From ada3f4fdefef12f99a2bfc586057b19e4d6b037b Mon Sep 17 00:00:00 2001 From: QingChuanWS <2754693100@qq.com> Date: Thu, 10 Dec 2020 17:11:03 +0800 Subject: [PATCH] update tflite components --- .../TencentOS_tiny_STM32L496ZGTx.dbgconf | 77 - .../RTE/_TencentOS_tiny/RTE_Components.h | 21 - .../TencentOS_tiny.uvoptx | 26 +- .../TencentOS_tiny.uvprojx | 12 +- .../tensorflow/core/public/version.h | 0 .../tensorflow/lite/c/builtin_op_data.h | 0 .../tensorflow/lite/c/common.h | 0 .../tensorflow/lite/core/api/error_reporter.h | 0 .../lite/core/api/flatbuffer_conversions.h | 0 .../tensorflow/lite/core/api/op_resolver.h | 0 .../tensorflow/lite/core/api/profiler.h | 0 .../tensorflow/lite/core/api/tensor_utils.h | 0 .../tensorflow/lite/kernels/internal/common.h | 0 .../lite/kernels/internal/compatibility.h | 0 .../lite/kernels/internal/cppmath.h | 0 .../tensorflow/lite/kernels/internal/max.h | 0 .../tensorflow/lite/kernels/internal/min.h | 0 .../kernels/internal/optimized/neon_check.h | 0 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components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/person_model_int8/person_detect_model_data.cc create mode 100644 components/tflite_micro/Tensorflow Lite Micro使用指南.md diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/DebugConfig/TencentOS_tiny_STM32L496ZGTx.dbgconf b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/DebugConfig/TencentOS_tiny_STM32L496ZGTx.dbgconf deleted file mode 100644 index 979440d7..00000000 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/DebugConfig/TencentOS_tiny_STM32L496ZGTx.dbgconf +++ /dev/null @@ -1,77 +0,0 @@ -// File: STM32L4x5_4x6.dbgconf -// Version: 1.0.0 -// Note: refer to STM32L4x5 and STM32L4x6 Reference manual (RM0351) -// refer to STM32L475xx STM32L476xx STM32L486xx STM32L496xx STM32L4A6xx datasheets - -// <<< Use Configuration Wizard in Context Menu >>> - -// Debug MCU configuration register (DBGMCU_CR) -// DBG_STANDBY Debug Standby mode -// DBG_STOP Debug Stop mode -// DBG_SLEEP Debug Sleep mode -// -DbgMCU_CR = 0x00000007; - -// Debug MCU APB1 freeze register1 (DBGMCU_APB1FZR1) -// Reserved bits must be kept at reset value -// DBG_LPTIM1_STOP LPTIM1 counter stopped when core is halted -// DBG_CAN2_STOP bxCAN2 stopped when core is halted -// DBG_CAN1_STOP bxCAN1 stopped when core is halted -// DBG_I2C3_STOP I2C3 SMBUS timeout counter stopped when core is halted -// DBG_I2C2_STOP I2C2 SMBUS timeout counter stopped when core is halted -// DBG_I2C1_STOP I2C1 SMBUS timeout counter stopped when core is halted -// DBG_IWDG_STOP Independent watchdog counter stopped when core is halted -// DBG_WWDG_STOP Window watchdog counter stopped when core is halted -// DBG_RTC_STOP RTC counter stopped when core is halted -// DBG_TIM7_STOP TIM7 counter stopped when core is halted -// DBG_TIM6_STOP TIM6 counter stopped when core is halted -// DBG_TIM5_STOP TIM5 counter stopped when core is halted -// DBG_TIM4_STOP TIM4 counter stopped when core is halted -// DBG_TIM3_STOP TIM3 counter stopped when core is halted -// DBG_TIM2_STOP TIM2 counter stopped when core is halted -// -DbgMCU_APB1_Fz1 = 0x00000000; - -// Debug MCU APB1 freeze register 2 (DBGMCU_APB1FZR2) -// Reserved bits must be kept at reset value -// DBG_LPTIM2_STOP LPTIM2 counter stopped when core is halted -// DBG_I2C4_STOP I2C4 SMBUS timeout counter stopped when core is halted -// -DbgMCU_APB1_Fz2 = 0x00000000; - -// Debug MCU APB2 freeze register (DBGMCU_APB2FZR) -// Reserved bits must be kept at reset value -// DBG_TIM17_STOP TIM17 counter stopped when core is halted -// DBG_TIM16_STOP TIM16 counter stopped when core is halted -// DBG_TIM15_STOP TIM15 counter stopped when core is halted -// DBG_TIM8_STOP TIM8 counter stopped when core is halted -// DBG_TIM1_STOP TIM1 counter stopped when core is halted -// -DbgMCU_APB2_Fz = 0x00000000; - -// TPIU Pin Routing (TRACECLK fixed on Pin PE2) -// TRACECLK: Pin PE2 -// TRACED0 -// ETM Trace Data 0 -// <0x00040003=> Pin PE3 -// <0x00020001=> Pin PC1 -// TRACED1 -// ETM Trace Data 1 -// <0x00040004=> Pin PE4 -// <0x0002000A=> Pin PC10 -// TRACED2 -// ETM Trace Data 2 -// <0x00040005=> Pin PE5 -// <0x00030002=> Pin PD2 -// TRACED3 -// ETM Trace Data 3 -// <0x00040006=> Pin PE6 -// <0x0002000C=> Pin PC12 -// -TraceClk_Pin = 0x00040002; -TraceD0_Pin = 0x00040003; -TraceD1_Pin = 0x00040004; -TraceD2_Pin = 0x00040005; -TraceD3_Pin = 0x00040006; - -// <<< end of configuration section >>> diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/RTE/_TencentOS_tiny/RTE_Components.h b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/RTE/_TencentOS_tiny/RTE_Components.h deleted file mode 100644 index 248f644f..00000000 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/RTE/_TencentOS_tiny/RTE_Components.h +++ /dev/null @@ -1,21 +0,0 @@ - -/* - * Auto generated Run-Time-Environment Configuration File - * *** Do not modify ! *** - * - * Project: 'TencentOS_tiny' - * Target: 'TencentOS_tiny' - */ - -#ifndef RTE_COMPONENTS_H -#define RTE_COMPONENTS_H - - -/* - * Define the Device Header File: - */ -#define CMSIS_device_header "stm32l4xx.h" - - - -#endif /* RTE_COMPONENTS_H */ diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx index c09f8373..21877009 100644 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx +++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx @@ -1139,25 +1139,13 @@ tensorflow - 0 + 1 0 0 0 10 71 - 4 - 0 - 0 - 0 - ..\..\..\..\components\tflite_micro\STM32L496-lib\tflm_person_detection.lib - tflm_person_detection.lib - 0 - 0 - - - 10 - 72 1 0 0 @@ -1167,6 +1155,18 @@ 0 0 + + 10 + 72 + 4 + 0 + 0 + 0 + ..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\tflm_person_detection.lib + tflm_person_detection.lib + 0 + 0 + diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx index 6916022e..f8cb62dc 100644 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx +++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx @@ -339,7 +339,7 @@ USE_HAL_DRIVER,STM32L496xx,NUCLEO_STM32L496ZG - ..\..\BSP\Inc;..\..\..\..\platform\vendor_bsp\st\STM32L4xx_HAL_Driver\Inc;..\..\..\..\platform\vendor_bsp\st\STM32L4xx_HAL_Driver\Inc\Legacy;..\..\..\..\platform\vendor_bsp\st\CMSIS\Device\ST\STM32L4xx\Include;..\..\..\..\platform\vendor_bsp\st\CMSIS\Include;..\..\..\..\arch\arm\arm-v7m\common\include;..\..\..\..\arch\arm\arm-v7m\cortex-m4\armcc;..\..\..\..\kernel\core\include;..\..\..\..\kernel\pm\include;..\..\..\..\osal\cmsis_os;..\..\..\..\examples\hello_world;..\..\TOS_CONFIG;..\..\..\..\net\at\include;..\..\..\..\kernel\hal\include;..\..\BSP\Hardware\Inc;..\..\..\..\components\tflite_micro\STM32L496-lib;..\..\..\..\components\tflite_micro\STM32L496-lib\third_party\flatbuffers\include;..\..\..\..\components\tflite_micro\STM32L496-lib\third_party\gemmlowp;..\..\..\..\components\tflite_micro\STM32L496-lib\third_party\kissfft;..\..\..\..\components\tflite_micro\STM32L496-lib\third_party\ruy;..\..\..\..\components\tflite_micro\STM32L496-lib\tensorflow\lite\micro\tools\make\downloads + ..\..\BSP\Inc;..\..\..\..\platform\vendor_bsp\st\STM32L4xx_HAL_Driver\Inc;..\..\..\..\platform\vendor_bsp\st\STM32L4xx_HAL_Driver\Inc\Legacy;..\..\..\..\platform\vendor_bsp\st\CMSIS\Device\ST\STM32L4xx\Include;..\..\..\..\platform\vendor_bsp\st\CMSIS\Include;..\..\..\..\arch\arm\arm-v7m\common\include;..\..\..\..\arch\arm\arm-v7m\cortex-m4\armcc;..\..\..\..\kernel\core\include;..\..\..\..\kernel\pm\include;..\..\..\..\osal\cmsis_os;..\..\..\..\examples\hello_world;..\..\TOS_CONFIG;..\..\..\..\net\at\include;..\..\..\..\kernel\hal\include;..\..\BSP\Hardware\Inc;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\third_party\flatbuffers\include;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\third_party\gemmlowp;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\third_party\kissfft;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\third_party\ruy;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\tensorflow\lite\micro\tools\make\downloads @@ -778,16 +778,16 @@ tensorflow - - tflm_person_detection.lib - 4 - ..\..\..\..\components\tflite_micro\STM32L496-lib\tflm_person_detection.lib - regarget.c 1 ..\..\..\..\components\tflite_micro\KEIL\regarget.c + + tflm_person_detection.lib + 4 + ..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\tflm_person_detection.lib + diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/core/public/version.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/core/public/version.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/core/public/version.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/core/public/version.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/c/builtin_op_data.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/c/builtin_op_data.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/c/builtin_op_data.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/c/builtin_op_data.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/c/common.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/c/common.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/c/common.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/c/common.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/error_reporter.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/error_reporter.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/error_reporter.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/error_reporter.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/flatbuffer_conversions.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/flatbuffer_conversions.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/flatbuffer_conversions.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/flatbuffer_conversions.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/op_resolver.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/op_resolver.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/op_resolver.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/op_resolver.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/profiler.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/profiler.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/profiler.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/profiler.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/tensor_utils.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/tensor_utils.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/core/api/tensor_utils.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/core/api/tensor_utils.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/common.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/common.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/common.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/common.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/compatibility.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/compatibility.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/compatibility.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/compatibility.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/cppmath.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/cppmath.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/cppmath.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/cppmath.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/max.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/max.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/max.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/max.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/min.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/min.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/min.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/min.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/optimized/neon_check.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/optimized/neon_check.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/optimized/neon_check.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/optimized/neon_check.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/quantization_util.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/quantization_util.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/quantization_util.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/quantization_util.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/add.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/add.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/add.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/add.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/arg_min_max.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/arg_min_max.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/arg_min_max.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/arg_min_max.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/binary_function.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/binary_function.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/binary_function.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/binary_function.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/ceil.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/ceil.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/ceil.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/ceil.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/comparisons.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/comparisons.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/comparisons.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/comparisons.h diff --git a/components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/concatenation.h b/components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/concatenation.h similarity index 100% rename from components/tflite_micro/STM32L496-lib/tensorflow/lite/kernels/internal/reference/concatenation.h rename to components/tflite_micro/ARM_CortexM4_lib/tensorflow/lite/kernels/internal/reference/concatenation.h diff --git 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TfLiteIntArrayGetSizeInBytes(int size) { - static TfLiteIntArray dummy; - return sizeof(dummy) + sizeof(dummy.data[0]) * size; -} - -int TfLiteIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b) { - if (a == b) return 1; - if (a == NULL || b == NULL) return 0; - return TfLiteIntArrayEqualsArray(a, b->size, b->data); -} - -int TfLiteIntArrayEqualsArray(const TfLiteIntArray* a, int b_size, - const int b_data[]) { - if (a == NULL) return (b_size == 0); - if (a->size != b_size) return 0; - int i = 0; - for (; i < a->size; i++) - if (a->data[i] != b_data[i]) return 0; - return 1; -} - -#ifndef TF_LITE_STATIC_MEMORY - -TfLiteIntArray* TfLiteIntArrayCreate(int size) { - TfLiteIntArray* ret = - (TfLiteIntArray*)malloc(TfLiteIntArrayGetSizeInBytes(size)); - ret->size = size; - return ret; -} - -TfLiteIntArray* TfLiteIntArrayCopy(const TfLiteIntArray* src) { - if (!src) return NULL; - TfLiteIntArray* ret = TfLiteIntArrayCreate(src->size); - if (ret) { - memcpy(ret->data, src->data, src->size * sizeof(int)); - } - return ret; -} - -void TfLiteIntArrayFree(TfLiteIntArray* a) { free(a); } - -#endif // TF_LITE_STATIC_MEMORY - -int TfLiteFloatArrayGetSizeInBytes(int size) { - static TfLiteFloatArray dummy; - return sizeof(dummy) + sizeof(dummy.data[0]) * size; -} - -#ifndef TF_LITE_STATIC_MEMORY - -TfLiteFloatArray* TfLiteFloatArrayCreate(int size) { - TfLiteFloatArray* ret = - (TfLiteFloatArray*)malloc(TfLiteFloatArrayGetSizeInBytes(size)); - ret->size = size; - return ret; -} - -void TfLiteFloatArrayFree(TfLiteFloatArray* a) { free(a); } - -void TfLiteTensorDataFree(TfLiteTensor* t) { - if (t->allocation_type == kTfLiteDynamic || - t->allocation_type == kTfLitePersistentRo) { - free(t->data.raw); - } - t->data.raw = NULL; -} - -void TfLiteQuantizationFree(TfLiteQuantization* quantization) { - if (quantization->type == kTfLiteAffineQuantization) { - TfLiteAffineQuantization* q_params = - (TfLiteAffineQuantization*)(quantization->params); - if (q_params->scale) { - TfLiteFloatArrayFree(q_params->scale); - q_params->scale = NULL; - } - if (q_params->zero_point) { - TfLiteIntArrayFree(q_params->zero_point); - q_params->zero_point = NULL; - } - free(q_params); - } - quantization->params = NULL; - quantization->type = kTfLiteNoQuantization; -} - -void TfLiteSparsityFree(TfLiteSparsity* sparsity) { - if (sparsity == NULL) { - return; - } - - if (sparsity->traversal_order) { - TfLiteIntArrayFree(sparsity->traversal_order); - sparsity->traversal_order = NULL; - } - - if (sparsity->block_map) { - TfLiteIntArrayFree(sparsity->block_map); - sparsity->block_map = NULL; - } - - if (sparsity->dim_metadata) { - int i = 0; - for (; i < sparsity->dim_metadata_size; i++) { - TfLiteDimensionMetadata metadata = sparsity->dim_metadata[i]; - if (metadata.format == kTfLiteDimSparseCSR) { - TfLiteIntArrayFree(metadata.array_segments); - metadata.array_segments = NULL; - TfLiteIntArrayFree(metadata.array_indices); - metadata.array_indices = NULL; - } - } - free(sparsity->dim_metadata); - sparsity->dim_metadata = NULL; - } - - free(sparsity); -} - -void TfLiteTensorFree(TfLiteTensor* t) { - TfLiteTensorDataFree(t); - if (t->dims) TfLiteIntArrayFree(t->dims); - t->dims = NULL; - - if (t->dims_signature) { - TfLiteIntArrayFree((TfLiteIntArray *) t->dims_signature); - } - t->dims_signature = NULL; - - TfLiteQuantizationFree(&t->quantization); - TfLiteSparsityFree(t->sparsity); - t->sparsity = NULL; -} - -void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, - TfLiteQuantizationParams quantization, char* buffer, - size_t size, TfLiteAllocationType allocation_type, - const void* allocation, bool is_variable, - TfLiteTensor* tensor) { - TfLiteTensorFree(tensor); - tensor->type = type; - tensor->name = name; - tensor->dims = dims; - tensor->params = quantization; - tensor->data.raw = buffer; - tensor->bytes = size; - tensor->allocation_type = allocation_type; - tensor->allocation = allocation; - tensor->is_variable = is_variable; - - tensor->quantization.type = kTfLiteNoQuantization; - tensor->quantization.params = NULL; -} - -void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor) { - if (tensor->allocation_type != kTfLiteDynamic && - tensor->allocation_type != kTfLitePersistentRo) { - return; - } - // TODO(b/145340303): Tensor data should be aligned. - if (!tensor->data.raw) { - tensor->data.raw = malloc(num_bytes); - } else if (num_bytes > tensor->bytes) { - tensor->data.raw = realloc(tensor->data.raw, num_bytes); - } - tensor->bytes = num_bytes; -} -#endif // TF_LITE_STATIC_MEMORY - -const char* TfLiteTypeGetName(TfLiteType type) { - switch (type) { - case kTfLiteNoType: - return "NOTYPE"; - case kTfLiteFloat32: - return "FLOAT32"; - case kTfLiteInt16: - return "INT16"; - case kTfLiteInt32: - return "INT32"; - case kTfLiteUInt8: - return "UINT8"; - case kTfLiteInt8: - return "INT8"; - case kTfLiteInt64: - return "INT64"; - case kTfLiteBool: - return "BOOL"; - case kTfLiteComplex64: - return "COMPLEX64"; - case kTfLiteComplex128: - return "COMPLEX128"; - case kTfLiteString: - return "STRING"; - case kTfLiteFloat16: - return "FLOAT16"; - case kTfLiteFloat64: - return "FLOAT64"; - } - return "Unknown type"; -} - -TfLiteDelegate TfLiteDelegateCreate() { - TfLiteDelegate d = { - .data_ = NULL, - .Prepare = NULL, - .CopyFromBufferHandle = NULL, - .CopyToBufferHandle = NULL, - .FreeBufferHandle = NULL, - .flags = kTfLiteDelegateFlagsNone, - }; - return d; -} diff --git a/components/tflite_micro/Source/tensorflow/lite/core/api/error_reporter.cc b/components/tflite_micro/Source/tensorflow/lite/core/api/error_reporter.cc deleted file mode 100644 index 7070eaa5..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/core/api/error_reporter.cc +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/core/api/error_reporter.h" -#include - -namespace tflite { - -int ErrorReporter::Report(const char* format, ...) { - va_list args; - va_start(args, format); - int code = Report(format, args); - va_end(args); - return code; -} - -// TODO(aselle): Make the name of ReportError on context the same, so -// we can use the ensure functions w/o a context and w/ a reporter. -int ErrorReporter::ReportError(void*, const char* format, ...) { - va_list args; - va_start(args, format); - int code = Report(format, args); - va_end(args); - return code; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/core/api/flatbuffer_conversions.cc b/components/tflite_micro/Source/tensorflow/lite/core/api/flatbuffer_conversions.cc deleted file mode 100644 index 7fb04f5b..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/core/api/flatbuffer_conversions.cc +++ /dev/null @@ -1,1739 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/core/api/flatbuffer_conversions.h" - -#include -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -namespace { - -// Utility class for safely allocating POD data. This is useful for avoiding -// leaks in cases where op params are allocated but fail to propagate to the -// parsed op data (e.g., when model parameters are invalid). -class SafeBuiltinDataAllocator { - public: - class BuiltinDataDeleter { - public: - explicit BuiltinDataDeleter(BuiltinDataAllocator* allocator) - : allocator_(allocator) {} - - void operator()(void* data) { allocator_->Deallocate(data); } - - private: - BuiltinDataAllocator* allocator_; - }; - - template - using BuiltinDataPtr = std::unique_ptr; - - explicit SafeBuiltinDataAllocator(BuiltinDataAllocator* allocator) - : allocator_(allocator) {} - - template - BuiltinDataPtr Allocate() { - return BuiltinDataPtr(allocator_->AllocatePOD(), - BuiltinDataDeleter(allocator_)); - } - - private: - BuiltinDataAllocator* allocator_; -}; - -// All the Parse functions take some pointers as params and this function has -// the common DCHECKs to catch if any of those are nullptr. -void CheckParsePointerParams(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - TFLITE_DCHECK(op != nullptr); - TFLITE_DCHECK(error_reporter != nullptr); - TFLITE_DCHECK(allocator != nullptr); - TFLITE_DCHECK(builtin_data != nullptr); -} - -// Copies the contents from the flatbuffer int vector `flatbuffer` into the -// int array `buffer`. `flat_vector` and `buffer` represent the same -// configuration operation for a given operation. -TfLiteStatus FlatBufferIntVectorToArray( - int max_size_of_buffer, const flatbuffers::Vector* flat_vector, - int* buffer, ErrorReporter* error_reporter, const char* op_name) { - if (!flat_vector) { - TF_LITE_REPORT_ERROR(error_reporter, - "Input array not provided for operation '%s'.\n", - op_name); - return kTfLiteError; - } else { - size_t num_dimensions = flat_vector->size(); - if (num_dimensions > max_size_of_buffer / sizeof(int)) { - TF_LITE_REPORT_ERROR( - error_reporter, - "Found too many dimensions in the input array of operation '%s'.\n", - op_name); - return kTfLiteError; - } else { - for (size_t i = 0; i < num_dimensions; ++i) { - buffer[i] = flat_vector->Get(i); - } - } - } - return kTfLiteOk; -} - -// Converts the flatbuffer activation to what is used at runtime. -TfLiteFusedActivation ConvertActivation(ActivationFunctionType activation) { - switch (activation) { - case ActivationFunctionType_NONE: - return kTfLiteActNone; - case ActivationFunctionType_RELU: - return kTfLiteActRelu; - case ActivationFunctionType_RELU_N1_TO_1: - return kTfLiteActReluN1To1; - case ActivationFunctionType_RELU6: - return kTfLiteActRelu6; - case ActivationFunctionType_TANH: - return kTfLiteActTanh; - case ActivationFunctionType_SIGN_BIT: - return kTfLiteActSignBit; - } - return kTfLiteActNone; -} - -// Converts the flatbuffer padding enum to what is used at runtime. -TfLitePadding ConvertPadding(Padding padding) { - switch (padding) { - case Padding_SAME: - return kTfLitePaddingSame; - case Padding_VALID: - return kTfLitePaddingValid; - } - return kTfLitePaddingUnknown; -} - -#ifndef TF_LITE_STATIC_MEMORY -TfLiteStatus ParseOpDataTfLite(const Operator* op, BuiltinOperator op_type, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - auto parseLSHProjectionType = [](LSHProjectionType type) { - switch (type) { - case LSHProjectionType_SPARSE: - return kTfLiteLshProjectionSparse; - case LSHProjectionType_DENSE: - return kTfLiteLshProjectionDense; - default: - return kTfLiteLshProjectionUnknown; - } - }; - auto parseCombinerType = [](CombinerType type) { - switch (type) { - case CombinerType_MEAN: - return kTfLiteCombinerTypeMean; - case CombinerType_SQRTN: - return kTfLiteCombinerTypeSqrtn; - case CombinerType_SUM: - default: - return kTfLiteCombinerTypeSum; - } - }; - - SafeBuiltinDataAllocator safe_allocator(allocator); - *builtin_data = nullptr; - switch (op_type) { - case BuiltinOperator_ABS: { - return ParseAbs(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_ADD: { - return ParseAdd(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_ARG_MAX: { - return ParseArgMax(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_ARG_MIN: { - return ParseArgMin(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_AVERAGE_POOL_2D: { - return ParsePool(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_CEIL: { - return ParseCeil(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_CONCATENATION: { - return ParseConcatenation(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_CONV_2D: { - return ParseConv2D(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_DEPTHWISE_CONV_2D: { - return ParseDepthwiseConv2D(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_DEQUANTIZE: { - return ParseDequantize(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_FLOOR: { - return ParseFloor(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_FULLY_CONNECTED: { - return ParseFullyConnected(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_GREATER: { - return ParseGreater(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_GREATER_EQUAL: { - return ParseGreaterEqual(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_HARD_SWISH: { - return ParseHardSwish(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_L2_NORMALIZATION: { - return ParseL2Normalization(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_L2_POOL_2D: { - return ParsePool(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_LESS: { - return ParseLess(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_LESS_EQUAL: { - return ParseLessEqual(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_LOG: { - return ParseLog(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_LOGICAL_AND: { - return ParseLogicalAnd(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_LOGICAL_NOT: { - return ParseLogicalNot(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_LOGICAL_OR: { - return ParseLogicalOr(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_LOGISTIC: { - return ParseLogistic(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_MAXIMUM: { - return ParseMaximum(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_MAX_POOL_2D: { - return ParsePool(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_MEAN: { - return ParseReducer(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_MINIMUM: { - return ParseMinimum(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_MUL: { - return ParseMul(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_NEG: { - return ParseNeg(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_NOT_EQUAL: { - return ParseNotEqual(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_PACK: { - return ParsePack(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_PAD: { - return ParsePad(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_PADV2: { - return ParsePadV2(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_PRELU: { - return ParsePrelu(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_QUANTIZE: { - return ParseQuantize(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_REDUCE_ANY: { - return ParseReducer(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_REDUCE_MAX: { - return ParseReducer(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_REDUCE_MIN: { - return ParseReducer(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_REDUCE_PROD: { - return ParseReducer(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_RELU: { - return ParseRelu(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_RELU6: { - return ParseRelu6(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_RESHAPE: { - return ParseReshape(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: { - return ParseResizeNearestNeighbor(op, error_reporter, allocator, - builtin_data); - } - - case BuiltinOperator_ROUND: { - return ParseRound(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_RSQRT: { - return ParseRsqrt(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_SIN: { - return ParseSin(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_SOFTMAX: { - return ParseSoftmax(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_SPLIT: { - return ParseSplit(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_SQRT: { - return ParseSqrt(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_SQUARE: { - return ParseSquare(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_STRIDED_SLICE: { - return ParseStridedSlice(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_SUB: { - return ParseSub(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_SUM: { - return ParseReducer(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_SVDF: { - return ParseSvdf(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_TANH: { - return ParseTanh(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_UNPACK: { - return ParseUnpack(op, error_reporter, allocator, builtin_data); - } - - case BuiltinOperator_CAST: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = op->builtin_options_as_CastOptions()) { - TF_LITE_ENSURE_STATUS(ConvertTensorType(schema_params->in_data_type(), - ¶ms->in_data_type, - error_reporter)); - TF_LITE_ENSURE_STATUS(ConvertTensorType(schema_params->out_data_type(), - ¶ms->out_data_type, - error_reporter)); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_LSH_PROJECTION: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* lshParams = - op->builtin_options_as_LSHProjectionOptions()) { - params->type = parseLSHProjectionType(lshParams->type()); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* sequence_rnn_params = - op->builtin_options_as_SequenceRNNOptions()) { - params->activation = - ConvertActivation(sequence_rnn_params->fused_activation_function()); - params->time_major = sequence_rnn_params->time_major(); - params->asymmetric_quantize_inputs = - sequence_rnn_params->asymmetric_quantize_inputs(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: { - auto params = - safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* bidi_sequence_rnn_params = - op->builtin_options_as_BidirectionalSequenceRNNOptions()) { - params->activation = ConvertActivation( - bidi_sequence_rnn_params->fused_activation_function()); - params->time_major = bidi_sequence_rnn_params->time_major(); - params->merge_outputs = bidi_sequence_rnn_params->merge_outputs(); - params->asymmetric_quantize_inputs = - bidi_sequence_rnn_params->asymmetric_quantize_inputs(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_RNN: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* rnn_params = op->builtin_options_as_RNNOptions()) { - params->activation = - ConvertActivation(rnn_params->fused_activation_function()); - params->asymmetric_quantize_inputs = - rnn_params->asymmetric_quantize_inputs(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: { - auto params = - safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* embedding_params = - op->builtin_options_as_EmbeddingLookupSparseOptions()) { - params->combiner = parseCombinerType(embedding_params->combiner()); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - - case BuiltinOperator_HASHTABLE_LOOKUP: - // no-op. - return kTfLiteOk; - case BuiltinOperator_DIV: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = op->builtin_options_as_DivOptions()) { - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = - op->builtin_options_as_LocalResponseNormalizationOptions()) { - params->radius = schema_params->radius(); - params->bias = schema_params->bias(); - params->alpha = schema_params->alpha(); - params->beta = schema_params->beta(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_LSTM: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* lstm_params = op->builtin_options_as_LSTMOptions()) { - params->activation = - ConvertActivation(lstm_params->fused_activation_function()); - params->cell_clip = lstm_params->cell_clip(); - params->proj_clip = lstm_params->proj_clip(); - switch (lstm_params->kernel_type()) { - case LSTMKernelType_FULL: - params->kernel_type = kTfLiteLSTMFullKernel; - break; - case LSTMKernelType_BASIC: - params->kernel_type = kTfLiteLSTMBasicKernel; - break; - default: - TF_LITE_REPORT_ERROR(error_reporter, - "Unhandled LSTM kernel type: %d", - lstm_params->kernel_type()); - return kTfLiteError; - } - params->asymmetric_quantize_inputs = - lstm_params->asymmetric_quantize_inputs(); - } else { - TF_LITE_REPORT_ERROR(error_reporter, - "No valid LSTM builtin options exist"); - return kTfLiteError; - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: { - auto params = - safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* seq_lstm_params = - op->builtin_options_as_UnidirectionalSequenceLSTMOptions()) { - params->activation = - ConvertActivation(seq_lstm_params->fused_activation_function()); - params->cell_clip = seq_lstm_params->cell_clip(); - params->proj_clip = seq_lstm_params->proj_clip(); - params->time_major = seq_lstm_params->time_major(); - params->asymmetric_quantize_inputs = - seq_lstm_params->asymmetric_quantize_inputs(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: { - auto params = - safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* bidi_lstm_params = - op->builtin_options_as_BidirectionalSequenceLSTMOptions()) { - params->activation = - ConvertActivation(bidi_lstm_params->fused_activation_function()); - params->cell_clip = bidi_lstm_params->cell_clip(); - params->proj_clip = bidi_lstm_params->proj_clip(); - params->merge_outputs = bidi_lstm_params->merge_outputs(); - params->time_major = bidi_lstm_params->time_major(); - params->asymmetric_quantize_inputs = - bidi_lstm_params->asymmetric_quantize_inputs(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_RESIZE_BILINEAR: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = - op->builtin_options_as_ResizeBilinearOptions()) { - params->align_corners = schema_params->align_corners(); - params->half_pixel_centers = schema_params->half_pixel_centers(); - } else { - // Some older models did not populate the ResizeBilinearOptions field in - // the flatbuffer, so ensure it's set to a sensible default. - params->align_corners = false; - params->half_pixel_centers = false; - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_SKIP_GRAM: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* skip_gram_params = - op->builtin_options_as_SkipGramOptions()) { - params->ngram_size = skip_gram_params->ngram_size(); - params->max_skip_size = skip_gram_params->max_skip_size(); - params->include_all_ngrams = skip_gram_params->include_all_ngrams(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_SPACE_TO_DEPTH: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = - op->builtin_options_as_SpaceToDepthOptions()) { - params->block_size = schema_params->block_size(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_DEPTH_TO_SPACE: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = - op->builtin_options_as_DepthToSpaceOptions()) { - params->block_size = schema_params->block_size(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_GATHER: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - params->axis = 0; - if (const auto* gather_params = op->builtin_options_as_GatherOptions()) { - params->axis = gather_params->axis(); - } - - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_SPLIT_V: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = op->builtin_options_as_SplitVOptions()) { - params->num_splits = schema_params->num_splits(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_SQUEEZE: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = op->builtin_options_as_SqueezeOptions()) { - const auto* squeeze_dims = schema_params->squeeze_dims(); - TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray( - sizeof(params->squeeze_dims), squeeze_dims, params->squeeze_dims, - error_reporter, "squeeze")); - params->num_squeeze_dims = squeeze_dims->size(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_TRANSPOSE_CONV: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* transpose_conv_params = - op->builtin_options_as_TransposeConvOptions()) { - params->padding = ConvertPadding(transpose_conv_params->padding()); - params->stride_width = transpose_conv_params->stride_w(); - params->stride_height = transpose_conv_params->stride_h(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_SPARSE_TO_DENSE: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* sparse_to_dense_params = - op->builtin_options_as_SparseToDenseOptions()) { - params->validate_indices = sparse_to_dense_params->validate_indices(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_SHAPE: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = op->builtin_options_as_ShapeOptions()) { - TF_LITE_ENSURE_STATUS(ConvertTensorType( - schema_params->out_type(), ¶ms->out_type, error_reporter)); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_DELEGATE: { - // TODO(ycling): Revisit when supporting saving delegated models. - TF_LITE_REPORT_ERROR(error_reporter, - "DELEGATE op shouldn't exist in model."); - return kTfLiteError; - } - case BuiltinOperator_FAKE_QUANT: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = - op->builtin_options_as_FakeQuantOptions()) { - params->min = schema_params->min(); - params->max = schema_params->max(); - params->num_bits = schema_params->num_bits(); - params->narrow_range = schema_params->narrow_range(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_ONE_HOT: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* schema_params = op->builtin_options_as_OneHotOptions()) { - params->axis = schema_params->axis(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_LEAKY_RELU: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* leaky_relu_params = - op->builtin_options_as_LeakyReluOptions()) { - params->alpha = leaky_relu_params->alpha(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_MIRROR_PAD: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - const auto* mirror_pad_params = op->builtin_options_as_MirrorPadOptions(); - if (mirror_pad_params != nullptr) { - params->mode = - mirror_pad_params->mode() == tflite::MirrorPadMode_REFLECT - ? TfLiteMirrorPaddingMode::kTfLiteMirrorPaddingReflect - : TfLiteMirrorPaddingMode::kTfLiteMirrorPaddingSymmetric; - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_UNIQUE: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - const auto* unique_params = op->builtin_options_as_UniqueOptions(); - if (unique_params != nullptr) { - params->index_out_type = - unique_params->idx_out_type() == tflite::TensorType_INT64 - ? TfLiteType::kTfLiteInt64 - : TfLiteType::kTfLiteInt32; - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_REVERSE_SEQUENCE: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* reverse_seq_params = - op->builtin_options_as_ReverseSequenceOptions()) { - params->seq_dim = reverse_seq_params->seq_dim(); - params->batch_dim = reverse_seq_params->batch_dim(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_IF: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* if_params = op->builtin_options_as_IfOptions()) { - params->then_subgraph_index = if_params->then_subgraph_index(); - params->else_subgraph_index = if_params->else_subgraph_index(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_WHILE: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* while_params = op->builtin_options_as_WhileOptions()) { - params->cond_subgraph_index = while_params->cond_subgraph_index(); - params->body_subgraph_index = while_params->body_subgraph_index(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - case BuiltinOperator_BATCH_MATMUL: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* bmm_params = - op->builtin_options_as_BatchMatMulOptions()) { - params->adj_x = bmm_params->adj_x(); - params->adj_y = bmm_params->adj_y(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } - // Below are the ops with no builtin_data structure. - case BuiltinOperator_BATCH_TO_SPACE_ND: - // TODO(aselle): Implement call in BuiltinOptions, but nullptrs are - // ok for now, since there is no call implementation either. - case BuiltinOperator_CALL: - case BuiltinOperator_CONCAT_EMBEDDINGS: - case BuiltinOperator_COS: - case BuiltinOperator_CUSTOM: - case BuiltinOperator_ELU: - case BuiltinOperator_EMBEDDING_LOOKUP: - case BuiltinOperator_EQUAL: - case BuiltinOperator_EXP: - case BuiltinOperator_EXPAND_DIMS: - case BuiltinOperator_LOG_SOFTMAX: - case BuiltinOperator_MATRIX_DIAG: - case BuiltinOperator_MATRIX_SET_DIAG: - case BuiltinOperator_RELU_N1_TO_1: - case BuiltinOperator_SELECT: - case BuiltinOperator_SELECT_V2: - case BuiltinOperator_SLICE: - case BuiltinOperator_SPACE_TO_BATCH_ND: - case BuiltinOperator_TILE: - case BuiltinOperator_TOPK_V2: - case BuiltinOperator_TRANSPOSE: - case BuiltinOperator_POW: - case BuiltinOperator_FLOOR_DIV: - case BuiltinOperator_ZEROS_LIKE: - case BuiltinOperator_FILL: - case BuiltinOperator_FLOOR_MOD: - case BuiltinOperator_RANGE: - case BuiltinOperator_SQUARED_DIFFERENCE: - case BuiltinOperator_REVERSE_V2: - case BuiltinOperator_ADD_N: - case BuiltinOperator_GATHER_ND: - case BuiltinOperator_WHERE: - case BuiltinOperator_RANK: - case BuiltinOperator_NON_MAX_SUPPRESSION_V4: - case BuiltinOperator_NON_MAX_SUPPRESSION_V5: - case BuiltinOperator_SCATTER_ND: - case BuiltinOperator_DENSIFY: - case BuiltinOperator_SEGMENT_SUM: - return kTfLiteOk; - } - return kTfLiteError; -} // NOLINT[readability/fn_size] -#endif // !defined(TF_LITE_STATIC_MEMORY) -} // namespace - -TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type, - ErrorReporter* error_reporter) { - switch (tensor_type) { - case TensorType_FLOAT16: - *type = kTfLiteFloat16; - return kTfLiteOk; - case TensorType_FLOAT32: - *type = kTfLiteFloat32; - return kTfLiteOk; - case TensorType_FLOAT64: - *type = kTfLiteFloat64; - return kTfLiteOk; - case TensorType_INT16: - *type = kTfLiteInt16; - return kTfLiteOk; - case TensorType_INT32: - *type = kTfLiteInt32; - return kTfLiteOk; - case TensorType_UINT8: - *type = kTfLiteUInt8; - return kTfLiteOk; - case TensorType_INT8: - *type = kTfLiteInt8; - return kTfLiteOk; - case TensorType_INT64: - *type = kTfLiteInt64; - return kTfLiteOk; - case TensorType_STRING: - *type = kTfLiteString; - return kTfLiteOk; - case TensorType_BOOL: - *type = kTfLiteBool; - return kTfLiteOk; - case TensorType_COMPLEX64: - *type = kTfLiteComplex64; - return kTfLiteOk; - case TensorType_COMPLEX128: - *type = kTfLiteComplex128; - return kTfLiteOk; - default: - *type = kTfLiteNoType; - TF_LITE_REPORT_ERROR(error_reporter, - "Unsupported data type %d in tensor\n", tensor_type); - return kTfLiteError; - } -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseAbs(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseAdd(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const AddOptions* schema_params = op->builtin_options_as_AddOptions(); - - if (schema_params != nullptr) { - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - params->pot_scale_int16 = schema_params->pot_scale_int16(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -TfLiteStatus ParseArgMax(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const ArgMaxOptions* schema_params = op->builtin_options_as_ArgMaxOptions(); - - if (schema_params != nullptr) { - TF_LITE_ENSURE_STATUS(ConvertTensorType( - schema_params->output_type(), ¶ms->output_type, error_reporter)); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -TfLiteStatus ParseArgMin(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const ArgMinOptions* schema_params = op->builtin_options_as_ArgMinOptions(); - - if (schema_params != nullptr) { - TF_LITE_ENSURE_STATUS(ConvertTensorType( - schema_params->output_type(), ¶ms->output_type, error_reporter)); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseCeil(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseConcatenation(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const ConcatenationOptions* schema_params = - op->builtin_options_as_ConcatenationOptions(); - - if (schema_params != nullptr) { - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - params->axis = schema_params->axis(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -TfLiteStatus ParseConv2D(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const Conv2DOptions* schema_params = op->builtin_options_as_Conv2DOptions(); - - if (schema_params != nullptr) { - params->padding = ConvertPadding(schema_params->padding()); - params->stride_width = schema_params->stride_w(); - params->stride_height = schema_params->stride_h(); - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - - params->dilation_width_factor = schema_params->dilation_w_factor(); - params->dilation_height_factor = schema_params->dilation_h_factor(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseCos(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseDepthwiseConv2D(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const DepthwiseConv2DOptions* schema_params = - op->builtin_options_as_DepthwiseConv2DOptions(); - - if (schema_params != nullptr) { - params->padding = ConvertPadding(schema_params->padding()); - params->stride_width = schema_params->stride_w(); - params->stride_height = schema_params->stride_h(); - params->depth_multiplier = schema_params->depth_multiplier(); - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - - params->dilation_width_factor = schema_params->dilation_w_factor(); - params->dilation_height_factor = schema_params->dilation_h_factor(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseDequantize(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseEqual(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseFloor(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseFullyConnected(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const FullyConnectedOptions* schema_params = - op->builtin_options_as_FullyConnectedOptions(); - - if (schema_params != nullptr) { - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - params->keep_num_dims = schema_params->keep_num_dims(); - params->asymmetric_quantize_inputs = - schema_params->asymmetric_quantize_inputs(); - - switch (schema_params->weights_format()) { - case FullyConnectedOptionsWeightsFormat_DEFAULT: - params->weights_format = kTfLiteFullyConnectedWeightsFormatDefault; - break; - case FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8: - params->weights_format = - kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8; - break; - default: - TF_LITE_REPORT_ERROR(error_reporter, - "Unhandled fully-connected weights format."); - return kTfLiteError; - } - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseGreater(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseGreaterEqual(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseHardSwish(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseL2Normalization(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const L2NormOptions* schema_params = op->builtin_options_as_L2NormOptions(); - - if (schema_params != nullptr) { - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseLess(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseLessEqual(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseLog(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseLogicalAnd(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseLogicalNot(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseLogicalOr(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseLogistic(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseMaximum(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseMinimum(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseMul(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const MulOptions* schema_params = op->builtin_options_as_MulOptions(); - - if (schema_params != nullptr) { - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseNeg(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseNotEqual(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -TfLiteStatus ParsePack(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const PackOptions* schema_params = op->builtin_options_as_PackOptions(); - - if (schema_params != nullptr) { - params->values_count = schema_params->values_count(); - params->axis = schema_params->axis(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParsePad(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParsePadV2(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParsePool(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const Pool2DOptions* schema_params = op->builtin_options_as_Pool2DOptions(); - - if (schema_params != nullptr) { - params->padding = ConvertPadding(schema_params->padding()); - params->stride_width = schema_params->stride_w(); - params->stride_height = schema_params->stride_h(); - params->filter_width = schema_params->filter_width(); - params->filter_height = schema_params->filter_height(); - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParsePrelu(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseQuantize(const Operator*, ErrorReporter*, - BuiltinDataAllocator*, void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseReducer(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const ReducerOptions* schema_params = op->builtin_options_as_ReducerOptions(); - - if (schema_params != nullptr) { - params->keep_dims = schema_params->keep_dims(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseRelu(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseRelu6(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseReshape(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const ReshapeOptions* schema_params = op->builtin_options_as_ReshapeOptions(); - - if (schema_params != nullptr) { - const flatbuffers::Vector* new_shape = schema_params->new_shape(); - // TODO(b/147203660): We need to figure out when dynamic reshape - // (new_shape is a tensor) happens, why the option is not a nullptr. - // But nonethless, we should only copy when new_shape is not a nullptr. - if (new_shape != nullptr) { - TF_LITE_ENSURE_STATUS( - FlatBufferIntVectorToArray(sizeof(params->shape), new_shape, - params->shape, error_reporter, "reshape")); - params->num_dimensions = new_shape->size(); - } else { - // TODO(b/157480169) TODO(b/147203660): We should either return - // kTfLiteError or fill in some reasonable defaults in the params struct. - // We are not doing so until we better undertand the ramifications of - // changing the legacy behavior. - } - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -TfLiteStatus ParseResizeNearestNeighbor(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const ResizeNearestNeighborOptions* schema_params = - op->builtin_options_as_ResizeNearestNeighborOptions(); - - if (schema_params != nullptr) { - params->align_corners = schema_params->align_corners(); - params->half_pixel_centers = schema_params->half_pixel_centers(); - } else { - params->align_corners = false; - params->half_pixel_centers = false; - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseRound(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseRsqrt(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseSin(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseSoftmax(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const SoftmaxOptions* schema_params = op->builtin_options_as_SoftmaxOptions(); - - if (schema_params != nullptr) { - params->beta = schema_params->beta(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -TfLiteStatus ParseSplit(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const SplitOptions* schema_params = op->builtin_options_as_SplitOptions(); - - if (schema_params != nullptr) { - params->num_splits = schema_params->num_splits(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseSqrt(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseSquare(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseStridedSlice(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const StridedSliceOptions* schema_params = - op->builtin_options_as_StridedSliceOptions(); - - if (schema_params != nullptr) { - params->begin_mask = schema_params->begin_mask(); - params->end_mask = schema_params->end_mask(); - params->ellipsis_mask = schema_params->ellipsis_mask(); - params->new_axis_mask = schema_params->new_axis_mask(); - params->shrink_axis_mask = schema_params->shrink_axis_mask(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -TfLiteStatus ParseSub(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const SubOptions* schema_params = op->builtin_options_as_SubOptions(); - - if (schema_params != nullptr) { - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - params->pot_scale_int16 = schema_params->pot_scale_int16(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -TfLiteStatus ParseSvdf(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const SVDFOptions* schema_params = op->builtin_options_as_SVDFOptions(); - if (schema_params != nullptr) { - params->rank = schema_params->rank(); - params->activation = - ConvertActivation(schema_params->fused_activation_function()); - params->asymmetric_quantize_inputs = - schema_params->asymmetric_quantize_inputs(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -// We have this parse function instead of directly returning kTfLiteOk from the -// switch-case in ParseOpData because this function is used as part of the -// selective registration for the OpResolver implementation in micro. -TfLiteStatus ParseTanh(const Operator*, ErrorReporter*, BuiltinDataAllocator*, - void**) { - return kTfLiteOk; -} - -TfLiteStatus ParseUnpack(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { - CheckParsePointerParams(op, error_reporter, allocator, builtin_data); - - SafeBuiltinDataAllocator safe_allocator(allocator); - std::unique_ptr - params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - - const UnpackOptions* schema_params = op->builtin_options_as_UnpackOptions(); - - if (schema_params != nullptr) { - params->num = schema_params->num(); - params->axis = schema_params->axis(); - } else { - // TODO(b/157480169): We should either return kTfLiteError or fill in some - // reasonable defaults in the params struct. We are not doing so until we - // better undertand the ramifications of changing the legacy behavior. - } - - *builtin_data = params.release(); - return kTfLiteOk; -} - -TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data) { -// TODO(b/145762662): It would be preferable to have the build graph for TF Lite -// Micro not have the ParseOpData function at all. This would require splitting -// the current file into two separate files, one of which defines the -// ParseOpData function and the other that defines the operator specific parse -// functions (e.g. ParseAdd). -// -// Such a split was attempted but was not worth the effort at the time because -// of the following reasons: -// * We could either duplicate the functions and the SafeBuiltinDataAllocator -// class in the anonymous namespace of this file, or attempt to make a common -// library with these helper functions and class. -// * Making a common library with a separate build target was not feasible as -// it introduced circular dependencies due to the ErrorReporter and a common -// .cc and .h within the same api build target the also cause circular -// dependencies due to the BuiltinDataAllocator class. -// * If all the builtin operators were to have their own parse functions, or we -// were ok with some amount of code duplication, then this split of the .cc -// files would be a lot more feasible. -#ifdef TF_LITE_STATIC_MEMORY - TF_LITE_REPORT_ERROR( - error_reporter, - "ParseOpData is unsupported on TfLiteMicro, please use the operator " - "specific parse functions (e.g. ParseAdd etc.).\n"); - return kTfLiteError; -#else - return ParseOpDataTfLite(op, op_type, error_reporter, allocator, - builtin_data); -#endif -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/core/api/op_resolver.cc b/components/tflite_micro/Source/tensorflow/lite/core/api/op_resolver.cc deleted file mode 100644 index c239d9ed..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/core/api/op_resolver.cc +++ /dev/null @@ -1,66 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/core/api/op_resolver.h" - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" - -namespace tflite { - -TfLiteStatus GetRegistrationFromOpCode( - const OperatorCode* opcode, const OpResolver& op_resolver, - ErrorReporter* error_reporter, const TfLiteRegistration** registration) { - TfLiteStatus status = kTfLiteOk; - *registration = nullptr; - auto builtin_code = opcode->builtin_code(); - int version = opcode->version(); - - if (builtin_code > BuiltinOperator_MAX || - builtin_code < BuiltinOperator_MIN) { - TF_LITE_REPORT_ERROR( - error_reporter, - "Op builtin_code out of range: %d. Are you using old TFLite binary " - "with newer model?", - builtin_code); - status = kTfLiteError; - } else if (builtin_code != BuiltinOperator_CUSTOM) { - *registration = op_resolver.FindOp(builtin_code, version); - if (*registration == nullptr) { - TF_LITE_REPORT_ERROR( - error_reporter, - "Didn't find op for builtin opcode '%s' version '%d'\n", - EnumNameBuiltinOperator(builtin_code), version); - status = kTfLiteError; - } - } else if (!opcode->custom_code()) { - TF_LITE_REPORT_ERROR( - error_reporter, - "Operator with CUSTOM builtin_code has no custom_code.\n"); - status = kTfLiteError; - } else { - const char* name = opcode->custom_code()->c_str(); - *registration = op_resolver.FindOp(name, version); - if (*registration == nullptr) { - // Do not report error for unresolved custom op, we do the final check - // while preparing ops. - status = kTfLiteError; - } - } - return status; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/core/api/tensor_utils.cc b/components/tflite_micro/Source/tensorflow/lite/core/api/tensor_utils.cc deleted file mode 100644 index 3aac16b6..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/core/api/tensor_utils.cc +++ /dev/null @@ -1,50 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/core/api/tensor_utils.h" - -#include - -#include "tensorflow/lite/c/common.h" - -namespace tflite { - -TfLiteStatus ResetVariableTensor(TfLiteTensor* tensor) { - if (!tensor->is_variable) { - return kTfLiteOk; - } - // TODO(b/115961645): Implement - If a variable tensor has a buffer, reset it - // to the value of the buffer. - int value = 0; - if (tensor->type == kTfLiteInt8) { - value = tensor->params.zero_point; - } - // TODO(b/139446230): Provide a platform header to better handle these - // specific scenarios. -#if __ANDROID__ || defined(__x86_64__) || defined(__i386__) || \ - defined(__i386) || defined(__x86__) || defined(__X86__) || \ - defined(_X86_) || defined(_M_IX86) || defined(_M_X64) - memset(tensor->data.raw, value, tensor->bytes); -#else - char* raw_ptr = tensor->data.raw; - for (size_t i = 0; i < tensor->bytes; ++i) { - *raw_ptr = value; - raw_ptr++; - } -#endif - return kTfLiteOk; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/kernels/internal/quantization_util.cc b/components/tflite_micro/Source/tensorflow/lite/kernels/internal/quantization_util.cc deleted file mode 100644 index cf431cff..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/kernels/internal/quantization_util.cc +++ /dev/null @@ -1,395 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/quantization_util.h" - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" - -namespace tflite { - -namespace { -// These constants are used to manipulate the binary representation of doubles. -// Double-precision binary64 floating point format is: -// Bit | 63 | 62-52 | 51-0 | -// | Sign | Exponent | Fraction | -// To avoid 64-bit integers as much as possible, I break this into high and -// low 32-bit chunks. High is: -// Bit | 31 | 30-20 | 19-0 | -// | Sign | Exponent | High Fraction | -// Low is: -// Bit | 31-0 | -// | Low Fraction | -// We then access the components through logical bit-wise operations to -// extract the parts needed, with the positions and masks derived from the -// layout shown above. -constexpr uint64_t kSignMask = 0x8000000000000000LL; -constexpr uint64_t kExponentMask = 0x7ff0000000000000LL; -constexpr int32_t kExponentShift = 52; -constexpr int32_t kExponentBias = 1023; -constexpr uint32_t kExponentIsBadNum = 0x7ff; -constexpr uint64_t kFractionMask = 0x000fffffffc00000LL; -constexpr uint32_t kFractionShift = 22; -constexpr uint32_t kFractionRoundingMask = 0x003fffff; -constexpr uint32_t kFractionRoundingThreshold = 0x00200000; -} // namespace - -void QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, - int* shift) { - if (double_multiplier == 0.) { - *quantized_multiplier = 0; - *shift = 0; - return; - } -#ifdef TFLITE_EMULATE_FLOAT - // If we're trying to avoid the use of floating-point instructions (for - // example on microcontrollers) then use an alternative implementation - // that only requires integer and bitwise operations. To enable this, you - // need to set the define during the build process for your platform. - int64_t q_fixed = IntegerFrExp(double_multiplier, shift); -#else // TFLITE_EMULATE_FLOAT - const double q = std::frexp(double_multiplier, shift); - auto q_fixed = static_cast(TfLiteRound(q * (1ll << 31))); -#endif // TFLITE_EMULATE_FLOAT - TFLITE_CHECK(q_fixed <= (1ll << 31)); - if (q_fixed == (1ll << 31)) { - q_fixed /= 2; - ++*shift; - } - TFLITE_CHECK_LE(q_fixed, std::numeric_limits::max()); - // A shift amount smaller than -31 would cause all bits to be shifted out - // and thus all results would be zero. We implement that instead with - // q_fixed==0, so as to avoid hitting issues with right-shift - // operations with shift amounts greater than 31. Note that this happens - // roughly when abs(double_multiplier) < 2^-31 and the present handling means - // that we're effectively flushing tiny double_multiplier's to zero. - // We could conceivably handle values in the range (roughly) [32, 63] - // as 'denormals' i.e. (shift==0, q_fixed < 2^30). In that point of view - // the present handling is just doing 'flush denormals to zero'. We could - // reconsider and actually generate nonzero denormals if a need arises. - if (*shift < -31) { - *shift = 0; - q_fixed = 0; - } - *quantized_multiplier = static_cast(q_fixed); -} - -void QuantizeMultiplierGreaterThanOne(double double_multiplier, - int32_t* quantized_multiplier, - int* left_shift) { - TFLITE_CHECK_GT(double_multiplier, 1.); - QuantizeMultiplier(double_multiplier, quantized_multiplier, left_shift); - TFLITE_CHECK_GE(*left_shift, 0); -} - -void QuantizeMultiplierSmallerThanOneExp(double double_multiplier, - int32_t* quantized_multiplier, - int* left_shift) { - TFLITE_CHECK_LT(double_multiplier, 1.); - TFLITE_CHECK_GT(double_multiplier, 0.); - int shift; - QuantizeMultiplier(double_multiplier, quantized_multiplier, &shift); - TFLITE_CHECK_LE(shift, 0); - *left_shift = shift; -} - -int64_t IntegerFrExp(double input, int* shift) { - // Make sure our assumptions about the double layout hold. - TFLITE_CHECK_EQ(8, sizeof(double)); - - // We want to access the bits of the input double value directly, which is - // tricky to do safely, so use a union to handle the casting. - union { - double double_value; - uint64_t double_as_uint; - } cast_union; - cast_union.double_value = input; - const uint64_t u = cast_union.double_as_uint; - - // If the bitfield is all zeros apart from the sign bit, this is a normalized - // zero value, so return standard values for this special case. - if ((u & ~kSignMask) == 0) { - *shift = 0; - return 0; - } - - // Deal with NaNs and Infs, which are always indicated with a fixed pattern in - // the exponent, and distinguished by whether the fractions are zero or - // non-zero. - const uint32_t exponent_part = ((u & kExponentMask) >> kExponentShift); - if (exponent_part == kExponentIsBadNum) { - *shift = std::numeric_limits::max(); - if (u & kFractionMask) { - // NaN, so just return zero (with the exponent set to INT_MAX). - return 0; - } else { - // Infinity, so return +/- INT_MAX. - if (u & kSignMask) { - return std::numeric_limits::min(); - } else { - return std::numeric_limits::max(); - } - } - } - - // The shift is fairly easy to extract from the high bits of the double value, - // just by masking it out and applying a bias. The std::frexp() implementation - // always returns values between 0.5 and 1.0 though, whereas the exponent - // assumes 1.0 to 2.0 is the standard range, so I add on one to match that - // interface. - *shift = (exponent_part - kExponentBias) + 1; - - // There's an implicit high bit in the double format definition, so make sure - // we include that at the top, and then reconstruct the rest of the fractional - // value from the remaining fragments. - int64_t fraction = 0x40000000 + ((u & kFractionMask) >> kFractionShift); - - // We're cutting off some bits at the bottom, so to exactly match the standard - // frexp implementation here we'll apply rounding by adding one to the least - // significant bit of the result if the discarded portion is over half of the - // maximum. - if ((u & kFractionRoundingMask) > kFractionRoundingThreshold) { - fraction += 1; - } - // Negate the fraction if the sign bit was set. - if (u & kSignMask) { - fraction *= -1; - } - - return fraction; -} - -double DoubleFromFractionAndShift(int64_t fraction, int shift) { - union { - double double_value; - uint64_t double_as_uint; - } result; - - // Detect NaNs and infinities. - if (shift == std::numeric_limits::max()) { - if (fraction == 0) { - return std::numeric_limits::quiet_NaN(); - } else if (fraction > 0) { - return std::numeric_limits::infinity(); - } else { - return -std::numeric_limits::infinity(); - } - } - - // Return a normalized zero for a zero fraction. - if (fraction == 0) { - result.double_as_uint = 0; - return result.double_value; - } - - bool is_negative = (fraction < 0); - int64_t encoded_fraction = is_negative ? -fraction : fraction; - int64_t encoded_shift = (shift - 1); - while (encoded_fraction < 0x40000000) { - encoded_fraction *= 2; - encoded_shift -= 1; - } - while (encoded_fraction > 0x80000000) { - encoded_fraction /= 2; - encoded_shift += 1; - } - encoded_fraction -= 0x40000000; - if (encoded_shift < -1022) { - encoded_shift = -1023; - } else if (encoded_shift > 1022) { - encoded_shift = 1023; - } - encoded_shift += kExponentBias; - uint64_t encoded_sign = is_negative ? kSignMask : 0; - result.double_as_uint = encoded_sign | (encoded_shift << kExponentShift) | - (encoded_fraction << kFractionShift); - return result.double_value; -} - -double IntegerDoubleMultiply(double a, double b) { - int a_shift; - const int64_t a_fraction = IntegerFrExp(a, &a_shift); - int b_shift; - const int64_t b_fraction = IntegerFrExp(b, &b_shift); - // Detect NaNs and infinities. - if (a_shift == std::numeric_limits::max() || - (b_shift == std::numeric_limits::max())) { - return std::numeric_limits::quiet_NaN(); - } - const int result_shift = a_shift + b_shift + 1; - const int64_t result_fraction = (a_fraction * b_fraction) >> 32; - return DoubleFromFractionAndShift(result_fraction, result_shift); -} - -int IntegerDoubleCompare(double a, double b) { - int a_shift; - const int64_t a_fraction = IntegerFrExp(a, &a_shift); - int b_shift; - const int64_t b_fraction = IntegerFrExp(b, &b_shift); - - // Detect NaNs and infinities. - if (a_shift == std::numeric_limits::max() || - (b_shift == std::numeric_limits::max())) { - return 1; - } - - if ((a_fraction == 0) && (b_fraction < 0)) { - return 1; - } else if ((a_fraction < 0) && (b_fraction == 0)) { - return -1; - } else if (a_shift < b_shift) { - return -1; - } else if (a_shift > b_shift) { - return 1; - } else if (a_fraction < b_fraction) { - return -1; - } else if (a_fraction > b_fraction) { - return 1; - } else { - return 0; - } -} - -void PreprocessSoftmaxScaling(double beta, double input_scale, - int input_integer_bits, - int32_t* quantized_multiplier, int* left_shift) { - // If the overall multiplier (input and beta) is large, then exp() of an - // input difference of 1 scaled by this will be large. In other words, we - // can cap the multiplier and know that, when it is used, the output will be - // (round to) zero wherever the input is not at the maximum value. - - // If the overall scale is less than one, and input_integer_bits=0, then the - // result is double equivalent of Q0.31 (actually with more precision). Thus - // this generates a Q(input_integer_bits).(31-input_integer_bits) - // representation. -#ifdef TFLITE_EMULATE_FLOAT - const double input_beta = IntegerDoubleMultiply(beta, input_scale); - int shift; - int64_t fraction = IntegerFrExp(input_beta, &shift); - shift += (31 - input_integer_bits); - double input_beta_real_multiplier = - DoubleFromFractionAndShift(fraction, shift); - if (IntegerDoubleCompare(input_beta_real_multiplier, (1ll << 31) - 1.0) > 0) { - input_beta_real_multiplier = (1ll << 31) - 1.0; - } -#else // TFLITE_EMULATE_FLOAT - const double input_beta_real_multiplier = std::min( - beta * input_scale * (1 << (31 - input_integer_bits)), (1ll << 31) - 1.0); -#endif // TFLITE_EMULATE_FLOAT - - QuantizeMultiplierGreaterThanOne(input_beta_real_multiplier, - quantized_multiplier, left_shift); -} - -void PreprocessLogSoftmaxScalingExp(double beta, double input_scale, - int input_integer_bits, - int32_t* quantized_multiplier, - int* left_shift, - int32_t* reverse_scaling_divisor, - int* reverse_scaling_left_shift) { - PreprocessSoftmaxScaling(beta, input_scale, input_integer_bits, - quantized_multiplier, left_shift); - - // Also calculate what amounts to the inverse scaling factor for the input. - const double real_reverse_scaling_divisor = - (1 << (31 - *left_shift)) / static_cast(*quantized_multiplier); - tflite::QuantizeMultiplierSmallerThanOneExp(real_reverse_scaling_divisor, - reverse_scaling_divisor, - reverse_scaling_left_shift); -} - -int CalculateInputRadius(int input_integer_bits, int input_left_shift, - int total_signed_bits) { -#ifdef TFLITE_EMULATE_FLOAT - int64_t result = (1 << input_integer_bits) - 1; - result <<= (total_signed_bits - input_integer_bits); - result >>= input_left_shift; - return result; -#else // TFLITE_EMULATE_FLOAT - const double max_input_rescaled = - 1.0 * ((1 << input_integer_bits) - 1) * - (1ll << (total_signed_bits - input_integer_bits)) / - (1ll << input_left_shift); - // Tighten bound using floor. Suppose that we could use the exact value. - // After scaling the difference, the result would be at the maximum. Thus we - // must ensure that our value has lower magnitude. - return static_cast(std::floor(max_input_rescaled)); -#endif // TFLITE_EMULATE_FLOAT -} - -void NudgeQuantizationRange(const float min, const float max, - const int quant_min, const int quant_max, - float* nudged_min, float* nudged_max, - float* nudged_scale) { - // This code originates from tensorflow/core/kernels/fake_quant_ops_functor.h. - const float quant_min_float = static_cast(quant_min); - const float quant_max_float = static_cast(quant_max); - *nudged_scale = (max - min) / (quant_max_float - quant_min_float); - const float zero_point_from_min = quant_min_float - min / *nudged_scale; - uint16_t nudged_zero_point; - if (zero_point_from_min < quant_min_float) { - nudged_zero_point = static_cast(quant_min); - } else if (zero_point_from_min > quant_max_float) { - nudged_zero_point = static_cast(quant_max); - } else { - nudged_zero_point = static_cast(TfLiteRound(zero_point_from_min)); - } - *nudged_min = (quant_min_float - nudged_zero_point) * (*nudged_scale); - *nudged_max = (quant_max_float - nudged_zero_point) * (*nudged_scale); -} - -void FakeQuantizeArray(const float nudged_scale, const float nudged_min, - const float nudged_max, const float* input_data, - float* output_data, const float size) { - // This code originates from tensorflow/core/kernels/fake_quant_ops_functor.h. - const float inv_nudged_scale = 1.0f / nudged_scale; - - for (int i = 0; i < size; i++) { - const float src_val = input_data[i]; - const float clamped = std::min(nudged_max, std::max(nudged_min, src_val)); - const float clamped_shifted = clamped - nudged_min; - const float dst_val = - TfLiteRound(clamped_shifted * inv_nudged_scale) * nudged_scale + - nudged_min; - output_data[i] = dst_val; - } -} - -bool CheckedLog2(const float x, int* log2_result) { - // Using TfLiteRound instead of std::round and std::log instead of - // std::log2 to work around these functions being missing in a toolchain - // used in some TensorFlow tests as of May 2018. - const float x_log2 = std::log(x) * (1.0f / std::log(2.0f)); - const float x_log2_rounded = TfLiteRound(x_log2); - const float x_log2_fracpart = x_log2 - x_log2_rounded; - - *log2_result = static_cast(x_log2_rounded); - return std::abs(x_log2_fracpart) < 1e-3f; -} - -void QuantizeMultiplierArray(const double* effective_scales, size_t size, - int32_t* effective_scale_significand, - int* effective_shift) { - for (size_t i = 0; i < size; ++i) { - QuantizeMultiplier(effective_scales[i], &effective_scale_significand[i], - &effective_shift[i]); - } -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/kernels/kernel_util.cc b/components/tflite_micro/Source/tensorflow/lite/kernels/kernel_util.cc deleted file mode 100644 index 74c8c88d..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/kernels/kernel_util.cc +++ /dev/null @@ -1,325 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/kernels/kernel_util.h" - -#include -#include - -#include -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" - -namespace tflite { - -const TfLiteTensor* GetInput(const TfLiteContext* context, - const TfLiteNode* node, int index) { - if (context->tensors != nullptr) { - return &context->tensors[node->inputs->data[index]]; - } else { - return context->GetTensor(context, node->inputs->data[index]); - } -} - -TfLiteTensor* GetVariableInput(TfLiteContext* context, const TfLiteNode* node, - int index) { - TfLiteTensor* tensor = nullptr; - if (context->tensors != nullptr) { - tensor = &context->tensors[node->inputs->data[index]]; - } else { - tensor = context->GetTensor(context, node->inputs->data[index]); - } - return tensor->is_variable ? tensor : nullptr; -} - -TfLiteTensor* GetOutput(TfLiteContext* context, const TfLiteNode* node, - int index) { - if (context->tensors != nullptr) { - return &context->tensors[node->outputs->data[index]]; - } else { - return context->GetTensor(context, node->outputs->data[index]); - } -} - -const TfLiteTensor* GetOptionalInputTensor(const TfLiteContext* context, - const TfLiteNode* node, int index) { - const bool use_tensor = index < node->inputs->size && - node->inputs->data[index] != kTfLiteOptionalTensor; - if (use_tensor) { - if (context->tensors != nullptr) { - return &context->tensors[node->inputs->data[index]]; - } else { - return context->GetTensor(context, node->inputs->data[index]); - } - } - return nullptr; -} - -// Per-axis -TfLiteStatus PopulateConvolutionQuantizationParams( - TfLiteContext* context, const TfLiteTensor* input, - const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output, - const TfLiteFusedActivation& activation, int32_t* multiplier, int* shift, - int32_t* output_activation_min, int32_t* output_activation_max, - int32_t* per_channel_multiplier, int* per_channel_shift) { - const auto* affine_quantization = - reinterpret_cast(filter->quantization.params); - return PopulateConvolutionQuantizationParams( - context, input, filter, bias, output, activation, multiplier, shift, - output_activation_min, output_activation_max, per_channel_multiplier, - per_channel_shift, affine_quantization->scale->size); -} - -// Per-axis & per-tensor -TfLiteStatus PopulateConvolutionQuantizationParams( - TfLiteContext* context, const TfLiteTensor* input, - const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output, - const TfLiteFusedActivation& activation, int32_t* multiplier, int* shift, - int32_t* output_activation_min, int32_t* output_activation_max, - int32_t* per_channel_multiplier, int* per_channel_shift, int num_channels) { - TF_LITE_ENSURE_EQ(context, input->quantization.type, - kTfLiteAffineQuantization); - TF_LITE_ENSURE_EQ(context, filter->quantization.type, - kTfLiteAffineQuantization); - // TODO(jianlijianli): Enable bias type check and bias scale == input scale - // * filter scale for each channel in affine quantization once bias - // quantization is properly populated. - // TF_LITE_ENSURE_EQ(context, bias->quantization.type, - // kTfLiteAffineQuantization); - - // Check data type. - const auto* affine_quantization = - reinterpret_cast(filter->quantization.params); - TF_LITE_ENSURE(context, affine_quantization); - TF_LITE_ENSURE(context, affine_quantization->scale); - const bool is_per_channel = affine_quantization->scale->size > 1; - if (is_per_channel) { - // Currently only Int8/Int16 is supported for per channel quantization. - TF_LITE_ENSURE(context, - input->type == kTfLiteInt8 || input->type == kTfLiteInt16); - TF_LITE_ENSURE_EQ(context, filter->type, kTfLiteInt8); - TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size, num_channels); - TF_LITE_ENSURE_EQ( - context, num_channels, - filter->dims->data[affine_quantization->quantized_dimension]); - } - - // Populate multiplier and shift using affine quantization. - const float input_scale = input->params.scale; - const float output_scale = output->params.scale; - const float* filter_scales = affine_quantization->scale->data; - for (int i = 0; i < num_channels; ++i) { - // If per-tensor quantization parameter is specified, broadcast it along the - // quantization dimension (channels_out). - const float scale = is_per_channel ? filter_scales[i] : filter_scales[0]; - const double filter_scale = static_cast(scale); - const double effective_output_scale = static_cast(input_scale) * - filter_scale / - static_cast(output_scale); - int32_t significand; - int channel_shift; - QuantizeMultiplier(effective_output_scale, &significand, &channel_shift); - per_channel_multiplier[i] = significand; - per_channel_shift[i] = channel_shift; - } - - // Populate scalar quantization parameters. - // This check on legacy quantization parameters is kept only for backward - // compatibility. - if (input->type == kTfLiteUInt8) { - // Check bias scale == input scale * filter scale. - double real_multiplier = 0.0; - TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler( - context, input, filter, bias, output, &real_multiplier)); - int exponent; - - // Populate quantization parameters with multiplier and shift. - QuantizeMultiplier(real_multiplier, multiplier, &exponent); - *shift = -exponent; - } - if (input->type == kTfLiteInt8 || input->type == kTfLiteUInt8 || - input->type == kTfLiteInt16) { - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, activation, output, output_activation_min, - output_activation_max)); - } - return kTfLiteOk; -} - -TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, - const TfLiteTensor* input, - const TfLiteTensor* filter, - const TfLiteTensor* bias, - TfLiteTensor* output, - double* multiplier) { - const double input_product_scale = static_cast(input->params.scale) * - static_cast(filter->params.scale); - // TODO(ahentz): The following conditions must be guaranteed by the training - // pipeline. - if (bias) { - const double bias_scale = static_cast(bias->params.scale); - // Here we're making sure the input_product_scale & bias_scale are about the - // same. Since we have: - // (output - output_zp) * output_scale = - // input_product_scale * input_product + bias * bias_scale ---- (0) - // - // (0) equals: - // (input_product + bias) * input_product_scale ----- (1) - // + - // bias * (bias_scale - input_product_scale) ------ (2) - // - // For the real kernel computation, we're doing (1), so we really need to - // make sure (2) has minimum impact on the output, so: - // bias * (bias_scale - input_product_scale) / output_scale should be - // a small number for an integer. - // Since normally bias should be within a small range. - // We should expect (bias_scale - input_product_scale) / output_scale to - // be a small number like 0.02. - const double scale_diff = std::abs(input_product_scale - bias_scale); - const double output_scale = static_cast(output->params.scale); - - TF_LITE_ENSURE(context, scale_diff / output_scale <= 0.02); - } - return GetQuantizedConvolutionMultipler(context, input, filter, output, - multiplier); -} - -TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, - const TfLiteTensor* input, - const TfLiteTensor* filter, - TfLiteTensor* output, - double* multiplier) { - const double input_product_scale = - static_cast(input->params.scale * filter->params.scale); - TF_LITE_ENSURE(context, input_product_scale >= 0); - *multiplier = input_product_scale / static_cast(output->params.scale); - - return kTfLiteOk; -} - -namespace { -void CalculateActivationRangeQuantizedImpl(TfLiteFusedActivation activation, - int32_t qmin, int32_t qmax, - TfLiteTensor* output, - int32_t* act_min, int32_t* act_max) { - const auto scale = output->params.scale; - const auto zero_point = output->params.zero_point; - - auto quantize = [scale, zero_point](float f) { - return zero_point + static_cast(TfLiteRound(f / scale)); - }; - - if (activation == kTfLiteActRelu) { - *act_min = std::max(qmin, quantize(0.0)); - *act_max = qmax; - } else if (activation == kTfLiteActRelu6) { - *act_min = std::max(qmin, quantize(0.0)); - *act_max = std::min(qmax, quantize(6.0)); - } else if (activation == kTfLiteActReluN1To1) { - *act_min = std::max(qmin, quantize(-1.0)); - *act_max = std::min(qmax, quantize(1.0)); - } else { - *act_min = qmin; - *act_max = qmax; - } -} -} // namespace - -TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context, - TfLiteFusedActivation activation, - TfLiteTensor* output, - int32_t* act_min, - int32_t* act_max) { - int32_t qmin = 0; - int32_t qmax = 0; - if (output->type == kTfLiteUInt8) { - qmin = std::numeric_limits::min(); - qmax = std::numeric_limits::max(); - } else if (output->type == kTfLiteInt8) { - qmin = std::numeric_limits::min(); - qmax = std::numeric_limits::max(); - } else if (output->type == kTfLiteInt16) { - qmin = std::numeric_limits::min(); - qmax = std::numeric_limits::max(); - } else { - TF_LITE_ENSURE(context, false); - } - - CalculateActivationRangeQuantizedImpl(activation, qmin, qmax, output, act_min, - act_max); - return kTfLiteOk; -} - -bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2) { - return TfLiteIntArrayEqual(input1->dims, input2->dims); -} - -// TODO(petewarden): Having macros around this is ugly, look at other strategies -// before replicating this approach elsewhere. -#ifndef TF_LITE_STATIC_MEMORY -TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - TfLiteIntArray** output_shape) { - int dims1 = NumDimensions(input1); - int dims2 = NumDimensions(input2); - int out_dims = std::max(dims1, dims2); - if (NumElements(input1) == 0) { - *output_shape = TfLiteIntArrayCopy(input1->dims); - return kTfLiteOk; - } - std::unique_ptr shape( - TfLiteIntArrayCreate(out_dims), TfLiteIntArrayFree); - for (int i = 0; i < out_dims; ++i) { - int d1 = i >= dims1 ? 1 : SizeOfDimension(input1, dims1 - i - 1); - int d2 = i >= dims2 ? 1 : SizeOfDimension(input2, dims2 - i - 1); - TF_LITE_ENSURE(context, d1 == d2 || d1 == 1 || d2 == 1); - shape->data[out_dims - i - 1] = std::max(d1, d2); - } - *output_shape = shape.release(); - return kTfLiteOk; -} - -TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - const TfLiteTensor* input3, - TfLiteIntArray** output_shape) { - int dims1 = NumDimensions(input1); - int dims2 = NumDimensions(input2); - int dims3 = NumDimensions(input3); - int out_dims = std::max(std::max(dims1, dims2), dims3); - std::unique_ptr shape( - TfLiteIntArrayCreate(out_dims), TfLiteIntArrayFree); - for (int i = 0; i < out_dims; ++i) { - int d1 = i >= dims1 ? 1 : SizeOfDimension(input1, dims1 - i - 1); - int d2 = i >= dims2 ? 1 : SizeOfDimension(input2, dims2 - i - 1); - int d3 = i >= dims3 ? 1 : SizeOfDimension(input3, dims3 - i - 1); - int max_value = std::max(std::max(d1, d2), d3); - TF_LITE_ENSURE(context, d1 == 1 || d1 == max_value); - TF_LITE_ENSURE(context, d2 == 1 || d2 == max_value); - TF_LITE_ENSURE(context, d3 == 1 || d3 == max_value); - shape->data[out_dims - i - 1] = max_value; - } - *output_shape = shape.release(); - return kTfLiteOk; -} -#endif // TF_LITE_STATIC_MEMORY - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/all_ops_resolver.cc b/components/tflite_micro/Source/tensorflow/lite/micro/all_ops_resolver.cc deleted file mode 100644 index ff461cb9..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/all_ops_resolver.cc +++ /dev/null @@ -1,91 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/all_ops_resolver.h" - -#include "tensorflow/lite/micro/kernels/micro_ops.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace custom { -TfLiteRegistration* Register_ETHOSU(); -const char* GetString_ETHOSU(); -} // namespace custom -} // namespace micro -} // namespace ops - -AllOpsResolver::AllOpsResolver() { - // Please keep this list of Builtin Operators in alphabetical order. - AddAbs(); - AddAdd(); - AddArgMax(); - AddArgMin(); - AddAveragePool2D(); - AddCeil(); - AddConcatenation(); - AddConv2D(); - AddCos(); - AddDepthwiseConv2D(); - AddDequantize(); - AddEqual(); - AddFloor(); - AddFullyConnected(); - AddGreater(); - AddGreaterEqual(); - AddHardSwish(); - AddL2Normalization(); - AddLess(); - AddLessEqual(); - AddLog(); - AddLogicalAnd(); - AddLogicalNot(); - AddLogicalOr(); - AddLogistic(); - AddMaximum(); - AddMaxPool2D(); - AddMean(); - AddMinimum(); - AddMul(); - AddNeg(); - AddNotEqual(); - AddPack(); - AddPad(); - AddPadV2(); - AddPrelu(); - AddQuantize(); - AddRelu(); - AddRelu6(); - AddReshape(); - AddResizeNearestNeighbor(); - AddRound(); - AddRsqrt(); - AddSin(); - AddSoftmax(); - AddSplit(); - AddSqrt(); - AddSquare(); - AddStridedSlice(); - AddSub(); - AddSvdf(); - AddTanh(); - AddUnpack(); - - // TODO(b/159644355): Figure out if custom Ops belong in AllOpsResolver. - TfLiteRegistration* registration = - tflite::ops::micro::custom::Register_ETHOSU(); - if (registration) { - AddCustom(tflite::ops::micro::custom::GetString_ETHOSU(), registration); - } -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/benchmarks/keyword_scrambled_model_data.cc b/components/tflite_micro/Source/tensorflow/lite/micro/benchmarks/keyword_scrambled_model_data.cc deleted file mode 100644 index 834f44ca..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/benchmarks/keyword_scrambled_model_data.cc +++ /dev/null @@ -1,2898 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/benchmarks/keyword_scrambled_model_data.h" - -// Keep model aligned to 8 bytes to guarantee aligned 64-bit accesses. -alignas(8) const unsigned char g_keyword_scrambled_model_data[] = { - 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, - 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, - 0x0e, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xd0, 0x6e, 0x00, 0x00, - 0xe4, 0x85, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, - 0xbc, 0x6e, 0x00, 0x00, 0xac, 0x56, 0x00, 0x00, 0x9c, 0x52, 0x00, 0x00, - 0x8c, 0x51, 0x00, 0x00, 0x7c, 0x4d, 0x00, 0x00, 0x2c, 0x4d, 0x00, 0x00, - 0x1c, 0x49, 0x00, 0x00, 0x0c, 0x45, 0x00, 0x00, 0xfc, 0x43, 0x00, 0x00, - 0xec, 0x3f, 0x00, 0x00, 0x9c, 0x3f, 0x00, 0x00, 0x8c, 0x3b, 0x00, 0x00, - 0x7c, 0x37, 0x00, 0x00, 0x6c, 0x36, 0x00, 0x00, 0x5c, 0x32, 0x00, 0x00, - 0x0c, 0x32, 0x00, 0x00, 0xfc, 0x2d, 0x00, 0x00, 0xec, 0x29, 0x00, 0x00, - 0xdc, 0x28, 0x00, 0x00, 0xcc, 0x24, 0x00, 0x00, 0x7c, 0x24, 0x00, 0x00, - 0x6c, 0x22, 0x00, 0x00, 0x5c, 0x1a, 0x00, 0x00, 0xcc, 0x19, 0x00, 0x00, - 0xbc, 0x15, 0x00, 0x00, 0xac, 0x0d, 0x00, 0x00, 0x1c, 0x0d, 0x00, 0x00, - 0x0c, 0x09, 0x00, 0x00, 0xfc, 0x00, 0x00, 0x00, 0x6c, 0x00, 0x00, 0x00, - 0x1c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x2a, 0x91, 0xff, 0xff, - 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x34, 0xe1, 0x4f, 0xa1, - 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0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x60, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0xc4, 0x00, 0x00, 0x00, - 0xb4, 0x00, 0x00, 0x00, 0xa4, 0x00, 0x00, 0x00, 0x98, 0x00, 0x00, 0x00, - 0x8c, 0x00, 0x00, 0x00, 0x80, 0x00, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, - 0x68, 0x00, 0x00, 0x00, 0x5c, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00, - 0x44, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, - 0x20, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0a, 0x00, - 0x0c, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, - 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x96, 0xff, 0xff, 0xff, - 0x00, 0x00, 0x00, 0x72, 0x9e, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x19, - 0xa6, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x09, 0xae, 0xff, 0xff, 0xff, - 0x00, 0x00, 0x00, 0x1b, 0xb6, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x1b, - 0xbe, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x1b, 0xc6, 0xff, 0xff, 0xff, - 0x00, 0x00, 0x00, 0x09, 0xce, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x1b, - 0xd6, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x09, 0xde, 0xff, 0xff, 0xff, - 0x00, 0x00, 0x00, 0x1b, 0xe6, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x09, - 0xfa, 0xff, 0xff, 0xff, 0x00, 0x1b, 0x06, 0x00, 0x06, 0x00, 0x05, 0x00, - 0x06, 0x00, 0x00, 0x00, 0x00, 0x09, 0x06, 0x00, 0x08, 0x00, 0x07, 0x00, - 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1b}; - -const unsigned int g_keyword_scrambled_model_data_length = 34520; diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/debug_log.cc b/components/tflite_micro/Source/tensorflow/lite/micro/debug_log.cc deleted file mode 100644 index 966b0bb6..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/debug_log.cc +++ /dev/null @@ -1,27 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/debug_log.h" -#include "stdio.h" - -#ifdef __cplusplus -extern "C" { -#endif - -void DebugLog(const char *s) { printf("%s", s); } - -#ifdef __cplusplus -} -#endif diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.cc b/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.cc deleted file mode 100644 index bc409b5b..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.cc +++ /dev/null @@ -1,25 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h" - -// This dummy implementation writes person and no person scores to the error -// console. Real applications will want to take some custom action instead, and -// should implement their own versions of this function. -void RespondToDetection(tflite::ErrorReporter* error_reporter, - int8_t person_score, int8_t no_person_score) { - TF_LITE_REPORT_ERROR(error_reporter, "person score:%d no person score %d", - person_score, no_person_score); -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/image_provider.cc b/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/image_provider.cc deleted file mode 100644 index 0b295228..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/image_provider.cc +++ /dev/null @@ -1,26 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h" -#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h" - -TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width, - int image_height, int channels, int8_t* image_data, - uint8_t * hardware_input) { - for (int i = 0; i < image_width * image_height * channels; ++i) { - image_data[i] = hardware_input[i]; - } - return kTfLiteOk; -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc b/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc deleted file mode 100644 index 8d248ab7..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc +++ /dev/null @@ -1,119 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h" - -#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h" -#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h" -#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h" -#include "tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" -#include "tensorflow/lite/micro/micro_interpreter.h" -#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" -#include "tensorflow/lite/schema/schema_generated.h" -#include "tensorflow/lite/version.h" - -// Globals, used for compatibility with Arduino-style sketches. -namespace { -tflite::ErrorReporter* error_reporter = nullptr; -const tflite::Model* model = nullptr; -tflite::MicroInterpreter* interpreter = nullptr; -TfLiteTensor* input = nullptr; - -// In order to use optimized tensorflow lite kernels, a signed int8_t quantized -// model is preferred over the legacy unsigned model format. This means that -// throughout this project, input images must be converted from unisgned to -// signed format. The easiest and quickest way to convert from unsigned to -// signed 8-bit integers is to subtract 128 from the unsigned value to get a -// signed value. - -// An area of memory to use for input, output, and intermediate arrays. -constexpr int kTensorArenaSize = 115 * 1024; -static uint8_t tensor_arena[kTensorArenaSize]; -} // namespace - -// The name of this function is important for Arduino compatibility. -void person_detect_init() { - // Set up logging. Google style is to avoid globals or statics because of - // lifetime uncertainty, but since this has a trivial destructor it's okay. - // NOLINTNEXTLINE(runtime-global-variables) - static tflite::MicroErrorReporter micro_error_reporter; - error_reporter = µ_error_reporter; - - // Map the model into a usable data structure. This doesn't involve any - // copying or parsing, it's a very lightweight operation. - model = tflite::GetModel(g_person_detect_model_data); - if (model->version() != TFLITE_SCHEMA_VERSION) { - TF_LITE_REPORT_ERROR(error_reporter, - "Model provided is schema version %d not equal " - "to supported version %d.", - model->version(), TFLITE_SCHEMA_VERSION); - return; - } - - // Pull in only the operation implementations we need. - // This relies on a complete list of all the ops needed by this graph. - // An easier approach is to just use the AllOpsResolver, but this will - // incur some penalty in code space for op implementations that are not - // needed by this graph. - // - // tflite::AllOpsResolver resolver; - // NOLINTNEXTLINE(runtime-global-variables) - static tflite::MicroMutableOpResolver<5> micro_op_resolver; - micro_op_resolver.AddAveragePool2D(); - micro_op_resolver.AddConv2D(); - micro_op_resolver.AddDepthwiseConv2D(); - micro_op_resolver.AddReshape(); - micro_op_resolver.AddSoftmax(); - - // Build an interpreter to run the model with. - // NOLINTNEXTLINE(runtime-global-variables) - static tflite::MicroInterpreter static_interpreter( - model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter); - interpreter = &static_interpreter; - - // Allocate memory from the tensor_arena for the model's tensors. - TfLiteStatus allocate_status = interpreter->AllocateTensors(); - if (allocate_status != kTfLiteOk) { - TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed"); - return; - } - - // Get information about the memory area to use for the model's input. - input = interpreter->input(0); -} - -// The name of this function is important for Arduino compatibility. -int person_detect(uint8_t * hardware_input) { - // Get image from provider. - if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels, - input->data.int8, hardware_input)) { - TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed."); - } - - // Run the model on this input and make sure it succeeds. - if (kTfLiteOk != interpreter->Invoke()) { - TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed."); - } - - TfLiteTensor* output = interpreter->output(0); - - // Process the inference results. - int8_t person_score = output->data.uint8[kPersonIndex]; - int8_t no_person_score = output->data.uint8[kNotAPersonIndex]; - RespondToDetection(error_reporter, person_score, no_person_score); - if(person_score >= no_person_score + 50) return 1; - else return 0; -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/model_settings.cc b/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/model_settings.cc deleted file mode 100644 index c7359b8f..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/examples/person_detection_experimental/model_settings.cc +++ /dev/null @@ -1,21 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h" - -const char* kCategoryLabels[kCategoryCount] = { - "notperson", - "person", -}; diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/activations.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/activations.cc deleted file mode 100644 index 2bdc0b51..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/activations.cc +++ /dev/null @@ -1,285 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace activations { -namespace { - -struct ReluOpData { - ReluParams params; -}; - -struct Relu6OpData { - int8_t six_int8; - int8_t zero_int8; - uint8_t six_uint8; - uint8_t zero_uint8; -}; - -} // namespace - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -template -inline void ReluQuantized(const ReluOpData& data, - const RuntimeShape& input_shape, - const RuntimeShape& output_shape, const T* input_data, - T* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - const int32_t val = static_cast(input_data[i]); - int32_t clamped = - data.params.output_offset + - MultiplyByQuantizedMultiplier(val - data.params.input_offset, - data.params.output_multiplier, - data.params.output_shift); - clamped = std::max(data.params.quantized_activation_min, clamped); - clamped = std::min(data.params.quantized_activation_max, clamped); - output_data[i] = static_cast(clamped); - } -} - -template -inline void CalculateReluOpData(const TfLiteTensor* input, TfLiteTensor* output, - ReluOpData* data) { - float act_min = 0.0; - float act_max = std::numeric_limits::infinity(); - double real_multiplier = - static_cast(input->params.scale / output->params.scale); - - const RuntimeShape input_shape = GetTensorShape(input); - const RuntimeShape output_shape = GetTensorShape(output); - - QuantizeMultiplier(real_multiplier, &data->params.output_multiplier, - &data->params.output_shift); - - data->params.quantized_activation_min = std::max( - static_cast(std::numeric_limits::min()), - output->params.zero_point + - static_cast(roundf(act_min / output->params.scale))); - data->params.quantized_activation_max = - act_max == std::numeric_limits::infinity() - ? static_cast(std::numeric_limits::max()) - : std::min(static_cast(std::numeric_limits::max()), - output->params.zero_point + - static_cast( - roundf(act_max / output->params.scale))); - data->params.input_offset = input->params.zero_point; - data->params.output_offset = output->params.zero_point; -} - -inline void ReluFloat(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - const float val = input_data[i]; - const float lower = 0.0f; - const float clamped = val < lower ? lower : val; - output_data[i] = clamped; - } -} - -inline void Relu6Float(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - const float val = input_data[i]; - const float upper = 6.0f; - const float lower = 0.0f; - const float clamped = val > upper ? upper : val < lower ? lower : val; - output_data[i] = clamped; - } -} - -template -inline void Relu6Quantized(Q lower, Q upper, const RuntimeShape& input_shape, - const Q* input_data, - const RuntimeShape& output_shape, Q* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - const Q val = input_data[i]; - const Q clamped = val > upper ? upper : val < lower ? lower : val; - output_data[i] = clamped; - } -} - -void* ReluInit(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(ReluOpData)); -} - -TfLiteStatus ReluPrepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - ReluOpData* data = static_cast(node->user_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - if (input->type == kTfLiteInt8) { - CalculateReluOpData(input, output, data); - } else if (input->type == kTfLiteUInt8) { - CalculateReluOpData(input, output, data); - } - - return kTfLiteOk; -} - -TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const ReluOpData& data = *(static_cast(node->user_data)); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - switch (input->type) { - case kTfLiteFloat32: { - ReluFloat(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - - return kTfLiteOk; - } - case kTfLiteInt8: { - ReluQuantized(data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } - case kTfLiteUInt8: { - ReluQuantized(data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } - default: { - TF_LITE_KERNEL_LOG(context, "Only float32 is supported currently, got %s", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - } -} - -void* Relu6Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(Relu6OpData)); -} - -TfLiteStatus Relu6Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - Relu6OpData* data = static_cast(node->user_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - - if (input->type == kTfLiteInt8) { - data->six_int8 = FloatToAsymmetricQuantizedInt8(6.0f, input->params.scale, - input->params.zero_point); - data->zero_int8 = input->params.zero_point; - } else if (input->type == kTfLiteUInt8) { - data->six_uint8 = FloatToAsymmetricQuantizedUInt8(6.0f, input->params.scale, - input->params.zero_point); - data->zero_uint8 = input->params.zero_point; - } - - return kTfLiteOk; -} - -TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const Relu6OpData& data = *(static_cast(node->user_data)); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - switch (input->type) { - case kTfLiteFloat32: { - Relu6Float(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - - return kTfLiteOk; - } - case kTfLiteInt8: { - Relu6Quantized(data.zero_int8, data.six_int8, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } - case kTfLiteUInt8: { - Relu6Quantized(data.zero_uint8, data.six_uint8, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } - default: { - TF_LITE_KERNEL_LOG(context, "Only float32 is supported currently, got %s", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - } -} - -} // namespace activations - -TfLiteRegistration Register_RELU() { - return {/*init=*/activations::ReluInit, - /*free=*/nullptr, - /*prepare=*/activations::ReluPrepare, - /*invoke=*/activations::ReluEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_RELU6() { - return {/*init=*/activations::Relu6Init, - /*free=*/nullptr, - /*prepare=*/activations::Relu6Prepare, - /*invoke=*/activations::Relu6Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/activations_test.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/activations_test.cc deleted file mode 100644 index f7466f9b..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/activations_test.cc +++ /dev/null @@ -1,378 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "mcu_init.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/all_ops_resolver.h" -#include "tensorflow/lite/micro/kernels/kernel_runner.h" -#include "tensorflow/lite/micro/testing/micro_test.h" -#include "tensorflow/lite/micro/testing/test_utils.h" - -namespace tflite { -namespace testing { -namespace { - -void TestReluFloat(const int* input_dims_data, const float* input_data, - const int* output_dims_data, const float* golden, - float* output_data) { - TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data); - TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data); - const int output_elements_count = ElementCount(*output_dims); - - constexpr int inputs_size = 1; - constexpr int outputs_size = 1; - constexpr int tensors_size = inputs_size + outputs_size; - TfLiteTensor tensors[tensors_size] = { - CreateFloatTensor(input_data, input_dims), - CreateFloatTensor(output_data, output_dims), - }; - - int inputs_array_data[] = {1, 0}; - TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); - int outputs_array_data[] = {1, 1}; - TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); - - const TfLiteRegistration registration = ops::micro::Register_RELU(); - micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array, - outputs_array, - /*builtin_data=*/nullptr, micro_test::reporter); - - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare()); - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke()); - - for (int i = 0; i < output_elements_count; ++i) { - TF_LITE_MICRO_EXPECT_NEAR(golden[i], output_data[i], 1e-5f); - } -} - -void TestRelu6Float(const int* input_dims_data, const float* input_data, - const int* output_dims_data, const float* golden, - float* output_data) { - TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data); - TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data); - const int output_elements_count = ElementCount(*output_dims); - - constexpr int inputs_size = 1; - constexpr int outputs_size = 1; - constexpr int tensors_size = inputs_size + outputs_size; - TfLiteTensor tensors[tensors_size] = { - CreateFloatTensor(input_data, input_dims), - CreateFloatTensor(output_data, output_dims), - }; - - int inputs_array_data[] = {1, 0}; - TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); - int outputs_array_data[] = {1, 1}; - TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); - - const TfLiteRegistration registration = ops::micro::Register_RELU6(); - micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array, - outputs_array, - /*builtin_data=*/nullptr, micro_test::reporter); - - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare()); - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke()); - - for (int i = 0; i < output_elements_count; ++i) { - TF_LITE_MICRO_EXPECT_NEAR(golden[i], output_data[i], 1e-5f); - } -} - -void TestReluUint8(const int* input_dims_data, const float* input_data, - uint8_t* input_data_quantized, const float input_scale, - const int input_zero_point, const float* golden, - uint8_t* golden_quantized, const int* output_dims_data, - const float output_scale, const int output_zero_point, - uint8_t* output_data) { - TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data); - TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data); - const int output_elements_count = ElementCount(*output_dims); - - constexpr int inputs_size = 1; - constexpr int outputs_size = 1; - constexpr int tensors_size = inputs_size + outputs_size; - TfLiteTensor tensors[tensors_size] = { - CreateQuantizedTensor(input_data, input_data_quantized, input_dims, - input_scale, input_zero_point), - CreateQuantizedTensor(output_data, output_dims, output_scale, - output_zero_point), - }; - - int inputs_array_data[] = {1, 0}; - TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); - int outputs_array_data[] = {1, 1}; - TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); - - const TfLiteRegistration registration = ops::micro::Register_RELU(); - micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array, - outputs_array, - /*builtin_data=*/nullptr, micro_test::reporter); - - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare()); - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke()); - - AsymmetricQuantize(golden, golden_quantized, output_elements_count, - output_scale, output_zero_point); - - for (int i = 0; i < output_elements_count; ++i) { - TF_LITE_MICRO_EXPECT_EQ(golden_quantized[i], output_data[i]); - } -} - -void TestRelu6Uint8(const int* input_dims_data, const float* input_data, - uint8_t* input_data_quantized, const float input_scale, - const int input_zero_point, const float* golden, - uint8_t* golden_quantized, const int* output_dims_data, - const float output_scale, const int output_zero_point, - uint8_t* output_data) { - TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data); - TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data); - const int output_elements_count = ElementCount(*output_dims); - - constexpr int inputs_size = 1; - constexpr int outputs_size = 1; - constexpr int tensors_size = inputs_size + outputs_size; - TfLiteTensor tensors[tensors_size] = { - CreateQuantizedTensor(input_data, input_data_quantized, input_dims, - input_scale, input_zero_point), - CreateQuantizedTensor(output_data, output_dims, output_scale, - output_zero_point), - }; - - int inputs_array_data[] = {1, 0}; - TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); - int outputs_array_data[] = {1, 1}; - TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); - - const TfLiteRegistration registration = ops::micro::Register_RELU6(); - micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array, - outputs_array, - /*builtin_data=*/nullptr, micro_test::reporter); - - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare()); - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke()); - - AsymmetricQuantize(golden, golden_quantized, output_elements_count, - output_scale, output_zero_point); - - for (int i = 0; i < output_elements_count; ++i) { - TF_LITE_MICRO_EXPECT_EQ(golden_quantized[i], output_data[i]); - } -} - -void TestReluInt8(const int* input_dims_data, const float* input_data, - int8_t* input_data_quantized, const float input_scale, - const int input_zero_point, const float* golden, - int8_t* golden_quantized, const int* output_dims_data, - const float output_scale, const int output_zero_point, - int8_t* output_data) { - TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data); - TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data); - const int output_elements_count = ElementCount(*output_dims); - constexpr int inputs_size = 1; - constexpr int outputs_size = 1; - constexpr int tensors_size = inputs_size + outputs_size; - TfLiteTensor tensors[tensors_size] = { - CreateQuantizedTensor(input_data, input_data_quantized, input_dims, - input_scale, input_zero_point), - CreateQuantizedTensor(output_data, output_dims, output_scale, - output_zero_point), - }; - - int inputs_array_data[] = {1, 0}; - TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); - int outputs_array_data[] = {1, 1}; - TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); - - const TfLiteRegistration registration = ops::micro::Register_RELU(); - micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array, - outputs_array, - /*builtin_data=*/nullptr, micro_test::reporter); - - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare()); - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke()); - - AsymmetricQuantize(golden, golden_quantized, output_elements_count, - output_scale, output_zero_point); - - for (int i = 0; i < output_elements_count; ++i) { - TF_LITE_MICRO_EXPECT_EQ(golden_quantized[i], output_data[i]); - } -} - -void TestRelu6Int8(const int* input_dims_data, const float* input_data, - int8_t* input_data_quantized, const float input_scale, - const int input_zero_point, const float* golden, - int8_t* golden_quantized, const int* output_dims_data, - const float output_scale, const int output_zero_point, - int8_t* output_data) { - TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data); - TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data); - const int output_elements_count = ElementCount(*output_dims); - constexpr int inputs_size = 1; - constexpr int outputs_size = 1; - constexpr int tensors_size = inputs_size + outputs_size; - TfLiteTensor tensors[tensors_size] = { - CreateQuantizedTensor(input_data, input_data_quantized, input_dims, - input_scale, input_zero_point), - CreateQuantizedTensor(output_data, output_dims, output_scale, - output_zero_point), - }; - - int inputs_array_data[] = {1, 0}; - TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); - int outputs_array_data[] = {1, 1}; - TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); - - const TfLiteRegistration registration = ops::micro::Register_RELU6(); - micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array, - outputs_array, - /*builtin_data=*/nullptr, micro_test::reporter); - - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare()); - TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke()); - - AsymmetricQuantize(golden, golden_quantized, output_elements_count, - output_scale, output_zero_point); - - for (int i = 0; i < output_elements_count; ++i) { - TF_LITE_MICRO_EXPECT_EQ(golden_quantized[i], output_data[i]); - } -} - -} // namespace -} // namespace testing -} // namespace tflite - -TF_LITE_MICRO_TESTS_BEGIN - -TF_LITE_MICRO_TEST(SimpleReluTestFloat) { - const int output_elements_count = 10; - const int input_shape[] = {2, 1, 5}; - const float input_data[] = { - 1.0, 2.0, 3.0, 4.0, 5.0, -1.0, -2.0, -3.0, -4.0, -5.0, - }; - const float golden[] = {1.0, 2.0, 3.0, 4.0, 5.0, 0, 0, 0, 0, 0}; - const int output_shape[] = {2, 1, 5}; - float output_data[output_elements_count]; - tflite::testing::TestReluFloat(input_shape, input_data, output_shape, golden, - output_data); -} - -TF_LITE_MICRO_TEST(SimpleRelu6TestFloat) { - const int output_elements_count = 10; - float output_data[output_elements_count]; - const int input_shape[] = {2, 1, 5}; - const float input_data[] = {4.0, 5.0, 6.0, 7.0, 8.0, - -4.0, -5.0, -6.0, -7.0, -8.0}; - const int output_shape[] = {2, 1, 5}; - const float golden[] = { - 4.0, 5.0, 6.0, 6.0, 6.0, 0.0, 0.0, 0.0, 0.0, 0.0, - }; - - tflite::testing::TestRelu6Float(input_shape, input_data, output_shape, golden, - output_data); -} - -TF_LITE_MICRO_TEST(SimpleReluTestUint8) { - const int elements_count = 10; - - const int input_shape[] = {2, 1, 5}; - const float input_data[] = {1, 2, 3, 4, 5, -1, -2, -3, -4, -5}; - uint8_t input_quantized[elements_count]; - const int output_shape[] = {2, 1, 5}; - const float golden[] = {1, 2, 3, 4, 5, 0, 0, 0, 0, 0}; - uint8_t golden_quantized[elements_count]; - uint8_t output_data[elements_count]; - - const float input_scale = 0.5f; - const int input_zero_point = 127; - const float output_scale = 0.5f; - const int output_zero_point = 127; - - tflite::testing::TestReluUint8(input_shape, input_data, input_quantized, - input_scale, input_zero_point, golden, - golden_quantized, output_shape, output_scale, - output_zero_point, output_data); -} - -TF_LITE_MICRO_TEST(SimpleRelu6TestUint8) { - const int elements_count = 10; - - const int input_shape[] = {2, 1, 5}; - const float input_data[] = {4, 5, 6, 7, 8, -1, -2, -3, -4, -5}; - uint8_t input_quantized[elements_count]; - const int output_shape[] = {2, 1, 5}; - const float golden[] = {4, 5, 6, 6, 6, 0, 0, 0, 0, 0}; - uint8_t golden_quantized[elements_count]; - uint8_t output_data[elements_count]; - - const float input_scale = 0.5f; - const int input_zero_point = 127; - const float output_scale = 0.5f; - const int output_zero_point = 127; - - tflite::testing::TestRelu6Uint8(input_shape, input_data, input_quantized, - input_scale, input_zero_point, golden, - golden_quantized, output_shape, output_scale, - output_zero_point, output_data); -} - -TF_LITE_MICRO_TEST(SimpleReluTestInt8) { - const int elements_count = 10; - - const int input_shape[] = {2, 1, 5}; - const float input_data[] = {1, 2, 3, 4, 5, -1, -2, -3, -4, -5}; - int8_t input_quantized[elements_count]; - const int output_shape[] = {2, 1, 5}; - const float golden[] = {1, 2, 3, 4, 5, 0, 0, 0, 0, 0}; - int8_t golden_quantized[elements_count]; - int8_t output_data[elements_count]; - - const float input_scale = 0.5f; - const int input_zero_point = 0; - const float output_scale = 0.5f; - const int output_zero_point = 0; - - tflite::testing::TestReluInt8(input_shape, input_data, input_quantized, - input_scale, input_zero_point, golden, - golden_quantized, output_shape, output_scale, - output_zero_point, output_data); -} - -TF_LITE_MICRO_TEST(SimpleRelu6TestInt8) { - const int elements_count = 10; - - const int input_shape[] = {2, 1, 5}; - const float input_data[] = {4, 5, 6, 7, 8, -1, -2, -3, -4, -5}; - int8_t input_quantized[elements_count]; - const int output_shape[] = {2, 1, 5}; - const float golden[] = {4, 5, 6, 6, 6, 0, 0, 0, 0, 0}; - int8_t golden_quantized[elements_count]; - int8_t output_data[elements_count]; - - const float input_scale = 0.5f; - const int input_zero_point = 127; - const float output_scale = 0.5f; - const int output_zero_point = 127; - - tflite::testing::TestRelu6Int8(input_shape, input_data, input_quantized, - input_scale, input_zero_point, golden, - golden_quantized, output_shape, output_scale, - output_zero_point, output_data); -} - -TF_LITE_MICRO_TESTS_END diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/add.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/add.cc deleted file mode 100644 index 79a04875..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/add.cc +++ /dev/null @@ -1,240 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/add.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/add.h" -#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/memory_helpers.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace add { - -constexpr int kInputTensor1 = 0; -constexpr int kInputTensor2 = 1; -constexpr int kOutputTensor = 0; - -struct OpData { - bool requires_broadcast; - - // These fields are used in both the general 8-bit -> 8bit quantized path, - // and the special 16-bit -> 16bit quantized path - int input1_shift; - int input2_shift; - int32_t output_activation_min; - int32_t output_activation_max; - - // These fields are used only in the general 8-bit -> 8bit quantized path - int32_t input1_multiplier; - int32_t input2_multiplier; - int32_t output_multiplier; - int output_shift; - int left_shift; - int32_t input1_offset; - int32_t input2_offset; - int32_t output_offset; - - // Used only for float evals: - float output_activation_min_f32; - float output_activation_max_f32; -}; - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteAddParams* params, - const TfLiteTensor* input1, - const TfLiteTensor* input2, TfLiteTensor* output, - OpData* data) { - data->requires_broadcast = !HaveSameShapes(input1, input2); - - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - // 8bit -> 8bit general quantized path, with general rescalings - data->input1_offset = -input1->params.zero_point; - data->input2_offset = -input2->params.zero_point; - data->output_offset = output->params.zero_point; - data->left_shift = 20; - const double twice_max_input_scale = - 2 * static_cast( - std::max(input1->params.scale, input2->params.scale)); - const double real_input1_multiplier = - static_cast(input1->params.scale) / twice_max_input_scale; - const double real_input2_multiplier = - static_cast(input2->params.scale) / twice_max_input_scale; - const double real_output_multiplier = - twice_max_input_scale / - ((1 << data->left_shift) * static_cast(output->params.scale)); - - QuantizeMultiplierSmallerThanOneExp( - real_input1_multiplier, &data->input1_multiplier, &data->input1_shift); - - QuantizeMultiplierSmallerThanOneExp( - real_input2_multiplier, &data->input2_multiplier, &data->input2_shift); - - QuantizeMultiplierSmallerThanOneExp( - real_output_multiplier, &data->output_multiplier, &data->output_shift); - - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, params->activation, output, &data->output_activation_min, - &data->output_activation_max)); - } else if (output->type == kTfLiteFloat32) { - CalculateActivationRange(params->activation, - &data->output_activation_min_f32, - &data->output_activation_max_f32); - } - - return kTfLiteOk; -} - -void EvalAdd(TfLiteContext* context, TfLiteNode* node, TfLiteAddParams* params, - const OpData* data, const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) { - tflite::ArithmeticParams op_params; - SetActivationParams(data->output_activation_min_f32, - data->output_activation_max_f32, &op_params); -#define TF_LITE_ADD(opname) \ - reference_ops::opname(op_params, tflite::micro::GetTensorShape(input1), \ - tflite::micro::GetTensorData(input1), \ - tflite::micro::GetTensorShape(input2), \ - tflite::micro::GetTensorData(input2), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output)) - if (data->requires_broadcast) { - TF_LITE_ADD(BroadcastAdd4DSlow); - } else { - TF_LITE_ADD(Add); - } -#undef TF_LITE_ADD -} - -TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteAddParams* params, const OpData* data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output) { - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - tflite::ArithmeticParams op_params; - op_params.left_shift = data->left_shift; - op_params.input1_offset = data->input1_offset; - op_params.input1_multiplier = data->input1_multiplier; - op_params.input1_shift = data->input1_shift; - op_params.input2_offset = data->input2_offset; - op_params.input2_multiplier = data->input2_multiplier; - op_params.input2_shift = data->input2_shift; - op_params.output_offset = data->output_offset; - op_params.output_multiplier = data->output_multiplier; - op_params.output_shift = data->output_shift; - SetActivationParams(data->output_activation_min, - data->output_activation_max, &op_params); - bool need_broadcast = reference_ops::ProcessBroadcastShapes( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), &op_params); -#define TF_LITE_ADD(type, opname, dtype) \ - type::opname(op_params, tflite::micro::GetTensorShape(input1), \ - tflite::micro::GetTensorData(input1), \ - tflite::micro::GetTensorShape(input2), \ - tflite::micro::GetTensorData(input2), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output)); - if (output->type == kTfLiteInt8) { - if (need_broadcast) { - TF_LITE_ADD(reference_integer_ops, BroadcastAdd4DSlow, int8_t); - } else { - TF_LITE_ADD(reference_integer_ops, Add, int8_t); - } - } else { - if (need_broadcast) { - TF_LITE_ADD(reference_ops, BroadcastAdd4DSlow, uint8_t); - } else { - TF_LITE_ADD(reference_ops, Add, uint8_t); - } - } -#undef TF_LITE_ADD - } - - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); - const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - OpData* data = static_cast(node->user_data); - auto* params = reinterpret_cast(node->builtin_data); - - TF_LITE_ENSURE_STATUS( - CalculateOpData(context, params, input1, input2, output, data)); - - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - if (output->type == kTfLiteFloat32) { - EvalAdd(context, node, params, data, input1, input2, output); - } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - TF_LITE_ENSURE_OK(context, EvalAddQuantized(context, node, params, data, - input1, input2, output)); - } else { - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(output->type), output->type); - return kTfLiteError; - } - - return kTfLiteOk; -} - -} // namespace add - -TfLiteRegistration Register_ADD() { - return {/*init=*/add::Init, - /*free=*/nullptr, - /*prepare=*/add::Prepare, - /*invoke=*/add::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/arg_min_max.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/arg_min_max.cc deleted file mode 100644 index 12ac0019..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/arg_min_max.cc +++ /dev/null @@ -1,133 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/arg_min_max.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace arg_min_max { - -constexpr int kInputTensor = 0; -constexpr int kAxis = 1; -constexpr int kOutputTensor = 0; - -template -inline void ArgMinMaxHelper(const RuntimeShape& input1_shape, - const T1* input1_data, const T3* input2_data, - const RuntimeShape& output_shape, T2* output_data, - bool is_arg_max) { - if (is_arg_max) { - reference_ops::ArgMinMax(input1_shape, input1_data, input2_data, - output_shape, output_data, micro::Greater()); - } else { - reference_ops::ArgMinMax(input1_shape, input1_data, input2_data, - output_shape, output_data, micro::Less()); - } -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node, bool is_arg_max) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* axis = - tflite::micro::GetEvalInput(context, node, kAxis); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - -#define TF_LITE_ARG_MIN_MAX(data_type, axis_type, output_type) \ - ArgMinMaxHelper(tflite::micro::GetTensorShape(input), \ - tflite::micro::GetTensorData(input), \ - tflite::micro::GetTensorData(axis), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output), \ - is_arg_max) - if (axis->type == kTfLiteInt32) { - if (output->type == kTfLiteInt32) { - switch (input->type) { - case kTfLiteFloat32: - TF_LITE_ARG_MIN_MAX(float, int32_t, int32_t); - break; - case kTfLiteUInt8: - TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int32_t); - break; - case kTfLiteInt8: - TF_LITE_ARG_MIN_MAX(int8_t, int32_t, int32_t); - break; - default: - TF_LITE_KERNEL_LOG(context, - "Only float32, uint8_t and int8_t are " - "supported currently, got %s.", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - } else { - TF_LITE_KERNEL_LOG(context, - "Only int32_t are supported currently, got %s.", - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else { - TF_LITE_KERNEL_LOG(context, "Only int32_t are supported currently, got %s.", - TfLiteTypeGetName(axis->type)); - return kTfLiteError; - } - -#undef TF_LITE_ARG_MIN_MAX - - return kTfLiteOk; -} - -TfLiteStatus ArgMinEval(TfLiteContext* context, TfLiteNode* node) { - return Eval(context, node, false); -} - -TfLiteStatus ArgMaxEval(TfLiteContext* context, TfLiteNode* node) { - return Eval(context, node, true); -} - -} // namespace arg_min_max - -TfLiteRegistration Register_ARG_MAX() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/arg_min_max::ArgMaxEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_ARG_MIN() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/arg_min_max::ArgMinEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/ceil.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/ceil.cc deleted file mode 100644 index 3bce8a73..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/ceil.cc +++ /dev/null @@ -1,74 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/ceil.h" - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace ceil { - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32); - TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type); - TF_LITE_ENSURE_EQ(context, output->bytes, input->bytes); - TF_LITE_ENSURE_EQ(context, output->dims->size, input->dims->size); - for (int i = 0; i < output->dims->size; ++i) { - TF_LITE_ENSURE_EQ(context, output->dims->data[i], input->dims->data[i]); - } - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - reference_ops::Ceil(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - - return kTfLiteOk; -} -} // namespace ceil - -TfLiteRegistration Register_CEIL() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ceil::Prepare, - /*invoke=*/ceil::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/circular_buffer.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/circular_buffer.cc deleted file mode 100644 index b5a8ae1b..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/circular_buffer.cc +++ /dev/null @@ -1,178 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/add.h" -#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -/* - * The circular buffer custom operator is used to implement strided streaming - * convolutions on TFLite Micro. Each time this operator is invoked, it checks - * whether or not to run, based on a predetermined stride in time. If the op - * runs, it inserts the input into the end of the output buffer and shifts the - * output values towards the start of the buffer. It discards the oldest value - * in the output buffer. - * - * Input: [, , , ] - * - * After shifting: - * Output: [, , , ] - * - * We make some assumptions in this custom operator: - * - Input shape must be [1, 1, 1, depth] - * - Output shape must be [1, num_slots, 1, depth] - * - Input and output types must match. - * - Input and output quantization params must be identical. - */ -namespace tflite { -namespace ops { -namespace micro { -namespace circular_buffer { - -namespace { - -// The CircularBuffer op has one input and one output tensor. -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -// TODO(b/149795762): Add this to TfLiteStatus enum. -constexpr int kTfLiteAbort = -9; - -// These fields control the stride period of a strided streaming model. This op -// returns kTfLiteAbort until cycles_until_run-- is zero. At this time, -// cycles_until_run is reset to cycles_max. -struct OpData { - int cycles_until_run; - int cycles_max; -}; - -// These constants represent constants specific to the music detect model. -// They exist until (b/132070898) is fixed. -constexpr int kMaxOpDataSize = 7; -int op_data_counter = 0; -OpData op_data_array[kMaxOpDataSize]; - -} // namespace - -void Free(TfLiteContext* context, void* buffer) { op_data_counter = 0; } - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE(context, input != nullptr); - TF_LITE_ENSURE(context, output != nullptr); - TF_LITE_ENSURE_EQ(context, 1, output->dims->data[0]); - TF_LITE_ENSURE_EQ(context, 1, input->dims->data[0]); - TF_LITE_ENSURE_EQ(context, 1, input->dims->data[1]); - TF_LITE_ENSURE_EQ(context, 1, output->dims->data[2]); - TF_LITE_ENSURE_EQ(context, 1, input->dims->data[2]); - TF_LITE_ENSURE_EQ(context, output->dims->data[3], input->dims->data[3]); - - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - - // The circular buffer custom operator currently only supports int8_t. - TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteInt8); - - // TODO(b/132070898): Use statically slotted OpData structures until a - // scratch memory API is ready. - TFLITE_DCHECK_LE(op_data_counter, kMaxOpDataSize); - OpData* op_data = &op_data_array[op_data_counter++]; - // The last circular buffer layer (length 5) simply accumulates outputs, and - // does not run periodically. - // TODO(b/150001379): Move this special case logic to the tflite flatbuffer. - if (output->dims->data[1] == 5) { - op_data->cycles_max = 1; - } else { - op_data->cycles_max = 2; - } - op_data->cycles_until_run = op_data->cycles_max; - node->user_data = op_data; - - return kTfLiteOk; -} - -// Shifts buffer over by the output depth, and write new input to end of buffer. -// num_slots is the number of samples stored in the output buffer. -// depth is the size of each sample. -void EvalInt8(const int8_t* input, int num_slots, int depth, int8_t* output) { - memmove(output, &output[depth], (num_slots - 1) * depth); - memcpy(&output[(num_slots - 1) * depth], input, depth); -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - OpData* data = reinterpret_cast(node->user_data); - - int num_slots = output->dims->data[1]; - int depth = output->dims->data[3]; - - if (input->type == kTfLiteInt8) { - EvalInt8(tflite::micro::GetTensorData(input), num_slots, depth, - tflite::micro::GetTensorData(output)); - } else { - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - - if (--data->cycles_until_run != 0) { - // Signal the interpreter to end current run if the delay before op invoke - // has not been reached. - // TODO(b/149795762): Add kTfLiteAbort to TfLiteStatus enum. - return static_cast(kTfLiteAbort); - } - - // If prepare is ever called more than one time (for example, when testing the - // ambient model, the interpreter is created a few times), this op data - // counter needs to be reset so that future instances do not overrun this op - // data array. - op_data_counter = 0; - - data->cycles_until_run = data->cycles_max; - - return kTfLiteOk; -} - -} // namespace circular_buffer - -TfLiteRegistration* Register_CIRCULAR_BUFFER() { - static TfLiteRegistration r = {/*init=*/nullptr, - /*free=*/circular_buffer::Free, - /*prepare=*/circular_buffer::Prepare, - /*invoke=*/circular_buffer::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; - return &r; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/add.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/add.cc deleted file mode 100644 index 6db88839..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/add.cc +++ /dev/null @@ -1,246 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/add.h" - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/add.h" -#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/memory_helpers.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace add { - -constexpr int kInputTensor1 = 0; -constexpr int kInputTensor2 = 1; -constexpr int kOutputTensor = 0; - -struct OpData { - bool requires_broadcast; - - // These fields are used in both the general 8-bit -> 8bit quantized path, - // and the special 16-bit -> 16bit quantized path - int input1_shift; - int input2_shift; - int32_t output_activation_min; - int32_t output_activation_max; - - // These fields are used only in the general 8-bit -> 8bit quantized path - int32_t input1_multiplier; - int32_t input2_multiplier; - int32_t output_multiplier; - int output_shift; - int left_shift; - int32_t input1_offset; - int32_t input2_offset; - int32_t output_offset; -}; - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteAddParams* params, - const TfLiteTensor* input1, - const TfLiteTensor* input2, TfLiteTensor* output, - OpData* data) { - data->requires_broadcast = !HaveSameShapes(input1, input2); - - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - // 8bit -> 8bit general quantized path, with general rescalings - data->input1_offset = -input1->params.zero_point; - data->input2_offset = -input2->params.zero_point; - data->output_offset = output->params.zero_point; - data->left_shift = 20; - const double twice_max_input_scale = - 2 * static_cast( - std::max(input1->params.scale, input2->params.scale)); - const double real_input1_multiplier = - static_cast(input1->params.scale) / twice_max_input_scale; - const double real_input2_multiplier = - static_cast(input2->params.scale) / twice_max_input_scale; - const double real_output_multiplier = - twice_max_input_scale / - ((1 << data->left_shift) * static_cast(output->params.scale)); - - QuantizeMultiplierSmallerThanOneExp( - real_input1_multiplier, &data->input1_multiplier, &data->input1_shift); - - QuantizeMultiplierSmallerThanOneExp( - real_input2_multiplier, &data->input2_multiplier, &data->input2_shift); - - QuantizeMultiplierSmallerThanOneExp( - real_output_multiplier, &data->output_multiplier, &data->output_shift); - - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, params->activation, output, &data->output_activation_min, - &data->output_activation_max)); - } - - return kTfLiteOk; -} - -void EvalAdd(TfLiteContext* context, TfLiteNode* node, TfLiteAddParams* params, - const OpData* data, const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); - tflite::ArithmeticParams op_params; - SetActivationParams(output_activation_min, output_activation_max, &op_params); -#define TF_LITE_ADD(opname) \ - reference_ops::opname(op_params, tflite::micro::GetTensorShape(input1), \ - tflite::micro::GetTensorData(input1), \ - tflite::micro::GetTensorShape(input2), \ - tflite::micro::GetTensorData(input2), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output)) - if (data->requires_broadcast) { - TF_LITE_ADD(BroadcastAdd4DSlow); - } else { - TF_LITE_ADD(Add); - } -#undef TF_LITE_ADD -} - -TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteAddParams* params, const OpData* data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output) { - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - tflite::ArithmeticParams op_params; - op_params.left_shift = data->left_shift; - op_params.input1_offset = data->input1_offset; - op_params.input1_multiplier = data->input1_multiplier; - op_params.input1_shift = data->input1_shift; - op_params.input2_offset = data->input2_offset; - op_params.input2_multiplier = data->input2_multiplier; - op_params.input2_shift = data->input2_shift; - op_params.output_offset = data->output_offset; - op_params.output_multiplier = data->output_multiplier; - op_params.output_shift = data->output_shift; - SetActivationParams(data->output_activation_min, - data->output_activation_max, &op_params); - bool need_broadcast = reference_ops::ProcessBroadcastShapes( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), &op_params); -#define TF_LITE_ADD(type, opname, dtype) \ - type::opname(op_params, tflite::micro::GetTensorShape(input1), \ - tflite::micro::GetTensorData(input1), \ - tflite::micro::GetTensorShape(input2), \ - tflite::micro::GetTensorData(input2), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output)); - if (output->type == kTfLiteInt8) { - if (need_broadcast) { - TF_LITE_ADD(reference_integer_ops, BroadcastAdd4DSlow, int8_t); - } else { - arm_elementwise_add_s8( - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorData(input2), - op_params.input1_offset, op_params.input1_multiplier, - op_params.input1_shift, op_params.input2_offset, - op_params.input2_multiplier, op_params.input2_shift, - op_params.left_shift, tflite::micro::GetTensorData(output), - op_params.output_offset, op_params.output_multiplier, - op_params.output_shift, op_params.quantized_activation_min, - op_params.quantized_activation_max, - MatchingElementsSize(tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorShape(output))); - } - } else { - if (need_broadcast) { - TF_LITE_ADD(reference_ops, BroadcastAdd4DSlow, uint8_t); - } else { - TF_LITE_ADD(reference_ops, Add, uint8_t); - } - } -#undef TF_LITE_ADD - } - - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); - const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - OpData* data = static_cast(node->user_data); - auto* params = reinterpret_cast(node->builtin_data); - - TF_LITE_ENSURE_STATUS( - CalculateOpData(context, params, input1, input2, output, data)); - - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - if (output->type == kTfLiteFloat32) { - EvalAdd(context, node, params, data, input1, input2, output); - } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - TF_LITE_ENSURE_OK(context, EvalAddQuantized(context, node, params, data, - input1, input2, output)); - } else { - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(output->type), output->type); - return kTfLiteError; - } - - return kTfLiteOk; -} - -} // namespace add - -TfLiteRegistration Register_ADD() { - return {/*init=*/add::Init, - /*free=*/nullptr, - /*prepare=*/add::Prepare, - /*invoke=*/add::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/conv.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/conv.cc deleted file mode 100644 index cf1ce8cb..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/conv.cc +++ /dev/null @@ -1,455 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/conv.h" - -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/padding.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace conv { - -constexpr int kInputTensor = 0; -constexpr int kFilterTensor = 1; -constexpr int kBiasTensor = 2; -constexpr int kOutputTensor = 0; -constexpr int kMaxChannels = 256; - -// Conv is quantized along dimension 0: -// https://www.tensorflow.org/lite/performance/quantization_spec -constexpr int kConvQuantizedDimension = 0; - -struct OpData { - TfLitePaddingValues padding; - - // Cached tensor zero point values for quantized operations. - int32_t input_zero_point; - int32_t filter_zero_point; - int32_t output_zero_point; - - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - - // Per channel output multiplier and shift. - // TODO(b/141139247): Allocate these dynamically when possible. - int32_t per_channel_output_multiplier[kMaxChannels]; - int32_t per_channel_output_shift[kMaxChannels]; - - // The range of the fused activation layer. For example for kNone and - // uint8_t these would be 0 and 255. - int32_t output_activation_min; - int32_t output_activation_max; - - // Index to buffer for optimizations if applicable. - int buffer_idx; -}; - -inline PaddingType RuntimePaddingType(TfLitePadding padding) { - switch (padding) { - case TfLitePadding::kTfLitePaddingSame: - return PaddingType::kSame; - case TfLitePadding::kTfLitePaddingValid: - return PaddingType::kValid; - case TfLitePadding::kTfLitePaddingUnknown: - default: - return PaddingType::kNone; - } -} - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, - TfLiteConvParams* params, int width, int height, - int filter_width, int filter_height, int out_width, - int out_height, const TfLiteType data_type, - OpData* data) { - bool has_bias = node->inputs->size == 3; - // Check number of inputs/outputs - TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); - - // Matching GetWindowedOutputSize in TensorFlow. - auto padding = params->padding; - data->padding = ComputePaddingHeightWidth( - params->stride_height, params->stride_width, - params->dilation_height_factor, params->dilation_width_factor, height, - width, filter_height, filter_width, padding, &out_height, &out_width); - - // Note that quantized inference requires that all tensors have their - // parameters set. This is usually done during quantized training. - if (data_type != kTfLiteFloat32) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); - const TfLiteTensor* bias = - GetOptionalInputTensor(context, node, kBiasTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - int num_channels = filter->dims->data[kConvQuantizedDimension]; - - TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams( - context, input, filter, bias, output, params->activation, - &data->output_multiplier, &data->output_shift, - &data->output_activation_min, &data->output_activation_max, - data->per_channel_output_multiplier, - reinterpret_cast(data->per_channel_output_shift), num_channels)); - } - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { -#if defined(__ARM_FEATURE_DSP) || defined(__ARM_FEATURE_MVE) - int32_t buf_size = 0; - - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - auto* params = reinterpret_cast(node->builtin_data); - auto* data = reinterpret_cast(node->user_data); - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); - const TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - RuntimeShape input_shape = GetTensorShape(input); - RuntimeShape output_shape = GetTensorShape(output); - - // Initialize cmsis-nn input dimensions - cmsis_nn_dims input_dims; - input_dims.n = MatchingDim(input_shape, 0, output_shape, 0); - input_dims.h = input->dims->data[1]; - input_dims.w = input->dims->data[2]; - input_dims.c = input_shape.Dims(3); - - // Initialize cmsis-nn filter dimensions - cmsis_nn_dims filter_dims; - filter_dims.n = output_shape.Dims(3); - filter_dims.h = filter->dims->data[1]; - filter_dims.w = filter->dims->data[2]; - filter_dims.c = input_dims.c; - - // Initialize cmsis-nn output dimensions - cmsis_nn_dims output_dims; - output_dims.n = input_dims.n; - output_dims.h = output->dims->data[1]; - output_dims.w = output->dims->data[2]; - output_dims.c = output_shape.Dims(3); - - TF_LITE_ENSURE_STATUS(CalculateOpData( - context, node, params, input_dims.w, input_dims.h, filter_dims.w, - filter_dims.h, output_dims.w, output_dims.h, input->type, data)); - - data->input_zero_point = input->params.zero_point; - data->filter_zero_point = filter->params.zero_point; - data->output_zero_point = output->params.zero_point; - - if (input->type == kTfLiteInt8) { - // Initialize cmsis-nn convolution parameters - cmsis_nn_conv_params conv_params; - conv_params.input_offset = -input->params.zero_point; - conv_params.output_offset = output->params.zero_point; - conv_params.stride.h = params->stride_height; - conv_params.stride.w = params->stride_width; - conv_params.dilation.h = params->dilation_height_factor; - conv_params.dilation.w = params->dilation_width_factor; - conv_params.padding.h = data->padding.height; - conv_params.padding.w = data->padding.width; - conv_params.activation.min = data->output_activation_min; - conv_params.activation.max = data->output_activation_max; - - buf_size = arm_convolve_wrapper_s8_get_buffer_size( - &conv_params, &input_dims, &filter_dims, &output_dims); - } - - if (buf_size > 0) { - TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena( - context, buf_size, &data->buffer_idx)); - } else { - data->buffer_idx = -1; - } -#endif - return kTfLiteOk; -} - -TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteConvParams* params, const OpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* im2col, - TfLiteEvalTensor* hwcn_weights, - TfLiteEvalTensor* output) { - const int32_t input_offset = -data.input_zero_point; - const int32_t filter_offset = -data.filter_zero_point; - const int32_t output_offset = data.output_zero_point; - - ConvParams op_params; - op_params.padding_type = RuntimePaddingType(params->padding); - op_params.padding_values.width = data.padding.width; - op_params.padding_values.height = data.padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.input_offset = input_offset; - op_params.weights_offset = filter_offset; - op_params.output_offset = output_offset; - op_params.output_multiplier = data.output_multiplier; - op_params.output_shift = -data.output_shift; - op_params.quantized_activation_min = data.output_activation_min; - op_params.quantized_activation_max = data.output_activation_max; - reference_ops::Conv(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), - tflite::micro::GetTensorShape(im2col), - tflite::micro::GetTensorData(im2col), nullptr); - return kTfLiteOk; -} - -TfLiteStatus EvalQuantizedPerChannel( - TfLiteContext* context, TfLiteNode* node, TfLiteConvParams* params, - const OpData& data, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output, TfLiteEvalTensor* im2col) { - // Initialize cmsis-nn convolution parameters - cmsis_nn_conv_params conv_params; - conv_params.input_offset = -data.input_zero_point; - conv_params.output_offset = data.output_zero_point; - conv_params.stride.h = params->stride_height; - conv_params.stride.w = params->stride_width; - conv_params.dilation.h = params->dilation_height_factor; - conv_params.dilation.w = params->dilation_width_factor; - conv_params.padding.h = data.padding.height; - conv_params.padding.w = data.padding.width; - conv_params.activation.min = data.output_activation_min; - conv_params.activation.max = data.output_activation_max; - - // Initialize cmsis-nn per channel quantization parameters - cmsis_nn_per_channel_quant_params quant_params; - quant_params.multiplier = - const_cast(data.per_channel_output_multiplier); - quant_params.shift = const_cast(data.per_channel_output_shift); - -#if defined(__ARM_FEATURE_DSP) || defined(__ARM_FEATURE_MVE) - RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter); - RuntimeShape input_shape = tflite::micro::GetTensorShape(input); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - RuntimeShape bias_shape = tflite::micro::GetTensorShape(bias); - - // Consistency check. - TFLITE_DCHECK_LE(conv_params.activation.min, conv_params.activation.max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batch_size = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (tflite::micro::GetTensorData(bias)) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - // Initialize cmsis-nn dimensions - // Input - cmsis_nn_dims input_dims; - input_dims.n = batch_size; - input_dims.h = input_shape.Dims(1); - input_dims.w = input_shape.Dims(2); - input_dims.c = input_depth; - - // Filter - cmsis_nn_dims filter_dims; - filter_dims.n = output_depth; - filter_dims.h = filter_shape.Dims(1); - filter_dims.w = filter_shape.Dims(2); - filter_dims.c = input_depth; - - // Bias - cmsis_nn_dims bias_dims; - bias_dims.n = 1; - bias_dims.h = 1; - bias_dims.w = 1; - bias_dims.c = output_depth; - - // Output - cmsis_nn_dims output_dims; - output_dims.n = batch_size; - output_dims.h = output_shape.Dims(1); - output_dims.w = output_shape.Dims(2); - output_dims.c = output_depth; - - // Initialize cmsis-nn context - cmsis_nn_context ctx; - ctx.buf = nullptr; - ctx.size = 0; - - if (data.buffer_idx > -1) { - ctx.buf = context->GetScratchBuffer(context, data.buffer_idx); - // Note: ctx.size is currently not used in cmsis-nn. - // The buffer should be allocated in the Prepare function through - // arm_convolve_wrapper_s8_get_buffer_size - } - - // arm_convolve_wrapper_s8 dispatches the optimized kernel accordingly with - // the parameters passed - arm_status status = arm_convolve_wrapper_s8( - &ctx, &conv_params, &quant_params, &input_dims, - tflite::micro::GetTensorData(input), &filter_dims, - tflite::micro::GetTensorData(filter), &bias_dims, - tflite::micro::GetTensorData(bias), &output_dims, - tflite::micro::GetTensorData(output)); - - if (status == ARM_MATH_SUCCESS) { - return kTfLiteOk; - } else { - return kTfLiteError; - } - -#else -#pragma message( \ - "CMSIS-NN optimization for conv not available for this target. Using reference kernel.") - - ConvParams op_params; - conv_params.input_offset = -data.input_zero_point; - conv_params.output_offset = data.output_zero_point; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.padding_values.height = data.padding.height; - op_params.padding_values.width = data.padding.width; - op_params.quantized_activation_min = data->output_activation_min; - op_params.quantized_activation_max = data->output_activation_max; - - reference_integer_ops::ConvPerChannel( - op_params, data->per_channel_output_multiplier, - data->per_channel_output_shift, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - -#endif - return kTfLiteOk; -} - -TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteConvParams* params, const OpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, TfLiteEvalTensor* im2col, - TfLiteEvalTensor* hwcn_weights, - TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); - // TODO(b/154032858): Investigate removing extra copies. - ConvParams op_params; - op_params.padding_type = RuntimePaddingType(params->padding); - op_params.padding_values.width = data.padding.width; - op_params.padding_values.height = data.padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - - reference_ops::Conv(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), - tflite::micro::GetTensorShape(im2col), - tflite::micro::GetTensorData(im2col)); - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* filter = - tflite::micro::GetEvalInput(context, node, kFilterTensor); - const TfLiteEvalTensor* bias = - (NumInputs(node) == 3) - ? tflite::micro::GetEvalInput(context, node, kBiasTensor) - : nullptr; - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData& data = *(static_cast(node->user_data)); - - switch (input->type) { // Already know in/out types are same. - case kTfLiteFloat32: - EvalFloat(context, node, params, data, input, filter, bias, nullptr, - nullptr, output); - break; - case kTfLiteInt8: - return EvalQuantizedPerChannel(context, node, params, data, input, filter, - bias, output, nullptr); - break; - case kTfLiteUInt8: - return EvalQuantized(context, node, params, data, input, filter, bias, - nullptr, nullptr, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace conv - -TfLiteRegistration Register_CONV_2D() { - return {/*init=*/conv::Init, - /*free=*/nullptr, - /*prepare=*/conv::Prepare, - /*invoke=*/conv::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/depthwise_conv.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/depthwise_conv.cc deleted file mode 100644 index 42ac15a0..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/depthwise_conv.cc +++ /dev/null @@ -1,478 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h" - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h" -#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/padding.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace depthwise_conv { -namespace { - -constexpr int kInputTensor = 0; -constexpr int kFilterTensor = 1; -constexpr int kBiasTensor = 2; -constexpr int kOutputTensor = 0; -constexpr int kMaxChannels = 256; - -// Depthwise conv is quantized along dimension 3: -// https://www.tensorflow.org/lite/performance/quantization_spec -constexpr int kDepthwiseConvQuantizedDimension = 3; - -struct OpData { - TfLitePaddingValues padding; - - // Cached tensor zero point values for quantized operations. - int32_t input_zero_point; - int32_t filter_zero_point; - int32_t output_zero_point; - - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - - // Per channel output multiplier and shift. - // TODO: Allocate dynamic buffers when b/158779832 is resolved - int32_t per_channel_output_multiplier[kMaxChannels]; - int32_t per_channel_output_shift[kMaxChannels]; - // The range of the fused activation layer. For example for kNone and - // uint8_t these would be 0 and 255. - int32_t output_activation_min; - int32_t output_activation_max; - // Index to buffer for optimizations if applicable. - int buffer_idx; -}; - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, - TfLiteDepthwiseConvParams* params, int width, - int height, int filter_width, int filter_height, - const TfLiteType data_type, OpData* data) { - bool has_bias = node->inputs->size == 3; - // Check number of inputs/outputs - TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); - - int unused_output_height, unused_output_width; - // Set buffer index to a reset value - data->buffer_idx = -1; - data->padding = ComputePaddingHeightWidth( - params->stride_height, params->stride_width, 1, 1, height, width, - filter_height, filter_width, params->padding, &unused_output_height, - &unused_output_width); - - // Note that quantized inference requires that all tensors have their - // parameters set. This is usually done during quantized training. - if (data_type != kTfLiteFloat32) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); - const TfLiteTensor* bias = - GetOptionalInputTensor(context, node, kBiasTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - int num_channels = filter->dims->data[kDepthwiseConvQuantizedDimension]; - - return tflite::PopulateConvolutionQuantizationParams( - context, input, filter, bias, output, params->activation, - &data->output_multiplier, &data->output_shift, - &data->output_activation_min, &data->output_activation_max, - data->per_channel_output_multiplier, - reinterpret_cast(data->per_channel_output_shift), num_channels); - } - return kTfLiteOk; -} - -} // namespace - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - OpData* data = static_cast(node->user_data); - auto* params = - reinterpret_cast(node->builtin_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - const TfLiteType data_type = input->type; - int width = SizeOfDimension(input, 2); - int height = SizeOfDimension(input, 1); - int filter_width = SizeOfDimension(filter, 2); - int filter_height = SizeOfDimension(filter, 1); - - if (input->type == kTfLiteInt8) { - // Allocate memory for per-channel quantization parameters - const int num_channels = - filter->dims->data[kDepthwiseConvQuantizedDimension]; - TFLITE_DCHECK_LE(num_channels, kMaxChannels); - - TF_LITE_ENSURE_EQ(context, filter->quantization.type, - kTfLiteAffineQuantization); - - // All per-channel quantized tensors need valid zero point and scale arrays. - const auto* affine_quantization = - reinterpret_cast( - filter->quantization.params); - TF_LITE_ENSURE(context, affine_quantization); - TF_LITE_ENSURE(context, affine_quantization->scale); - TF_LITE_ENSURE(context, affine_quantization->zero_point); - TF_LITE_ENSURE( - context, affine_quantization->scale->size == 1 || - affine_quantization->scale->size == - filter->dims->data[kDepthwiseConvQuantizedDimension]); - TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size, - affine_quantization->zero_point->size); - } - - TF_LITE_ENSURE_STATUS(CalculateOpData(context, node, params, width, height, - filter_width, filter_height, data_type, - data)); - - data->input_zero_point = input->params.zero_point; - data->filter_zero_point = filter->params.zero_point; - data->output_zero_point = output->params.zero_point; - - if (input->type == kTfLiteInt8) { - RuntimeShape input_shape = GetTensorShape(input); - RuntimeShape output_shape = GetTensorShape(output); - RuntimeShape filter_shape = GetTensorShape(filter); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - const int batch_size = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(output_shape, 3, filter_shape, 3); - TFLITE_DCHECK_EQ(batch_size, 1); /* Only batch = 1 is supported */ - - cmsis_nn_dims input_dims; - input_dims.n = batch_size; - input_dims.h = height; - input_dims.w = width; - input_dims.c = input_shape.Dims(3); - - cmsis_nn_dims filter_dims; - filter_dims.n = 1; - filter_dims.h = filter_height; - filter_dims.w = filter_width; - filter_dims.c = output_depth; - - cmsis_nn_dims output_dims; - output_dims.n = batch_size; - output_dims.h = output_shape.Dims(1); - output_dims.w = output_shape.Dims(2); - output_dims.c = output_depth; - - cmsis_nn_dw_conv_params dw_conv_params; - dw_conv_params.padding.h = data->padding.height; - dw_conv_params.padding.w = data->padding.width; - - const int32_t buf_size = arm_depthwise_conv_wrapper_s8_get_buffer_size( - &dw_conv_params, &input_dims, &filter_dims, &output_dims); - - if (buf_size > 0) { - TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena( - context, buf_size, &data->buffer_idx)); - } else { - data->buffer_idx = -1; - } - } - return kTfLiteOk; -} - -void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteDepthwiseConvParams* params, const OpData* data, - const TfLiteEvalTensor* input, const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); - - tflite::DepthwiseParams op_params; - // Padding type is ignored, but still set. - op_params.padding_type = PaddingType::kSame; - op_params.padding_values.width = data->padding.width; - op_params.padding_values.height = data->padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.depth_multiplier = params->depth_multiplier; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - - tflite::reference_ops::DepthwiseConv( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node, - TfLiteDepthwiseConvParams* params, OpData* data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output) { - cmsis_nn_dw_conv_params dw_conv_params; - dw_conv_params.dilation.h = params->dilation_height_factor; - dw_conv_params.dilation.w = params->dilation_width_factor; - // Call to reference implementation can be removed when dilation is supported - // in the optimized implementations. - if (1 == dw_conv_params.dilation.h && 1 == dw_conv_params.dilation.w) { - dw_conv_params.input_offset = -data->input_zero_point; - dw_conv_params.output_offset = data->output_zero_point; - dw_conv_params.stride.h = params->stride_height; - dw_conv_params.stride.w = params->stride_width; - dw_conv_params.padding.h = data->padding.height; - dw_conv_params.padding.w = data->padding.width; - // TODO(b/130439627): Use calculated value for clamping. - dw_conv_params.activation.min = std::numeric_limits::min(); - dw_conv_params.activation.max = std::numeric_limits::max(); - dw_conv_params.ch_mult = params->depth_multiplier; - - cmsis_nn_per_channel_quant_params quant_params; - quant_params.multiplier = data->per_channel_output_multiplier; - quant_params.shift = data->per_channel_output_shift; - - RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter); - RuntimeShape input_shape = tflite::micro::GetTensorShape(input); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - RuntimeShape bias_shape = tflite::micro::GetTensorShape(bias); - - TFLITE_DCHECK_LE(dw_conv_params.activation.min, - dw_conv_params.activation.max); - - const int batch_size = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - - if (tflite::micro::GetTensorData(bias)) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - cmsis_nn_dims input_dims; - input_dims.n = batch_size; - input_dims.h = input_shape.Dims(1); - input_dims.w = input_shape.Dims(2); - input_dims.c = input_shape.Dims(3); - - cmsis_nn_dims filter_dims; - filter_dims.n = filter_shape.Dims(0); - filter_dims.h = filter_shape.Dims(1); - filter_dims.w = filter_shape.Dims(2); - filter_dims.c = output_depth; - - cmsis_nn_dims bias_dims; - bias_dims.n = 1; - bias_dims.h = 1; - bias_dims.w = 1; - bias_dims.c = output_depth; - - cmsis_nn_dims output_dims; - output_dims.n = batch_size; - output_dims.h = output_shape.Dims(1); - output_dims.w = output_shape.Dims(2); - output_dims.c = output_depth; - - cmsis_nn_context ctx; - ctx.buf = nullptr; - /* 'size' is unused */ - ctx.size = 0; - - if (data->buffer_idx > -1) { - ctx.buf = context->GetScratchBuffer(context, data->buffer_idx); - } - - TFLITE_DCHECK_EQ( - arm_depthwise_conv_wrapper_s8( - &ctx, &dw_conv_params, &quant_params, &input_dims, - tflite::micro::GetTensorData(input), &filter_dims, - tflite::micro::GetTensorData(filter), &bias_dims, - tflite::micro::GetTensorData(bias), &output_dims, - tflite::micro::GetTensorData(output)), - ARM_MATH_SUCCESS); - } else { - DepthwiseParams op_params; - op_params.padding_type = PaddingType::kSame; - op_params.padding_values.width = data->padding.width; - op_params.padding_values.height = data->padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.depth_multiplier = params->depth_multiplier; - op_params.input_offset = -data->input_zero_point; - op_params.weights_offset = 0; - op_params.output_offset = data->output_zero_point; - // TODO(b/130439627): Use calculated value for clamping. - op_params.quantized_activation_min = std::numeric_limits::min(); - op_params.quantized_activation_max = std::numeric_limits::max(); - - reference_integer_ops::DepthwiseConvPerChannel( - op_params, data->per_channel_output_multiplier, - data->per_channel_output_shift, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } -} - -void EvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteDepthwiseConvParams* params, const OpData* data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output) { - const int32_t input_offset = -data->input_zero_point; - const int32_t filter_offset = -data->filter_zero_point; - const int32_t output_offset = data->output_zero_point; - - tflite::DepthwiseParams op_params; - // Padding type is ignored, but still set. - op_params.padding_type = PaddingType::kSame; - op_params.padding_values.width = data->padding.width; - op_params.padding_values.height = data->padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.depth_multiplier = params->depth_multiplier; - op_params.quantized_activation_min = data->output_activation_min; - op_params.quantized_activation_max = data->output_activation_max; - op_params.input_offset = input_offset; - op_params.weights_offset = filter_offset; - op_params.output_offset = output_offset; - op_params.output_multiplier = data->output_multiplier; - // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. - op_params.output_shift = -data->output_shift; - - if (1 == op_params.dilation_width_factor && - 1 == op_params.dilation_height_factor) { - RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - RuntimeShape input_shape = tflite::micro::GetTensorShape(input); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - arm_depthwise_conv_u8_basic_ver1( - tflite::micro::GetTensorData(input), input_width, input_height, - input_depth, tflite::micro::GetTensorData(filter), - filter_width, filter_height, op_params.depth_multiplier, - op_params.padding_values.width, op_params.padding_values.height, - op_params.stride_width, op_params.stride_height, - op_params.dilation_width_factor, op_params.dilation_height_factor, - tflite::micro::GetTensorData(bias), op_params.input_offset, - op_params.weights_offset, op_params.output_offset, - tflite::micro::GetTensorData(output), output_width, - output_height, op_params.quantized_activation_min, - op_params.quantized_activation_max, op_params.output_shift, - op_params.output_multiplier); - } else { - tflite::reference_ops::DepthwiseConv( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - auto* params = - reinterpret_cast(node->builtin_data); - OpData& data = *(static_cast(node->user_data)); - - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* filter = - tflite::micro::GetEvalInput(context, node, kFilterTensor); - const TfLiteEvalTensor* bias = - (NumInputs(node) == 3) - ? tflite::micro::GetEvalInput(context, node, kBiasTensor) - : nullptr; - - // TODO(aselle): Consider whether float conv and quantized conv should be - // separate ops to avoid dispatch overhead here. - switch (input->type) { // Already know in/out types are same. - case kTfLiteFloat32: - EvalFloat(context, node, params, &data, input, filter, bias, output); - break; - case kTfLiteInt8: - EvalQuantizedPerChannel(context, node, params, &data, input, filter, bias, - output); - break; - case kTfLiteUInt8: - EvalQuantized(context, node, params, &data, input, filter, bias, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace depthwise_conv - -TfLiteRegistration Register_DEPTHWISE_CONV_2D() { - return {/*init=*/depthwise_conv::Init, - /*free=*/nullptr, - /*prepare=*/depthwise_conv::Prepare, - /*invoke=*/depthwise_conv::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/fully_connected.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/fully_connected.cc deleted file mode 100644 index 8af92e6d..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/fully_connected.cc +++ /dev/null @@ -1,350 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/fully_connected.h" - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace fully_connected { -namespace { - -struct OpData { - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - // The range of the fused activation layer. For example for kNone and - // uint8_t these would be 0 and 255. - int32_t output_activation_min; - int32_t output_activation_max; - // The index of the temporary tensor where the quantized inputs are cached. - int input_quantized_index; - // Index to buffer for optimizations if applicable. - int buffer_idx; - - // Cached tensor zero point values for quantized operations. - int32_t input_zero_point; - int32_t filter_zero_point; - int32_t output_zero_point; -}; - -constexpr int kInputTensor = 0; -constexpr int kWeightsTensor = 1; -constexpr int kBiasTensor = 2; -constexpr int kOutputTensor = 0; - -TfLiteStatus CalculateOpData(TfLiteContext* context, - TfLiteFusedActivation activation, - TfLiteType data_type, const TfLiteTensor* input, - const TfLiteTensor* filter, - const TfLiteTensor* bias, TfLiteTensor* output, - OpData* data) { - TfLiteStatus status = kTfLiteOk; - // Set buffer index to a reset value - data->buffer_idx = -1; - if (data_type != kTfLiteFloat32) { - double real_multiplier = 0.0; - TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler( - context, input, filter, bias, output, &real_multiplier)); - int exponent; - QuantizeMultiplier(real_multiplier, &data->output_multiplier, &exponent); - data->output_shift = -exponent; - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, activation, output, &data->output_activation_min, - &data->output_activation_max)); - data->input_zero_point = input->params.zero_point; - data->filter_zero_point = filter->params.zero_point; - data->output_zero_point = output->params.zero_point; - } - return status; -} - -} // namespace - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - OpData* data = static_cast(node->user_data); - const auto params = - static_cast(node->builtin_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor); - const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - TF_LITE_ENSURE_MSG(context, input->type == filter->type, - "Hybrid models are not supported on TFLite Micro."); - TF_LITE_ENSURE_STATUS(CalculateOpData(context, params->activation, - input->type, input, filter, bias, - output, data)); - - if (input->type == kTfLiteInt8 && nullptr != GetTensorData(bias)) { - RuntimeShape filter_shape = GetTensorShape(filter); - RuntimeShape output_shape = GetTensorShape(output); - - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2); - const int filter_dim_count = filter_shape.DimensionsCount(); - cmsis_nn_dims filter_dims; - filter_dims.n = filter_shape.Dims(filter_dim_count - 1); - filter_dims.h = 1; - filter_dims.w = 1; - filter_dims.c = output_shape.Dims(1); - - const int32_t buf_size = - arm_fully_connected_s8_get_buffer_size(&filter_dims); - - if (buf_size > 0) { - TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena( - context, buf_size, &data->buffer_idx)); - } else { - data->buffer_idx = -1; - } - } - return kTfLiteOk; -} - -TfLiteStatus EvalQuantizedInt8(TfLiteContext* context, TfLiteNode* node, - const OpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output) { - // The 'if' condition can be removed when null handling of bias is added to - // arm_fully_connected_s8 - if (nullptr != tflite::micro::GetTensorData(bias)) { - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2); - const int batches = output_shape.Dims(0); - const int output_depth = output_shape.Dims(1); - const RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - const RuntimeShape input_shape = tflite::micro::GetTensorShape(input); - - cmsis_nn_fc_params fc_params; - fc_params.input_offset = -data.input_zero_point; - fc_params.output_offset = data.output_zero_point; - fc_params.activation.min = data.output_activation_min; - fc_params.activation.max = data.output_activation_max; - - cmsis_nn_per_tensor_quant_params quant_params; - quant_params.multiplier = data.output_multiplier; - // TODO(b/138810107): Figure out whether output shift should be inverted - quant_params.shift = -data.output_shift; - - cmsis_nn_dims input_dims; - input_dims.n = batches; - input_dims.h = input_shape.Dims(1); - input_dims.w = input_shape.Dims(2); - input_dims.c = input_shape.Dims(3); - - cmsis_nn_dims filter_dims; - filter_dims.n = accum_depth; - filter_dims.h = 1; - filter_dims.w = 1; - filter_dims.c = output_depth; - - cmsis_nn_dims bias_dims; - bias_dims.n = 1; - bias_dims.h = 1; - bias_dims.w = 1; - bias_dims.c = output_depth; - - cmsis_nn_dims output_dims; - output_dims.n = batches; - output_dims.h = 1; - output_dims.w = 1; - output_dims.c = output_depth; - - cmsis_nn_context ctx; - ctx.buf = nullptr; - ctx.size = 0; - - if (data.buffer_idx > -1) { - ctx.buf = context->GetScratchBuffer(context, data.buffer_idx); - } - - TF_LITE_ENSURE_EQ( - context, - arm_fully_connected_s8( - &ctx, &fc_params, &quant_params, &input_dims, - tflite::micro::GetTensorData(input), &filter_dims, - tflite::micro::GetTensorData(filter), &bias_dims, - tflite::micro::GetTensorData(bias), &output_dims, - tflite::micro::GetTensorData(output)), - ARM_MATH_SUCCESS); - } else { - tflite::FullyConnectedParams op_params; - op_params.input_offset = -data.input_zero_point; - op_params.weights_offset = -data.filter_zero_point; - op_params.output_offset = data.output_zero_point; - op_params.output_multiplier = data.output_multiplier; - // TODO(b/138810107): Figure out whether output shift should be inverted - op_params.output_shift = -data.output_shift; - op_params.quantized_activation_min = data.output_activation_min; - op_params.quantized_activation_max = data.output_activation_max; - - reference_integer_ops::FullyConnected( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - return kTfLiteOk; -} - -TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, - const OpData& data, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output) { - const int32_t input_offset = -data.input_zero_point; - const int32_t filter_offset = -data.filter_zero_point; - const int32_t output_offset = data.output_zero_point; - - tflite::FullyConnectedParams op_params; - op_params.input_offset = input_offset; - op_params.weights_offset = filter_offset; - op_params.output_offset = output_offset; - op_params.output_multiplier = data.output_multiplier; - // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. - op_params.output_shift = -data.output_shift; - op_params.quantized_activation_min = data.output_activation_min; - op_params.quantized_activation_max = data.output_activation_max; - -#define TF_LITE_FULLY_CONNECTED(output_data_type) \ - reference_ops::FullyConnected( \ - op_params, tflite::micro::GetTensorShape(input), \ - tflite::micro::GetTensorData(input), \ - tflite::micro::GetTensorShape(filter), \ - tflite::micro::GetTensorData(filter), \ - tflite::micro::GetTensorShape(bias), \ - tflite::micro::GetTensorData(bias), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output)) - switch (output->type) { - case kTfLiteUInt8: - TF_LITE_FULLY_CONNECTED(uint8_t); - break; - case kTfLiteInt16: - TF_LITE_FULLY_CONNECTED(int16_t); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(output->type), output->type); - return kTfLiteError; - } - - return kTfLiteOk; -} - -TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteFusedActivation activation, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(activation, &output_activation_min, - &output_activation_max); - tflite::FullyConnectedParams op_params; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - tflite::reference_ops::FullyConnected( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->builtin_data != nullptr); - const auto* params = - static_cast(node->builtin_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* filter = - tflite::micro::GetEvalInput(context, node, kWeightsTensor); - const TfLiteEvalTensor* bias = - tflite::micro::GetEvalInput(context, node, kBiasTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData& data = *(static_cast(node->user_data)); - - // Checks in Prepare ensure input, output and filter types are all the same. - switch (input->type) { - case kTfLiteFloat32: - return EvalFloat(context, node, params->activation, input, filter, bias, - output); - case kTfLiteInt8: - return EvalQuantizedInt8(context, node, data, input, filter, bias, - output); - - case kTfLiteUInt8: - return EvalQuantized(context, node, data, input, filter, bias, output); - - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace fully_connected - -TfLiteRegistration Register_FULLY_CONNECTED() { - return {/*init=*/fully_connected::Init, - /*free=*/nullptr, - /*prepare=*/fully_connected::Prepare, - /*invoke=*/fully_connected::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/mul.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/mul.cc deleted file mode 100644 index 00d884eb..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/mul.cc +++ /dev/null @@ -1,218 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/mul.h" - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/mul.h" -#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/memory_helpers.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace mul { - -constexpr int kInput1Tensor = 0; -constexpr int kInput2Tensor = 1; -constexpr int kOutputTensor = 0; - -struct OpData { - int32_t output_activation_min; - int32_t output_activation_max; - - int32_t output_multiplier; - int output_shift; - - // Cached tensor zero point values for quantized operations. - int32_t input1_zero_point; - int32_t input2_zero_point; - int32_t output_zero_point; -}; - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, OpData* data) { - const TfLiteTensor* input1 = GetInput(context, node, kInput1Tensor); - const TfLiteTensor* input2 = GetInput(context, node, kInput2Tensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type); - - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, params->activation, output, &data->output_activation_min, - &data->output_activation_max)); - - double real_multiplier = - input1->params.scale * input2->params.scale / output->params.scale; - QuantizeMultiplier(real_multiplier, &data->output_multiplier, - &data->output_shift); - } - - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - const TfLiteTensor* input1 = GetInput(context, node, kInput1Tensor); - const TfLiteTensor* input2 = GetInput(context, node, kInput2Tensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - if (output->dims->size == 0) { - return AllocateOutputDimensionsFromInput(context, input1, input2, output); - } - - TFLITE_DCHECK(node->builtin_data != nullptr); - auto* params = reinterpret_cast(node->builtin_data); - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - data->input1_zero_point = input1->params.zero_point; - data->input2_zero_point = input2->params.zero_point; - data->output_zero_point = output->params.zero_point; - CalculateOpData(context, node, params, data); - - return kTfLiteOk; -} - -void EvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, const OpData& data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) { - if (output->type == kTfLiteInt8 || output->type == kTfLiteUInt8) { - tflite::ArithmeticParams op_params; - SetActivationParams(data.output_activation_min, data.output_activation_max, - &op_params); - op_params.input1_offset = -data.input1_zero_point; - op_params.input2_offset = -data.input2_zero_point; - op_params.output_offset = data.output_zero_point; - op_params.output_multiplier = data.output_multiplier; - op_params.output_shift = data.output_shift; - bool need_broadcast = reference_ops::ProcessBroadcastShapes( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), &op_params); - -#define TF_LITE_MUL(type, opname, dtype) \ - type::opname(op_params, tflite::micro::GetTensorShape(input1), \ - tflite::micro::GetTensorData(input1), \ - tflite::micro::GetTensorShape(input2), \ - tflite::micro::GetTensorData(input2), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output)); - - if (output->type == kTfLiteInt8) { - if (need_broadcast) { - TF_LITE_MUL(reference_integer_ops, BroadcastMul4DSlow, int8_t); - } else { - arm_elementwise_mul_s8( - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorData(input2), - op_params.input1_offset, op_params.input2_offset, - tflite::micro::GetTensorData(output), - op_params.output_offset, op_params.output_multiplier, - op_params.output_shift, op_params.quantized_activation_min, - op_params.quantized_activation_max, - MatchingElementsSize(tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorShape(output))); - } - } else if (output->type == kTfLiteUInt8) { - if (need_broadcast) { - TF_LITE_MUL(reference_ops, BroadcastMul4DSlow, uint8_t); - } else { - TF_LITE_MUL(reference_ops, Mul, uint8_t); - } - } -#undef TF_LITE_MUL - } -} - -void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); - tflite::ArithmeticParams op_params; - SetActivationParams(output_activation_min, output_activation_max, &op_params); - - bool need_broadcast = reference_ops::ProcessBroadcastShapes( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), &op_params); -#define TF_LITE_MUL(opname) \ - reference_ops::opname(op_params, tflite::micro::GetTensorShape(input1), \ - tflite::micro::GetTensorData(input1), \ - tflite::micro::GetTensorShape(input2), \ - tflite::micro::GetTensorData(input2), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output)); - - if (need_broadcast) { - TF_LITE_MUL(BroadcastMul4DSlow); - } else { - TF_LITE_MUL(Mul); - } -#undef TF_LITE_MUL -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInput1Tensor); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInput2Tensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData& data = *(static_cast(node->user_data)); - - switch (input1->type) { - case kTfLiteUInt8: - case kTfLiteInt8: - EvalQuantized(context, node, params, data, input1, input2, output); - break; - case kTfLiteFloat32: - EvalFloat(context, node, params, input1, input2, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input1->type), input1->type); - return kTfLiteError; - } - - return kTfLiteOk; -} -} // namespace mul - -TfLiteRegistration Register_MUL() { - return {mul::Init, nullptr /* Free */, mul::Prepare, mul::Eval}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/pooling.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/pooling.cc deleted file mode 100644 index 4229b2c2..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/pooling.cc +++ /dev/null @@ -1,397 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/kernels/internal/reference/pooling.h" - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "flatbuffers/base.h" // from @flatbuffers -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/padding.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace pooling { - -namespace { - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -struct OpData { - TfLitePaddingValues padding; - // Index to buffer for optimizations if applicable. - int buffer_idx; - - int32_t activation_min; - int32_t activation_max; -}; - -TfLiteStatus CalculateOpData(TfLiteContext* context, - const TfLitePoolParams* params, - const TfLiteTensor* input, TfLiteTensor* output, - OpData* data) { - // input: batch, height, width, channel - int height = SizeOfDimension(input, 1); - int width = SizeOfDimension(input, 2); - - int out_height, out_width; - - data->padding = ComputePaddingHeightWidth( - params->stride_height, params->stride_width, - /*dilation_rate_height=*/1, - /*dilation_rate_width=*/1, height, width, params->filter_height, - params->filter_width, params->padding, &out_height, &out_width); - - if (input->type != kTfLiteFloat32) { - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, params->activation, output, &data->activation_min, - &data->activation_max)); - TFLITE_DCHECK_LE(data->activation_min, data->activation_max); - } - - // Set buffer index to a reset value - data->buffer_idx = -1; - - return kTfLiteOk; -} - -void AverageEvalFloat(const TfLiteContext* context, const TfLiteNode* node, - const TfLitePoolParams* params, const OpData& data, - const TfLiteEvalTensor* input, TfLiteEvalTensor* output) { - float activation_min, activation_max; - CalculateActivationRange(params->activation, &activation_min, - &activation_max); - - PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data.padding.height; - op_params.padding_values.width = data.padding.width; - op_params.float_activation_min = activation_min; - op_params.float_activation_max = activation_max; - reference_ops::AveragePool(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void AverageEvalQuantized(TfLiteContext* context, const TfLiteNode* node, - const TfLitePoolParams* params, const OpData& data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output) { - TFLITE_DCHECK(input->type == kTfLiteUInt8 || input->type == kTfLiteInt8); - - PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data.padding.height; - op_params.padding_values.width = data.padding.width; - op_params.quantized_activation_min = data.activation_min; - op_params.quantized_activation_max = data.activation_max; - - if (input->type == kTfLiteUInt8) { - reference_ops::AveragePool(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - RuntimeShape input_shape = tflite::micro::GetTensorShape(input); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - - cmsis_nn_dims input_dims; - input_dims.n = 1; - input_dims.h = input_shape.Dims(1); - input_dims.w = input_shape.Dims(2); - input_dims.c = depth; - - cmsis_nn_dims output_dims; - output_dims.n = 1; - output_dims.h = output_shape.Dims(1); - output_dims.w = output_shape.Dims(2); - output_dims.c = depth; - - cmsis_nn_pool_params pool_params; - pool_params.stride.h = params->stride_height; - pool_params.stride.w = params->stride_width; - pool_params.padding.h = data.padding.height; - pool_params.padding.w = data.padding.width; - pool_params.activation.min = data.activation_min; - pool_params.activation.max = data.activation_max; - - cmsis_nn_dims filter_dims; - filter_dims.n = 1; - filter_dims.h = params->filter_height; - filter_dims.w = params->filter_width; - filter_dims.c = 1; - - cmsis_nn_context ctx; - ctx.buf = nullptr; - ctx.size = 0; - if (data.buffer_idx > -1) { - ctx.buf = context->GetScratchBuffer(context, data.buffer_idx); - } - - TFLITE_DCHECK_EQ( - arm_avgpool_s8(&ctx, &pool_params, &input_dims, - tflite::micro::GetTensorData(input), - &filter_dims, &output_dims, - tflite::micro::GetTensorData(output)), - ARM_MATH_SUCCESS); - } -} - -void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLitePoolParams* params, const OpData& data, - const TfLiteEvalTensor* input, TfLiteEvalTensor* output) { - float activation_min, activation_max; - CalculateActivationRange(params->activation, &activation_min, - &activation_max); - tflite::PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data.padding.height; - op_params.padding_values.width = data.padding.width; - op_params.float_activation_min = activation_min; - op_params.float_activation_max = activation_max; - reference_ops::MaxPool(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void MaxEvalQuantizedUInt8(TfLiteContext* context, TfLiteNode* node, - TfLitePoolParams* params, const OpData& data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output) { - tflite::PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data.padding.height; - op_params.padding_values.width = data.padding.width; - op_params.quantized_activation_min = data.activation_min; - op_params.quantized_activation_max = data.activation_max; - reference_ops::MaxPool(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -TfLiteStatus MaxEvalInt8(TfLiteContext* context, const TfLiteNode* node, - const TfLitePoolParams* params, const OpData& data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output) { - RuntimeShape input_shape = tflite::micro::GetTensorShape(input); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - - cmsis_nn_dims input_dims; - input_dims.n = 1; - input_dims.h = input_shape.Dims(1); - input_dims.w = input_shape.Dims(2); - input_dims.c = depth; - - cmsis_nn_dims output_dims; - output_dims.n = 1; - output_dims.h = output_shape.Dims(1); - output_dims.w = output_shape.Dims(2); - output_dims.c = depth; - - cmsis_nn_pool_params pool_params; - pool_params.stride.h = params->stride_height; - pool_params.stride.w = params->stride_width; - pool_params.padding.h = data.padding.height; - pool_params.padding.w = data.padding.width; - pool_params.activation.min = data.activation_min; - pool_params.activation.max = data.activation_max; - - cmsis_nn_dims filter_dims; - filter_dims.n = 1; - filter_dims.h = params->filter_height; - filter_dims.w = params->filter_width; - filter_dims.c = 1; - - cmsis_nn_context ctx; - ctx.buf = nullptr; - ctx.size = 0; - if (data.buffer_idx > -1) { - ctx.buf = context->GetScratchBuffer(context, data.buffer_idx); - } - - TFLITE_DCHECK_EQ( - arm_max_pool_s8(&ctx, &pool_params, &input_dims, - tflite::micro::GetTensorData(input), &filter_dims, - &output_dims, - tflite::micro::GetTensorData(output)), - ARM_MATH_SUCCESS); - - return kTfLiteOk; -} - -} // namespace - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus MaxPrepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - OpData* data = static_cast(node->user_data); - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_STATUS(CalculateOpData(context, params, input, output, data)); - - return kTfLiteOk; -} - -TfLiteStatus AveragePrepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - OpData* data = static_cast(node->user_data); - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_STATUS(CalculateOpData(context, params, input, output, data)); - - if (input->type == kTfLiteInt8) { - RuntimeShape input_shape = GetTensorShape(input); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - - RuntimeShape output_shape = GetTensorShape(output); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int output_width = output_shape.Dims(2); - - const int32_t buffer_size = - arm_avgpool_s8_get_buffer_size(output_width, depth); - - if (buffer_size > 0) { - TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena( - context, buffer_size, &data->buffer_idx)); - } else { - data->buffer_idx = -1; - } - } - return kTfLiteOk; -} - -TfLiteStatus AverageEval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - const OpData& data = *(static_cast(node->user_data)); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - // Inputs and outputs share the same type, guaranteed by the converter. - switch (input->type) { - case kTfLiteFloat32: - AverageEvalFloat(context, node, params, data, input, output); - break; - case kTfLiteUInt8: - case kTfLiteInt8: - AverageEvalQuantized(context, node, params, data, input, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Input type %s is not currently supported", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus MaxEval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - const OpData& data = *(static_cast(node->user_data)); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - switch (input->type) { - case kTfLiteFloat32: - MaxEvalFloat(context, node, params, data, input, output); - break; - case kTfLiteUInt8: - MaxEvalQuantizedUInt8(context, node, params, data, input, output); - break; - case kTfLiteInt8: - MaxEvalInt8(context, node, params, data, input, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s not currently supported.", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace pooling - -TfLiteRegistration Register_AVERAGE_POOL_2D() { - return {/*init=*/pooling::Init, - /*free=*/nullptr, - /*prepare=*/pooling::AveragePrepare, - /*invoke=*/pooling::AverageEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_MAX_POOL_2D() { - return {/*init=*/pooling::Init, - /*free=*/nullptr, - /*prepare=*/pooling::MaxPrepare, - /*invoke=*/pooling::MaxEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/softmax.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/softmax.cc deleted file mode 100644 index 194bba4f..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/cmsis-nn/softmax.cc +++ /dev/null @@ -1,175 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/softmax.h" - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace activations { -namespace { - -TfLiteStatus CalculateSoftmaxParams(TfLiteContext* context, - const TfLiteTensor* input, - TfLiteTensor* output, - const TfLiteSoftmaxParams* params, - SoftmaxParams* op_data) { - if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) { - if (input->type == kTfLiteUInt8) { - TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteUInt8); - TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteInt8); - if (output->type == kTfLiteInt16) { - TF_LITE_ENSURE_EQ(context, output->params.zero_point, -32768); - // NOTE: Current int16_t softmax output does not require symmetric - // scaling - // - so no need to verify scale here. - } else { - TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteInt8); - TF_LITE_ENSURE_EQ(context, output->params.zero_point, -128); - TF_LITE_ENSURE(context, output->params.scale == 1.f / 256); - } - } - - static const int kScaledDiffIntegerBits = 5; - - int input_left_shift; - tflite::PreprocessSoftmaxScaling( - static_cast(params->beta), - static_cast(input->params.scale), kScaledDiffIntegerBits, - &op_data->input_multiplier, &input_left_shift); - op_data->input_left_shift = input_left_shift; - op_data->diff_min = - -1.0 * tflite::CalculateInputRadius(kScaledDiffIntegerBits, - op_data->input_left_shift); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32); - TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32); - op_data->beta = static_cast(params->beta); - } - return kTfLiteOk; -} - -} // namespace - -void* SoftmaxInit(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(SoftmaxParams)); -} - -TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) { - auto* params = static_cast(node->builtin_data); - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - const TfLiteTensor* input = GetInput(context, node, 0); - TF_LITE_ENSURE(context, NumDimensions(input) >= 1); - - TfLiteTensor* output = GetOutput(context, node, 0); - - TFLITE_DCHECK(node->user_data != nullptr); - SoftmaxParams* data = static_cast(node->user_data); - return CalculateSoftmaxParams(context, input, output, params, data); -} - -// Takes a tensor and performs softmax along the last dimension. -void SoftmaxFloat(const TfLiteEvalTensor* input, TfLiteEvalTensor* output, - const SoftmaxParams& op_data) { - tflite::reference_ops::Softmax(op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void SoftmaxQuantized(const TfLiteEvalTensor* input, TfLiteEvalTensor* output, - const SoftmaxParams& op_data) { - const auto input_shape = tflite::micro::GetTensorShape(input); - const auto output_shape = tflite::micro::GetTensorShape(output); - - if (input->type == kTfLiteUInt8) { - tflite::reference_ops::Softmax( - op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - if (output->type == kTfLiteInt16) { - tflite::reference_ops::Softmax( - op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - arm_softmax_s8(tflite::micro::GetTensorData(input), outer_size, - depth, op_data.input_multiplier, op_data.input_left_shift, - op_data.diff_min, - tflite::micro::GetTensorData(output)); - } - } -} - -TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - - TFLITE_DCHECK(node->user_data != nullptr); - const SoftmaxParams& data = - *(static_cast(node->user_data)); - - switch (input->type) { - case kTfLiteFloat32: { - SoftmaxFloat(input, output, data); - return kTfLiteOk; - } - case kTfLiteInt8: - case kTfLiteUInt8: { - SoftmaxQuantized(input, output, data); - return kTfLiteOk; - } - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } -} - -} // namespace activations - -TfLiteRegistration Register_SOFTMAX() { - return {/*init=*/activations::SoftmaxInit, - /*free=*/nullptr, - /*prepare=*/activations::SoftmaxPrepare, - /*invoke=*/activations::SoftmaxEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/comparisons.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/comparisons.cc deleted file mode 100644 index 6b01f78c..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/comparisons.cc +++ /dev/null @@ -1,722 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/kernels/internal/reference/comparisons.h" - -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace comparisons { -namespace { - -struct OpData { - ComparisonParams params; -}; - -constexpr int kInputTensor1 = 0; -constexpr int kInputTensor2 = 1; -constexpr int kOutputTensor = 0; - -TfLiteStatus EqualEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); - RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - bool* output_data = tflite::micro::GetTensorData(output); - - bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); - switch (input1->type) { - case kTfLiteBool: - requires_broadcast - ? reference_ops::Broadcast4DSlowEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::EqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteFloat32: - requires_broadcast - ? reference_ops::Broadcast4DSlowEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::EqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt32: - requires_broadcast - ? reference_ops::Broadcast4DSlowEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::EqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt64: - requires_broadcast - ? reference_ops::Broadcast4DSlowEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::EqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteUInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::EqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::EqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input1->type), input1->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -// TODO(renjieliu): Refactor the logic to avoid duplications. -TfLiteStatus NotEqualEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); - RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - bool* output_data = tflite::micro::GetTensorData(output); - - bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); - switch (input1->type) { - case kTfLiteBool: - requires_broadcast - ? reference_ops::Broadcast4DSlowNotEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::NotEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteFloat32: - requires_broadcast - ? reference_ops::Broadcast4DSlowNotEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::NotEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt32: - requires_broadcast - ? reference_ops::Broadcast4DSlowNotEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::NotEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt64: - requires_broadcast - ? reference_ops::Broadcast4DSlowNotEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::NotEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteUInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowNotEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::NotEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowNotEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::NotEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input1->type), input1->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); - RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - bool* output_data = tflite::micro::GetTensorData(output); - - bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); - switch (input1->type) { - case kTfLiteFloat32: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt32: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt64: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteUInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input1->type), input1->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); - RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - bool* output_data = tflite::micro::GetTensorData(output); - - bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); - switch (input1->type) { - case kTfLiteFloat32: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt32: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt64: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteUInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowGreaterEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::GreaterEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input1->type), input1->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); - RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - bool* output_data = tflite::micro::GetTensorData(output); - - bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); - switch (input1->type) { - case kTfLiteFloat32: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt32: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt64: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteUInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input1->type), input1->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus LessEqualEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1); - RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2); - RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - bool* output_data = tflite::micro::GetTensorData(output); - - bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2); - switch (input1->type) { - case kTfLiteFloat32: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt32: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt64: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessEqualNoScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteUInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - case kTfLiteInt8: - requires_broadcast - ? reference_ops::Broadcast4DSlowLessEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data) - : reference_ops::LessEqualWithScaling( - data->params, input1_shape, - tflite::micro::GetTensorData(input1), input2_shape, - tflite::micro::GetTensorData(input2), output_shape, - output_data); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input1->type), input1->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); - const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); - - if (input1->type == kTfLiteUInt8 || input1->type == kTfLiteInt8) { - auto input1_offset = -input1->params.zero_point; - auto input2_offset = -input2->params.zero_point; - const int kLeftShift = 8; - - int32_t input1_multiplier; - int input1_shift; - QuantizeMultiplierSmallerThanOneExp( - static_cast(input1->params.scale), &input1_multiplier, - &input1_shift); - int32_t input2_multiplier; - int input2_shift; - QuantizeMultiplierSmallerThanOneExp( - static_cast(input2->params.scale), &input2_multiplier, - &input2_shift); - - data->params.left_shift = kLeftShift; - data->params.input1_offset = input1_offset; - data->params.input1_multiplier = input1_multiplier; - data->params.input1_shift = input1_shift; - data->params.input2_offset = input2_offset; - data->params.input2_multiplier = input2_multiplier; - data->params.input2_shift = input2_shift; - } - - return kTfLiteOk; -} - -} // namespace comparisons - -TfLiteRegistration Register_EQUAL() { - return {/*init=*/comparisons::Init, - /*free=*/nullptr, - /*prepare=*/comparisons::Prepare, - /*invoke=*/comparisons::EqualEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_NOT_EQUAL() { - return {/*init=*/comparisons::Init, - /*free=*/nullptr, - /*prepare=*/comparisons::Prepare, - /*invoke=*/comparisons::NotEqualEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_GREATER() { - return {/*init=*/comparisons::Init, - /*free=*/nullptr, - /*prepare=*/comparisons::Prepare, - /*invoke=*/comparisons::GreaterEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_GREATER_EQUAL() { - return {/*init=*/comparisons::Init, - /*free=*/nullptr, - /*prepare=*/comparisons::Prepare, - /*invoke=*/comparisons::GreaterEqualEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_LESS() { - return {/*init=*/comparisons::Init, - /*free=*/nullptr, - /*prepare=*/comparisons::Prepare, - /*invoke=*/comparisons::LessEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_LESS_EQUAL() { - return {/*init=*/comparisons::Init, - /*free=*/nullptr, - /*prepare=*/comparisons::Prepare, - /*invoke=*/comparisons::LessEqualEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/concatenation.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/concatenation.cc deleted file mode 100644 index 1db8fd2c..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/concatenation.cc +++ /dev/null @@ -1,264 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/kernels/internal/reference/concatenation.h" - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace concatenation { - -constexpr int kMaxInputNum = 10; // Maximum number of input tensors -constexpr int kOutputTensor = 0; - -struct OpData { - ConcatenationParams params; -}; - -// Handles negative axis index, coerces to positive index value. -inline int CalculatePositiveAxis(int axis, const TfLiteTensor* output_tensor) { - if (axis >= 0) { - return axis; - } else { - return NumDimensions(output_tensor) + axis; - } -} - -// The following functions are helpers to get tensor data in the format that the -// reference op implementation expects. They provide the same functionality as -// class VectorOfTensors and class VectorOfQuantizedTensors in TFLite. - -// Gets shapes from a list of tensors. -inline void GetAllInputTensorShapes(const TfLiteContext* context, - const TfLiteNode* node, - RuntimeShape all_shapes[kMaxInputNum]) { - TFLITE_DCHECK(context != nullptr); - TFLITE_DCHECK(node != nullptr); - for (int i = 0; i < node->inputs->size; ++i) { - const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i); - RuntimeShape shape = tflite::micro::GetTensorShape(t); - all_shapes[i].ReplaceWith(shape.DimensionsCount(), shape.DimsData()); - } -} - -// Get shape pointers from a list of shapes. -inline void GetShapesPointers(const RuntimeShape* shapes, size_t num, - const RuntimeShape* pointers[]) { - for (size_t i = 0; i < num; ++i) { - pointers[i] = &shapes[i]; - } -} - -// Gets data pointers from a list of tensors. -template -inline void GetAllInputTensorData(const TfLiteContext* context, - const TfLiteNode* node, - T* all_data[kMaxInputNum]) { - TFLITE_DCHECK(context != nullptr); - TFLITE_DCHECK(node != nullptr); - for (int i = 0; i < node->inputs->size; ++i) { - const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i); - all_data[i] = tflite::micro::GetTensorData(t); - } -} - -template -void EvalUnquantized(TfLiteContext* context, TfLiteNode* node) { - // Collect the shapes and data pointer of input tensors - RuntimeShape inputs_shape[kMaxInputNum]; - const RuntimeShape* inputs_shape_ptr[kMaxInputNum]; - const data_type* inputs_data[kMaxInputNum]; - GetAllInputTensorShapes(context, node, inputs_shape); - GetShapesPointers(inputs_shape, node->inputs->size, inputs_shape_ptr); - GetAllInputTensorData(context, node, inputs_data); - - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - reference_ops::Concatenation(data->params, inputs_shape_ptr, inputs_data, - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void EvalQuantizedUInt8(TfLiteContext* context, TfLiteNode* node) { - // Collect the shapes and data pointer of input tensors - RuntimeShape inputs_shape[kMaxInputNum]; - const RuntimeShape* inputs_shape_ptr[kMaxInputNum]; - const uint8_t* inputs_data[kMaxInputNum]; - GetAllInputTensorShapes(context, node, inputs_shape); - GetShapesPointers(inputs_shape, node->inputs->size, inputs_shape_ptr); - GetAllInputTensorData(context, node, inputs_data); - - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - reference_ops::ConcatenationWithScaling( - data->params, inputs_shape_ptr, inputs_data, - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - // This function only checks the types. Additional shape validations are - // performed in the reference implementation called during Eval(). - const TfLiteConcatenationParams* params = - reinterpret_cast(node->builtin_data); - - TfLiteType input_type = GetInput(context, node, 0)->type; - TfLiteType output_type = GetOutput(context, node, kOutputTensor)->type; - - // Check activation and input type - TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone); - TF_LITE_ENSURE(context, - input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8 || - input_type == kTfLiteInt8 || input_type == kTfLiteInt32 || - input_type == kTfLiteInt64); - - // Output type must match input type - TF_LITE_ENSURE_EQ(context, output_type, input_type); - - // This implementation does not support large number of input tensors - const int num_inputs = NumInputs(node); - TF_LITE_ENSURE(context, num_inputs <= kMaxInputNum); - - // Shapes with dimensions >4 are not yet supported with static allocation. - for (int i = 0; i < num_inputs; ++i) { - const TfLiteTensor* input = GetInput(context, node, i); - int num_dimensions = NumDimensions(input); - - if (num_dimensions > 4) { - TF_LITE_KERNEL_LOG(context, - "Op Concatenation does not currently support num dimensions >4 " - "Tensor has %d dimensions.", - num_dimensions); - return kTfLiteError; - } - } - - // Calculate OpData. - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - switch (output_type) { // Already know in/outtypes are same. - case kTfLiteFloat32: - case kTfLiteInt32: - case kTfLiteInt64: { - data->params.axis = CalculatePositiveAxis(params->axis, output); - data->params.inputs_count = node->inputs->size; - break; - } - case kTfLiteUInt8: - case kTfLiteInt8: { - data->params.axis = CalculatePositiveAxis(params->axis, output); - data->params.inputs_count = node->inputs->size; - - float* input_scales = - reinterpret_cast(context->AllocatePersistentBuffer( - context, node->inputs->size * sizeof(float))); - - int32_t* input_zero_points = - reinterpret_cast(context->AllocatePersistentBuffer( - context, node->inputs->size * sizeof(int32_t))); - - // Allocate persistent scale and zeropoint buffers. - // Store input scale and zero point values in OpParams: - for (int i = 0; i < node->inputs->size; ++i) { - const TfLiteTensor* t = GetInput(context, node, i); - input_scales[i] = t->params.scale; - input_zero_points[i] = t->params.zero_point; - } - - data->params.input_scale = input_scales; - data->params.input_zeropoint = input_zero_points; - data->params.output_zeropoint = output->params.zero_point; - data->params.output_scale = output->params.scale; - break; - } - default: - TF_LITE_KERNEL_LOG(context, "Op Concatenation does not currently support Type '%s'.", - TfLiteTypeGetName(output_type)); - return kTfLiteError; - } - - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TfLiteType output_type = GetOutput(context, node, kOutputTensor)->type; - - switch (output_type) { // Already know in/outtypes are same. - case kTfLiteFloat32: - EvalUnquantized(context, node); - break; - case kTfLiteInt32: - EvalUnquantized(context, node); - break; - case kTfLiteUInt8: - EvalQuantizedUInt8(context, node); - break; - case kTfLiteInt8: - EvalUnquantized(context, node); - break; - case kTfLiteInt64: - EvalUnquantized(context, node); - break; - - default: - TF_LITE_KERNEL_LOG(context, "Op Concatenation does not currently support Type '%s'.", - TfLiteTypeGetName(output_type)); - return kTfLiteError; - } - - return kTfLiteOk; -} - -} // namespace concatenation - -TfLiteRegistration Register_CONCATENATION() { - return {/*init=*/concatenation::Init, - /*free=*/nullptr, - /*prepare=*/concatenation::Prepare, - /*invoke=*/concatenation::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/conv.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/conv.cc deleted file mode 100644 index 6601213f..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/conv.cc +++ /dev/null @@ -1,334 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/conv.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/padding.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace conv { - -constexpr int kInputTensor = 0; -constexpr int kFilterTensor = 1; -constexpr int kBiasTensor = 2; -constexpr int kOutputTensor = 0; - -// Conv is quantized along dimension 0: -// https://www.tensorflow.org/lite/performance/quantization_spec -constexpr int kConvQuantizedDimension = 0; - -// This file has 2 implementation of Conv. - -struct OpData { - TfLitePaddingValues padding; - - // Cached tensor zero point values for quantized operations. - int32_t input_zero_point; - int32_t filter_zero_point; - int32_t output_zero_point; - - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - - // Per channel output multiplier and shift. - int32_t* per_channel_output_multiplier; - int32_t* per_channel_output_shift; - - // The range of the fused activation layer. For example for kNone and - // uint8_t these would be 0 and 255. - int32_t output_activation_min; - int32_t output_activation_max; -}; - -inline PaddingType RuntimePaddingType(TfLitePadding padding) { - switch (padding) { - case TfLitePadding::kTfLitePaddingSame: - return PaddingType::kSame; - case TfLitePadding::kTfLitePaddingValid: - return PaddingType::kValid; - case TfLitePadding::kTfLitePaddingUnknown: - default: - return PaddingType::kNone; - } -} - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, - const TfLiteConvParams* params, int width, - int height, int filter_width, int filter_height, - int out_width, int out_height, - const TfLiteType data_type, OpData* data) { - bool has_bias = node->inputs->size == 3; - // Check number of inputs/outputs - TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); - - // Matching GetWindowedOutputSize in TensorFlow. - auto padding = params->padding; - data->padding = ComputePaddingHeightWidth( - params->stride_height, params->stride_width, - params->dilation_height_factor, params->dilation_width_factor, height, - width, filter_height, filter_width, padding, &out_height, &out_width); - - // Note that quantized inference requires that all tensors have their - // parameters set. This is usually done during quantized training. - if (data_type != kTfLiteFloat32) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); - const TfLiteTensor* bias = - GetOptionalInputTensor(context, node, kBiasTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - int output_channels = filter->dims->data[kConvQuantizedDimension]; - - TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams( - context, input, filter, bias, output, params->activation, - &data->output_multiplier, &data->output_shift, - &data->output_activation_min, &data->output_activation_max, - data->per_channel_output_multiplier, - reinterpret_cast(data->per_channel_output_shift), - output_channels)); - } - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - OpData* data = static_cast(node->user_data); - const auto params = static_cast(node->builtin_data); - - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); - - int input_width = input->dims->data[2]; - int input_height = input->dims->data[1]; - int filter_width = filter->dims->data[2]; - int filter_height = filter->dims->data[1]; - int output_width = output->dims->data[2]; - int output_height = output->dims->data[1]; - - // Dynimically allocate per-channel quantization parameters. - const int num_channels = filter->dims->data[kConvQuantizedDimension]; - data->per_channel_output_multiplier = - reinterpret_cast(context->AllocatePersistentBuffer( - context, num_channels * sizeof(int32_t))); - data->per_channel_output_shift = - reinterpret_cast(context->AllocatePersistentBuffer( - context, num_channels * sizeof(int32_t))); - - // All per-channel quantized tensors need valid zero point and scale arrays. - if (input->type == kTfLiteInt8) { - TF_LITE_ENSURE_EQ(context, filter->quantization.type, - kTfLiteAffineQuantization); - - const auto* affine_quantization = - static_cast(filter->quantization.params); - TF_LITE_ENSURE(context, affine_quantization); - TF_LITE_ENSURE(context, affine_quantization->scale); - TF_LITE_ENSURE(context, affine_quantization->zero_point); - - TF_LITE_ENSURE(context, - affine_quantization->scale->size == 1 || - affine_quantization->scale->size == - filter->dims->data[kConvQuantizedDimension]); - TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size, - affine_quantization->zero_point->size); - } - - TF_LITE_ENSURE_STATUS(CalculateOpData( - context, node, params, input_width, input_height, filter_width, - filter_height, output_width, output_height, input->type, data)); - - data->input_zero_point = input->params.zero_point; - data->filter_zero_point = filter->params.zero_point; - data->output_zero_point = output->params.zero_point; - - return kTfLiteOk; -} // namespace conv - -void EvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteConvParams* params, const OpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, const TfLiteEvalTensor* bias, - TfLiteEvalTensor* im2col, TfLiteEvalTensor* hwcn_weights, - TfLiteEvalTensor* output) { - const int32_t input_offset = -data.input_zero_point; - const int32_t filter_offset = -data.filter_zero_point; - const int32_t output_offset = data.output_zero_point; - - // TODO(b/154032858): Investigate removing extra copies. - ConvParams op_params; - op_params.padding_type = RuntimePaddingType(params->padding); - op_params.padding_values.width = data.padding.width; - op_params.padding_values.height = data.padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.input_offset = input_offset; - op_params.weights_offset = filter_offset; - op_params.output_offset = output_offset; - op_params.output_multiplier = data.output_multiplier; - op_params.output_shift = -data.output_shift; - op_params.quantized_activation_min = data.output_activation_min; - op_params.quantized_activation_max = data.output_activation_max; - reference_ops::Conv(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), - tflite::micro::GetTensorShape(im2col), - tflite::micro::GetTensorData(im2col), nullptr); -} - -void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node, - TfLiteConvParams* params, const OpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output, - TfLiteEvalTensor* im2col) { - // TODO(b/154032858): Investigate removing extra copies. - ConvParams op_params; - op_params.input_offset = -data.input_zero_point; - op_params.output_offset = data.output_zero_point; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.padding_values.height = data.padding.height; - op_params.padding_values.width = data.padding.width; - op_params.quantized_activation_min = data.output_activation_min; - op_params.quantized_activation_max = data.output_activation_max; - - reference_integer_ops::ConvPerChannel( - op_params, data.per_channel_output_multiplier, - data.per_channel_output_shift, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteConvParams* params, const OpData& data, - const TfLiteEvalTensor* input, const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, TfLiteEvalTensor* im2col, - TfLiteEvalTensor* hwcn_weights, TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); - // TODO(b/154032858): Investigate removing extra copies. - ConvParams op_params; - op_params.padding_type = RuntimePaddingType(params->padding); - op_params.padding_values.width = data.padding.width; - op_params.padding_values.height = data.padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - - reference_ops::Conv(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), - tflite::micro::GetTensorShape(im2col), - tflite::micro::GetTensorData(im2col)); -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* filter = - tflite::micro::GetEvalInput(context, node, kFilterTensor); - const TfLiteEvalTensor* bias = - (NumInputs(node) == 3) - ? tflite::micro::GetEvalInput(context, node, kBiasTensor) - : nullptr; - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData& data = *(static_cast(node->user_data)); - - switch (input->type) { // Already know in/out types are same. - case kTfLiteFloat32: - EvalFloat(context, node, params, data, input, filter, bias, nullptr, - nullptr, output); - break; - case kTfLiteInt8: - EvalQuantizedPerChannel(context, node, params, data, input, filter, bias, - output, nullptr); - break; - case kTfLiteUInt8: - EvalQuantized(context, node, params, data, input, filter, bias, nullptr, - nullptr, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace conv - -TfLiteRegistration Register_CONV_2D() { - return {/*init=*/conv::Init, - /*free=*/nullptr, - /*prepare=*/conv::Prepare, - /*invoke=*/conv::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/depthwise_conv.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/depthwise_conv.cc deleted file mode 100644 index 2f6083d5..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/depthwise_conv.cc +++ /dev/null @@ -1,327 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h" -#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/padding.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace depthwise_conv { -namespace { - -constexpr int kInputTensor = 0; -constexpr int kFilterTensor = 1; -constexpr int kBiasTensor = 2; -constexpr int kOutputTensor = 0; - -// Depthwise conv is quantized along dimension 3: -// https://www.tensorflow.org/lite/performance/quantization_spec -constexpr int kDepthwiseConvQuantizedDimension = 3; - -struct OpData { - TfLitePaddingValues padding; - - // Cached tensor zero point values for quantized operations. - int32_t input_zero_point; - int32_t filter_zero_point; - int32_t output_zero_point; - - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - - // Per channel output multiplier and shift. - int32_t* per_channel_output_multiplier; - int32_t* per_channel_output_shift; - // The range of the fused activation layer. For example for kNone and - // uint8_t these would be 0 and 255. - int32_t output_activation_min; - int32_t output_activation_max; -}; - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, - TfLiteDepthwiseConvParams* params, int width, - int height, int filter_width, int filter_height, - const TfLiteType data_type, OpData* data) { - bool has_bias = node->inputs->size == 3; - // Check number of inputs/outputs - TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); - - int unused_output_height, unused_output_width; - data->padding = ComputePaddingHeightWidth( - params->stride_height, params->stride_width, 1, 1, height, width, - filter_height, filter_width, params->padding, &unused_output_height, - &unused_output_width); - - // Note that quantized inference requires that all tensors have their - // parameters set. This is usually done during quantized training. - if (data_type != kTfLiteFloat32) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); - const TfLiteTensor* bias = - GetOptionalInputTensor(context, node, kBiasTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - int num_channels = filter->dims->data[kDepthwiseConvQuantizedDimension]; - - return tflite::PopulateConvolutionQuantizationParams( - context, input, filter, bias, output, params->activation, - &data->output_multiplier, &data->output_shift, - &data->output_activation_min, &data->output_activation_max, - data->per_channel_output_multiplier, - reinterpret_cast(data->per_channel_output_shift), num_channels); - } - return kTfLiteOk; -} - -} // namespace - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - auto* params = - reinterpret_cast(node->builtin_data); - OpData* data = static_cast(node->user_data); - - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); - - const TfLiteType data_type = input->type; - int width = SizeOfDimension(input, 2); - int height = SizeOfDimension(input, 1); - int filter_width = SizeOfDimension(filter, 2); - int filter_height = SizeOfDimension(filter, 1); - - // Per channel quantization is only needed for int8_t inference. For other - // quantized types, only a single scale and zero point is needed. - const int num_channels = filter->dims->data[kDepthwiseConvQuantizedDimension]; - // Dynimically allocate per-channel quantization parameters. - data->per_channel_output_multiplier = - reinterpret_cast(context->AllocatePersistentBuffer( - context, num_channels * sizeof(int32_t))); - data->per_channel_output_shift = - reinterpret_cast(context->AllocatePersistentBuffer( - context, num_channels * sizeof(int32_t))); - - // All per-channel quantized tensors need valid zero point and scale arrays. - if (input->type == kTfLiteInt8) { - TF_LITE_ENSURE_EQ(context, filter->quantization.type, - kTfLiteAffineQuantization); - - const auto* affine_quantization = - reinterpret_cast( - filter->quantization.params); - TF_LITE_ENSURE(context, affine_quantization); - TF_LITE_ENSURE(context, affine_quantization->scale); - TF_LITE_ENSURE(context, affine_quantization->zero_point); - TF_LITE_ENSURE( - context, affine_quantization->scale->size == 1 || - affine_quantization->scale->size == - filter->dims->data[kDepthwiseConvQuantizedDimension]); - TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size, - affine_quantization->zero_point->size); - } - - TF_LITE_ENSURE_STATUS(CalculateOpData(context, node, params, width, height, - filter_width, filter_height, data_type, - data)); - - data->input_zero_point = input->params.zero_point; - data->filter_zero_point = filter->params.zero_point; - data->output_zero_point = output->params.zero_point; - - return kTfLiteOk; -} - -void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteDepthwiseConvParams* params, const OpData& data, - const TfLiteEvalTensor* input, const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); - - tflite::DepthwiseParams op_params; - // Padding type is ignored, but still set. - op_params.padding_type = PaddingType::kSame; - op_params.padding_values.width = data.padding.width; - op_params.padding_values.height = data.padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.depth_multiplier = params->depth_multiplier; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - - tflite::reference_ops::DepthwiseConv( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node, - TfLiteDepthwiseConvParams* params, - const OpData& data, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output) { - DepthwiseParams op_params; - op_params.padding_type = PaddingType::kSame; - op_params.padding_values.width = data.padding.width; - op_params.padding_values.height = data.padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.depth_multiplier = params->depth_multiplier; - op_params.input_offset = -data.input_zero_point; - op_params.weights_offset = 0; - op_params.output_offset = data.output_zero_point; - // TODO(b/130439627): Use calculated value for clamping. - op_params.quantized_activation_min = std::numeric_limits::min(); - op_params.quantized_activation_max = std::numeric_limits::max(); - - reference_integer_ops::DepthwiseConvPerChannel( - op_params, data.per_channel_output_multiplier, - data.per_channel_output_shift, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void EvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteDepthwiseConvParams* params, const OpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output) { - const int32_t input_offset = -data.input_zero_point; - const int32_t filter_offset = -data.filter_zero_point; - const int32_t output_offset = data.output_zero_point; - - tflite::DepthwiseParams op_params; - // Padding type is ignored, but still set. - op_params.padding_type = PaddingType::kSame; - op_params.padding_values.width = data.padding.width; - op_params.padding_values.height = data.padding.height; - op_params.stride_width = params->stride_width; - op_params.stride_height = params->stride_height; - op_params.dilation_width_factor = params->dilation_width_factor; - op_params.dilation_height_factor = params->dilation_height_factor; - op_params.depth_multiplier = params->depth_multiplier; - op_params.quantized_activation_min = data.output_activation_min; - op_params.quantized_activation_max = data.output_activation_max; - op_params.input_offset = input_offset; - op_params.weights_offset = filter_offset; - op_params.output_offset = output_offset; - op_params.output_multiplier = data.output_multiplier; - // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. - op_params.output_shift = -data.output_shift; - - tflite::reference_ops::DepthwiseConv( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - auto* params = - reinterpret_cast(node->builtin_data); - const OpData& data = *(static_cast(node->user_data)); - - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* filter = - tflite::micro::GetEvalInput(context, node, kFilterTensor); - const TfLiteEvalTensor* bias = - (NumInputs(node) == 3) - ? tflite::micro::GetEvalInput(context, node, kBiasTensor) - : nullptr; - - // TODO(aselle): Consider whether float conv and quantized conv should be - // separate ops to avoid dispatch overhead here. - switch (input->type) { // Already know in/out types are same. - case kTfLiteFloat32: - EvalFloat(context, node, params, data, input, filter, bias, output); - break; - case kTfLiteInt8: - EvalQuantizedPerChannel(context, node, params, data, input, filter, bias, - output); - break; - case kTfLiteUInt8: - EvalQuantized(context, node, params, data, input, filter, bias, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace depthwise_conv - -TfLiteRegistration Register_DEPTHWISE_CONV_2D() { - return {/*init=*/depthwise_conv::Init, - /*free=*/nullptr, - /*prepare=*/depthwise_conv::Prepare, - /*invoke=*/depthwise_conv::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/dequantize.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/dequantize.cc deleted file mode 100644 index df501887..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/dequantize.cc +++ /dev/null @@ -1,164 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/dequantize.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/quantize.h" -#include "tensorflow/lite/kernels/internal/reference/requantize.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace dequantize { - -struct OpData { - tflite::DequantizationParams quantization_params; - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - int32_t output_zero_point; -}; - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - // TODO(b/140515557): Add cached dequant to improve hybrid model performance. - const TfLiteTensor* input = GetInput(context, node, 0); - TfLiteTensor* output = GetOutput(context, node, 0); - - TF_LITE_ENSURE(context, input->type == kTfLiteUInt8 || - input->type == kTfLiteInt8 || - input->type == kTfLiteInt16); - TF_LITE_ENSURE( - context, output->type == kTfLiteFloat32 || output->type == kTfLiteInt32); - - if (output->type == kTfLiteInt32) { - const double effective_output_scale = - static_cast(input->params.scale) / - static_cast(output->params.scale); - QuantizeMultiplier(effective_output_scale, &data->output_multiplier, - &data->output_shift); - } - - data->quantization_params.zero_point = input->params.zero_point; - data->quantization_params.scale = static_cast(input->params.scale); - data->output_zero_point = output->params.zero_point; - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - - if (output->type == kTfLiteFloat32) { - switch (input->type) { - case kTfLiteUInt8: - reference_ops::Dequantize(data->quantization_params, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - case kTfLiteInt8: - reference_ops::Dequantize(data->quantization_params, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - case kTfLiteInt16: - reference_ops::Dequantize(data->quantization_params, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - default: - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else if (output->type == kTfLiteInt32) { - int flat_size = MatchingFlatSize(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorShape(output)); - switch (input->type) { - case kTfLiteInt16: { - reference_ops::Requantize( - tflite::micro::GetTensorData(input), flat_size, - data->output_multiplier, data->output_shift, - data->quantization_params.zero_point, data->output_zero_point, - tflite::micro::GetTensorData(output)); - break; - } - case kTfLiteInt8: { - reference_ops::Requantize( - tflite::micro::GetTensorData(input), flat_size, - data->output_multiplier, data->output_shift, - data->quantization_params.zero_point, data->output_zero_point, - tflite::micro::GetTensorData(output)); - break; - } - default: - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else { - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - - return kTfLiteOk; -} - -} // namespace dequantize - -TfLiteRegistration Register_DEQUANTIZE() { - return {/*init=*/dequantize::Init, - /*free=*/nullptr, - /*prepare=*/dequantize::Prepare, - /*invoke=*/dequantize::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/elementwise.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/elementwise.cc deleted file mode 100644 index 64880344..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/elementwise.cc +++ /dev/null @@ -1,212 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace elementwise { -namespace { - -bool IsNumericSupportedType(const TfLiteType type) { - return type == kTfLiteFloat32; -} - -bool IsLogicalSupportedType(const TfLiteType type) { - return type == kTfLiteBool; -} - -typedef bool (*IsSupportedType)(TfLiteType); -template -TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) { - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - const TfLiteTensor* input = GetInput(context, node, 0); - TfLiteTensor* output = GetOutput(context, node, 0); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - if (!IsSupportedType(input->type)) { - TF_LITE_KERNEL_LOG(context, "Input data type %s (%d) is not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -template -inline TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node, - T func(T), TfLiteType expected_type) { - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, expected_type); - const size_t num_elements = ElementCount(*input->dims); - const T* in_data = tflite::micro::GetTensorData(input); - T* out_data = tflite::micro::GetTensorData(output); - for (size_t i = 0; i < num_elements; ++i) { - out_data[i] = func(in_data[i]); - } - return kTfLiteOk; -} - -inline TfLiteStatus EvalNumeric(TfLiteContext* context, TfLiteNode* node, - float float_func(float)) { - return EvalImpl(context, node, float_func, kTfLiteFloat32); -} - -inline TfLiteStatus EvalLogical(TfLiteContext* context, TfLiteNode* node, - bool bool_func(bool)) { - return EvalImpl(context, node, bool_func, kTfLiteBool); -} - -TfLiteStatus AbsEval(TfLiteContext* context, TfLiteNode* node) { - return EvalNumeric(context, node, std::abs); -} - -TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) { - return EvalNumeric(context, node, std::sin); -} - -TfLiteStatus CosEval(TfLiteContext* context, TfLiteNode* node) { - return EvalNumeric(context, node, std::cos); -} - -TfLiteStatus LogEval(TfLiteContext* context, TfLiteNode* node) { - return EvalNumeric(context, node, std::log); -} - -TfLiteStatus SqrtEval(TfLiteContext* context, TfLiteNode* node) { - return EvalNumeric(context, node, std::sqrt); -} - -TfLiteStatus RsqrtEval(TfLiteContext* context, TfLiteNode* node) { - return EvalNumeric(context, node, [](float f) { return 1.f / std::sqrt(f); }); -} - -TfLiteStatus SquareEval(TfLiteContext* context, TfLiteNode* node) { - return EvalNumeric(context, node, [](float f) { return f * f; }); -} - -TfLiteStatus LogicalNotEval(TfLiteContext* context, TfLiteNode* node) { - return EvalLogical(context, node, [](bool v) { return !v; }); -} - -} // namespace -} // namespace elementwise - -TfLiteRegistration Register_ABS() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ - elementwise::GenericPrepare, - /*invoke=*/elementwise::AbsEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_SIN() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ - elementwise::GenericPrepare, - /*invoke=*/elementwise::SinEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_COS() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ - elementwise::GenericPrepare, - /*invoke=*/elementwise::CosEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_LOG() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ - elementwise::GenericPrepare, - /*invoke=*/elementwise::LogEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_SQRT() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ - elementwise::GenericPrepare, - /*invoke=*/elementwise::SqrtEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_RSQRT() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ - elementwise::GenericPrepare, - /*invoke=*/elementwise::RsqrtEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_SQUARE() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ - elementwise::GenericPrepare, - /*invoke=*/elementwise::SquareEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_LOGICAL_NOT() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/ - elementwise::GenericPrepare, - /*invoke=*/elementwise::LogicalNotEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/ethosu.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/ethosu.cc deleted file mode 100644 index eac6cea8..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/ethosu.cc +++ /dev/null @@ -1,32 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// -// This is a stub file for non-Ethos platforms -// -#include "tensorflow/lite/c/common.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace custom { -TfLiteRegistration* Register_ETHOSU() { return nullptr; } - -const char* GetString_ETHOSU() { return ""; } - -} // namespace custom -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/floor.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/floor.cc deleted file mode 100644 index b8be1cf0..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/floor.cc +++ /dev/null @@ -1,57 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/floor.h" - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace floor { - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - reference_ops::Floor(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; -} -} // namespace floor - -TfLiteRegistration Register_FLOOR() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/floor::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/fully_connected.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/fully_connected.cc deleted file mode 100644 index 03078f89..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/fully_connected.cc +++ /dev/null @@ -1,256 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/fully_connected.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace fully_connected { -namespace { - -struct OpData { - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - // The range of the fused activation layer. For example for kNone and - // uint8_t these would be 0 and 255. - int32_t output_activation_min; - int32_t output_activation_max; - // The index of the temporary tensor where the quantized inputs are cached. - int input_quantized_index; - // Cached zero point values of tensors. - int32_t input_zero_point; - int32_t filter_zero_point; - int32_t output_zero_point; -}; - -constexpr int kInputTensor = 0; -constexpr int kWeightsTensor = 1; -constexpr int kBiasTensor = 2; -constexpr int kOutputTensor = 0; - -TfLiteStatus CalculateOpData(TfLiteContext* context, - TfLiteFusedActivation activation, - TfLiteType data_type, const TfLiteTensor* input, - const TfLiteTensor* filter, - const TfLiteTensor* bias, TfLiteTensor* output, - OpData* data) { - TfLiteStatus status = kTfLiteOk; - if (data_type != kTfLiteFloat32) { - double real_multiplier = 0.0; - TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler( - context, input, filter, bias, output, &real_multiplier)); - int exponent; - QuantizeMultiplier(real_multiplier, &data->output_multiplier, &exponent); - data->output_shift = -exponent; - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, activation, output, &data->output_activation_min, - &data->output_activation_max)); - - data->input_zero_point = input->params.zero_point; - data->filter_zero_point = filter->params.zero_point; - data->output_zero_point = output->params.zero_point; - } - return status; -} - -} // namespace - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - OpData* data = static_cast(node->user_data); - const auto params = - static_cast(node->builtin_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor); - const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - TF_LITE_ENSURE_MSG(context, input->type == filter->type, - "Hybrid models are not supported on TFLite Micro."); - - return CalculateOpData(context, params->activation, input->type, input, - filter, bias, output, data); -} - -TfLiteStatus EvalQuantizedInt8(TfLiteContext* context, TfLiteNode* node, - const OpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output) { - tflite::FullyConnectedParams op_params; - op_params.input_offset = -data.input_zero_point; - op_params.weights_offset = -data.filter_zero_point; - op_params.output_offset = data.output_zero_point; - op_params.output_multiplier = data.output_multiplier; - // TODO(b/138810107): Figure out whether output shift should be inverted - op_params.output_shift = -data.output_shift; - op_params.quantized_activation_min = data.output_activation_min; - op_params.quantized_activation_max = data.output_activation_max; - - reference_integer_ops::FullyConnected( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; -} - -TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, - const OpData& data, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output) { - const int32_t input_offset = -data.input_zero_point; - const int32_t filter_offset = -data.filter_zero_point; - const int32_t output_offset = data.output_zero_point; - - tflite::FullyConnectedParams op_params; - op_params.input_offset = input_offset; - op_params.weights_offset = filter_offset; - op_params.output_offset = output_offset; - op_params.output_multiplier = data.output_multiplier; - // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. - op_params.output_shift = -data.output_shift; - op_params.quantized_activation_min = data.output_activation_min; - op_params.quantized_activation_max = data.output_activation_max; - -#define TF_LITE_FULLY_CONNECTED(output_data_type) \ - reference_ops::FullyConnected( \ - op_params, tflite::micro::GetTensorShape(input), \ - tflite::micro::GetTensorData(input), \ - tflite::micro::GetTensorShape(filter), \ - tflite::micro::GetTensorData(filter), \ - tflite::micro::GetTensorShape(bias), \ - tflite::micro::GetTensorData(bias), \ - tflite::micro::GetTensorShape(output), \ - tflite::micro::GetTensorData(output)) - switch (output->type) { - case kTfLiteUInt8: - TF_LITE_FULLY_CONNECTED(uint8_t); - break; - case kTfLiteInt16: - TF_LITE_FULLY_CONNECTED(int16_t); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(output->type), output->type); - return kTfLiteError; - } - - return kTfLiteOk; -} - -TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteFusedActivation activation, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(activation, &output_activation_min, - &output_activation_max); - tflite::FullyConnectedParams op_params; - op_params.float_activation_min = output_activation_min; - op_params.float_activation_max = output_activation_max; - tflite::reference_ops::FullyConnected( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->builtin_data != nullptr); - const auto* params = - static_cast(node->builtin_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* filter = - tflite::micro::GetEvalInput(context, node, kWeightsTensor); - const TfLiteEvalTensor* bias = - tflite::micro::GetEvalInput(context, node, kBiasTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData& data = *(static_cast(node->user_data)); - - // Checks in Prepare ensure input, output and filter types are all the same. - switch (input->type) { - case kTfLiteFloat32: - return EvalFloat(context, node, params->activation, input, filter, bias, - output); - case kTfLiteInt8: - return EvalQuantizedInt8(context, node, data, input, filter, bias, - output); - - case kTfLiteUInt8: - return EvalQuantized(context, node, data, input, filter, bias, output); - - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace fully_connected - -TfLiteRegistration Register_FULLY_CONNECTED() { - return {/*init=*/fully_connected::Init, - /*free=*/nullptr, - /*prepare=*/fully_connected::Prepare, - /*invoke=*/fully_connected::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/hard_swish.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/hard_swish.cc deleted file mode 100644 index 11e1d1a7..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/hard_swish.cc +++ /dev/null @@ -1,140 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/hard_swish.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace hard_swish { - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -void* HardSwishInit(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(HardSwishParams)); -} - -TfLiteStatus HardSwishPrepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) { - HardSwishParams* params = static_cast(node->user_data); - - params->input_zero_point = input->params.zero_point; - params->output_zero_point = output->params.zero_point; - - const float input_scale = input->params.scale; - const float hires_input_scale = (1.0f / 128.0f) * input_scale; - const float reluish_scale = 3.0f / 32768.0f; - const float output_scale = output->params.scale; - - const double output_multiplier = - static_cast(hires_input_scale / output_scale); - int32_t output_multiplier_fixedpoint_int32; - QuantizeMultiplier(output_multiplier, &output_multiplier_fixedpoint_int32, - ¶ms->output_multiplier_exponent); - DownScaleInt32ToInt16Multiplier( - output_multiplier_fixedpoint_int32, - ¶ms->output_multiplier_fixedpoint_int16); - - TF_LITE_ENSURE(context, params->output_multiplier_exponent <= 0); - - const double reluish_multiplier = - static_cast(hires_input_scale / reluish_scale); - int32_t reluish_multiplier_fixedpoint_int32; - QuantizeMultiplier(reluish_multiplier, &reluish_multiplier_fixedpoint_int32, - ¶ms->reluish_multiplier_exponent); - DownScaleInt32ToInt16Multiplier( - reluish_multiplier_fixedpoint_int32, - ¶ms->reluish_multiplier_fixedpoint_int16); - } - - return kTfLiteOk; -} - -TfLiteStatus HardSwishEval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - HardSwishParams* params = static_cast(node->user_data); - - switch (input->type) { - case kTfLiteFloat32: { - tflite::reference_ops::HardSwish( - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } break; - case kTfLiteUInt8: { - tflite::reference_ops::HardSwish( - *params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } break; - case kTfLiteInt8: { - tflite::reference_ops::HardSwish( - *params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } break; - default: { - TF_LITE_KERNEL_LOG( - context, - "Only float32/int8_t/uint8_t are supported currently, got %s", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - } - return kTfLiteOk; -} - -} // namespace hard_swish - -TfLiteRegistration Register_HARD_SWISH() { - return {/*init=*/hard_swish::HardSwishInit, - /*free=*/nullptr, - /*prepare=*/hard_swish::HardSwishPrepare, - /*invoke=*/hard_swish::HardSwishEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/kernel_runner.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/kernel_runner.cc deleted file mode 100644 index cef6c01c..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/kernel_runner.cc +++ /dev/null @@ -1,165 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/kernels/kernel_runner.h" - -namespace tflite { -namespace micro { - -namespace { -constexpr size_t kBufferAlignment = 16; -} // namespace - -// TODO(b/161841696): Consider moving away from global arena buffers: -constexpr int KernelRunner::kNumScratchBuffers_; -constexpr int KernelRunner::kKernelRunnerBufferSize_; -uint8_t KernelRunner::kKernelRunnerBuffer_[]; - -KernelRunner::KernelRunner(const TfLiteRegistration& registration, - TfLiteTensor* tensors, int tensors_size, - TfLiteIntArray* inputs, TfLiteIntArray* outputs, - void* builtin_data, ErrorReporter* error_reporter) - : allocator_(SimpleMemoryAllocator::Create( - error_reporter, kKernelRunnerBuffer_, kKernelRunnerBufferSize_)), - registration_(registration), - tensors_(tensors), - error_reporter_(error_reporter) { - // Prepare TfLiteContext: - context_.impl_ = static_cast(this); - context_.ReportError = ReportOpError; - context_.recommended_num_threads = 1; - context_.GetTensor = GetTensor; - context_.GetEvalTensor = GetEvalTensor; - context_.AllocatePersistentBuffer = AllocatePersistentBuffer; - context_.RequestScratchBufferInArena = RequestScratchBufferInArena; - context_.GetScratchBuffer = GetScratchBuffer; - - // Prepare TfLiteNode: - node_.inputs = inputs; - node_.outputs = outputs; - node_.builtin_data = builtin_data; -} - -TfLiteStatus KernelRunner::InitAndPrepare(const char* init_data) { - if (registration_.init) { - node_.user_data = registration_.init(&context_, init_data, /*length=*/0); - } - if (registration_.prepare) { - TF_LITE_ENSURE_STATUS(registration_.prepare(&context_, &node_)); - } - return kTfLiteOk; -} - -TfLiteStatus KernelRunner::Invoke() { - if (registration_.invoke == nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, - "TfLiteRegistration missing invoke function pointer!"); - return kTfLiteError; - } - return registration_.invoke(&context_, &node_); -} - -TfLiteTensor* KernelRunner::GetTensor(const struct TfLiteContext* context, - int tensor_index) { - TFLITE_DCHECK(context != nullptr); - KernelRunner* runner = reinterpret_cast(context->impl_); - TFLITE_DCHECK(runner != nullptr); - - return &runner->tensors_[tensor_index]; -} - -TfLiteEvalTensor* KernelRunner::GetEvalTensor( - const struct TfLiteContext* context, int tensor_index) { - TFLITE_DCHECK(context != nullptr); - KernelRunner* runner = reinterpret_cast(context->impl_); - TFLITE_DCHECK(runner != nullptr); - - TfLiteEvalTensor* eval_tensor = - reinterpret_cast(runner->allocator_->AllocateTemp( - sizeof(TfLiteEvalTensor), alignof(TfLiteEvalTensor))); - TFLITE_DCHECK(eval_tensor != nullptr); - - // In unit tests, the TfLiteTensor pointer contains the source of truth for - // buffers and values: - eval_tensor->data = runner->tensors_[tensor_index].data; - eval_tensor->dims = runner->tensors_[tensor_index].dims; - eval_tensor->type = runner->tensors_[tensor_index].type; - return eval_tensor; -} - -void* KernelRunner::AllocatePersistentBuffer(TfLiteContext* context, - size_t bytes) { - TFLITE_DCHECK(context != nullptr); - KernelRunner* runner = reinterpret_cast(context->impl_); - TFLITE_DCHECK(runner != nullptr); - - return runner->allocator_->AllocateFromTail(bytes, kBufferAlignment); -} - -TfLiteStatus KernelRunner::RequestScratchBufferInArena(TfLiteContext* context, - size_t bytes, - int* buffer_index) { - TFLITE_DCHECK(context != nullptr); - TFLITE_DCHECK(buffer_index != nullptr); - - KernelRunner* runner = reinterpret_cast(context->impl_); - TFLITE_DCHECK(runner != nullptr); - - if (runner->scratch_buffer_count_ == kNumScratchBuffers_) { - TF_LITE_REPORT_ERROR( - runner->error_reporter_, - "Exceeded the maximum number of scratch tensors allowed (%d).", - kNumScratchBuffers_); - return kTfLiteError; - } - - // For tests, we allocate scratch buffers from the tail and keep them around - // for the lifetime of model. This means that the arena size in the tests will - // be more than what we would have if the scratch buffers could share memory. - runner->scratch_buffers_[runner->scratch_buffer_count_] = - runner->allocator_->AllocateFromTail(bytes, kBufferAlignment); - TFLITE_DCHECK(runner->scratch_buffers_[runner->scratch_buffer_count_] != - nullptr); - - *buffer_index = runner->scratch_buffer_count_++; - return kTfLiteOk; -} - -void* KernelRunner::GetScratchBuffer(TfLiteContext* context, int buffer_index) { - TFLITE_DCHECK(context != nullptr); - KernelRunner* runner = reinterpret_cast(context->impl_); - TFLITE_DCHECK(runner != nullptr); - - TFLITE_DCHECK(runner->scratch_buffer_count_ <= kNumScratchBuffers_); - if (buffer_index >= runner->scratch_buffer_count_) { - return nullptr; - } - return runner->scratch_buffers_[buffer_index]; -} - -void KernelRunner::ReportOpError(struct TfLiteContext* context, - const char* format, ...) { - TFLITE_DCHECK(context != nullptr); - KernelRunner* runner = reinterpret_cast(context->impl_); - TFLITE_DCHECK(runner != nullptr); - - va_list args; - va_start(args, format); - TF_LITE_REPORT_ERROR(runner->error_reporter_, format, args); - va_end(args); -} - -} // namespace micro -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/kernel_util.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/kernel_util.cc deleted file mode 100644 index 1ddfc1d3..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/kernel_util.cc +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -#include "tensorflow/lite/c/common.h" - -namespace tflite { -namespace micro { - -bool HaveSameShapes(const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2) { - TFLITE_DCHECK(input1 != nullptr); - TFLITE_DCHECK(input2 != nullptr); - return TfLiteIntArrayEqual(input1->dims, input2->dims); -} - -} // namespace micro -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/l2norm.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/l2norm.cc deleted file mode 100644 index f864efa2..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/l2norm.cc +++ /dev/null @@ -1,155 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h" -#include "tensorflow/lite/kernels/internal/reference/l2normalization.h" -#include "tensorflow/lite/kernels/internal/tensor.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace l2norm { - -namespace { - -// This file has two implementation of L2Norm. -enum KernelType { - kReference, - kGenericOptimized, -}; - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -} // namespace - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - auto* params = reinterpret_cast(node->builtin_data); - L2NormalizationParams* data = - static_cast(node->user_data); - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE(context, NumDimensions(input) <= 4); - - TF_LITE_ENSURE(context, output->type == kTfLiteFloat32 || - output->type == kTfLiteUInt8 || - output->type == kTfLiteInt8); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - data->input_zero_point = input->params.zero_point; - } else if (output->type == kTfLiteFloat32) { - data->input_zero_point = 0; - } - - // TODO(ahentz): For some reason our implementations don't support - // activations. - TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone); - - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, - sizeof(L2NormalizationParams)); -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const L2NormalizationParams& data = - *(static_cast(node->user_data)); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - // TODO(b/143912164): instead of hardcode the epsilon here, we should read it - // from tensorflow, i.e., adding a params. - // We don't compute epsilon for quantized kernel: - // - // epsilon_float = (epsilon_quant - zp) * scale - // so - // espsilon_quant = epsilon_float / scale + zp - // We know epsilon_float is just a very small number to avoid division by - // zero error, and scale is > 1, so the integer value of epsilon for quant - // is just dominated by the zero point. - // Also, GetInvSqrtQuantizedMultiplierExp handles the scenario where the sum - // of input value squared is zero case well. - // So we don't even need to do handle the epsilon for quantized kernel case. - const float epsilon = 1e-6f; - if (output->type == kTfLiteFloat32) { - reference_ops::L2Normalization(data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), - epsilon); - } else if (output->type == kTfLiteUInt8) { - reference_ops::L2Normalization( - data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else if (output->type == kTfLiteInt8) { - const auto input_shape = tflite::micro::GetTensorShape(input); - const auto output_shape = tflite::micro::GetTensorShape(output); - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - reference_integer_ops::L2Normalization( - data.input_zero_point, outer_size, depth, - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorData(output)); - } else { - TF_LITE_KERNEL_LOG(context, "Output type is %s, requires float.", - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - - return kTfLiteOk; -} - -} // namespace l2norm - -TfLiteRegistration Register_L2NORM_REF() { - return {/*init=*/l2norm::Init, - /*free=*/nullptr, - /*prepare=*/l2norm::Prepare, - /*invoke=*/l2norm::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_L2_NORMALIZATION() { return Register_L2NORM_REF(); } - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/logical.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/logical.cc deleted file mode 100644 index f4033ba8..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/logical.cc +++ /dev/null @@ -1,105 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/reference/binary_function.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace logical { -namespace { - -// Input/output tensor index. -constexpr int kInputTensor1 = 0; -constexpr int kInputTensor2 = 1; -constexpr int kOutputTensor = 0; - -TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node, - bool (*func)(bool, bool)) { - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - if (tflite::micro::HaveSameShapes(input1, input2)) { - reference_ops::BinaryFunction( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), func); - } else { - reference_ops::BroadcastBinaryFunction4DSlow( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), func); - } - - return kTfLiteOk; -} - -bool LogicalOr(bool x, bool y) { return x || y; } - -TfLiteStatus LogicalOrEval(TfLiteContext* context, TfLiteNode* node) { - return LogicalImpl(context, node, LogicalOr); -} - -bool LogicalAnd(bool x, bool y) { return x && y; } - -TfLiteStatus LogicalAndEval(TfLiteContext* context, TfLiteNode* node) { - return LogicalImpl(context, node, LogicalAnd); -} - -} // namespace -} // namespace logical - -TfLiteRegistration Register_LOGICAL_OR() { - // Init, Free, Prepare, Eval are satisfying the Interface required by - // TfLiteRegistration. - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/logical::LogicalOrEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_LOGICAL_AND() { - // Init, Free, Prepare, Eval are satisfying the Interface required by - // TfLiteRegistration. - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/logical::LogicalAndEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/logistic.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/logistic.cc deleted file mode 100644 index 7a371da2..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/logistic.cc +++ /dev/null @@ -1,148 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/logistic.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace activations { -namespace { -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -struct OpData { - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -TfLiteStatus CalculateArithmeticOpData(TfLiteContext* context, TfLiteNode* node, - OpData* data) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - if (input->type == kTfLiteInt8) { - TF_LITE_ENSURE_EQ(context, output->params.zero_point, - std::numeric_limits::min()); - - static constexpr int kInputIntegerBits = 4; - const double input_real_multiplier = - static_cast(input->params.scale) * - static_cast(1 << (31 - kInputIntegerBits)); - - data->input_zero_point = input->params.zero_point; - - const double q = std::frexp(input_real_multiplier, &data->input_left_shift); - data->input_multiplier = static_cast(TfLiteRound(q * (1ll << 31))); - - data->input_range_radius = - CalculateInputRadius(kInputIntegerBits, data->input_left_shift, 31); - } - return kTfLiteOk; -} -} // namespace - -void* LogisticInit(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus LogisticPrepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - return CalculateArithmeticOpData(context, node, data); -} - -TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - if (input->type == kTfLiteFloat32) { - switch (output->type) { - case kTfLiteFloat32: { - reference_ops::Logistic(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } - default: - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else if (input->type == kTfLiteInt8) { - switch (output->type) { - case kTfLiteInt8: { - reference_integer_ops::Logistic( - data->input_zero_point, data->input_range_radius, - data->input_multiplier, data->input_left_shift, - NumElements(input->dims), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } - default: - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else { - // TODO(b/141211002): Also support other data types once we have supported - // temporary tensors in TFLM. - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace activations - -TfLiteRegistration Register_LOGISTIC() { - return {/*init=*/activations::LogisticInit, - /*free=*/nullptr, - /*prepare=*/activations::LogisticPrepare, - /*invoke=*/activations::LogisticEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/maximum_minimum.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/maximum_minimum.cc deleted file mode 100644 index a7c343bf..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/maximum_minimum.cc +++ /dev/null @@ -1,148 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/maximum_minimum.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace maximum_minimum { -namespace { - -// This file has a reference implementation of TFMaximum/TFMinimum. -enum KernelType { - kReference, -}; - -constexpr int kInputTensor1 = 0; -constexpr int kInputTensor2 = 1; -constexpr int kOutputTensor = 0; - -struct OpContext { - OpContext(TfLiteContext* context, TfLiteNode* node) { - input1 = tflite::micro::GetEvalInput(context, node, kInputTensor1); - input2 = tflite::micro::GetEvalInput(context, node, kInputTensor2); - output = tflite::micro::GetEvalOutput(context, node, kOutputTensor); - } - const TfLiteEvalTensor* input1; - const TfLiteEvalTensor* input2; - TfLiteEvalTensor* output; -}; - -struct MaximumOp { - template - static data_type op(data_type el1, data_type el2) { - return el1 > el2 ? el1 : el2; - } -}; - -struct MinimumOp { - template - static data_type op(data_type el1, data_type el2) { - return el1 < el2 ? el1 : el2; - } -}; - -} // namespace - -template -void TFLiteOperation(TfLiteContext* context, TfLiteNode* node, - const OpContext& op_context) { - reference_ops::MaximumMinimumBroadcastSlow( - tflite::micro::GetTensorShape(op_context.input1), - tflite::micro::GetTensorData(op_context.input1), - tflite::micro::GetTensorShape(op_context.input2), - tflite::micro::GetTensorData(op_context.input2), - tflite::micro::GetTensorShape(op_context.output), - tflite::micro::GetTensorData(op_context.output), - op_type::template op); -} - -template -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - OpContext op_context(context, node); - - if (kernel_type == kReference) { - switch (op_context.output->type) { - case kTfLiteFloat32: - TFLiteOperation(context, node, op_context); - break; - case kTfLiteUInt8: - TFLiteOperation(context, node, op_context); - break; - case kTfLiteInt8: - TFLiteOperation(context, node, op_context); - break; - case kTfLiteInt32: - TFLiteOperation(context, node, op_context); - break; - case kTfLiteInt64: - TFLiteOperation(context, node, op_context); - break; - default: - TF_LITE_KERNEL_LOG(context, - "Type %s (%d) is not supported by Maximum/Minimum.", - TfLiteTypeGetName(op_context.output->type), - op_context.output->type); - return kTfLiteError; - } - } else { - TF_LITE_KERNEL_LOG(context, - "Kernel type not supported by Maximum/Minimum."); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace maximum_minimum - -TfLiteRegistration Register_MAXIMUM() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/ - maximum_minimum::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_MINIMUM() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/ - maximum_minimum::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/mul.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/mul.cc deleted file mode 100644 index 36e41a36..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/mul.cc +++ /dev/null @@ -1,233 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/mul.h" - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/mul.h" -#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/memory_helpers.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace mul { -namespace { - -constexpr int kInput1Tensor = 0; -constexpr int kInput2Tensor = 1; -constexpr int kOutputTensor = 0; - -struct OpData { - int32_t input1_zero_point; - int32_t input2_zero_point; - - int32_t output_activation_min; - int32_t output_activation_max; - int32_t output_zero_point; - int32_t output_multiplier; - int output_shift; - - float output_activation_min_f32; - float output_activation_max_f32; -}; - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, OpData* data) { - const TfLiteTensor* input1 = GetInput(context, node, kInput1Tensor); - const TfLiteTensor* input2 = GetInput(context, node, kInput2Tensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type); - - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, params->activation, output, &data->output_activation_min, - &data->output_activation_max)); - - double real_multiplier = static_cast(input1->params.scale) * - static_cast(input2->params.scale) / - static_cast(output->params.scale); - QuantizeMultiplier(real_multiplier, &data->output_multiplier, - &data->output_shift); - - data->input1_zero_point = input1->params.zero_point; - data->input2_zero_point = input2->params.zero_point; - data->output_zero_point = output->params.zero_point; - } else { - CalculateActivationRange(params->activation, - &data->output_activation_min_f32, - &data->output_activation_max_f32); - } - - return kTfLiteOk; -} - -} // namespace - -void EvalQuantized(TfLiteContext* context, TfLiteNode* node, const OpData* data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) { - tflite::ArithmeticParams op_params = {}; - op_params.quantized_activation_min = data->output_activation_min; - op_params.quantized_activation_max = data->output_activation_max; - op_params.float_activation_max = data->output_activation_max_f32; - op_params.input1_offset = -data->input1_zero_point; - op_params.input2_offset = -data->input2_zero_point; - op_params.output_offset = data->output_zero_point; - op_params.output_multiplier = data->output_multiplier; - op_params.output_shift = data->output_shift; - - bool need_broadcast = reference_ops::ProcessBroadcastShapes( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), &op_params); - - if (output->type == kTfLiteInt8) { - if (need_broadcast) { - reference_integer_ops::BroadcastMul4DSlow( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - reference_integer_ops::Mul(op_params, - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - } else if (output->type == kTfLiteUInt8) { - if (need_broadcast) { - reference_integer_ops::BroadcastMul4DSlow( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - reference_integer_ops::Mul(op_params, - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - } -} - -void EvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, const OpData* data, - const TfLiteEvalTensor* input1, const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output) { - tflite::ArithmeticParams op_params = {}; - op_params.float_activation_min = data->output_activation_min_f32; - op_params.float_activation_max = data->output_activation_max_f32; - - bool need_broadcast = reference_ops::ProcessBroadcastShapes( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), &op_params); - - if (need_broadcast) { - reference_ops::BroadcastMul4DSlow( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - reference_ops::Mul(op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->builtin_data != nullptr); - auto* params = reinterpret_cast(node->builtin_data); - - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - return CalculateOpData(context, node, params, data); -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->builtin_data != nullptr); - auto* params = reinterpret_cast(node->builtin_data); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInput1Tensor); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInput2Tensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - switch (input1->type) { - case kTfLiteUInt8: - case kTfLiteInt8: - EvalQuantized(context, node, data, input1, input2, output); - break; - case kTfLiteFloat32: - EvalFloat(context, node, params, data, input1, input2, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input1->type), input1->type); - return kTfLiteError; - } - - return kTfLiteOk; -} -} // namespace mul - -TfLiteRegistration Register_MUL() { - return {/*init=*/mul::Init, - /*free=*/nullptr, - /*prepare=*/mul::Prepare, - /*invoke=*/mul::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/neg.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/neg.cc deleted file mode 100644 index 74a95ca3..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/neg.cc +++ /dev/null @@ -1,66 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/neg.h" - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace neg { - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - switch (input->type) { - // TODO(wangtz): handle for kTfLiteInt8 - case kTfLiteFloat32: - reference_ops::Negate(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace neg - -TfLiteRegistration Register_NEG() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/neg::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pack.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pack.cc deleted file mode 100644 index d332fc63..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pack.cc +++ /dev/null @@ -1,127 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace pack { -namespace { - -constexpr int kOutputTensor = 0; - -template -TfLiteStatus PackImpl(TfLiteContext* context, TfLiteNode* node, - TfLiteEvalTensor* output, int values_count, int axis) { - const TfLiteEvalTensor* input0 = - tflite::micro::GetEvalInput(context, node, 0); - - const int dimensions = output->dims->size; - const TfLiteIntArray* input_dims = input0->dims; - const TfLiteIntArray* output_dims = output->dims; - - if (axis < 0) { - axis += dimensions; - } - - int outer_size = 1; - for (int i = 0; i < axis; ++i) { - outer_size *= output_dims->data[i]; - } - int copy_size = 1; - for (int i = axis + 1; i < dimensions; ++i) { - copy_size *= output_dims->data[i]; - } - int input_size = 1; - for (int i = 0; i < input_dims->size; ++i) { - input_size *= input_dims->data[i]; - } - TFLITE_DCHECK_EQ(input_size, copy_size * outer_size); - - T* output_data = tflite::micro::GetTensorData(output); - - for (int i = 0; i < values_count; ++i) { - const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i); - const T* input_data = tflite::micro::GetTensorData(t); - for (int k = 0; k < outer_size; ++k) { - const T* input_ptr = input_data + copy_size * k; - int loc = k * values_count * copy_size + i * copy_size; - T* output_ptr = output_data + loc; - for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j]; - } - } - - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLitePackParams* data = - reinterpret_cast(node->builtin_data); - - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - switch (output->type) { - case kTfLiteFloat32: { - return PackImpl(context, node, output, data->values_count, - data->axis); - } - case kTfLiteUInt8: { - return PackImpl(context, node, output, data->values_count, - data->axis); - } - case kTfLiteInt8: { - return PackImpl(context, node, output, data->values_count, - data->axis); - } - case kTfLiteInt32: { - return PackImpl(context, node, output, data->values_count, - data->axis); - } - case kTfLiteInt64: { - return PackImpl(context, node, output, data->values_count, - data->axis); - } - default: { - TF_LITE_KERNEL_LOG(context, "Type '%s' is not supported by pack.", - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } - - return kTfLiteOk; -} - -} // namespace -} // namespace pack - -TfLiteRegistration Register_PACK() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/pack::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pad.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pad.cc deleted file mode 100644 index 39f86cbf..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pad.cc +++ /dev/null @@ -1,251 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/kernels/internal/reference/pad.h" - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace pad { -namespace { - -struct OpData { - PadParams params; - int32_t output_zero_point; -}; - -} // namespace - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - const TfLiteTensor* input = GetInput(context, node, /*index=*/0); - const TfLiteTensor* paddings = GetInput(context, node, /*index=*/1); - const TfLiteTensor* constant_values = - NumInputs(node) == 3 ? GetInput(context, node, /*index=*/2) : nullptr; - TfLiteTensor* output = GetOutput(context, node, /*index=*/0); - - TF_LITE_ENSURE_EQ(context, input->type, output->type); - - // Current implementations rely on the inputs being <= 4D. - TF_LITE_ENSURE(context, NumDimensions(input) <= - reference_ops::PadKernelMaxDimensionCount()); - - if (constant_values != nullptr) { - TF_LITE_ENSURE_EQ(context, input->type, constant_values->type); - // Ensure that constant_values is a scalar. - TF_LITE_ENSURE_EQ(context, NumElements(constant_values), 1); - } - - // There must be a pair of paddings for each output dimension. - TF_LITE_ENSURE_EQ(context, GetTensorShape(paddings).FlatSize(), - output->dims->size * 2); - - // On Micro, outputs must be properly sized by the converter. - // NOTE: This data is only available because the paddings buffer is stored in - // the flatbuffer: - TF_LITE_ENSURE(context, IsConstantTensor(paddings)); - const int32_t* paddings_data = GetTensorData(paddings); - for (int i = 0; i < output->dims->size; i++) { - int output_dim = output->dims->data[i]; - int expected_dim = - input->dims->data[i] + paddings_data[i * 2] + paddings_data[i * 2 + 1]; - TF_LITE_ENSURE_EQ(context, output_dim, expected_dim); - } - - // Calculate OpData: - data->params.resizing_category = ResizingCategory::kGenericResize; - const int paddings_total = GetTensorShape(paddings).FlatSize(); - if (paddings_total == 8 && (paddings_data[0] == 0 && paddings_data[1] == 0) && - (paddings_data[6] == 0 && paddings_data[7] == 0)) { - data->params.resizing_category = ResizingCategory::kImageStyle; - } - - const int num_input_dimensions = NumDimensions(input); - data->params.left_padding_count = num_input_dimensions; - data->params.right_padding_count = num_input_dimensions; - - for (int idx = num_input_dimensions - 1; idx >= 0; --idx) { - data->params.left_padding[idx] = paddings_data[idx * 2]; - data->params.right_padding[idx] = paddings_data[idx * 2 + 1]; - } - - if (input->type == kTfLiteInt8 || input->type == kTfLiteUInt8) { - if (constant_values == nullptr) { - // Quantized Pad requires that 0 is represented in the quantized - // range. - if (input->type == kTfLiteUInt8) { - TF_LITE_ENSURE(context, output->params.zero_point >= - std::numeric_limits::min()); - TF_LITE_ENSURE(context, output->params.zero_point <= - std::numeric_limits::max()); - } else { - TF_LITE_ENSURE(context, output->params.zero_point >= - std::numeric_limits::min()); - TF_LITE_ENSURE(context, output->params.zero_point <= - std::numeric_limits::max()); - } - } else { - // Quantized Pad requires that 'constant_values' is represented in the - // same quantized range as the input and output tensors. - TF_LITE_ENSURE_EQ(context, output->params.zero_point, - constant_values->params.zero_point); - TF_LITE_ENSURE_EQ(context, static_cast(output->params.scale), - static_cast(constant_values->params.scale)); - } - data->output_zero_point = output->params.zero_point; - } - - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, /*index=*/0); - const TfLiteEvalTensor* constant_values = - NumInputs(node) == 3 - ? tflite::micro::GetEvalInput(context, node, /*index=*/2) - : nullptr; - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, /*index=*/0); - - switch (input->type) { - case kTfLiteFloat32: { - float pad_value = - constant_values == nullptr - ? 0.f - : *tflite::micro::GetTensorData(constant_values); - if (data->params.resizing_category == ResizingCategory::kImageStyle) { - reference_ops::PadImageStyle( - data->params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), &pad_value, - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - &pad_value, tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - } break; - case kTfLiteUInt8: { - uint8_t pad_value; - if (constant_values == nullptr) { - pad_value = static_cast(data->output_zero_point); - } else { - pad_value = *tflite::micro::GetTensorData(constant_values); - } - if (data->params.resizing_category == ResizingCategory::kImageStyle) { - reference_ops::PadImageStyle( - data->params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), &pad_value, - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - &pad_value, tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - } break; - case kTfLiteInt8: { - int8_t pad_value; - if (constant_values == nullptr) { - pad_value = static_cast(data->output_zero_point); - } else { - pad_value = *tflite::micro::GetTensorData(constant_values); - } - if (data->params.resizing_category == ResizingCategory::kImageStyle) { - reference_ops::PadImageStyle( - data->params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), &pad_value, - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - &pad_value, tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - } break; - case kTfLiteInt32: { - int32_t pad_value = - constant_values == nullptr - ? 0 - : *tflite::micro::GetTensorData(constant_values); - reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - &pad_value, tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } break; - default: - - TF_LITE_KERNEL_LOG(context, "Type %s not currently supported by Pad.", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } -#undef TF_LITE_PAD - return kTfLiteOk; -} - -} // namespace pad - -TfLiteRegistration Register_PAD() { - return {/*init=*/pad::Init, - /*free=*/nullptr, - /*prepare=*/pad::Prepare, - /*invoke=*/pad::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -// Also register Pad as PadV2. -TfLiteRegistration Register_PADV2() { - return {/*init=*/pad::Init, - /*free=*/nullptr, - /*prepare=*/pad::Prepare, - /*invoke=*/pad::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pooling.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pooling.cc deleted file mode 100644 index 90d48aae..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/pooling.cc +++ /dev/null @@ -1,267 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/kernels/internal/reference/pooling.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/padding.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace pooling { - -namespace { - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -struct OpData { - TfLitePaddingValues padding; - int32_t activation_min; - int32_t activation_max; - float activation_min_f32; - float activation_max_f32; -}; - -TfLiteStatus CalculateOpData(const TfLiteContext* context, - const TfLitePoolParams* params, - const TfLiteTensor* input, - const TfLiteTensor* output, OpData* data) { - // input: batch, height, width, channel - int height = SizeOfDimension(input, 1); - int width = SizeOfDimension(input, 2); - - int out_height, out_width; - - data->padding = ComputePaddingHeightWidth( - params->stride_height, params->stride_width, - /*dilation_rate_height=*/1, - /*dilation_rate_width=*/1, height, width, params->filter_height, - params->filter_width, params->padding, &out_height, &out_width); - - return kTfLiteOk; -} - -void AverageEvalFloat(const TfLiteContext* context, const TfLiteNode* node, - const TfLitePoolParams* params, const OpData* data, - const TfLiteEvalTensor* input, TfLiteEvalTensor* output) { - PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data->padding.height; - op_params.padding_values.width = data->padding.width; - op_params.float_activation_min = data->activation_min_f32; - op_params.float_activation_max = data->activation_max_f32; - reference_ops::AveragePool(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void AverageEvalQuantized(TfLiteContext* context, const TfLiteNode* node, - const TfLitePoolParams* params, const OpData* data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output) { - TFLITE_DCHECK(input->type == kTfLiteUInt8 || input->type == kTfLiteInt8); - - PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data->padding.height; - op_params.padding_values.width = data->padding.width; - op_params.quantized_activation_min = data->activation_min; - op_params.quantized_activation_max = data->activation_max; - - if (input->type == kTfLiteUInt8) { - reference_ops::AveragePool(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - reference_integer_ops::AveragePool( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } -} - -void MaxEvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLitePoolParams* params, const OpData* data, - const TfLiteEvalTensor* input, TfLiteEvalTensor* output) { - tflite::PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data->padding.height; - op_params.padding_values.width = data->padding.width; - op_params.float_activation_min = data->activation_min_f32; - op_params.float_activation_max = data->activation_max_f32; - reference_ops::MaxPool(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void MaxEvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLitePoolParams* params, const OpData* data, - const TfLiteEvalTensor* input, TfLiteEvalTensor* output) { - tflite::PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data->padding.height; - op_params.padding_values.width = data->padding.width; - op_params.quantized_activation_min = data->activation_min; - op_params.quantized_activation_max = data->activation_max; - - if (input->type == kTfLiteUInt8) { - reference_ops::MaxPool(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - reference_integer_ops::MaxPool( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } -} -} // namespace - -TfLiteStatus AverageEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->builtin_data != nullptr); - auto* params = reinterpret_cast(node->builtin_data); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - // Inputs and outputs share the same type, guaranteed by the converter. - switch (input->type) { - case kTfLiteFloat32: - AverageEvalFloat(context, node, params, data, input, output); - break; - case kTfLiteUInt8: - case kTfLiteInt8: - AverageEvalQuantized(context, node, params, data, input, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Input type %s is not currently supported", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus MaxEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->builtin_data != nullptr); - auto* params = reinterpret_cast(node->builtin_data); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - switch (input->type) { - case kTfLiteFloat32: - MaxEvalFloat(context, node, params, data, input, output); - break; - case kTfLiteUInt8: - case kTfLiteInt8: - MaxEvalQuantized(context, node, params, data, input, output); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s not currently supported.", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->builtin_data != nullptr); - auto* params = reinterpret_cast(node->builtin_data); - - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_STATUS(CalculateOpData(context, params, input, output, data)); - - if (input->type == kTfLiteFloat32) { - CalculateActivationRange(params->activation, &data->activation_min_f32, - &data->activation_max_f32); - } else if (input->type == kTfLiteInt8 || input->type == kTfLiteUInt8) { - CalculateActivationRangeQuantized(context, params->activation, output, - &data->activation_min, - &data->activation_max); - } - - return kTfLiteOk; -} - -} // namespace pooling - -TfLiteRegistration Register_AVERAGE_POOL_2D() { - return {/*init=*/pooling::Init, - /*free=*/nullptr, - /*prepare=*/pooling::Prepare, - /*invoke=*/pooling::AverageEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -TfLiteRegistration Register_MAX_POOL_2D() { - return {/*init=*/pooling::Init, - /*free=*/nullptr, - /*prepare=*/pooling::Prepare, - /*invoke=*/pooling::MaxEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/prelu.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/prelu.cc deleted file mode 100644 index 8665dbc2..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/prelu.cc +++ /dev/null @@ -1,166 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/prelu.h" - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace activations { -namespace { - -TfLiteStatus CalculatePreluParams(const TfLiteTensor* input, - const TfLiteTensor* alpha, - TfLiteTensor* output, PreluParams* params) { - if (output->type == kTfLiteInt8 || output->type == kTfLiteUInt8 || - output->type == kTfLiteInt16) { - double real_multiplier_1 = static_cast(input->params.scale) / - static_cast(output->params.scale); - double real_multiplier_2 = static_cast(input->params.scale) * - static_cast(alpha->params.scale) / - static_cast(output->params.scale); - QuantizeMultiplier(real_multiplier_1, ¶ms->output_multiplier_1, - ¶ms->output_shift_1); - QuantizeMultiplier(real_multiplier_2, ¶ms->output_multiplier_2, - ¶ms->output_shift_2); - - params->input_offset = -input->params.zero_point; - params->alpha_offset = -alpha->params.zero_point; - params->output_offset = output->params.zero_point; - } - - return kTfLiteOk; -} - -} // namespace - -inline void BroadcastPrelu4DSlowFloat( - const RuntimeShape& unextended_input1_shape, const float* input1_data, - const RuntimeShape& unextended_input2_shape, const float* input2_data, - const RuntimeShape& unextended_output_shape, float* output_data) { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - - for (int b = 0; b < output_shape.Dims(0); ++b) { - for (int y = 0; y < output_shape.Dims(1); ++y) { - for (int x = 0; x < output_shape.Dims(2); ++x) { - for (int c = 0; c < output_shape.Dims(3); ++c) { - auto out_idx = Offset(output_shape, b, y, x, c); - auto in1_idx = SubscriptToIndex(desc1, b, y, x, c); - auto in2_idx = SubscriptToIndex(desc2, b, y, x, c); - auto in1_val = input1_data[in1_idx]; - auto in2_val = input2_data[in2_idx]; - output_data[out_idx] = in1_val >= 0.0f ? in1_val : in1_val * in2_val; - } - } - } - } -} - -void* PreluInit(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(PreluParams)); -} - -TfLiteStatus PreluPrepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - PreluParams* params = static_cast(node->user_data); - - const TfLiteTensor* input = GetInput(context, node, 0); - const TfLiteTensor* alpha = GetInput(context, node, 1); - TfLiteTensor* output = GetOutput(context, node, 0); - - return CalculatePreluParams(input, alpha, output, params); -} - -TfLiteStatus PreluEval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const PreluParams& params = - *(static_cast(node->user_data)); - - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - const TfLiteEvalTensor* alpha = tflite::micro::GetEvalInput(context, node, 1); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - - switch (input->type) { - case kTfLiteFloat32: { - BroadcastPrelu4DSlowFloat(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(alpha), - tflite::micro::GetTensorData(alpha), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } break; - case kTfLiteUInt8: { - reference_ops::BroadcastPrelu4DSlow( - params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(alpha), - tflite::micro::GetTensorData(alpha), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } break; - case kTfLiteInt8: { - reference_ops::BroadcastPrelu4DSlow( - params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(alpha), - tflite::micro::GetTensorData(alpha), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } break; - default: - TF_LITE_KERNEL_LOG( - context, "Only float32 and uint8_t are supported currently, got %d.", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } -} - -} // namespace activations - -TfLiteRegistration Register_PRELU() { - return {/*init=*/activations::PreluInit, - /*free=*/nullptr, - /*prepare=*/activations::PreluPrepare, - /*invoke=*/activations::PreluEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/quantize.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/quantize.cc deleted file mode 100644 index 83237906..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/quantize.cc +++ /dev/null @@ -1,178 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/kernels/internal/reference/quantize.h" - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/requantize.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace quantize { - -struct OpData { - tflite::QuantizationParams quantization_params; - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - - int32_t input_zero_point; -}; - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - const TfLiteTensor* input = GetInput(context, node, 0); - TfLiteTensor* output = GetOutput(context, node, 0); - - // TODO(b/128934713): Add support for fixed-point per-channel quantization. - // Currently this only support affine per-layer quantization. - TF_LITE_ENSURE_EQ(context, output->quantization.type, - kTfLiteAffineQuantization); - const auto* affine_quantization = - reinterpret_cast(output->quantization.params); - TF_LITE_ENSURE(context, affine_quantization); - TF_LITE_ENSURE(context, affine_quantization->scale); - TF_LITE_ENSURE(context, affine_quantization->scale->size == 1); - - TF_LITE_ENSURE(context, input->type == kTfLiteFloat32 || - input->type == kTfLiteInt16 || - input->type == kTfLiteInt8); - TF_LITE_ENSURE(context, - output->type == kTfLiteUInt8 || output->type == kTfLiteInt8); - - if ((input->type == kTfLiteInt16 || input->type == kTfLiteInt8) && - output->type == kTfLiteInt8) { - double effective_scale = - static_cast(input->params.scale / output->params.scale); - - QuantizeMultiplier(effective_scale, &data->output_multiplier, - &data->output_shift); - } - - data->quantization_params.zero_point = output->params.zero_point; - data->quantization_params.scale = static_cast(output->params.scale); - - data->input_zero_point = input->params.zero_point; - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - - if (input->type == kTfLiteFloat32) { - switch (output->type) { - case kTfLiteInt8: - reference_ops::AffineQuantize( - data->quantization_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - case kTfLiteUInt8: - reference_ops::AffineQuantize( - data->quantization_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - default: - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else if (input->type == kTfLiteInt16) { - size_t size = ElementCount(*input->dims); - switch (output->type) { - case kTfLiteInt8: - reference_ops::Requantize(tflite::micro::GetTensorData(input), - size, data->output_multiplier, - data->output_shift, data->input_zero_point, - data->quantization_params.zero_point, - tflite::micro::GetTensorData(output)); - break; - default: - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else if (input->type == kTfLiteInt8) { - // Int8 to Int8 requantization, required if the input and output tensors - // have different scales and/or zero points. - size_t size = ElementCount(*input->dims); - switch (output->type) { - case kTfLiteInt8: - reference_ops::Requantize(tflite::micro::GetTensorData(input), - size, data->output_multiplier, - data->output_shift, data->input_zero_point, - data->quantization_params.zero_point, - tflite::micro::GetTensorData(output)); - break; - default: - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else { - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - - return kTfLiteOk; -} - -} // namespace quantize - -// This Op (QUANTIZE) quantizes the input and produces quantized output. -// AffineQuantize takes scale and zero point and quantizes the float value to -// quantized output, in int8_t or uint8_t format. -TfLiteRegistration Register_QUANTIZE() { - return {/*init=*/quantize::Init, - /*free=*/nullptr, - /*prepare=*/quantize::Prepare, - /*invoke=*/quantize::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/reduce.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/reduce.cc deleted file mode 100644 index 5cae7824..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/reduce.cc +++ /dev/null @@ -1,139 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/reduce.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace reduce { - -constexpr int kMaxNumberOfAxis = 4; -constexpr int kMaxNumberOfReducedAxis = 2; - -TfLiteStatus PrepareSimple(TfLiteContext* context, TfLiteNode* node) { - // Inputs Tensor (dtype depends on quantization): - // [0] = Input - // [1] = Axis - - // Outputs Tensor (dtype depends on quantization): - // [0] = Output - - // Validate number of inputs and outputs - TF_LITE_ENSURE_EQ(context, node->inputs->size, 2); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); - - // Validate axis type - const TfLiteTensor* axis = GetInput(context, node, 1); - TF_LITE_ENSURE_TYPES_EQ(context, axis->type, kTfLiteInt32); - return kTfLiteOk; -} - -TfLiteStatus PrepareMeanOrSum(TfLiteContext* context, TfLiteNode* node) { - TF_LITE_ENSURE_OK(context, PrepareSimple(context, node)); - // TODO(b/144955155): Support uint8_t(b/144955155) and int8_t(b/144955018) - return kTfLiteOk; -} - -void ResolveAxis(const int* axis_data, int axis_count, - tflite::MeanParams* op_params) { - int i = 0; - for (; i < axis_count; ++i) { - op_params->axis[i] = static_cast(axis_data[i]); - } - for (; i < 4; ++i) { - op_params->axis[i] = 1; - } - op_params->axis_count = axis_count; -} - -TfLiteStatus EvalMean(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 1); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - TfLiteReducerParams* params = - reinterpret_cast(node->builtin_data); - - int num_axis = static_cast(ElementCount(*axis->dims)); - int temp_index[kMaxNumberOfAxis]; - int resolved_axis[kMaxNumberOfReducedAxis]; - - switch (input->type) { - case kTfLiteFloat32: { - tflite::MeanParams op_params; - ResolveAxis(tflite::micro::GetTensorData(axis), num_axis, - &op_params); - // TODO(b/146571391): Support only 4D Input and 2D Axis for Mean until - // scratch tensor allocation has been implemented in (b/132070898) - bool is_valid_inputs = - (input->dims->size == 4 && op_params.axis_count == 2 && - ((op_params.axis[0] == 1 && op_params.axis[1] == 2) || - (op_params.axis[0] == 2 && op_params.axis[1] == 1))); - TF_LITE_ENSURE_MSG( - context, is_valid_inputs == true, - "Number of Input " - "dimensions != 4 OR the Axis is not either [1, 2] or [2, 1]"); - // TODO(b/139102329): Handle the below special case in the combined - // reference method. - // Defer to specialized implementation for 4D Mean across axes 1 & 2. - if (params->keep_dims) { - reference_ops::Mean(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - TF_LITE_ENSURE( - context, - reference_ops::Mean( - tflite::micro::GetTensorData(input), input->dims->data, - input->dims->size, tflite::micro::GetTensorData(output), - output->dims->data, output->dims->size, - tflite::micro::GetTensorData(axis), num_axis, - params->keep_dims, temp_index, resolved_axis, - tflite::micro::GetTensorData(output))); - } - } break; - default: - // TODO(b/144955155): Support uint8_t(b/144955155) and int8_t(b/144955018) - TF_LITE_ENSURE_MSG(context, false, - "Currently, only float32 input type " - "is supported."); - } - return kTfLiteOk; -} -} // namespace reduce - -TfLiteRegistration Register_MEAN() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/reduce::PrepareMeanOrSum, - /*invoke=*/reduce::EvalMean, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/reshape.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/reshape.cc deleted file mode 100644 index a865892b..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/reshape.cc +++ /dev/null @@ -1,116 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/memory_helpers.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace reshape { - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -TfLiteStatus ReshapeOutput(TfLiteContext* context, TfLiteNode* node) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - // Tensorflow's Reshape allows one of the shape components to have the - // special -1 value, meaning it will be calculated automatically based on the - // input. Here we calculate what that dimension should be so that the number - // of output elements in the same as the number of input elements. - int num_input_elements = NumElements(input); - TfLiteIntArray* output_shape = output->dims; - - if (NumInputs(node) == 1 && // Legacy scalar supported with params. - output_shape->size == 1 && output_shape->data[0] == 0) { - // Legacy tflite models use a shape parameter of [0] to indicate scalars, - // so adjust accordingly. TODO(b/111614235): Allow zero-sized buffers during - // toco conversion. - output_shape->size = 0; - } - - int num_output_elements = 1; - int stretch_dim = -1; - for (int i = 0; i < output_shape->size; ++i) { - int value = output_shape->data[i]; - if (value == -1) { - TF_LITE_ENSURE_EQ(context, stretch_dim, -1); - stretch_dim = i; - } else { - num_output_elements *= value; - } - } - if (stretch_dim != -1) { - output_shape->data[stretch_dim] = num_input_elements / num_output_elements; - num_output_elements *= output_shape->data[stretch_dim]; - } - - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - TF_LITE_ENSURE_EQ(context, num_input_elements, num_output_elements); - return kTfLiteOk; -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - TF_LITE_ENSURE_EQ(context, ReshapeOutput(context, node), kTfLiteOk); - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - // TODO(b/162522304): storing input bytes in OpData increases some models - // significantly, possibly due to alignment issues. - size_t input_bytes; - TF_LITE_ENSURE_STATUS(TfLiteTypeSizeOf(input->type, &input_bytes)); - input_bytes *= ElementCount(*input->dims); - - // Do nothing for in-place reshape. - if (input->data.raw != output->data.raw) { - // Otherwise perform reshape with copy. - for (size_t i = 0; i < input_bytes; ++i) { - output->data.raw[i] = input->data.raw[i]; - } - } - return kTfLiteOk; -} - -} // namespace reshape - -TfLiteRegistration Register_RESHAPE() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/reshape::Prepare, - /*invoke=*/reshape::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc deleted file mode 100644 index 222cb3a0..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc +++ /dev/null @@ -1,123 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace resize_nearest_neighbor { - -constexpr int kInputTensor = 0; -constexpr int kSizeTensor = 1; -constexpr int kOutputTensor = 0; - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { -#if defined(DEBUG) - TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* size = GetInput(context, node, kSizeTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - // Our current implementations rely on the input being 4D, - // and the size being 1D tensor with exactly 2 elements. - TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4); - TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1); - TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32); - TF_LITE_ENSURE_EQ(context, size->dims->data[0], 2); - - output->type = input->type; - - if (!IsConstantTensor(size)) { - TF_LITE_KERNEL_LOG(context, "Dynamic tensors are unsupported in tfmicro."); - return kTfLiteError; - } -#endif - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = - reinterpret_cast(node->builtin_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* size = - tflite::micro::GetEvalInput(context, node, kSizeTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - tflite::ResizeNearestNeighborParams op_params; - op_params.align_corners = params->align_corners; - op_params.half_pixel_centers = false; - - if (output->type == kTfLiteFloat32) { - reference_ops::ResizeNearestNeighbor( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(size), - tflite::micro::GetTensorData(size), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else if (output->type == kTfLiteUInt8) { - reference_ops::ResizeNearestNeighbor( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(size), - tflite::micro::GetTensorData(size), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else if (output->type == kTfLiteInt8) { - reference_ops::ResizeNearestNeighbor( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(size), - tflite::micro::GetTensorData(size), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - TF_LITE_KERNEL_LOG(context, - "Output type is %d, requires float, uint8_t or int8_t.", - output->type); - return kTfLiteError; - } - - return kTfLiteOk; -} -} // namespace resize_nearest_neighbor - -TfLiteRegistration Register_RESIZE_NEAREST_NEIGHBOR() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/resize_nearest_neighbor::Prepare, - /*invoke=*/resize_nearest_neighbor::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/round.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/round.cc deleted file mode 100644 index 7b4adfc6..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/round.cc +++ /dev/null @@ -1,74 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/round.h" - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace round { - -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32); - TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type); - TF_LITE_ENSURE_EQ(context, output->bytes, input->bytes); - TF_LITE_ENSURE_EQ(context, output->dims->size, input->dims->size); - for (int i = 0; i < output->dims->size; ++i) { - TF_LITE_ENSURE_EQ(context, output->dims->data[i], input->dims->data[i]); - } - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - reference_ops::Round(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - - return kTfLiteOk; -} -} // namespace round - -TfLiteRegistration Register_ROUND() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/round::Prepare, - /*invoke=*/round::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/softmax.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/softmax.cc deleted file mode 100644 index e85c1a4a..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/softmax.cc +++ /dev/null @@ -1,169 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/softmax.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace activations { -namespace { - -TfLiteStatus CalculateSoftmaxParams(TfLiteContext* context, - const TfLiteTensor* input, - TfLiteTensor* output, - const TfLiteSoftmaxParams* params, - SoftmaxParams* op_data) { - if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) { - if (input->type == kTfLiteUInt8) { - TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteUInt8); - TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteInt8); - if (output->type == kTfLiteInt16) { - TF_LITE_ENSURE_EQ(context, output->params.zero_point, -32768); - // NOTE: Current int16_t softmax output does not require symmetric - // scaling - // - so no need to verify scale here. - } else { - TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteInt8); - TF_LITE_ENSURE_EQ(context, output->params.zero_point, -128); - TF_LITE_ENSURE(context, output->params.scale == 1.f / 256); - } - } - - static const int kScaledDiffIntegerBits = 5; - - int input_left_shift; - tflite::PreprocessSoftmaxScaling( - static_cast(params->beta), - static_cast(input->params.scale), kScaledDiffIntegerBits, - &op_data->input_multiplier, &input_left_shift); - op_data->input_left_shift = input_left_shift; - op_data->diff_min = - -1.0 * tflite::CalculateInputRadius(kScaledDiffIntegerBits, - op_data->input_left_shift); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32); - TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32); - op_data->beta = static_cast(params->beta); - } - return kTfLiteOk; -} - -} // namespace - -// Takes a tensor and performs softmax along the last dimension. -void SoftmaxFloat(const TfLiteEvalTensor* input, TfLiteEvalTensor* output, - const SoftmaxParams& op_data) { - tflite::reference_ops::Softmax(op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -} - -void SoftmaxQuantized(const TfLiteEvalTensor* input, TfLiteEvalTensor* output, - const SoftmaxParams& op_data) { - if (input->type == kTfLiteUInt8) { - tflite::reference_ops::Softmax( - op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - if (output->type == kTfLiteInt16) { - tflite::reference_ops::Softmax( - op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - tflite::reference_ops::Softmax( - op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - } -} - -void* SoftmaxInit(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(SoftmaxParams)); -} - -TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) { - auto* params = static_cast(node->builtin_data); - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - const TfLiteTensor* input = GetInput(context, node, 0); - TF_LITE_ENSURE(context, NumDimensions(input) >= 1); - - TfLiteTensor* output = GetOutput(context, node, 0); - - TFLITE_DCHECK(node->user_data != nullptr); - SoftmaxParams* data = static_cast(node->user_data); - return CalculateSoftmaxParams(context, input, output, params, data); -} - -TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - - TFLITE_DCHECK(node->user_data != nullptr); - SoftmaxParams* data = static_cast(node->user_data); - - switch (input->type) { - case kTfLiteFloat32: { - SoftmaxFloat(input, output, *data); - return kTfLiteOk; - } - case kTfLiteInt8: - case kTfLiteUInt8: { - SoftmaxQuantized(input, output, *data); - return kTfLiteOk; - } - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } -} -} // namespace activations - -TfLiteRegistration Register_SOFTMAX() { - return {/*init=*/activations::SoftmaxInit, - /*free=*/nullptr, - /*prepare=*/activations::SoftmaxPrepare, - /*invoke=*/activations::SoftmaxEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/split.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/split.cc deleted file mode 100644 index 9bff0b70..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/split.cc +++ /dev/null @@ -1,134 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace split { - -template -TfLiteStatus SplitImpl(TfLiteContext* context, TfLiteNode* node, - const TfLiteEvalTensor* input, int axis_value) { - const int output_count = NumOutputs(node); - const TfLiteIntArray* input_dims = input->dims; - const TfLiteEvalTensor* output0 = - tflite::micro::GetEvalOutput(context, node, 0); - const TfLiteIntArray* output_dims = output0->dims; - - const int split_dimensions = input_dims->size; - int axis = axis_value < 0 ? axis_value + split_dimensions : axis_value; - - TFLITE_DCHECK_LT(axis, split_dimensions); - TFLITE_DCHECK_EQ(output_dims->size, split_dimensions); - - int64_t split_size = output_dims->data[axis] * output_count; - - TFLITE_DCHECK_EQ(split_size, input_dims->data[axis]); - int64_t outer_size = 1; - for (int i = 0; i < axis; ++i) { - outer_size *= input_dims->data[i]; - } - - int64_t base_inner_size = 1; - for (int i = axis + 1; i < split_dimensions; ++i) { - base_inner_size *= input_dims->data[i]; - } - - const T* input_ptr = tflite::micro::GetTensorData(input); - for (int k = 0; k < outer_size; ++k) { - for (int i = 0; i < output_count; ++i) { - TfLiteEvalTensor* t = tflite::micro::GetEvalOutput(context, node, i); - T* output_data = tflite::micro::GetTensorData(t); - const int copy_size = output_dims->data[axis] * base_inner_size; - T* output_ptr = output_data + k * copy_size; - for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j]; - input_ptr += copy_size; - } - } - - return kTfLiteOk; -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - const TfLiteTensor* axis = GetInput(context, node, 0); - - // Dynamic output tensors are needed if axis tensor is not constant. - // But Micro doesn't support dynamic memory allocation, so we only support - // constant axis tensor for now. - TF_LITE_ENSURE_MSG(context, IsConstantTensor(axis), - "Non constant axis tensor not supported"); - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 0); - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 1); - - int axis_value = tflite::micro::GetTensorData(axis)[0]; - if (axis_value < 0) { - axis_value += input->dims->size; - } - - TF_LITE_ENSURE(context, axis_value >= 0); - TF_LITE_ENSURE(context, axis_value < input->dims->size); - - switch (input->type) { - case kTfLiteFloat32: { - return SplitImpl(context, node, input, axis_value); - } - case kTfLiteUInt8: { - return SplitImpl(context, node, input, axis_value); - } - case kTfLiteInt8: { - return SplitImpl(context, node, input, axis_value); - } - case kTfLiteInt16: { - return SplitImpl(context, node, input, axis_value); - } - case kTfLiteInt32: { - return SplitImpl(context, node, input, axis_value); - } - default: - TF_LITE_KERNEL_LOG(context, "Type %s currently not supported.", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } -#undef TF_LITE_SPLIT - - return kTfLiteOk; -} - -} // namespace split - -TfLiteRegistration Register_SPLIT() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/split::Prepare, - /*invoke=*/split::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/strided_slice.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/strided_slice.cc deleted file mode 100644 index 2dbe6e1d..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/strided_slice.cc +++ /dev/null @@ -1,192 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/kernels/internal/reference/strided_slice.h" - -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace strided_slice { - -constexpr int kInputTensor = 0; -constexpr int kBeginTensor = 1; -constexpr int kEndTensor = 2; -constexpr int kStridesTensor = 3; -constexpr int kOutputTensor = 0; - -struct StridedSliceContext { - StridedSliceContext(TfLiteContext* context, TfLiteNode* node) { - params = reinterpret_cast(node->builtin_data); - input = GetInput(context, node, kInputTensor); - begin = GetInput(context, node, kBeginTensor); - end = GetInput(context, node, kEndTensor); - strides = GetInput(context, node, kStridesTensor); - output = GetOutput(context, node, kOutputTensor); - dims = NumDimensions(input); - } - const TfLiteStridedSliceParams* params; - const TfLiteTensor* input; - const TfLiteTensor* begin; - const TfLiteTensor* end; - const TfLiteTensor* strides; - TfLiteTensor* output; - int dims; -}; - -// This Op only supports 1-4D cases and since we use the reference 4D -// implementation, the 1-3D tensors are mapped to 4D. -const int kMaxDim = 4; - -tflite::StridedSliceParams BuildStridedSliceParams( - StridedSliceContext* op_context) { - tflite::StridedSliceParams op_params; - op_params.start_indices_count = op_context->dims; - op_params.stop_indices_count = op_context->dims; - op_params.strides_count = op_context->dims; - - for (int i = 0; i < op_context->dims; ++i) { - op_params.start_indices[i] = GetTensorData(op_context->begin)[i]; - op_params.stop_indices[i] = GetTensorData(op_context->end)[i]; - op_params.strides[i] = GetTensorData(op_context->strides)[i]; - } - - op_params.begin_mask = op_context->params->begin_mask; - op_params.ellipsis_mask = 0; - op_params.end_mask = op_context->params->end_mask; - op_params.new_axis_mask = 0; - op_params.shrink_axis_mask = op_context->params->shrink_axis_mask; - return op_params; -} - -// Processes the indexing tensors (begin, end and strides) to resize the -// output tensor. This function is callable from both Prepare() and Eval() as -// long as the caller ensures the indexing tensors are present. -TfLiteStatus CheckOutputSize(TfLiteContext* context, - StridedSliceContext* op_context) { - using ::tflite::strided_slice::StartForAxis; - using ::tflite::strided_slice::StopForAxis; - TfLiteIntArray* output_shape = op_context->output->dims; - int shape_size = 0; - auto op_params = BuildStridedSliceParams(op_context); - auto input_shape = GetTensorShape(op_context->input); - for (int idx = 0; idx < op_context->dims; ++idx) { - int32_t stride = GetTensorData(op_context->strides)[idx]; - TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero"); - int32_t begin = StartForAxis(op_params, input_shape, idx); - int32_t end = StopForAxis(op_params, input_shape, idx, begin); - - // When shrinking an axis, the end position does not matter (and can be - // incorrect when negative indexing is used, see Issue #19260). Always use - // begin + 1 to generate a length 1 slice, since begin has - // already been adjusted for negative indices by StartForAxis. - const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx); - if (shrink_axis) { - end = begin + 1; - } - - // This is valid for both positive and negative strides - int32_t dim_shape = std::ceil((end - begin) / static_cast(stride)); - dim_shape = dim_shape < 0 ? 0 : dim_shape; - if (!shrink_axis) { - TF_LITE_ENSURE_EQ(context, output_shape->data[shape_size], dim_shape); - shape_size++; - } - } - TF_LITE_ENSURE_EQ(context, output_shape->size, shape_size); - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(StridedSliceParams)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - StridedSliceParams* op_params = - static_cast(node->user_data); - TF_LITE_ENSURE_EQ(context, NumInputs(node), 4); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - StridedSliceContext op_context(context, node); - TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim, - "input dim should not exceed 4"); - auto params = BuildStridedSliceParams(&op_context); - memcpy(op_params, ¶ms, sizeof(StridedSliceParams)); - return CheckOutputSize(context, &op_context); -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - const StridedSliceParams& op_params = - *(static_cast(node->user_data)); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - switch (output->type) { - case kTfLiteFloat32: - reference_ops::StridedSlice(op_params, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - case kTfLiteUInt8: - reference_ops::StridedSlice( - op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - case kTfLiteInt8: - reference_ops::StridedSlice(op_params, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - return kTfLiteOk; -} -} // namespace strided_slice - -TfLiteRegistration Register_STRIDED_SLICE() { - return {/*init=*/strided_slice::Init, - /*free=*/nullptr, - /*prepare=*/strided_slice::Prepare, - /*invoke=*/strided_slice::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/sub.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/sub.cc deleted file mode 100644 index 8ba15949..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/sub.cc +++ /dev/null @@ -1,253 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/sub.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace sub { - -constexpr int kInputTensor1 = 0; -constexpr int kInputTensor2 = 1; -constexpr int kOutputTensor = 0; - -struct OpData { - bool requires_broadcast; - - // These fields are used in both the general 8-bit -> 8bit quantized path, - // and the special 16-bit -> 16bit quantized path - int input1_shift; - int input2_shift; - int32_t output_activation_min; - int32_t output_activation_max; - - // These fields are used only in the general 8-bit -> 8bit quantized path - int32_t input1_multiplier; - int32_t input2_multiplier; - int32_t output_multiplier; - int output_shift; - int left_shift; - int32_t input1_offset; - int32_t input2_offset; - int32_t output_offset; -}; - -TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteSubParams* params, - const TfLiteTensor* input1, - const TfLiteTensor* input2, TfLiteTensor* output, - OpData* data) { - data->requires_broadcast = !HaveSameShapes(input1, input2); - - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - // 8bit -> 8bit general quantized path, with general rescalings - data->input1_offset = -input1->params.zero_point; - data->input2_offset = -input2->params.zero_point; - data->output_offset = output->params.zero_point; - data->left_shift = 20; - const float twice_max_input_scale = - 2 * std::max(input1->params.scale, input2->params.scale); - const double real_input1_multiplier = - static_cast(input1->params.scale / twice_max_input_scale); - const double real_input2_multiplier = - static_cast(input2->params.scale / twice_max_input_scale); - const double real_output_multiplier = - static_cast(twice_max_input_scale / - ((1 << data->left_shift) * output->params.scale)); - - QuantizeMultiplierSmallerThanOneExp( - real_input1_multiplier, &data->input1_multiplier, &data->input1_shift); - - QuantizeMultiplierSmallerThanOneExp( - real_input2_multiplier, &data->input2_multiplier, &data->input2_shift); - - QuantizeMultiplierSmallerThanOneExp( - real_output_multiplier, &data->output_multiplier, &data->output_shift); - - TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( - context, params->activation, output, &data->output_activation_min, - &data->output_activation_max)); - } - - return kTfLiteOk; -} - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - TFLITE_DCHECK(node->builtin_data != nullptr); - - OpData* data = static_cast(node->user_data); - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1); - const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_STATUS( - CalculateOpData(context, params, input1, input2, output, data)); - return kTfLiteOk; -} - -void EvalSub(TfLiteContext* context, TfLiteNode* node, TfLiteSubParams* params, - const OpData* data, const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) { - float output_activation_min, output_activation_max; - CalculateActivationRange(params->activation, &output_activation_min, - &output_activation_max); - tflite::ArithmeticParams op_params; - SetActivationParams(output_activation_min, output_activation_max, &op_params); - if (data->requires_broadcast) { - tflite::reference_ops::BroadcastSubSlow( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - tflite::reference_ops::SubWithActivation( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } -} - -TfLiteStatus EvalSubQuantized(TfLiteContext* context, TfLiteNode* node, - TfLiteSubParams* params, const OpData* data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output) { - if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - tflite::ArithmeticParams op_params; - op_params.left_shift = data->left_shift; - op_params.input1_offset = data->input1_offset; - op_params.input1_multiplier = data->input1_multiplier; - op_params.input1_shift = data->input1_shift; - op_params.input2_offset = data->input2_offset; - op_params.input2_multiplier = data->input2_multiplier; - op_params.input2_shift = data->input2_shift; - op_params.output_offset = data->output_offset; - op_params.output_multiplier = data->output_multiplier; - op_params.output_shift = data->output_shift; - SetActivationParams(data->output_activation_min, - data->output_activation_max, &op_params); - bool need_broadcast = reference_ops::ProcessBroadcastShapes( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorShape(input2), &op_params); - - if (output->type == kTfLiteInt8) { - if (need_broadcast) { - tflite::reference_ops::BroadcastSubSlow( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - tflite::reference_ops::Sub( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - } else { - if (need_broadcast) { - tflite::reference_ops::BroadcastSubSlow( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - tflite::reference_ops::Sub( - op_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - } - } - - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData& data = *(static_cast(node->user_data)); - - if (output->type == kTfLiteFloat32) { - EvalSub(context, node, params, &data, input1, input2, output); - } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8) { - TF_LITE_ENSURE_OK(context, EvalSubQuantized(context, node, params, &data, - input1, input2, output)); - } else { - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(output->type), output->type); - return kTfLiteError; - } - - return kTfLiteOk; -} - -} // namespace sub - -TfLiteRegistration Register_SUB() { - return {/*init=*/sub::Init, - /*free=*/nullptr, - /*prepare=*/sub::Prepare, - /*invoke=*/sub::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/svdf.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/svdf.cc deleted file mode 100644 index 5cb8e06f..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/svdf.cc +++ /dev/null @@ -1,548 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/activation_utils.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace svdf { -namespace { - -struct OpData { - int32_t effective_scale_1_a; - int32_t effective_scale_2_a; - // b versions of each scale are kept at int since the numbers are just the - // shift value - typically between [-32, 32]. - int effective_scale_1_b; - int effective_scale_2_b; - int scratch_tensor_index; - int scratch_output_tensor_index; - - // Cached tensor zero point values for quantized operations. - int input_zero_point; - int output_zero_point; -}; - -/** - * This version of SVDF is specific to TFLite Micro. It contains the following - * differences between the TFLite version: - * - * 1.) Scratch tensor allocation - scratch tensors must be known ahead of time - * for the Micro interpreter. - * 2.) Output dimensions - the TFLite version determines output size and runtime - * and resizes the output tensor. Micro runtime does not support tensor - * resizing. - */ -static inline void ApplyTimeWeightsBiasAndActivation( - int batch_size, int memory_size, int num_filters, int num_units, int rank, - const float* const __restrict__ weights_time_ptr, - const float* const __restrict__ bias_ptr, TfLiteFusedActivation activation, - float* const __restrict__ state_ptr, float* const __restrict__ scratch_ptr, - float* const __restrict__ output_ptr) { - // Compute matmul(activation_state, weights_time). - for (int b = 0; b < batch_size; ++b) { - // Perform batched vector dot product: - float* scratch_ptr_batch = scratch_ptr + b * num_filters; - const float* vector1_ptr = weights_time_ptr; - const float* vector2_ptr = state_ptr + b * memory_size * num_filters; - for (int i = 0; i < num_filters; ++i) { - *scratch_ptr_batch = 0.f; - for (int j = 0; j < memory_size; ++j) { - *scratch_ptr_batch += *vector1_ptr++ * *vector2_ptr++; - } - scratch_ptr_batch++; - } - } - - // Initialize output with bias if provided. - if (bias_ptr) { - // VectorBatchVectorAssign - for (int i = 0; i < batch_size; ++i) { - float* output_data = output_ptr + i * num_units; - const float* bias_data = bias_ptr; - for (int j = 0; j < num_units; ++j) { - *output_data++ = *bias_data++; - } - } - } else { - float* output_data = output_ptr; - for (int i = 0; i < batch_size * num_units; ++i) { - *output_data++ = 0.0f; - } - } - - // Reduction sum. - for (int b = 0; b < batch_size; ++b) { - float* output_ptr_batch = output_ptr + b * num_units; - float* scratch_ptr_batch = scratch_ptr + b * num_filters; - - // Reduction sum vector - for (int i = 0; i < num_units; ++i) { - for (int j = 0; j < rank; j++) { - output_ptr_batch[i] += *scratch_ptr_batch++; - } - } - } - - // Apply activation. - for (int b = 0; b < batch_size; ++b) { - float* output_ptr_batch = output_ptr + b * num_units; - for (int i = 0; i < num_units; ++i) { - *output_ptr_batch = ActivationValFloat(activation, *output_ptr_batch); - ++output_ptr_batch; - } - } -} - -inline void EvalFloatSVDF( - TfLiteContext* context, TfLiteNode* node, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* weights_feature, - const TfLiteEvalTensor* weights_time, const TfLiteEvalTensor* bias, - const TfLiteSVDFParams* params, int scratch_tensor_index, - TfLiteEvalTensor* activation_state, TfLiteEvalTensor* output) { - const int rank = params->rank; - const int batch_size = input->dims->data[0]; - const int input_size = input->dims->data[1]; - const int num_filters = weights_feature->dims->data[0]; - const int num_units = num_filters / rank; - const int memory_size = weights_time->dims->data[1]; - - const float* weights_feature_ptr = - tflite::micro::GetTensorData(weights_feature); - const float* weights_time_ptr = - tflite::micro::GetTensorData(weights_time); - const float* bias_ptr = tflite::micro::GetTensorData(bias); - const float* input_ptr = tflite::micro::GetTensorData(input); - - float* state_ptr = tflite::micro::GetTensorData(activation_state); - - TFLITE_DCHECK(context != nullptr); - TFLITE_DCHECK(context->GetScratchBuffer != nullptr); - - float* scratch_ptr = static_cast( - context->GetScratchBuffer(context, scratch_tensor_index)); - - float* output_ptr = tflite::micro::GetTensorData(output); - - // Left shift the activation_state. - { - float* new_state_start = state_ptr; - const float* old_state_start = state_ptr + 1; - const float* old_state_end = - state_ptr + batch_size * num_filters * memory_size; - while (old_state_start != old_state_end) { - *new_state_start++ = *old_state_start++; - } - } - - // Note: no need to clear the latest activation, matmul is not accumulative. - - // Compute conv1d(inputs, weights_feature). - // The activation_state's rightmost column is used to save current cycle - // activation. This is achieved by starting at state_ptr[memory_size - 1] and - // having the stride equal to memory_size. - - // Perform batched matrix vector multiply operation: - { - const float* matrix = weights_feature_ptr; - const float* vector = input_ptr; - float* result = &state_ptr[memory_size - 1]; - float* result_in_batch = result; - for (int i = 0; i < batch_size; ++i) { - const float* matrix_ptr = matrix; - for (int j = 0; j < num_filters; ++j) { - float dot_prod = 0.0f; - const float* vector_in_batch = vector + i * input_size; - for (int k = 0; k < input_size; ++k) { - dot_prod += *matrix_ptr++ * *vector_in_batch++; - } - *result_in_batch = dot_prod; - result_in_batch += memory_size; - } - } - } - - ApplyTimeWeightsBiasAndActivation( - batch_size, memory_size, num_filters, num_units, rank, weights_time_ptr, - bias_ptr, params->activation, state_ptr, scratch_ptr, output_ptr); -} - -void EvalIntegerSVDF(TfLiteContext* context, TfLiteNode* node, - const TfLiteEvalTensor* input_tensor, - const TfLiteEvalTensor* weights_feature_tensor, - const TfLiteEvalTensor* weights_time_tensor, - const TfLiteEvalTensor* bias_tensor, - const TfLiteSVDFParams* params, - TfLiteEvalTensor* activation_state_tensor, - TfLiteEvalTensor* output_tensor, const OpData& data) { - const int n_rank = params->rank; - const int n_batch = input_tensor->dims->data[0]; - const int n_input = input_tensor->dims->data[1]; - const int n_filter = weights_feature_tensor->dims->data[0]; - const int n_unit = n_filter / n_rank; - const int n_memory = weights_time_tensor->dims->data[1]; - - TFLITE_DCHECK(context != nullptr); - TFLITE_DCHECK(context->GetScratchBuffer != nullptr); - - int32_t* scratch_tensor = static_cast( - context->GetScratchBuffer(context, data.scratch_tensor_index)); - int32_t* scratch_output_tensor = static_cast( - context->GetScratchBuffer(context, data.scratch_output_tensor_index)); - - // Shift states. - int16_t* const state_ptr = - tflite::micro::GetTensorData(activation_state_tensor); - - // Left shift the activation_state. - { - int16_t* new_state_start = state_ptr; - const int16_t* old_state_start = state_ptr + 1; - const int16_t* old_state_end = state_ptr + n_batch * n_filter * n_memory; - while (old_state_start != old_state_end) { - *new_state_start++ = *old_state_start++; - } - } - - // Note: no need to clear the latest activation, matmul is not accumulative. - - // Feature matmul. - { - int16_t* state = - tflite::micro::GetTensorData(activation_state_tensor); - const int8_t* input = tflite::micro::GetTensorData(input_tensor); - const int8_t* weight_feature = - tflite::micro::GetTensorData(weights_feature_tensor); - const int32_t output_max = std::numeric_limits::max(); - const int32_t output_min = std::numeric_limits::min(); - int16_t* result_in_batch = state + (n_memory - 1); - for (int b = 0; b < n_batch; b++) { - const int8_t* matrix_ptr = weight_feature; - for (int r = 0; r < n_filter; r++) { - int32_t dot_prod = 0; - const int8_t* vector_in_batch = input + b * n_input; - for (int c = 0; c < n_input; c++) { - dot_prod += - *matrix_ptr++ * (*vector_in_batch++ - data.input_zero_point); - } - dot_prod = MultiplyByQuantizedMultiplier( - dot_prod, data.effective_scale_1_a, data.effective_scale_1_b); - dot_prod = std::min(std::max(output_min, dot_prod), output_max); - // This assumes state is symmetrically quantized. Otherwise last bit of - // state should be initialized to its zero point and accumulate the - // dot_prod. - // Equivalent as the following: - // result_in_batch = zero point, which happens to be zero. - // result_in_batch += dot_prod_56. - *result_in_batch = dot_prod; - result_in_batch += n_memory; - } - } - } - - // Time. - { - for (int b = 0; b < n_batch; ++b) { - int32_t* scratch_ptr_batch = scratch_tensor + b * n_filter; - - // Perform batched vector dot product: - const int16_t* vector1_ptr = - tflite::micro::GetTensorData(weights_time_tensor); - const int16_t* vector2_ptr = - tflite::micro::GetTensorData(activation_state_tensor) + - b * n_memory * n_filter; - - for (int i = 0; i < n_filter; i++) { - *scratch_ptr_batch = 0; - for (int j = 0; j < n_memory; j++) { - *scratch_ptr_batch += *vector1_ptr++ * *vector2_ptr++; - } - scratch_ptr_batch++; - } - } - } - - // Reduce, add bias, rescale, activation. - { - // Add bias. - if (bias_tensor) { - // Vector batch assign: - const int32_t* bias_data = - tflite::micro::GetTensorData(bias_tensor); - for (int i = 0; i < n_batch; ++i) { - int32_t* output_ptr = scratch_output_tensor + i * n_unit; - const int32_t* bias_ptr = bias_data; - for (int j = 0; j < n_unit; ++j) { - *output_ptr++ = *bias_ptr++; - } - } - } else { - int32_t* output_ptr = scratch_output_tensor; - for (int i = 0; i < n_batch * n_unit; ++i) { - *output_ptr++ = 0; - } - } - - // Reduce. - for (int b = 0; b < n_batch; ++b) { - int32_t* output_temp_ptr = scratch_output_tensor + b * n_unit; - int32_t* scratch_ptr_batch = scratch_tensor + b * n_filter; - - // Reduction sum vector - for (int i = 0; i < n_unit; ++i) { - for (int j = 0; j < n_rank; ++j) { - output_temp_ptr[i] += *scratch_ptr_batch++; - } - } - } - - // Rescale. - const int32_t output_max = std::numeric_limits::max(); - const int32_t output_min = std::numeric_limits::min(); - for (int i = 0; i < n_batch * n_unit; ++i) { - int32_t x1 = scratch_output_tensor[i]; - int32_t x2 = MultiplyByQuantizedMultiplier(x1, data.effective_scale_2_a, - data.effective_scale_2_b); - int32_t x3 = x2 + data.output_zero_point; - int32_t x4 = std::min(std::max(output_min, x3), output_max); - tflite::micro::GetTensorData(output_tensor)[i] = - static_cast(x4); - } - } -} - -} // namespace - -// Input tensors. -constexpr int kInputTensor = 0; -constexpr int kWeightsFeatureTensor = 1; -constexpr int kWeightsTimeTensor = 2; -constexpr int kBiasTensor = 3; -// This is a variable tensor, and will be modified by this op. -constexpr int kInputActivationStateTensor = 4; - -// Output tensor. -constexpr int kOutputTensor = 0; - -void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->builtin_data != nullptr); - - const auto* params = static_cast(node->builtin_data); - - // Validate Tensor Inputs (dtype depends on quantization): - // [0] = Input, {2, batch_size, input_size} - // [1] = Weights Feature, {2, num_filters, input_size} - // [2] = Weights Time, {2, num_filters, memory_size} - // [3] = Bias (optional), {1, num_units} - // [4] = Activation State (variable), - // {2, batch_size, memory_size * num_filters} - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const TfLiteTensor* weights_feature = - GetInput(context, node, kWeightsFeatureTensor); - const TfLiteTensor* weights_time = - GetInput(context, node, kWeightsTimeTensor); - const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor); - const TfLiteTensor* activation_state = - GetInput(context, node, kInputActivationStateTensor); - - // Define input constants based on input tensor definition above: - const int rank = params->rank; - const int input_size = input->dims->data[1]; - const int batch_size = input->dims->data[0]; - const int num_filters = weights_feature->dims->data[0]; - TF_LITE_ENSURE_EQ(context, num_filters % rank, 0); - const int num_units = num_filters / rank; - const int memory_size = weights_time->dims->data[1]; - - // Validate Input Tensor: - TF_LITE_ENSURE(context, - input->type == kTfLiteFloat32 || input->type == kTfLiteInt8); - TF_LITE_ENSURE_EQ(context, NumDimensions(input), 2); - - // Validate Tensor Output: - // [0] = float/int8_t, {2, batch_size, num_units} - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - TF_LITE_ENSURE_EQ(context, NumDimensions(output), 2); - TF_LITE_ENSURE_EQ(context, output->dims->data[0], batch_size); - TF_LITE_ENSURE_EQ(context, output->dims->data[1], num_units); - - // Validate Weights Feature Input Tensor: - TF_LITE_ENSURE_EQ(context, NumDimensions(weights_feature), 2); - TF_LITE_ENSURE_EQ(context, weights_feature->dims->data[1], input_size); - - // Validate Weights Time Input Tensor: - TF_LITE_ENSURE_EQ(context, NumDimensions(weights_time), 2); - TF_LITE_ENSURE_EQ(context, weights_time->dims->data[0], num_filters); - TF_LITE_ENSURE_EQ(context, weights_time->dims->data[1], memory_size); - - // Validate Optional Bias Input Tensor: - if (bias != nullptr) { - TF_LITE_ENSURE_EQ(context, bias->dims->data[0], num_units); - } - - // Validate Activation State Input Tensor: - TF_LITE_ENSURE_EQ(context, NumDimensions(activation_state), 2); - TF_LITE_ENSURE_EQ(context, activation_state->dims->data[0], batch_size); - TF_LITE_ENSURE_EQ(context, activation_state->dims->data[1], - memory_size * num_filters); - // Since is_variable is not part of TFLiteEvalTensor, check is_variable here. - TF_LITE_ENSURE_EQ(context, activation_state->is_variable, true); - - TF_LITE_ENSURE_EQ(context, node->inputs->size, 5); - - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = static_cast(node->user_data); - - if (input->type == kTfLiteInt8) { - TF_LITE_ENSURE_EQ(context, weights_feature->type, kTfLiteInt8); - TF_LITE_ENSURE_EQ(context, weights_time->type, kTfLiteInt16); - TF_LITE_ENSURE_EQ(context, activation_state->type, kTfLiteInt16); - if (bias != nullptr) { - TF_LITE_ENSURE_EQ(context, bias->type, kTfLiteInt32); - } - - TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteInt8); - - const double effective_scale_1 = static_cast( - input->params.scale * weights_feature->params.scale / - activation_state->params.scale); - const double effective_scale_2 = - static_cast(activation_state->params.scale * - weights_time->params.scale / output->params.scale); - - // TODO(b/162018098): Use TF_LITE_ENSURE_NEAR when it is ready. - TF_LITE_ENSURE( - context, - std::abs(static_cast(bias->params.scale) - - static_cast(activation_state->params.scale * - weights_time->params.scale)) < 1e-5); - - QuantizeMultiplier(effective_scale_1, &(data->effective_scale_1_a), - &(data->effective_scale_1_b)); - QuantizeMultiplier(effective_scale_2, &(data->effective_scale_2_a), - &(data->effective_scale_2_b)); - - data->input_zero_point = input->params.zero_point; - data->output_zero_point = output->params.zero_point; - - TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr); - - const TfLiteStatus scratch_status = context->RequestScratchBufferInArena( - context, batch_size * num_filters * sizeof(int32_t), - &(data->scratch_tensor_index)); - TF_LITE_ENSURE_OK(context, scratch_status); - - const TfLiteStatus scratch_output_status = - context->RequestScratchBufferInArena( - context, batch_size * num_units * sizeof(int32_t), - &(data->scratch_output_tensor_index)); - TF_LITE_ENSURE_OK(context, scratch_output_status); - } else { - TF_LITE_ENSURE_EQ(context, weights_feature->type, kTfLiteFloat32); - TF_LITE_ENSURE_EQ(context, weights_time->type, kTfLiteFloat32); - TF_LITE_ENSURE_EQ(context, activation_state->type, kTfLiteFloat32); - if (bias != nullptr) { - TF_LITE_ENSURE_EQ(context, bias->type, kTfLiteFloat32); - } - TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteFloat32); - - TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr); - const TfLiteStatus scratch_status = context->RequestScratchBufferInArena( - context, batch_size * num_filters * sizeof(float), - &(data->scratch_tensor_index)); - TF_LITE_ENSURE_OK(context, scratch_status); - } - - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - TFLITE_DCHECK(node->user_data != nullptr); - const OpData& data = *(static_cast(node->user_data)); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - const TfLiteEvalTensor* weights_feature = - tflite::micro::GetEvalInput(context, node, kWeightsFeatureTensor); - const TfLiteEvalTensor* weights_time = - tflite::micro::GetEvalInput(context, node, kWeightsTimeTensor); - const TfLiteEvalTensor* bias = - (NumInputs(node) == 5) - ? tflite::micro::GetEvalInput(context, node, kBiasTensor) - : nullptr; - TfLiteEvalTensor* activation_state = tflite::micro::GetMutableEvalInput( - context, node, kInputActivationStateTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - switch (weights_feature->type) { - case kTfLiteFloat32: { - EvalFloatSVDF(context, node, input, weights_feature, weights_time, bias, - params, data.scratch_tensor_index, activation_state, - output); - return kTfLiteOk; - break; - } - - case kTfLiteInt8: { - EvalIntegerSVDF(context, node, input, weights_feature, weights_time, bias, - params, activation_state, output, data); - return kTfLiteOk; - break; - } - - default: - TF_LITE_KERNEL_LOG(context, "Type %s not currently supported.", - TfLiteTypeGetName(weights_feature->type)); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace svdf - -TfLiteRegistration Register_SVDF() { - return {/*init=*/svdf::Init, - /*free=*/nullptr, - /*prepare=*/svdf::Prepare, - /*invoke=*/svdf::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/tanh.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/tanh.cc deleted file mode 100644 index 5fa32f8f..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/tanh.cc +++ /dev/null @@ -1,154 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/tanh.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace activations { -namespace { -constexpr int kInputTensor = 0; -constexpr int kOutputTensor = 0; - -struct OpData { - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -void* TanhInit(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus CalculateArithmeticOpData(TfLiteContext* context, TfLiteNode* node, - OpData* data) { - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - TfLiteTensor* output = GetOutput(context, node, kOutputTensor); - - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - - if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) { - static constexpr int kInputIntegerBits = 4; - const double input_real_multiplier = - static_cast(input->params.scale) * - static_cast(1 << (31 - kInputIntegerBits)); - - const double q = std::frexp(input_real_multiplier, &data->input_left_shift); - data->input_multiplier = static_cast(TfLiteRound(q * (1ll << 31))); - - data->input_range_radius = - CalculateInputRadius(kInputIntegerBits, data->input_left_shift, 31); - } - return kTfLiteOk; -} - -TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - - OpData* data = static_cast(node->user_data); - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - data->input_zero_point = input->params.zero_point; - return CalculateArithmeticOpData(context, node, data); -} - -} // namespace - -TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - TFLITE_DCHECK(node->user_data != nullptr); - const OpData& data = *(static_cast(node->user_data)); - - switch (input->type) { - case kTfLiteFloat32: { - reference_ops::Tanh(tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } break; - case kTfLiteInt16: { - TanhParams params; - params.input_left_shift = data.input_left_shift; - reference_ops::Tanh(params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } break; - case kTfLiteUInt8: { - TanhParams params; - params.input_zero_point = data.input_zero_point; - params.input_range_radius = data.input_range_radius; - params.input_multiplier = data.input_multiplier; - params.input_left_shift = data.input_left_shift; - reference_ops::Tanh(params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - - return kTfLiteOk; - } break; - case kTfLiteInt8: { - reference_integer_ops::Tanh( - data.input_zero_point, data.input_range_radius, data.input_multiplier, - data.input_left_shift, NumElements(input->dims), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorData(output)); - return kTfLiteOk; - } break; - default: - TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } -} - -} // namespace activations - -TfLiteRegistration Register_TANH() { - return {/*init=*/activations::TanhInit, - /*free=*/nullptr, - /*prepare=*/activations::TanhPrepare, - /*invoke=*/activations::TanhEval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/unpack.cc b/components/tflite_micro/Source/tensorflow/lite/micro/kernels/unpack.cc deleted file mode 100644 index 557cc57a..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/kernels/unpack.cc +++ /dev/null @@ -1,121 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite { -namespace ops { -namespace micro { -namespace unpack { -namespace { - -constexpr int kInputTensor = 0; - -template -TfLiteStatus UnpackImpl(TfLiteContext* context, TfLiteNode* node, - const TfLiteEvalTensor* input, int output_count, - int axis) { - const TfLiteEvalTensor* output0 = - tflite::micro::GetEvalOutput(context, node, 0); - const TfLiteIntArray* input_dims = input->dims; - const TfLiteIntArray* output_dims = output0->dims; - const int dimensions = input_dims->size; - - if (axis < 0) { - axis += input->dims->size; - } - - TFLITE_DCHECK_LT(axis, dimensions); - - int outer_size = 1; - for (int i = 0; i < axis; ++i) { - outer_size *= input_dims->data[i]; - } - int copy_size = 1; - for (int i = axis + 1; i < dimensions; ++i) { - copy_size *= input_dims->data[i]; - } - int output_size = 1; - for (int i = 0; i < output_dims->size; ++i) { - output_size *= output_dims->data[i]; - } - TFLITE_DCHECK_EQ(output_size, copy_size * outer_size); - - const T* input_data = tflite::micro::GetTensorData(input); - - for (int i = 0; i < output_count; ++i) { - TfLiteEvalTensor* t = tflite::micro::GetEvalOutput(context, node, i); - T* output_data = tflite::micro::GetTensorData(t); - for (int k = 0; k < outer_size; ++k) { - T* output_ptr = output_data + copy_size * k; - int loc = k * output_count * copy_size + i * copy_size; - const T* input_ptr = input_data + loc; - for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j]; - } - } - - return kTfLiteOk; -} - -TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - TfLiteUnpackParams* data = - reinterpret_cast(node->builtin_data); - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kInputTensor); - - switch (input->type) { - case kTfLiteFloat32: { - return UnpackImpl(context, node, input, data->num, data->axis); - } - case kTfLiteInt32: { - return UnpackImpl(context, node, input, data->num, data->axis); - } - case kTfLiteUInt8: { - return UnpackImpl(context, node, input, data->num, data->axis); - } - case kTfLiteInt8: { - return UnpackImpl(context, node, input, data->num, data->axis); - } - default: { - TF_LITE_KERNEL_LOG(context, "Type '%s' is not supported by unpack.", - TfLiteTypeGetName(input->type)); - return kTfLiteError; - } - } - - return kTfLiteOk; -} -} // namespace -} // namespace unpack - -TfLiteRegistration Register_UNPACK() { - return {/*init=*/nullptr, - /*free=*/nullptr, - /*prepare=*/nullptr, - /*invoke=*/unpack::Eval, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0}; -} - -} // namespace micro -} // namespace ops -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/memory_helpers.cc b/components/tflite_micro/Source/tensorflow/lite/micro/memory_helpers.cc deleted file mode 100644 index c6180cb4..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/memory_helpers.cc +++ /dev/null @@ -1,155 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/memory_helpers.h" - -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/api/flatbuffer_conversions.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -uint8_t* AlignPointerUp(uint8_t* data, size_t alignment) { - std::uintptr_t data_as_uintptr_t = reinterpret_cast(data); - uint8_t* aligned_result = reinterpret_cast( - ((data_as_uintptr_t + (alignment - 1)) / alignment) * alignment); - return aligned_result; -} - -uint8_t* AlignPointerDown(uint8_t* data, size_t alignment) { - std::uintptr_t data_as_uintptr_t = reinterpret_cast(data); - uint8_t* aligned_result = - reinterpret_cast((data_as_uintptr_t / alignment) * alignment); - return aligned_result; -} - -size_t AlignSizeUp(size_t size, size_t alignment) { - size_t aligned_size = (((size + (alignment - 1)) / alignment) * alignment); - return aligned_size; -} - -TfLiteStatus TfLiteTypeSizeOf(TfLiteType type, size_t* size) { - switch (type) { - case kTfLiteFloat32: - *size = sizeof(float); - break; - case kTfLiteInt16: - *size = sizeof(int16_t); - break; - case kTfLiteInt32: - *size = sizeof(int32_t); - break; - case kTfLiteUInt8: - *size = sizeof(uint8_t); - break; - case kTfLiteInt8: - *size = sizeof(int8_t); - break; - case kTfLiteInt64: - *size = sizeof(int64_t); - break; - case kTfLiteBool: - *size = sizeof(bool); - break; - case kTfLiteComplex64: - *size = sizeof(float) * 2; - break; - case kTfLiteComplex128: - *size = sizeof(double) * 2; - break; - default: - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus BytesRequiredForTensor(const tflite::Tensor& flatbuffer_tensor, - size_t* bytes, size_t* type_size, - ErrorReporter* error_reporter) { - int element_count = 1; - // If flatbuffer_tensor.shape == nullptr, then flatbuffer_tensor is a scalar - // so has 1 element. - if (flatbuffer_tensor.shape() != nullptr) { - for (size_t n = 0; n < flatbuffer_tensor.shape()->Length(); ++n) { - element_count *= flatbuffer_tensor.shape()->Get(n); - } - } - - TfLiteType tf_lite_type; - TF_LITE_ENSURE_STATUS(ConvertTensorType(flatbuffer_tensor.type(), - &tf_lite_type, error_reporter)); - TF_LITE_ENSURE_STATUS(TfLiteTypeSizeOf(tf_lite_type, type_size)); - *bytes = element_count * (*type_size); - return kTfLiteOk; -} - -TfLiteStatus TfLiteEvalTensorByteLength(const TfLiteEvalTensor* eval_tensor, - size_t* out_bytes) { - TFLITE_DCHECK(out_bytes != nullptr); - - int element_count = 1; - // If eval_tensor->dims == nullptr, then tensor is a scalar so has 1 element. - if (eval_tensor->dims != nullptr) { - for (int n = 0; n < eval_tensor->dims->size; ++n) { - element_count *= eval_tensor->dims->data[n]; - } - } - size_t type_size; - TF_LITE_ENSURE_STATUS(TfLiteTypeSizeOf(eval_tensor->type, &type_size)); - *out_bytes = element_count * type_size; - return kTfLiteOk; -} - -TfLiteStatus AllocateOutputDimensionsFromInput(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - TfLiteTensor* output) { - const TfLiteTensor* input = nullptr; - - TF_LITE_ENSURE(context, input1->dims != nullptr); - TF_LITE_ENSURE(context, input2->dims != nullptr); - TF_LITE_ENSURE(context, output->dims->size == 0); - - input = input1->dims->size > input2->dims->size ? input1 : input2; - TF_LITE_ENSURE(context, output->type == input->type); - - size_t size = 0; - TfLiteTypeSizeOf(input->type, &size); - const int dimensions_count = tflite::GetTensorShape(input).DimensionsCount(); - for (int i = 0; i < dimensions_count; i++) { - size *= input->dims->data[i]; - } - - output->bytes = size; - - output->dims = - reinterpret_cast(context->AllocatePersistentBuffer( - context, TfLiteIntArrayGetSizeInBytes(size))); - - output->dims->size = input->dims->size; - for (int i = 0; i < dimensions_count; i++) { - output->dims->data[i] = input->dims->data[i]; - } - - return kTfLiteOk; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/memory_planner/greedy_memory_planner.cc b/components/tflite_micro/Source/tensorflow/lite/micro/memory_planner/greedy_memory_planner.cc deleted file mode 100644 index 39991ab7..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/memory_planner/greedy_memory_planner.cc +++ /dev/null @@ -1,437 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h" - -namespace tflite { - -// Simple stable in-place sort function. Not time-efficient for large arrays. -// Would normally be in an anonymous namespace to keep it private, but we want -// to be able to test it externally. -void ReverseSortInPlace(int* values, int* ids, int size) { - bool any_swapped; - do { - any_swapped = false; - for (int i = 1; i < size; ++i) { - if (values[i - 1] < values[i]) { - const int value_temp = values[i - 1]; - values[i - 1] = values[i]; - values[i] = value_temp; - const int id_temp = ids[i - 1]; - ids[i - 1] = ids[i]; - ids[i] = id_temp; - any_swapped = true; - } - } - } while (any_swapped); -} - -GreedyMemoryPlanner::GreedyMemoryPlanner(unsigned char* scratch_buffer, - int scratch_buffer_size) - : buffer_count_(0), need_to_calculate_offsets_(true) { - // Allocate the arrays we need within the scratch buffer arena. - max_buffer_count_ = scratch_buffer_size / per_buffer_size(); - - unsigned char* next_free = scratch_buffer; - requirements_ = reinterpret_cast(next_free); - next_free += sizeof(BufferRequirements) * max_buffer_count_; - - buffer_sizes_sorted_ = reinterpret_cast(next_free); - next_free += sizeof(int) * max_buffer_count_; - - buffer_ids_sorted_ = reinterpret_cast(next_free); - next_free += sizeof(int) * max_buffer_count_; - - buffers_sorted_by_offset_ = reinterpret_cast(next_free); - next_free += sizeof(ListEntry) * max_buffer_count_; - - buffer_offsets_ = reinterpret_cast(next_free); -} - -GreedyMemoryPlanner::~GreedyMemoryPlanner() { - // We don't own the scratch buffer, so don't deallocate anything. -} - -TfLiteStatus GreedyMemoryPlanner::AddBuffer( - tflite::ErrorReporter* error_reporter, int size, int first_time_used, - int last_time_used) { - if (buffer_count_ >= max_buffer_count_) { - TF_LITE_REPORT_ERROR(error_reporter, "Too many buffers (max is %d)", - max_buffer_count_); - return kTfLiteError; - } - BufferRequirements* current = &requirements_[buffer_count_]; - current->size = size; - current->first_time_used = first_time_used; - current->last_time_used = last_time_used; - current->offline_offset = kOnlinePlannedBuffer; - ++buffer_count_; - need_to_calculate_offsets_ = true; - return kTfLiteOk; -} - -TfLiteStatus GreedyMemoryPlanner::AddBuffer( - tflite::ErrorReporter* error_reporter, int size, int first_time_used, - int last_time_used, int offline_offset) { - BufferRequirements* current = &requirements_[buffer_count_]; - if (AddBuffer(error_reporter, size, first_time_used, last_time_used) != - kTfLiteOk) { - return kTfLiteError; - } - current->offline_offset = offline_offset; - return kTfLiteOk; -} - -bool GreedyMemoryPlanner::DoesEntryOverlapInTime( - const GreedyMemoryPlanner::ListEntry* entry, const int first_time_used, - const int last_time_used) const { - const BufferRequirements* entry_requirements = - &requirements_[entry->requirements_index]; - if (entry_requirements->first_time_used > last_time_used) { - return false; - } - if (first_time_used > entry_requirements->last_time_used) { - return false; - } - return true; -} - -GreedyMemoryPlanner::ListEntry* -GreedyMemoryPlanner::NextSimultaneouslyActiveBuffer( - const GreedyMemoryPlanner::ListEntry* start, const int first_time_used, - const int last_time_used) { - ListEntry* result = nullptr; - ListEntry* candidate_next_entry; - if (start == nullptr) { - candidate_next_entry = &buffers_sorted_by_offset_[first_entry_index_]; - } else { - if (start->next_entry_index == -1) { - return nullptr; - } - candidate_next_entry = &buffers_sorted_by_offset_[start->next_entry_index]; - } - do { - if (DoesEntryOverlapInTime(candidate_next_entry, first_time_used, - last_time_used)) { - result = candidate_next_entry; - break; - } - if (candidate_next_entry->next_entry_index == -1) { - break; - } - candidate_next_entry = - &buffers_sorted_by_offset_[candidate_next_entry->next_entry_index]; - } while (true); - return result; -} - -void GreedyMemoryPlanner::CalculateOffsetsIfNeeded() { - if (!need_to_calculate_offsets_ || (buffer_count_ == 0)) { - return; - } - need_to_calculate_offsets_ = false; - - // Start off by ordering the buffers in descending order of size. - // This helps find a more compact layout. Intuitively, you can think - // about putting the large buffers in place first, and then the - // smaller buffers can fit in the gaps, rather than fragmenting the - // gaps with small buffers at the beginning. Add offline planned offsets - // first in the list, since they have a predetermined offset. - int idx_from_tail = buffer_count_; - int idx_from_head = 0; - for (int i = 0; i < buffer_count_; ++i) { - if (requirements_[i].offline_offset == kOnlinePlannedBuffer) { - idx_from_tail--; - buffer_sizes_sorted_[idx_from_tail] = requirements_[i].size; - buffer_ids_sorted_[idx_from_tail] = i; - buffer_offsets_[i] = -1; - } else { - buffer_sizes_sorted_[idx_from_head] = requirements_[i].size; - buffer_ids_sorted_[idx_from_head] = i; - buffer_offsets_[i] = requirements_[i].offline_offset; - idx_from_head++; - } - } - - // This sorting algorithm is naive, and may end up taking a very long time - // with hundreds of buffers. Do not sort the offline planned offsets. - ReverseSortInPlace(&buffer_sizes_sorted_[idx_from_head], - &buffer_ids_sorted_[idx_from_head], - buffer_count_ - idx_from_head); - - // Initialize the first entry to the first buffer in - // buffer_ids_sorted_. - // - If there are no offline planned offsets, the largest buffer will be - // first, and the buffers will be handled in size order. - // - If offline offsets are present, these will be handled first in order - // for the greedy algorithm to utilized gaps in the offline plan. - first_entry_index_ = 0; - next_free_entry_ = 1; - ListEntry* first_entry = &buffers_sorted_by_offset_[first_entry_index_]; - first_entry->next_entry_index = -1; // to mark the entry as end of list - int buffer_id = buffer_ids_sorted_[0]; - first_entry->requirements_index = buffer_id; - if (requirements_[buffer_id].offline_offset == kOnlinePlannedBuffer) { - buffer_offsets_[buffer_id] = 0; - } - first_entry->offset = buffer_offsets_[buffer_id]; - - // Work through the rest of the buffers to find a good gap to place each one. - for (int i = 1; i < buffer_count_; ++i) { - // The id is the order the buffer was originally added by the client. - buffer_id = buffer_ids_sorted_[i]; - // Look at what size and time range the buffer needs to be active. - BufferRequirements* wanted_requirements = &requirements_[buffer_id]; - const int wanted_size = wanted_requirements->size; - const int wanted_first_time_used = wanted_requirements->first_time_used; - const int wanted_last_time_used = wanted_requirements->last_time_used; - - // Find the first buffer that's active in our time range. All placed - // buffers are stored in the order of their starting position in the arena - // so that it's easy to find the next buffer in memory, and so the gap. - // The candidate_entry variable holds the buffer that we're considering - // placing the current buffer after. - - int candidate_offset = 0; - // Loop through the offset-ordered list of buffers, looking for gaps. - if (wanted_requirements->offline_offset == kOnlinePlannedBuffer) { - ListEntry* prior_entry = nullptr; - while (true) { - // Find out what the next active buffer is. - ListEntry* next_entry = NextSimultaneouslyActiveBuffer( - prior_entry, wanted_first_time_used, wanted_last_time_used); - - if (prior_entry) { - BufferRequirements* candidate_requirements = - &requirements_[prior_entry->requirements_index]; - const int prior_entry_offset = - prior_entry->offset + candidate_requirements->size; - if (prior_entry_offset > candidate_offset) { - candidate_offset = prior_entry_offset; - } - } - if (next_entry == nullptr) { - // We're at the end of the list, so we can always append the buffer - // here. - break; - } - // Find out how much space there is between us and the next buffer. - const int gap = next_entry->offset - candidate_offset; - if (gap >= wanted_size) { - // This entry has a big enough gap between it and the next, so - // use it! - break; - } - // The gap wasn't big enough, so move on to another candidate. - prior_entry = next_entry; - } - } else { - // Offline planned offset are to be considered constant - candidate_offset = wanted_requirements->offline_offset; - } - // At this point, we've either found a gap (possibly at the end of the - // list) and want to place the buffer there, or there are no other active - // buffers in this time range and so we can put it at offset zero. - // Record the buffer's offset in our plan. - buffer_offsets_[buffer_id] = candidate_offset; - // Add the newly-placed buffer to our offset-ordered list, so that - // subsequent passes can fit in their buffers around it. - ListEntry* new_entry = &buffers_sorted_by_offset_[next_free_entry_]; - new_entry->offset = candidate_offset; - new_entry->requirements_index = buffer_id; - const int new_entry_index = next_free_entry_; - ++next_free_entry_; - - if (first_entry->offset > candidate_offset) { - // The new entry offset is smaller than the first entry offset => - // replace the first entry - first_entry = new_entry; - first_entry->next_entry_index = first_entry_index_; - first_entry_index_ = new_entry_index; - } else { - ListEntry* current_entry = first_entry; - // Make sure that we insert the buffer at the correct place in the - // buffer-offset-ordered list - while (true) { - const int next_entry_index = current_entry->next_entry_index; - if (next_entry_index == -1) { - // We're at the end of the list, so just add the new entry here. - current_entry->next_entry_index = new_entry_index; - new_entry->next_entry_index = -1; - break; - } - // not at the end of the list -> take a look at next entry - ListEntry* next_entry = &buffers_sorted_by_offset_[next_entry_index]; - if (next_entry->offset > candidate_offset) { - // We're at the right spot to do an insertion and retain the sorting - // order, so place the new entry here. - new_entry->next_entry_index = current_entry->next_entry_index; - current_entry->next_entry_index = new_entry_index; - break; - } - current_entry = next_entry; - } - } - } -} - -size_t GreedyMemoryPlanner::GetMaximumMemorySize() { - CalculateOffsetsIfNeeded(); - if (buffer_count_ == 0) { - return 0; - } - ListEntry* entry = &buffers_sorted_by_offset_[first_entry_index_]; - size_t max_size = 0; - while (entry) { - BufferRequirements* requirements = - &requirements_[entry->requirements_index]; - // TODO(b/148246793): Update all size and offset variables types from - // int to size_t - const size_t current_size = entry->offset + requirements->size; - if (current_size > max_size) { - max_size = current_size; - } - if (entry->next_entry_index == -1) { - break; - } - entry = &buffers_sorted_by_offset_[entry->next_entry_index]; - } - return max_size; -} - -void GreedyMemoryPlanner::PrintMemoryPlan(ErrorReporter* error_reporter) { - CalculateOffsetsIfNeeded(); - - for (int i = 0; i < buffer_count_; ++i) { - TF_LITE_REPORT_ERROR( - error_reporter, - "Planner buffer ID: %d, calculated offset: %d, size required: %d, " - "first_time_created: %d, " - "last_time_used: %d", - i, buffer_offsets_[i], requirements_[i].size, - requirements_[i].first_time_used, requirements_[i].last_time_used); - } - - constexpr int kLineWidth = 80; - int max_size = kLineWidth; - int max_time = 0; - for (int i = 0; i < buffer_count_; ++i) { - BufferRequirements* requirements = &requirements_[i]; - const int offset = buffer_offsets_[i]; - const int last_time_used = requirements->last_time_used; - const int size = offset + requirements->size; - if (size > max_size) { - max_size = size; - } - if (last_time_used > max_time) { - max_time = last_time_used; - } - } - - char line[kLineWidth + 1]; - for (int t = 0; t <= max_time; ++t) { - for (int c = 0; c < kLineWidth; ++c) { - line[c] = '.'; - } - for (int i = 0; i < buffer_count_; ++i) { - BufferRequirements* requirements = &requirements_[i]; - if ((t < requirements->first_time_used) || - (t > requirements->last_time_used)) { - continue; - } - const int offset = buffer_offsets_[i]; - if (offset == -1) { - continue; - } - const int size = requirements->size; - const int line_start = (offset * kLineWidth) / max_size; - const int line_end = ((offset + size) * kLineWidth) / max_size; - for (int n = line_start; n < line_end; ++n) { - if (line[n] == '.') { - char display; - if (i < 10) { - display = '0' + i; - } else if (i < 36) { - display = 'a' + (i - 10); - } else if (i < 62) { - display = 'A' + (i - 36); - } else { - display = '*'; - } - line[n] = display; - } else { - line[n] = '!'; - } - } - } - line[kLineWidth] = 0; - TF_LITE_REPORT_ERROR(error_reporter, "%s", (const char*)line); - } -} - -int GreedyMemoryPlanner::GetBufferCount() { return buffer_count_; } - -TfLiteStatus GreedyMemoryPlanner::GetOffsetForBuffer( - tflite::ErrorReporter* error_reporter, int buffer_index, int* offset) { - CalculateOffsetsIfNeeded(); - if ((buffer_index < 0) || (buffer_index >= buffer_count_)) { - TF_LITE_REPORT_ERROR(error_reporter, - "buffer index %d is outside range 0 to %d", - buffer_index, buffer_count_); - return kTfLiteError; - } - *offset = buffer_offsets_[buffer_index]; - return kTfLiteOk; -} - -bool GreedyMemoryPlanner::DoAnyBuffersOverlap(ErrorReporter* error_reporter) { - CalculateOffsetsIfNeeded(); - bool were_overlaps_found = false; - for (int i = 0; i < buffer_count_; ++i) { - BufferRequirements* a_requirements = &requirements_[i]; - const int a_start_offset = buffer_offsets_[i]; - const int a_first_time_used = a_requirements->first_time_used; - const int a_last_time_used = a_requirements->last_time_used; - const int a_end_offset = a_start_offset + a_requirements->size; - for (int j = 0; j < buffer_count_; ++j) { - if (i == j) { - continue; - } - BufferRequirements* b_requirements = &requirements_[j]; - const int b_start_offset = buffer_offsets_[j]; - const int b_first_time_used = b_requirements->first_time_used; - const int b_last_time_used = b_requirements->last_time_used; - const int b_end_offset = b_start_offset + b_requirements->size; - if ((a_first_time_used > b_last_time_used) || - (b_first_time_used > a_last_time_used)) { - // Buffers don't overlap in time. - continue; - } - if ((a_start_offset >= b_end_offset) || - (b_start_offset >= a_end_offset)) { - // No overlap in memory. - continue; - } - were_overlaps_found = true; - TF_LITE_REPORT_ERROR( - error_reporter, "Overlap: %d (%d=>%d, %d->%d) vs %d (%d=>%d, %d->%d)", - i, a_first_time_used, a_last_time_used, a_start_offset, a_end_offset, - j, b_first_time_used, b_last_time_used, b_start_offset, b_end_offset); - } - } - return were_overlaps_found; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc b/components/tflite_micro/Source/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc deleted file mode 100644 index d25a4f22..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc +++ /dev/null @@ -1,54 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/memory_planner/linear_memory_planner.h" - -namespace tflite { - -LinearMemoryPlanner::LinearMemoryPlanner() - : current_buffer_count_(0), next_free_offset_(0) {} -LinearMemoryPlanner::~LinearMemoryPlanner() {} - -TfLiteStatus LinearMemoryPlanner::AddBuffer( - tflite::ErrorReporter* error_reporter, int size, int first_time_used, - int last_time_used) { - if (current_buffer_count_ >= kMaxBufferCount) { - TF_LITE_REPORT_ERROR(error_reporter, "Too many buffers (max is %d)", - kMaxBufferCount); - return kTfLiteError; - } - buffer_offsets_[current_buffer_count_] = next_free_offset_; - next_free_offset_ += size; - ++current_buffer_count_; - return kTfLiteOk; -} - -size_t LinearMemoryPlanner::GetMaximumMemorySize() { return next_free_offset_; } - -int LinearMemoryPlanner::GetBufferCount() { return current_buffer_count_; } - -TfLiteStatus LinearMemoryPlanner::GetOffsetForBuffer( - tflite::ErrorReporter* error_reporter, int buffer_index, int* offset) { - if ((buffer_index < 0) || (buffer_index >= current_buffer_count_)) { - TF_LITE_REPORT_ERROR(error_reporter, - "buffer index %d is outside range 0 to %d", - buffer_index, current_buffer_count_); - return kTfLiteError; - } - *offset = buffer_offsets_[buffer_index]; - return kTfLiteOk; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/micro_allocator.cc b/components/tflite_micro/Source/tensorflow/lite/micro/micro_allocator.cc deleted file mode 100644 index 881b9b9a..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/micro_allocator.cc +++ /dev/null @@ -1,1076 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/micro_allocator.h" - -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/api/flatbuffer_conversions.h" -#include "tensorflow/lite/core/api/op_resolver.h" -#include "tensorflow/lite/core/api/tensor_utils.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/memory_helpers.h" -#include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h" -#include "tensorflow/lite/micro/memory_planner/memory_planner.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" -#include "tensorflow/lite/micro/simple_memory_allocator.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { - -namespace { -// Used to hold information used during allocation calculations. -struct AllocationInfo { - size_t bytes; - void** output_ptr; - int first_created; - int last_used; - int32_t offline_offset; - bool needs_allocating; -}; - -// We align tensor buffers to 16-byte boundaries, since this is a common -// requirement for SIMD extensions. -constexpr int kBufferAlignment = 16; -constexpr char kOfflineMemAllocMetadata[] = "OfflineMemoryAllocation"; -const TfLiteIntArray kZeroLengthIntArray = {0, {}}; - -class MicroBuiltinDataAllocator : public BuiltinDataAllocator { - public: - explicit MicroBuiltinDataAllocator(SimpleMemoryAllocator* memory_allocator) - : memory_allocator_(memory_allocator) {} - - void* Allocate(size_t size, size_t alignment_hint) override { - return memory_allocator_->AllocateFromTail(size, alignment_hint); - } - void Deallocate(void* data) override { - // Do not deallocate, builtin data needs to be available for the life time - // of the model. - } - - private: - SimpleMemoryAllocator* memory_allocator_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -#if !defined(__clang__) -// Helper function to check flatbuffer metadata correctness. This function is -// not called by default. Hence it's not linked in to the final binary code. -TfLiteStatus CheckOfflinePlannedOffsets(const Model* model, - ErrorReporter* error_reporter) { - // Suppress compile warning for unused function - (void)CheckOfflinePlannedOffsets; - - if (model->metadata()) { - for (size_t i = 0; i < model->metadata()->size(); ++i) { - auto metadata = model->metadata()->Get(i); - if (strncmp(metadata->name()->c_str(), kOfflineMemAllocMetadata, - strlen(kOfflineMemAllocMetadata)) == 0) { - auto* subgraphs = model->subgraphs(); - const SubGraph* subgraph = (*subgraphs)[0]; - const flatbuffers::Vector>* tensors = - subgraph->tensors(); - const flatbuffers::Vector>* buffers = - model->buffers(); - int nbr_tflite_tensors = tensors->size(); - auto* buffer = (*buffers)[metadata->buffer()]; - auto* array = buffer->data(); - const uint32_t* metadata_buffer = (uint32_t*)array->data(); - int version = metadata_buffer[0]; - int subgraph_idx = metadata_buffer[1]; - const int nbr_offline_offsets = metadata_buffer[2]; -#ifndef TF_LITE_STRIP_ERROR_STRINGS - int* offline_planner_offsets = (int*)&metadata_buffer[3]; -#endif - - TF_LITE_REPORT_ERROR(error_reporter, "==== Model metadata info: ====="); - TF_LITE_REPORT_ERROR(error_reporter, - "Offline planner metadata found, version %d, " - "subgraph %d, nbr offline offsets %d", - version, subgraph_idx, nbr_offline_offsets); - for (int j = 0; j < nbr_offline_offsets; ++j) { - TF_LITE_REPORT_ERROR( - error_reporter, - "Offline planner tensor index %d, offline offset: %d", j, - offline_planner_offsets[j]); - } - - if (version != 1) { - TF_LITE_REPORT_ERROR(error_reporter, "Version not supported! (%d)\n", - version); - return kTfLiteError; - } - if (subgraph_idx != 0) { - TF_LITE_REPORT_ERROR(error_reporter, - "Only 1 subgraph supported! Subgraph idx (%d)\n", - subgraph_idx); - return kTfLiteError; - } - if (nbr_tflite_tensors != nbr_offline_offsets) { - TF_LITE_REPORT_ERROR(error_reporter, - "Nbr of offline buffer offsets (%d) in metadata " - "not equal nbr tensors (%d)\n", - nbr_offline_offsets, nbr_tflite_tensors); - return kTfLiteError; - } - } - } - } - return kTfLiteOk; -} -#endif - -// A helper class to construct AllocationInfo array. This array contains the -// lifetime of tensors / scratch_buffer and will be used to calculate the memory -// plan. Methods need to be called in order from `Init`, `Add*`, to `Finish`. -class AllocationInfoBuilder { - public: - AllocationInfoBuilder(ErrorReporter* reporter, - SimpleMemoryAllocator* allocator) - : reporter_(reporter), allocator_(allocator) {} - - // Initializes the builder by allocating AllocationInfo array from the - // simple memory allocator. - TfLiteStatus Init(size_t tensor_count, size_t scratch_buffer_count) { - tensor_count_ = tensor_count; - buffer_count_ = scratch_buffer_count; - return Allocate(); - } - - // Check if model contains offline planned buffer offsets. - // - If there's no metadata available, offline_planner_offsets is not set - // - If there's metadata available, offline_planner_offsets will point to the - // first offset in the metadata buffer list. - TfLiteStatus GetOfflinePlannedOffsets( - const Model* model, const int32_t** offline_planner_offsets); - - // Add allocaiton information for the tensors. - TfLiteStatus AddTensors(const SubGraph* subgraph, - const int32_t* offline_offsets, - TfLiteEvalTensor* eval_tensors); - - // Add allocation information for the scratch buffers. - TfLiteStatus AddScratchBuffers(internal::ScratchBufferHandle* buffer_handles); - - // Returns a pointer to the built AllocationInfo array. - const AllocationInfo* Finish() const { return info_; } - size_t Size() const { return tensor_count_ + buffer_count_; } - - private: - // Allocate the output AllocationInfo array from the allocator_; - TfLiteStatus Allocate(); - - ErrorReporter* reporter_ = nullptr; - SimpleMemoryAllocator* allocator_ = nullptr; - size_t tensor_count_ = 0; - size_t buffer_count_ = 0; - AllocationInfo* info_ = nullptr; -}; - -TfLiteStatus AllocationInfoBuilder::Allocate() { - size_t bytes = sizeof(AllocationInfo) * Size(); - info_ = reinterpret_cast( - allocator_->AllocateFromTail(bytes, alignof(AllocationInfo))); - if (info_ == nullptr) { - TF_LITE_REPORT_ERROR( - reporter_, - "Failed to allocate memory for allocation_info, %d bytes required", - bytes); - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus AllocationInfoBuilder::AddTensors(const SubGraph* subgraph, - const int32_t* offline_offsets, - TfLiteEvalTensor* eval_tensors) { - TFLITE_DCHECK(eval_tensors != nullptr); - - // Set up allocation info for all tensors. - for (size_t i = 0; i < tensor_count_; ++i) { - AllocationInfo* current = &info_[i]; - current->output_ptr = &(eval_tensors[i].data.data); - - TF_LITE_ENSURE_STATUS( - TfLiteEvalTensorByteLength(&eval_tensors[i], ¤t->bytes)); - - current->first_created = -1; - current->last_used = -1; - current->needs_allocating = (eval_tensors[i].data.data == nullptr) && - (!subgraph->tensors()->Get(i)->is_variable()); - if (offline_offsets) { - current->offline_offset = offline_offsets[i]; - } else { - current->offline_offset = kOnlinePlannedBuffer; - } - } - - for (size_t i = 0; i < subgraph->inputs()->size(); ++i) { - const int tensor_index = subgraph->inputs()->Get(i); - AllocationInfo* current = &info_[tensor_index]; - current->first_created = 0; - } - - // Mark all outputs as persistent to the end of the invocation. - for (size_t i = 0; i < subgraph->outputs()->size(); ++i) { - const int tensor_index = subgraph->outputs()->Get(i); - AllocationInfo* current = &info_[tensor_index]; - current->last_used = subgraph->operators()->size() - 1; - } - - // Figure out when the first and last use of each tensor is. - for (int i = (subgraph->operators()->size() - 1); i >= 0; --i) { - const auto* op = subgraph->operators()->Get(i); - for (size_t n = 0; n < op->inputs()->size(); ++n) { - const int tensor_index = op->inputs()->Get(n); - AllocationInfo* current = &info_[tensor_index]; - if (((current->last_used == -1) || (current->last_used < i))) { - current->last_used = i; - } - } - for (size_t n = 0; n < op->outputs()->size(); ++n) { - const int tensor_index = op->outputs()->Get(n); - AllocationInfo* current = &info_[tensor_index]; - if ((current->first_created == -1) || (current->first_created > i)) { - current->first_created = i; - } - } - } - - // Work out which tensors need to be allocated. - for (size_t i = 0; i < tensor_count_; ++i) { - AllocationInfo* current = &info_[i]; - const bool is_read_only = - (current->first_created == -1) && (current->last_used != -1); - if (is_read_only) { - current->needs_allocating = false; - } - const bool has_partial_lifetime = - !is_read_only && - ((current->first_created == -1) || (current->last_used == -1)); - if (has_partial_lifetime && current->needs_allocating) { - TF_LITE_REPORT_ERROR( - reporter_, - "Logic error in memory planner, tensor %d has an invalid lifetime: " - "first_created: %d, last_used: %d", - i, current->first_created, current->last_used); - return kTfLiteError; - } - } - return kTfLiteOk; -} - -// The tensor offsets will be encoded in the metadata:[Metadata] field of the -// Model. The following encoding applies: -// -// | Metadata component | Value | -// | name:string | “OfflineMemoryAllocation” | -// | buffer:unit | Index of buffer containing memory allocation data | -// -// The buffer contents for the memory allocation is a list of 32-bit integers. -// The number of tensors, n, must be equal to the number of tensors defined in -// the model. The following encoding applies: -// -// | Offset | Value | -// | 0 | Offline allocation format version – set to 0 | -// | 1 | Subgraph index to which this allocation applies | -// | 2 | Number offsets following: n | -// | 3 | Arena byte offset of tensor #0 or -1 to allocate at runtime | -// | 4 | Arena byte offset of tensor #1 or -1 to allocate at runtime | -// | 3+(n-1) | Arena byte offset of tensor #(n-1) or -1 to allocate at runtime | -TfLiteStatus AllocationInfoBuilder::GetOfflinePlannedOffsets( - const Model* model, const int32_t** offline_planner_offsets) { - if (model->metadata()) { - for (size_t i = 0; i < model->metadata()->size(); ++i) { - auto metadata = model->metadata()->Get(i); - if (strncmp(metadata->name()->c_str(), kOfflineMemAllocMetadata, - strlen(kOfflineMemAllocMetadata)) == 0) { - const flatbuffers::Vector>* buffers = - model->buffers(); - auto* buffer = (*buffers)[metadata->buffer()]; - auto* array = buffer->data(); - const uint32_t* metadata_buffer = - reinterpret_cast(array->data()); - const size_t nbr_tensors = static_cast(metadata_buffer[2]); - *offline_planner_offsets = - reinterpret_cast(&metadata_buffer[3]); - - if (tensor_count_ != nbr_tensors) { - TF_LITE_REPORT_ERROR(reporter_, - "Nbr of offline buffer offsets (%d) in metadata " - "not equal nbr tensors (%d)\n", - nbr_tensors, tensor_count_); - return kTfLiteError; - } - } - } - } - return kTfLiteOk; -} - -TfLiteStatus AllocationInfoBuilder::AddScratchBuffers( - internal::ScratchBufferHandle* buffer_handles) { - // Set up allocation info for buffers. - for (size_t i = tensor_count_; i < tensor_count_ + buffer_count_; ++i) { - AllocationInfo* current = &info_[i]; - internal::ScratchBufferHandle* handle = - &(buffer_handles[i - tensor_count_]); - current->output_ptr = reinterpret_cast(&handle->data); - current->bytes = handle->bytes; - current->first_created = handle->node_idx; - current->last_used = handle->node_idx; - current->needs_allocating = true; - current->offline_offset = kOnlinePlannedBuffer; - } - return kTfLiteOk; -} - -TfLiteStatus CreatePlan(ErrorReporter* error_reporter, - GreedyMemoryPlanner* planner, - const AllocationInfo* allocation_info, - size_t allocation_info_size) { - // Add the tensors to our allocation plan. - for (size_t i = 0; i < allocation_info_size; ++i) { - const AllocationInfo* current = &allocation_info[i]; - if (current->needs_allocating) { - size_t aligned_bytes_required = - AlignSizeUp(current->bytes, kBufferAlignment); - if (current->offline_offset == kOnlinePlannedBuffer) { - TF_LITE_ENSURE_STATUS( - planner->AddBuffer(error_reporter, aligned_bytes_required, - current->first_created, current->last_used)); - } else { - TF_LITE_ENSURE_STATUS(planner->AddBuffer( - error_reporter, aligned_bytes_required, current->first_created, - current->last_used, current->offline_offset)); - } - } - } - return kTfLiteOk; -} - -TfLiteStatus CommitPlan(ErrorReporter* error_reporter, MemoryPlanner* planner, - uint8_t* starting_point, - const AllocationInfo* allocation_info, - size_t allocation_info_size) { - // Figure out the actual memory addresses for each buffer, based on the plan. - int planner_index = 0; - for (size_t i = 0; i < allocation_info_size; ++i) { - const AllocationInfo* current = &allocation_info[i]; - if (current->needs_allocating) { - int offset = -1; - TF_LITE_ENSURE_STATUS( - planner->GetOffsetForBuffer(error_reporter, planner_index, &offset)); - *current->output_ptr = reinterpret_cast(starting_point + offset); - ++planner_index; - } - } - return kTfLiteOk; -} -} // namespace - -namespace internal { - -// Handles architecture safe mapping of flatbuffer vectors to a TfLite*Array -// struct. Matching types are required (e.g. float and TfLiteFloatArray). -// Big-endian systems will always allocate dimension array data in the tail -// (persistent) section. -template -TfLiteStatus FlatBufferVectorToTfLiteTypeArray( - SimpleMemoryAllocator* allocator, ErrorReporter* error_reporter, - const flatbuffers::Vector* flatbuffer_array, - kTfLiteArrayType** result) { - TFLITE_DCHECK(error_reporter != nullptr); - TFLITE_DCHECK(flatbuffer_array != nullptr); - // TODO(b/159668691): Consider adding type assertion or breaking this function - // into multiple functions for each type. std::is_same is c++11 and has a - // special updated constructor in c++17 that requires a string argument. - if (FLATBUFFERS_LITTLEENDIAN) { - // On little-endian machines, TfLite*Array happens to have the same memory - // layout as flatbuffers:Vector, so we can - // reinterpret_cast the flatbuffer vector and avoid a copy and malloc. - *result = const_cast( - reinterpret_cast(flatbuffer_array)); - } else { - // Big-endian architecture can not use the same memory layout as - // flatbuffers::Vector. Allocate from the tail and - // copy values from the flatbuffer into the newly allocated chunk. - kTfLiteArrayType* array = - reinterpret_cast(allocator->AllocateFromTail( - TfLiteIntArrayGetSizeInBytes(flatbuffer_array->Length()), - alignof(kTfLiteArrayType))); - if (array == nullptr) { - TF_LITE_REPORT_ERROR( - error_reporter, - "Failed to allocate %d bytes of memory to copy an array.", - TfLiteIntArrayGetSizeInBytes(flatbuffer_array->Length())); - return kTfLiteError; - } - array->size = flatbuffer_array->Length(); - for (int i = 0; i < array->size; ++i) { - array->data[i] = flatbuffer_array->Get(i); - } - *result = array; - } - return kTfLiteOk; -} - -// Returns a pointer to any buffer associated with the flatbuffer tensor. Can -// return nullptr if no buffer is found. -void* GetFlatbufferTensorBuffer( - const tflite::Tensor& flatbuffer_tensor, - const flatbuffers::Vector>* buffers) { - // We need to figure out where the actual contents of this tensor are stored - // in memory. We'll check to see if there's a serialized buffer (pretty much - // the same as a constant op in TensorFlow) associated with this tensor first, - // and if there is update the runtime structure to point to its location in - // memory. - // First see if there's any buffer information in the serialized tensor. - // TODO(b/160894903): Add better unit tests that validate flatbuffer values. - void* out_buffer = nullptr; - if (auto* buffer = (*buffers)[flatbuffer_tensor.buffer()]) { - // If we've found a buffer, does it have any data? - if (auto* array = buffer->data()) { - // If it has any data, is the data size larger than zero? - if (array->size()) { - // We've found a buffer with valid data, so update the runtime tensor - // data structure to point to it. - out_buffer = const_cast(static_cast(array->data())); - } - } - // TODO(petewarden): It's not clear in what circumstances we could have a - // buffer in the serialized tensor, but it doesn't have any data in it. Is - // that a validly-generated file, and if so what does it mean, or is it an - // error condition? It would be good to tighten up the specification to make - // it less ambiguous. - } - return out_buffer; -} - -TfLiteStatus InitializeTfLiteTensorFromFlatbuffer( - SimpleMemoryAllocator* allocator, bool allocate_temp, - const tflite::Tensor& flatbuffer_tensor, - const flatbuffers::Vector>* buffers, - ErrorReporter* error_reporter, TfLiteTensor* result) { - TFLITE_DCHECK(result != nullptr); - - *result = {}; - // Make sure the serialized type is one we know how to deal with, and convert - // it from a flatbuffer enum into a constant used by the kernel C API. - TF_LITE_ENSURE_STATUS(ConvertTensorType(flatbuffer_tensor.type(), - &result->type, error_reporter)); - // Make sure we remember if the serialized tensor is designated as a variable. - result->is_variable = flatbuffer_tensor.is_variable(); - - result->data.data = GetFlatbufferTensorBuffer(flatbuffer_tensor, buffers); - - // TODO(petewarden): Some of these paths aren't getting enough testing - // coverage, so we should figure out some tests that exercise them. - if (result->data.data == nullptr) { - // The tensor contents haven't been set from a serialized buffer, so - // make a note that they will be allocated from memory. The actual - // allocation won't happen until later. - result->allocation_type = kTfLiteArenaRw; - } else { - // We set the data from a serialized buffer, so record tha. - result->allocation_type = kTfLiteMmapRo; - } - - // Figure out what the size in bytes of the buffer is and store it. - size_t type_size; - TF_LITE_ENSURE_STATUS(BytesRequiredForTensor( - flatbuffer_tensor, &result->bytes, &type_size, error_reporter)); - - if (flatbuffer_tensor.shape() == nullptr) { - // flatbuffer_tensor.shape() can return a nullptr in the case of a scalar - // tensor. - result->dims = const_cast(&kZeroLengthIntArray); - } else { - // TFLM doesn't allow reshaping the tensor which requires dynamic memory - // allocation so it is safe to drop the const qualifier. In the future, if - // we really want to update the tensor shape, we can always pass in a new - // TfLiteIntArray - especially we have to do so if the dimension is - TF_LITE_ENSURE_STATUS(FlatBufferVectorToTfLiteTypeArray( - allocator, error_reporter, flatbuffer_tensor.shape(), &(result->dims))); - } - - // Copy the quantization information from the serialized data. - const auto* src_quantization = flatbuffer_tensor.quantization(); - if (src_quantization && src_quantization->scale() && - (src_quantization->scale()->size() > 0) && - src_quantization->zero_point() && - (src_quantization->zero_point()->size() > 0)) { - // Always populate the TfLiteTensor.params field, even if there are - // per-channel quantization parameters. - result->params.scale = src_quantization->scale()->Get(0); - // Note that the zero_point field in the FlatBuffers schema is a 64-bit - // integer, but the zero_point field in the TfLiteQuantizationParams struct - // is a 32-bit integer. - result->params.zero_point = - static_cast(src_quantization->zero_point()->Get(0)); - - // Populate per-channel quantization params. - int channels = src_quantization->scale()->size(); - TfLiteAffineQuantization* quantization = - allocate_temp - ? reinterpret_cast( - allocator->AllocateTemp(sizeof(TfLiteAffineQuantization), - alignof(TfLiteAffineQuantization))) - : reinterpret_cast( - allocator->AllocateFromTail( - sizeof(TfLiteAffineQuantization), - alignof(TfLiteAffineQuantization))); - if (quantization == nullptr) { - TF_LITE_REPORT_ERROR(error_reporter, - "Unable to allocate TfLiteAffineQuantization.\n"); - return kTfLiteError; - } - - // TODO(b/153688719): Reduce tail allocation by using a global zero-point - // buffer. This value can not be reused from the flatbuffer since the - // zero_point is stored as a int64_t. - quantization->zero_point = - allocate_temp - ? reinterpret_cast(allocator->AllocateTemp( - TfLiteIntArrayGetSizeInBytes(channels), - alignof(TfLiteIntArray))) - : reinterpret_cast(allocator->AllocateFromTail( - TfLiteIntArrayGetSizeInBytes(channels), - alignof(TfLiteIntArray))); - if (quantization->zero_point == nullptr) { - TF_LITE_REPORT_ERROR(error_reporter, - "Unable to allocate quantization->zero_point.\n"); - return kTfLiteError; - } - - TF_LITE_ENSURE_STATUS(FlatBufferVectorToTfLiteTypeArray( - allocator, error_reporter, src_quantization->scale(), - &quantization->scale)); - - quantization->zero_point->size = channels; - int* zero_point_data = quantization->zero_point->data; - for (int i = 0; i < channels; i++) { - zero_point_data[i] = src_quantization->zero_point()->Get(i); - } - // TODO(rocky): Need to add a micro_allocator test case that fails when - // this is not copied: - quantization->quantized_dimension = src_quantization->quantized_dimension(); - - result->quantization = {kTfLiteAffineQuantization, quantization}; - } - return kTfLiteOk; -} - -TfLiteStatus InitializeTfLiteEvalTensorFromFlatbuffer( - SimpleMemoryAllocator* allocator, const tflite::Tensor& flatbuffer_tensor, - const flatbuffers::Vector>* buffers, - ErrorReporter* error_reporter, TfLiteEvalTensor* result) { - *result = {}; - // Make sure the serialized type is one we know how to deal with, and convert - // it from a flatbuffer enum into a constant used by the kernel C API. - TF_LITE_ENSURE_STATUS(ConvertTensorType(flatbuffer_tensor.type(), - &result->type, error_reporter)); - - result->data.data = GetFlatbufferTensorBuffer(flatbuffer_tensor, buffers); - - if (flatbuffer_tensor.shape() == nullptr) { - // flatbuffer_tensor.shape() can return a nullptr in the case of a scalar - // tensor. - result->dims = const_cast(&kZeroLengthIntArray); - } else { - TF_LITE_ENSURE_STATUS(FlatBufferVectorToTfLiteTypeArray( - allocator, error_reporter, flatbuffer_tensor.shape(), &(result->dims))); - } - return kTfLiteOk; -} - -} // namespace internal - -MicroAllocator::MicroAllocator(SimpleMemoryAllocator* memory_allocator, - ErrorReporter* error_reporter) - : memory_allocator_(memory_allocator), - error_reporter_(error_reporter), - model_is_allocating_(false) {} - -MicroAllocator::~MicroAllocator() {} - -MicroAllocator* MicroAllocator::Create(uint8_t* tensor_arena, size_t arena_size, - ErrorReporter* error_reporter) { - uint8_t* aligned_arena = AlignPointerUp(tensor_arena, kBufferAlignment); - if (aligned_arena != tensor_arena) { - TF_LITE_REPORT_ERROR( - error_reporter, - "%d bytes lost due to alignment. To avoid this loss, please make sure " - "the tensor_arena is 16 bytes aligned.", - aligned_arena - tensor_arena); - } - size_t aligned_arena_size = tensor_arena + arena_size - aligned_arena; - return Create(SimpleMemoryAllocator::Create(error_reporter, aligned_arena, - aligned_arena_size), - error_reporter); -} - -MicroAllocator* MicroAllocator::Create(SimpleMemoryAllocator* memory_allocator, - ErrorReporter* error_reporter) { - TFLITE_DCHECK(memory_allocator != nullptr); - TFLITE_DCHECK(error_reporter != nullptr); - - uint8_t* allocator_buffer = memory_allocator->AllocateFromTail( - sizeof(MicroAllocator), alignof(MicroAllocator)); - MicroAllocator* allocator = - new (allocator_buffer) MicroAllocator(memory_allocator, error_reporter); - return allocator; -} - -TfLiteStatus MicroAllocator::StartModelAllocation( - const Model* model, const MicroOpResolver& op_resolver, - NodeAndRegistration** node_and_registrations, - TfLiteEvalTensor** eval_tensors) { - TFLITE_DCHECK(model != nullptr); - - if (model_is_allocating_) { - TF_LITE_REPORT_ERROR(error_reporter_, - "MicroAllocator: Model allocation started before " - "finishing previously allocated model"); - return kTfLiteError; - } - - model_is_allocating_ = true; - - TF_LITE_ENSURE_STATUS(AllocateTfLiteEvalTensors(model, eval_tensors)); - TF_LITE_ENSURE_STATUS( - AllocateNodeAndRegistrations(model, node_and_registrations)); - TF_LITE_ENSURE_STATUS(PrepareNodeAndRegistrationDataFromFlatbuffer( - model, op_resolver, *node_and_registrations)); - - return kTfLiteOk; -} - -TfLiteStatus MicroAllocator::FinishModelAllocation( - const Model* model, TfLiteEvalTensor* eval_tensors) { - if (!model_is_allocating_) { - TF_LITE_REPORT_ERROR(error_reporter_, - "MicroAllocator: Model allocation finished before " - "starting allocating model"); - return kTfLiteError; - } - - const SubGraph* subgraph = GetSubGraphFromModel(model); - TFLITE_DCHECK(subgraph != nullptr); - - TF_LITE_ENSURE_STATUS(CommitStaticMemoryPlan(model, subgraph, eval_tensors)); - TF_LITE_ENSURE_STATUS(AllocateVariables(subgraph, eval_tensors)); - - model_is_allocating_ = false; - return kTfLiteOk; -} - -void* MicroAllocator::AllocatePersistentBuffer(size_t bytes) { - return memory_allocator_->AllocateFromTail(bytes, kBufferAlignment); -} - -TfLiteStatus MicroAllocator::RequestScratchBufferInArena(int node_id, - size_t bytes, - int* buffer_idx) { - // A consistency check to make sure scratch_buffer_handles_ is contiguous i.e. - // scratch_buffer_handles_ is pointing to the last allocation from memory - // allocator. - if (scratch_buffer_handles_ != nullptr && - reinterpret_cast(scratch_buffer_handles_) != - memory_allocator_->GetTail()) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Internal error: AllocateFromTail can not be called " - "between two RequestScratchBufferInArena calls."); - return kTfLiteError; - } - - internal::ScratchBufferHandle* handle = - reinterpret_cast( - memory_allocator_->AllocateFromTail( - sizeof(internal::ScratchBufferHandle), - alignof(internal::ScratchBufferHandle))); - if (handle == nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Failed to register scratch buffer handle for node %s", - node_id); - return kTfLiteError; - } - *handle = {}; - handle->bytes = bytes; - handle->node_idx = node_id; - *buffer_idx = scratch_buffer_count_; - scratch_buffer_count_ += 1; - // scratch_buffer_handles_ is in reverse order. The following code ensures - // that scratch_buffers[0] is pointing to the newly allocated handle. - scratch_buffer_handles_ = handle; - return kTfLiteOk; -} - -void* MicroAllocator::GetScratchBuffer(int buffer_idx) const { - if (static_cast(buffer_idx) >= scratch_buffer_count_) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Buffer %d not found. %d buffers available.", - buffer_idx, scratch_buffer_count_); - return nullptr; - } - // scratch_buffer_handles_ is in reverse order. - return scratch_buffer_handles_[scratch_buffer_count_ - buffer_idx - 1].data; -} - -size_t MicroAllocator::used_bytes() const { - return memory_allocator_->GetUsedBytes(); -} - -TfLiteStatus MicroAllocator::AllocateNodeAndRegistrations( - const Model* model, NodeAndRegistration** node_and_registrations) { - TFLITE_DCHECK(node_and_registrations); - - const SubGraph* subgraph = GetSubGraphFromModel(model); - TFLITE_DCHECK(subgraph != nullptr); - - NodeAndRegistration* output = reinterpret_cast( - memory_allocator_->AllocateFromTail( - sizeof(NodeAndRegistration) * subgraph->operators()->size(), - alignof(NodeAndRegistration))); - if (output == nullptr) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Failed to allocate memory for node_and_registrations."); - return kTfLiteError; - } - *node_and_registrations = output; - return kTfLiteOk; -} - -TfLiteStatus MicroAllocator::PrepareNodeAndRegistrationDataFromFlatbuffer( - const Model* model, const MicroOpResolver& op_resolver, - NodeAndRegistration* node_and_registrations) { - TFLITE_DCHECK(model != nullptr); - TFLITE_DCHECK(node_and_registrations != nullptr); - - const SubGraph* subgraph = GetSubGraphFromModel(model); - TFLITE_DCHECK(subgraph != nullptr); - - TfLiteStatus status = kTfLiteOk; - auto* opcodes = model->operator_codes(); - MicroBuiltinDataAllocator builtin_data_allocator(memory_allocator_); - for (size_t i = 0; i < subgraph->operators()->size(); ++i) { - const auto* op = subgraph->operators()->Get(i); - const size_t index = op->opcode_index(); - if (index >= opcodes->size()) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Missing registration for opcode_index %d\n", index); - return kTfLiteError; - } - auto* opcode = (*opcodes)[index]; - status = - GetRegistrationFromOpCode(opcode, op_resolver, error_reporter_, - &(node_and_registrations[i].registration)); - if (status != kTfLiteOk) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Failed to get registration from op code %s\n ", - EnumNameBuiltinOperator(opcode->builtin_code())); - return status; - } - const auto* registration = node_and_registrations[i].registration; - if (registration == nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, "Skipping op for opcode_index %d\n", - index); - return kTfLiteError; - } - BuiltinOperator op_type = - static_cast(registration->builtin_code); - - const char* custom_data = nullptr; - size_t custom_data_size = 0; - unsigned char* builtin_data = nullptr; - - if (op_type == BuiltinOperator_CUSTOM) { - // Custom Ops may or may not have a non-null custom_options field. - if (op->custom_options() != nullptr) { - custom_data = - reinterpret_cast(op->custom_options()->data()); - custom_data_size = op->custom_options()->size(); - } - } else { - if (op->custom_options() != nullptr) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Unsupported behavior: found builtin operator %s with custom " - "options.\n", - EnumNameBuiltinOperator(op_type)); - return kTfLiteError; - } - - MicroOpResolver::BuiltinParseFunction parser = - op_resolver.GetOpDataParser(op_type); - if (parser == nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, "Did not find a parser for %s", - EnumNameBuiltinOperator(op_type)); - - return kTfLiteError; - } - TF_LITE_ENSURE_STATUS(parser(op, error_reporter_, &builtin_data_allocator, - (void**)(&builtin_data))); - } - - TfLiteIntArray* inputs_array; - TF_LITE_ENSURE_STATUS(internal::FlatBufferVectorToTfLiteTypeArray( - memory_allocator_, error_reporter_, op->inputs(), &inputs_array)); - - TfLiteIntArray* outputs_array; - TF_LITE_ENSURE_STATUS(internal::FlatBufferVectorToTfLiteTypeArray( - memory_allocator_, error_reporter_, op->outputs(), &outputs_array)); - - TfLiteNode* node = &(node_and_registrations[i].node); - *node = {}; - node->inputs = inputs_array; - node->outputs = outputs_array; - node->builtin_data = reinterpret_cast(builtin_data); - node->custom_initial_data = custom_data; - node->custom_initial_data_size = custom_data_size; - } - - return kTfLiteOk; -} - -TfLiteTensor* MicroAllocator::AllocatePersistentTfLiteTensor( - const Model* model, TfLiteEvalTensor* eval_tensors, int tensor_index) { - const SubGraph* subgraph = GetSubGraphFromModel(model); - TFLITE_DCHECK(subgraph != nullptr); - - // This value is allocated from persistent arena space. It is guaranteed to be - // around for the lifetime of the application. - TfLiteTensor* tensor = - AllocatePersistentTfLiteTensorInternal(model, eval_tensors, tensor_index); - - // Populate any fields from the flatbuffer, since this TfLiteTensor struct is - // allocated in the persistent section of the arena, ensure that additional - // allocations also take place in that section of the arena. - if (PopulateTfLiteTensorFromFlatbuffer(model, subgraph, tensor, tensor_index, - /*allocate_temp=*/false) != - kTfLiteOk) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Failed to populate a persistent TfLiteTensor struct " - "from flatbuffer data!"); - return nullptr; - } - - if (eval_tensors != nullptr) { - // Tensor buffers that are allocated at runtime (e.g. non-weight buffers) - // and not located in the flatbuffer are stored on the pre-allocated list of - // TfLiteEvalTensors structs. These structs are the source of truth, simply - // point the corresponding buffer to the new TfLiteTensor data value. - tensor->data.data = eval_tensors[tensor_index].data.data; - } - return tensor; -} - -TfLiteTensor* MicroAllocator::AllocateTempTfLiteTensor( - const Model* model, TfLiteEvalTensor* eval_tensors, int tensor_index) { - const SubGraph* subgraph = GetSubGraphFromModel(model); - TFLITE_DCHECK(subgraph != nullptr); - - // This value is allocated from temporary arena space. It is guaranteed to be - // around for at least the scope of the calling function. Since this struct - // allocation takes place in temp space, no need to own or cleanup. - TfLiteTensor* tensor = - reinterpret_cast(memory_allocator_->AllocateTemp( - sizeof(TfLiteTensor), alignof(TfLiteTensor))); - - // Populate any fields from the flatbuffer, since this TfLiteTensor struct is - // allocated in the temp section of the arena, ensure that additional - // allocations also take place in that section of the arena. - if (PopulateTfLiteTensorFromFlatbuffer(model, subgraph, tensor, tensor_index, - /*allocate_temp=*/true) != kTfLiteOk) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Failed to populate a temp TfLiteTensor struct from flatbuffer data!"); - return nullptr; - } - - if (eval_tensors != nullptr) { - // Tensor buffers that are allocated at runtime (e.g. non-weight buffers) - // and not located in the flatbuffer are stored on the pre-allocated list of - // TfLiteEvalTensors structs. These structs are the source of truth, simply - // point the corresponding buffer to the new TfLiteTensor data value. - tensor->data.data = eval_tensors[tensor_index].data.data; - } - return tensor; -} - -void MicroAllocator::ResetTempAllocations() { - memory_allocator_->ResetTempAllocations(); -} - -TfLiteStatus MicroAllocator::AllocateTfLiteEvalTensors( - const Model* model, TfLiteEvalTensor** eval_tensors) { - TFLITE_DCHECK(eval_tensors != nullptr); - - const SubGraph* subgraph = GetSubGraphFromModel(model); - TFLITE_DCHECK(subgraph != nullptr); - - size_t alloc_count = subgraph->tensors()->size(); - TfLiteEvalTensor* tensors = - reinterpret_cast(memory_allocator_->AllocateFromTail( - sizeof(TfLiteEvalTensor) * alloc_count, alignof(TfLiteEvalTensor))); - if (tensors == nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Failed to allocate memory for context->eval_tensors, " - "%d bytes required", - sizeof(TfLiteEvalTensor) * alloc_count); - return kTfLiteError; - } - - for (size_t i = 0; i < alloc_count; ++i) { - TfLiteStatus status = internal::InitializeTfLiteEvalTensorFromFlatbuffer( - memory_allocator_, *subgraph->tensors()->Get(i), model->buffers(), - error_reporter_, &tensors[i]); - if (status != kTfLiteOk) { - TF_LITE_REPORT_ERROR(error_reporter_, "Failed to initialize tensor %d", - i); - return kTfLiteError; - } - } - *eval_tensors = tensors; - return kTfLiteOk; -} - -TfLiteStatus MicroAllocator::AllocateVariables(const SubGraph* subgraph, - TfLiteEvalTensor* eval_tensors) { - for (size_t i = 0; i < subgraph->tensors()->size(); ++i) { - auto* tensor = subgraph->tensors()->Get(i); - if (tensor->is_variable()) { - size_t buffer_size; - TF_LITE_ENSURE_STATUS( - TfLiteEvalTensorByteLength(&eval_tensors[i], &buffer_size)); - - eval_tensors[i].data.data = - memory_allocator_->AllocateFromTail(buffer_size, kBufferAlignment); - - if (eval_tensors[i].data.data == nullptr) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Failed to allocate variable tensor of size %d", - buffer_size); - return kTfLiteError; - } - } - } - return kTfLiteOk; -} - -TfLiteTensor* MicroAllocator::AllocatePersistentTfLiteTensorInternal( - const Model* model, TfLiteEvalTensor* eval_tensors, int tensor_index) { - return reinterpret_cast(memory_allocator_->AllocateFromTail( - sizeof(TfLiteTensor), alignof(TfLiteTensor))); -} - -TfLiteStatus MicroAllocator::PopulateTfLiteTensorFromFlatbuffer( - const Model* model, const SubGraph* subgraph, TfLiteTensor* tensor, - int tensor_index, bool allocate_temp) { - // TODO(b/160894903): This method serves as a stub to ensure quantized - // allocations in the tail can be recorded. Once all kernels have been ported - // to the new API this can be dropped. - return internal::InitializeTfLiteTensorFromFlatbuffer( - memory_allocator_, allocate_temp, *subgraph->tensors()->Get(tensor_index), - model->buffers(), error_reporter_, tensor); -} - -ErrorReporter* MicroAllocator::error_reporter() const { - return error_reporter_; -} - -const SubGraph* MicroAllocator::GetSubGraphFromModel(const Model* model) { - auto* subgraphs = model->subgraphs(); - if (subgraphs->size() != 1) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Only 1 subgraph is currently supported.\n"); - return nullptr; - } - return (*subgraphs)[0]; -} - -TfLiteStatus MicroAllocator::CommitStaticMemoryPlan( - const Model* model, const SubGraph* subgraph, - TfLiteEvalTensor* eval_tensors) { - size_t head_usage = 0; - // Create static memory plan - // 1. Calculate AllocationInfo to know the lifetime of each tensor/buffer. - // 2. Add them into the planner (such as the GreedyMemoryPlanner). - // 3. Static memory planning using the planner. - // 4. Set tensor/buffer pointers based on the offsets from the previous step. - // Note that AllocationInfo is only needed for creating the plan. It will be - // thrown away when the child allocator (tmp_allocator) goes out of scope. - { - // TODO(b/162595810): Use temp allocation buffer instead of a stack - // instance: - SimpleMemoryAllocator tmp_allocator(error_reporter_, - memory_allocator_->GetBufferHead(), - memory_allocator_->GetTail()); - - AllocationInfoBuilder builder(error_reporter_, &tmp_allocator); - TF_LITE_ENSURE_STATUS( - builder.Init(subgraph->tensors()->size(), scratch_buffer_count_)); - - const int32_t* offline_planner_offsets = nullptr; - TF_LITE_ENSURE_STATUS( - builder.GetOfflinePlannedOffsets(model, &offline_planner_offsets)); - TF_LITE_ENSURE_STATUS( - builder.AddTensors(subgraph, offline_planner_offsets, eval_tensors)); - - TF_LITE_ENSURE_STATUS(builder.AddScratchBuffers(scratch_buffer_handles_)); - const AllocationInfo* allocation_info = builder.Finish(); - - // Remaining arena size that memory planner can use for calculating offsets. - size_t remaining_arena_size = - tmp_allocator.GetAvailableMemory(kBufferAlignment); - uint8_t* planner_arena = - tmp_allocator.AllocateTemp(remaining_arena_size, kBufferAlignment); - TF_LITE_ENSURE(error_reporter_, planner_arena != nullptr); - GreedyMemoryPlanner planner(planner_arena, remaining_arena_size); - TF_LITE_ENSURE_STATUS( - CreatePlan(error_reporter_, &planner, allocation_info, builder.Size())); - - size_t actual_available_arena_size = - memory_allocator_->GetAvailableMemory(kBufferAlignment); - // Make sure we have enough arena size. - if (planner.GetMaximumMemorySize() > actual_available_arena_size) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Arena size is too small for activation buffers. Needed %d but only " - "%d was available.", - planner.GetMaximumMemorySize(), actual_available_arena_size); - return kTfLiteError; - } - - // Commit the plan. - TF_LITE_ENSURE_STATUS(CommitPlan(error_reporter_, &planner, - memory_allocator_->GetBufferHead(), - allocation_info, builder.Size())); - head_usage = planner.GetMaximumMemorySize(); - } - - TF_LITE_ENSURE_STATUS( - memory_allocator_->EnsureHeadSize(head_usage, kBufferAlignment)); - return kTfLiteOk; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/micro_error_reporter.cc b/components/tflite_micro/Source/tensorflow/lite/micro/micro_error_reporter.cc deleted file mode 100644 index 6d8361cd..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/micro_error_reporter.cc +++ /dev/null @@ -1,41 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/micro_error_reporter.h" - -#include - -#ifndef TF_LITE_STRIP_ERROR_STRINGS -#include "tensorflow/lite/micro/debug_log.h" -#include "tensorflow/lite/micro/micro_string.h" -#endif - -namespace tflite { - -int MicroErrorReporter::Report(const char* format, va_list args) { -#ifndef TF_LITE_STRIP_ERROR_STRINGS - // Only pulling in the implementation of this function for builds where we - // expect to make use of it to be extra cautious about not increasing the code - // size. - static constexpr int kMaxLogLen = 256; - char log_buffer[kMaxLogLen]; - MicroVsnprintf(log_buffer, kMaxLogLen, format, args); - DebugLog(log_buffer); - DebugLog("\r\n"); -#endif - return 0; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/micro_interpreter.cc b/components/tflite_micro/Source/tensorflow/lite/micro/micro_interpreter.cc deleted file mode 100644 index 8c2f8e03..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/micro_interpreter.cc +++ /dev/null @@ -1,447 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/micro/micro_interpreter.h" - -#include -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/api/tensor_utils.h" -#include "tensorflow/lite/micro/memory_helpers.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" -#include "tensorflow/lite/micro/micro_profiler.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { -namespace { - -#ifndef TF_LITE_STRIP_ERROR_STRINGS -const char* OpNameFromRegistration(const TfLiteRegistration* registration) { - if (registration->builtin_code == BuiltinOperator_CUSTOM) { - return registration->custom_name; - } else { - return EnumNameBuiltinOperator(BuiltinOperator(registration->builtin_code)); - } -} -#endif // !defined(TF_LITE_STRIP_ERROR_STRINGS) - -} // namespace - -namespace internal { - -ContextHelper::ContextHelper(ErrorReporter* error_reporter, - MicroAllocator* allocator, const Model* model) - : allocator_(allocator), error_reporter_(error_reporter), model_(model) {} - -void* ContextHelper::AllocatePersistentBuffer(TfLiteContext* ctx, - size_t bytes) { - return reinterpret_cast(ctx->impl_) - ->allocator_->AllocatePersistentBuffer(bytes); -} - -TfLiteStatus ContextHelper::RequestScratchBufferInArena(TfLiteContext* ctx, - size_t bytes, - int* buffer_idx) { - ContextHelper* helper = reinterpret_cast(ctx->impl_); - return helper->allocator_->RequestScratchBufferInArena( - helper->current_node_idx_, bytes, buffer_idx); -} - -void* ContextHelper::GetScratchBuffer(TfLiteContext* ctx, int buffer_idx) { - return reinterpret_cast(ctx->impl_) - ->allocator_->GetScratchBuffer(buffer_idx); -} - -void ContextHelper::ReportOpError(struct TfLiteContext* context, - const char* format, ...) { -#ifndef TF_LITE_STRIP_ERROR_STRINGS - ContextHelper* helper = static_cast(context->impl_); - va_list args; - va_start(args, format); - TF_LITE_REPORT_ERROR(helper->error_reporter_, format, args); - va_end(args); -#endif -} - -TfLiteTensor* ContextHelper::GetTensor(const struct TfLiteContext* context, - int tensor_idx) { - ContextHelper* helper = static_cast(context->impl_); - return helper->allocator_->AllocateTempTfLiteTensor( - helper->model_, helper->eval_tensors_, tensor_idx); -} - -TfLiteEvalTensor* ContextHelper::GetEvalTensor( - const struct TfLiteContext* context, int tensor_idx) { - ContextHelper* helper = reinterpret_cast(context->impl_); - return &helper->eval_tensors_[tensor_idx]; -} - -void ContextHelper::SetNodeIndex(int idx) { current_node_idx_ = idx; } - -void ContextHelper::SetTfLiteEvalTensors(TfLiteEvalTensor* eval_tensors) { - eval_tensors_ = eval_tensors; -} - -} // namespace internal - -MicroInterpreter::MicroInterpreter(const Model* model, - const MicroOpResolver& op_resolver, - uint8_t* tensor_arena, - size_t tensor_arena_size, - ErrorReporter* error_reporter, - tflite::Profiler* profiler) - : model_(model), - op_resolver_(op_resolver), - error_reporter_(error_reporter), - allocator_(*MicroAllocator::Create(tensor_arena, tensor_arena_size, - error_reporter)), - tensors_allocated_(false), - initialization_status_(kTfLiteError), - eval_tensors_(nullptr), - context_helper_(error_reporter_, &allocator_, model), - input_tensor_(nullptr), - output_tensor_(nullptr) { - Init(profiler); -} - -MicroInterpreter::MicroInterpreter(const Model* model, - const MicroOpResolver& op_resolver, - MicroAllocator* allocator, - ErrorReporter* error_reporter, - tflite::Profiler* profiler) - : model_(model), - op_resolver_(op_resolver), - error_reporter_(error_reporter), - allocator_(*allocator), - tensors_allocated_(false), - initialization_status_(kTfLiteError), - eval_tensors_(nullptr), - context_helper_(error_reporter_, &allocator_, model), - input_tensor_(nullptr), - output_tensor_(nullptr) { - Init(profiler); -} - -MicroInterpreter::~MicroInterpreter() { - if (node_and_registrations_ != nullptr) { - for (size_t i = 0; i < subgraph_->operators()->size(); ++i) { - TfLiteNode* node = &(node_and_registrations_[i].node); - const TfLiteRegistration* registration = - node_and_registrations_[i].registration; - // registration is allocated outside the interpreter, so double check to - // make sure it's not nullptr; - if (registration != nullptr && registration->free != nullptr) { - registration->free(&context_, node->user_data); - } - } - } -} - -void MicroInterpreter::Init(tflite::Profiler* profiler) { - const flatbuffers::Vector>* subgraphs = - model_->subgraphs(); - if (subgraphs->size() != 1) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Only 1 subgraph is currently supported.\n"); - initialization_status_ = kTfLiteError; - return; - } - subgraph_ = (*subgraphs)[0]; - - context_.impl_ = static_cast(&context_helper_); - context_.ReportError = context_helper_.ReportOpError; - context_.GetTensor = context_helper_.GetTensor; - context_.GetEvalTensor = context_helper_.GetEvalTensor; - context_.recommended_num_threads = 1; - context_.profiler = profiler; - - initialization_status_ = kTfLiteOk; -} - -void MicroInterpreter::CorrectTensorEndianness(TfLiteEvalTensor* tensorCorr) { - int32_t tensorSize = 1; - for (int d = 0; d < tensorCorr->dims->size; ++d) - tensorSize *= reinterpret_cast(tensorCorr->dims->data)[d]; - - switch (tensorCorr->type) { - case TfLiteType::kTfLiteFloat32: - CorrectTensorDataEndianness(tensorCorr->data.f, tensorSize); - break; - case TfLiteType::kTfLiteFloat16: - CorrectTensorDataEndianness(tensorCorr->data.f16, tensorSize); - break; - case TfLiteType::kTfLiteInt64: - CorrectTensorDataEndianness(tensorCorr->data.i64, tensorSize); - break; - case TfLiteType::kTfLiteInt32: - CorrectTensorDataEndianness(tensorCorr->data.i32, tensorSize); - break; - case TfLiteType::kTfLiteInt16: - CorrectTensorDataEndianness(tensorCorr->data.i16, tensorSize); - break; - case TfLiteType::kTfLiteComplex64: - CorrectTensorDataEndianness(tensorCorr->data.c64, tensorSize); - break; - case TfLiteType::kTfLiteComplex128: - CorrectTensorDataEndianness(tensorCorr->data.c128, tensorSize); - break; - default: - // Do nothing for other data types. - break; - } -} - -template -void MicroInterpreter::CorrectTensorDataEndianness(T* data, int32_t size) { - for (int32_t i = 0; i < size; ++i) { - data[i] = flatbuffers::EndianScalar(data[i]); - } -} - -TfLiteStatus MicroInterpreter::AllocateTensors() { - if (allocator_.StartModelAllocation(model_, op_resolver_, - &node_and_registrations_, - &eval_tensors_) != kTfLiteOk) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Failed starting model allocation.\n"); - initialization_status_ = kTfLiteError; - return kTfLiteError; - } - - // Update the pointer now that TfLiteEvalTensor allocation has completed on - // the context helper. - // TODO(b/16157777): This call would not be needed if ContextHelper rolled - // into the interpreter. - context_helper_.SetTfLiteEvalTensors(eval_tensors_); - - // If the system is big endian then convert weights from the flatbuffer from - // little to big endian on startup so that it does not need to be done during - // inference. - // NOTE: This requires that the flatbuffer is held in memory which can be - // modified by this process. - if (!FLATBUFFERS_LITTLEENDIAN) { - for (size_t t = 0; t < subgraph_->tensors()->size(); ++t) { - if (auto* buffer = - (*model_->buffers())[subgraph_->tensors()->Get(t)->buffer()]) { - // If we've found a buffer, does it have any data? - if (auto* array = buffer->data()) { - // If it has any data, is the data size larger than zero? - if (array->size()) { - // Update the endianness of the corresponding eval tensor since that - // struct holds the buffer used at inference time. - CorrectTensorEndianness(&eval_tensors_[t]); - } - } - } - } - } - - // Only allow AllocatePersistentBuffer in Init stage. - context_.AllocatePersistentBuffer = context_helper_.AllocatePersistentBuffer; - context_.RequestScratchBufferInArena = nullptr; - context_.GetScratchBuffer = nullptr; - - for (size_t i = 0; i < subgraph_->operators()->size(); ++i) { - context_helper_.SetNodeIndex(i); - auto* node = &(node_and_registrations_[i].node); - auto* registration = node_and_registrations_[i].registration; - size_t init_data_size; - const char* init_data; - if (registration->builtin_code == BuiltinOperator_CUSTOM) { - init_data = reinterpret_cast(node->custom_initial_data); - init_data_size = node->custom_initial_data_size; - } else { - init_data = reinterpret_cast(node->builtin_data); - init_data_size = 0; - } - if (registration->init) { - node->user_data = - registration->init(&context_, init_data, init_data_size); - } - } - context_helper_.SetNodeIndex(-1); - - // Both AllocatePersistentBuffer and RequestScratchBufferInArena is - // available in Prepare stage. - context_.RequestScratchBufferInArena = - context_helper_.RequestScratchBufferInArena; - for (size_t i = 0; i < subgraph_->operators()->size(); ++i) { - // Set node idx to annotate the lifetime for scratch buffers. - context_helper_.SetNodeIndex(i); - auto* node = &(node_and_registrations_[i].node); - auto* registration = node_and_registrations_[i].registration; - if (registration->prepare) { - TfLiteStatus prepare_status = registration->prepare(&context_, node); - if (prepare_status != kTfLiteOk) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Node %s (number %df) failed to prepare with status %d", - OpNameFromRegistration(registration), i, prepare_status); - return kTfLiteError; - } - } - allocator_.ResetTempAllocations(); - } - context_helper_.SetNodeIndex(-1); - - // Prepare is done, we're ready for Invoke. Memory allocation is no longer - // allowed. Kernels can only fetch scratch buffers via GetScratchBuffer. - context_.AllocatePersistentBuffer = nullptr; - context_.RequestScratchBufferInArena = nullptr; - context_.GetScratchBuffer = context_helper_.GetScratchBuffer; - - TF_LITE_ENSURE_OK(&context_, - allocator_.FinishModelAllocation(model_, eval_tensors_)); - TF_LITE_ENSURE_STATUS(ResetVariableTensors()); - - tensors_allocated_ = true; - return kTfLiteOk; -} - -TfLiteStatus MicroInterpreter::Invoke() { - if (initialization_status_ != kTfLiteOk) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Invoke() called after initialization failed\n"); - return kTfLiteError; - } - - // Ensure tensors are allocated before the interpreter is invoked to avoid - // difficult to debug segfaults. - if (!tensors_allocated_) { - TF_LITE_ENSURE_OK(&context_, AllocateTensors()); - } - - for (size_t i = 0; i < subgraph_->operators()->size(); ++i) { - auto* node = &(node_and_registrations_[i].node); - auto* registration = node_and_registrations_[i].registration; - - if (registration->invoke) { - TfLiteStatus invoke_status; -#ifndef NDEBUG // Omit profiler overhead from release builds. - // The case where profiler == nullptr is handled by - // ScopedOperatorProfile. - tflite::Profiler* profiler = - reinterpret_cast(context_.profiler); - ScopedOperatorProfile scoped_profiler( - profiler, OpNameFromRegistration(registration), i); -#endif - invoke_status = registration->invoke(&context_, node); - - // All TfLiteTensor structs used in the kernel are allocated from temp - // memory in the allocator. This creates a chain of allocations in the - // temp section. The call below resets the chain of allocations to - // prepare for the next call. - allocator_.ResetTempAllocations(); - - if (invoke_status == kTfLiteError) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Node %s (number %d) failed to invoke with status %d", - OpNameFromRegistration(registration), i, invoke_status); - return kTfLiteError; - } else if (invoke_status != kTfLiteOk) { - return invoke_status; - } - } - } - return kTfLiteOk; -} - -TfLiteTensor* MicroInterpreter::input(size_t index) { - const size_t length = inputs_size(); - if (index >= length) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Input index %d out of range (length is %d)", index, - length); - return nullptr; - } - if (index != 0) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Input tensors not at index 0 are allocated from the " - "persistent memory arena. Repeat calls will cause excess " - "allocation!"); - return allocator_.AllocatePersistentTfLiteTensor(model_, eval_tensors_, - inputs().Get(index)); - } - if (input_tensor_ == nullptr) { - input_tensor_ = allocator_.AllocatePersistentTfLiteTensor( - model_, eval_tensors_, inputs().Get(index)); - } - return input_tensor_; -} - -TfLiteTensor* MicroInterpreter::output(size_t index) { - const size_t length = outputs_size(); - if (index >= length) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Output index %d out of range (length is %d)", index, - length); - return nullptr; - } - if (index != 0) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Output tensors not at index 0 are allocated from the " - "persistent memory arena. Repeat calls will cause excess " - "allocation!"); - return allocator_.AllocatePersistentTfLiteTensor(model_, eval_tensors_, - outputs().Get(index)); - } - if (output_tensor_ == nullptr) { - // TODO(b/160894903): This API will allocate TfLiteTensor structs from - // persistent (tail) memory and cache on this pointer. - output_tensor_ = allocator_.AllocatePersistentTfLiteTensor( - model_, eval_tensors_, outputs().Get(index)); - } - return output_tensor_; -} - -TfLiteTensor* MicroInterpreter::tensor(size_t index) { - const size_t length = tensors_size(); - if (index >= length) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Tensor index %d out of range (length is %d)", index, - length); - return nullptr; - } - return allocator_.AllocatePersistentTfLiteTensor(model_, eval_tensors_, - index); -} - -TfLiteStatus MicroInterpreter::ResetVariableTensors() { - for (size_t i = 0; i < subgraph_->tensors()->size(); ++i) { - auto* tensor = subgraph_->tensors()->Get(i); - if (tensor->is_variable()) { - size_t buffer_size; - TF_LITE_ENSURE_STATUS( - TfLiteEvalTensorByteLength(&eval_tensors_[i], &buffer_size)); - - int value = 0; - if (tensor->type() == tflite::TensorType_INT8) { - value = tensor->quantization()->zero_point()->Get(0); - } - memset(eval_tensors_[i].data.raw, value, buffer_size); - } - } - - return kTfLiteOk; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/micro_optional_debug_tools.cc b/components/tflite_micro/Source/tensorflow/lite/micro/micro_optional_debug_tools.cc deleted file mode 100644 index a2eb067a..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/micro_optional_debug_tools.cc +++ /dev/null @@ -1,186 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/micro/micro_optional_debug_tools.h" - -// `cinttypes` requires `__STDC_FORMAT_MACROS` to be defined to expose `PRId32`. -#ifndef __STDC_FORMAT_MACROS -#define __STDC_FORMAT_MACROS -#endif - -#include -#include -#include -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/memory_helpers.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_interpreter.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { -namespace { - -std::vector flatbuffersVector2StdVector( - const flatbuffers::Vector& fVector) { - std::vector stdVector; - stdVector.reserve(fVector.size()); - for (size_t i = 0; i < fVector.size(); i++) { - stdVector.push_back(fVector.Get(i)); - } - return stdVector; -} - -void PrintIntVector(const std::vector& v) { - for (const auto& it : v) { - printf(" %d", it); - } - printf("\n"); -} - -void PrintTfLiteIntVector(const TfLiteIntArray* v) { - if (!v) { - printf(" (null)\n"); - return; - } - for (int k = 0; k < v->size; k++) { - printf(" %d", v->data[k]); - } - printf("\n"); -} - -const char* TensorTypeName(TfLiteType type) { - switch (type) { - case kTfLiteNoType: - return "kTfLiteNoType"; - case kTfLiteFloat32: - return "kTfLiteFloat32"; - case kTfLiteInt32: - return "kTfLiteInt32"; - case kTfLiteUInt8: - return "kTfLiteUInt8"; - case kTfLiteInt8: - return "kTfLiteInt8"; - case kTfLiteInt64: - return "kTfLiteInt64"; - case kTfLiteString: - return "kTfLiteString"; - case kTfLiteBool: - return "kTfLiteBool"; - case kTfLiteInt16: - return "kTfLiteInt16"; - case kTfLiteComplex64: - return "kTfLiteComplex64"; - case kTfLiteComplex128: - return "kTfLiteComplex128"; - case kTfLiteFloat16: - return "kTfLiteFloat16"; - case kTfLiteFloat64: - return "kTfLiteFloat64"; - } - return "(invalid)"; -} - -const char* AllocTypeName(TfLiteAllocationType type) { - switch (type) { - case kTfLiteMemNone: - return "kTfLiteMemNone"; - case kTfLiteMmapRo: - return "kTfLiteMmapRo"; - case kTfLiteDynamic: - return "kTfLiteDynamic"; - case kTfLiteArenaRw: - return "kTfLiteArenaRw"; - case kTfLiteArenaRwPersistent: - return "kTfLiteArenaRwPersistent"; - case kTfLitePersistentRo: - return "kTfLitePersistentRo"; - } - return "(invalid)"; -} -} // namespace - -// Helper function to print model flatbuffer data. This function is not called -// by default. Hence it's not linked in to the final binary code. -void PrintModelData(const Model* model, ErrorReporter* error_reporter) { -#ifndef TF_LITE_STRIP_ERROR_STRINGS - auto* subgraphs = model->subgraphs(); - const SubGraph* subgraph = (*subgraphs)[0]; - const flatbuffers::Vector>* tensors = - subgraph->tensors(); - const flatbuffers::Vector>* buffers = - model->buffers(); - TF_LITE_REPORT_ERROR(error_reporter, "==== Model info: ====="); - for (size_t i = 0; i < tensors->size(); ++i) { - const tflite::Tensor& flatbuffer_tensor = *tensors->Get(i); - size_t type_size, tensor_size; - auto* buffer = (*buffers)[flatbuffer_tensor.buffer()]; - auto* array = buffer->data(); - int array_size = 0; - if (array) { - array_size = array->size(); - } - BytesRequiredForTensor(flatbuffer_tensor, &tensor_size, &type_size, - error_reporter); - TF_LITE_REPORT_ERROR( - error_reporter, "Tensor index: %d arena tensor %d size %d ", i, - !array_size && !flatbuffer_tensor.is_variable(), tensor_size); - } -#endif -} - -// Prints a dump of what tensors and what nodes are in the interpreter. -void PrintInterpreterState(MicroInterpreter* interpreter) { - printf("Interpreter has %zu tensors and %zu nodes\n", - interpreter->tensors_size(), interpreter->operators_size()); - printf("Inputs:"); - PrintIntVector(flatbuffersVector2StdVector(interpreter->inputs())); - printf("Outputs:"); - PrintIntVector(flatbuffersVector2StdVector(interpreter->outputs())); - printf("\n"); - - for (size_t tensor_index = 0; tensor_index < interpreter->tensors_size(); - tensor_index++) { - TfLiteTensor* tensor = interpreter->tensor(static_cast(tensor_index)); - printf("Tensor %3zu %10s %15s %10zu bytes (%4.1f MB) ", tensor_index, - TensorTypeName(tensor->type), AllocTypeName(tensor->allocation_type), - tensor->bytes, static_cast(tensor->bytes / (1 << 20))); - PrintTfLiteIntVector(tensor->dims); - } - printf("\n"); - - for (size_t node_index = 0; node_index < interpreter->operators_size(); - node_index++) { - const NodeAndRegistration node_and_reg = - interpreter->node_and_registration(static_cast(node_index)); - const TfLiteNode& node = node_and_reg.node; - const TfLiteRegistration* reg = node_and_reg.registration; - if (reg->custom_name != nullptr) { - printf("Node %3zu Operator Custom Name %s\n", node_index, - reg->custom_name); - } else { - printf("Node %3zu Operator Builtin Code %3" PRId32 " %s\n", node_index, - reg->builtin_code, EnumNamesBuiltinOperator()[reg->builtin_code]); - } - printf(" Inputs:"); - PrintTfLiteIntVector(node.inputs); - printf(" Outputs:"); - PrintTfLiteIntVector(node.outputs); - } -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/micro_profiler.cc b/components/tflite_micro/Source/tensorflow/lite/micro/micro_profiler.cc deleted file mode 100644 index 83fb9f64..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/micro_profiler.cc +++ /dev/null @@ -1,42 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/micro_profiler.h" - -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/micro_time.h" - -namespace tflite { - -MicroProfiler::MicroProfiler(tflite::ErrorReporter* reporter) - : reporter_(reporter) {} - -uint32_t MicroProfiler::BeginEvent(const char* tag, EventType event_type, - int64_t event_metadata1, - int64_t event_metadata2) { - start_time_ = GetCurrentTimeTicks(); - TFLITE_DCHECK(tag != nullptr); - event_tag_ = tag; - return 0; -} - -void MicroProfiler::EndEvent(uint32_t event_handle) { -#ifndef TF_LITE_STRIP_ERROR_STRINGS - int32_t end_time = GetCurrentTimeTicks(); - TF_LITE_REPORT_ERROR(reporter_, "%s took %d cycles\n", event_tag_, - end_time - start_time_); -#endif -} -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/micro_string.cc b/components/tflite_micro/Source/tensorflow/lite/micro/micro_string.cc deleted file mode 100644 index 6d6495ed..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/micro_string.cc +++ /dev/null @@ -1,305 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Implements debug logging for numbers by converting them into strings and then -// calling the main DebugLog(char*) function. These are separated into a -// different file so that platforms can just implement the string output version -// of DebugLog() and then get the numerical variations without requiring any -// more code. - -#include "tensorflow/lite/micro/micro_string.h" - -#include -#include - -namespace { - -// Int formats can need up to 10 bytes for the value plus a single byte for the -// sign. -constexpr int kMaxIntCharsNeeded = 10 + 1; -// Hex formats can need up to 8 bytes for the value plus two bytes for the "0x". -constexpr int kMaxHexCharsNeeded = 8 + 2; - -// Float formats can need up to 7 bytes for the fraction plus 3 bytes for "x2^" -// plus 3 bytes for the exponent and a single sign bit. -constexpr float kMaxFloatCharsNeeded = 7 + 3 + 3 + 1; - -// All input buffers to the number conversion functions must be this long. -const int kFastToBufferSize = 48; - -// Reverses a zero-terminated string in-place. -char* ReverseStringInPlace(char* start, char* end) { - char* p1 = start; - char* p2 = end - 1; - while (p1 < p2) { - char tmp = *p1; - *p1++ = *p2; - *p2-- = tmp; - } - return start; -} - -// Appends a string to a string, in-place. You need to pass in the maximum -// string length as the second argument. -char* StrCatStr(char* main, int main_max_length, const char* to_append) { - char* current = main; - while (*current != 0) { - ++current; - } - char* current_end = main + (main_max_length - 1); - while ((*to_append != 0) && (current < current_end)) { - *current = *to_append; - ++current; - ++to_append; - } - *current = 0; - return current; -} - -// Populates the provided buffer with an ASCII representation of the number. -char* FastUInt32ToBufferLeft(uint32_t i, char* buffer, int base) { - char* start = buffer; - do { - int32_t digit = i % base; - char character; - if (digit < 10) { - character = '0' + digit; - } else { - character = 'a' + (digit - 10); - } - *buffer++ = character; - i /= base; - } while (i > 0); - *buffer = 0; - ReverseStringInPlace(start, buffer); - return buffer; -} - -// Populates the provided buffer with an ASCII representation of the number. -char* FastInt32ToBufferLeft(int32_t i, char* buffer) { - uint32_t u = i; - if (i < 0) { - *buffer++ = '-'; - u = -u; - } - return FastUInt32ToBufferLeft(u, buffer, 10); -} - -// Converts a number to a string and appends it to another. -char* StrCatInt32(char* main, int main_max_length, int32_t number) { - char number_string[kFastToBufferSize]; - FastInt32ToBufferLeft(number, number_string); - return StrCatStr(main, main_max_length, number_string); -} - -// Converts a number to a string and appends it to another. -char* StrCatUInt32(char* main, int main_max_length, uint32_t number, int base) { - char number_string[kFastToBufferSize]; - FastUInt32ToBufferLeft(number, number_string, base); - return StrCatStr(main, main_max_length, number_string); -} - -// Populates the provided buffer with ASCII representation of the float number. -// Avoids the use of any floating point instructions (since these aren't -// supported on many microcontrollers) and as a consequence prints values with -// power-of-two exponents. -char* FastFloatToBufferLeft(float f, char* buffer) { - char* current = buffer; - char* current_end = buffer + (kFastToBufferSize - 1); - // Access the bit fields of the floating point value to avoid requiring any - // float instructions. These constants are derived from IEEE 754. - const uint32_t sign_mask = 0x80000000; - const uint32_t exponent_mask = 0x7f800000; - const int32_t exponent_shift = 23; - const int32_t exponent_bias = 127; - const uint32_t fraction_mask = 0x007fffff; - const uint32_t u = *reinterpret_cast(&f); - const int32_t exponent = - ((u & exponent_mask) >> exponent_shift) - exponent_bias; - const uint32_t fraction = (u & fraction_mask); - // Expect ~0x2B1B9D3 for fraction. - if (u & sign_mask) { - *current = '-'; - current += 1; - } - *current = 0; - // These are special cases for infinities and not-a-numbers. - if (exponent == 128) { - if (fraction == 0) { - current = StrCatStr(current, (current_end - current), "Inf"); - return current; - } else { - current = StrCatStr(current, (current_end - current), "NaN"); - return current; - } - } - // 0x007fffff (8388607) represents 0.99... for the fraction, so to print the - // correct decimal digits we need to scale our value before passing it to the - // conversion function. This scale should be 10000000/8388608 = 1.1920928955. - // We can approximate this using multiply-adds and right-shifts using the - // values in this array. The 1. portion of the number string is printed out - // in a fixed way before the fraction, below. - const int32_t scale_shifts_size = 13; - const int8_t scale_shifts[13] = {3, 4, 8, 11, 13, 14, 17, - 18, 19, 20, 21, 22, 23}; - uint32_t scaled_fraction = fraction; - for (int i = 0; i < scale_shifts_size; ++i) { - scaled_fraction += (fraction >> scale_shifts[i]); - } - *current = '1'; - current += 1; - *current = '.'; - current += 1; - *current = 0; - - // Prepend leading zeros to fill in all 7 bytes of the fraction. Truncate - // zeros off the end of the fraction. Every fractional value takes 7 bytes. - // For example, 2500 would be written into the buffer as 0002500 since it - // represents .00025. - constexpr int kMaxFractionalDigits = 7; - - // Abort early if there is not enough space in the buffer. - if (current_end - current <= kMaxFractionalDigits) { - return current; - } - - // Pre-fill buffer with zeros to ensure zero-truncation works properly. - for (int i = 1; i < kMaxFractionalDigits; i++) { - *(current + i) = '0'; - } - - // Track how large the fraction is to add leading zeros. - char* previous = current; - current = StrCatUInt32(current, (current_end - current), scaled_fraction, 10); - int fraction_digits = current - previous; - int leading_zeros = kMaxFractionalDigits - fraction_digits; - - // Overwrite the null terminator from StrCatUInt32 to ensure zero-trunctaion - // works properly. - *current = '0'; - - // Shift fraction values and prepent zeros. - for (int i = 0; i < fraction_digits; i++) { - current--; - *(current + leading_zeros) = *current; - *current = '0'; - } - current += kMaxFractionalDigits; - - // Truncate trailing zeros for cleaner logs. Ensure we leave at least one - // fractional character for the case when scaled_fraction is 0. - while (*(current - 1) == '0' && (current - 1) > previous) { - current--; - } - *current = 0; - current = StrCatStr(current, (current_end - current), "*2^"); - current = StrCatInt32(current, (current_end - current), exponent); - return current; -} - -int FormatInt32(char* output, int32_t i) { - return static_cast(FastInt32ToBufferLeft(i, output) - output); -} - -int FormatUInt32(char* output, uint32_t i) { - return static_cast(FastUInt32ToBufferLeft(i, output, 10) - output); -} - -int FormatHex(char* output, uint32_t i) { - return static_cast(FastUInt32ToBufferLeft(i, output, 16) - output); -} - -int FormatFloat(char* output, float i) { - return static_cast(FastFloatToBufferLeft(i, output) - output); -} - -} // namespace - -extern "C" int MicroVsnprintf(char* output, int len, const char* format, - va_list args) { - int output_index = 0; - const char* current = format; - // One extra character must be left for the null terminator. - const int usable_length = len - 1; - while (*current != '\0' && output_index < usable_length) { - if (*current == '%') { - current++; - switch (*current) { - case 'd': - // Cut off log message if format could exceed log buffer length. - if (usable_length - output_index < kMaxIntCharsNeeded) { - output[output_index++] = '\0'; - return output_index; - } - output_index += - FormatInt32(&output[output_index], va_arg(args, int32_t)); - current++; - break; - case 'u': - if (usable_length - output_index < kMaxIntCharsNeeded) { - output[output_index++] = '\0'; - return output_index; - } - output_index += - FormatUInt32(&output[output_index], va_arg(args, uint32_t)); - current++; - break; - case 'x': - if (usable_length - output_index < kMaxHexCharsNeeded) { - output[output_index++] = '\0'; - return output_index; - } - output[output_index++] = '0'; - output[output_index++] = 'x'; - output_index += - FormatHex(&output[output_index], va_arg(args, uint32_t)); - current++; - break; - case 'f': - if (usable_length - output_index < kMaxFloatCharsNeeded) { - output[output_index++] = '\0'; - return output_index; - } - output_index += - FormatFloat(&output[output_index], va_arg(args, double)); - current++; - break; - case '%': - output[output_index++] = *current++; - break; - case 's': - char* string = va_arg(args, char*); - int string_idx = 0; - while (string_idx + output_index < usable_length && - string[string_idx] != '\0') { - output[output_index++] = string[string_idx++]; - } - current++; - } - } else { - output[output_index++] = *current++; - } - } - output[output_index++] = '\0'; - return output_index; -} - -extern "C" int MicroSnprintf(char* output, int len, const char* format, ...) { - va_list args; - va_start(args, format); - int bytes_written = MicroVsnprintf(output, len, format, args); - va_end(args); - return bytes_written; -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/micro_time.cc b/components/tflite_micro/Source/tensorflow/lite/micro/micro_time.cc deleted file mode 100644 index 09119de8..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/micro_time.cc +++ /dev/null @@ -1,44 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Reference implementation of timer functions. Platforms are not required to -// implement these timer methods, but they are required to enable profiling. - -// On platforms that have a POSIX stack or C library, it can be written using -// methods from or clock() from . - -// To add an equivalent function for your own platform, create your own -// implementation file, and place it in a subfolder with named after the OS -// you're targeting. For example, see the Cortex M bare metal version in -// tensorflow/lite/micro/bluepill/micro_time.cc or the mbed one on -// tensorflow/lite/micro/mbed/micro_time.cc. - -#include "tensorflow/lite/micro/micro_time.h" - -namespace tflite { - -// Reference implementation of the ticks_per_second() function that's required -// for a platform to support Tensorflow Lite for Microcontrollers profiling. -// This returns 0 by default because timing is an optional feature that builds -// without errors on platforms that do not need it. -int32_t ticks_per_second() { return 0; } - -// Reference implementation of the GetCurrentTimeTicks() function that's -// required for a platform to support Tensorflow Lite for Microcontrollers -// profiling. This returns 0 by default because timing is an optional feature -// that builds without errors on platforms that do not need it. -int32_t GetCurrentTimeTicks() { return 0; } - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/micro_utils.cc b/components/tflite_micro/Source/tensorflow/lite/micro/micro_utils.cc deleted file mode 100644 index ff885fa0..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/micro_utils.cc +++ /dev/null @@ -1,279 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/micro_utils.h" - -#include -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/op_macros.h" - -namespace tflite { - -namespace { - -static const uint8_t kAsymmetricUInt8Min = 0; -static const uint8_t kAsymmetricUInt8Max = UINT8_MAX; -static const uint8_t kSymmetricUInt8Min = 1; -static const uint8_t kSymmetricUInt8Max = UINT8_MAX; -static const int8_t kAsymmetricInt8Min = INT8_MIN; -static const int8_t kAsymmetricInt8Max = INT8_MAX; -static const int kSymmetricInt8Scale = kAsymmetricInt8Max; - -static const int16_t kAsymmetricInt16Min = INT16_MIN; -static const int16_t kAsymmetricInt16Max = INT16_MAX; -static const int kSymmetricInt16Scale = kAsymmetricInt16Max; - -static const int32_t kAsymmetricInt32Max = INT32_MAX; -static const int kSymmetricInt32Scale = kAsymmetricInt32Max; - -} // namespace - -int ElementCount(const TfLiteIntArray& dims) { - int result = 1; - for (int i = 0; i < dims.size; ++i) { - result *= dims.data[i]; - } - return result; -} - -// Converts a float value into an unsigned eight-bit quantized value. -uint8_t FloatToAsymmetricQuantizedUInt8(const float value, const float scale, - const int zero_point) { - int32_t result = round(value / scale) + zero_point; - if (result < kAsymmetricUInt8Min) { - result = kAsymmetricUInt8Min; - } - if (result > kAsymmetricUInt8Max) { - result = kAsymmetricUInt8Max; - } - return result; -} - -uint8_t FloatToSymmetricQuantizedUInt8(const float value, const float scale) { - int32_t result = round(value / scale); - if (result < kSymmetricUInt8Min) { - result = kSymmetricUInt8Min; - } - if (result > kSymmetricUInt8Max) { - result = kSymmetricUInt8Max; - } - return result; -} - -int8_t FloatToAsymmetricQuantizedInt8(const float value, const float scale, - const int zero_point) { - int32_t result = round(value / scale) + zero_point; - if (result < kAsymmetricInt8Min) { - result = kAsymmetricInt8Min; - } - if (result > kAsymmetricInt8Max) { - result = kAsymmetricInt8Max; - } - return result; -} - -int16_t FloatToAsymmetricQuantizedInt16(const float value, const float scale, - const int zero_point) { - int32_t result = round(value / scale) + zero_point; - if (result < kAsymmetricInt16Min) { - result = kAsymmetricInt16Min; - } - if (result > kAsymmetricInt16Max) { - result = kAsymmetricInt16Max; - } - return result; -} - -int8_t FloatToSymmetricQuantizedInt8(const float value, const float scale) { - return FloatToAsymmetricQuantizedInt8(value, scale, 0.0f); -} - -int32_t FloatToSymmetricQuantizedInt32(const float value, const float scale) { - float quantized = round(value / scale); - if (static_cast(quantized) > INT_MAX) { - quantized = static_cast(INT_MAX); - } else if (quantized < INT_MIN) { - quantized = static_cast INT_MIN; - } - - return static_cast(quantized); -} - -void AsymmetricQuantize(const float* input, int8_t* output, int num_elements, - float scale, int zero_point) { - for (int i = 0; i < num_elements; i++) { - output[i] = FloatToAsymmetricQuantizedInt8(input[i], scale, zero_point); - } -} - -void AsymmetricQuantize(const float* input, uint8_t* output, int num_elements, - float scale, int zero_point) { - for (int i = 0; i < num_elements; i++) { - output[i] = FloatToAsymmetricQuantizedUInt8(input[i], scale, zero_point); - } -} - -void AsymmetricQuantize(const float* input, int16_t* output, int num_elements, - float scale, int zero_point) { - for (int i = 0; i < num_elements; i++) { - output[i] = FloatToAsymmetricQuantizedInt16(input[i], scale, zero_point); - } -} - -void SymmetricQuantize(const float* input, int32_t* output, int num_elements, - float scale) { - for (int i = 0; i < num_elements; i++) { - output[i] = FloatToSymmetricQuantizedInt32(input[i], scale); - } -} - -void SymmetricPerChannelQuantize(const float* input, int32_t* output, - int num_elements, int num_channels, - float* scales) { - int elements_per_channel = num_elements / num_channels; - for (int i = 0; i < num_channels; i++) { - for (int j = 0; j < elements_per_channel; j++) { - output[i * elements_per_channel + j] = FloatToSymmetricQuantizedInt32( - input[i * elements_per_channel + j], scales[i]); - } - } -} - -void SignedSymmetricPerChannelQuantize(const float* values, - TfLiteIntArray* dims, - int quantized_dimension, - int8_t* quantized_values, - float* scaling_factors) { - int input_size = ElementCount(*dims); - int channel_count = dims->data[quantized_dimension]; - int per_channel_size = input_size / channel_count; - - int stride; - int channel_stride; - if (quantized_dimension == 0) { - stride = 1; - channel_stride = per_channel_size; - } else if (quantized_dimension == 3) { - stride = channel_count; - channel_stride = 1; - } else { - TF_LITE_FATAL("quantized dimension must be 0 or 3"); - } - - // Calculate scales for each channel. - for (int channel = 0; channel < channel_count; channel++) { - float min = 0; - float max = 0; - - for (int i = 0; i < per_channel_size; i++) { - int idx = channel * channel_stride + i * stride; - min = fminf(min, values[idx]); - max = fmaxf(max, values[idx]); - } - scaling_factors[channel] = - fmaxf(fabs(min), fabs(max)) / kSymmetricInt8Scale; - for (int i = 0; i < per_channel_size; i++) { - int idx = channel * channel_stride + i * stride; - const int32_t quantized_value = - static_cast(roundf(values[idx] / scaling_factors[channel])); - // Clamp: just in case some odd numeric offset. - quantized_values[idx] = fminf( - kSymmetricInt8Scale, fmaxf(-kSymmetricInt8Scale, quantized_value)); - } - } -} - -void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims, - int8_t* quantized_values, float* scaling_factor) { - int input_size = ElementCount(*dims); - - float min = 0; - float max = 0; - for (int i = 0; i < input_size; i++) { - min = fminf(min, values[i]); - max = fmaxf(max, values[i]); - } - *scaling_factor = fmaxf(fabs(min), fabs(max)) / kSymmetricInt8Scale; - for (int i = 0; i < input_size; i++) { - const int32_t quantized_value = - static_cast(roundf(values[i] / *scaling_factor)); - // Clamp: just in case some odd numeric offset. - quantized_values[i] = fminf(kSymmetricInt8Scale, - fmaxf(-kSymmetricInt8Scale, quantized_value)); - } -} - -void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims, - int16_t* quantized_values, float* scaling_factor) { - int input_size = ElementCount(*dims); - - float min = 0; - float max = 0; - for (int i = 0; i < input_size; i++) { - min = fminf(min, values[i]); - max = fmaxf(max, values[i]); - } - *scaling_factor = fmaxf(fabs(min), fabs(max)) / kSymmetricInt16Scale; - for (int i = 0; i < input_size; i++) { - const int32_t quantized_value = - static_cast(roundf(values[i] / *scaling_factor)); - // Clamp: just in case some odd numeric offset. - quantized_values[i] = fminf(kSymmetricInt16Scale, - fmaxf(-kSymmetricInt16Scale, quantized_value)); - } -} - -void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims, - int32_t* quantized_values, float* scaling_factor) { - int input_size = ElementCount(*dims); - - float min = 0; - float max = 0; - for (int i = 0; i < input_size; i++) { - min = fminf(min, values[i]); - max = fmaxf(max, values[i]); - } - - *scaling_factor = - fmaxf(fabs(min), fabs(max)) / static_cast(kSymmetricInt32Scale); - for (int i = 0; i < input_size; i++) { - const int32_t quantized_value = - static_cast(roundf(values[i] / *scaling_factor)); - // Clamp: just in case some odd numeric offset. - quantized_values[i] = fminf( - static_cast(kSymmetricInt32Scale), - fmaxf(static_cast(-kSymmetricInt32Scale), quantized_value)); - } -} - -void SymmetricQuantize(const float* values, TfLiteIntArray* dims, - uint8_t* quantized_values, float* scaling_factor) { - SignedSymmetricQuantize(values, dims, - reinterpret_cast(quantized_values), - scaling_factor); -} - -void SymmetricDequantize(const int8_t* values, const int size, - const float dequantization_scale, - float* dequantized_values) { - for (int i = 0; i < size; ++i) { - dequantized_values[i] = values[i] * dequantization_scale; - } -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/recording_micro_allocator.cc b/components/tflite_micro/Source/tensorflow/lite/micro/recording_micro_allocator.cc deleted file mode 100644 index 7e11523f..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/recording_micro_allocator.cc +++ /dev/null @@ -1,230 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/recording_micro_allocator.h" - -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/recording_simple_memory_allocator.h" - -namespace tflite { - -RecordingMicroAllocator::RecordingMicroAllocator( - RecordingSimpleMemoryAllocator* recording_memory_allocator, - ErrorReporter* error_reporter) - : MicroAllocator(recording_memory_allocator, error_reporter), - recording_memory_allocator_(recording_memory_allocator) {} - -RecordingMicroAllocator* RecordingMicroAllocator::Create( - uint8_t* tensor_arena, size_t arena_size, ErrorReporter* error_reporter) { - TFLITE_DCHECK(error_reporter != nullptr); - - RecordingSimpleMemoryAllocator* simple_memory_allocator = - RecordingSimpleMemoryAllocator::Create(error_reporter, tensor_arena, - arena_size); - TFLITE_DCHECK(simple_memory_allocator != nullptr); - - uint8_t* allocator_buffer = simple_memory_allocator->AllocateFromTail( - sizeof(RecordingMicroAllocator), alignof(RecordingMicroAllocator)); - RecordingMicroAllocator* allocator = new (allocator_buffer) - RecordingMicroAllocator(simple_memory_allocator, error_reporter); - return allocator; -} - -RecordedAllocation RecordingMicroAllocator::GetRecordedAllocation( - RecordedAllocationType allocation_type) const { - switch (allocation_type) { - case RecordedAllocationType::kTfLiteEvalTensorData: - return recorded_tflite_eval_tensor_data_; - case RecordedAllocationType::kPersistentTfLiteTensorData: - return recorded_persistent_tflite_tensor_data_; - case RecordedAllocationType::kPersistentTfLiteTensorQuantizationData: - return recorded_persistent_tflite_tensor_quantization_data_; - case RecordedAllocationType::kTfLiteTensorVariableBufferData: - return recorded_tflite_tensor_variable_buffer_data_; - case RecordedAllocationType::kNodeAndRegistrationArray: - return recorded_node_and_registration_array_data_; - case RecordedAllocationType::kOpData: - return recorded_op_data_; - } - TF_LITE_REPORT_ERROR(error_reporter(), "Invalid allocation type supplied: %d", - allocation_type); - return RecordedAllocation(); -} - -const RecordingSimpleMemoryAllocator* -RecordingMicroAllocator::GetSimpleMemoryAllocator() const { - return recording_memory_allocator_; -} - -void RecordingMicroAllocator::PrintAllocations() const { - TF_LITE_REPORT_ERROR( - error_reporter(), - "[RecordingMicroAllocator] Arena allocation total %d bytes", - recording_memory_allocator_->GetUsedBytes()); - TF_LITE_REPORT_ERROR( - error_reporter(), - "[RecordingMicroAllocator] Arena allocation head %d bytes", - recording_memory_allocator_->GetHeadUsedBytes()); - TF_LITE_REPORT_ERROR( - error_reporter(), - "[RecordingMicroAllocator] Arena allocation tail %d bytes", - recording_memory_allocator_->GetTailUsedBytes()); - PrintRecordedAllocation(RecordedAllocationType::kTfLiteEvalTensorData, - "TfLiteEvalTensor data", "allocations"); - PrintRecordedAllocation(RecordedAllocationType::kPersistentTfLiteTensorData, - "Persistent TfLiteTensor data", "tensors"); - PrintRecordedAllocation( - RecordedAllocationType::kPersistentTfLiteTensorQuantizationData, - "Persistent TfLiteTensor quantization data", "allocations"); - PrintRecordedAllocation( - RecordedAllocationType::kTfLiteTensorVariableBufferData, - "TfLiteTensor variable buffer data", "allocations"); - PrintRecordedAllocation(RecordedAllocationType::kNodeAndRegistrationArray, - "NodeAndRegistration struct", - "NodeAndRegistration structs"); - PrintRecordedAllocation(RecordedAllocationType::kOpData, - "Operator runtime data", "OpData structs"); -} - -void RecordingMicroAllocator::PrintRecordedAllocation( - RecordedAllocationType allocation_type, const char* allocation_name, - const char* allocation_description) const { -#ifndef TF_LITE_STRIP_ERROR_STRINGS - RecordedAllocation allocation = GetRecordedAllocation(allocation_type); - TF_LITE_REPORT_ERROR( - error_reporter(), - "[RecordingMicroAllocator] '%s' used %d bytes with alignment overhead " - "(requested %d bytes for %d %s)", - allocation_name, allocation.used_bytes, allocation.requested_bytes, - allocation.count, allocation_description); -#endif -} - -TfLiteStatus RecordingMicroAllocator::AllocateNodeAndRegistrations( - const Model* model, NodeAndRegistration** node_and_registrations) { - RecordedAllocation allocations = SnapshotAllocationUsage(); - - TfLiteStatus status = MicroAllocator::AllocateNodeAndRegistrations( - model, node_and_registrations); - - RecordAllocationUsage(allocations, - recorded_node_and_registration_array_data_); - // The allocation count in SimpleMemoryAllocator will only be 1. To provide - // better logging, decrement by 1 and add in the actual number of operators - // used in the graph: - // The allocation for this recording will always be 1. This is because the - // parent class mallocs one large allocation for the number of nodes in the - // graph (e.g. sizeof(NodeAndRegistration) * num_nodes). - // To prevent extra overhead and potential for fragmentation, manually adjust - // the accounting by decrementing by 1 and adding the actual number of nodes - // used in the graph: - recorded_node_and_registration_array_data_.count += - GetSubGraphFromModel(model)->operators()->size() - 1; - return status; -} - -TfLiteStatus -RecordingMicroAllocator::PrepareNodeAndRegistrationDataFromFlatbuffer( - const Model* model, const MicroOpResolver& op_resolver, - NodeAndRegistration* node_and_registrations) { - RecordedAllocation allocations = SnapshotAllocationUsage(); - - TfLiteStatus status = - MicroAllocator::PrepareNodeAndRegistrationDataFromFlatbuffer( - model, op_resolver, node_and_registrations); - - RecordAllocationUsage(allocations, recorded_op_data_); - return status; -} - -TfLiteStatus RecordingMicroAllocator::AllocateTfLiteEvalTensors( - const Model* model, TfLiteEvalTensor** eval_tensors) { - RecordedAllocation allocations = SnapshotAllocationUsage(); - - TfLiteStatus status = - MicroAllocator::AllocateTfLiteEvalTensors(model, eval_tensors); - - RecordAllocationUsage(allocations, recorded_tflite_eval_tensor_data_); - // The allocation for this recording will always be 1. This is because the - // parent class mallocs one large allocation for the number of tensors in the - // graph (e.g. sizeof(TfLiteEvalTensor) * num_tensors). - // To prevent extra overhead and potential for fragmentation, manually adjust - // the accounting by decrementing by 1 and adding the actual number of tensors - // used in the graph: - recorded_tflite_eval_tensor_data_.count += - GetSubGraphFromModel(model)->tensors()->size() - 1; - return status; -} - -TfLiteStatus RecordingMicroAllocator::AllocateVariables( - const SubGraph* subgraph, TfLiteEvalTensor* eval_tensors) { - RecordedAllocation allocations = SnapshotAllocationUsage(); - - TfLiteStatus status = - MicroAllocator::AllocateVariables(subgraph, eval_tensors); - - RecordAllocationUsage(allocations, - recorded_tflite_tensor_variable_buffer_data_); - return status; -} - -TfLiteTensor* RecordingMicroAllocator::AllocatePersistentTfLiteTensorInternal( - const Model* model, TfLiteEvalTensor* eval_tensors, int tensor_index) { - RecordedAllocation allocations = SnapshotAllocationUsage(); - - TfLiteTensor* result = MicroAllocator::AllocatePersistentTfLiteTensorInternal( - model, eval_tensors, tensor_index); - - RecordAllocationUsage(allocations, recorded_persistent_tflite_tensor_data_); - return result; -} - -TfLiteStatus RecordingMicroAllocator::PopulateTfLiteTensorFromFlatbuffer( - const Model* model, const SubGraph* subgraph, TfLiteTensor* tensor, - int tensor_index, bool allocate_temp) { - RecordedAllocation allocations = SnapshotAllocationUsage(); - - TfLiteStatus status = MicroAllocator::PopulateTfLiteTensorFromFlatbuffer( - model, subgraph, tensor, tensor_index, allocate_temp); - - RecordAllocationUsage(allocations, - recorded_persistent_tflite_tensor_quantization_data_); - return status; -} - -RecordedAllocation RecordingMicroAllocator::SnapshotAllocationUsage() const { - return {/*requested_bytes=*/recording_memory_allocator_->GetRequestedBytes(), - /*used_bytes=*/recording_memory_allocator_->GetUsedBytes(), - /*count=*/recording_memory_allocator_->GetAllocatedCount()}; -} - -void RecordingMicroAllocator::RecordAllocationUsage( - const RecordedAllocation& snapshotted_allocation, - RecordedAllocation& recorded_allocation) { - recorded_allocation.requested_bytes += - recording_memory_allocator_->GetRequestedBytes() - - snapshotted_allocation.requested_bytes; - recorded_allocation.used_bytes += - recording_memory_allocator_->GetUsedBytes() - - snapshotted_allocation.used_bytes; - recorded_allocation.count += - recording_memory_allocator_->GetAllocatedCount() - - snapshotted_allocation.count; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/recording_simple_memory_allocator.cc b/components/tflite_micro/Source/tensorflow/lite/micro/recording_simple_memory_allocator.cc deleted file mode 100644 index ef2e9f31..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/recording_simple_memory_allocator.cc +++ /dev/null @@ -1,83 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/recording_simple_memory_allocator.h" - -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" - -namespace tflite { - -RecordingSimpleMemoryAllocator::RecordingSimpleMemoryAllocator( - ErrorReporter* error_reporter, uint8_t* buffer_head, size_t buffer_size) - : SimpleMemoryAllocator(error_reporter, buffer_head, buffer_size), - requested_head_bytes_(0), - requested_tail_bytes_(0), - used_bytes_(0), - alloc_count_(0) {} - -RecordingSimpleMemoryAllocator::~RecordingSimpleMemoryAllocator() {} - -RecordingSimpleMemoryAllocator* RecordingSimpleMemoryAllocator::Create( - ErrorReporter* error_reporter, uint8_t* buffer_head, size_t buffer_size) { - TFLITE_DCHECK(error_reporter != nullptr); - TFLITE_DCHECK(buffer_head != nullptr); - RecordingSimpleMemoryAllocator tmp = - RecordingSimpleMemoryAllocator(error_reporter, buffer_head, buffer_size); - - uint8_t* allocator_buffer = - tmp.AllocateFromTail(sizeof(RecordingSimpleMemoryAllocator), - alignof(RecordingSimpleMemoryAllocator)); - // Use the default copy constructor to populate internal states. - return new (allocator_buffer) RecordingSimpleMemoryAllocator(tmp); -} - -size_t RecordingSimpleMemoryAllocator::GetRequestedBytes() const { - return requested_head_bytes_ + requested_tail_bytes_; -} - -size_t RecordingSimpleMemoryAllocator::GetUsedBytes() const { - return used_bytes_; -} - -size_t RecordingSimpleMemoryAllocator::GetAllocatedCount() const { - return alloc_count_; -} - -TfLiteStatus RecordingSimpleMemoryAllocator::EnsureHeadSize(size_t size, - size_t alignment) { - const uint8_t* previous_head = GetHead(); - TfLiteStatus status = SimpleMemoryAllocator::EnsureHeadSize(size, alignment); - if (status == kTfLiteOk) { - used_bytes_ += GetHead() - previous_head; - requested_head_bytes_ = size; - } - return status; -} - -uint8_t* RecordingSimpleMemoryAllocator::AllocateFromTail(size_t size, - size_t alignment) { - const uint8_t* previous_tail = GetTail(); - uint8_t* result = SimpleMemoryAllocator::AllocateFromTail(size, alignment); - if (result != nullptr) { - used_bytes_ += previous_tail - GetTail(); - requested_tail_bytes_ += size; - alloc_count_++; - } - return result; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/simple_memory_allocator.cc b/components/tflite_micro/Source/tensorflow/lite/micro/simple_memory_allocator.cc deleted file mode 100644 index bea1a9d7..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/simple_memory_allocator.cc +++ /dev/null @@ -1,154 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/simple_memory_allocator.h" - -#include -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/memory_helpers.h" - -namespace tflite { - -SimpleMemoryAllocator::SimpleMemoryAllocator(ErrorReporter* error_reporter, - uint8_t* buffer_head, - uint8_t* buffer_tail) - : error_reporter_(error_reporter), - buffer_head_(buffer_head), - buffer_tail_(buffer_tail), - head_(buffer_head), - tail_(buffer_tail), - temp_(buffer_head_) {} - -SimpleMemoryAllocator::SimpleMemoryAllocator(ErrorReporter* error_reporter, - uint8_t* buffer, - size_t buffer_size) - : SimpleMemoryAllocator(error_reporter, buffer, buffer + buffer_size) {} - -/* static */ -SimpleMemoryAllocator* SimpleMemoryAllocator::Create( - ErrorReporter* error_reporter, uint8_t* buffer_head, size_t buffer_size) { - TFLITE_DCHECK(error_reporter != nullptr); - TFLITE_DCHECK(buffer_head != nullptr); - SimpleMemoryAllocator tmp = - SimpleMemoryAllocator(error_reporter, buffer_head, buffer_size); - - // Allocate enough bytes from the buffer to create a SimpleMemoryAllocator. - // The new instance will use the current adjusted tail buffer from the tmp - // allocator instance. - uint8_t* allocator_buffer = tmp.AllocateFromTail( - sizeof(SimpleMemoryAllocator), alignof(SimpleMemoryAllocator)); - // Use the default copy constructor to populate internal states. - return new (allocator_buffer) SimpleMemoryAllocator(tmp); -} - -SimpleMemoryAllocator::~SimpleMemoryAllocator() {} - -TfLiteStatus SimpleMemoryAllocator::EnsureHeadSize(size_t size, - size_t alignment) { - if (head_ != temp_) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Internal error: EnsureHeadSize() needs to be called after" - "ResetTempAllocations()."); - return kTfLiteError; - } - - uint8_t* const aligned_result = AlignPointerUp(buffer_head_, alignment); - if (aligned_result + size < head_) { - // Size is below the current head size, just return. - return kTfLiteOk; - } - - const size_t available_memory = tail_ - aligned_result; - if (available_memory < size) { - TF_LITE_REPORT_ERROR( - error_reporter_, - "Failed to adjust head size. Requested: %u, available %u, missing: %u", - size, available_memory, size - available_memory); - return kTfLiteError; - } - head_ = aligned_result + size; - temp_ = head_; - - return kTfLiteOk; -} - -uint8_t* SimpleMemoryAllocator::AllocateFromTail(size_t size, - size_t alignment) { - uint8_t* const aligned_result = AlignPointerDown(tail_ - size, alignment); - if (aligned_result < head_) { -#ifndef TF_LITE_STRIP_ERROR_STRINGS - const size_t missing_memory = head_ - aligned_result; - TF_LITE_REPORT_ERROR(error_reporter_, - "Failed to allocate tail memory. Requested: %u, " - "available %u, missing: %u", - size, size - missing_memory, missing_memory); -#endif - return nullptr; - } - tail_ = aligned_result; - return aligned_result; -} - -uint8_t* SimpleMemoryAllocator::AllocateTemp(size_t size, size_t alignment) { - uint8_t* const aligned_result = AlignPointerUp(temp_, alignment); - const size_t available_memory = tail_ - aligned_result; - if (available_memory < size) { - TF_LITE_REPORT_ERROR(error_reporter_, - "Failed to allocate temp memory. Requested: %u, " - "available %u, missing: %u", - size, available_memory, size - available_memory); - return nullptr; - } - temp_ = aligned_result + size; - return aligned_result; -} - -void SimpleMemoryAllocator::ResetTempAllocations() { temp_ = head_; } - -uint8_t* SimpleMemoryAllocator::GetHead() const { return head_; } - -uint8_t* SimpleMemoryAllocator::GetBufferHead() const { return buffer_head_; } - -uint8_t* SimpleMemoryAllocator::GetTail() const { return tail_; } - -size_t SimpleMemoryAllocator::GetHeadUsedBytes() const { - return head_ - buffer_head_; -} - -size_t SimpleMemoryAllocator::GetTailUsedBytes() const { - return buffer_tail_ - tail_; -} - -size_t SimpleMemoryAllocator::GetAvailableMemory(size_t alignment) const { - uint8_t* const aligned_head = AlignPointerUp(head_, alignment); - uint8_t* const aligned_tail = AlignPointerDown(tail_, alignment); - return aligned_tail - aligned_head; -} - -size_t SimpleMemoryAllocator::GetUsedBytes() const { - return GetBufferSize() - (tail_ - head_); -} - -size_t SimpleMemoryAllocator::GetBufferSize() const { - return buffer_tail_ - buffer_head_; -} - -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/test_helpers.cc b/components/tflite_micro/Source/tensorflow/lite/micro/test_helpers.cc deleted file mode 100644 index 23c7ca96..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/test_helpers.cc +++ /dev/null @@ -1,1008 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/test_helpers.h" - -#include -#include -#include -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/all_ops_resolver.h" -#include "tensorflow/lite/micro/micro_utils.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite { -namespace testing { -namespace { - -class StackAllocator : public flatbuffers::Allocator { - public: - StackAllocator() : data_(data_backing_), data_size_(0) {} - - uint8_t* allocate(size_t size) override { - TFLITE_DCHECK((data_size_ + size) <= kStackAllocatorSize); - uint8_t* result = data_; - data_ += size; - data_size_ += size; - return result; - } - - void deallocate(uint8_t* p, size_t) override {} - - static StackAllocator& instance() { - // Avoid using true dynamic memory allocation to be portable to bare metal. - static char inst_memory[sizeof(StackAllocator)]; - static StackAllocator* inst = new (inst_memory) StackAllocator; - return *inst; - } - - static constexpr size_t kStackAllocatorSize = 8192; - - private: - uint8_t data_backing_[kStackAllocatorSize]; - uint8_t* data_; - int data_size_; -}; - -flatbuffers::FlatBufferBuilder* BuilderInstance() { - static char inst_memory[sizeof(flatbuffers::FlatBufferBuilder)]; - static flatbuffers::FlatBufferBuilder* inst = - new (inst_memory) flatbuffers::FlatBufferBuilder( - StackAllocator::kStackAllocatorSize, &StackAllocator::instance()); - return inst; -} - -// A wrapper around FlatBuffer API to help build model easily. -class ModelBuilder { - public: - typedef int32_t Tensor; - typedef int Operator; - typedef int Node; - - // `builder` needs to be available until BuildModel is called. - explicit ModelBuilder(flatbuffers::FlatBufferBuilder* builder) - : builder_(builder) {} - - // Registers an operator that will be used in the model. - Operator RegisterOp(BuiltinOperator op, const char* custom_code, - int32_t version); - - // Adds a tensor to the model. - Tensor AddTensor(TensorType type, std::initializer_list shape) { - return AddTensorImpl(type, /* is_variable */ false, shape); - } - - // Adds a variable tensor to the model. - Tensor AddVariableTensor(TensorType type, - std::initializer_list shape) { - return AddTensorImpl(type, /* is_variable */ true, shape); - } - - // Adds a node to the model with given input and output Tensors. - Node AddNode(Operator op, std::initializer_list inputs, - std::initializer_list outputs); - - void AddMetadata(const char* description_string, - const int32_t* metadata_buffer_data, size_t num_elements); - - // Constructs the flatbuffer model using `builder_` and return a pointer to - // it. The returned model has the same lifetime as `builder_`. - const Model* BuildModel(std::initializer_list inputs, - std::initializer_list outputs); - - private: - // Adds a tensor to the model. - Tensor AddTensorImpl(TensorType type, bool is_variable, - std::initializer_list shape); - - flatbuffers::FlatBufferBuilder* builder_; - - static constexpr int kMaxOperatorCodes = 10; - flatbuffers::Offset operator_codes_[kMaxOperatorCodes]; - int next_operator_code_id_ = 0; - - static constexpr int kMaxOperators = 50; - flatbuffers::Offset operators_[kMaxOperators]; - int next_operator_id_ = 0; - - static constexpr int kMaxTensors = 50; - flatbuffers::Offset tensors_[kMaxTensors]; - - static constexpr int kMaxMetadataBuffers = 10; - - static constexpr int kMaxMetadatas = 10; - flatbuffers::Offset metadata_[kMaxMetadatas]; - - flatbuffers::Offset metadata_buffers_[kMaxMetadataBuffers]; - - int nbr_of_metadata_buffers_ = 0; - - int next_tensor_id_ = 0; -}; - -ModelBuilder::Operator ModelBuilder::RegisterOp(BuiltinOperator op, - const char* custom_code, - int32_t version) { - TFLITE_DCHECK(next_operator_code_id_ <= kMaxOperatorCodes); - operator_codes_[next_operator_code_id_] = - tflite::CreateOperatorCodeDirect(*builder_, op, custom_code, version); - next_operator_code_id_++; - return next_operator_code_id_ - 1; -} - -ModelBuilder::Node ModelBuilder::AddNode( - ModelBuilder::Operator op, - std::initializer_list inputs, - std::initializer_list outputs) { - TFLITE_DCHECK(next_operator_id_ <= kMaxOperators); - operators_[next_operator_id_] = tflite::CreateOperator( - *builder_, op, builder_->CreateVector(inputs.begin(), inputs.size()), - builder_->CreateVector(outputs.begin(), outputs.size()), - BuiltinOptions_NONE); - next_operator_id_++; - return next_operator_id_ - 1; -} - -void ModelBuilder::AddMetadata(const char* description_string, - const int32_t* metadata_buffer_data, - size_t num_elements) { - metadata_[ModelBuilder::nbr_of_metadata_buffers_] = - CreateMetadata(*builder_, builder_->CreateString(description_string), - 1 + ModelBuilder::nbr_of_metadata_buffers_); - - metadata_buffers_[nbr_of_metadata_buffers_] = tflite::CreateBuffer( - *builder_, builder_->CreateVector((uint8_t*)metadata_buffer_data, - sizeof(uint32_t) * num_elements)); - - ModelBuilder::nbr_of_metadata_buffers_++; -} - -const Model* ModelBuilder::BuildModel( - std::initializer_list inputs, - std::initializer_list outputs) { - // Model schema requires an empty buffer at idx 0. - size_t buffer_size = 1 + ModelBuilder::nbr_of_metadata_buffers_; - flatbuffers::Offset buffers[kMaxMetadataBuffers]; - buffers[0] = tflite::CreateBuffer(*builder_); - - // Place the metadata buffers first in the buffer since the indices for them - // have already been set in AddMetadata() - for (int i = 1; i < ModelBuilder::nbr_of_metadata_buffers_ + 1; ++i) { - buffers[i] = metadata_buffers_[i - 1]; - } - - // TFLM only supports single subgraph. - constexpr size_t subgraphs_size = 1; - const flatbuffers::Offset subgraphs[subgraphs_size] = { - tflite::CreateSubGraph( - *builder_, builder_->CreateVector(tensors_, next_tensor_id_), - builder_->CreateVector(inputs.begin(), inputs.size()), - builder_->CreateVector(outputs.begin(), outputs.size()), - builder_->CreateVector(operators_, next_operator_id_), - builder_->CreateString("test_subgraph"))}; - - flatbuffers::Offset model_offset; - if (ModelBuilder::nbr_of_metadata_buffers_ > 0) { - model_offset = tflite::CreateModel( - *builder_, 0, - builder_->CreateVector(operator_codes_, next_operator_code_id_), - builder_->CreateVector(subgraphs, subgraphs_size), - builder_->CreateString("teset_model"), - builder_->CreateVector(buffers, buffer_size), 0, - builder_->CreateVector(metadata_, - ModelBuilder::nbr_of_metadata_buffers_)); - } else { - model_offset = tflite::CreateModel( - *builder_, 0, - builder_->CreateVector(operator_codes_, next_operator_code_id_), - builder_->CreateVector(subgraphs, subgraphs_size), - builder_->CreateString("teset_model"), - builder_->CreateVector(buffers, buffer_size)); - } - - tflite::FinishModelBuffer(*builder_, model_offset); - void* model_pointer = builder_->GetBufferPointer(); - const Model* model = flatbuffers::GetRoot(model_pointer); - return model; -} - -ModelBuilder::Tensor ModelBuilder::AddTensorImpl( - TensorType type, bool is_variable, std::initializer_list shape) { - TFLITE_DCHECK(next_tensor_id_ <= kMaxTensors); - tensors_[next_tensor_id_] = tflite::CreateTensor( - *builder_, builder_->CreateVector(shape.begin(), shape.size()), type, - /* buffer */ 0, /* name */ 0, /* quantization */ 0, - /* is_variable */ is_variable, - /* sparsity */ 0); - next_tensor_id_++; - return next_tensor_id_ - 1; -} - -const Model* BuildSimpleStatefulModel() { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* fb_builder = BuilderInstance(); - - ModelBuilder model_builder(fb_builder); - - const int op_id = - model_builder.RegisterOp(BuiltinOperator_CUSTOM, "simple_stateful_op", 0); - const int input_tensor = model_builder.AddTensor(TensorType_UINT8, {3}); - const int median_tensor = model_builder.AddTensor(TensorType_UINT8, {3}); - const int invoke_count_tensor = - model_builder.AddTensor(TensorType_INT32, {1}); - - model_builder.AddNode(op_id, {input_tensor}, - {median_tensor, invoke_count_tensor}); - return model_builder.BuildModel({input_tensor}, - {median_tensor, invoke_count_tensor}); -} - -const Model* BuildSimpleModelWithBranch() { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* fb_builder = BuilderInstance(); - - ModelBuilder model_builder(fb_builder); - /* Model structure - | t0 - +------| - | v - | +---------+ - | | n0 | - | | | - | +---------+ - v + - | - +---------+ | t1 - | n1 | | - | | | - +---------+ | - | | - t2 | v - | +---------+ - +-->| n2 | - | | - +-------|-+ - |t3 - v - */ - const int op_id = - model_builder.RegisterOp(BuiltinOperator_CUSTOM, "mock_custom", - /* version= */ 0); - const int t0 = model_builder.AddTensor(TensorType_FLOAT32, {2, 2, 3}); - const int t1 = model_builder.AddTensor(TensorType_FLOAT32, {2, 2, 3}); - const int t2 = model_builder.AddTensor(TensorType_FLOAT32, {2, 2, 3}); - const int t3 = model_builder.AddTensor(TensorType_FLOAT32, {2, 2, 3}); - model_builder.AddNode(op_id, {t0}, {t1}); // n0 - model_builder.AddNode(op_id, {t0}, {t2}); // n1 - model_builder.AddNode(op_id, {t1, t2}, {t3}); // n2 - return model_builder.BuildModel({t0}, {t3}); -} - -const Model* BuildModelWithOfflinePlanning(int number_of_tensors, - const int32_t* metadata_buffer, - NodeConnection* node_conn, - int num_conns) { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* fb_builder = BuilderInstance(); - - ModelBuilder model_builder(fb_builder); - - const int op_id = - model_builder.RegisterOp(BuiltinOperator_CUSTOM, "mock_custom", - /* version= */ 0); - - for (int i = 0; i < number_of_tensors; ++i) { - model_builder.AddTensor(TensorType_FLOAT32, {2, 2, 3}); - } - - for (int i = 0; i < num_conns; ++i) { - model_builder.AddNode(op_id, node_conn[i].input, node_conn[i].output); - } - - model_builder.AddMetadata( - "OfflineMemoryAllocation", metadata_buffer, - number_of_tensors + tflite::testing::kOfflinePlannerHeaderSize); - - return model_builder.BuildModel(node_conn[0].input, - node_conn[num_conns - 1].output); -} - -const Model* BuildSimpleMockModel() { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* builder = BuilderInstance(); - - constexpr size_t buffer_data_size = 1; - const uint8_t buffer_data[buffer_data_size] = {21}; - constexpr size_t buffers_size = 2; - const Offset buffers[buffers_size] = { - CreateBuffer(*builder), - CreateBuffer(*builder, - builder->CreateVector(buffer_data, buffer_data_size))}; - constexpr size_t tensor_shape_size = 1; - const int32_t tensor_shape[tensor_shape_size] = {1}; - constexpr size_t tensors_size = 4; - const Offset tensors[tensors_size] = { - CreateTensor(*builder, - builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, - builder->CreateString("test_input_tensor"), 0, false), - CreateTensor(*builder, - builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_UINT8, 1, - builder->CreateString("test_weight_tensor"), 0, false), - CreateTensor(*builder, - builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, - builder->CreateString("test_output_tensor"), 0, false), - CreateTensor(*builder, - builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, - builder->CreateString("test_output2_tensor"), 0, false), - }; - constexpr size_t inputs_size = 1; - const int32_t inputs[inputs_size] = {0}; - constexpr size_t outputs_size = 2; - const int32_t outputs[outputs_size] = {2, 3}; - constexpr size_t operator_inputs_size = 2; - const int32_t operator_inputs[operator_inputs_size] = {0, 1}; - constexpr size_t operator_outputs_size = 1; - const int32_t operator_outputs[operator_outputs_size] = {2}; - const int32_t operator2_outputs[operator_outputs_size] = {3}; - constexpr size_t operators_size = 2; - const Offset operators[operators_size] = { - CreateOperator( - *builder, 0, - builder->CreateVector(operator_inputs, operator_inputs_size), - builder->CreateVector(operator_outputs, operator_outputs_size), - BuiltinOptions_NONE), - CreateOperator( - *builder, 0, - builder->CreateVector(operator_inputs, operator_inputs_size), - builder->CreateVector(operator2_outputs, operator_outputs_size), - BuiltinOptions_NONE), - }; - constexpr size_t subgraphs_size = 1; - const Offset subgraphs[subgraphs_size] = { - CreateSubGraph(*builder, builder->CreateVector(tensors, tensors_size), - builder->CreateVector(inputs, inputs_size), - builder->CreateVector(outputs, outputs_size), - builder->CreateVector(operators, operators_size), - builder->CreateString("test_subgraph"))}; - constexpr size_t operator_codes_size = 1; - const Offset operator_codes[operator_codes_size] = { - CreateOperatorCodeDirect(*builder, BuiltinOperator_CUSTOM, "mock_custom", - 0)}; - const Offset model_offset = CreateModel( - *builder, 0, builder->CreateVector(operator_codes, operator_codes_size), - builder->CreateVector(subgraphs, subgraphs_size), - builder->CreateString("test_model"), - builder->CreateVector(buffers, buffers_size)); - FinishModelBuffer(*builder, model_offset); - void* model_pointer = builder->GetBufferPointer(); - const Model* model = flatbuffers::GetRoot(model_pointer); - return model; -} - -const Model* BuildComplexMockModel() { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* builder = BuilderInstance(); - - constexpr size_t buffer_data_size = 1; - const uint8_t buffer_data_1[buffer_data_size] = {21}; - const uint8_t buffer_data_2[buffer_data_size] = {21}; - const uint8_t buffer_data_3[buffer_data_size] = {21}; - constexpr size_t buffers_size = 7; - const Offset buffers[buffers_size] = { - // Op 1 buffers: - CreateBuffer(*builder), - CreateBuffer(*builder), - CreateBuffer(*builder, - builder->CreateVector(buffer_data_1, buffer_data_size)), - // Op 2 buffers: - CreateBuffer(*builder), - CreateBuffer(*builder, - builder->CreateVector(buffer_data_2, buffer_data_size)), - // Op 3 buffers: - CreateBuffer(*builder), - CreateBuffer(*builder, - builder->CreateVector(buffer_data_3, buffer_data_size)), - }; - constexpr size_t tensor_shape_size = 1; - const int32_t tensor_shape[tensor_shape_size] = {1}; - - constexpr size_t tensors_size = 10; - const Offset tensors[tensors_size] = { - // Op 1 inputs: - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, builder->CreateString("test_input_tensor_1"), 0, - false /* is_variable */), - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 1, builder->CreateString("test_variable_tensor_1"), - 0, true /* is_variable */), - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_UINT8, 2, builder->CreateString("test_weight_tensor_1"), 0, - false /* is_variable */), - // Op 1 output / Op 2 input: - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, builder->CreateString("test_output_tensor_1"), 0, - false /* is_variable */), - // Op 2 inputs: - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 1, builder->CreateString("test_variable_tensor_2"), - 0, true /* is_variable */), - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_UINT8, 2, builder->CreateString("test_weight_tensor_2"), 0, - false /* is_variable */), - // Op 2 output / Op 3 input: - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, builder->CreateString("test_output_tensor_2"), 0, - false /* is_variable */), - // Op 3 inputs: - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 1, builder->CreateString("test_variable_tensor_3"), - 0, true /* is_variable */), - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_UINT8, 2, builder->CreateString("test_weight_tensor_3"), 0, - false /* is_variable */), - // Op 3 output: - CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, builder->CreateString("test_output_tensor_3"), 0, - false /* is_variable */), - }; - - constexpr size_t operators_size = 3; - Offset operators[operators_size]; - { - // Set Op 1 attributes: - constexpr size_t operator_inputs_size = 3; - const int32_t operator_inputs[operator_inputs_size] = {0, 1, 2}; - constexpr size_t operator_outputs_size = 1; - const int32_t operator_outputs[operator_outputs_size] = {3}; - - operators[0] = {CreateOperator( - *builder, 0, - builder->CreateVector(operator_inputs, operator_inputs_size), - builder->CreateVector(operator_outputs, operator_outputs_size), - BuiltinOptions_NONE)}; - } - - { - // Set Op 2 attributes - constexpr size_t operator_inputs_size = 3; - const int32_t operator_inputs[operator_inputs_size] = {3, 4, 5}; - constexpr size_t operator_outputs_size = 1; - const int32_t operator_outputs[operator_outputs_size] = {6}; - - operators[1] = {CreateOperator( - *builder, 0, - builder->CreateVector(operator_inputs, operator_inputs_size), - builder->CreateVector(operator_outputs, operator_outputs_size), - BuiltinOptions_NONE)}; - } - - { - // Set Op 3 attributes - constexpr size_t operator_inputs_size = 3; - const int32_t operator_inputs[operator_inputs_size] = {6, 7, 8}; - constexpr size_t operator_outputs_size = 1; - const int32_t operator_outputs[operator_outputs_size] = {9}; - - operators[2] = {CreateOperator( - *builder, 0, - builder->CreateVector(operator_inputs, operator_inputs_size), - builder->CreateVector(operator_outputs, operator_outputs_size), - BuiltinOptions_NONE)}; - } - - constexpr size_t inputs_size = 1; - const int32_t inputs[inputs_size] = {0}; - constexpr size_t outputs_size = 1; - const int32_t outputs[outputs_size] = {9}; - - constexpr size_t subgraphs_size = 1; - const Offset subgraphs[subgraphs_size] = { - CreateSubGraph(*builder, builder->CreateVector(tensors, tensors_size), - builder->CreateVector(inputs, inputs_size), - builder->CreateVector(outputs, outputs_size), - builder->CreateVector(operators, operators_size), - builder->CreateString("test_subgraph"))}; - - constexpr size_t operator_codes_size = 1; - const Offset operator_codes[operator_codes_size] = { - CreateOperatorCodeDirect(*builder, BuiltinOperator_CUSTOM, "mock_custom", - 0)}; - - const Offset model_offset = CreateModel( - *builder, 0, builder->CreateVector(operator_codes, operator_codes_size), - builder->CreateVector(subgraphs, subgraphs_size), - builder->CreateString("test_model"), - builder->CreateVector(buffers, buffers_size)); - - FinishModelBuffer(*builder, model_offset); - void* model_pointer = builder->GetBufferPointer(); - const Model* model = flatbuffers::GetRoot(model_pointer); - return model; -} - -} // namespace - -const TfLiteRegistration* SimpleStatefulOp::getRegistration() { - return GetMutableRegistration(); -} - -TfLiteRegistration* SimpleStatefulOp::GetMutableRegistration() { - static TfLiteRegistration r; - r.init = Init; - r.prepare = Prepare; - r.invoke = Invoke; - return &r; -} - -void* SimpleStatefulOp::Init(TfLiteContext* context, const char* buffer, - size_t length) { - TFLITE_DCHECK(context->AllocateBufferForEval == nullptr); - TFLITE_DCHECK(context->GetScratchBuffer == nullptr); - TFLITE_DCHECK(context->RequestScratchBufferInArena == nullptr); - - void* raw = context->AllocatePersistentBuffer(context, sizeof(OpData)); - OpData* data = reinterpret_cast(raw); - *data = {}; - return raw; -} - -TfLiteStatus SimpleStatefulOp::Prepare(TfLiteContext* context, - TfLiteNode* node) { - OpData* data = reinterpret_cast(node->user_data); - - // Make sure that the input is in uint8_t with at least 1 data entry. - const TfLiteTensor* input = tflite::GetInput(context, node, kInputTensor); - if (input->type != kTfLiteUInt8) return kTfLiteError; - if (NumElements(input->dims) == 0) return kTfLiteError; - - // Allocate a temporary buffer with the same size of input for sorting. - TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena( - context, sizeof(uint8_t) * NumElements(input->dims), - &data->sorting_buffer)); - return kTfLiteOk; -} - -TfLiteStatus SimpleStatefulOp::Invoke(TfLiteContext* context, - TfLiteNode* node) { - OpData* data = reinterpret_cast(node->user_data); - data->invoke_count += 1; - - const TfLiteTensor* input = GetInput(context, node, kInputTensor); - const uint8_t* input_data = GetTensorData(input); - int size = NumElements(input->dims); - - uint8_t* sorting_buffer = reinterpret_cast( - context->GetScratchBuffer(context, data->sorting_buffer)); - // Copy inputs data to the sorting buffer. We don't want to mutate the input - // tensor as it might be used by a another node. - for (int i = 0; i < size; i++) { - sorting_buffer[i] = input_data[i]; - } - - // In place insertion sort on `sorting_buffer`. - for (int i = 1; i < size; i++) { - for (int j = i; j > 0 && sorting_buffer[j] < sorting_buffer[j - 1]; j--) { - std::swap(sorting_buffer[j], sorting_buffer[j - 1]); - } - } - - TfLiteTensor* median = GetOutput(context, node, kMedianTensor); - uint8_t* median_data = GetTensorData(median); - TfLiteTensor* invoke_count = GetOutput(context, node, kInvokeCount); - int32_t* invoke_count_data = GetTensorData(invoke_count); - - median_data[0] = sorting_buffer[size / 2]; - invoke_count_data[0] = data->invoke_count; - return kTfLiteOk; -} - -const TfLiteRegistration* MockCustom::getRegistration() { - return GetMutableRegistration(); -} - -TfLiteRegistration* MockCustom::GetMutableRegistration() { - static TfLiteRegistration r; - r.init = Init; - r.prepare = Prepare; - r.invoke = Invoke; - r.free = Free; - return &r; -} - -void* MockCustom::Init(TfLiteContext* context, const char* buffer, - size_t length) { - // We don't support delegate in TFL micro. This is a weak check to test if - // context struct being zero-initialized. - TFLITE_DCHECK(context->ReplaceNodeSubsetsWithDelegateKernels == nullptr); - freed_ = false; - // Do nothing. - return nullptr; -} - -void MockCustom::Free(TfLiteContext* context, void* buffer) { freed_ = true; } - -TfLiteStatus MockCustom::Prepare(TfLiteContext* context, TfLiteNode* node) { - return kTfLiteOk; -} - -TfLiteStatus MockCustom::Invoke(TfLiteContext* context, TfLiteNode* node) { - const TfLiteTensor* input = tflite::GetInput(context, node, 0); - const int32_t* input_data = input->data.i32; - const TfLiteTensor* weight = tflite::GetInput(context, node, 1); - const uint8_t* weight_data = weight->data.uint8; - TfLiteTensor* output = GetOutput(context, node, 0); - int32_t* output_data = output->data.i32; - output_data[0] = - 0; // Catch output tensor sharing memory with an input tensor - output_data[0] = input_data[0] + weight_data[0]; - return kTfLiteOk; -} - -bool MockCustom::freed_ = false; - -AllOpsResolver GetOpResolver() { - AllOpsResolver op_resolver; - op_resolver.AddCustom("mock_custom", MockCustom::GetMutableRegistration()); - op_resolver.AddCustom("simple_stateful_op", - SimpleStatefulOp::GetMutableRegistration()); - - return op_resolver; -} - -const Model* GetSimpleMockModel() { - static Model* model = nullptr; - if (!model) { - model = const_cast(BuildSimpleMockModel()); - } - return model; -} - -const Model* GetComplexMockModel() { - static Model* model = nullptr; - if (!model) { - model = const_cast(BuildComplexMockModel()); - } - return model; -} - -const Model* GetSimpleModelWithBranch() { - static Model* model = nullptr; - if (!model) { - model = const_cast(BuildSimpleModelWithBranch()); - } - return model; -} - -const Model* GetModelWithOfflinePlanning(int num_tensors, - const int32_t* metadata_buffer, - NodeConnection* node_conn, - int num_conns) { - const Model* model = BuildModelWithOfflinePlanning( - num_tensors, metadata_buffer, node_conn, num_conns); - return model; -} - -const Model* GetSimpleStatefulModel() { - static Model* model = nullptr; - if (!model) { - model = const_cast(BuildSimpleStatefulModel()); - } - return model; -} - -const Tensor* Create1dFlatbufferTensor(int size, bool is_variable) { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* builder = BuilderInstance(); - constexpr size_t tensor_shape_size = 1; - const int32_t tensor_shape[tensor_shape_size] = {size}; - const Offset tensor_offset = CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, builder->CreateString("test_tensor"), 0, - is_variable); - builder->Finish(tensor_offset); - void* tensor_pointer = builder->GetBufferPointer(); - const Tensor* tensor = flatbuffers::GetRoot(tensor_pointer); - return tensor; -} - -const Tensor* CreateQuantizedFlatbufferTensor(int size) { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* builder = BuilderInstance(); - const Offset quant_params = - CreateQuantizationParameters( - *builder, - /*min=*/builder->CreateVector({0.1f}), - /*max=*/builder->CreateVector({0.2f}), - /*scale=*/builder->CreateVector({0.3f}), - /*zero_point=*/builder->CreateVector({100ll})); - - constexpr size_t tensor_shape_size = 1; - const int32_t tensor_shape[tensor_shape_size] = {size}; - const Offset tensor_offset = CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, builder->CreateString("test_tensor"), quant_params, - false); - builder->Finish(tensor_offset); - void* tensor_pointer = builder->GetBufferPointer(); - const Tensor* tensor = flatbuffers::GetRoot(tensor_pointer); - return tensor; -} - -const Tensor* CreateMissingQuantizationFlatbufferTensor(int size) { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* builder = BuilderInstance(); - const Offset quant_params = - CreateQuantizationParameters(*builder, 0, 0, 0, 0, - QuantizationDetails_NONE, 0, 0); - constexpr size_t tensor_shape_size = 1; - const int32_t tensor_shape[tensor_shape_size] = {size}; - const Offset tensor_offset = CreateTensor( - *builder, builder->CreateVector(tensor_shape, tensor_shape_size), - TensorType_INT32, 0, builder->CreateString("test_tensor"), quant_params, - false); - builder->Finish(tensor_offset); - void* tensor_pointer = builder->GetBufferPointer(); - const Tensor* tensor = flatbuffers::GetRoot(tensor_pointer); - return tensor; -} - -const flatbuffers::Vector>* -CreateFlatbufferBuffers() { - using flatbuffers::Offset; - flatbuffers::FlatBufferBuilder* builder = BuilderInstance(); - constexpr size_t buffers_size = 1; - const Offset buffers[buffers_size] = { - CreateBuffer(*builder), - }; - const flatbuffers::Offset>> - buffers_offset = builder->CreateVector(buffers, buffers_size); - builder->Finish(buffers_offset); - void* buffers_pointer = builder->GetBufferPointer(); - const flatbuffers::Vector>* result = - flatbuffers::GetRoot>>( - buffers_pointer); - return result; -} - -int TestStrcmp(const char* a, const char* b) { - if ((a == nullptr) || (b == nullptr)) { - return -1; - } - while ((*a != 0) && (*a == *b)) { - a++; - b++; - } - return *reinterpret_cast(a) - - *reinterpret_cast(b); -} - -// Wrapper to forward kernel errors to the interpreter's error reporter. -void ReportOpError(struct TfLiteContext* context, const char* format, ...) { -#ifndef TF_LITE_STRIP_ERROR_STRINGS - ErrorReporter* error_reporter = static_cast(context->impl_); - va_list args; - va_start(args, format); - TF_LITE_REPORT_ERROR(error_reporter, format, args); - va_end(args); -#endif -} - -// Create a TfLiteIntArray from an array of ints. The first element in the -// supplied array must be the size of the array expressed as an int. -TfLiteIntArray* IntArrayFromInts(const int* int_array) { - return const_cast( - reinterpret_cast(int_array)); -} - -// Create a TfLiteFloatArray from an array of floats. The first element in the -// supplied array must be the size of the array expressed as a float. -TfLiteFloatArray* FloatArrayFromFloats(const float* floats) { - static_assert(sizeof(float) == sizeof(int), - "assumes sizeof(float) == sizeof(int) to perform casting"); - int size = static_cast(floats[0]); - *reinterpret_cast(const_cast(floats)) = size; - return reinterpret_cast(const_cast(floats)); -} - -TfLiteTensor CreateTensor(TfLiteIntArray* dims, bool is_variable) { - TfLiteTensor result; - result.dims = dims; - result.params = {}; - result.quantization = {kTfLiteNoQuantization, nullptr}; - result.is_variable = is_variable; - result.allocation_type = kTfLiteMemNone; - return result; -} - -TfLiteTensor CreateFloatTensor(const float* data, TfLiteIntArray* dims, - bool is_variable) { - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteFloat32; - result.data.f = const_cast(data); - result.bytes = ElementCount(*dims) * sizeof(float); - return result; -} - -void PopulateFloatTensor(TfLiteTensor* tensor, float* begin, float* end) { - float* p = begin; - float* v = tensor->data.f; - while (p != end) { - *v++ = *p++; - } -} - -TfLiteTensor CreateBoolTensor(const bool* data, TfLiteIntArray* dims, - bool is_variable) { - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteBool; - result.data.b = const_cast(data); - result.bytes = ElementCount(*dims) * sizeof(bool); - return result; -} - -TfLiteTensor CreateInt32Tensor(const int32_t* data, TfLiteIntArray* dims, - bool is_variable) { - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteInt32; - result.data.i32 = const_cast(data); - result.bytes = ElementCount(*dims) * sizeof(int32_t); - return result; -} - -TfLiteTensor CreateQuantizedTensor(const uint8_t* data, TfLiteIntArray* dims, - float scale, int zero_point, - bool is_variable) { - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteUInt8; - result.data.uint8 = const_cast(data); - result.params = {scale, zero_point}; - result.quantization = {kTfLiteAffineQuantization, nullptr}; - result.bytes = ElementCount(*dims) * sizeof(uint8_t); - return result; -} - -TfLiteTensor CreateQuantizedTensor(const int8_t* data, TfLiteIntArray* dims, - float scale, int zero_point, - bool is_variable) { - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteInt8; - result.data.int8 = const_cast(data); - result.params = {scale, zero_point}; - result.quantization = {kTfLiteAffineQuantization, nullptr}; - result.bytes = ElementCount(*dims) * sizeof(int8_t); - return result; -} - -TfLiteTensor CreateQuantizedTensor(const int16_t* data, TfLiteIntArray* dims, - float scale, int zero_point, - bool is_variable) { - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteInt16; - result.data.i16 = const_cast(data); - result.params = {scale, zero_point}; - result.quantization = {kTfLiteAffineQuantization, nullptr}; - result.bytes = ElementCount(*dims) * sizeof(int16_t); - return result; -} - -TfLiteTensor CreateQuantizedBiasTensor(const float* data, int32_t* quantized, - TfLiteIntArray* dims, float input_scale, - float weights_scale, bool is_variable) { - float bias_scale = input_scale * weights_scale; - tflite::SymmetricQuantize(data, quantized, ElementCount(*dims), bias_scale); - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteInt32; - result.data.i32 = const_cast(quantized); - // Quantized int32_t tensors always have a zero point of 0, since the range of - // int32_t values is large, and because zero point costs extra cycles during - // processing. - result.params = {bias_scale, 0}; - result.quantization = {kTfLiteAffineQuantization, nullptr}; - result.bytes = ElementCount(*dims) * sizeof(int32_t); - return result; -} - -// Quantizes int32_t bias tensor with per-channel weights determined by input -// scale multiplied by weight scale for each channel. -TfLiteTensor CreatePerChannelQuantizedBiasTensor( - const float* input, int32_t* quantized, TfLiteIntArray* dims, - float input_scale, float* weight_scales, float* scales, int* zero_points, - TfLiteAffineQuantization* affine_quant, int quantized_dimension, - bool is_variable) { - int input_size = ElementCount(*dims); - int num_channels = dims->data[quantized_dimension]; - // First element is reserved for array length - zero_points[0] = num_channels; - scales[0] = static_cast(num_channels); - float* scales_array = &scales[1]; - for (int i = 0; i < num_channels; i++) { - scales_array[i] = input_scale * weight_scales[i]; - zero_points[i + 1] = 0; - } - - SymmetricPerChannelQuantize(input, quantized, input_size, num_channels, - scales_array); - - affine_quant->scale = FloatArrayFromFloats(scales); - affine_quant->zero_point = IntArrayFromInts(zero_points); - affine_quant->quantized_dimension = quantized_dimension; - - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteInt32; - result.data.i32 = const_cast(quantized); - result.quantization = {kTfLiteAffineQuantization, affine_quant}; - result.bytes = ElementCount(*dims) * sizeof(int32_t); - return result; -} - -TfLiteTensor CreateSymmetricPerChannelQuantizedTensor( - const float* input, int8_t* quantized, TfLiteIntArray* dims, float* scales, - int* zero_points, TfLiteAffineQuantization* affine_quant, - int quantized_dimension, bool is_variable) { - int channel_count = dims->data[quantized_dimension]; - scales[0] = static_cast(channel_count); - zero_points[0] = channel_count; - - SignedSymmetricPerChannelQuantize(input, dims, quantized_dimension, quantized, - &scales[1]); - - for (int i = 0; i < channel_count; i++) { - zero_points[i + 1] = 0; - } - - affine_quant->scale = FloatArrayFromFloats(scales); - affine_quant->zero_point = IntArrayFromInts(zero_points); - affine_quant->quantized_dimension = quantized_dimension; - - TfLiteTensor result = CreateTensor(dims, is_variable); - result.type = kTfLiteInt8; - result.data.int8 = const_cast(quantized); - result.quantization = {kTfLiteAffineQuantization, affine_quant}; - result.bytes = ElementCount(*dims) * sizeof(int8_t); - return result; -} - -size_t GetModelTensorCount(const Model* model) { - auto* subgraphs = model->subgraphs(); - if (subgraphs) { - return (*subgraphs)[0]->tensors()->size(); - } - return 0; -} - -} // namespace testing -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/testing/test_conv_model.cc b/components/tflite_micro/Source/tensorflow/lite/micro/testing/test_conv_model.cc deleted file mode 100644 index 358479c3..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/testing/test_conv_model.cc +++ /dev/null @@ -1,1799 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/testing/test_conv_model.h" - -extern const unsigned char kTestConvModelData[] = { - 0x24, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x12, 0x00, 0x1c, 0x00, 0x04, 0x00, - 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, 0x00, 0x00, 0x18, 0x00, - 0x12, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xb4, 0x52, 0x00, 0x00, - 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0x00, 0x00, 0x18, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, - 0x88, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x68, 0x00, 0x00, 0x00, - 0x28, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0xff, 0xff, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x14, 0x00, 0x04, 0x00, 0x08, 0x00, - 0x0c, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00, - 0x24, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, - 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xf0, 0x77, 0x80, 0x3b, - 0x01, 0x00, 0x00, 0x00, 0xf0, 0xee, 0x7f, 0x3f, 0x01, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x63, 0x6f, 0x6e, 0x76, - 0x32, 0x64, 0x5f, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x5f, 0x69, 0x6e, 0x74, - 0x38, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, - 0x06, 0x00, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x54, 0x00, 0x00, 0x00, - 0x40, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, - 0x04, 0x00, 0x00, 0x00, 0xca, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x06, - 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x07, 0x00, - 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, 0xe6, 0xff, 0xff, 0xff, - 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, - 0x06, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x16, 0x0a, 0x00, - 0x0e, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0a, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x11, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0a, 0x00, - 0x0c, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0a, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x03, 0x03, 0x00, 0x00, 0x00}; - -const unsigned int kTestConvModelDataSize = 21344; diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/testing/test_utils.cc b/components/tflite_micro/Source/tensorflow/lite/micro/testing/test_utils.cc deleted file mode 100644 index 4d931bdd..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/testing/test_utils.cc +++ /dev/null @@ -1,240 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/testing/test_utils.h" - -#include "tensorflow/lite/micro/simple_memory_allocator.h" - -namespace tflite { -namespace testing { - -namespace { -// TODO(b/141330728): Refactor out of test_utils.cc -// The variables below (and the AllocatePersistentBuffer function) are only -// needed for the kernel tests and benchmarks, i.e. where we do not have an -// interpreter object, and the fully featured MicroAllocator. -// Currently, these need to be sufficient for all the kernel_tests. If that -// becomes problematic, we can investigate allowing the arena_size to be -// specified for each call to PopulatContext. -constexpr size_t kArenaSize = 10000; -uint8_t raw_arena_[kArenaSize]; -SimpleMemoryAllocator* simple_memory_allocator_ = nullptr; -constexpr size_t kBufferAlignment = 16; - -// We store the pointer to the ith scratch buffer to implement the Request/Get -// ScratchBuffer API for the tests. scratch_buffers_[i] will be the ith scratch -// buffer and will still be allocated from within raw_arena_. -constexpr int kNumScratchBuffers = 5; -uint8_t* scratch_buffers_[kNumScratchBuffers]; -int scratch_buffer_count_ = 0; - -// Note that the context parameter in this function is only needed to match the -// signature of TfLiteContext::AllocatePersistentBuffer and isn't needed in the -// implementation because we are assuming a single global -// simple_memory_allocator_ -void* AllocatePersistentBuffer(TfLiteContext* context, size_t bytes) { - TFLITE_DCHECK(simple_memory_allocator_ != nullptr); - return simple_memory_allocator_->AllocateFromTail(bytes, kBufferAlignment); -} - -TfLiteStatus RequestScratchBufferInArena(TfLiteContext* context, size_t bytes, - int* buffer_index) { - TFLITE_DCHECK(simple_memory_allocator_ != nullptr); - TFLITE_DCHECK(buffer_index != nullptr); - - if (scratch_buffer_count_ == kNumScratchBuffers) { - TF_LITE_REPORT_ERROR( - static_cast(context->impl_), - "Exceeded the maximum number of scratch tensors allowed (%d).", - kNumScratchBuffers); - return kTfLiteError; - } - - // For tests, we allocate scratch buffers from the tail and keep them around - // for the lifetime of model. This means that the arena size in the tests will - // be more than what we would have if the scratch buffers could share memory. - scratch_buffers_[scratch_buffer_count_] = - simple_memory_allocator_->AllocateFromTail(bytes, kBufferAlignment); - TFLITE_DCHECK(scratch_buffers_[scratch_buffer_count_] != nullptr); - - *buffer_index = scratch_buffer_count_++; - return kTfLiteOk; -} - -void* GetScratchBuffer(TfLiteContext* context, int buffer_index) { - TFLITE_DCHECK(scratch_buffer_count_ <= kNumScratchBuffers); - if (buffer_index >= scratch_buffer_count_) { - return nullptr; - } - return scratch_buffers_[buffer_index]; -} - -TfLiteTensor* GetTensor(const struct TfLiteContext* context, int subgraph_idx) { - // TODO(b/160894903): Return this value from temp allocated memory. - return &context->tensors[subgraph_idx]; -} - -} // namespace - -uint8_t F2Q(float value, float min, float max) { - int32_t result = ZeroPointFromMinMax(min, max) + - (value / ScaleFromMinMax(min, max)) + 0.5f; - if (result < std::numeric_limits::min()) { - result = std::numeric_limits::min(); - } - if (result > std::numeric_limits::max()) { - result = std::numeric_limits::max(); - } - return result; -} - -// Converts a float value into a signed eight-bit quantized value. -int8_t F2QS(float value, float min, float max) { - return F2Q(value, min, max) + std::numeric_limits::min(); -} - -int32_t F2Q32(float value, float scale) { - double quantized = static_cast(value / scale); - if (quantized > std::numeric_limits::max()) { - quantized = std::numeric_limits::max(); - } else if (quantized < std::numeric_limits::min()) { - quantized = std::numeric_limits::min(); - } - return static_cast(quantized); -} - -// TODO(b/141330728): Move this method elsewhere as part clean up. -void PopulateContext(TfLiteTensor* tensors, int tensors_size, - ErrorReporter* error_reporter, TfLiteContext* context) { - simple_memory_allocator_ = - SimpleMemoryAllocator::Create(error_reporter, raw_arena_, kArenaSize); - TFLITE_DCHECK(simple_memory_allocator_ != nullptr); - scratch_buffer_count_ = 0; - - context->tensors_size = tensors_size; - context->tensors = tensors; - context->impl_ = static_cast(error_reporter); - context->GetExecutionPlan = nullptr; - context->ResizeTensor = nullptr; - context->ReportError = ReportOpError; - context->AddTensors = nullptr; - context->GetNodeAndRegistration = nullptr; - context->ReplaceNodeSubsetsWithDelegateKernels = nullptr; - context->recommended_num_threads = 1; - context->GetExternalContext = nullptr; - context->SetExternalContext = nullptr; - - context->GetTensor = GetTensor; - context->GetEvalTensor = nullptr; - - context->AllocatePersistentBuffer = AllocatePersistentBuffer; - context->RequestScratchBufferInArena = RequestScratchBufferInArena; - context->GetScratchBuffer = GetScratchBuffer; - - for (int i = 0; i < tensors_size; ++i) { - if (context->tensors[i].is_variable) { - ResetVariableTensor(&context->tensors[i]); - } - } -} - -TfLiteTensor CreateQuantizedTensor(const uint8_t* data, TfLiteIntArray* dims, - float min, float max, bool is_variable) { - TfLiteTensor result; - result.type = kTfLiteUInt8; - result.data.uint8 = const_cast(data); - result.dims = dims; - result.params = {ScaleFromMinMax(min, max), - ZeroPointFromMinMax(min, max)}; - result.allocation_type = kTfLiteMemNone; - result.bytes = ElementCount(*dims) * sizeof(uint8_t); - result.is_variable = false; - return result; -} - -TfLiteTensor CreateQuantizedTensor(const int8_t* data, TfLiteIntArray* dims, - float min, float max, bool is_variable) { - TfLiteTensor result; - result.type = kTfLiteInt8; - result.data.int8 = const_cast(data); - result.dims = dims; - result.params = {ScaleFromMinMax(min, max), - ZeroPointFromMinMax(min, max)}; - result.allocation_type = kTfLiteMemNone; - result.bytes = ElementCount(*dims) * sizeof(int8_t); - result.is_variable = is_variable; - return result; -} - -TfLiteTensor CreateQuantizedTensor(float* data, uint8_t* quantized_data, - TfLiteIntArray* dims, bool is_variable) { - TfLiteTensor result; - SymmetricQuantize(data, dims, quantized_data, &result.params.scale); - result.data.uint8 = quantized_data; - result.type = kTfLiteUInt8; - result.dims = dims; - result.params.zero_point = 128; - result.allocation_type = kTfLiteMemNone; - result.bytes = ElementCount(*dims) * sizeof(uint8_t); - result.is_variable = is_variable; - return result; -} - -TfLiteTensor CreateQuantizedTensor(float* data, int8_t* quantized_data, - TfLiteIntArray* dims, bool is_variable) { - TfLiteTensor result; - SignedSymmetricQuantize(data, dims, quantized_data, &result.params.scale); - result.data.int8 = quantized_data; - result.type = kTfLiteInt8; - result.dims = dims; - result.params.zero_point = 0; - result.allocation_type = kTfLiteMemNone; - result.bytes = ElementCount(*dims) * sizeof(int8_t); - result.is_variable = is_variable; - return result; -} - -TfLiteTensor CreateQuantizedTensor(float* data, int16_t* quantized_data, - TfLiteIntArray* dims, bool is_variable) { - TfLiteTensor result; - SignedSymmetricQuantize(data, dims, quantized_data, &result.params.scale); - result.data.i16 = quantized_data; - result.type = kTfLiteInt16; - result.dims = dims; - result.params.zero_point = 0; - result.allocation_type = kTfLiteMemNone; - result.bytes = ElementCount(*dims) * sizeof(int16_t); - result.is_variable = is_variable; - return result; -} - -TfLiteTensor CreateQuantized32Tensor(const int32_t* data, TfLiteIntArray* dims, - float scale, bool is_variable) { - TfLiteTensor result; - result.type = kTfLiteInt32; - result.data.i32 = const_cast(data); - result.dims = dims; - // Quantized int32_t tensors always have a zero point of 0, since the range of - // int32_t values is large, and because zero point costs extra cycles during - // processing. - result.params = {scale, 0}; - result.allocation_type = kTfLiteMemNone; - result.bytes = ElementCount(*dims) * sizeof(int32_t); - result.is_variable = is_variable; - return result; -} - -} // namespace testing -} // namespace tflite diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c deleted file mode 100644 index 69c68777..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_activations_q15.c - * Description: Q15 neural network activation function using direct table look-up - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/DSP/Include/arm_common_tables.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - -/** - * @brief neural network activation function using direct table look-up - * - * @note Refer header file for details. - * - */ - -void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type) -{ - uint16_t i = size; - q15_t *pIn = data; - q15_t *pOut = data; - uint16_t shift_size = 8 + 3 - int_width; - uint32_t bit_mask = 0x7FF >> int_width; - uint32_t full_frac = bit_mask + 1; - const q15_t *lookup_table; - - switch (type) - { - case ARM_SIGMOID: - lookup_table = sigmoidTable_q15; - break; - case ARM_TANH: - default: - lookup_table = tanhTable_q15; - break; - } - - while (i) - { - q15_t out; - q15_t in = *pIn++; - q15_t frac = (uint32_t) in & bit_mask; - q15_t value = lookup_table[(uint8_t)(in >> shift_size)]; - if ((in >> shift_size) != 0x7f) - { - q15_t value2 = lookup_table[(uint8_t)(1 + ((uint8_t)(in >> shift_size)))]; - /* doing the interpolation here for better accuracy */ - out = ((q31_t) (full_frac - frac) * value + (q31_t) value2 * frac) >> shift_size; - } else - { - /* the largest positive value does not have a right side for linear interpolation */ - out = value; - } - - *pOut++ = out; - i--; - } - -} - -/** - * @} end of Acti group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c deleted file mode 100644 index 19476ed5..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_activations_q7.c - * Description: Q7 neural network activation function using direct table look-up - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/DSP/Include/arm_common_tables.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - - /** - * @brief Q7 neural network activation function using direct table look-up - * @param[in,out] data pointer to input - * @param[in] size number of elements - * @param[in] int_width bit-width of the integer part, assume to be smaller than 3 - * @param[in] type type of activation functions - * - * @details - * - * This is the direct table look-up approach. - * - * Assume here the integer part of the fixed-point is <= 3. - * More than 3 just not making much sense, makes no difference with - * saturation followed by any of these activation functions. - */ - -void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type) -{ - uint16_t i = size; - q7_t *pIn = data; - q7_t *pOut = data; - q7_t in; - q7_t out; - uint16_t shift_size = 3 - int_width; - const q7_t *lookup_table; - switch (type) - { - case ARM_SIGMOID: - lookup_table = sigmoidTable_q7; - break; - case ARM_TANH: - default: - lookup_table = tanhTable_q7; - break; - } - while (i) - { - in = *pIn++; - out = lookup_table[(uint8_t) (in >> shift_size)]; - *pOut++ = out; - i--; - } -} - -/** - * @} end of Acti group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu6_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu6_s8.c deleted file mode 100644 index 63f29f48..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu6_s8.c +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_relu6_s8.c - * Description: Basic s8 version of ReLU6 - * - * $Date: Spetember 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - - /* - * Basic ReLU6 function - * - * Refer to header file for details. - * - */ - -void arm_relu6_s8(q7_t *data, uint16_t size) -{ - int32_t i; - - for (i = 0; i < size; i++) - { - int32_t ip = data[i]; - - ip = MAX(ip, 0); - data[i] = MIN(ip, 6); - } -} - -/** - * @} end of Acti group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c deleted file mode 100644 index be5450de..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c +++ /dev/null @@ -1,104 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_relu_q15.c - * Description: Q15 version of ReLU - * - * $Date: February 27, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - -/** - * @brief Q15 RELU function - * @param[in,out] data pointer to input - * @param[in] size number of elements - * - * @details - * - * Optimized relu with QSUB instructions. - * - */ - -void arm_relu_q15(q15_t *data, uint16_t size) -{ - -#if defined(ARM_MATH_DSP) - /* Run the following code for M cores with DSP extension */ - - uint16_t i = size >> 1; - q15_t *input = data; - q15_t *output = data; - q31_t in; - q31_t buf; - q31_t mask; - - while (i) - { - in = read_q15x2_ia(&input); - - /* extract the first bit */ - buf = __ROR(in & 0x80008000, 15); - - /* if MSB=1, mask will be 0xFF, 0x0 otherwise */ - mask = __QSUB16(0x00000000, buf); - - write_q15x2_ia(&output, in & (~mask)); - i--; - } - - if (size & 0x1) - { - if (*input < 0) - { - *input = 0; - } - input++; - } -#else - /* Run the following code as reference implementation for M cores without DSP extension */ - uint16_t i; - - for (i = 0; i < size; i++) - { - if (data[i] < 0) - data[i] = 0; - } - -#endif /* ARM_MATH_DSP */ -} - -/** - * @} end of Acti group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c deleted file mode 100644 index 724d7b49..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c +++ /dev/null @@ -1,109 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_relu_q7.c - * Description: Q7 version of ReLU - * - * $Date: May 29, 2020 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Acti - * @{ - */ - - /** - * @brief Q7 RELU function - * @param[in,out] data pointer to input - * @param[in] size number of elements - * - * @details - * - * Optimized relu with QSUB instructions. - * - */ - -void arm_relu_q7(q7_t *data, uint16_t size) -{ - -#if defined(ARM_MATH_DSP) - /* Run the following code for M cores with DSP extension */ - - uint16_t i = size >> 2; - q7_t *input = data; - q7_t *output = data; - q31_t in; - q31_t buf; - q31_t mask; - - while (i) - { - in = read_q7x4_ia(&input); - - /* extract the first bit */ - buf = (int32_t)__ROR((uint32_t)in & 0x80808080, 7); - - /* if MSB=1, mask will be 0xFF, 0x0 otherwise */ - mask = __QSUB8(0x00000000, buf); - - write_q7x4_ia(&output, in & (~mask)); - - i--; - } - - i = size & 0x3; - while (i) - { - if (*input < 0) - { - *input = 0; - } - input++; - i--; - } - -#else - /* Run the following code as reference implementation for cores without DSP extension */ - - uint16_t i; - - for (i = 0; i < size; i++) - { - if (data[i] < 0) - data[i] = 0; - } - -#endif -} - -/** - * @} end of Acti group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_add_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_add_s8.c deleted file mode 100644 index 0ab9e975..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_add_s8.c +++ /dev/null @@ -1,252 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_elementwise_add_s8 - * Description: Element wise add - * - * $Date: February 27, 2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" -#if defined(ARM_MATH_MVEI) -#include "arm_helium_utils.h" -#endif - -#if defined(ARM_MATH_MVEI) -#define SAT_INPUT_VECT(__INPUT_V, __MULT, __SHIFT) \ - __INPUT_V = arm_sat_doubling_high_mult_mve(__INPUT_V, __MULT); \ - __INPUT_V = arm_divide_by_power_of_two_mve(__INPUT_V, -__SHIFT); -#endif - -#define SAT_INPUT(__INPUT, __MULT, __SHIFT) \ - __INPUT = arm_nn_sat_doubling_high_mult(__INPUT, __MULT); \ - __INPUT = arm_nn_divide_by_power_of_two(__INPUT, -__SHIFT); - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup BasicMath - * @{ - */ - -/* - * s8 element wise add - * - * Refer header file for details. - * - */ - -/* Note: __SHIFT is expected to be <=0 */ - - -arm_status -arm_elementwise_add_s8(const int8_t *input_1_vect, - const int8_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_1_mult, - const int32_t input_1_shift, - const int32_t input_2_offset, - const int32_t input_2_mult, - const int32_t input_2_shift, - const int32_t left_shift, - int8_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const uint32_t block_size) -{ -#if defined(ARM_MATH_MVEI) - int32_t count = (int32_t)block_size; - - while (count > 0) - { - int32x4_t vect_1; - int32x4_t vect_2; - - mve_pred16_t p = vctp32q((uint32_t)count); - - vect_1 = vldrbq_z_s32(input_1_vect, p); - vect_2 = vldrbq_z_s32(input_2_vect, p); - - vect_1 = vaddq_s32(vect_1, vdupq_n_s32(input_1_offset)); - vect_2 = vaddq_s32(vect_2, vdupq_n_s32(input_2_offset)); - - vect_1 = vshlq_r_s32(vect_1, left_shift); - vect_2 = vshlq_r_s32(vect_2, left_shift); - - SAT_INPUT_VECT(vect_1, input_1_mult, input_1_shift); - SAT_INPUT_VECT(vect_2, input_2_mult, input_2_shift); - - vect_1 = vaddq_s32(vect_1, vect_2); - SAT_INPUT_VECT(vect_1, out_mult, out_shift); - - vect_1 = vaddq_n_s32(vect_1, out_offset); - - vect_1 = vmaxq_s32(vect_1, vdupq_n_s32(out_activation_min)); - vect_1 = vminq_s32(vect_1, vdupq_n_s32(out_activation_max)); - - input_1_vect += 4; - input_2_vect += 4; - vstrbq_p_s32(output, vect_1, p); - - output += 4; - count -= 4; - } -#else - uint32_t loop_count; - int32_t input_1; - int32_t input_2; - int32_t sum; - -#if defined(ARM_MATH_DSP) - int32_t a_1, b_1, a_2, b_2; - - int32_t offset_1_packed, offset_2_packed; - - int8_t r1, r2, r3, r4; - - offset_1_packed = (input_1_offset << 16U) | (input_1_offset & 0x0FFFFL); - offset_2_packed = (input_2_offset << 16U) | (input_2_offset & 0x0FFFFL); - - loop_count = block_size >> 2; - - while (loop_count > 0U) - { - /* 4 outputs are calculated in one loop. The order of calculation is follows the order of output sign extension - intrinsic */ - input_1_vect = read_and_pad_reordered(input_1_vect, &b_1, &a_1); - input_2_vect = read_and_pad_reordered(input_2_vect, &b_2, &a_2); - - a_1 = __SADD16(a_1, offset_1_packed); - b_1 = __SADD16(b_1, offset_1_packed); - - a_2 = __SADD16(a_2, offset_2_packed); - b_2 = __SADD16(b_2, offset_2_packed); - - /* Sum 1 */ - input_1 = (int16_t)(b_1 & 0x0FFFFL) << left_shift; - SAT_INPUT(input_1, input_1_mult, input_1_shift); - - input_2 = (int16_t)(b_2 & 0x0FFFFL) << left_shift; - SAT_INPUT(input_2, input_2_mult, input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - r1 = (q7_t)sum; - - /* Sum 3 */ - input_1 = (int16_t)((b_1 >> 16) & 0x0FFFFL) << left_shift; - SAT_INPUT(input_1, input_1_mult, input_1_shift); - - input_2 = (int16_t)((b_2 >> 16) & 0x0FFFFL) << left_shift; - SAT_INPUT(input_2, input_2_mult, input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - r3 = (q7_t)sum; - - /* Sum 2 */ - input_1 = (int16_t)(a_1 & 0x0FFFFL) << left_shift; - SAT_INPUT(input_1, input_1_mult, input_1_shift); - - input_2 = (int16_t)(a_2 & 0x0FFFFL) << left_shift; - SAT_INPUT(input_2, input_2_mult, input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - r2 = (q7_t)sum; - - /* Sum 4 */ - input_1 = (int16_t)((a_1 >> 16) & 0x0FFFFL) << left_shift; - SAT_INPUT(input_1, input_1_mult, input_1_shift); - - input_2 = (int16_t)((a_2 >> 16) & 0x0FFFFL) << left_shift; - SAT_INPUT(input_2, input_2_mult, input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - r4 = (q7_t)sum; - - write_q7x4_ia(&output, __PACKq7(r1, r2, r3, r4)); - - loop_count--; - } - - loop_count = block_size & 0x3; -#else - loop_count = block_size; -#endif - - while (loop_count > 0U) - { - /* C = A + B */ - - input_1 = (*input_1_vect++ + input_1_offset) << left_shift; - input_2 = (*input_2_vect++ + input_2_offset) << left_shift; - - input_1 = arm_nn_sat_doubling_high_mult(input_1, input_1_mult); - input_1 = arm_nn_divide_by_power_of_two(input_1, -input_1_shift); - - input_2 = arm_nn_sat_doubling_high_mult(input_2, input_2_mult); - input_2 = arm_nn_divide_by_power_of_two(input_2, -input_2_shift); - - sum = input_1 + input_2; - SAT_INPUT(sum, out_mult, out_shift); - sum += out_offset; - - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - - *output++ = (q7_t)sum; - - /* Decrement loop counter */ - loop_count--; - } - -#endif /* ARM_MATH_MVEI */ - - return (ARM_MATH_SUCCESS); -} - -/** - * @} end of BasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_mul_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_mul_s8.c deleted file mode 100644 index 55303b77..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/BasicMathFunctions/arm_elementwise_mul_s8.c +++ /dev/null @@ -1,202 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_elementwise_mul_s8 - * Description: Element wise multiplication - * - * $Date: May 29, 2020 - * $Revision: V.1.0.3 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup BasicMath - * @{ - */ - -/** - * @brief s8 element wise multiplication of two vectors - * - * @note Refer header file for details. - * - */ - -arm_status -arm_elementwise_mul_s8(const int8_t *input_1_vect, - const int8_t *input_2_vect, - const int32_t input_1_offset, - const int32_t input_2_offset, - int8_t *output, - const int32_t out_offset, - const int32_t out_mult, - const int32_t out_shift, - const int32_t out_activation_min, - const int32_t out_activation_max, - const uint32_t block_size) -{ - - int32_t loop_count; -#if defined(ARM_MATH_MVEI) - - loop_count = (block_size + 3) / 4; - uint32_t num_elements = block_size; - - for (int i = 0; i < loop_count; i++) - { - mve_pred16_t p = vctp32q(num_elements); - - int32x4_t input_1 = vldrbq_z_s32(input_1_vect, p); - input_1 = vaddq_n_s32(input_1, input_1_offset); - - int32x4_t input_2 = vldrbq_z_s32(input_2_vect, p); - input_2 = vaddq_n_s32(input_2, input_2_offset); - - int32x4_t res_0 = vmulq_s32(input_1, input_2); - - res_0 = arm_requantize_mve_32x4(res_0, vdupq_n_s32(out_mult), vdupq_n_s32(out_shift)); - - res_0 += vdupq_n_s32(out_offset); - - res_0 = vmaxq_s32(res_0, vdupq_n_s32(out_activation_min)); - res_0 = vminq_s32(res_0, vdupq_n_s32(out_activation_max)); - - vstrbq_p_s32(output, res_0, p); - input_1_vect += 4; - input_2_vect += 4; - output += 4; - num_elements -= 4; - } - -#else - int32_t input_1; - int32_t input_2; - int32_t mul_res; - -#if defined(ARM_MATH_DSP) - int32_t a_1, b_1, a_2, b_2; - - int32_t offset_1_packed, offset_2_packed; - - int8_t r1, r2, r3, r4; - - offset_1_packed = (input_1_offset << 16U) | (input_1_offset & 0x0FFFFL); - offset_2_packed = (input_2_offset << 16U) | (input_2_offset & 0x0FFFFL); - - loop_count = block_size >> 2; - - while (loop_count > 0U) - { - /* 4 outputs are calculated in one loop. The order of calculation is follows the order of output sign extension - intrinsic */ - input_1_vect = read_and_pad_reordered(input_1_vect, &b_1, &a_1); - input_2_vect = read_and_pad_reordered(input_2_vect, &b_2, &a_2); - - a_1 = __SADD16(a_1, offset_1_packed); - b_1 = __SADD16(b_1, offset_1_packed); - - a_2 = __SADD16(a_2, offset_2_packed); - b_2 = __SADD16(b_2, offset_2_packed); - - /* Mul 1 */ - input_1 = (int16_t)(b_1 & 0x0FFFFL); - input_2 = (int16_t)(b_2 & 0x0FFFFL); - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - r1 = (q7_t)mul_res; - - /* Mul 3 */ - input_1 = (int16_t)((b_1 >> 16U) & 0x0FFFFL); - input_2 = (int16_t)((b_2 >> 16U) & 0x0FFFFL); - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - r3 = (q7_t)mul_res; - - /* Mul 2 */ - input_1 = (int16_t)(a_1 & 0x0FFFFL); - input_2 = (int16_t)(a_2 & 0x0FFFFL); - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - r2 = (q7_t)mul_res; - - /* Mul 4 */ - input_1 = (int16_t)((a_1 >> 16U) & 0x0FFFFL); - input_2 = (int16_t)((a_2 >> 16U) & 0x0FFFFL); - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - r4 = (q7_t)mul_res; - - write_q7x4_ia(&output, __PACKq7(r1, r2, r3, r4)); - - loop_count--; - } - - loop_count = block_size & 0x3; -#else - loop_count = block_size; -#endif - - while (loop_count > 0U) - { - /* C = A * B */ - - input_1 = *input_1_vect++ + input_1_offset; - input_2 = *input_2_vect++ + input_2_offset; - - mul_res = input_1 * input_2; - mul_res = arm_nn_requantize(mul_res, out_mult, out_shift) + out_offset; - - mul_res = MAX(mul_res, out_activation_min); - mul_res = MIN(mul_res, out_activation_max); - - *output++ = (q7_t)mul_res; - - /* Decrement loop counter */ - loop_count--; - } -#endif - return ARM_MATH_SUCCESS; -} - -/** - * @} end of BasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_w.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_w.c deleted file mode 100644 index 20b68bc7..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_w.c +++ /dev/null @@ -1,65 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_concatenation_s8_w.c - * Description: s8 version of concatenation along the W axis - * - * $Date: October 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Concatenation - * @{ - */ - - /* - * s8 version of concatenation along the W axis - * - * Refer to header file for details. - * - */ -void arm_concatenation_s8_w(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint32_t offset_w) -{ - const uint32_t input_copy_size = input_x * input_y * input_z * input_w; - - output += offset_w * (input_x * input_y * input_z); - - memcpy(output, input, input_copy_size); -} - -/** - * @} end of Concatenation group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_x.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_x.c deleted file mode 100644 index b1ff364a..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_x.c +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_concatenation_s8_x.c - * Description: s8 version of concatenation along the X axis - * - * $Date: October 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Concatenation - * @{ - */ - - /* - * s8 version of concatenation along the X axis - * - * Refer to header file for details. - * - */ -void arm_concatenation_s8_x(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_x, - const uint32_t offset_x) -{ - const uint32_t num_iterations = input_y * input_z * input_w; - - output += offset_x; - - uint32_t i; - - // Copy per row - for (i = 0; i < num_iterations; ++i) - { - memcpy(output, input, input_x); - input += input_x; - output += output_x; - } -} - -/** - * @} end of Concatenation group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_y.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_y.c deleted file mode 100644 index 47913687..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_y.c +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_concatenation_s8_y.c - * Description: s8 version of concatenation along the Y axis - * - * $Date: October 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Concatenation - * @{ - */ - - /* - * s8 version of concatenation along the Y axis - * - * Refer to header file for details. - * - */ -void arm_concatenation_s8_y(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_y, - const uint32_t offset_y) -{ - const uint32_t num_iterations = input_z * input_w; - const uint32_t input_copy_size = input_x * input_y; - const uint32_t output_stride = input_x * output_y; - - output += offset_y * input_x; - uint32_t i; - - // Copy per tile - for (i = 0; i < num_iterations; ++i) - { - memcpy(output, input, input_copy_size); - input += input_copy_size; - output += output_stride; - } -} - -/** - * @} end of Concatenation group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_z.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_z.c deleted file mode 100644 index 0d0bf500..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConcatenationFunctions/arm_concatenation_s8_z.c +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_concatenation_s8_z.c - * Description: s8 version of concatenation along the Z axis - * - * $Date: October 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Concatenation - * @{ - */ - - /* - * s8 version of concatenation along the Z axis - * - * Refer to header file for details. - * - */ -void arm_concatenation_s8_z(const int8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_z, - const uint16_t input_w, - int8_t *output, - const uint16_t output_z, - const uint32_t offset_z) -{ - const uint32_t input_copy_size = input_x * input_y * input_z; - const uint32_t output_stride = input_x * input_y * output_z; - - output += offset_z * (input_x * input_y); - - uint32_t i; - - for (i = 0; i < input_w; ++i) - { - memcpy(output, input, input_copy_size); - input += input_copy_size; - output += output_stride; - } -} - -/** - * @} end of Concatenation group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1_x_n_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1_x_n_s8.c deleted file mode 100644 index f2af65bb..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1_x_n_s8.c +++ /dev/null @@ -1,199 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_1_x_n_s8.c - * Description: s8 version of 1xN convolution using symmetric quantization. - * - * $Date: May 18, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * 1xN s8 convolution function. - * - * Refer header file for details. - * - */ - -arm_status arm_convolve_1_x_n_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data) -{ - (void)bias_dims; - arm_status status = ARM_MATH_SUCCESS; - if (output_dims->w % 4 != 0) - { - status = ARM_MATH_SIZE_MISMATCH; - goto out; - } - -#if defined(ARM_MATH_MVEI) - q15_t *buffer_a = (q15_t *)ctx->buf; - - const uint16_t input_x = input_dims->w; - const uint16_t kernel_x = filter_dims->w; - const uint16_t output_x = output_dims->w; - const uint16_t output_ch = output_dims->c; - const uint16_t input_ch = input_dims->c; - const uint16_t pad_x = conv_params->padding.w; - const uint16_t stride_x = conv_params->stride.w; - - const int32_t input_offset = conv_params->input_offset; - const int32_t out_offset = conv_params->output_offset; - const int32_t out_activation_min = conv_params->activation.min; - const int32_t out_activation_max = conv_params->activation.max; - int32_t *output_mult = quant_params->multiplier; - int32_t *output_shift = quant_params->shift; - - for (int i_out_x = 0; i_out_x <= (output_x - 4); i_out_x += 4) - { - int32_t input_begin_idx[4]; - int32_t ker_begin_idx[4]; - int32_t ker_end_idx[4]; - - for (int i = 0; i < 4; i++) - { - const int32_t est_input_x_idx = stride_x * (i_out_x + i) - pad_x; - input_begin_idx[i] = MAX(0, est_input_x_idx); - ker_begin_idx[i] = MAX(0, -est_input_x_idx); - ker_end_idx[i] = MIN(kernel_x, input_x - est_input_x_idx); - } - - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32x4_t s_offset; - int32_t acc[4]; - if ((ker_begin_idx[0] != 0) || (ker_end_idx[3] != kernel_x)) - { - int32_t sum_row[4]; - - (void)arm_nn_mat_mul_core_1x_s8((ker_end_idx[0] - ker_begin_idx[0]) * input_ch, - input_data + input_begin_idx[0] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch) + (ker_begin_idx[0] * input_ch), - &sum_row[0], - &acc[0]); - (void)arm_nn_mat_mul_core_1x_s8((ker_end_idx[1] - ker_begin_idx[1]) * input_ch, - input_data + input_begin_idx[1] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch) + (ker_begin_idx[1] * input_ch), - &sum_row[1], - &acc[1]); - - (void)arm_nn_mat_mul_core_1x_s8((ker_end_idx[2] - ker_begin_idx[2]) * input_ch, - input_data + input_begin_idx[2] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch) + (ker_begin_idx[2] * input_ch), - &sum_row[2], - &acc[2]); - - (void)arm_nn_mat_mul_core_1x_s8((ker_end_idx[3] - ker_begin_idx[3]) * input_ch, - input_data + input_begin_idx[3] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch) + (ker_begin_idx[3] * input_ch), - &sum_row[3], - &acc[3]); - - s_offset = vldrwq_s32(sum_row); - } - else - { - int32_t sum_row; - (void)arm_nn_mat_mul_core_4x_s8(kernel_x * input_ch, - stride_x * input_ch, - input_data + input_begin_idx[0] * input_ch, - filter_data + (input_ch * kernel_x * i_out_ch), - &sum_row, - acc); - - s_offset = vdupq_n_s32(sum_row); - } - int32x4_t res = vldrwq_s32(acc); - s_offset = vmulq_n_s32(s_offset, input_offset); - - res = vaddq_n_s32(res, bias_data[i_out_ch]); - res = vaddq_s32(res, s_offset); - res = arm_requantize_mve(res, output_mult[i_out_ch], output_shift[i_out_ch]); - res = vaddq_n_s32(res, out_offset); - - res = vmaxq_s32(res, vdupq_n_s32(out_activation_min)); - res = vminq_s32(res, vdupq_n_s32(out_activation_max)); - - const uint32x4_t scatter_offset = {0, output_ch, output_ch * 2, output_ch * 3}; - vstrbq_scatter_offset_s32(output_data, scatter_offset, res); - output_data++; - } - output_data += (3 * output_ch); - } - -#else - status = arm_convolve_s8(ctx, - conv_params, - quant_params, - input_dims, - input_data, - filter_dims, - filter_data, - bias_dims, - bias_data, - output_dims, - output_data); -#endif - -out: - /* Return to application */ - return status; -} - -int32_t arm_convolve_1_x_n_s8_get_buffer_size(const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims) -{ -#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI) - return (2 * input_dims->c * filter_dims->w * filter_dims->h) * sizeof(int16_t); -#else - (void)input_dims; - (void)filter_dims; - return 0; -#endif -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c deleted file mode 100644 index 0725e615..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c +++ /dev/null @@ -1,236 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_1x1_HWC_q7_fast_nonsquare.c - * Description: Fast Q7 version of 1x1 convolution (non-square shape) - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/** - * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is optimized for convolution with 1x1 kernel size (i.e., dim_kernel_x=1 - * and dim_kernel_y=1). It can be used for the second half of MobileNets [1] after depthwise - * separable convolution. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - * - * [1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications - * https://arxiv.org/abs/1704.04861 - */ - -arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - (void)dim_im_in_y; - int16_t i_out_y, i_out_x; - int16_t i_ch_out; - - /* ----------------------- - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1 - || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - /* This part implements the im2col function */ - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_out_y * dim_im_in_x + i_out_x) * ch_im_in, pBuffer, - ch_im_in); - pBuffer += ch_im_in; - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* check if there is left-over for compute */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++) - { - q31_t sum = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift); - const q15_t *pB = bufferA; - /* basically each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2; - - while (colCnt) - { - - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad_reordered(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut = (q7_t) __SSAT((sum >> out_shift), 8); - pOut++; - - } - - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1 - || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out_y; j++) - { - for (k = 0; k < dim_im_out_x; k++) - { - conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel_y; m++) - { - for (n = 0; n < dim_kernel_x; n++) - { - // if-for implementation - in_row = stride_y * j + m - padding_y; - in_col = stride_x * k + n - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * - wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_y + n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_s8_fast.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_s8_fast.c deleted file mode 100644 index 75e4c8e3..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_s8_fast.c +++ /dev/null @@ -1,182 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_1x1_s8_fast.c - * Description: Fast q7 version of 1x1 convolution (non-square shape) - * - * $Date: May 29, 2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" - -#define DIM_KER_X (1U) -#define DIM_KER_Y (1U) - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Fast s8 version for 1x1 convolution (non-square shape) - * - * Refer header file for details. - * - */ - -arm_status arm_convolve_1x1_s8_fast(const cmsis_nn_context *ctx, - const cmsis_nn_conv_params *conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input_data, - const cmsis_nn_dims *filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims *bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims *output_dims, - q7_t *output_data) -{ - if (input_dims->c % 4 != 0 || - conv_params->padding.w != 0 || conv_params->padding.h != 0 || - conv_params->stride.w != 1 || conv_params->stride.h != 1) - { - return ARM_MATH_SIZE_MISMATCH; - } - - (void)ctx; - (void)filter_dims; - (void)bias_dims; - -#if defined(ARM_MATH_MVEI) - - const int32_t col_len = input_dims->w * input_dims->h * input_dims->n; - const int32_t output_ch = output_dims->c; - const int32_t input_ch = input_dims->c; - const int32_t input_offset = conv_params->input_offset; - const int32_t out_offset = conv_params->output_offset; - const int32_t out_activation_min = conv_params->activation.min; - const int32_t out_activation_max = conv_params->activation.max; - int32_t *output_mult = quant_params->multiplier; - int32_t *output_shift = quant_params->shift; - - for (int i_items = 0; i_items <= (col_len - 4); i_items += 4) - { - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t sum_row = 0; - int32_t temp_out[4]; - - (void)arm_nn_mat_mul_core_4x_s8(input_ch, - input_ch, - input_data + i_items * input_ch, - filter_data + i_out_ch * input_ch, - &sum_row, - temp_out); - int32x4_t res = vldrwq_s32(temp_out); - - res = vaddq_n_s32(res, bias_data[i_out_ch]); - sum_row = sum_row * input_offset; - res = vaddq_n_s32(res, sum_row); - res = arm_requantize_mve(res, output_mult[i_out_ch], output_shift[i_out_ch]); - res = vaddq_n_s32(res, out_offset); - - res = vmaxq_s32(res, vdupq_n_s32(out_activation_min)); - res = vminq_s32(res, vdupq_n_s32(out_activation_max)); - - const uint32x4_t scatter_offset = {0, (uint32_t)output_ch, - (uint32_t)output_ch * 2, - (uint32_t)output_ch * 3}; - vstrbq_scatter_offset_s32(output_data, scatter_offset, res); - output_data++; - } - output_data += (3 * output_ch); - } - - /* Handle left over elements */ - for (int i_items = (col_len & ~0x3); i_items < col_len; i_items++) - { - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t sum_row = 0; - - int32_t acc; - (void)arm_nn_mat_mul_core_1x_s8(input_ch, - input_data + i_items * input_ch, - filter_data + i_out_ch * input_ch, - &sum_row, - &acc); - - acc += bias_data[i_out_ch]; - sum_row = (sum_row * input_offset); - acc += sum_row; - acc = arm_nn_requantize(acc, output_mult[i_out_ch], output_shift[i_out_ch]); - acc += out_offset; - - acc = MAX(acc, out_activation_min); - acc = MIN(acc, out_activation_max); - *output_data++ = acc; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M processors with or without DSP extension */ - - const int32_t lhs_rows = input_dims->w * input_dims->h * input_dims->n; - const int32_t rhs_rows = output_dims->c; - const int32_t rhs_cols = input_dims->c; - - arm_nn_mat_mult_nt_t_s8(input_data, - filter_data, - bias_data, - output_data, - quant_params->multiplier, - quant_params->shift, - lhs_rows, - rhs_rows, - rhs_cols, - conv_params->input_offset, - conv_params->output_offset, - conv_params->activation.min, - conv_params->activation.max); - -#endif - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -int32_t arm_convolve_1x1_s8_fast_get_buffer_size(const cmsis_nn_dims *input_dims) -{ - (void)input_dims; - return 0; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c deleted file mode 100644 index d7d25f44..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c +++ /dev/null @@ -1,207 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q15_basic.c - * Description: Q15 version of convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Basic Q15 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * bufferA size: ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * This basic version is designed to work for any input tensor and weight - * dimension. - */ - -arm_status -arm_convolve_HWC_q15_basic(const q15_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - uint16_t im2col_out_pixel_index = 0; - q15_t *pBuffer = bufferA; - q15_t *pOut = Im_out; - q15_t *im_buffer = bufferA; - const q15_t *pA; - int i; - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* Filling 0 for out-of-bound paddings */ - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in); - } - pBuffer += ch_im_in; - } - } - - pA = wt; - for (i = 0; i < ch_im_out; i++) - { - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - const q15_t *pB = im_buffer; - uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; - while (colCnt) - { - q31_t inA1 = arm_nn_read_q15x2_ia(&pA); - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inA2 = arm_nn_read_q15x2_ia(&pA); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB); - - sum = __SMLAD(inA1, inB1, sum); - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut = (q15_t) __SSAT((sum >> out_shift), 16); - pOut++; - } - - /* counter reset */ - pBuffer = im_buffer; - im2col_out_pixel_index++; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c deleted file mode 100644 index 221d64f0..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c +++ /dev/null @@ -1,255 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q15_fast.c - * Description: Fast Q15 version of convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Fast Q15 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in is multiple of 2 - * - * ch_im_out is multipe of 2 - * - */ - -arm_status -arm_convolve_HWC_q15_fast(const q15_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - q15_t *pBuffer = bufferA; - q15_t *im_buffer = bufferA; - q15_t *pOut = Im_out; - - if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (i_out_x & 0x1) - { - int i; - /* initialize the matrix pointers for A */ - const q15_t *pA = wt; - - /* set up the second output pointers */ - q15_t *pOut2 = pOut + ch_im_out; - - /* this loop over rows in A */ - for (i = 0; i < ch_im_out; i += 2) - { - /* setup pointers for B */ - const q15_t *pB = im_buffer; - const q15_t *pB2 = pB + ch_im_in * dim_kernel * dim_kernel; - - /* aling the second pointer for A */ - const q15_t *pA2 = pA + ch_im_in * dim_kernel * dim_kernel; - - /* init the sum with bias */ - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 1; - /* accumulate over the vector */ - while (colCnt) - { - q31_t inA1 = arm_nn_read_q15x2_ia(&pA); - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inA2 = arm_nn_read_q15x2_ia(&pA2); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA1, inB1, sum); - sum2 = __SMLAD(inA1, inB2, sum2); - sum3 = __SMLAD(inA2, inB1, sum3); - sum4 = __SMLAD(inA2, inB2, sum4); - - colCnt--; - } /* while over colCnt */ - colCnt = ch_im_in * dim_kernel * dim_kernel & 0x1; - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inB1 = *pB++; - q15_t inA2 = *pA2++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - sum3 += inA2 * inB1; - sum4 += inA2 * inB2; - colCnt--; - } /* while over colCnt */ - *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16); - *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16); - *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16); - *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16); - - /* skip the row computed with A2 */ - pA += ch_im_in * dim_kernel * dim_kernel; - } /* for over ch_im_out */ - - pOut += ch_im_out; - /* counter reset */ - pBuffer = im_buffer; - } - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c deleted file mode 100644 index 8b1fdc7d..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c +++ /dev/null @@ -1,265 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q15_fast.c - * Description: Fast Q15 version of convolution - * - * $Date: 24. May 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Fast Q15 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in is multiple of 2 - * - * ch_im_out is multipe of 2 - * - */ - -arm_status -arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q15_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q15_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q15_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - q15_t *pBuffer = bufferA; - q15_t *im_buffer = bufferA; - q15_t *pOut = Im_out; - - if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (i_out_x & 0x1) - { - int i; - /* initialize the matrix pointers for A */ - const q15_t *pA = wt; - - /* set up the second output pointers */ - q15_t *pOut2 = pOut + ch_im_out; - - /* this loop over rows in A */ - for (i = 0; i < ch_im_out; i += 2) - { - /* setup pointers for B */ - const q15_t *pB = im_buffer; - const q15_t *pB2 = pB + ch_im_in * dim_kernel_y * dim_kernel_x; - - /* aling the second pointer for A */ - const q15_t *pA2 = pA + ch_im_in * dim_kernel_y * dim_kernel_x; - - /* init the sum with bias */ - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 1; - /* accumulate over the vector */ - while (colCnt) - { - q31_t inA1 = arm_nn_read_q15x2_ia(&pA); - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inA2 = arm_nn_read_q15x2_ia(&pA2); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA1, inB1, sum); - sum2 = __SMLAD(inA1, inB2, sum2); - sum3 = __SMLAD(inA2, inB1, sum3); - sum4 = __SMLAD(inA2, inB2, sum4); - - colCnt--; - } /* while over colCnt */ - colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x1; - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inB1 = *pB++; - q15_t inA2 = *pA2++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - sum3 += inA2 * inB1; - sum4 += inA2 * inB2; - colCnt--; - } /* while over colCnt */ - *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16); - *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16); - *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16); - *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16); - - /* skip the row computed with A2 */ - pA += ch_im_in * dim_kernel_y * dim_kernel_x; - } /* for over ch_im_out */ - - pOut += ch_im_out; - /* counter reset */ - pBuffer = im_buffer; - } - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out_y; j++) - { - for (k = 0; k < dim_im_out_x; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel_y; m++) - { - for (n = 0; n < dim_kernel_x; n++) - { - in_row = stride_y * j + m - padding_y; - in_col = stride_x * k + n - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel_x * dim_kernel_y + (m * dim_kernel_x + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c deleted file mode 100644 index 912f8347..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c +++ /dev/null @@ -1,279 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_RGB.c - * Description: Q7 version of convolution for RGB image - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Q7 convolution function for RGB image - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in equals 3 - * - * This kernel is written exclusively for convolution with ch_im_in - * equals 3. This applies on the first layer of CNNs which has input - * image with RGB format. - */ - -arm_status -arm_convolve_HWC_q7_RGB(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, const uint16_t dim_im_out, q15_t * bufferA, q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - // check if number of input channels is 3 - if (ch_im_in != 3) - { - return ARM_MATH_SIZE_MISMATCH; - } - // This part implements the im2col function - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* Equivalent to arm_fill_q15(0, pBuffer, ch_im_in) with assumption: ch_im_in = 3 */ - *__SIMD32(pBuffer) = 0x0; - *(pBuffer + 2) = 0; - pBuffer += 3; - } else - { - /* - * Equivalent to: - * arm_q7_to_q15_no_shift( (q7_t*)Im_in+(i_ker_y*dim_im_in+i_ker_x)*3, pBuffer, 3); - */ - - const q7_t *pPixel = Im_in + (i_ker_y * dim_im_in + i_ker_x) * 3; - q31_t buf = arm_nn_read_q7x4(pPixel); - - union arm_nnword top; - union arm_nnword bottom; - - top.word = __SXTB16(buf); - bottom.word = __SXTB16(__ROR(buf, 8)); - -#ifndef ARM_MATH_BIG_ENDIAN - /* - * little-endian, | omit | 3rd | 2nd | 1st | - * MSB LSB - * top | 3rd | 1st |; bottom | omit | 2nd | - * - * version 1, need to swap 2nd and 3rd weight - * *__SIMD32(pBuffer) = top.word; - * *(pBuffer+2) = bottom.half_words[0]; - * - * version 2, no weight shuffling required - */ - *pBuffer++ = top.half_words[0]; - *__SIMD32(pBuffer) = __PKHBT(bottom.word, top.word, 0); -#else - /* - * big-endian, | 1st | 2nd | 3rd | omit | - * MSB LSB - * top | 2nd | omit |; bottom | 1st | 3rd | - * - * version 1, need to swap 2nd and 3rd weight - * *__SIMD32(pBuffer) = bottom.word; - * *(pBuffer+2) = top.half_words[1]; - * - * version 2, no weight shuffling required - */ - *pBuffer++ = bottom.half_words[0]; - *__SIMD32(pBuffer) = __PKHTB(top.word, bottom.word, 0); -#endif - pBuffer += 2; - } - } - } - - if (pBuffer == bufferA + 2 * 3 * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15(wt, bufferA, - ch_im_out, - 3 * dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* left-over because odd number of output pixels */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - - for (i = 0; i < ch_im_out; i++) - { - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - q15_t *pB = bufferA; - /* basically each time it process 4 entries */ - uint16_t colCnt = 3 * dim_kernel * dim_kernel >> 2; - - while (colCnt) - { - - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia((const q15_t **)&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia((const q15_t **)&pB); - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = 3 * dim_kernel * dim_kernel & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - } - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - // check if number of input channels is 3 - if (ch_im_in != 3) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - /* if-for implementation */ - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return (ARM_MATH_SUCCESS); -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c deleted file mode 100644 index 2e6147b0..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c +++ /dev/null @@ -1,231 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_basic.c - * Description: Q7 version of convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Basic Q7 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * This basic version is designed to work for any input tensor and weight - * dimension. - */ - -arm_status -arm_convolve_HWC_q7_basic(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* Filling 0 for out-of-bound paddings */ - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* Copying the pixel data to column */ - arm_q7_to_q15_no_shift((q7_t *) - Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - /* Computation is filed for every 2 columns */ - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15(wt, bufferA, - ch_im_out, - ch_im_in * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* left-over because odd number of output pixels */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - - for (i = 0; i < ch_im_out; i++) - { - /* Load the accumulator with bias first */ - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - - /* Point to the beging of the im2col buffer */ - const q15_t *pB = bufferA; - - /* Each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; - - while (colCnt) - { - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - } - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - // if-for implementation - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c deleted file mode 100644 index d50318db..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c +++ /dev/null @@ -1,229 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_basic.c - * Description: Q7 version of convolution - * - * $Date: 13. July 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Basic Q7 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns ARM_MATH_SUCCESS - */ - -arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* Filling 0 for out-of-bound paddings */ - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - /* Copying the pixel data to column */ - arm_q7_to_q15_no_shift((q7_t *) - Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - /* Computation is filed for every 2 columns */ - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_y * dim_kernel_x) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15(wt, bufferA, - ch_im_out, - ch_im_in * - dim_kernel_y * dim_kernel_x, bias_shift, out_shift, bias, pOut); - - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* left-over because odd number of output pixels */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - - for (i = 0; i < ch_im_out; i++) - { - /* Load the accumulator with bias first */ - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - - /* Point to the beging of the im2col buffer */ - const q15_t *pB = bufferA; - - /* Each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 2; - - while (colCnt) - { - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - } - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out_y; j++) - { - for (k = 0; k < dim_im_out_x; k++) - { - conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel_y; m++) - { - for (n = 0; n < dim_kernel_x; n++) - { - // if-for implementation - in_row = stride_y * j + m - padding_y; - in_col = stride_x * k + n - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * - wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + - (m * dim_kernel_x + n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c deleted file mode 100644 index 0352d6f8..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c +++ /dev/null @@ -1,408 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_fast.c - * Description: Fast Q7 version of convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - - /** - * @brief Fast Q7 convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in is multiple of 4 ( because of the SIMD32 read and swap ) - * - * ch_im_out is multipe of 2 ( bacause 2x2 mat_mult kernel ) - * - * The im2col converts the Q7 tensor input into Q15 column, which is stored in - * bufferA. There is reordering happenning during this im2col process with - * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and - * third elements are swapped. - * - * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the - * GEMM computation with the reordered columns. - * - * To speed-up the determination of the padding condition, we split the - * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}. - * This reduces the total number of boundary condition checks and improves - * the data copying performance. - */ - -arm_status -arm_convolve_HWC_q7_fast(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - /* - * Here we split the entire matrix into three regions depending on the padding situation - * Top: i_out_y from 0 to padding - 1 - * Middle: i_out_y from padding to dim_im_out-padding-1 - * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1 - */ - - /* top part */ - for (i_out_y = 0; i_out_y < padding; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift - ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* middle part, here we also divide the x into left, mid and right */ - for (; i_out_y < dim_im_out - padding; i_out_y++) - { - - /* left part */ - for (i_out_x = 0; i_out_x < padding; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift - ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - - /* mid part */ - for (; i_out_x < dim_im_out - padding; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in - + - (i_ker_y * - dim_im_in + - i_out_x * - stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel); - pBuffer += ch_im_in * dim_kernel; - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - - /* right part */ - for (; i_out_x < dim_im_out; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift - ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - for (; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift - ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, - bufferA, - ch_im_out, - ch_im_in - * - dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* check if there is left-over for compute */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - - for (i = 0; i < ch_im_out; i++) - { - q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); - const q15_t *pB = bufferA; - /* each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2; - - while (colCnt) - { - - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad_reordered(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut = (q7_t) __SSAT((sum >> out_shift), 8); - pOut++; - - } - - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out; j++) - { - for (k = 0; k < dim_im_out; k++) - { - conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel; m++) - { - for (n = 0; n < dim_kernel; n++) - { - // if-for implementation - in_row = stride * j + m - padding; - in_col = stride * k + n - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += - Im_in[(in_row * dim_im_in + in_col) * ch_im_in + - l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + - n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c deleted file mode 100644 index 31b4fdc2..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c +++ /dev/null @@ -1,379 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_HWC_q7_fast_nonsquare.c - * Description: Fast Q7 version of convolution (non-sqaure shape) - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/** - * @brief Fast Q7 convolution function (non-sqaure shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding size x - * @param[in] padding_y padding size y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is multiple of 4 - * ch_im_out is multiple of 2 - */ - -arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* ----------------------- - * Here we use bufferA as q15_t internally as computation are done with q15_t level - * im2col are done to output in q15_t format from q7_t input - */ - - q15_t *pBuffer = bufferA; - q7_t *pOut = Im_out; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - /* - * Here we split the entire matrix into three regions depending on the padding situation - * Top: i_out_y from 0 to padding - 1 - * Middle: i_out_y from padding to dim_im_out-padding-1 - * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1 - */ - - /* top part */ - for (i_out_y = 0; i_out_y < padding_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, - pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* middle part, here we also divide the x into left, mid and right */ - for (; i_out_y < dim_im_out_y - padding_y; i_out_y++) - { - - /* left part */ - for (i_out_x = 0; i_out_x < padding_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, - pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - - /* mid part */ - for (; i_out_x < dim_im_out_x - padding_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + - (i_ker_y * dim_im_in_x + i_out_x * stride_x - padding_x) * ch_im_in, - pBuffer, ch_im_in * dim_kernel_x); - pBuffer += ch_im_in * dim_kernel_x; - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - - /* right part */ - for (; i_out_x < dim_im_out_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, - pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - for (; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - /* This part implements the im2col function */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q15(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); - } else - { - arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, - pBuffer, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y) - { - pOut = - arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y, - bias_shift, out_shift, bias, pOut); - /* counter reset */ - pBuffer = bufferA; - } - } - } - - /* check if there is left-over for compute */ - if (pBuffer != bufferA) - { - const q7_t *pA = wt; - int i; - for (i = 0; i < ch_im_out; i++) - { - q31_t sum = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - const q15_t *pB = bufferA; - /* basically each time it process 4 entries */ - uint16_t colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2; - - while (colCnt) - { - - q31_t inA1, inA2; - q31_t inB1, inB2; - - pA = read_and_pad_reordered(pA, &inA1, &inA2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA1, inB1, sum); - inB2 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inA2, inB2, sum); - - colCnt--; - } - colCnt = (ch_im_in * dim_kernel_y * dim_kernel_x) & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - sum += inA1 * inB1; - colCnt--; - } - *pOut = (q7_t) __SSAT((sum >> out_shift), 8); - pOut++; - - } - - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i, j, k, l, m, n; - int conv_out; - int in_row, in_col; - - if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0) - { - /* check if the input dimension meets the constraints */ - return ARM_MATH_SIZE_MISMATCH; - } - - for (i = 0; i < ch_im_out; i++) - { - for (j = 0; j < dim_im_out_y; j++) - { - for (k = 0; k < dim_im_out_x; k++) - { - conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (m = 0; m < dim_kernel_y; m++) - { - for (n = 0; n < dim_kernel_x; n++) - { - /* if-for implementation */ - in_row = stride_y * j + m - padding_y; - in_col = stride_x * k + n - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - for (l = 0; l < ch_im_in; l++) - { - conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * - wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in + l]; - } - } - } - } - Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c deleted file mode 100644 index fd304734..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c +++ /dev/null @@ -1,372 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_s8.c - * Description: s8 version of convolution using symmetric quantization. - * - * $Date: May 29, 2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Basic s8 convolution function. - * - * Refer header file for details. Optimal use case for the DSP/MVE implementation is when input and output channels - * are multiples of 4 or atleast greater than 4. - * - */ - -arm_status arm_convolve_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data) -{ - q15_t *buffer_a = (q15_t *)ctx->buf; - - const uint16_t input_batches = input_dims->n; - const uint16_t input_x = input_dims->w; - const uint16_t input_y = input_dims->h; - const uint16_t input_ch = input_dims->c; - const uint16_t kernel_x = filter_dims->w; - const uint16_t kernel_y = filter_dims->h; - const uint16_t output_x = output_dims->w; - const uint16_t output_y = output_dims->h; - const uint16_t output_ch = output_dims->c; - - const uint16_t pad_x = conv_params->padding.w; - const uint16_t pad_y = conv_params->padding.h; - const uint16_t stride_x = conv_params->stride.w; - const uint16_t stride_y = conv_params->stride.h; - - const int32_t input_offset = conv_params->input_offset; - const int32_t out_offset = conv_params->output_offset; - const int32_t out_activation_min = conv_params->activation.min; - const int32_t out_activation_max = conv_params->activation.max; - int32_t *output_mult = quant_params->multiplier; - int32_t *output_shift = quant_params->shift; - - int i_batch; - for (i_batch = 0; i_batch < input_batches; i_batch++) - { -#if defined(ARM_MATH_MVEI) - (void)bias_dims; - /* Generate upto four columns from the input tensor a GEMM computation */ - q7_t *im2col_buf = (q7_t *)buffer_a; - q7_t *out = output_data; - int32_t buffer_fill_cnt = 0; - int32_t padded = 0; - const int32_t num_elem = kernel_x * kernel_y * input_ch; - - /* This part implements the im2col function */ - for (int i_out_y = 0; i_out_y < output_y; i_out_y++) - { - for (int i_out_x = 0; i_out_x < output_x; i_out_x++) - { - for (int i_ker_y = i_out_y * stride_y - pad_y; i_ker_y < i_out_y * stride_y - pad_y + kernel_y; i_ker_y++) - { - for (int i_ker_x = i_out_x * stride_x - pad_x; i_ker_x < i_out_x * stride_x - pad_x + kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x) - { - memset(im2col_buf, (int8_t)-input_offset, sizeof(q7_t) * input_ch); - padded = 1; - } - else - { - arm_memcpy_q7(im2col_buf, input_data + (i_ker_y * input_x + i_ker_x) * input_ch, input_ch); - } - im2col_buf += input_ch; - } - } - - buffer_fill_cnt++; - - /* Computation is filed for every 4 columns */ - if (buffer_fill_cnt == 4 && (padded == 0)) - { - buffer_fill_cnt = 0; - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t sum_row; - int32_t acc[4]; - - (void)arm_nn_mat_mul_core_4x_s8(num_elem, - num_elem, - (q7_t *)buffer_a, - filter_data + num_elem * i_out_ch, - &sum_row, - acc); - int32x4_t s_offset = vdupq_n_s32(sum_row); - - int32x4_t res = vldrwq_s32(acc); - s_offset = vmulq_n_s32(s_offset, input_offset); - - res = vaddq_n_s32(res, bias_data[i_out_ch]); - res = vaddq_s32(res, s_offset); - res = arm_requantize_mve(res, output_mult[i_out_ch], output_shift[i_out_ch]); - res = vaddq_n_s32(res, out_offset); - - res = vmaxq_s32(res, vdupq_n_s32(out_activation_min)); - res = vminq_s32(res, vdupq_n_s32(out_activation_max)); - - const uint32x4_t scatter_offset = {0, output_ch, output_ch * 2, output_ch * 3}; - vstrbq_scatter_offset_s32(out, scatter_offset, res); - out++; - } - out += (3 * output_ch); - im2col_buf = (q7_t *)buffer_a; - } - else if (buffer_fill_cnt == 4 && (padded != 0)) - { - buffer_fill_cnt = 0; - out = arm_nn_mat_mult_s8(filter_data, - (q7_t *)buffer_a, - output_ch, - 4, - output_shift, - output_mult, - out_offset, - input_offset, - 0, - out_activation_min, - out_activation_max, - num_elem, - bias_data, - out); - - im2col_buf = (q7_t *)buffer_a; - padded = 0; - } - } - } - /* Handle left over columns */ - if (buffer_fill_cnt != 0) - { - out = arm_nn_mat_mult_s8(filter_data, - (q7_t *)buffer_a, - output_ch, - buffer_fill_cnt, - output_shift, - output_mult, - out_offset, - input_offset, - 0, - out_activation_min, - out_activation_max, - num_elem, - bias_data, - out); - } - -#elif defined(ARM_MATH_DSP) - (void)bias_dims; - int32_t i_out_y, i_out_x, i_ker_y, i_ker_x; - - /* Generate two columns from the input tensor a GEMM computation */ - q15_t *two_column_buf = buffer_a; - q7_t *out = output_data; - - /* This part implements the im2col function */ - for (i_out_y = 0; i_out_y < output_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < output_x; i_out_x++) - { - for (i_ker_y = i_out_y * stride_y - pad_y; i_ker_y < i_out_y * stride_y - pad_y + kernel_y; i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - pad_x; i_ker_x < i_out_x * stride_x - pad_x + kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x) - { - /* Filling 0 for out-of-bound paddings */ - memset(two_column_buf, 0, sizeof(q15_t) * input_ch); - } - else - { - /* Copying the pixel data to column */ - arm_q7_to_q15_with_offset(input_data + (i_ker_y * input_x + i_ker_x) * input_ch, two_column_buf, input_ch, input_offset); - } - two_column_buf += input_ch; - } - } - - /* Computation is filed for every 2 columns */ - if (two_column_buf == buffer_a + 2 * input_ch * kernel_y * kernel_x) - { - out = - arm_nn_mat_mult_kernel_s8_s16(filter_data, - buffer_a, - output_ch, - output_shift, - output_mult, - out_offset, - out_activation_min, - out_activation_max, - input_ch * kernel_y * kernel_x, - bias_data, - out); - - /* counter reset */ - two_column_buf = buffer_a; - } - } - } - - /* left-over because odd number of output pixels */ - if (two_column_buf != buffer_a) - { - const q7_t *ker_a = filter_data; - int i; - - for (i = 0; i < output_ch; i++) - { - /* Load the accumulator with bias first */ - q31_t sum = bias_data[i]; - - /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */ - const q15_t *ip_as_col = buffer_a; - - /* 4 multiply and accumulates are done in one loop. */ - uint16_t col_count = (input_ch * kernel_y * kernel_x) >> 2; - - while (col_count) - { - q31_t ker_a1, ker_a2; - q31_t ip_b1, ip_b2; - - ker_a = read_and_pad(ker_a, &ker_a1, &ker_a2); - - ip_b1 = arm_nn_read_q15x2_ia(&ip_as_col); - sum = __SMLAD(ker_a1, ip_b1, sum); - ip_b2 = arm_nn_read_q15x2_ia(&ip_as_col); - sum = __SMLAD(ker_a2, ip_b2, sum); - - col_count--; - } - /* Handle left over mac */ - col_count = input_ch * kernel_y * kernel_x & 0x3; - while (col_count) - { - q7_t ker_a1 = *ker_a++; - q15_t ip_b1 = *ip_as_col++; - sum += ker_a1 * ip_b1; - col_count--; - } - - sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]); - sum += out_offset; - sum = MAX(sum, out_activation_min); - sum = MIN(sum, out_activation_max); - *out++ = (q7_t)sum; - } - } -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - (void)buffer_a; - int32_t i_out_ch, i_out_y, i_out_x, i_input_ch, i_ker_y, i_ker_x; - int32_t conv_out; - - for (i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - for (i_out_y = 0; i_out_y < output_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < output_x; i_out_x++) - { - conv_out = bias_data[i_out_ch]; - - const int32_t base_idx_y = stride_y * i_out_y - pad_y; - const int32_t base_idx_x = stride_x * i_out_x - pad_x; - - const int32_t ker_y_start = MAX(0, -base_idx_y); - const int32_t ker_x_start = MAX(0, -base_idx_x); - - const int32_t ker_y_end = MIN(kernel_y, input_y - base_idx_y); - const int32_t ker_x_end = MIN(kernel_x, input_x - base_idx_x); - - for (i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) - { - for (i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) - { - const int32_t in_row = base_idx_y + i_ker_y; - const int32_t in_col = base_idx_x + i_ker_x; - for (i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) - { - conv_out += - (input_data[(in_row * input_x + in_col) * input_ch + i_input_ch] + input_offset) * - filter_data[i_out_ch * input_ch * kernel_y * kernel_x + - (i_ker_y * kernel_x + i_ker_x) * input_ch + i_input_ch]; - } - } - } - conv_out = arm_nn_requantize(conv_out, output_mult[i_out_ch], output_shift[i_out_ch]); - conv_out += out_offset; - conv_out = MAX(conv_out, out_activation_min); - conv_out = MIN(conv_out, out_activation_max); - output_data[i_out_ch + (i_out_y * output_x + i_out_x) * output_ch] = (int8_t)conv_out; - } - } - } -#endif - /* Advance to the next batch */ - input_data += (input_x * input_y * input_ch); - output_data += (output_x * output_y * output_ch); - } - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -int32_t arm_convolve_s8_get_buffer_size(const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims) -{ -#if defined(ARM_MATH_DSP) - return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t); -#else - (void)input_dims; - (void)filter_dims; - return 0; -#endif -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_wrapper_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_wrapper_s8.c deleted file mode 100644 index 5cef066c..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_wrapper_s8.c +++ /dev/null @@ -1,148 +0,0 @@ -/* - * Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_convolve_wrapper_s8.c - * Description: s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in cmsis-nn to perform the convolution. - * - * $Date: May 18, 2020 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nn_types.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Convolution layer - * - * Refer header file for details. - * - */ - -arm_status arm_convolve_wrapper_s8(const cmsis_nn_context* ctx, - const cmsis_nn_conv_params* conv_params, - const cmsis_nn_per_channel_quant_params* quant_params, - const cmsis_nn_dims* input_dims, - const q7_t *input_data, - const cmsis_nn_dims* filter_dims, - const q7_t *filter_data, - const cmsis_nn_dims* bias_dims, - const int32_t *bias_data, - const cmsis_nn_dims* output_dims, - q7_t *output_data) -{ - if ((conv_params->padding.w == 0) && - (conv_params->padding.h == 0) && - (input_dims->c % 4 == 0) && - (conv_params->stride.w == 1) && - (conv_params->stride.h == 1) && - (filter_dims->w == 1) && - (filter_dims->h == 1)) - { - return arm_convolve_1x1_s8_fast(ctx, - conv_params, - quant_params, - input_dims, - input_data, - filter_dims, - filter_data, - bias_dims, - bias_data, - output_dims, - output_data); - } - else if ((output_dims->h == 1) && - (input_dims->h == 1) && - (filter_dims->h == 1) && - (output_dims->w % 4 == 0) && - (input_dims->n == 1)) - { - return arm_convolve_1_x_n_s8(ctx, - conv_params, - quant_params, - input_dims, - input_data, - filter_dims, - filter_data, - bias_dims, - bias_data, - output_dims, - output_data); - } - else - { - return arm_convolve_s8(ctx, - conv_params, - quant_params, - input_dims, - input_data, - filter_dims, - filter_data, - bias_dims, - bias_data, - output_dims, - output_data); - } -} - -int32_t arm_convolve_wrapper_s8_get_buffer_size(const cmsis_nn_conv_params* conv_params, - const cmsis_nn_dims* input_dims, - const cmsis_nn_dims* filter_dims, - const cmsis_nn_dims* output_dims) -{ - if ((conv_params->padding.w == 0) && - (conv_params->padding.h == 0) && - (input_dims->c % 4 == 0) && - (conv_params->stride.w == 1) && - (conv_params->stride.h == 1) && - (filter_dims->w == 1) && - (filter_dims->h == 1)) - { - return arm_convolve_1x1_s8_fast_get_buffer_size(input_dims); - } - else if ((output_dims->h == 1) && - (input_dims->h == 1) && - (filter_dims->h == 1) && - (output_dims->w % 4 == 0) && - (input_dims->n == 1)) - { - return arm_convolve_1_x_n_s8_get_buffer_size(input_dims, filter_dims); - } - else - { - return arm_convolve_s8_get_buffer_size(input_dims, filter_dims); - } -} - -/** - * @} end of NNConv group - */ - diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_3x3_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_3x3_s8.c deleted file mode 100644 index 5705b4bb..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_3x3_s8.c +++ /dev/null @@ -1,213 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_3x3_s8.c - * Description: Optimized s8 depthwise convolution function for channel - * multiplier of 1 and 3x3 kernel size. - * - * $Date: May 14, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Optimized s8 depthwise convolution function with constraint that - * in_channel == out_channel and kernel_x == kernel_y == 3 with pads at most 1 - * - * Refer prototype header file for details. - * - */ - -arm_status arm_depthwise_conv_3x3_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *kernel, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - (void)ctx; - (void)bias_dims; - - const int32_t input_x = input_dims->w; - const int32_t input_y = input_dims->h; - const int32_t input_ch = input_dims->c; - const int32_t output_ch = output_dims->c; - const int32_t pad_x = dw_conv_params->padding.w; - const int32_t pad_y = dw_conv_params->padding.h; - const int32_t stride_x = dw_conv_params->stride.w; - const int32_t stride_y = dw_conv_params->stride.h; - const int32_t *output_shift = quant_params->shift; - const int32_t *output_mult = quant_params->multiplier; - const int32_t output_x = output_dims->w; - const int32_t output_y = output_dims->h; - const int32_t output_offset = dw_conv_params->output_offset; - const int32_t input_offset = dw_conv_params->input_offset; - const int32_t output_activation_min = dw_conv_params->activation.min; - const int32_t output_activation_max = dw_conv_params->activation.max; - - /* Check input constraints input_ch == output_ch */ - if (input_ch != output_ch) - { - return ARM_MATH_SIZE_MISMATCH; - } - /* Check input constraints pad_x <= 1 */ - if (pad_x > 1 || filter_dims->w != 3 || filter_dims->h != 3) - { - return ARM_MATH_ARGUMENT_ERROR; - } - - for (int32_t in_h = -pad_y, out_h = 0, out_idx = 0; out_h < output_y; in_h += stride_y, ++out_h) - { - for (int32_t in_w = -pad_x, out_w = 0, ker_h_start = MAX(0, -in_h); out_w < output_x; in_w += stride_x, ++out_w) - { - int32_t in_ch = 0; - int32_t ker_w_start = MAX(0, -in_w); - - for (; in_ch <= (input_ch - 4); in_ch += 4) - { - int32_t out_buff0 = bias[in_ch + 0]; - int32_t out_buff1 = bias[in_ch + 1]; - int32_t out_buff2 = bias[in_ch + 2]; - int32_t out_buff3 = bias[in_ch + 3]; - - const int8_t *input_ptr = input + (in_h + ker_h_start) * (input_ch * input_x) + in_w * input_ch + in_ch; - const int8_t *kernel_ptr = kernel + ker_h_start * (input_ch * 3) + in_ch; - - for (int32_t ker_h = ker_h_start; ker_h < MIN(3, input_y - in_h); ++ker_h) - { - int32_t in_val = 0; - int32_t ker_val = 0; - - if (ker_w_start == 0) - { - in_val = arm_nn_read_q7x4(input_ptr); - ker_val = arm_nn_read_q7x4(kernel_ptr); - - out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val; - out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8); - out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16); - out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24); - } - - in_val = arm_nn_read_q7x4(input_ptr + input_ch); - ker_val = arm_nn_read_q7x4(kernel_ptr + input_ch); - - out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val; - out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8); - out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16); - out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24); - - if ((input_x - in_w) >= 3) - { - in_val = arm_nn_read_q7x4(input_ptr + (input_ch << 1)); - ker_val = arm_nn_read_q7x4(kernel_ptr + (input_ch << 1)); - - out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val; - out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8); - out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16); - out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24); - } - - input_ptr += (input_ch * input_x); - kernel_ptr += (input_ch * 3); - } - - out_buff0 = arm_nn_requantize(out_buff0, output_mult[in_ch + 0], output_shift[in_ch + 0]); - out_buff1 = arm_nn_requantize(out_buff1, output_mult[in_ch + 1], output_shift[in_ch + 1]); - out_buff2 = arm_nn_requantize(out_buff2, output_mult[in_ch + 2], output_shift[in_ch + 2]); - out_buff3 = arm_nn_requantize(out_buff3, output_mult[in_ch + 3], output_shift[in_ch + 3]); - - out_buff0 += output_offset; - out_buff1 += output_offset; - out_buff2 += output_offset; - out_buff3 += output_offset; - - out_buff0 = MIN(MAX(out_buff0, output_activation_min), output_activation_max); - out_buff1 = MIN(MAX(out_buff1, output_activation_min), output_activation_max); - out_buff2 = MIN(MAX(out_buff2, output_activation_min), output_activation_max); - out_buff3 = MIN(MAX(out_buff3, output_activation_min), output_activation_max); - - output[out_idx++] = (int8_t)out_buff0; - output[out_idx++] = (int8_t)out_buff1; - output[out_idx++] = (int8_t)out_buff2; - output[out_idx++] = (int8_t)out_buff3; - } - - // Leftover - for (; in_ch < input_ch; ++in_ch) - { - int32_t out_buff = bias[in_ch]; - - const int8_t *input_ptr = input + (in_h + ker_h_start) * (input_ch * input_x) + in_w * input_ch + in_ch; - const int8_t *kernel_ptr = kernel + ker_h_start * (input_ch * 3) + in_ch; - - for (int32_t ker_h = ker_h_start; ker_h < MIN(3, input_y - in_h); ++ker_h) - { - if (ker_w_start == 0) - { - out_buff += (*(input_ptr) + input_offset) * *(kernel_ptr); - } - - out_buff += (*(input_ptr + input_ch) + input_offset) * *(kernel_ptr + input_ch); - - if ((input_x - in_w) >= 3) - { - out_buff += (*(input_ptr + (input_ch << 1)) + input_offset) * *(kernel_ptr + (input_ch << 1)); - } - - input_ptr += (input_ch * input_x); - kernel_ptr += (input_ch * 3); - } - - out_buff = arm_nn_requantize(out_buff, output_mult[in_ch], output_shift[in_ch]); - out_buff += output_offset; - out_buff = MIN(MAX(out_buff, output_activation_min), output_activation_max); - output[out_idx++] = (int8_t)out_buff; - } - } - } - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8.c deleted file mode 100644 index 4819a0cc..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8.c +++ /dev/null @@ -1,249 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_s8.c - * Description: s8 version of depthwise convolution. - * - * $Date: May 14, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -static void depthwise_conv_s8_mult_4(const int8_t *input, - const int32_t input_x, - const int32_t input_y, - const int32_t input_ch, - const int8_t *kernel, - const int32_t output_ch, - const int32_t ch_mult, - const int32_t kernel_x, - const int32_t kernel_y, - const int32_t pad_x, - const int32_t pad_y, - const int32_t stride_x, - const int32_t stride_y, - const int32_t *bias, - int8_t *output, - const int32_t *output_shift, - const int32_t *output_mult, - const int32_t output_x, - const int32_t output_y, - const int32_t output_offset, - const int32_t input_offset, - const int32_t output_activation_min, - const int32_t output_activation_max) -{ - for (int32_t in_h = -pad_y, out_h = 0, out_idx = 0; out_h < output_y; in_h += stride_y, ++out_h) - { - for (int32_t in_w = -pad_x, out_w = 0, ker_h_start = MAX(0, -in_h); out_w < output_x; in_w += stride_x, ++out_w) - { - for (int32_t in_ch = 0, out_ch = 0, ker_w_start = MAX(0, -in_w); out_ch < output_ch; ++in_ch, out_ch += ch_mult) - { - for (int mult_tile = 0; mult_tile < ch_mult; mult_tile += 4) - { - int32_t out_buff[4]; - - out_buff[0] = bias[out_ch + 0 + mult_tile]; - out_buff[1] = bias[out_ch + 1 + mult_tile]; - out_buff[2] = bias[out_ch + 2 + mult_tile]; - out_buff[3] = bias[out_ch + 3 + mult_tile]; - - for (int32_t ker_h = ker_h_start; ker_h < MIN(kernel_y, input_y - in_h); ++ker_h) - { - int32_t ker_idx = ker_h * (output_ch * kernel_x) + ker_w_start * output_ch + out_ch; - int32_t in_idx = (in_h + ker_h) * (input_ch * input_x) + in_w * input_ch + in_ch; - - for (int32_t ker_w = ker_w_start; ker_w < MIN(kernel_x, input_x - in_w); ++ker_w, ker_idx += output_ch) - { - int32_t in_val = input[in_idx + ker_w * input_ch] + input_offset; - out_buff[0] += in_val * kernel[ker_idx + 0 + mult_tile]; - out_buff[1] += in_val * kernel[ker_idx + 1 + mult_tile]; - out_buff[2] += in_val * kernel[ker_idx + 2 + mult_tile]; - out_buff[3] += in_val * kernel[ker_idx + 3 + mult_tile]; - } - } -#if defined(ARM_MATH_MVEI) - (void)out_idx; - int32x4_t res = vldrwq_s32(out_buff); - res = arm_requantize_mve_32x4(res, vldrwq_s32(&output_mult[out_ch + mult_tile]), vldrwq_s32(&output_shift[out_ch + mult_tile])); - res = vaddq_n_s32(res, output_offset); - - res = vmaxq_s32(res, vdupq_n_s32(output_activation_min)); - res = vminq_s32(res, vdupq_n_s32(output_activation_max)); - vstrbq_s32(output, res); - output += 4; -#else - out_buff[0] = arm_nn_requantize(out_buff[0], output_mult[out_ch + 0 + mult_tile], output_shift[out_ch + 0 + mult_tile]); - out_buff[1] = arm_nn_requantize(out_buff[1], output_mult[out_ch + 1 + mult_tile], output_shift[out_ch + 1 + mult_tile]); - out_buff[2] = arm_nn_requantize(out_buff[2], output_mult[out_ch + 2 + mult_tile], output_shift[out_ch + 2 + mult_tile]); - out_buff[3] = arm_nn_requantize(out_buff[3], output_mult[out_ch + 3 + mult_tile], output_shift[out_ch + 3 + mult_tile]); - - out_buff[0] += output_offset; - out_buff[1] += output_offset; - out_buff[2] += output_offset; - out_buff[3] += output_offset; - - out_buff[0] = MIN(MAX(out_buff[0], output_activation_min), output_activation_max); - out_buff[1] = MIN(MAX(out_buff[1], output_activation_min), output_activation_max); - out_buff[2] = MIN(MAX(out_buff[2], output_activation_min), output_activation_max); - out_buff[3] = MIN(MAX(out_buff[3], output_activation_min), output_activation_max); - - output[out_idx++] = (int8_t)out_buff[0]; - output[out_idx++] = (int8_t)out_buff[1]; - output[out_idx++] = (int8_t)out_buff[2]; - output[out_idx++] = (int8_t)out_buff[3]; - -#endif - } - } - } - } -} - -static void depthwise_conv_s8_generic(const q7_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_ch, - const q7_t *kernel, - const uint16_t output_ch, - const uint16_t ch_mult, - const uint16_t kernel_x, - const uint16_t kernel_y, - const uint16_t pad_x, - const uint16_t pad_y, - const uint16_t stride_x, - const uint16_t stride_y, - const int32_t *bias, - q7_t *output, - const int32_t *output_shift, - const int32_t *output_mult, - const uint16_t output_x, - const uint16_t output_y, - const int32_t output_offset, - const int32_t input_offset, - const int32_t output_activation_min, - const int32_t output_activation_max) -{ - (void)output_ch; - int i_out = 0; - for (int i_out_y = 0; i_out_y < output_y; i_out_y++) - { - const int16_t base_idx_y = (i_out_y * stride_y) - pad_y; - for (int i_out_x = 0; i_out_x < output_x; i_out_x++) - { - const int16_t base_idx_x = (i_out_x * stride_x) - pad_x; - for (int i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) - { - for (int i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult++) - { - const int idx_out_ch = i_ch_mult + i_input_ch * ch_mult; - int32_t acc_0; - /* Condition for kernel start dimension: (base_idx_ + ker__start) >= 0 */ - const int ker_y_start = MAX(0, -base_idx_y); - const int ker_x_start = MAX(0, -base_idx_x); - /* Condition for kernel end dimension: (base_idx_ + ker__end) < input_ */ - const int ker_y_end = MIN(kernel_y, input_y - base_idx_y); - const int ker_x_end = MIN(kernel_x, input_x - base_idx_x); - acc_0 = bias[idx_out_ch]; - - for (int i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) - { - const int32_t idx_y = base_idx_y + i_ker_y; - for (int i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) - { - const int32_t idx_x = base_idx_x + i_ker_x; - int32_t idx_0 = (idx_y * input_x + idx_x) * input_ch + i_input_ch; - int32_t ker_idx_0 = (i_ker_y * kernel_x + i_ker_x) * (input_ch * ch_mult) + idx_out_ch; - - acc_0 += (input[idx_0] + input_offset) * kernel[ker_idx_0]; - } - } - - /* Requantize and clamp output to provided range */ - acc_0 = arm_nn_requantize(acc_0, output_mult[idx_out_ch], output_shift[idx_out_ch]); - acc_0 += output_offset; - acc_0 = MAX(acc_0, output_activation_min); - acc_0 = MIN(acc_0, output_activation_max); - - output[i_out++] = acc_0; - } - } - } - } -} - -/* - * Basic s8 depthwise convolution function. - * - * Refer header file for details. - * Optimization using DSP extension is not available for the generic case where channel multiplier is > 1. - * - */ -arm_status arm_depthwise_conv_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *kernel, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - (void)dw_conv_params->dilation; - (void)ctx; - - if (dw_conv_params->ch_mult % 4 == 0) - { - depthwise_conv_s8_mult_4(input, input_dims->w, input_dims->h, input_dims->c, kernel, output_dims->c, dw_conv_params->ch_mult, filter_dims->w, filter_dims->h, - dw_conv_params->padding.w, dw_conv_params->padding.h, dw_conv_params->stride.w, dw_conv_params->stride.h, bias, output, - quant_params->shift, quant_params->multiplier, output_dims->w, output_dims->h, dw_conv_params->output_offset, - dw_conv_params->input_offset, dw_conv_params->activation.min, dw_conv_params->activation.max); - } - else - { - depthwise_conv_s8_generic(input, input_dims->w, input_dims->h, input_dims->c, kernel, output_dims->c, dw_conv_params->ch_mult, filter_dims->w, filter_dims->h, - dw_conv_params->padding.w, dw_conv_params->padding.h, dw_conv_params->stride.w, dw_conv_params->stride.h, bias, output, - quant_params->shift, quant_params->multiplier, output_dims->w, output_dims->h, dw_conv_params->output_offset, - dw_conv_params->input_offset, dw_conv_params->activation.min, dw_conv_params->activation.max); - } - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8_opt.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8_opt.c deleted file mode 100644 index 3587b9c9..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8_opt.c +++ /dev/null @@ -1,425 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_s8_opt.c - * Description: Optimized s8 depthwise separable convolution function for - * channel multiplier of 1. - * - * $Date: May 29, 2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * Optimized s8 depthwise convolution function with constraint that in_channel equals out_channel - * - * Refer prototype header file for details. - * - */ - -arm_status arm_depthwise_conv_s8_opt(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *kernel, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - const int32_t input_x = input_dims->w; - const int32_t input_y = input_dims->h; - const int32_t input_ch = input_dims->c; - const int32_t output_ch = output_dims->c; - const int32_t kernel_x = filter_dims->w; - const int32_t kernel_y = filter_dims->h; - const int32_t pad_x = dw_conv_params->padding.w; - const int32_t pad_y = dw_conv_params->padding.h; - const int32_t stride_x = dw_conv_params->stride.w; - const int32_t stride_y = dw_conv_params->stride.h; - const int32_t *output_shift = quant_params->shift; - const int32_t *output_mult = quant_params->multiplier; - const int32_t output_x = output_dims->w; - const int32_t output_y = output_dims->h; - const int32_t output_offset = dw_conv_params->output_offset; - const int32_t input_offset = dw_conv_params->input_offset; - const int32_t output_activation_min = dw_conv_params->activation.min; - const int32_t output_activation_max = dw_conv_params->activation.max; - q15_t *buffer_a = (q15_t *)ctx->buf; - - /* Check input constraints input_ch == output_ch */ - if (input_ch != output_ch) - { - return ARM_MATH_SIZE_MISMATCH; - } -#ifdef ARM_MATH_MVEI - (void)bias_dims; - /* Generate two columns from the input tensor */ - q7_t *lhs_buffer = (q7_t *)buffer_a; - q7_t *out = output; - int padded = 0; - int buffer_count = 0; - const int32_t kernel_size = kernel_x * kernel_y; - - /* This part implements the im2col function */ - for (int i_out_y = 0, base_idx_y = -pad_y; i_out_y < output_y; base_idx_y += stride_y, i_out_y++) - { - for (int i_out_x = 0, base_idx_x = -pad_x; i_out_x < output_x; base_idx_x += stride_x, i_out_x++) - { - for (int i_ker_y = base_idx_y; i_ker_y < base_idx_y + kernel_y; i_ker_y++) - { - for (int i_ker_x = base_idx_x; i_ker_x < base_idx_x + kernel_x; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x) - { - arm_memset_q7(lhs_buffer, (int8_t)-input_offset, (uint32_t)input_ch); - padded = 1; - } - else - { - arm_memcpy_q7(lhs_buffer, input + (i_ker_y * input_x + i_ker_x) * input_ch, (uint32_t)input_ch); - } - lhs_buffer += input_ch; - } - } - buffer_count++; - - if (buffer_count == 4) - { - lhs_buffer = (q7_t *)buffer_a; - if (padded == 0) - { - out = arm_nn_depthwise_conv_nt_t_s8(lhs_buffer, - kernel, - input_offset, - input_ch, - output_shift, - output_mult, - output_offset, - output_activation_min, - output_activation_max, - kernel_size, - bias, - out); - } - else - { - out = arm_nn_depthwise_conv_nt_t_padded_s8(lhs_buffer, - kernel, - input_offset, - input_ch, - output_shift, - output_mult, - output_offset, - output_activation_min, - output_activation_max, - kernel_size, - bias, - out); - padded = 0; - } - buffer_count = 0; - } - } - } - - /* Handle left over buffers */ - lhs_buffer = (q7_t *)buffer_a; - - for (int i_buf = 0; i_buf < buffer_count; i_buf++) - { - int32_t loop_count = (input_ch + 3) / 4; - - int32_t num_ch_to_process = input_ch; - for (int i_loop_cnt = 0, offset = 0; i_loop_cnt < loop_count; - num_ch_to_process -= 4, offset += 4, i_loop_cnt++) - { - const int8_t *col_0 = lhs_buffer + (kernel_size * input_ch * i_buf) + offset; - const int8_t *row_0 = kernel + offset; - int32x4_t out_0 = vldrwq_s32(&bias[offset]); - - for (int i_ker = 0; i_ker < kernel_size; i_ker++) - { - const int32x4_t ker_0 = vldrbq_s32(row_0); - - int32x4_t ip_0 = vldrbq_s32(col_0); - ip_0 = vaddq_n_s32(ip_0, input_offset); - out_0 += vmulq_s32(ip_0, ker_0); - - col_0 += input_ch; - row_0 += input_ch; - } - - const int32x4_t mult = vldrwq_s32(&output_mult[offset]); - const int32x4_t shift = vldrwq_s32(&output_shift[offset]); - - out_0 = arm_requantize_mve_32x4(out_0, mult, shift); - out_0 = vaddq_n_s32(out_0, output_offset); - out_0 = vmaxq_s32(out_0, vdupq_n_s32(output_activation_min)); - out_0 = vminq_s32(out_0, vdupq_n_s32(output_activation_max)); - mve_pred16_t p = vctp32q((uint32_t)num_ch_to_process); - vstrbq_p_s32(out, out_0, p); - - out += 4; - } - - const int tail_ch = input_ch & 0x3; - if (tail_ch != 0) - { - out -= (4 - tail_ch); - } - } - -#elif defined(ARM_MATH_DSP) - (void)bias_dims; - /* Run the following code in cores using DSP extension */ - q15_t *const col_buffer_start = buffer_a; - q15_t *col_buffer = col_buffer_start; - const int32_t *const bias_start_pos = bias; - const q31_t *const out_mult_start_pos = output_mult; - const q31_t *const out_shift_start_pos = output_shift; - uint16_t row_count; - uint16_t row_shift; - - for (int i_out_y = 0; i_out_y < output_y; i_out_y++) - { - const int16_t base_idx_y = (i_out_y * stride_y) - pad_y; - for (int i_out_x = 0; i_out_x < output_x; i_out_x++) - { - const int16_t base_idx_x = (i_out_x * stride_x) - pad_x; - - /* Out of bounds is only considered for the y axis as it provides a contiguous zero'ing opportunity than along - the x axis */ - const int ker_y_start = MAX(0, -base_idx_y); - /* Condition for kernel end dimension: (base_idx_y + ker_y_end) < input_y */ - const int ker_y_end = MIN(kernel_y, input_y - base_idx_y); - - int32_t index = 0; - if (ker_y_start != 0) - { - memset(&col_buffer[index], 0, (kernel_x * input_ch) * ker_y_start * sizeof(q15_t)); - index += (kernel_x * input_ch) * ker_y_start; - } - - for (int i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) - { - const int32_t idx_y = base_idx_y + i_ker_y; - - for (int i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++) - { - const int32_t idx_x = base_idx_x + i_ker_x; - if (idx_x < 0 || idx_x >= input_x) - { - memset(&col_buffer[index], 0, input_ch * sizeof(q15_t)); - } - else - { - arm_q7_to_q15_with_offset((q7_t *)input + (idx_y * input_x + idx_x) * input_ch, &col_buffer[index], input_ch, input_offset); - } - index += input_ch; - } - } - - const int diff = kernel_y - ker_y_end; - if (diff != 0) - { - memset(&col_buffer[index], 0, (kernel_x * input_ch) * diff * sizeof(q15_t)); - } - - row_count = output_ch / 4; - row_shift = 0; - bias = bias_start_pos; - output_mult = out_mult_start_pos; - output_shift = out_shift_start_pos; - - while (row_count) - { - q31_t sum = *bias++; - q31_t sum_2 = *bias++; - q31_t sum_3 = *bias++; - q31_t sum_4 = *bias++; - - uint16_t col_count = (kernel_x * kernel_y) / 2; - q15_t *col_pos = col_buffer_start + row_shift; - const q7_t *row_pos = kernel + row_shift; - row_shift += 4; - - while (col_count) - { - /* General idea is to read 4 + 4 (input, kernel) pair and re-arrange them in the right order to - use in a SMLAD instruction . One run of this loop produces 4 partial outputs with 8 MACs. */ - /* Note: variable names can be improved here to align with rows and columns. */ - q31_t ip_a1, ip_a2, ip_b1, ip_b2, op_a, op_b, op_c; - /* Read 4 weights */ - ip_b1 = arm_nn_read_q7x4(row_pos); - ip_a1 = arm_nn_read_q7x4(row_pos + input_ch); - op_a = arm_nn_read_q15x2(col_pos); - op_b = arm_nn_read_q15x2(col_pos + input_ch); - - ip_a2 = __SXTB16(ip_b1); - ip_b1 = __SXTB16(__ROR(ip_b1, 8)); - - ip_b2 = __SXTB16(ip_a1); - ip_a1 = __SXTB16(__ROR(ip_a1, 8)); - - op_c = __PKHBT(op_b, op_a, 16); - op_a = __PKHTB(op_b, op_a, 16); - op_b = __PKHBT(ip_b2, ip_a2, 16); - sum = __SMLAD(op_c, op_b, sum); - - op_b = __PKHBT(ip_b1, ip_a1, 16); - sum_2 = __SMLAD(op_a, op_b, sum_2); - - op_a = arm_nn_read_q15x2(col_pos + 2); - op_b = arm_nn_read_q15x2(col_pos + input_ch + 2); - - op_c = __PKHBT(op_b, op_a, 16); - op_a = __PKHTB(op_b, op_a, 16); - op_b = __PKHTB(ip_a2, ip_b2, 16); - sum_3 = __SMLAD(op_c, op_b, sum_3); - - op_b = __PKHTB(ip_a1, ip_b1, 16); - sum_4 = __SMLAD(op_a, op_b, sum_4); - - row_pos += input_ch << 1; - col_pos += input_ch << 1; - col_count--; - } - - col_count = (kernel_x * kernel_y) & 0x1; - while (col_count) - { - sum += row_pos[0] * col_pos[0]; - sum_2 += row_pos[1] * col_pos[1]; - sum_3 += row_pos[2] * col_pos[2]; - sum_4 += row_pos[3] * col_pos[3]; - - row_pos += input_ch; - col_pos += input_ch; - - col_count--; - } - sum = arm_nn_requantize(sum, *output_mult++, *output_shift++); - sum += output_offset; - sum = MAX(sum, output_activation_min); - sum = MIN(sum, output_activation_max); - *output++ = (q7_t)sum; - - sum_2 = arm_nn_requantize(sum_2, *output_mult++, *output_shift++); - sum_2 += output_offset; - sum_2 = MAX(sum_2, output_activation_min); - sum_2 = MIN(sum_2, output_activation_max); - *output++ = (q7_t)sum_2; - sum_3 = arm_nn_requantize(sum_3, *output_mult++, *output_shift++); - sum_3 += output_offset; - sum_3 = MAX(sum_3, output_activation_min); - sum_3 = MIN(sum_3, output_activation_max); - *output++ = (q7_t)sum_3; - - sum_4 = arm_nn_requantize(sum_4, *output_mult++, *output_shift++); - sum_4 += output_offset; - sum_4 = MAX(sum_4, output_activation_min); - sum_4 = MIN(sum_4, output_activation_max); - *output++ = (q7_t)sum_4; - - row_count--; - } - - row_count = output_ch & 0x3; - while (row_count) - { - q15_t *col_pos = col_buffer_start + row_shift; - const q7_t *row_pos = kernel + row_shift; - q31_t sum = *bias++; - const uint16_t col_count = (kernel_x * kernel_y); - row_shift += 1; - - for (int i = 0; i < col_count; i++) - { - sum += row_pos[i * input_ch] * col_pos[i * input_ch]; - } - sum = arm_nn_requantize(sum, *output_mult++, *output_shift++); - sum += output_offset; - sum = MAX(sum, output_activation_min); - sum = MIN(sum, output_activation_max); - *output++ = (q7_t)sum; - - row_count--; - } - - // clear counter and pointers - col_buffer = col_buffer_start; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - return arm_depthwise_conv_s8(ctx, - dw_conv_params, - quant_params, - input_dims, - input, - filter_dims, - kernel, - bias_dims, - bias, - output_dims, - output); -#endif /* ARM_MATH_MVEI | ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -int32_t arm_depthwise_conv_s8_opt_get_buffer_size(const cmsis_nn_dims *input_dims, - const cmsis_nn_dims *filter_dims) -{ -#if defined(ARM_MATH_MVEI) - /* The + 4 accounts for out of bounds read of the lhs buffers in the *_nt_t_* functions. */ - return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t) + 4; -#elif defined(ARM_MATH_DSP) - return (input_dims->c * filter_dims->w * filter_dims->h) * sizeof(int16_t); -#else - (void)input_dims; - (void)filter_dims; - return 0; -#endif -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c deleted file mode 100644 index d5fa36c6..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c +++ /dev/null @@ -1,294 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_u8_basic_ver1.c - * Description: u8 depthwise convolution function - * - * $Date: May 29, 2020 - * $Revision: V.1.1.0 - * - * Target : Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -static void depthwise_conv_u8_mult_4(const uint8_t *input, - const int32_t input_x, - const int32_t input_y, - const int32_t input_ch, - const uint8_t *kernel, - const int32_t output_ch, - const int32_t ch_mult, - const int32_t kernel_x, - const int32_t kernel_y, - const int32_t pad_x, - const int32_t pad_y, - const int32_t stride_x, - const int32_t stride_y, - const int32_t *bias, - uint8_t *output, - const int32_t output_shift, - const int32_t output_mult, - const int32_t output_x, - const int32_t output_y, - const int32_t output_offset, - const int32_t input_offset, - const int32_t filter_offset, - const int32_t output_activation_min, - const int32_t output_activation_max) -{ - for (int32_t in_h = -pad_y, out_h = 0, out_idx = 0; out_h < output_y; in_h += stride_y, ++out_h) - { - for (int32_t in_w = -pad_x, out_w = 0, ker_h_start = MAX(0, -in_h); out_w < output_x; in_w += stride_x, ++out_w) - { - for (int32_t in_ch = 0, out_ch = 0, ker_w_start = MAX(0, -in_w); out_ch < output_ch; ++in_ch, out_ch += ch_mult) - { - for (int mult_tile = 0; mult_tile < ch_mult; mult_tile += 4) - { - int32_t out_buff[4]; - - out_buff[0] = 0; - out_buff[1] = 0; - out_buff[2] = 0; - out_buff[3] = 0; - - for (int32_t ker_h = ker_h_start; ker_h < MIN(kernel_y, input_y - in_h); ++ker_h) - { - int32_t ker_idx = ker_h * (output_ch * kernel_x) + ker_w_start * output_ch + out_ch; - int32_t in_idx = (in_h + ker_h) * (input_ch * input_x) + in_w * input_ch + in_ch; - - for (int32_t ker_w = ker_w_start; ker_w < MIN(kernel_x, input_x - in_w); ++ker_w, ker_idx += output_ch) - { - int32_t in_val = input[in_idx + ker_w * input_ch] + input_offset; - out_buff[0] += in_val * (kernel[ker_idx + 0 + mult_tile] + filter_offset); - out_buff[1] += in_val * (kernel[ker_idx + 1 + mult_tile] + filter_offset); - out_buff[2] += in_val * (kernel[ker_idx + 2 + mult_tile] + filter_offset); - out_buff[3] += in_val * (kernel[ker_idx + 3 + mult_tile] + filter_offset); - } - } - - if (bias != NULL) - { - out_buff[0] += bias[out_ch + 0 + mult_tile]; - out_buff[1] += bias[out_ch + 1 + mult_tile]; - out_buff[2] += bias[out_ch + 2 + mult_tile]; - out_buff[3] += bias[out_ch + 3 + mult_tile]; - } - out_buff[0] = arm_nn_requantize(out_buff[0], output_mult, output_shift); - out_buff[1] = arm_nn_requantize(out_buff[1], output_mult, output_shift); - out_buff[2] = arm_nn_requantize(out_buff[2], output_mult, output_shift); - out_buff[3] = arm_nn_requantize(out_buff[3], output_mult, output_shift); - - out_buff[0] += output_offset; - out_buff[1] += output_offset; - out_buff[2] += output_offset; - out_buff[3] += output_offset; - - out_buff[0] = MIN(MAX(out_buff[0], output_activation_min), output_activation_max); - out_buff[1] = MIN(MAX(out_buff[1], output_activation_min), output_activation_max); - out_buff[2] = MIN(MAX(out_buff[2], output_activation_min), output_activation_max); - out_buff[3] = MIN(MAX(out_buff[3], output_activation_min), output_activation_max); - - output[out_idx++] = (uint8_t)out_buff[0]; - output[out_idx++] = (uint8_t)out_buff[1]; - output[out_idx++] = (uint8_t)out_buff[2]; - output[out_idx++] = (uint8_t)out_buff[3]; - } - } - } - } -} - -static void depthwise_conv_u8_generic(const uint8_t *input, - const int32_t input_x, - const int32_t input_y, - const int32_t input_ch, - const uint8_t *kernel, - const int32_t output_ch, - const int32_t ch_mult, - const int32_t kernel_x, - const int32_t kernel_y, - const int32_t pad_x, - const int32_t pad_y, - const int32_t stride_x, - const int32_t stride_y, - const int32_t *bias, - uint8_t *output, - const int32_t output_shift, - const int32_t output_mult, - const int32_t output_x, - const int32_t output_y, - const int32_t output_offset, - const int32_t input_offset, - const int32_t filter_offset, - const int32_t output_activation_min, - const int32_t output_activation_max) -{ - (void)output_ch; - int i_out = 0; - for (int i_out_y = 0; i_out_y < output_y; i_out_y++) - { - const int16_t base_idx_y = (i_out_y * stride_y) - pad_y; - for (int i_out_x = 0; i_out_x < output_x; i_out_x++) - { - const int16_t base_idx_x = (i_out_x * stride_x) - pad_x; - for (int i_input_ch = 0; i_input_ch < input_ch; i_input_ch++) - { - for (int i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult++) - { - const int idx_out_ch = i_ch_mult + i_input_ch * ch_mult; - int32_t acc_0; - /* Condition for kernel start dimension: (base_idx_ + ker__start) >= 0 */ - const int ker_y_start = MAX(0, -base_idx_y); - const int ker_x_start = MAX(0, -base_idx_x); - /* Condition for kernel end dimension: (base_idx_ + ker__end) < input_ */ - const int ker_y_end = MIN(kernel_y, input_y - base_idx_y); - const int ker_x_end = MIN(kernel_x, input_x - base_idx_x); - acc_0 = 0; - - for (int i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++) - { - const int32_t idx_y = base_idx_y + i_ker_y; - for (int i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++) - { - const int32_t idx_x = base_idx_x + i_ker_x; - int32_t idx_0 = (idx_y * input_x + idx_x) * input_ch + i_input_ch; - int32_t ker_idx_0 = (i_ker_y * kernel_x + i_ker_x) * (input_ch * ch_mult) + idx_out_ch; - - acc_0 += (input[idx_0] + input_offset) * (kernel[ker_idx_0] + filter_offset); - } - } - if (bias != NULL) - { - acc_0 += bias[idx_out_ch]; - } - - /* Requantize and clamp output to provided range */ - acc_0 = arm_nn_requantize(acc_0, output_mult, output_shift); - acc_0 += output_offset; - acc_0 = MAX(acc_0, output_activation_min); - acc_0 = MIN(acc_0, output_activation_max); - - output[i_out++] = acc_0; - } - } - } - } -} - -/** - * @brief uint8 depthwise convolution function with asymmetric quantization - * - * @param[in] input Pointer to input tensor - * @param[in] input_x Width of input tensor - * @param[in] input_y Height of input tensor - * @param[in] input_ch Channels in input tensor - * @param[in] kernel Pointer to kernel weights - * @param[in] kernel_x Width of kernel - * @param[in] kernel_y Height of kernel - * @param[in] ch_mult Number of channel multiplier - * @param[in] pad_x Padding sizes x - * @param[in] pad_y Padding sizes y - * @param[in] stride_x Convolution stride along the width - * @param[in] stride_y Convolution stride along the height - * @param[in] dilation_x Dilation along width. Not used and intended for future enhancement. - * @param[in] dilation_y Dilation along height. Not used and intended for future enhancement. - * @param[in] bias Pointer to optional bias values. If no bias is - * availble, NULL is expected - * @param[in] input_offset Input tensor zero offset - * @param[in] filter_offset Kernel tensor zero offset - * @param[in] output_offset Output tensor zero offset - * @param[in,out] output Pointer to output tensor - * @param[in] output_x Width of output tensor - * @param[in] output_y Height of output tensor - * @param[in] output_activation_min Minimum value to clamp the output to. Range : {0, 255} - * @param[in] output_activation_max Minimum value to clamp the output to. Range : {0, 255} - * @param[in] output_shift Amount of right-shift for output - * @param[in] output_mult Output multiplier for requantization - * @return The function returns one of the following - * ARM_MATH_SIZE_MISMATCH - Not supported dimension of tensors - * ARM_MATH_SUCCESS - Successful operation - * ARM_MATH_ARGUMENT_ERROR - Implementation not available - * - * - */ - -arm_status arm_depthwise_conv_u8_basic_ver1(const uint8_t *input, - const uint16_t input_x, - const uint16_t input_y, - const uint16_t input_ch, - const uint8_t *kernel, - const uint16_t kernel_x, - const uint16_t kernel_y, - const int16_t ch_mult, - const int16_t pad_x, - const int16_t pad_y, - const int16_t stride_x, - const int16_t stride_y, - const int16_t dilation_x, - const int16_t dilation_y, - const int32_t *bias, - const int32_t input_offset, - const int32_t filter_offset, - const int32_t output_offset, - uint8_t *output, - const uint16_t output_x, - const uint16_t output_y, - const int32_t output_activation_min, - const int32_t output_activation_max, - const int32_t output_shift, - const int32_t output_mult) -{ - (void)dilation_x; - (void)dilation_y; - - if (ch_mult % 4 == 0) - { - depthwise_conv_u8_mult_4(input, input_x, input_y, input_ch, kernel, ch_mult * input_ch, ch_mult, - kernel_x, kernel_y, pad_x, pad_y, stride_x, stride_y, bias, output, - output_shift, output_mult, output_x, output_y, output_offset, input_offset, - filter_offset, output_activation_min, output_activation_max); - } - else - { - depthwise_conv_u8_generic(input, input_x, input_y, input_ch, kernel, ch_mult * input_ch, ch_mult, - kernel_x, kernel_y, pad_x, pad_y, stride_x, stride_y, bias, - output, output_shift, output_mult, output_x, output_y, output_offset, - input_offset, filter_offset, output_activation_min, output_activation_max); - } - - /* Return to application */ - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s8.c deleted file mode 100644 index 42d425f0..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s8.c +++ /dev/null @@ -1,133 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_conv_wrapper_s8.c - * Description: Wrapper API to select appropriate depthwise conv API based - * on dimensions. - * - * $Date: May 29, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/* - * s8 Depthwise conv wrapper function - * - * Refer header file for details. - * - */ -arm_status arm_depthwise_conv_wrapper_s8(const cmsis_nn_context *ctx, - const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_per_channel_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *filter, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - arm_status status = ARM_MATH_SUCCESS; - if (1 == dw_conv_params->ch_mult) - { -#if !defined(ARM_MATH_MVEI) - if ((filter_dims->w == 3) && (filter_dims->h == 3) && (dw_conv_params->padding.h <= 1)) - { - status = arm_depthwise_conv_3x3_s8(ctx, - dw_conv_params, - quant_params, - input_dims, - input, - filter_dims, - filter, - bias_dims, - bias, - output_dims, - output); - } - else -#endif - { - status = arm_depthwise_conv_s8_opt(ctx, - dw_conv_params, - quant_params, - input_dims, - input, - filter_dims, - filter, - bias_dims, - bias, - output_dims, - output); - } - } - else - { - status = arm_depthwise_conv_s8(ctx, - dw_conv_params, - quant_params, - input_dims, - input, - filter_dims, - filter, - bias_dims, - bias, - output_dims, - output); - } - - /* Return to application */ - return status; -} - -int32_t arm_depthwise_conv_wrapper_s8_get_buffer_size(const cmsis_nn_dw_conv_params *dw_conv_params, - const cmsis_nn_dims *input_dims, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims) -{ - (void)dw_conv_params; - int32_t size = 0; - - if (input_dims->c == output_dims->c) - { - size = arm_depthwise_conv_s8_opt_get_buffer_size(input_dims, filter_dims); - } - - return size; -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c deleted file mode 100644 index 2767ff47..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c +++ /dev/null @@ -1,418 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_separable_conv_HWC_q7.c - * Description: Q7 depthwise separable convolution function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/** - * @brief Q7 depthwise separable convolution function - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * @details - * - * Buffer size: - * - * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel - * - * bufferB size: 0 - * - * Input dimension constraints: - * - * ch_im_in equals ch_im_out - * - * Implementation: - * There are 3 nested loop here: - * Inner loop: calculate each output value with MAC instruction over an accumulator - * Mid loop: loop over different output channel - * Outer loop: loop over different output (x, y) - */ - -arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out, - q15_t * bufferA, - q7_t * bufferB) -{ - (void)bufferB; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_out_y, i_out_x; - int16_t i_ker_y, i_ker_x; - q7_t *colBuffer = (q7_t *) bufferA; - q7_t *pBuffer = colBuffer; - const q7_t *pBias = bias; - q7_t *pOut = Im_out; - uint16_t rowCnt; - uint16_t row_shift; - - /* do some checking here, basically ch_im_in == ch_im_out */ - if (ch_im_in != ch_im_out) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - /* we first do im2col here */ - for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++) - { - for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in) - { - /* arm_fill_q7(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, ch_im_in); - } else - { - /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - /* we will do the computation here for each channel */ - rowCnt = ch_im_out >> 2; - row_shift = 0; - pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = (dim_kernel * dim_kernel) >> 1; - q7_t *pB = colBuffer + row_shift; - const q7_t *pA = wt + row_shift; - row_shift += 4; - -#ifdef USE_INTRINSIC - -#ifndef ARM_MATH_BIG_ENDIAN - - while (colCnt) - { - q31_t inA1, inA2, inB1, inB2, opA, opB; - - inB1 = arm_nn_read_q7x4(pB); - pB += ch_im_in; - opB = arm_nn_read_q7x4(pB); - pB += ch_im_in; - inB2 = __PKHTB(opB, inB1, 16); - inB1 = __PKHBT(inB1, opB, 16); - inA1 = arm_nn_read_q7x4(pA); - pA += ch_im_in; - opB = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inA2 = __PKHTB(opB, inA1, 16); - inA1 = __PKHBT(inA1, opB, 16); - opA = __SXTB16(inA1); - opB = __SXTB16(inB1); - sum = __SMLAD(opA, opB, sum); - opA = __SXTB16(__ROR(inA1, 8)); - opB = __SXTB16(__ROR(inB1, 8)); - sum2 = __SMLAD(opA, opB, sum2); - opA = __SXTB16(inA2); - opB = __SXTB16(inB2); - sum3 = __SMLAD(opA, opB, sum3); - opA = __SXTB16(__ROR(inA2, 8)); - opB = __SXTB16(__ROR(inB2, 8)); - sum4 = __SMLAD(opA, opB, sum4); - colCnt--; - } -#else - - while (colCnt) - { - q31_t inA1, inA2, inB1, inB2, opA, opB; - - inB1 = arm_nn_read_q7x4(pB); - pB += ch_im_in; - opB = arm_nn_read_q7x4(pB); - pB += ch_im_in; - inB2 = __PKHBT(opB, inB1, 16); - inB1 = __PKHTB(inB1, opB, 16); - inA1 = arm_nn_read_q7x4(pA); - pA += ch_im_in; - opB = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inA2 = __PKHBT(opB, inA1, 16); - inA1 = __PKHTB(inA1, opB, 16); - opA = __SXTB16(inA1); - opB = __SXTB16(inB1); - sum2 = __SMLAD(opA, opB, sum2); - opA = __SXTB16(__ROR(inA1, 8)); - opB = __SXTB16(__ROR(inB1, 8)); - sum = __SMLAD(opA, opB, sum); - opA = __SXTB16(inA2); - opB = __SXTB16(inB2); - sum4 = __SMLAD(opA, opB, sum4); - opA = __SXTB16(__ROR(inA2, 8)); - opB = __SXTB16(__ROR(inB2, 8)); - sum3 = __SMLAD(opA, opB, sum3); - colCnt--; - } - -#endif /* ARM_MATH_BIG_ENDIAN */ - -#else - -#ifndef ARM_MATH_BIG_ENDIAN - /* - * r0 r1 r2 r3 r4 r5 - * inA1, inA2, inB1, inB2, opA, opB - */ - - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r2, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "ldr.w r5, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "pkhtb r3, r5, r2, ASR #16\n" - "pkhbt r2, r2, r5, LSL #16\n" - "ldr.w r0, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "ldr.w r5, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "pkhtb r1, r5, r0, ASR #16\n" - "pkhbt r0, r0, r5, LSL #16\n" - "sxtb16 r4, r0\n" - "sxtb16 r5, r2\n" - "smlad %[sum], r4, r5, %[sum]\n" - "mov.w r4, r0, ror #8\n" - "mov.w r5, r2, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum2], r4, r5, %[sum2]\n" - "sxtb16 r4, r1\n" - "sxtb16 r5, r3\n" - "smlad %[sum3], r4, r5, %[sum3]\n" - "mov.w r4, r1, ror #8\n" - "mov.w r5, r3, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum4], r4, r5, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] - "+r"(sum),[sum2] "+r"(sum2), - [sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB), - [pA] "+r"(pA):[colCnt] - "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); -#else - /* - * r0 r1 r2 r3 r4 r5 - * inA1, inA2, inB1, inB2, opA, opB - */ - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r2, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "ldr.w r5, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "pkhbt r3, r5, r2, LSL #16\n" - "pkhtb r2, r2, r5, ASR #16\n" - "ldr.w r0, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "ldr.w r5, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "pkhbt r1, r5, r0, LSL #16\n" - "pkhtb r0, r0, r5, ASR #16\n" - "sxtb16 r4, r0\n" - "sxtb16 r5, r2\n" - "smlad %[sum2], r4, r5, %[sum2]\n" - "mov.w r4, r0, ror #8\n" - "mov.w r5, r2, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum], r4, r5, %[sum]\n" - "sxtb16 r4, r1\n" - "sxtb16 r5, r3\n" - "smlad %[sum4], r4, r5, %[sum4]\n" - "mov.w r4, r1, ror #8\n" - "mov.w r5, r3, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum3], r4, r5, %[sum3]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] - "+r"(sum),[sum2] "+r"(sum2), - [sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB), - [pA] "+r"(pA):[colCnt] - "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); - -#endif /* ARM_MATH_BIG_ENDIAN */ - -#endif /* USE_INTRINSIC */ - - colCnt = (dim_kernel * dim_kernel) & 0x1; - while (colCnt) - { - union arm_nnword inA, inB; - inA.word = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inB.word = arm_nn_read_q7x4(pB); - pB += ch_im_in; - sum += inA.bytes[0] * inB.bytes[0]; - sum2 += inA.bytes[1] * inB.bytes[1]; - sum3 += inA.bytes[2] * inB.bytes[2]; - sum4 += inA.bytes[3] * inB.bytes[3]; - colCnt--; - } - - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - rowCnt--; - } - - rowCnt = ch_im_out & 0x3; - while (rowCnt) - { - q7_t *pB = colBuffer + row_shift; - const q7_t *pA = wt + row_shift; - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = (dim_kernel * dim_kernel); - - row_shift += 1; - - while (colCnt) - { - q7_t A1 = *pA; - q7_t B1 = *pB; - pA += ch_im_in; - pB += ch_im_in; - sum += A1 * B1; - - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - rowCnt--; - } - - /* clear counter and pointers */ - pBuffer = colBuffer; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i_out_y, i_out_x, i_ch_out, i_ker_x, i_ker_y; - int conv_out; - - /* do some checking here, basically ch_im_in == ch_im_out */ - if (ch_im_in != ch_im_out) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++) - { - for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++) - { - // for each output - conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift); - for (i_ker_y = 0; i_ker_y < dim_kernel; i_ker_y++) - { - for (i_ker_x = 0; i_ker_x < dim_kernel; i_ker_x++) - { - int in_row = stride * i_out_y + i_ker_y - padding; - int in_col = stride * i_out_x + i_ker_x - padding; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in) - { - conv_out += - Im_in[(in_row * - dim_im_in + - in_col) * - ch_im_in + - i_ch_out] * wt[(i_ker_y * dim_kernel + i_ker_x) * ch_im_out + i_ch_out]; - } - } - } - Im_out[(i_out_y * dim_im_out + - i_out_x) * ch_im_out + i_ch_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return ARM_MATH_SUCCESS; - -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c deleted file mode 100644 index adeb8c45..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c +++ /dev/null @@ -1,413 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_depthwise_separable_conv_HWC_q7_nonsquare.c - * Description: Q7 depthwise separable convolution function (non-square shape) - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup NNConv - * @{ - */ - -/** - * @brief Q7 depthwise separable convolution function (non-square shape) - * @param[in] Im_in pointer to input tensor - * @param[in] dim_im_in_x input tensor dimention x - * @param[in] dim_im_in_y input tensor dimention y - * @param[in] ch_im_in number of input tensor channels - * @param[in] wt pointer to kernel weights - * @param[in] ch_im_out number of filters, i.e., output tensor channels - * @param[in] dim_kernel_x filter kernel size x - * @param[in] dim_kernel_y filter kernel size y - * @param[in] padding_x padding sizes x - * @param[in] padding_y padding sizes y - * @param[in] stride_x convolution stride x - * @param[in] stride_y convolution stride y - * @param[in] bias pointer to bias - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in,out] Im_out pointer to output tensor - * @param[in] dim_im_out_x output tensor dimension x - * @param[in] dim_im_out_y output tensor dimension y - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] bufferB pointer to buffer space for output - * @return The function returns either - * ARM_MATH_SIZE_MISMATCH or ARM_MATH_SUCCESS based on the outcome of size checking. - * - * This function is the version with full list of optimization tricks, but with - * some contraints: - * ch_im_in is equal to ch_im_out - * - */ - -arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in, - const uint16_t dim_im_in_x, - const uint16_t dim_im_in_y, - const uint16_t ch_im_in, - const q7_t * wt, - const uint16_t ch_im_out, - const uint16_t dim_kernel_x, - const uint16_t dim_kernel_y, - const uint16_t padding_x, - const uint16_t padding_y, - const uint16_t stride_x, - const uint16_t stride_y, - const q7_t * bias, - const uint16_t bias_shift, - const uint16_t out_shift, - q7_t * Im_out, - const uint16_t dim_im_out_x, - const uint16_t dim_im_out_y, - q15_t * bufferA, - q7_t * bufferB) -{ - - (void)bufferB; - -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - -/* - * Implementation: - * There are 3 nested loop here: - * Inner loop: calculate each output value with MAC instruction over an accumulator - * Mid loop: loop over different output channel - * Outer loop: loop over different output (x, y) - * - */ - - int16_t i_out_y, i_out_x; - int16_t i_ker_y, i_ker_x; - q7_t *colBuffer = (q7_t *) bufferA; - q7_t *pBuffer = colBuffer; - const q7_t *pBias = bias; - q7_t *pOut = Im_out; - uint16_t rowCnt; - uint16_t row_shift; - - /* do some checking here, basically ch_im_in == ch_im_out */ - if (ch_im_in != ch_im_out) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - /* we first do im2col here */ - for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; - i_ker_y++) - { - for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; - i_ker_x++) - { - if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) - { - /* arm_fill_q7(0, pBuffer, ch_im_in); */ - memset(pBuffer, 0, ch_im_in); - } else - { - /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */ - memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, ch_im_in); - } - pBuffer += ch_im_in; - } - } - - /* we will do the computation here for each channel */ - rowCnt = ch_im_out >> 2; - row_shift = 0; - pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = (dim_kernel_x * dim_kernel_y) >> 1; - q7_t *pB = colBuffer + row_shift; - const q7_t *pA = wt + row_shift; - row_shift += 4; - -#ifdef USE_INTRINSIC - -#ifndef ARM_MATH_BIG_ENDIAN - - while (colCnt) - { - q31_t inA1, inA2, inB1, inB2, opA, opB; - - inB1 = arm_nn_read_q7x4(pB); - pB += ch_im_in; - opB = arm_nn_read_q7x4(pB); - pB += ch_im_in; - inB2 = __PKHTB(opB, inB1, 16); - inB1 = __PKHBT(inB1, opB, 16); - inA1 = arm_nn_read_q7x4(pA); - pA += ch_im_in; - opB = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inA2 = __PKHTB(opB, inA1, 16); - inA1 = __PKHBT(inA1, opB, 16); - opA = __SXTB16(inA1); - opB = __SXTB16(inB1); - sum = __SMLAD(opA, opB, sum); - opA = __SXTB16(__ROR(inA1, 8)); - opB = __SXTB16(__ROR(inB1, 8)); - sum2 = __SMLAD(opA, opB, sum2); - opA = __SXTB16(inA2); - opB = __SXTB16(inB2); - sum3 = __SMLAD(opA, opB, sum3); - opA = __SXTB16(__ROR(inA2, 8)); - opB = __SXTB16(__ROR(inB2, 8)); - sum4 = __SMLAD(opA, opB, sum4); - colCnt--; - } -#else - - while (colCnt) - { - q31_t inA1, inA2, inB1, inB2, opA, opB; - - inB1 = arm_nn_read_q7x4(pB); - pB += ch_im_in; - opB = arm_nn_read_q7x4(pB); - pB += ch_im_in; - inB2 = __PKHBT(opB, inB1, 16); - inB1 = __PKHTB(inB1, opB, 16); - inA1 = arm_nn_read_q7x4(pA); - pA += ch_im_in; - opB = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inA2 = __PKHBT(opB, inA1, 16); - inA1 = __PKHTB(inA1, opB, 16); - opA = __SXTB16(inA1); - opB = __SXTB16(inB1); - sum2 = __SMLAD(opA, opB, sum2); - opA = __SXTB16(__ROR(inA1, 8)); - opB = __SXTB16(__ROR(inB1, 8)); - sum = __SMLAD(opA, opB, sum); - opA = __SXTB16(inA2); - opB = __SXTB16(inB2); - sum4 = __SMLAD(opA, opB, sum4); - opA = __SXTB16(__ROR(inA2, 8)); - opB = __SXTB16(__ROR(inB2, 8)); - sum3 = __SMLAD(opA, opB, sum3); - colCnt--; - } - -#endif /* ARM_MATH_BIG_ENDIAN */ - -#else - -#ifndef ARM_MATH_BIG_ENDIAN - // r0 r1 r2 r3 r4 r5 - // inA1, inA2, inB1, inB2, opA, opB - asm volatile ("COL_LOOP:\n" - "ldr.w r2, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "ldr.w r5, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "pkhtb r3, r5, r2, ASR #16\n" - "pkhbt r2, r2, r5, LSL #16\n" - "ldr.w r0, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "ldr.w r5, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "pkhtb r1, r5, r0, ASR #16\n" - "pkhbt r0, r0, r5, LSL #16\n" - "sxtb16 r4, r0\n" - "sxtb16 r5, r2\n" - "smlad %[sum], r4, r5, %[sum]\n" - "mov.w r4, r0, ror #8\n" - "mov.w r5, r2, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum2], r4, r5, %[sum2]\n" - "sxtb16 r4, r1\n" - "sxtb16 r5, r3\n" - "smlad %[sum3], r4, r5, %[sum3]\n" - "mov.w r4, r1, ror #8\n" - "mov.w r5, r3, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum4], r4, r5, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt), - [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); -#else - // r0 r1 r2 r3 r4 r5 - // inA1, inA2, inB1, inB2, opA, opB - asm volatile ("COL_LOOP:\n" - "ldr.w r2, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "ldr.w r5, [%[pB], #0]\n" - "add.w %[pB], %[pB], %[ch_im_in]\n" - "pkhbt r3, r5, r2, LSL #16\n" - "pkhtb r2, r2, r5, ASR #16\n" - "ldr.w r0, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "ldr.w r5, [%[pA], #0]\n" - "add.w %[pA], %[pA], %[ch_im_in]\n" - "pkhbt r1, r5, r0, LSL #16\n" - "pkhtb r0, r0, r5, ASR #16\n" - "sxtb16 r4, r0\n" - "sxtb16 r5, r2\n" - "smlad %[sum2], r4, r5, %[sum2]\n" - "mov.w r4, r0, ror #8\n" - "mov.w r5, r2, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum], r4, r5, %[sum]\n" - "sxtb16 r4, r1\n" - "sxtb16 r5, r3\n" - "smlad %[sum4], r4, r5, %[sum4]\n" - "mov.w r4, r1, ror #8\n" - "mov.w r5, r3, ror #8\n" - "sxtb16 r4, r4\n" - "sxtb16 r5, r5\n" - "smlad %[sum3], r4, r5, %[sum3]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt), - [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5"); -#endif /*ARM_MATH_BIG_ENDIAN */ - -#endif /* USE_INTRINSIC */ - - colCnt = (dim_kernel_x * dim_kernel_y) & 0x1; - while (colCnt) - { - union arm_nnword inA, inB; - inA.word = arm_nn_read_q7x4(pA); - pA += ch_im_in; - inB.word = arm_nn_read_q7x4(pB); - pB += ch_im_in; - sum += inA.bytes[0] * inB.bytes[0]; - sum2 += inA.bytes[1] * inB.bytes[1]; - sum3 += inA.bytes[2] * inB.bytes[2]; - sum4 += inA.bytes[3] * inB.bytes[3]; - colCnt--; - } - - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - rowCnt--; - } - - rowCnt = ch_im_out & 0x3; - while (rowCnt) - { - q7_t *pB = colBuffer + row_shift; - const q7_t *pA = wt + row_shift; - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = (dim_kernel_x * dim_kernel_y); - - row_shift += 1; - - while (colCnt) - { - q7_t A1 = *pA; - q7_t B1 = *pB; - pA += ch_im_in; - pB += ch_im_in; - sum += A1 * B1; - - colCnt--; - } - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - rowCnt--; - } - - // clear counter and pointers - pBuffer = colBuffer; - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int i_out_y, i_out_x, i_ch_out; - int i_ker_y, i_ker_x; - - /* do some checking here, basically ch_im_in == ch_im_out */ - if (ch_im_in != ch_im_out) - { - return ARM_MATH_SIZE_MISMATCH; - } - - for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) - { - for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) - { - for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++) - { - // for each output - int conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift); - for (i_ker_y = 0; i_ker_y < dim_kernel_y; i_ker_y++) - { - for (i_ker_x = 0; i_ker_x < dim_kernel_x; i_ker_x++) - { - int in_row = stride_y * i_out_y + i_ker_y - padding_y; - int in_col = stride_x * i_out_x + i_ker_x - padding_x; - if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) - { - conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + i_ch_out] * - wt[(i_ker_y * dim_kernel_x + i_ker_x) * ch_im_out + i_ch_out]; - } - } - } - Im_out[(i_out_y * dim_im_out_x + i_out_x) * ch_im_out + i_ch_out] = - (q7_t) __SSAT((conv_out >> out_shift), 8); - } - } - } - -#endif /* ARM_MATH_DSP */ - - - /* Return to application */ - return ARM_MATH_SUCCESS; - -} - -/** - * @} end of NNConv group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_depthwise_conv_s8_core.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_depthwise_conv_s8_core.c deleted file mode 100644 index f9106341..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_depthwise_conv_s8_core.c +++ /dev/null @@ -1,219 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_depthwise_conv_s8_core.c - * Description: Depthwise convolution on im2col buffers. - * - * $Date: May 29, 2020 - * $Revision: V.1.0.3 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/* - * Depthwise conv on an im2col buffer where the input channel equals - * output channel. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_depthwise_conv_s8_core(const q7_t *row, - const q15_t *col, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t kernel_size, - const int32_t *const output_bias, - q7_t *out) -{ -#if defined(ARM_MATH_MVEI) - int32_t ch_per_loop = num_ch / 4; - - const int32_t *bias = output_bias; - int8_t *out_tmp = out; - - int32_t idx = 0; - - while (ch_per_loop > 0) - { - int32x4_t ip_0; - int32x4_t ip_1; - int32_t ker_loop = kernel_size / 3; - int32x4_t out_0 = vldrwq_s32(bias); - int32x4_t out_1 = out_0; - bias += 4; - - const int32_t offset = idx * 4; - const int8_t *row_0 = row + offset; - const int16_t *col_0 = col + offset; - const int16_t *col_1 = col + kernel_size * num_ch + offset; - - int32x4_t ker_0 = vldrbq_s32(row_0); - - while (ker_loop > 0) - { - const int8_t *row_1 = row_0 + num_ch; - const int8_t *row_2 = row_0 + 2 * num_ch; - const int32x4_t ker_1 = vldrbq_s32(row_1); - const int32x4_t ker_2 = vldrbq_s32(row_2); - - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - col_0 += num_ch; - col_1 += num_ch; - - out_0 += vmulq_s32(ip_0, ker_0); - out_1 += vmulq_s32(ip_1, ker_0); - - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - col_0 += num_ch; - col_1 += num_ch; - - out_0 += vmulq_s32(ip_0, ker_1); - out_1 += vmulq_s32(ip_1, ker_1); - - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - col_0 += num_ch; - col_1 += num_ch; - - out_0 += vmulq_s32(ip_0, ker_2); - out_1 += vmulq_s32(ip_1, ker_2); - row_0 += 3 * num_ch; - - ker_0 = vldrbq_s32(row_0); - ker_loop--; - } - - idx++; - /* Handle tail kernel elements */ - ker_loop = kernel_size - ((kernel_size / 3) * 3); - while (ker_loop > 0) - { - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - - out_0 += vmulq_s32(ip_0, ker_0); - out_1 += vmulq_s32(ip_1, ker_0); - - col_0 += num_ch; - col_1 += num_ch; - - ip_0 = vldrhq_s32(col_0); - ip_1 = vldrhq_s32(col_1); - - row_0 += num_ch; - ker_0 = vldrbq_s32(row_0); - ker_loop--; - } - const int32x4_t mult = vldrwq_s32(out_mult); - const int32x4_t shift = vldrwq_s32(out_shift); - out_mult += 4; - out_shift += 4; - - out_0 = arm_requantize_mve_32x4(out_0, mult, shift); - out_1 = arm_requantize_mve_32x4(out_1, mult, shift); - - out_0 = vaddq_n_s32(out_0, out_offset); - out_0 = vmaxq_s32(out_0, vdupq_n_s32(activation_min)); - out_0 = vminq_s32(out_0, vdupq_n_s32(activation_max)); - vstrbq_s32(out_tmp, out_0); - - out_1 = vaddq_n_s32(out_1, out_offset); - out_1 = vmaxq_s32(out_1, vdupq_n_s32(activation_min)); - out_1 = vminq_s32(out_1, vdupq_n_s32(activation_max)); - vstrbq_s32(out_tmp + num_ch, out_1); - - out_tmp += 4; - ch_per_loop--; - } - - int32_t tail_ch = num_ch & 3; - if (tail_ch != 0) - { - int32_t ch_idx = (num_ch & ~3); - int32x4_t col_0_sum; - int32x4_t col_1_sum; - - const int32_t single_buffer_size = kernel_size * num_ch; - for (int i = 0; i < tail_ch; i++) - { - const int16_t *col_pos_0 = col + ch_idx; - const int16_t *col_pos_1 = col_pos_0 + single_buffer_size; - - const int8_t *row_pos = row + ch_idx; - int32_t sum_0 = bias[i]; - int32_t sum_1 = bias[i]; - - for (int j = 0; j < kernel_size; j++) - { - const int8_t row_val = row_pos[j * num_ch]; - sum_0 += row_val * col_pos_0[j * num_ch]; - sum_1 += row_val * col_pos_1[j * num_ch]; - } - col_0_sum[i] = sum_0; - col_1_sum[i] = sum_1; - - ch_idx++; - } - const mve_pred16_t p = vctp32q((uint32_t)tail_ch); - const int32x4_t mult = vldrwq_z_s32(out_mult, p); - const int32x4_t shift = vldrwq_z_s32(out_shift, p); - - col_0_sum = arm_requantize_mve_32x4(col_0_sum, mult, shift); - col_1_sum = arm_requantize_mve_32x4(col_1_sum, mult, shift); - - col_0_sum = vaddq_n_s32(col_0_sum, out_offset); - col_0_sum = vmaxq_s32(col_0_sum, vdupq_n_s32(activation_min)); - col_0_sum = vminq_s32(col_0_sum, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out_tmp, col_0_sum, p); - - col_1_sum = vaddq_n_s32(col_1_sum, out_offset); - col_1_sum = vmaxq_s32(col_1_sum, vdupq_n_s32(activation_min)); - col_1_sum = vminq_s32(col_1_sum, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out_tmp + num_ch, col_1_sum, p); - - out_tmp += tail_ch; - } - - return out_tmp + num_ch; -#else - (void)row; - (void)col; - (void)num_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)kernel_size; - (void)output_bias; - (void)out; - return NULL; -#endif -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c deleted file mode 100644 index 49b3ba47..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c +++ /dev/null @@ -1,179 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_kernel_q7_q15.c - * Description: Matrix-multiplication function for convolution - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - - /** - * @brief Matrix-multiplication function for convolution. - * - * @details Refer to header file for details. - * - */ - -q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA, - const q15_t * pInBuffer, - const uint16_t ch_im_out, - const uint16_t numCol_A, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut) -{ -#if defined (ARM_MATH_DSP) - /* set up the second output pointers */ - q7_t *pOut2 = pOut + ch_im_out; - const q7_t *pBias = bias; - - uint16_t rowCnt = ch_im_out >> 1; - /* this loop over rows in A */ - while (rowCnt) - { - /* setup pointers for B */ - const q15_t *pB = pInBuffer; - const q15_t *pB2 = pB + numCol_A; - - /* align the second pointer for A */ - const q7_t *pA2 = pA + numCol_A; - - /* init the sum with bias */ - q31_t sum = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = numCol_A >> 2; - /* accumulate over the vector */ - while (colCnt) - { - q31_t inA11, inA12, inA21, inA22; - - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - pA = read_and_pad(pA, &inA11, &inA12); - pA2 = read_and_pad(pA2, &inA21, &inA22); - - sum = __SMLAD(inA11, inB1, sum); - sum2 = __SMLAD(inA11, inB2, sum2); - sum3 = __SMLAD(inA21, inB1, sum3); - sum4 = __SMLAD(inA21, inB2, sum4); - - inB1 = arm_nn_read_q15x2_ia(&pB); - inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA12, inB1, sum); - sum2 = __SMLAD(inA12, inB2, sum2); - sum3 = __SMLAD(inA22, inB1, sum3); - sum4 = __SMLAD(inA22, inB2, sum4); - - colCnt--; - } /* while over colCnt */ - colCnt = numCol_A & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - q7_t inA2 = *pA2++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - sum3 += inA2 * inB1; - sum4 += inA2 * inB2; - colCnt--; - } /* while over colCnt */ - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - /* skip the row computed with A2 */ - pA += numCol_A; - rowCnt--; - } /* for over ch_im_out */ - - /* compute left-over row if any */ - if (ch_im_out & 0x1) - { - /* setup pointers for B */ - const q15_t *pB = pInBuffer; - const q15_t *pB2 = pB + numCol_A; - - /* load the bias */ - q31_t sum = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = numCol_A >> 2; - while (colCnt) - { - q31_t inA11, inA12; - - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - pA = read_and_pad(pA, &inA11, &inA12); - - sum = __SMLAD(inA11, inB1, sum); - sum2 = __SMLAD(inA11, inB2, sum2); - - inB1 = arm_nn_read_q15x2_ia(&pB); - inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA12, inB1, sum); - sum2 = __SMLAD(inA12, inB2, sum2); - - colCnt--; - } - colCnt = numCol_A & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - colCnt--; - } - - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - } - - pOut += ch_im_out; - - /* return the new output pointer with offset */ - return pOut; -#else - /* To be completed */ - return NULL; -#endif /* ARM_MATH_DSP */ - -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c deleted file mode 100644 index b5bbc397..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c +++ /dev/null @@ -1,129 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_kernel_q7_q15_reordered.c - * Description: Matrix-multiplication function for convolution with reordered columns - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/DSP/Include/arm_math.h" - - /** - * @brief Matrix-multiplication function for convolution with re-ordered input. - * - * @details Refer to header file for details. - * - */ - -q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA, - const q15_t * pInBuffer, - const uint16_t ch_im_out, - const uint16_t numCol_A, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut) -{ - -#if defined (ARM_MATH_DSP) - /* set up the second output pointers */ - q7_t *pOut2 = pOut + ch_im_out; - int i; - - /* this loop over rows in A */ - for (i = 0; i < ch_im_out; i += 2) - { - /* setup pointers for B */ - const q15_t *pB = pInBuffer; - const q15_t *pB2 = pB + numCol_A; - - /* align the second pointer for A */ - const q7_t *pA2 = pA + numCol_A; - - /* init the sum with bias */ - q31_t sum = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = numCol_A >> 2; - /* accumulate over the vector */ - while (colCnt) - { - q31_t inA11, inA12, inA21, inA22; - - q31_t inB1 = arm_nn_read_q15x2_ia(&pB); - q31_t inB2 = arm_nn_read_q15x2_ia(&pB2); - - pA = read_and_pad_reordered(pA, &inA11, &inA12); - pA2 = read_and_pad_reordered(pA2, &inA21, &inA22); - - sum = __SMLAD(inA11, inB1, sum); - sum2 = __SMLAD(inA11, inB2, sum2); - sum3 = __SMLAD(inA21, inB1, sum3); - sum4 = __SMLAD(inA21, inB2, sum4); - - inB1 = arm_nn_read_q15x2_ia(&pB); - inB2 = arm_nn_read_q15x2_ia(&pB2); - - sum = __SMLAD(inA12, inB1, sum); - sum2 = __SMLAD(inA12, inB2, sum2); - sum3 = __SMLAD(inA22, inB1, sum3); - sum4 = __SMLAD(inA22, inB2, sum4); - - colCnt--; - } /* while over colCnt */ - colCnt = numCol_A & 0x3; - while (colCnt) - { - q7_t inA1 = *pA++; - q15_t inB1 = *pB++; - q7_t inA2 = *pA2++; - q15_t inB2 = *pB2++; - - sum += inA1 * inB1; - sum2 += inA1 * inB2; - sum3 += inA2 * inB1; - sum4 += inA2 * inB2; - colCnt--; - } /* while over colCnt */ - *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - /* skip the row computed with A2 */ - pA += numCol_A; - } /* for over ch_im_out */ - - pOut += ch_im_out; - - /* return the new output pointer with offset */ - return pOut; -#else - /* To be completed */ - return NULL; -#endif /* ARM_MATH_DSP */ -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16.c deleted file mode 100644 index 9aa17c96..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16.c +++ /dev/null @@ -1,391 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_kernel_s8_s16.c - * Description: Matrix-multiplication function for convolution - * - * $Date: May 29, 2020 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/* - * Matrix-multiplication function for convolution with per-channel requantization. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_mat_mult_kernel_s8_s16(const q7_t *input_a, - const q15_t *input_b, - const uint16_t output_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t num_col_a, - const int32_t *const output_bias, - q7_t *out_0) -{ -#if defined(ARM_MATH_MVEI) -#define ROW_PER_LOOP (4) -#define COL_PER_LOOP (8) - - const q7_t *ip_a0_s8 = input_a; - q7_t *out_1 = out_0 + output_ch; - - const int32_t *bias = output_bias; - - int32_t row_count = output_ch / ROW_PER_LOOP; - - while (row_count) - { - const q15_t *ip_b0_s16 = input_b; - const q15_t *ip_b1_s16 = input_b + num_col_a; - - const q7_t *ip_a1_s8 = ip_a0_s8 + num_col_a; - const q7_t *ip_a2_s8 = ip_a0_s8 + num_col_a * 2; - const q7_t *ip_a3_s8 = ip_a0_s8 + num_col_a * 3; - - q31_t ch_0_out_n = bias[0]; - q31_t ch_1_out_n = bias[1]; - q31_t ch_2_out_n = bias[2]; - q31_t ch_3_out_n = bias[3]; - - q31_t ch_0_out_n1 = ch_0_out_n; - q31_t ch_1_out_n1 = ch_1_out_n; - q31_t ch_2_out_n1 = ch_2_out_n; - q31_t ch_3_out_n1 = ch_3_out_n; - bias += 4; - - int32_t col_count = num_col_a / COL_PER_LOOP; - - while (col_count) - { - // Load inputs - const int16x8_t ip_b0 = vld1q_s16(ip_b0_s16); - ip_b0_s16 += COL_PER_LOOP; - const int16x8_t ip_b1 = vld1q_s16(ip_b1_s16); - ip_b1_s16 += COL_PER_LOOP; - - // Load filters - const int16x8_t ip_a0 = vldrbq_s16(ip_a0_s8); - ip_a0_s8 += COL_PER_LOOP; - const int16x8_t ip_a1 = vldrbq_s16(ip_a1_s8); - ip_a1_s8 += COL_PER_LOOP; - const int16x8_t ip_a2 = vldrbq_s16(ip_a2_s8); - ip_a2_s8 += COL_PER_LOOP; - const int16x8_t ip_a3 = vldrbq_s16(ip_a3_s8); - ip_a3_s8 += COL_PER_LOOP; - - // MAC - ch_0_out_n += vmladavq_s16(ip_b0, ip_a0); - ch_1_out_n += vmladavq_s16(ip_b0, ip_a1); - ch_2_out_n += vmladavq_s16(ip_b0, ip_a2); - ch_3_out_n += vmladavq_s16(ip_b0, ip_a3); - ch_0_out_n1 += vmladavq_s16(ip_b1, ip_a0); - ch_1_out_n1 += vmladavq_s16(ip_b1, ip_a1); - ch_2_out_n1 += vmladavq_s16(ip_b1, ip_a2); - ch_3_out_n1 += vmladavq_s16(ip_b1, ip_a3); - - col_count--; - } - - /* Handle tail */ - col_count = (num_col_a & (COL_PER_LOOP - 1)) - 1; - while (col_count >= 0) - { - const int32_t b0 = ip_b0_s16[col_count]; - const int32_t b1 = ip_b1_s16[col_count]; - - ch_0_out_n += b0 * ip_a0_s8[col_count]; - ch_1_out_n += b0 * ip_a1_s8[col_count]; - ch_2_out_n += b0 * ip_a2_s8[col_count]; - ch_3_out_n += b0 * ip_a3_s8[col_count]; - - ch_0_out_n1 += b1 * ip_a0_s8[col_count]; - ch_1_out_n1 += b1 * ip_a1_s8[col_count]; - ch_2_out_n1 += b1 * ip_a2_s8[col_count]; - ch_3_out_n1 += b1 * ip_a3_s8[col_count]; - col_count--; - } - ip_a0_s8 += (num_col_a & (COL_PER_LOOP - 1)); - - int32x4_t out_vec_0; - int32x4_t out_vec_1; - out_vec_0[0] = ch_0_out_n; - out_vec_0[1] = ch_1_out_n; - out_vec_0[2] = ch_2_out_n; - out_vec_0[3] = ch_3_out_n; - - out_vec_1[0] = ch_0_out_n1; - out_vec_1[1] = ch_1_out_n1; - out_vec_1[2] = ch_2_out_n1; - out_vec_1[3] = ch_3_out_n1; - - int32x4_t mult = vldrwq_s32(out_mult); - int32x4_t shift = vldrwq_s32(out_shift); - out_mult += ROW_PER_LOOP; - out_shift += ROW_PER_LOOP; - - out_vec_0 = arm_requantize_mve_32x4(out_vec_0, mult, shift); - out_vec_1 = arm_requantize_mve_32x4(out_vec_1, mult, shift); - - out_vec_0 = vaddq_n_s32(out_vec_0, out_offset); - out_vec_0 = vmaxq_s32(out_vec_0, vdupq_n_s32(activation_min)); - out_vec_0 = vminq_s32(out_vec_0, vdupq_n_s32(activation_max)); - vstrbq_s32(out_0, out_vec_0); - out_0 += ROW_PER_LOOP; - - out_vec_1 = vaddq_n_s32(out_vec_1, out_offset); - out_vec_1 = vmaxq_s32(out_vec_1, vdupq_n_s32(activation_min)); - out_vec_1 = vminq_s32(out_vec_1, vdupq_n_s32(activation_max)); - vstrbq_s32(out_1, out_vec_1); - out_1 += ROW_PER_LOOP; - row_count--; - ip_a0_s8 += (num_col_a * 3); - } - - row_count = output_ch & (ROW_PER_LOOP - 1); - - if (row_count) - { - ip_a0_s8 = input_a + num_col_a * (output_ch & ~3); - const mve_pred16_t p = vctp32q((uint32_t)row_count); - int32x4_t out_vec_0 = vdupq_n_s32(0); - int32x4_t out_vec_1 = vdupq_n_s32(0); - int32x4_t mult_tail; - int32x4_t shift_tail; - - for (int i_ch = 0; i_ch < row_count; i_ch++) - { - int32_t output_0 = bias[i_ch]; - int32_t output_1 = bias[i_ch]; - const q15_t *ip_b0_s16 = input_b; - const q15_t *ip_b1_s16 = input_b + num_col_a; - - for (int i_idx = 0; i_idx < num_col_a; i_idx++) - { - output_0 += ip_b0_s16[i_idx] * ip_a0_s8[i_idx]; - output_1 += ip_b1_s16[i_idx] * ip_a0_s8[i_idx]; - } - - ip_a0_s8 += num_col_a; - out_vec_0[i_ch] = output_0; - out_vec_1[i_ch] = output_1; - mult_tail[i_ch] = out_mult[i_ch]; - shift_tail[i_ch] = out_shift[i_ch]; - } - out_vec_0 = arm_requantize_mve_32x4(out_vec_0, mult_tail, shift_tail); - out_vec_1 = arm_requantize_mve_32x4(out_vec_1, mult_tail, shift_tail); - - out_vec_0 = vaddq_n_s32(out_vec_0, out_offset); - out_vec_0 = vmaxq_s32(out_vec_0, vdupq_n_s32(activation_min)); - out_vec_0 = vminq_s32(out_vec_0, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out_0, out_vec_0, p); - - out_vec_1 = vaddq_n_s32(out_vec_1, out_offset); - out_vec_1 = vmaxq_s32(out_vec_1, vdupq_n_s32(activation_min)); - out_vec_1 = vminq_s32(out_vec_1, vdupq_n_s32(activation_max)); - - vstrbq_p_s32(out_1, out_vec_1, p); - out_1 += row_count; - } - - return out_1; - -#elif defined(ARM_MATH_DSP) - /* set up the second output pointers */ - q7_t *out_1 = out_0 + output_ch; - const int32_t *bias = output_bias; - - uint16_t row_count = output_ch / 2; - const q7_t *ip_a0 = input_a; - /* this loop over rows in A */ - while (row_count) - { - /* setup pointers for B */ - const q15_t *ip_b0 = input_b; - const q15_t *ip_b1 = ip_b0 + num_col_a; - - /* align the second pointer for A */ - const q7_t *ip_a1 = ip_a0 + num_col_a; - - /* Init accumulator with bias for channel N and N + 1 */ - q31_t ch_0_out_0 = *bias; - q31_t ch_0_out_1 = *bias++; - q31_t ch_1_out_0 = *bias; - q31_t ch_1_out_1 = *bias++; - - uint16_t col_count = num_col_a / 4; - /* accumulate over the vector */ - while (col_count) - { - q31_t a01, a02, a11, a12; - q31_t b0 = arm_nn_read_q15x2_ia(&ip_b0); - q31_t b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ip_a0 = read_and_pad(ip_a0, &a01, &a02); - ip_a1 = read_and_pad(ip_a1, &a11, &a12); - - ch_0_out_0 = __SMLAD(a01, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a01, b1, ch_0_out_1); - ch_1_out_0 = __SMLAD(a11, b0, ch_1_out_0); - ch_1_out_1 = __SMLAD(a11, b1, ch_1_out_1); - - b0 = arm_nn_read_q15x2_ia(&ip_b0); - b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ch_0_out_0 = __SMLAD(a02, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a02, b1, ch_0_out_1); - ch_1_out_0 = __SMLAD(a12, b0, ch_1_out_0); - ch_1_out_1 = __SMLAD(a12, b1, ch_1_out_1); - - col_count--; - } /* while over col_count */ - col_count = num_col_a & 0x3; - while (col_count) - { - q7_t a0 = *ip_a0++; - q15_t b0 = *ip_b0++; - q7_t a1 = *ip_a1++; - q15_t b1 = *ip_b1++; - - ch_0_out_0 += a0 * b0; - ch_0_out_1 += a0 * b1; - ch_1_out_0 += a1 * b0; - ch_1_out_1 += a1 * b1; - col_count--; - } /* while over col_count */ - - ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift); - ch_0_out_0 += out_offset; - ch_0_out_0 = MAX(ch_0_out_0, activation_min); - ch_0_out_0 = MIN(ch_0_out_0, activation_max); - *out_0++ = (q7_t)ch_0_out_0; - - ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift); - ch_0_out_1 += out_offset; - ch_0_out_1 = MAX(ch_0_out_1, activation_min); - ch_0_out_1 = MIN(ch_0_out_1, activation_max); - *out_1++ = (q7_t)ch_0_out_1; - out_mult++; - out_shift++; - - ch_1_out_0 = arm_nn_requantize(ch_1_out_0, *out_mult, *out_shift); - ch_1_out_0 += out_offset; - ch_1_out_0 = MAX(ch_1_out_0, activation_min); - ch_1_out_0 = MIN(ch_1_out_0, activation_max); - *out_0++ = (q7_t)ch_1_out_0; - - ch_1_out_1 = arm_nn_requantize(ch_1_out_1, *out_mult, *out_shift); - ch_1_out_1 += out_offset; - ch_1_out_1 = MAX(ch_1_out_1, activation_min); - ch_1_out_1 = MIN(ch_1_out_1, activation_max); - *out_1++ = (q7_t)ch_1_out_1; - out_mult++; - out_shift++; - - /* skip row */ - ip_a0 += num_col_a; - row_count--; - } - - /* compute the last odd numbered row if any */ - if (output_ch & 0x1) - { - /* setup pointers for B */ - const q15_t *ip_b0 = input_b; - const q15_t *ip_b1 = ip_b0 + num_col_a; - - /* load the bias */ - q31_t ch_0_out_0 = *bias; - q31_t ch_0_out_1 = *bias++; - - uint16_t col_count = num_col_a >> 2; - while (col_count) - { - q31_t a01, a02; - q31_t b0 = arm_nn_read_q15x2_ia(&ip_b0); - q31_t b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ip_a0 = read_and_pad(ip_a0, &a01, &a02); - - ch_0_out_0 = __SMLAD(a01, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a01, b1, ch_0_out_1); - - b0 = arm_nn_read_q15x2_ia(&ip_b0); - b1 = arm_nn_read_q15x2_ia(&ip_b1); - ch_0_out_0 = __SMLAD(a02, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a02, b1, ch_0_out_1); - - col_count--; - } - col_count = num_col_a & 0x3; - while (col_count) - { - q7_t a0 = *ip_a0++; - q15_t b0 = *ip_b0++; - q15_t b1 = *ip_b1++; - - ch_0_out_0 += a0 * b0; - ch_0_out_1 += a0 * b1; - col_count--; - } - ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift); - ch_0_out_0 += out_offset; - ch_0_out_0 = MAX(ch_0_out_0, activation_min); - ch_0_out_0 = MIN(ch_0_out_0, activation_max); - *out_0++ = (q7_t)ch_0_out_0; - - ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift); - ch_0_out_1 += out_offset; - ch_0_out_1 = MAX(ch_0_out_1, activation_min); - ch_0_out_1 = MIN(ch_0_out_1, activation_max); - *out_1++ = (q7_t)ch_0_out_1; - out_mult++; - out_shift++; - } - - out_0 += output_ch; - - /* return the new output pointer with offset */ - return out_0; -#else - (void)input_a; - (void)input_b; - (void)output_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)num_col_a; - (void)output_bias; - (void)out_0; - /* To be completed */ - return NULL; -#endif -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16_reordered.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16_reordered.c deleted file mode 100644 index dc5d36d4..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16_reordered.c +++ /dev/null @@ -1,201 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_kernel_s8_s16_reordered.c - * Description: Matrix-multiplication function for convolution with reordered columns - * - * $Date: February 27, 2020 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/DSP/Include/arm_math.h" - -/* - * Matrix-multiplication with re-ordered input and bias inputs for convolution with per-channel - * requantization. The re-ordering is a consequence of sign extension is done by the SXTB16 command. - * - * Refer header file for details. This function differs from arm_nn_mat_mult_kernel_s8_s16(), in that it uses - * read_and_pad_reordered() instead of arm_nn_mat_mult_kernel_s8_s16(). Investigating the cycles impact and - * unifying these two functions is a potential future improvement. - * - */ - -q7_t *arm_nn_mat_mult_kernel_s8_s16_reordered(const q7_t *input_a, - const q15_t *input_b, - const uint16_t output_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t num_col_a, - const int32_t *const output_bias, - q7_t *out_0) -{ -#if defined(ARM_MATH_DSP) - /* set up the second output pointers */ - q7_t *out_1 = out_0 + output_ch; - const int32_t *bias = output_bias; - - uint16_t row_count = output_ch / 2; - const q7_t *ip_a0 = input_a; - /* this loop over rows in A */ - while (row_count) - { - /* setup pointers for B */ - const q15_t *ip_b0 = input_b; - const q15_t *ip_b1 = ip_b0 + num_col_a; - - /* align the second pointer for A */ - const q7_t *ip_a1 = ip_a0 + num_col_a; - - /* Init accumulator with bias for channel N and N + 1 */ - q31_t ch_0_out_0 = *bias; - q31_t ch_0_out_1 = *bias++; - q31_t ch_1_out_0 = *bias; - q31_t ch_1_out_1 = *bias++; - - uint16_t col_count = num_col_a / 4; - /* accumulate over the vector */ - while (col_count) - { - q31_t a01, a02, a11, a12; - q31_t b0 = arm_nn_read_q15x2_ia(&ip_b0); - q31_t b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ip_a0 = read_and_pad_reordered(ip_a0, &a01, &a02); - ip_a1 = read_and_pad_reordered(ip_a1, &a11, &a12); - - ch_0_out_0 = __SMLAD(a01, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a01, b1, ch_0_out_1); - ch_1_out_0 = __SMLAD(a11, b0, ch_1_out_0); - ch_1_out_1 = __SMLAD(a11, b1, ch_1_out_1); - - b0 = arm_nn_read_q15x2_ia(&ip_b0); - b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ch_0_out_0 = __SMLAD(a02, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a02, b1, ch_0_out_1); - ch_1_out_0 = __SMLAD(a12, b0, ch_1_out_0); - ch_1_out_1 = __SMLAD(a12, b1, ch_1_out_1); - - col_count--; - } /* while over col_count */ - - ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift); - ch_0_out_0 += out_offset; - ch_0_out_0 = MAX(ch_0_out_0, activation_min); - ch_0_out_0 = MIN(ch_0_out_0, activation_max); - *out_0++ = (q7_t)ch_0_out_0; - - ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift); - ch_0_out_1 += out_offset; - ch_0_out_1 = MAX(ch_0_out_1, activation_min); - ch_0_out_1 = MIN(ch_0_out_1, activation_max); - *out_1++ = (q7_t)ch_0_out_1; - out_mult++; - out_shift++; - - ch_1_out_0 = arm_nn_requantize(ch_1_out_0, *out_mult, *out_shift); - ch_1_out_0 += out_offset; - ch_1_out_0 = MAX(ch_1_out_0, activation_min); - ch_1_out_0 = MIN(ch_1_out_0, activation_max); - *out_0++ = (q7_t)ch_1_out_0; - - ch_1_out_1 = arm_nn_requantize(ch_1_out_1, *out_mult, *out_shift); - ch_1_out_1 += out_offset; - ch_1_out_1 = MAX(ch_1_out_1, activation_min); - ch_1_out_1 = MIN(ch_1_out_1, activation_max); - *out_1++ = (q7_t)ch_1_out_1; - out_mult++; - out_shift++; - - /* skip row */ - ip_a0 += num_col_a; - row_count--; - } - - if (output_ch & 1) - { - /* setup pointers for B */ - const q15_t *ip_b0 = input_b; - const q15_t *ip_b1 = ip_b0 + num_col_a; - - /* Init accumulator with bias for channel N + 1 */ - q31_t ch_0_out_0 = *bias; - q31_t ch_0_out_1 = ch_0_out_0; - - int32_t col_count = num_col_a / 4; - while (col_count) - { - q31_t a01, a02; - q31_t b0 = arm_nn_read_q15x2_ia(&ip_b0); - q31_t b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ip_a0 = read_and_pad_reordered(ip_a0, &a01, &a02); - - ch_0_out_0 = __SMLAD(a01, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a01, b1, ch_0_out_1); - - b0 = arm_nn_read_q15x2_ia(&ip_b0); - b1 = arm_nn_read_q15x2_ia(&ip_b1); - - ch_0_out_0 = __SMLAD(a02, b0, ch_0_out_0); - ch_0_out_1 = __SMLAD(a02, b1, ch_0_out_1); - - col_count--; - } /* while over col_count */ - - ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift); - ch_0_out_0 += out_offset; - ch_0_out_0 = MAX(ch_0_out_0, activation_min); - ch_0_out_0 = MIN(ch_0_out_0, activation_max); - *out_0++ = (q7_t)ch_0_out_0; - - ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift); - ch_0_out_1 += out_offset; - ch_0_out_1 = MAX(ch_0_out_1, activation_min); - ch_0_out_1 = MIN(ch_0_out_1, activation_max); - *out_1++ = (q7_t)ch_0_out_1; - } - - out_0 += output_ch; - - /* return the new output pointer with offset */ - return out_0; -#else - (void)input_a; - (void)input_b; - (void)output_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)num_col_a; - (void)output_bias; - (void)out_0; - /* To be completed */ - return NULL; -#endif -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_s8.c deleted file mode 100644 index d0271d06..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_s8.c +++ /dev/null @@ -1,173 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_s8.c - * Description: General Matrix-multiplication function - * - * $Date: May 29, 2020 - * $Revision: V.2.0.3 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/* - * s8 General matrix multiplication function with per-channel requantization for upto 4 column batches. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_mat_mult_s8(const q7_t *input_row, - const q7_t *input_col, - const uint16_t output_ch, - const uint16_t col_batches, - const int32_t *output_shift, - const int32_t *output_mult, - const int32_t out_offset, - const int32_t col_offset, - const int32_t row_offset, - const int16_t activation_min, - const int16_t activation_max, - const uint16_t row_len, - const int32_t *const bias, - q7_t *out) -{ -#if defined(ARM_MATH_MVEI) - (void)row_offset; - if (col_batches == 4) - { - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t row_len_tmp = row_len; - const int8_t *ip_r0 = input_row + (i_out_ch * row_len); - const int8_t *ip_c0 = input_col; - const int8_t *ip_c1 = input_col + row_len; - const int8_t *ip_c2 = input_col + (2 * row_len); - const int8_t *ip_c3 = input_col + (3 * row_len); - - int32_t acc_0 = bias[i_out_ch]; - int32_t acc_1 = bias[i_out_ch]; - int32_t acc_2 = bias[i_out_ch]; - int32_t acc_3 = bias[i_out_ch]; - const int32_t row_loop_cnt = (row_len + 7) / 8; - - for (int i_row_loop = 0; i_row_loop < row_loop_cnt; i_row_loop++) - { - mve_pred16_t p = vctp16q((uint32_t)row_len_tmp); - const int16x8_t offset = vdupq_m_n_s16(vuninitializedq_s16(), col_offset, p); - row_len_tmp -= 8; - - int16x8_t r0 = vldrbq_z_s16(ip_r0, p); - ip_r0 += 8; - - int16x8_t c0 = vldrbq_z_s16(ip_c0, p); - ip_c0 += 8; - c0 = vaddq_m_s16(vuninitializedq_s16(), c0, offset, p); - - int16x8_t c1 = vldrbq_z_s16(ip_c1, p); - ip_c1 += 8; - c1 = vaddq_m_s16(vuninitializedq_s16(), c1, offset, p); - - int16x8_t c2 = vldrbq_z_s16(ip_c2, p); - ip_c2 += 8; - c2 = vaddq_m_s16(vuninitializedq_s16(), c2, offset, p); - - int16x8_t c3 = vldrbq_z_s16(ip_c3, p); - ip_c3 += 8; - c3 = vaddq_m_s16(vuninitializedq_s16(), c3, offset, p); - - acc_0 = vmladavaq_p_s16(acc_0, r0, c0, p); - acc_1 = vmladavaq_p_s16(acc_1, r0, c1, p); - acc_2 = vmladavaq_p_s16(acc_2, r0, c2, p); - acc_3 = vmladavaq_p_s16(acc_3, r0, c3, p); - } - - int32x4_t res = {acc_0, acc_1, acc_2, acc_3}; - res = arm_requantize_mve(res, output_mult[i_out_ch], output_shift[i_out_ch]); - res = vaddq_n_s32(res, out_offset); - - res = vmaxq_s32(res, vdupq_n_s32(activation_min)); - res = vminq_s32(res, vdupq_n_s32(activation_max)); - - const uint32x4_t scatter_offset = {0, output_ch, output_ch * 2, output_ch * 3}; - vstrbq_scatter_offset_s32(&out[i_out_ch], scatter_offset, res); - } - out += 4 * output_ch; - } - else - { - for (int i_col_batch = (col_batches & ~0x3); i_col_batch < (col_batches & 0x3); i_col_batch++) - { - for (int i_out_ch = 0; i_out_ch < output_ch; i_out_ch++) - { - int32_t row_len_tmp = row_len; - - const int8_t *ip_r0 = input_row + (i_out_ch * row_len); - const int8_t *ip_c0 = input_col + (i_col_batch * row_len); - int32_t acc_0 = bias[i_out_ch]; - const int32_t row_loop_cnt = (row_len + 7) / 8; - - for (int i_row_loop = 0; i_row_loop < row_loop_cnt; i_row_loop++) - { - const mve_pred16_t p = vctp16q((uint32_t)row_len_tmp); - const int16x8_t offset = vdupq_m_n_s16(vuninitializedq_s16(), col_offset, p); - row_len_tmp -= 8; - - int16x8_t r0 = vldrbq_z_s16(ip_r0, p); - ip_r0 += 8; - int16x8_t c0 = vldrbq_z_s16(ip_c0, p); - ip_c0 += 8; - - c0 = vaddq_m_s16(vuninitializedq_s16(), c0, offset, p); - acc_0 = vmladavaq_p_s16(acc_0, r0, c0, p); - } - - acc_0 = arm_nn_requantize(acc_0, output_mult[i_out_ch], output_shift[i_out_ch]); - acc_0 += out_offset; - acc_0 = MAX(acc_0, activation_min); - acc_0 = MIN(acc_0, activation_max); - out[i_out_ch] = (q7_t)acc_0; - } - out += output_ch; - } - } - return out; - -#else - (void)input_row; - (void)input_col; - (void)output_ch; - (void)col_batches; - (void)output_shift; - (void)output_mult; - (void)out_offset; - (void)col_offset; - (void)row_offset; - (void)activation_min; - (void)activation_max; - (void)row_len; - (void)bias; - (void)out; - return NULL; -#endif -} diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c deleted file mode 100644 index 92bffbfd..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c +++ /dev/null @@ -1,199 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_mat_q7_vec_q15.c - * Description: Mixed Q15-Q7 fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Mixed Q15-Q7 fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * vec_buffer size: 0 - * - * Q7_Q15 version of the fully connected layer - * - * Weights are in q7_t and Activations are in q15_t - * - */ - -arm_status -arm_fully_connected_mat_q7_vec_q15(const q15_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q15_t * pOut, - q15_t * vec_buffer) -{ - (void)vec_buffer; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q7_t *pB = pM; - const q7_t *pB2; - q15_t *pO = pOut; - const q7_t *pBias = bias; - const q15_t *pA = pV; - - uint16_t rowCnt = num_of_rows >> 1; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - pB2 = pB + dim_vec; - - while (colCnt) - { - q31_t inV, inM11, inM12, inM21, inM22; - pB = read_and_pad(pB, &inM11, &inM12); - pB2 = read_and_pad(pB2, &inM21, &inM22); - - inV = arm_nn_read_q15x2_ia(&pA); - - sum = __SMLAD(inV, inM11, sum); - sum2 = __SMLAD(inV, inM21, sum2); - - inV = arm_nn_read_q15x2_ia(&pA); - - sum = __SMLAD(inV, inM12, sum); - sum2 = __SMLAD(inV, inM22, sum2); - - colCnt--; - } - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - q7_t inM2 = *pB2++; - - sum += inV * inM; - sum2 += inV * inM2; - colCnt--; - } /* while over colCnt */ - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16)); - - /*adjust the pointers and counters */ - pB += dim_vec; - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x1; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) - { - q31_t inV1, inV2, inM11, inM12; - - pB = read_and_pad(pB, &inM11, &inM12); - - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM11, sum); - - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM12, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - - rowCnt--; - } - -#else - int i, j; - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - for (i = 0; i < num_of_rows; i++) - { - int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (j = 0; j < dim_vec; j++) - { - ip_out += pV[j] * pM[i * dim_vec + j]; - } - pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16); - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c deleted file mode 100644 index f2debef1..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c +++ /dev/null @@ -1,404 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_mat_q7_vec_q15_opt.c - * Description: Mixed Q15-Q7 opt fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Mixed Q15-Q7 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * vec_buffer size: 0 - * - * Q7_Q15 version of the fully connected layer - * - * Weights are in q7_t and Activations are in q15_t - * - * Limitation: x4 version requires weight reordering to work - * - * Here we use only one pointer to read 4 rows in the weight - * matrix. So if the original q7_t matrix looks like this: - * - * | a11 | a12 | a13 | a14 | a15 | a16 | a17 | - * - * | a21 | a22 | a23 | a24 | a25 | a26 | a27 | - * - * | a31 | a32 | a33 | a34 | a35 | a36 | a37 | - * - * | a41 | a42 | a43 | a44 | a45 | a46 | a47 | - * - * | a51 | a52 | a53 | a54 | a55 | a56 | a57 | - * - * | a61 | a62 | a63 | a64 | a65 | a66 | a67 | - * - * We operates on multiple-of-4 rows, so the first four rows becomes - * - * | a11 | a21 | a12 | a22 | a31 | a41 | a32 | a42 | - * - * | a13 | a23 | a14 | a24 | a33 | a43 | a34 | a44 | - * - * | a15 | a25 | a16 | a26 | a35 | a45 | a36 | a46 | - * - * The column left over will be in-order. - * which is: - * | a17 | a27 | a37 | a47 | - * - * For the left-over rows, we do 1x1 computation, so the data remains - * as its original order. - * - * So the stored weight matrix looks like this: - * - * | a11 | a21 | a12 | a22 | a31 | a41 | - * - * | a32 | a42 | a13 | a23 | a14 | a24 | - * - * | a33 | a43 | a34 | a44 | a15 | a25 | - * - * | a16 | a26 | a35 | a45 | a36 | a46 | - * - * | a17 | a27 | a37 | a47 | a51 | a52 | - * - * | a53 | a54 | a55 | a56 | a57 | a61 | - * - * | a62 | a63 | a64 | a65 | a66 | a67 | - * - */ - -arm_status -arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, const q7_t * bias, q15_t * pOut, q15_t * vec_buffer) -{ - - (void)vec_buffer; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q7_t *pB = pM; - q15_t *pO = pOut; - const q7_t *pBias = bias; - const q15_t *pA = pV; - - uint16_t rowCnt = num_of_rows >> 2; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 1; - - pA = pV; - -#ifdef USE_INTRINSIC - -#ifndef ARM_MATH_BIG_ENDIAN - - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM11, inV, sum); - sum2 = __SMLAD(inM12, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM13, inV, sum3); - sum4 = __SMLAD(inM14, inV, sum4); - colCnt--; - } - -#else - - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = *__SIMD32(pA)++; - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM12, inV, sum); - sum2 = __SMLAD(inM11, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM14, inV, sum3); - sum4 = __SMLAD(inM13, inV, sum4); - colCnt--; - } - -#endif /* ARM_MATH_BIG_ENDIAN */ - -#else - - /* - * register needed: - * loop counter: colCnt - * accumulators: sum, sum2, sum3, sum4 - * pointers: pB, pA - * weight data: inM11, inM12, inM13, inM14 - * activation data: inV - */ - -#ifndef ARM_MATH_BIG_ENDIAN - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #4\n" - "ldr.w r1, [%[pB]], #8\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r1, %[sum]\n" - "smlad %[sum2], r4, r0, %[sum2]\n" - "ldr.w r3, [%[pB], #-4]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r3, %[sum3]\n" - "smlad %[sum4], r4, r2, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); -#else - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #4\n" - "ldr.w r1, [%[pB]], #8\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r0, %[sum]\n" - "smlad %[sum2], r4, r1, %[sum2]\n" - "ldr.w r3, [%[pB], #-4]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r2, %[sum3]\n" - "smlad %[sum4], r4, r3, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); -#endif /* ARM_MATH_BIG_ENDIAN */ - -#endif /* USE_INTRINSIC */ - - colCnt = dim_vec & 0x1; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - q7_t inM2 = *pB++; - q7_t inM3 = *pB++; - q7_t inM4 = *pB++; - - sum += inV * inM; - sum2 += inV * inM2; - sum3 += inV * inM3; - sum4 += inV * inM4; - colCnt--; - } /* while over colCnt */ - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16)); - - /* adjust the pointers and counters */ - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) - { - q31_t inV1, inV2, inM11, inM12; - - pB = read_and_pad(pB, &inM11, &inM12); - - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM11, sum); - - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM12, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - - rowCnt--; - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - uint16_t rowCnt = num_of_rows >> 2; - const q7_t *pB = pM; - const q15_t *pA; - q15_t *pO = pOut; - const q7_t *pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 1; - - pA = pV; - - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inA2 = *pA++; - - q7_t inB1 = *pB++; - q7_t inB3 = *pB++; - q7_t inB2 = *pB++; - q7_t inB4 = *pB++; - - sum += inA1 * inB1 + inA2 * inB2; - sum2 += inA1 * inB3 + inA2 * inB4; - - inB1 = *pB++; - inB3 = *pB++; - inB2 = *pB++; - inB4 = *pB++; - - sum3 += inA1 * inB1 + inA2 * inB2; - sum4 += inA1 * inB3 + inA2 * inB4; - - colCnt--; - } - - colCnt = dim_vec & 0x1; - while (colCnt) - { - q15_t inA = *pA++; - q7_t inB = *pB++; - sum += inA * inB; - inB = *pB++; - sum2 += inA * inB; - inB = *pB++; - sum3 += inA * inB; - inB = *pB++; - sum4 += inA * inB; - - colCnt--; - } - *pO++ = (q15_t) __SSAT((sum >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16); - - rowCnt--; - } - - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - int j; - - pA = pV; - for (j = 0; j < dim_vec; j++) - { - q15_t inA = *pA++; - q7_t inB = *pB++; - ip_out += inA * inB; - } - *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16); - - rowCnt--; - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c deleted file mode 100644 index 79413cbd..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c +++ /dev/null @@ -1,193 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_q15.c - * Description: Q15 basic fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Q15 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * - * @details - * - * Buffer size: - * - * vec_buffer size: 0 - * - */ - -arm_status -arm_fully_connected_q15(const q15_t * pV, - const q15_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q15_t * bias, - q15_t * pOut, - q15_t * vec_buffer) -{ - (void)vec_buffer; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q15_t *pB = pM; - const q15_t *pB2 = pB + dim_vec; - q15_t *pO = pOut; - const q15_t *pA; - const q15_t *pBias = bias; - uint16_t rowCnt = num_of_rows >> 1; - - /* this loop loops over different output */ - while (rowCnt) { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - pB2 = pB + dim_vec; - - while (colCnt) - { - q31_t inV1, inM1, inM2; - inV1 = arm_nn_read_q15x2_ia(&pA); - inM1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV1, inM1, sum); - inM2 = arm_nn_read_q15x2_ia(&pB2); - sum2 = __SMLAD(inV1, inM2, sum2); - - inV1 = arm_nn_read_q15x2_ia(&pA); - inM1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV1, inM1, sum); - inM2 = arm_nn_read_q15x2_ia(&pB2); - sum2 = __SMLAD(inV1, inM2, sum2); - - colCnt--; - } - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q15_t inM = *pB++; - q15_t inM2 = *pB2++; - - sum += inV * inM; - sum2 += inV * inM2; - colCnt--; - } /* while over colCnt */ - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum2>> out_shift), 16)); - - /* adjust the pointers and counters */ - pB = pB + dim_vec; - rowCnt --; - } - - rowCnt = num_of_rows & 0x1; - - while (rowCnt) { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) { - q31_t inV1, inM1; - inV1 = arm_nn_read_q15x2_ia(&pA); - inM1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV1, inM1, sum); - - inV1 = arm_nn_read_q15x2_ia(&pA); - inM1 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV1, inM1, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while(colCnt) { - q15_t inV = *pA++; - q15_t inM = *pB++; - - sum += inV * inM; - - colCnt--; - } - - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - - rowCnt --; - } - -#else - int i, j; - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - for (i = 0; i < num_of_rows; i++) - { - int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (j = 0; j < dim_vec; j++) - { - ip_out += pV[j] * pM[i * dim_vec + j]; - } - pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16); - } - -#endif /* ARM_MATH_DSP */ - - /* Return to application */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c deleted file mode 100644 index d495342e..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c +++ /dev/null @@ -1,332 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_q15_opt.c - * Description: Q15 opt fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Q15 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * - * @details - * - * Buffer size: - * - * vec_buffer size: 0 - * - * Here we use only one pointer to read 4 rows in the weight - * matrix. So if the original matrix looks like this: - * - * | a11 | a12 | a13 | - * - * | a21 | a22 | a23 | - * - * | a31 | a32 | a33 | - * - * | a41 | a42 | a43 | - * - * | a51 | a52 | a53 | - * - * | a61 | a62 | a63 | - * - * We operates on multiple-of-4 rows, so the first four rows becomes - * - * | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 | - * - * | a13 | a23 | a33 | a43 | - * - * Remaining rows are kept the same original order. - * - * So the stored weight matrix looks like this: - * - * - * | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 | - * - * | a13 | a23 | a33 | a43 | a51 | a52 | a53 | a61 | - * - * | a62 | a63 | - */ - -arm_status -arm_fully_connected_q15_opt(const q15_t * pV, - const q15_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q15_t * bias, - q15_t * pOut, - q15_t * vec_buffer) -{ - (void)vec_buffer; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q15_t *pB = pM; - q15_t *pO = pOut; - const q15_t *pBias = bias; - const q15_t *pA = pV; - - uint16_t rowCnt = num_of_rows >> 2; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 1; - - pA = pV; - -#ifdef USE_INTRINSIC - - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q15x2_ia(&pB); - sum = __SMLAD(inV, inM11, sum); - inM12 = arm_nn_read_q15x2_ia(&pB); - sum2 = __SMLAD(inV, inM12, sum2); - inM13 = arm_nn_read_q15x2_ia(&pB); - sum3 = __SMLAD(inV, inM13, sum3); - inM14 = arm_nn_read_q15x2_ia(&pB); - sum4 = __SMLAD(inV, inM14, sum4); - colCnt--; - } - -#else - - /* - * register needed: - * loop counter: colCnt - * accumulators: sum, sum2, sum3, sum4 - * pointers: pB, pA - * weight data: inM11, inM12, inM13, inM14 - * activation data: inV - */ - - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #4\n" - "ldr.w r0, [%[pB]], #16\n" - "smlad %[sum], r4, r0, %[sum]\n" - "ldr.w r1, [%[pB] , #-12]\n" - "smlad %[sum2], r4, r1, %[sum2]\n" - "ldr.w r2, [%[pB] , #-8]\n" - "smlad %[sum3], r4, r2, %[sum3]\n" - "ldr.w r3, [%[pB] , #-4]\n" - "smlad %[sum4], r4, r3, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); - -#endif /* USE_INTRINSIC */ - - colCnt = dim_vec & 0x1; - while (colCnt) - { - - q15_t inV = *pA++; - q15_t inM = *pB++; - q15_t inM2 = *pB++; - q15_t inM3 = *pB++; - q15_t inM4 = *pB++; - - sum += inV * inM; - sum2 += inV * inM2; - sum3 += inV * inM3; - sum4 += inV * inM4; - colCnt--; - } /* while over colCnt */ - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16)); - *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16)); - - /* adjust the pointers and counters */ - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) - { - q31_t inV1, inV2, inM1, inM2; - - inM1 = arm_nn_read_q15x2_ia(&pB); - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM1, sum); - - inM2 = arm_nn_read_q15x2_ia(&pB); - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM2, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q15_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); - - rowCnt--; - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - uint16_t rowCnt = num_of_rows >> 2; - const q15_t *pB = pM; - const q15_t *pA; - q15_t *pO = pOut; - const q15_t *pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 1; - - pA = pV; - while (colCnt) - { - q15_t inA1 = *pA++; - q15_t inA2 = *pA++; - - q15_t inB1 = *pB++; - q15_t inB2 = *pB++; - sum += inA1 * inB1 + inA2 * inB2; - - inB1 = *pB++; - inB2 = *pB++; - sum2 += inA1 * inB1 + inA2 * inB2; - - inB1 = *pB++; - inB2 = *pB++; - sum3 += inA1 * inB1 + inA2 * inB2; - - inB1 = *pB++; - inB2 = *pB++; - sum4 += inA1 * inB1 + inA2 * inB2; - - colCnt--; - } - colCnt = dim_vec & 0x1; - while (colCnt) - { - q15_t inA = *pA++; - q15_t inB = *pB++; - sum += inA * inB; - inB = *pB++; - sum2 += inA * inB; - inB = *pB++; - sum3 += inA * inB; - inB = *pB++; - sum4 += inA * inB; - colCnt--; - } - *pO++ = (q15_t) __SSAT((sum >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16); - *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16); - - rowCnt--; - } - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - int j; - - pA = pV; - for (j = 0; j < dim_vec; j++) - { - q15_t inA = *pA++; - q15_t inB = *pB++; - ip_out += inA * inB; - } - *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16); - - rowCnt--; - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c deleted file mode 100644 index 12dc6f1c..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c +++ /dev/null @@ -1,198 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_q7.c - * Description: Q7 basic fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Q7 basic fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * vec_buffer size: dim_vec - * - * This basic function is designed to work with regular weight - * matrix without interleaving. - * - */ - -arm_status -arm_fully_connected_q7(const q7_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, const q7_t * bias, q7_t * pOut, q15_t * vec_buffer) -{ - -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q7_t *pB = pM; - const q7_t *pB2; - q7_t *pO = pOut; - const q7_t *pBias = bias; - const q15_t *pA; - uint16_t rowCnt = num_of_rows >> 1; - - /* expand the vector into the buffer */ - arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec); - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 2; - - pA = vec_buffer; - pB2 = pB + dim_vec; - - while (colCnt) - { - q31_t inV, inM11, inM12, inM21, inM22; - pB = read_and_pad_reordered(pB, &inM11, &inM12); - pB2 = read_and_pad_reordered(pB2, &inM21, &inM22); - - inV = arm_nn_read_q15x2_ia(&pA); - - sum = __SMLAD(inV, inM11, sum); - sum2 = __SMLAD(inV, inM21, sum2); - - inV = arm_nn_read_q15x2_ia(&pA); - - sum = __SMLAD(inV, inM12, sum); - sum2 = __SMLAD(inV, inM22, sum2); - - colCnt--; - } - colCnt = dim_vec & 0x3; - while (colCnt) - { - q7_t inV = *pA++; - q15_t inM = *pB++; - q15_t inM2 = *pB2++; - - sum += inV * inM; - sum2 += inV * inM2; - colCnt--; - } /* while over colCnt */ - *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8)); - *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8)); - - /* adjust the pointers and counters */ - pB += dim_vec; - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x1; - - while (rowCnt) - { - uint16_t colCnt = dim_vec >> 2; - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - pA = vec_buffer; - - while (colCnt) - { - q31_t inV1, inV2, inM11, inM12; - - pB = read_and_pad_reordered(pB, &inM11, &inM12); - - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM11, sum); - - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM12, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q7_t inV = *pA++; - q15_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8)); - - rowCnt--; - } - -#else - int i, j; - - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - for (i = 0; i < num_of_rows; i++) - { - int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift); - for (j = 0; j < dim_vec; j++) - { - ip_out += pV[j] * pM[i * dim_vec + j]; - } - pOut[i] = (q7_t) __SSAT((ip_out >> out_shift), 8); - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c deleted file mode 100644 index ee7faa9d..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c +++ /dev/null @@ -1,484 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_q7_opt.c - * Description: Q7 basic fully-connected layer function - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - - /** - * @brief Q7 opt fully-connected layer function - * @param[in] pV pointer to input vector - * @param[in] pM pointer to matrix weights - * @param[in] dim_vec length of the vector - * @param[in] num_of_rows number of rows in weight matrix - * @param[in] bias_shift amount of left-shift for bias - * @param[in] out_shift amount of right-shift for output - * @param[in] bias pointer to bias - * @param[in,out] pOut pointer to output vector - * @param[in,out] vec_buffer pointer to buffer space for input - * @return The function returns ARM_MATH_SUCCESS - * - * @details - * - * Buffer size: - * - * vec_buffer size: dim_vec - * - * This opt function is designed to work with interleaved weight - * matrix. The vector input is assumed in q7_t format, we call - * arm_q7_to_q15_no_shift_shuffle function to expand into - * q15_t format with certain weight re-ordering, refer to the function - * comments for more details. - * Here we use only one pointer to read 4 rows in the weight - * matrix. So if the original q7_t matrix looks like this: - * - * | a11 | a12 | a13 | a14 | a15 | a16 | a17 | - * - * | a21 | a22 | a23 | a24 | a25 | a26 | a27 | - * - * | a31 | a32 | a33 | a34 | a35 | a36 | a37 | - * - * | a41 | a42 | a43 | a44 | a45 | a46 | a47 | - * - * | a51 | a52 | a53 | a54 | a55 | a56 | a57 | - * - * | a61 | a62 | a63 | a64 | a65 | a66 | a67 | - * - * - * We operates on multiple-of-4 rows, so the first four rows becomes - * - * | a11 | a21 | a13 | a23 | a31 | a41 | a33 | a43 | - * - * | a12 | a22 | a14 | a24 | a32 | a42 | a34 | a44 | - * - * | a15 | a25 | a35 | a45 | a16 | a26 | a36 | a46 | - * - * So within the kernel, we first read the re-ordered vector in as: - * - * | b1 | b3 | and | b2 | b4 | - * - * the four q31_t weights will look like - * - * | a11 | a13 |, | a21 | a23 |, | a31 | a33 |, | a41 | a43 | - * - * | a12 | a14 |, | a22 | a24 |, | a32 | a34 |, | a42 | a44 | - * - * The column left over will be in-order. - * which is: - * - * | a17 | a27 | a37 | a47 | - * - * For the left-over rows, we do 1x1 computation, so the data remains - * as its original order. - * - * So the stored weight matrix looks like this: - * - * | a11 | a21 | a13 | a23 | a31 | a41 | - * - * | a33 | a43 | a12 | a22 | a14 | a24 | - * - * | a32 | a42 | a34 | a44 | a15 | a25 | - * - * | a35 | a45 | a16 | a26 | a36 | a46 | - * - * | a17 | a27 | a37 | a47 | a51 | a52 | - * - * | a53 | a54 | a55 | a56 | a57 | a61 | - * - * | a62 | a63 | a64 | a65 | a66 | a67 | - * - * - */ - -arm_status -arm_fully_connected_q7_opt(const q7_t * pV, - const q7_t * pM, - const uint16_t dim_vec, - const uint16_t num_of_rows, - const uint16_t bias_shift, - const uint16_t out_shift, - const q7_t * bias, - q7_t * pOut, - q15_t * vec_buffer) -{ - -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - const q7_t *pB = pM; - q7_t *pO = pOut; - const q7_t *pBias = bias; - const q15_t *pA; - uint16_t rowCnt = num_of_rows >> 2; - - arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec); - - while (rowCnt) - { - - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = vec_buffer; - -#ifdef USE_INTRINSIC - -#ifndef ARM_MATH_BIG_ENDIAN - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM11, inV, sum); - sum2 = __SMLAD(inM12, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM13, inV, sum3); - sum4 = __SMLAD(inM14, inV, sum4); - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM11, inV, sum); - sum2 = __SMLAD(inM12, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM13, inV, sum3); - sum4 = __SMLAD(inM14, inV, sum4); - colCnt--; - } -#else - while (colCnt) - { - q31_t inM11, inM12, inM13, inM14; - q31_t inV; - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM12, inV, sum); - sum2 = __SMLAD(inM11, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM14, inV, sum3); - sum4 = __SMLAD(inM13, inV, sum4); - - inV = arm_nn_read_q15x2_ia(&pA); - inM11 = arm_nn_read_q7x4_ia(&pB); - inM12 = __SXTB16(__ROR(inM11, 8)); - inM11 = __SXTB16(inM11); - sum = __SMLAD(inM12, inV, sum); - sum2 = __SMLAD(inM11, inV, sum2); - inM13 = arm_nn_read_q7x4_ia(&pB); - inM14 = __SXTB16(__ROR(inM13, 8)); - inM13 = __SXTB16(inM13); - sum3 = __SMLAD(inM14, inV, sum3); - sum4 = __SMLAD(inM13, inV, sum4); - colCnt--; - } -#endif /* ARM_MATH_BIG_ENDIAN */ - -#else - - /* - * register needed: - * loop counter: colCnt - * accumulators: sum, sum2, sum3, sum4 - * pointers: pB, pA - * weight data: inM11, inM12, inM13, inM14 - * activation data: inV - */ - -#ifndef ARM_MATH_BIG_ENDIAN - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #8\n" - "ldr.w r1, [%[pB]], #16\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r1, %[sum]\n" - "smlad %[sum2], r4, r0, %[sum2]\n" - "ldr.w r3, [%[pB], #-12]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r3, %[sum3]\n" - "smlad %[sum4], r4, r2, %[sum4]\n" - "ldr.w r4, [%[pA], #-4]\n" - "ldr.w r1, [%[pB], #-8]\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r1, %[sum]\n" - "smlad %[sum2], r4, r0, %[sum2]\n" - "ldr.w r3, [%[pB], #-4]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r3, %[sum3]\n" - "smlad %[sum4], r4, r2, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); -#else - asm volatile ("COL_LOOP_%=:\n" - "ldr.w r4, [%[pA]], #8\n" - "ldr.w r1, [%[pB]], #16\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r0, %[sum]\n" - "smlad %[sum2], r4, r1, %[sum2]\n" - "ldr.w r3, [%[pB], #-12]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r2, %[sum3]\n" - "smlad %[sum4], r4, r3, %[sum4]\n" - "ldr.w r4, [%[pA], #-4]\n" - "ldr.w r1, [%[pB], #-8]\n" - "mov.w r0, r1, ror #8\n" - "sxtb16 r0, r0\n" - "sxtb16 r1, r1\n" - "smlad %[sum], r4, r0, %[sum]\n" - "smlad %[sum2], r4, r1, %[sum2]\n" - "ldr.w r3, [%[pB], #-4]\n" - "mov.w r2, r3, ror #8\n" - "sxtb16 r2, r2\n" - "sxtb16 r3, r3\n" - "smlad %[sum3], r4, r2, %[sum3]\n" - "smlad %[sum4], r4, r3, %[sum4]\n" - "subs %[colCnt], #1\n" - "bne COL_LOOP_%=\n":[sum] "+r"(sum), - [sum2] "+r"(sum2),[sum3] "+r"(sum3), - [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); -#endif /* ARM_MATH_BIG_ENDIAN */ - -#endif /* USE_INTRINSIC */ - - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - q7_t inM2 = *pB++; - q7_t inM3 = *pB++; - q7_t inM4 = *pB++; - - sum += inV * inM; - sum2 += inV * inM2; - sum3 += inV * inM3; - sum4 += inV * inM4; - colCnt--; - } /* while over colCnt */ - *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8)); - *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8)); - *pO++ = (q7_t) (__SSAT((sum3 >> out_shift), 8)); - *pO++ = (q7_t) (__SSAT((sum4 >> out_shift), 8)); - - /* adjust the pointers and counters */ - rowCnt--; - } - - /* left-over part of the rows */ - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - uint16_t colCnt = dim_vec >> 2; - - pA = vec_buffer; - - while (colCnt) - { - q31_t inV1, inV2, inM11, inM12; - - pB = read_and_pad_reordered(pB, &inM11, &inM12); - - inV1 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV1, inM11, sum); - - inV2 = arm_nn_read_q15x2_ia(&pA); - sum = __SMLAD(inV2, inM12, sum); - - colCnt--; - } - - /* left-over of the vector */ - colCnt = dim_vec & 0x3; - while (colCnt) - { - q15_t inV = *pA++; - q7_t inM = *pB++; - sum += inV * inM; - colCnt--; - } - - *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8)); - - rowCnt--; - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - uint16_t rowCnt = num_of_rows >> 2; - const q7_t *pB = pM; - const q7_t *pA; - q7_t *pO = pOut; - const q7_t *pBias = bias; - - while (rowCnt) - { - q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - uint16_t colCnt = dim_vec >> 2; - - pA = pV; - - while (colCnt) - { - q7_t inA1 = *pA++; - q7_t inA3 = *pA++; - q7_t inA2 = *pA++; - q7_t inA4 = *pA++; - - q7_t inB1 = *pB++; - q7_t inB3 = *pB++; - q7_t inB2 = *pB++; - q7_t inB4 = *pB++; - - sum += inA1 * inB1 + inA2 * inB2; - sum2 += inA1 * inB3 + inA2 * inB4; - - inB1 = *pB++; - inB3 = *pB++; - inB2 = *pB++; - inB4 = *pB++; - - sum3 += inA1 * inB1 + inA2 * inB2; - sum4 += inA1 * inB3 + inA2 * inB4; - - inB1 = *pB++; - inB3 = *pB++; - inB2 = *pB++; - inB4 = *pB++; - - sum += inA3 * inB1 + inA4 * inB2; - sum2 += inA3 * inB3 + inA4 * inB4; - - inB1 = *pB++; - inB3 = *pB++; - inB2 = *pB++; - inB4 = *pB++; - - sum3 += inA3 * inB1 + inA4 * inB2; - sum4 += inA3 * inB3 + inA4 * inB4; - - colCnt--; - } - colCnt = dim_vec & 0x3; - while (colCnt) - { - q7_t inA = *pA++; - q7_t inB = *pB++; - sum += inA * inB; - inB = *pB++; - sum2 += inA * inB; - inB = *pB++; - sum3 += inA * inB; - inB = *pB++; - sum4 += inA * inB; - - colCnt--; - } - *pO++ = (q7_t) __SSAT((sum >> out_shift), 8); - *pO++ = (q7_t) __SSAT((sum2 >> out_shift), 8); - *pO++ = (q7_t) __SSAT((sum3 >> out_shift), 8); - *pO++ = (q7_t) __SSAT((sum4 >> out_shift), 8); - - rowCnt--; - } - - rowCnt = num_of_rows & 0x3; - - while (rowCnt) - { - int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); - - int j; - - pA = pV; - for (j = 0; j < dim_vec; j++) - { - q7_t inA = *pA++; - q7_t inB = *pB++; - ip_out += inA * inB; - } - *pO++ = (q7_t) __SSAT((ip_out >> out_shift), 8); - - rowCnt--; - } - -#endif /* ARM_MATH_DSP */ - - /* Return to ARM_MATH_SUCCESS */ - return (ARM_MATH_SUCCESS); - -} - -/** - * @} end of FC group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_s8.c deleted file mode 100644 index 9775aaaa..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_s8.c +++ /dev/null @@ -1,97 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_fully_connected_s8 - * Description: Fully connected function compatible with TF Lite. - * - * $Date: May 2, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M and Cortex-A cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup FC - * @{ - */ - -/* - * S8 basic fully-connected and matrix multiplication layer function for TensorFlow Lite - * - * Refer header file for details. - * - */ - -arm_status -arm_fully_connected_s8(const cmsis_nn_context *ctx, - const cmsis_nn_fc_params *fc_params, - const cmsis_nn_per_tensor_quant_params *quant_params, - const cmsis_nn_dims *input_dims, - const q7_t *input, - const cmsis_nn_dims *filter_dims, - const q7_t *kernel, - const cmsis_nn_dims *bias_dims, - const int32_t *bias, - const cmsis_nn_dims *output_dims, - q7_t *output) -{ - (void)bias_dims; - (void)ctx; - int32_t batch_cnt = input_dims->n; - - while (batch_cnt) - { - arm_nn_vec_mat_mult_t_s8(input, - kernel, - bias, - output, - fc_params->input_offset, - fc_params->filter_offset, - fc_params->output_offset, - quant_params->multiplier, - quant_params->shift, - filter_dims->n, /* col_dim or accum_depth */ - output_dims->c, /* row_dim or output_depth */ - fc_params->activation.min, - fc_params->activation.max); - input += filter_dims->n; - output += output_dims->c; - batch_cnt--; - } - return (ARM_MATH_SUCCESS); -} - -int32_t arm_fully_connected_s8_get_buffer_size(const cmsis_nn_dims *filter_dims) -{ - (void)filter_dims; - return 0; -} - -/** - * @} end of FC group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_accumulate_q7_to_q15.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_accumulate_q7_to_q15.c deleted file mode 100644 index 7d14834e..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_accumulate_q7_to_q15.c +++ /dev/null @@ -1,81 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_accumulate_q7_to_q15.c - * Description: Accumulate q7 vector into q15 one. - * - * $Date: May 29, 2020 - * $Revision: V.1.0.1 - * - * pSrc Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -void arm_nn_accumulate_q7_to_q15(q15_t *pDst, const q7_t *pSrc, uint32_t length) -{ - q15_t *pCnt = pDst; - const q7_t *pV = pSrc; - q31_t v1, v2, vo1, vo2; - int32_t cnt = length >> 2; - q31_t in; - - while (cnt > 0l) - { - q31_t value = arm_nn_read_q7x4_ia(&pV); - v1 = __SXTB16(__ROR((uint32_t)value, 8)); - v2 = __SXTB16(value); -#ifndef ARM_MATH_BIG_ENDIAN - vo2 = (q31_t)__PKHTB(v1, v2, 16); - vo1 = (q31_t)__PKHBT(v2, v1, 16); -#else - vo1 = (q31_t)__PKHTB(v1, v2, 16); - vo2 = (q31_t)__PKHBT(v2, v1, 16); -#endif - - in = arm_nn_read_q15x2(pCnt); - write_q15x2_ia(&pCnt, __QADD16(vo1, in)); - - in = arm_nn_read_q15x2(pCnt); - write_q15x2_ia(&pCnt, __QADD16(vo2, in)); - - cnt--; - } - cnt = length & 0x3; - while (cnt > 0l) - { - *pCnt++ += *pV++; - cnt--; - } -} - -/** - * @} end of NNBasicMath group - */ \ No newline at end of file diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_add_q7.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_add_q7.c deleted file mode 100644 index ea549f94..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_add_q7.c +++ /dev/null @@ -1,82 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_add_q7.c - * Description: Non saturating addition of elements of a q7 vector. - * - * $Date: July 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -void arm_nn_add_q7(const q7_t *input, q31_t *output, uint32_t block_size) -{ - uint32_t block_count; - q31_t result = 0; -#if defined(ARM_MATH_DSP) - /* Loop unrolling: Compute 4 outputs at a time */ - block_count = block_size >> 2U; - - while (block_count > 0U) - { - const int32_t mult_q15x2 = (1UL << 16) | 1UL; - q31_t in_q7x4 = arm_nn_read_q7x4_ia(&input); - q31_t temp_q15x2 = __SXTAB16(__SXTB16(in_q7x4), - __ROR((uint32_t)in_q7x4, 8)); - - result = __SMLAD(temp_q15x2, mult_q15x2, result); - - /* Decrement loop counter */ - block_count--; - } - - /* Loop unrolling: Compute remaining outputs */ - block_count = block_size & 0x3; -#else - block_count = block_size; -#endif - while (block_count > 0U) - { - /* Add and store result in destination buffer. */ - result += *input++; - - /* Decrement loop counter */ - block_count--; - } - - *output = result; -} - -/** - * @} end of NNBasicMath group - */ \ No newline at end of file diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_padded_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_padded_s8.c deleted file mode 100644 index 15e6b6dd..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_padded_s8.c +++ /dev/null @@ -1,169 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_depthwise_conv_nt_t_padded_s8.c - * Description: Depthwise convolution with padded matrices. - * - * $Date: March 17, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M processors with MVE extension - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * Depthwise convolution of transposed rhs matrix with 4 lhs matrices. One or more of the rhs matrices are padded. - * Dimensions are the same for lhs and rhs. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_depthwise_conv_nt_t_padded_s8(const q7_t *lhs, - const q7_t *rhs, - const int32_t input_offset, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t row_x_col, - const int32_t *const output_bias, - q7_t *out) -{ -#if defined(ARM_MATH_MVEI) - int32_t loop_count = (num_ch + 3) / 4; - const int32_t *bias = output_bias; - uint32_t num_ch_to_process = num_ch; - - for (int i_loop_cnt = 0, offset = 0; i_loop_cnt < loop_count; - num_ch_to_process -= 4, out += 4, offset += 4, i_loop_cnt++) - { - int32x4_t out_0 = vldrwq_s32(bias); - int32x4_t out_1 = out_0; - int32x4_t out_2 = out_0; - int32x4_t out_3 = out_0; - bias += 4; - - const int8_t *rhs_0 = rhs + offset; - const int8_t *lhs_0 = lhs + offset; - const int8_t *lhs_1 = lhs + row_x_col * num_ch + offset; - const int8_t *lhs_2 = lhs + (row_x_col * num_ch * 2) + offset; - const int8_t *lhs_3 = lhs + (row_x_col * num_ch * 3) + offset; - - for (int i_row_x_col = 0; i_row_x_col < row_x_col; i_row_x_col++) - { - const int32x4_t ker_0 = vldrbq_s32(rhs_0); - - int32x4_t ip_0 = vldrbq_s32(lhs_0); - ip_0 = vaddq_n_s32(ip_0, input_offset); - out_0 += vmulq_s32(ip_0, ker_0); - - int32x4_t ip_1 = vldrbq_s32(lhs_1); - ip_1 = vaddq_n_s32(ip_1, input_offset); - out_1 += vmulq_s32(ip_1, ker_0); - - int32x4_t ip_2 = vldrbq_s32(lhs_2); - ip_2 = vaddq_n_s32(ip_2, input_offset); - out_2 += vmulq_s32(ip_2, ker_0); - - int32x4_t ip_3 = vldrbq_s32(lhs_3); - ip_3 = vaddq_n_s32(ip_3, input_offset); - - out_3 += vmulq_s32(ip_3, ker_0); - - lhs_0 += num_ch; - lhs_1 += num_ch; - lhs_2 += num_ch; - lhs_3 += num_ch; - - rhs_0 += num_ch; - } - - const int32x4_t mult = vldrwq_s32(out_mult); - const int32x4_t shift = vldrwq_s32(out_shift); - out_mult += 4; - out_shift += 4; - - out_0 = arm_requantize_mve_32x4(out_0, mult, shift); - out_0 = vaddq_n_s32(out_0, out_offset); - out_0 = vmaxq_s32(out_0, vdupq_n_s32(activation_min)); - out_0 = vminq_s32(out_0, vdupq_n_s32(activation_max)); - mve_pred16_t p = vctp32q(num_ch_to_process); - vstrbq_p_s32(out, out_0, p); - - out_1 = arm_requantize_mve_32x4(out_1, mult, shift); - out_1 = vaddq_n_s32(out_1, out_offset); - out_1 = vmaxq_s32(out_1, vdupq_n_s32(activation_min)); - out_1 = vminq_s32(out_1, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + num_ch, out_1, p); - - out_2 = arm_requantize_mve_32x4(out_2, mult, shift); - out_2 = vaddq_n_s32(out_2, out_offset); - out_2 = vmaxq_s32(out_2, vdupq_n_s32(activation_min)); - out_2 = vminq_s32(out_2, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + 2 * num_ch, out_2, p); - - out_3 = arm_requantize_mve_32x4(out_3, mult, shift); - out_3 = vaddq_n_s32(out_3, out_offset); - out_3 = vmaxq_s32(out_3, vdupq_n_s32(activation_min)); - out_3 = vminq_s32(out_3, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + 3 * num_ch, out_3, p); - } - - const int tail_ch = num_ch & 0x3; - if (tail_ch != 0) - { - out -= (4 - tail_ch); - } - return out + (3 * num_ch); - -#else - (void)lhs; - (void)rhs; - (void)input_offset; - (void)num_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)row_x_col; - (void)output_bias; - (void)out; - return NULL; -#endif -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_s8.c deleted file mode 100644 index 2cfdef44..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_s8.c +++ /dev/null @@ -1,171 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_depthwise_conv_nt_t_s8.c - * Description: Depthwise convolution on matrices with no padding. - * - * $Date: March 17, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M processors with MVE extension. - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * Depthwise convolution of rhs matrix with 4 lhs matrices with no padding. Dimensions are the same for lhs and rhs. - * - * Refer header file for details. - * - */ - -q7_t *arm_nn_depthwise_conv_nt_t_s8(const q7_t *lhs, - const q7_t *rhs, - const int32_t input_offset, - const uint16_t num_ch, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t out_offset, - const int32_t activation_min, - const int32_t activation_max, - const uint16_t row_x_col, - const int32_t *const output_bias, - q7_t *out) -{ -#if defined(ARM_MATH_MVEI) - const int32_t *bias = output_bias; - int32_t loop_count = (num_ch + 3) / 4; - uint32_t num_ch_to_process = num_ch; - - for (int i_loop_cnt = 0, offset = 0; i_loop_cnt < loop_count; - num_ch_to_process -= 4, offset += 4, out += 4, i_loop_cnt++) - { - int32x4_t out_0 = vldrwq_s32(bias); - int32x4_t out_1 = out_0; - int32x4_t out_2 = out_0; - int32x4_t out_3 = out_0; - bias += 4; - - const int8_t *rhs_0 = rhs + offset; - const int8_t *lhs_0 = lhs + offset; - const int8_t *lhs_1 = lhs + row_x_col * num_ch + offset; - const int8_t *lhs_2 = lhs + (row_x_col * num_ch * 2) + offset; - const int8_t *lhs_3 = lhs + (row_x_col * num_ch * 3) + offset; - int32x4_t ker_sum = vdupq_n_s32(0); - - for (int i_row_x_col = 0; i_row_x_col < row_x_col; i_row_x_col++) - { - const int32x4_t ker_0 = vldrbq_s32(rhs_0); - ker_sum = vaddq_s32(ker_sum, ker_0); - - int32x4_t ip_0 = vldrbq_s32(lhs_0); - out_0 += vmulq_s32(ip_0, ker_0); - - int32x4_t ip_1 = vldrbq_s32(lhs_1); - out_1 += vmulq_s32(ip_1, ker_0); - - int32x4_t ip_2 = vldrbq_s32(lhs_2); - out_2 += vmulq_s32(ip_2, ker_0); - - int32x4_t ip_3 = vldrbq_s32(lhs_3); - out_3 += vmulq_s32(ip_3, ker_0); - - lhs_0 += num_ch; - lhs_1 += num_ch; - lhs_2 += num_ch; - lhs_3 += num_ch; - - rhs_0 += num_ch; - } - - ker_sum = vmulq_n_s32(ker_sum, input_offset); - out_0 = ker_sum + out_0; - out_1 = ker_sum + out_1; - out_2 = ker_sum + out_2; - out_3 = ker_sum + out_3; - - const int32x4_t mult = vldrwq_s32(out_mult); - const int32x4_t shift = vldrwq_s32(out_shift); - out_mult += 4; - out_shift += 4; - mve_pred16_t p = vctp32q(num_ch_to_process); - - out_0 = arm_requantize_mve_32x4(out_0, mult, shift); - out_0 = vaddq_n_s32(out_0, out_offset); - out_0 = vmaxq_s32(out_0, vdupq_n_s32(activation_min)); - out_0 = vminq_s32(out_0, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out, out_0, p); - - out_1 = arm_requantize_mve_32x4(out_1, mult, shift); - out_1 = vaddq_n_s32(out_1, out_offset); - out_1 = vmaxq_s32(out_1, vdupq_n_s32(activation_min)); - out_1 = vminq_s32(out_1, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + num_ch, out_1, p); - - out_2 = arm_requantize_mve_32x4(out_2, mult, shift); - out_2 = vaddq_n_s32(out_2, out_offset); - out_2 = vmaxq_s32(out_2, vdupq_n_s32(activation_min)); - out_2 = vminq_s32(out_2, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + 2 * num_ch, out_2, p); - - out_3 = arm_requantize_mve_32x4(out_3, mult, shift); - out_3 = vaddq_n_s32(out_3, out_offset); - out_3 = vmaxq_s32(out_3, vdupq_n_s32(activation_min)); - out_3 = vminq_s32(out_3, vdupq_n_s32(activation_max)); - vstrbq_p_s32(out + 3 * num_ch, out_3, p); - } - - const int tail_ch = num_ch & 0x3; - if (tail_ch != 0) - { - out -= (4 - tail_ch); - } - - return out + (3 * num_ch); -#else - (void)lhs; - (void)rhs; - (void)input_offset; - (void)num_ch; - (void)out_shift; - (void)out_mult; - (void)out_offset; - (void)activation_min; - (void)activation_max; - (void)row_x_col; - (void)output_bias; - (void)out; - return NULL; -#endif -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_1x_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_1x_s8.c deleted file mode 100644 index f57ee07f..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_1x_s8.c +++ /dev/null @@ -1,91 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mul_core_1x_s8.c - * Description: General Matrix-multiplication function - * - * $Date: January 20, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * s8 matrix multiplication to process 1 row - * - * Refer header file for details. - * - */ - -arm_status arm_nn_mat_mul_core_1x_s8(int32_t row_elements, - const int8_t *row_base, - const int8_t *col_base, - int32_t *const sum_col, - int32_t *const output) -{ - int32_t acc_n0 = 0; - int32_t sum_tmp = 0; - -#if defined(ARM_MATH_MVEI) && !defined(ARM_MATH_AUTOVECTORIZE) - - __asm volatile ( - " vldrb.8 q0, [%[col]], 16 \n" - " wlstp.8 lr, %[cnt], 1f \n" - "2: \n" - " vaddva.s8 %[sum], q0 \n" - " vldrb.8 q1, [%[row0]], 16 \n" - " vmladava.s8 %[out0], q0, q1 \n" - " vldrb.8 q0, [%[col]], 16 \n" - " letp lr, 2b \n" - "1: \n" - :[col] "+r"(col_base) - ,[sum] "+Te"(sum_tmp) - ,[row0] "+r"(row_base) - ,[out0] "+Te"(acc_n0) - :[cnt] "r"(row_elements) - :"q0","q1", "memory", "r14"); -#else - for (int i = 0; i < row_elements; i++) - { - sum_tmp += col_base[i]; - acc_n0 += row_base[i] * col_base[i]; - } -#endif - - *sum_col = sum_tmp; - *output = acc_n0; - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_4x_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_4x_s8.c deleted file mode 100644 index 0b240b70..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mul_core_4x_s8.c +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mul_core_4x_s8.c - * Description: General matrix multiplication function for MVE extension - * - * $Date: January 20, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M cores - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * s8 matrix multiplication to process 4 rows and one column - * - * Refer header file for details. - * - */ -arm_status arm_nn_mat_mul_core_4x_s8(const int32_t row_elements, - const int32_t offset, - const int8_t *row_base, - const int8_t *col_base, - int32_t *const sum_col, - int32_t *const output) -{ - int32_t acc_n0 = 0; - int32_t acc_n1 = 0; - int32_t acc_n2 = 0; - int32_t acc_n3 = 0; - - const int8_t *ip_row_0 = row_base; - const int8_t *ip_row_1 = row_base + offset; - const int8_t *ip_row_2 = row_base + (2 * offset); - const int8_t *ip_row_3 = row_base + (3 * offset); - int32_t sum_tmp = 0; - -#if defined(ARM_MATH_MVEI) && !defined(ARM_MATH_AUTOVECTORIZE) - __asm volatile( - " vldrb.8 q0, [%[col]], 16 \n" - " wlstp.8 lr, %[cnt], 1f \n" - "2: \n" - " vaddva.s8 %[sum], q0 \n" - " vldrb.8 q1, [%[row0]], 16 \n" - " vmladava.s8 %[out0], q0, q1 \n" - " vldrb.8 q2, [%[row1]], 16 \n" - " vmladava.s8 %[out1], q0, q2 \n" - " vldrb.8 q3, [%[row2]], 16 \n" - " vmladava.s8 %[out2], q0, q3 \n" - " vldrb.8 q4, [%[row3]], 16 \n" - " vmladava.s8 %[out3], q0, q4 \n" - " vldrb.8 q0, [%[col]], 16 \n" - " letp lr, 2b \n" - "1: \n" - :[col] "+r"(col_base) - ,[sum] "+Te"(sum_tmp) - ,[row0] "+r"(ip_row_0) - ,[row1] "+r"(ip_row_1) - ,[row2] "+r"(ip_row_2) - ,[row3] "+r"(ip_row_3) - ,[out0] "+Te"(acc_n0) - ,[out1] "+Te"(acc_n1) - ,[out2] "+Te"(acc_n2) - ,[out3] "+Te"(acc_n3) - : [cnt] "r"(row_elements) - : "q0", "q1", "q2", "q3", "q4", "memory", "r14"); -#else - for (int i = 0; i < row_elements; i++) - { - int32_t col = col_base[i]; - sum_tmp += col; - acc_n0 += ip_row_0[i] * col; - acc_n1 += ip_row_1[i] * col; - acc_n2 += ip_row_2[i] * col; - acc_n3 += ip_row_3[i] * col; - } -#endif - output[0] = acc_n0; - output[1] = acc_n1; - output[2] = acc_n2; - output[3] = acc_n3; - - *sum_col = sum_tmp; - - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s8.c deleted file mode 100644 index 392ab58c..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s8.c +++ /dev/null @@ -1,579 +0,0 @@ -/* - * Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mat_mult_s8_nt_t_s8 - * Description: Matrix multiplication support function with the right-hand-side (rhs) matrix transposed - * - * $Date: March 17 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -// Work around for https://github.com/ARMmbed/mbed-os/issues/12568 -#define __patched_SXTB16_RORn(op1, rotate) \ -({ \ - uint32_t result; \ - __ASM ("sxtb16 %0, %1, ROR %2" : "=r" (result) : "r" (op1), "i" (rotate) ); \ - result; \ -}) -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * s8 matrix multiplication with the right-hand-side matrix transposed - * - * Refer header file for details. - * - */ -arm_status arm_nn_mat_mult_nt_t_s8(const q7_t *lhs, - const q7_t *rhs, - const q31_t *bias, - q7_t *dst, - const int32_t *dst_multipliers, - const int32_t *dst_shifts, - const int32_t lhs_rows, - const int32_t rhs_rows, - const int32_t rhs_cols, - const int32_t lhs_offset, - const int32_t dst_offset, - const int32_t activation_min, - const int32_t activation_max) -{ -#if defined(ARM_MATH_DSP) - const int32_t off0 = rhs_cols - 4; - - for (int32_t rhs_rows_idx = 0; rhs_rows_idx <= (rhs_rows - 2); rhs_rows_idx += 2) - { - const q7_t *lhs_ptr = &lhs[0]; - q7_t *dst_ptr = &dst[0]; - - q31_t lhs_offset_contribution0 = 0; - q31_t lhs_offset_contribution1 = 0; - - for (int32_t x = 0; x < rhs_cols; ++x) - { - lhs_offset_contribution0 += rhs[x]; - lhs_offset_contribution1 += rhs[x + rhs_cols]; - } - - lhs_offset_contribution0 *= lhs_offset; - lhs_offset_contribution1 *= lhs_offset; - - lhs_offset_contribution0 += bias[rhs_rows_idx]; - lhs_offset_contribution1 += bias[rhs_rows_idx + 1]; - - int32_t lhs_rows_idx = lhs_rows >> 1; - - while (lhs_rows_idx) - { - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = lhs_offset_contribution0; - q31_t res01 = lhs_offset_contribution1; - q31_t res10 = lhs_offset_contribution0; - q31_t res11 = lhs_offset_contribution1; - - int32_t rhs_cols_idx = 0; - - q31_t val0, val1, val2, val3, val4, val5; - - for (; rhs_cols_idx <= (rhs_cols - 16); rhs_cols_idx += 16) - { - val1 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val2 = __SXTB16(val1); - val0 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val4 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val1 = __patched_SXTB16_RORn(val1, 8); - val0 = __patched_SXTB16_RORn(val0, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTB16(val4); - res00 = __SMLAD(val0, val1, res00); - val4 = __patched_SXTB16_RORn(val4, 8); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val0, val4, res01); - - // 4 x MAC res10, res11 - val0 = arm_nn_read_q7x4((const q7_t *)&lhs_ptr[off0]); - val3 = __SXTB16(val0); - val0 = __patched_SXTB16_RORn(val0, 8); - res10 = __SMLAD(val3, val2, res10); - res11 = __SMLAD(val3, val5, res11); - res10 = __SMLAD(val0, val1, res10); - val1 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res11 = __SMLAD(val0, val4, res11); - - val4 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = __SXTB16(val1); - val0 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val1 = __patched_SXTB16_RORn(val1, 8); - val0 = __patched_SXTB16_RORn(val0, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTB16(val4); - res00 = __SMLAD(val0, val1, res00); - val4 = __patched_SXTB16_RORn(val4, 8); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val0, val4, res01); - - // 4 x MAC res10, res11 - val0 = arm_nn_read_q7x4((const q7_t *)&lhs_ptr[off0]); - val3 = __SXTB16(val0); - val0 = __patched_SXTB16_RORn(val0, 8); - res10 = __SMLAD(val3, val2, res10); - res11 = __SMLAD(val3, val5, res11); - res10 = __SMLAD(val0, val1, res10); - val1 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res11 = __SMLAD(val0, val4, res11); - - val4 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = __SXTB16(val1); - val0 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val1 = __patched_SXTB16_RORn(val1, 8); - val0 = __patched_SXTB16_RORn(val0, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTB16(val4); - res00 = __SMLAD(val0, val1, res00); - val4 = __patched_SXTB16_RORn(val4, 8); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val0, val4, res01); - - // 4 x MAC res10, res11 - val0 = arm_nn_read_q7x4((const q7_t *)&lhs_ptr[off0]); - val3 = __SXTB16(val0); - val0 = __patched_SXTB16_RORn(val0, 8); - res10 = __SMLAD(val3, val2, res10); - res11 = __SMLAD(val3, val5, res11); - res10 = __SMLAD(val0, val1, res10); - val1 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res11 = __SMLAD(val0, val4, res11); - - val4 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = __SXTB16(val1); - val0 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val1 = __patched_SXTB16_RORn(val1, 8); - val0 = __patched_SXTB16_RORn(val0, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTB16(val4); - res00 = __SMLAD(val0, val1, res00); - val4 = __patched_SXTB16_RORn(val4, 8); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val0, val4, res01); - - // 4 x MAC res10, res11 - val0 = arm_nn_read_q7x4((const q7_t *)&lhs_ptr[off0]); - val3 = __SXTB16(val0); - val0 = __patched_SXTB16_RORn(val0, 8); - res10 = __SMLAD(val3, val2, res10); - res11 = __SMLAD(val3, val5, res11); - res10 = __SMLAD(val0, val1, res10); - res11 = __SMLAD(val0, val4, res11); - } - - for (; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q7_t rhs_value0 = rhs_ptr[0]; - q7_t rhs_value1 = rhs_ptr[rhs_cols]; - q7_t lhs_value = lhs_ptr[0]; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - lhs_value = lhs_ptr[rhs_cols]; - res10 += lhs_value * rhs_value0; - res11 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res01 = arm_nn_requantize(res01, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - res10 = arm_nn_requantize(res10, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res11 = arm_nn_requantize(res11, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - res10 += dst_offset; - res11 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - res10 = MAX(res10, activation_min); - res10 = MIN(res10, activation_max); - res11 = MAX(res11, activation_min); - res11 = MIN(res11, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr[1] = (q7_t)res01; - dst_ptr += rhs_rows; - dst_ptr[0] = (q7_t)res10; - dst_ptr[1] = (q7_t)res11; - dst_ptr += rhs_rows; - - lhs_ptr += rhs_cols; - - lhs_rows_idx--; - } - - // Left-over rows - if (lhs_rows % 2) - { - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = lhs_offset_contribution0; - q31_t res01 = lhs_offset_contribution1; - - int32_t rhs_cols_idx = 0; - - q31_t val0, val1, val2, val3, val4, val5; - for (; rhs_cols_idx <= (rhs_cols - 16); rhs_cols_idx += 16) - { - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val5 = __SXTB16(val2); - val4 = __SXTB16(val1); - val0 = __patched_SXTB16_RORn(val0, 8); - val2 = __patched_SXTB16_RORn(val2, 8); - val1 = __patched_SXTB16_RORn(val1, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val5, val3, res00); - res00 = __SMLAD(val2, val0, res00); - res01 = __SMLAD(val5, val4, res01); - res01 = __SMLAD(val2, val1, res01); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val5 = __SXTB16(val2); - val4 = __SXTB16(val1); - val0 = __patched_SXTB16_RORn(val0, 8); - val2 = __patched_SXTB16_RORn(val2, 8); - val1 = __patched_SXTB16_RORn(val1, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val5, val3, res00); - res00 = __SMLAD(val2, val0, res00); - res01 = __SMLAD(val5, val4, res01); - res01 = __SMLAD(val2, val1, res01); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val5 = __SXTB16(val2); - val4 = __SXTB16(val1); - val0 = __patched_SXTB16_RORn(val0, 8); - val2 = __patched_SXTB16_RORn(val2, 8); - val1 = __patched_SXTB16_RORn(val1, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val5, val3, res00); - res00 = __SMLAD(val2, val0, res00); - res01 = __SMLAD(val5, val4, res01); - res01 = __SMLAD(val2, val1, res01); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = arm_nn_read_q7x4((const q7_t *)&rhs_ptr[off0]); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val3 = __SXTB16(val0); - val5 = __SXTB16(val2); - val4 = __SXTB16(val1); - val0 = __patched_SXTB16_RORn(val0, 8); - val2 = __patched_SXTB16_RORn(val2, 8); - val1 = __patched_SXTB16_RORn(val1, 8); - - // 4 x MAC res00, res01 - res00 = __SMLAD(val5, val3, res00); - res00 = __SMLAD(val2, val0, res00); - res01 = __SMLAD(val5, val4, res01); - res01 = __SMLAD(val2, val1, res01); - } - - // Left-over accumulations - for (; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q7_t rhs_value0 = rhs_ptr[0]; - q7_t rhs_value1 = rhs_ptr[rhs_cols]; - q7_t lhs_value = lhs_ptr[0]; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res01 = arm_nn_requantize(res01, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr[1] = (q7_t)res01; - } - - rhs += 2 * rhs_cols; - dst += 2; - } - - if (rhs_rows % 2) - { - const q7_t *lhs_ptr = &lhs[0]; - q7_t *dst_ptr = &dst[0]; - - for (int32_t lhs_rows_idx = 0; lhs_rows_idx < lhs_rows; ++lhs_rows_idx) - { - const q7_t *rhs_ptr = &rhs[0]; - q31_t res00 = bias[rhs_rows - 1]; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value = rhs_ptr[0]; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows - 1], dst_shifts[rhs_rows - 1]); - - // Add offset - res00 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr += rhs_rows; - } - } -#else - for (int32_t rhs_rows_idx = 0; rhs_rows_idx <= (rhs_rows - 2); rhs_rows_idx += 2) - { - const q7_t *lhs_ptr = &lhs[0]; - q7_t *dst_ptr = &dst[0]; - - q31_t lhs_offset_contribution0 = 0; - q31_t lhs_offset_contribution1 = 0; - - for (int32_t x = 0; x < rhs_cols; ++x) - { - lhs_offset_contribution0 += rhs[x]; - lhs_offset_contribution1 += rhs[x + rhs_cols]; - } - - lhs_offset_contribution0 *= lhs_offset; - lhs_offset_contribution1 *= lhs_offset; - - lhs_offset_contribution0 += bias[rhs_rows_idx]; - lhs_offset_contribution1 += bias[rhs_rows_idx + 1]; - - int32_t lhs_rows_idx = lhs_rows >> 1; - - while (lhs_rows_idx) - { - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = lhs_offset_contribution0; - q31_t res01 = lhs_offset_contribution1; - q31_t res10 = lhs_offset_contribution0; - q31_t res11 = lhs_offset_contribution1; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q7_t rhs_value0 = rhs_ptr[0]; - q7_t rhs_value1 = rhs_ptr[rhs_cols]; - q7_t lhs_value = lhs_ptr[0]; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - lhs_value = lhs_ptr[rhs_cols]; - res10 += lhs_value * rhs_value0; - res11 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res01 = arm_nn_requantize(res01, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - res10 = arm_nn_requantize(res10, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res11 = arm_nn_requantize(res11, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - res10 += dst_offset; - res11 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - res10 = MAX(res10, activation_min); - res10 = MIN(res10, activation_max); - res11 = MAX(res11, activation_min); - res11 = MIN(res11, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr[1] = (q7_t)res01; - dst_ptr += rhs_rows; - dst_ptr[0] = (q7_t)res10; - dst_ptr[1] = (q7_t)res11; - dst_ptr += rhs_rows; - - lhs_ptr += rhs_cols; - - lhs_rows_idx--; - } - - // Left-over rows - if (lhs_rows % 2) - { - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = lhs_offset_contribution0; - q31_t res01 = lhs_offset_contribution1; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q7_t rhs_value0 = rhs_ptr[0]; - q7_t rhs_value1 = rhs_ptr[rhs_cols]; - q7_t lhs_value = lhs_ptr[0]; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows_idx], dst_shifts[rhs_rows_idx]); - res01 = arm_nn_requantize(res01, dst_multipliers[rhs_rows_idx + 1], dst_shifts[rhs_rows_idx + 1]); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr[1] = (q7_t)res01; - } - - rhs += 2 * rhs_cols; - dst += 2; - } - - if (rhs_rows % 2) - { - const q7_t *lhs_ptr = &lhs[0]; - q7_t *dst_ptr = &dst[0]; - - for (int32_t lhs_rows_idx = 0; lhs_rows_idx < lhs_rows; ++lhs_rows_idx) - { - const q7_t *rhs_ptr = &rhs[0]; - q31_t res00 = bias[rhs_rows - 1]; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value = rhs_ptr[0]; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multipliers[rhs_rows - 1], dst_shifts[rhs_rows - 1]); - - // Add offset - res00 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - - dst_ptr[0] = (q7_t)res00; - dst_ptr += rhs_rows; - } - } -#endif - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c deleted file mode 100644 index d84bc18b..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c +++ /dev/null @@ -1,145 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mult_q15.c - * Description: Q15 vector multiplication with variable output shifts - * - * $Date: 29. April 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - - -/** - * @brief Q7 vector multiplication with variable output shifts - * @param[in] *pSrcA pointer to the first input vector - * @param[in] *pSrcB pointer to the second input vector - * @param[out] *pDst pointer to the output vector - * @param[in] out_shift amount of right-shift for output - * @param[in] blockSize number of samples in each vector - * - * Scaling and Overflow Behavior: - * \par - * The function uses saturating arithmetic. - * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated. - */ - -void arm_nn_mult_q15( - q15_t * pSrcA, - q15_t * pSrcB, - q15_t * pDst, - const uint16_t out_shift, - uint32_t blockSize) -{ - uint32_t blkCnt; /* loop counters */ - -#if defined (ARM_MATH_DSP) - -/* Run the below code for Cortex-M4 and Cortex-M3 */ - q31_t inA1, inA2, inB1, inB2; /* temporary input variables */ - q15_t out1, out2, out3, out4; /* temporary output variables */ - q31_t mul1, mul2, mul3, mul4; /* temporary variables */ - - /* loop Unrolling */ - blkCnt = blockSize >> 2U; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. - ** a second loop below computes the remaining 1 to 3 samples. */ - while (blkCnt > 0U) - { - /* read two samples at a time from sourceA */ - inA1 = arm_nn_read_q15x2_ia((const q15_t **)&pSrcA); - /* read two samples at a time from sourceB */ - inB1 = arm_nn_read_q15x2_ia((const q15_t **)&pSrcB); - /* read two samples at a time from sourceA */ - inA2 = arm_nn_read_q15x2_ia((const q15_t **)&pSrcA); - /* read two samples at a time from sourceB */ - inB2 = arm_nn_read_q15x2_ia((const q15_t **)&pSrcB); - - /* multiply mul = sourceA * sourceB */ - mul1 = (q31_t) ((q15_t) (inA1 >> 16) * (q15_t) (inB1 >> 16)); - mul2 = (q31_t) ((q15_t) inA1 * (q15_t) inB1); - mul3 = (q31_t) ((q15_t) (inA2 >> 16) * (q15_t) (inB2 >> 16)); - mul4 = (q31_t) ((q15_t) inA2 * (q15_t) inB2); - - /* saturate result to 16 bit */ - out1 = (q15_t) __SSAT((q31_t) (mul1 + NN_ROUND(out_shift)) >> out_shift, 16); - out2 = (q15_t) __SSAT((q31_t) (mul2 + NN_ROUND(out_shift)) >> out_shift, 16); - out3 = (q15_t) __SSAT((q31_t) (mul3 + NN_ROUND(out_shift)) >> out_shift, 16); - out4 = (q15_t) __SSAT((q31_t) (mul4 + NN_ROUND(out_shift)) >> out_shift, 16); - - /* store the result */ -#ifndef ARM_MATH_BIG_ENDIAN - - *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16); - *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16); - -#else - - *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16); - *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16); - -#endif /* #ifndef ARM_MATH_BIG_ENDIAN */ - - /* Decrement the blockSize loop counter */ - blkCnt--; - } - - /* If the blockSize is not a multiple of 4, compute any remaining output samples here. - ** No loop unrolling is used. */ - blkCnt = blockSize % 0x4U; - -#else - - /* Run the below code for Cortex-M0 */ - - /* Initialize blkCnt with number of samples */ - blkCnt = blockSize; - -#endif /* #if defined (ARM_MATH_DSP) */ - - - while (blkCnt > 0U) - { - /* C = A * B */ - /* Multiply the inputs and store the result in the destination buffer */ - *pDst++ = (q15_t) __SSAT(((q31_t) ((q31_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 16); - - /* Decrement the blockSize loop counter */ - blkCnt--; - } -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c deleted file mode 100644 index d99c42a3..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_mult_q7.c - * Description: Q7 vector multiplication with variable output shifts - * - * $Date: 29. April 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/** - * @brief Q7 vector multiplication with variable output shifts - * @param[in] *pSrcA pointer to the first input vector - * @param[in] *pSrcB pointer to the second input vector - * @param[out] *pDst pointer to the output vector - * @param[in] out_shift amount of right-shift for output - * @param[in] blockSize number of samples in each vector - * - * Scaling and Overflow Behavior: - * \par - * The function uses saturating arithmetic. - * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated. - */ - -void arm_nn_mult_q7( - q7_t * pSrcA, - q7_t * pSrcB, - q7_t * pDst, - const uint16_t out_shift, - uint32_t blockSize) -{ - uint32_t blkCnt; /* loop counters */ - -#if defined (ARM_MATH_DSP) - -/* Run the below code for Cortex-M4 and Cortex-M3 */ - q7_t out1, out2, out3, out4; /* Temporary variables to store the product */ - - /* loop Unrolling */ - blkCnt = blockSize >> 2U; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. - ** a second loop below computes the remaining 1 to 3 samples. */ - while (blkCnt > 0U) - { - /* C = A * B */ - /* Multiply the inputs and store the results in temporary variables */ - out1 = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - out2 = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - out3 = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - out4 = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - - /* Store the results of 4 inputs in the destination buffer in single cycle by packing */ - *__SIMD32(pDst)++ = __PACKq7(out1, out2, out3, out4); - - /* Decrement the blockSize loop counter */ - blkCnt--; - } - - /* If the blockSize is not a multiple of 4, compute any remaining output samples here. - ** No loop unrolling is used. */ - blkCnt = blockSize % 0x4U; - -#else - - /* Run the below code for Cortex-M0 */ - - /* Initialize blkCnt with number of samples */ - blkCnt = blockSize; - -#endif /* #if defined (ARM_MATH_DSP) */ - - - while (blkCnt > 0U) - { - /* C = A * B */ - /* Multiply the inputs and store the result in the destination buffer */ - *pDst++ = (q7_t) __SSAT(((q15_t) ((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); - - /* Decrement the blockSize loop counter */ - blkCnt--; - } -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s8.c deleted file mode 100644 index 2551b189..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s8.c +++ /dev/null @@ -1,462 +0,0 @@ -/* - * Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nn_vec_mat_mult_t_s8 - * Description: s8 vector by matrix (transposed) multiplication - * - * $Date: April 2, 2020 - * $Revision: V.1.5.0 - * - * Target Processor: Cortex-M - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup NNBasicMath - * @{ - */ - -/* - * s8 vector(lhs) by matrix (transposed) multiplication - * - * Refer header file for details. - * - */ -arm_status arm_nn_vec_mat_mult_t_s8(const q7_t *lhs, - const q7_t *rhs, - const q31_t *bias, - q7_t *dst, - const int32_t lhs_offset, - const int32_t rhs_offset, - const int32_t dst_offset, - const int32_t dst_multiplier, - const int32_t dst_shift, - const int32_t rhs_cols, - const int32_t rhs_rows, - const int32_t activation_min, - const int32_t activation_max) -{ -#if defined(ARM_MATH_MVEI) - - const int16x8_t rhs_offset_vec = vdupq_n_s16((int16_t)rhs_offset); - const int16x8_t lhs_offset_vec = vdupq_n_s16((int16_t)lhs_offset); - - int32_t row_loop_cnt = rhs_rows / 4; - - for (int i_row_loop_cnt = 0; i_row_loop_cnt < row_loop_cnt; i_row_loop_cnt++) - { - int32_t acc1 = bias[0]; - int32_t acc2 = bias[1]; - int32_t acc3 = bias[2]; - int32_t acc4 = bias[3]; - bias += 4; - - int32x4_t acc; - const int32_t col_loop_cnt = (rhs_cols + 7) / 8; - - const int8_t *vec = lhs; - const int8_t *rhs_0 = rhs; - const int8_t *rhs_1 = rhs + rhs_cols; - const int8_t *rhs_2 = rhs + 2 * rhs_cols; - const int8_t *rhs_3 = rhs + 3 * rhs_cols; - - uint32_t col_cnt = (uint32_t)rhs_cols; - - for (int i = 0; i < col_loop_cnt; i++) - { - mve_pred16_t p = vctp16q(col_cnt); - col_cnt -= 8; - const int16x8_t tmp_b = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(vec, p), lhs_offset_vec, p); - - const int16x8_t tmp_a0 = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(rhs_0, p), rhs_offset_vec, p); - acc1 = vmladavaq_p_s16(acc1, tmp_a0, tmp_b, p); - - const int16x8_t tmp_a1 = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(rhs_1, p), rhs_offset_vec, p); - acc2 = vmladavaq_p_s16(acc2, tmp_a1, tmp_b, p); - - const int16x8_t tmp_a2 = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(rhs_2, p), rhs_offset_vec, p); - acc3 = vmladavaq_p_s16(acc3, tmp_a2, tmp_b, p); - - const int16x8_t tmp_a3 = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(rhs_3, p), rhs_offset_vec, p); - acc4 = vmladavaq_p_s16(acc4, tmp_a3, tmp_b, p); - - vec += 8; - rhs_0 += 8; - rhs_1 += 8; - rhs_2 += 8; - rhs_3 += 8; - } - rhs += 4 * rhs_cols; - - acc[0] = acc1; - acc[1] = acc2; - acc[2] = acc3; - acc[3] = acc4; - - acc = arm_requantize_mve(acc, dst_multiplier, dst_shift); - acc = vaddq_s32(acc, vdupq_n_s32(dst_offset)); - acc = vmaxq_s32(acc, vdupq_n_s32(activation_min)); - acc = vminq_s32(acc, vdupq_n_s32(activation_max)); - - vstrbq_s32(dst, acc); - dst += 4; - } - - row_loop_cnt = rhs_rows & 3; - - for (int i_row_loop_cnt = 0; i_row_loop_cnt < row_loop_cnt; - i_row_loop_cnt++) - { - int32_t acc = *bias++; - const int32_t col_loop_cnt = (rhs_cols + 7) / 8; - const int8_t *vec = lhs; - const int8_t *kernel_cur = rhs; - - uint32_t col_cnt = (uint32_t)rhs_cols; - - for (int i = 0; i < col_loop_cnt; i++) - { - mve_pred16_t p = vctp16q(col_cnt); - col_cnt -= 8; - const int16x8_t tmp_b = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(vec, p), lhs_offset_vec, p); - - const int16x8_t tmp_a = vaddq_m_s16(vuninitializedq_s16(), - vldrbq_z_s16(kernel_cur, p), rhs_offset_vec, p); - acc = vmladavaq_p_s16(acc, tmp_a, tmp_b, p); - vec += 8; - kernel_cur += 8; - } - rhs += rhs_cols; - - acc = arm_nn_requantize(acc, dst_multiplier, dst_shift); - acc += dst_offset; - - acc = MAX(acc, activation_min); - acc = MIN(acc, activation_max); - *dst++ = (int8_t)(acc); - } - -#elif defined(ARM_MATH_DSP) - const int32_t off0 = rhs_cols - 4; - const int16_t lhs_offset_s16 = lhs_offset; - const int16_t rhs_offset_s16 = rhs_offset; - - const uint32_t lhs_offset_s16x2 = __PKHBT(lhs_offset_s16, lhs_offset_s16, 16); - const uint32_t rhs_offset_s16x2 = __PKHBT(rhs_offset_s16, rhs_offset_s16, 16); - - for (int32_t rhs_rows_idx = 0; rhs_rows_idx <= (rhs_rows - 2); rhs_rows_idx += 2) - { - const q7_t *lhs_ptr = &lhs[0]; - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = *bias++; - q31_t res01 = *bias++; - - int32_t rhs_cols_idx = 0; - - q31_t val0, val1, val2, val3, val4, val5; - for (; rhs_cols_idx <= (rhs_cols - 16); rhs_cols_idx += 16) - { - // Read 4 x int8 values from the RHS matrix - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val2 = __SXTAB16(rhs_offset_s16x2, val0); - // Read 4 x int8 values from the LHS vector - val1 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val1); - // Read 4 x int8 values from the RHS matrix - val4 = arm_nn_read_q7x4((const q7_t *)rhs_ptr + off0); - val1 = __SXTAB16(lhs_offset_s16x2, __ROR(val1, 8)); - - // Perform the accumulations - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTAB16(rhs_offset_s16x2, val4); - res00 = __SMLAD(val1, val0, res00); - val4 = __SXTAB16(rhs_offset_s16x2, __ROR(val4, 8)); - // Read 4 x int8 values from the RHS matrix - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val1, val4, res01); - - val2 = __SXTAB16(rhs_offset_s16x2, val0); - // Read 4 x int8 values from the LHS vector - val1 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val1); - // Read 4 x int8 values from the RHS matrix - val4 = arm_nn_read_q7x4((const q7_t *)rhs_ptr + off0); - val1 = __SXTAB16(lhs_offset_s16x2, __ROR(val1, 8)); - - // Perform the accumulations - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTAB16(rhs_offset_s16x2, val4); - res00 = __SMLAD(val1, val0, res00); - val4 = __SXTAB16(rhs_offset_s16x2, __ROR(val4, 8)); - // Read 4 x int8 values from the RHS matrix - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val1, val4, res01); - - val2 = __SXTAB16(rhs_offset_s16x2, val0); - // Read 4 x int8 values from the LHS vector - val1 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val1); - // Read 4 x int8 values from the RHS matrix - val4 = arm_nn_read_q7x4((const q7_t *)rhs_ptr + off0); - val1 = __SXTAB16(lhs_offset_s16x2, __ROR(val1, 8)); - - // Perform the accumulations - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTAB16(rhs_offset_s16x2, val4); - res00 = __SMLAD(val1, val0, res00); - val4 = __SXTAB16(rhs_offset_s16x2, __ROR(val4, 8)); - // Read 4 x int8 values from the RHS matrix - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val1, val4, res01); - - val2 = __SXTAB16(rhs_offset_s16x2, val0); - // Read 4 x int8 values from the LHS vector - val1 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val1); - // Read 4 x int8 values from the RHS matrix - val4 = arm_nn_read_q7x4((const q7_t *)rhs_ptr + off0); - val1 = __SXTAB16(lhs_offset_s16x2, __ROR(val1, 8)); - - // Perform the accumulations - res00 = __SMLAD(val3, val2, res00); - val5 = __SXTAB16(rhs_offset_s16x2, val4); - res00 = __SMLAD(val1, val0, res00); - val4 = __SXTAB16(rhs_offset_s16x2, __ROR(val4, 8)); - res01 = __SMLAD(val3, val5, res01); - res01 = __SMLAD(val1, val4, res01); - } - - for (; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value0 = rhs_ptr[0] + rhs_offset; - q31_t rhs_value1 = rhs_ptr[rhs_cols] + rhs_offset; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multiplier, dst_shift); - res01 = arm_nn_requantize(res01, dst_multiplier, dst_shift); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - - *dst++ = (q7_t)res00; - *dst++ = (q7_t)res01; - - rhs += 2 * rhs_cols; - } - - if (rhs_rows % 2) - { - const q7_t *lhs_ptr = &lhs[0]; - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = *bias++; - - int32_t rhs_cols_idx = 0; - - q31_t val0, val1, val2, val3; - for (; rhs_cols_idx <= (rhs_cols - 16); rhs_cols_idx += 16) - { - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = __SXTAB16(rhs_offset_s16x2, val0); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val2); - val2 = __SXTAB16(lhs_offset_s16x2, __ROR(val2, 8)); - - // Partial accumulations - res00 = __SMLAD(val3, val1, res00); - res00 = __SMLAD(val2, val0, res00); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = __SXTAB16(rhs_offset_s16x2, val0); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val2); - val2 = __SXTAB16(lhs_offset_s16x2, __ROR(val2, 8)); - - // Partial accumulations - res00 = __SMLAD(val3, val1, res00); - res00 = __SMLAD(val2, val0, res00); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = __SXTAB16(rhs_offset_s16x2, val0); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val2); - val2 = __SXTAB16(lhs_offset_s16x2, __ROR(val2, 8)); - - // Partial accumulations - res00 = __SMLAD(val3, val1, res00); - res00 = __SMLAD(val2, val0, res00); - - val0 = arm_nn_read_q7x4_ia((const q7_t **)&rhs_ptr); - val1 = __SXTAB16(rhs_offset_s16x2, val0); - val2 = arm_nn_read_q7x4_ia((const q7_t **)&lhs_ptr); - val0 = __SXTAB16(rhs_offset_s16x2, __ROR(val0, 8)); - val3 = __SXTAB16(lhs_offset_s16x2, val2); - val2 = __SXTAB16(lhs_offset_s16x2, __ROR(val2, 8)); - - // Partial accumulations - res00 = __SMLAD(val3, val1, res00); - res00 = __SMLAD(val2, val0, res00); - } - - for (; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value0 = rhs_ptr[0] + rhs_offset; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value0; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multiplier, dst_shift); - - // Add offset - res00 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - - *dst = (q7_t)res00; - } - -#else - - for (int32_t rhs_rows_idx = 0; rhs_rows_idx <= (rhs_rows - 2); rhs_rows_idx += 2) - { - const q7_t *lhs_ptr = &lhs[0]; - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = *bias++; - q31_t res01 = *bias++; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value0 = rhs_ptr[0] + rhs_offset; - q31_t rhs_value1 = rhs_ptr[rhs_cols] + rhs_offset; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value0; - res01 += lhs_value * rhs_value1; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multiplier, dst_shift); - res01 = arm_nn_requantize(res01, dst_multiplier, dst_shift); - - // Add offset - res00 += dst_offset; - res01 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - res01 = MAX(res01, activation_min); - res01 = MIN(res01, activation_max); - - *dst++ = (q7_t)res00; - *dst++ = (q7_t)res01; - - rhs += 2 * rhs_cols; - } - - if (rhs_rows % 2) - { - const q7_t *lhs_ptr = &lhs[0]; - const q7_t *rhs_ptr = &rhs[0]; - - q31_t res00 = *bias++; - - for (int32_t rhs_cols_idx = 0; rhs_cols_idx < rhs_cols; ++rhs_cols_idx) - { - q31_t rhs_value0 = rhs_ptr[0] + rhs_offset; - q31_t lhs_value = lhs_ptr[0] + lhs_offset; - - res00 += lhs_value * rhs_value0; - - ++rhs_ptr; - ++lhs_ptr; - } - - // Quantize down - res00 = arm_nn_requantize(res00, dst_multiplier, dst_shift); - - // Add offset - res00 += dst_offset; - - // Clamp the result - res00 = MAX(res00, activation_min); - res00 = MIN(res00, activation_max); - - *dst = (q7_t)res00; - } -#endif - - return ARM_MATH_SUCCESS; -} - -/** - * @} end of NNBasicMath group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c deleted file mode 100644 index 57f97f68..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c +++ /dev/null @@ -1,297 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_nntables.c - * Description: Converts the elements of the Q7 vector to Q15 vector without left-shift - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @brief tables for various activation functions - * - * This file include the declaration of common tables. - * Most of them are used for activation functions - * - * Assumption: - * Unified table: input is 3.x format, i.e, range of [-8, 8) - * sigmoid(8) = 0.9996646498695336 - * tanh(8) = 0.9999997749296758 - * The accuracy here should be good enough - * - * 2-stage HL table: - * - * The entire input range is divided into two parts: - * - * Low range table: 0x000x xxxx or 0x111x xxxx - * table entry will be the binary number excluding the first - * two digits, i.e., 0x0x xxxx or 0x1x xxxx - * - * - * - * High range table 0x0010 0000 -- 0x0111 1111 - * 0x1000 0000 -- 0x1101 1111 - * - * For positive numbers, table entry will be - * 0x0010 0000 -- 0x0111 1111 minus 0x0010 0000 - * i.e., 0x0000 0000 - 0x0101 11111 - * - * same thing for the negative numbers, table entry will be - * 0x1000 0000 -- 0x1101 1111 minux 0x0010 0000 - * i.e., 0x0110 0000 - 0x1011 1111 - */ - -const q7_t sigmoidTable_q7[256] = { - 0x40, 0x42, 0x44, 0x46, 0x48, 0x4a, 0x4c, 0x4e, - 0x50, 0x52, 0x53, 0x55, 0x57, 0x59, 0x5a, 0x5c, - 0x5e, 0x5f, 0x61, 0x62, 0x63, 0x65, 0x66, 0x67, - 0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f, 0x70, - 0x71, 0x72, 0x72, 0x73, 0x74, 0x74, 0x75, 0x76, - 0x76, 0x77, 0x77, 0x78, 0x78, 0x79, 0x79, 0x7a, - 0x7a, 0x7a, 0x7b, 0x7b, 0x7b, 0x7c, 0x7c, 0x7c, - 0x7c, 0x7c, 0x7d, 0x7d, 0x7d, 0x7d, 0x7d, 0x7e, - 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x01, 0x01, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, - 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x04, - 0x04, 0x04, 0x04, 0x04, 0x05, 0x05, 0x05, 0x06, - 0x06, 0x06, 0x07, 0x07, 0x08, 0x08, 0x09, 0x09, - 0x0a, 0x0a, 0x0b, 0x0c, 0x0c, 0x0d, 0x0e, 0x0e, - 0x0f, 0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16, - 0x17, 0x19, 0x1a, 0x1b, 0x1d, 0x1e, 0x1f, 0x21, - 0x22, 0x24, 0x26, 0x27, 0x29, 0x2b, 0x2d, 0x2e, - 0x30, 0x32, 0x34, 0x36, 0x38, 0x3a, 0x3c, 0x3e, -}; - -const q15_t sigmoidTable_q15[256] = { - 0x4000, 0x4200, 0x43ff, 0x45fc, 0x47f5, 0x49eb, 0x4bdc, 0x4dc8, - 0x4fad, 0x518a, 0x5360, 0x552c, 0x56ef, 0x58a8, 0x5a57, 0x5bfb, - 0x5d93, 0x5f20, 0x60a1, 0x6216, 0x637f, 0x64db, 0x662b, 0x676f, - 0x68a6, 0x69d2, 0x6af1, 0x6c05, 0x6d0d, 0x6e09, 0x6efb, 0x6fe2, - 0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7, - 0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f, - 0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03, - 0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d, - 0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81, - 0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17, - 0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72, - 0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa, - 0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc, - 0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0, - 0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed, - 0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4, - 0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011, - 0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c, - 0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e, - 0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c, - 0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d, - 0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce, - 0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152, - 0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a, - 0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388, - 0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8, - 0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a, - 0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70, - 0x0f42, 0x101e, 0x1105, 0x11f7, 0x12f3, 0x13fb, 0x150f, 0x162e, - 0x175a, 0x1891, 0x19d5, 0x1b25, 0x1c81, 0x1dea, 0x1f5f, 0x20e0, - 0x226d, 0x2405, 0x25a9, 0x2758, 0x2911, 0x2ad4, 0x2ca0, 0x2e76, - 0x3053, 0x3238, 0x3424, 0x3615, 0x380b, 0x3a04, 0x3c01, 0x3e00, -}; - -const q15_t sigmoidLTable_q15[128] = { - 0x4000, 0x4100, 0x4200, 0x42ff, 0x43ff, 0x44fd, 0x45fc, 0x46f9, - 0x47f5, 0x48f1, 0x49eb, 0x4ae5, 0x4bdc, 0x4cd3, 0x4dc8, 0x4ebb, - 0x4fad, 0x509c, 0x518a, 0x5276, 0x5360, 0x5447, 0x552c, 0x560f, - 0x56ef, 0x57cd, 0x58a8, 0x5981, 0x5a57, 0x5b2a, 0x5bfb, 0x5cc9, - 0x5d93, 0x5e5b, 0x5f20, 0x5fe2, 0x60a1, 0x615d, 0x6216, 0x62cc, - 0x637f, 0x642e, 0x64db, 0x6584, 0x662b, 0x66ce, 0x676f, 0x680c, - 0x68a6, 0x693d, 0x69d2, 0x6a63, 0x6af1, 0x6b7c, 0x6c05, 0x6c8a, - 0x6d0d, 0x6d8d, 0x6e09, 0x6e84, 0x6efb, 0x6f70, 0x6fe2, 0x7051, - 0x0f42, 0x0faf, 0x101e, 0x1090, 0x1105, 0x117c, 0x11f7, 0x1273, - 0x12f3, 0x1376, 0x13fb, 0x1484, 0x150f, 0x159d, 0x162e, 0x16c3, - 0x175a, 0x17f4, 0x1891, 0x1932, 0x19d5, 0x1a7c, 0x1b25, 0x1bd2, - 0x1c81, 0x1d34, 0x1dea, 0x1ea3, 0x1f5f, 0x201e, 0x20e0, 0x21a5, - 0x226d, 0x2337, 0x2405, 0x24d6, 0x25a9, 0x267f, 0x2758, 0x2833, - 0x2911, 0x29f1, 0x2ad4, 0x2bb9, 0x2ca0, 0x2d8a, 0x2e76, 0x2f64, - 0x3053, 0x3145, 0x3238, 0x332d, 0x3424, 0x351b, 0x3615, 0x370f, - 0x380b, 0x3907, 0x3a04, 0x3b03, 0x3c01, 0x3d01, 0x3e00, 0x3f00, -}; - -const q15_t sigmoidHTable_q15[192] = { - 0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7, - 0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f, - 0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03, - 0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d, - 0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81, - 0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17, - 0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72, - 0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa, - 0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc, - 0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0, - 0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed, - 0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4, - 0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011, - 0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c, - 0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e, - 0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c, - 0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d, - 0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce, - 0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152, - 0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a, - 0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388, - 0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8, - 0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a, - 0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70, -}; - -const q7_t tanhTable_q7[256] = { - 0x00, 0x08, 0x10, 0x18, 0x1f, 0x27, 0x2e, 0x35, - 0x3b, 0x41, 0x47, 0x4c, 0x51, 0x56, 0x5a, 0x5e, - 0x61, 0x65, 0x68, 0x6a, 0x6d, 0x6f, 0x71, 0x72, - 0x74, 0x75, 0x76, 0x78, 0x78, 0x79, 0x7a, 0x7b, - 0x7b, 0x7c, 0x7c, 0x7d, 0x7d, 0x7e, 0x7e, 0x7e, - 0x7e, 0x7e, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, - 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x81, - 0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x82, - 0x82, 0x82, 0x82, 0x82, 0x83, 0x83, 0x84, 0x84, - 0x85, 0x85, 0x86, 0x87, 0x88, 0x88, 0x8a, 0x8b, - 0x8c, 0x8e, 0x8f, 0x91, 0x93, 0x96, 0x98, 0x9b, - 0x9f, 0xa2, 0xa6, 0xaa, 0xaf, 0xb4, 0xb9, 0xbf, - 0xc5, 0xcb, 0xd2, 0xd9, 0xe1, 0xe8, 0xf0, 0xf8, -}; - -const q15_t tanhTable_q15[256] = { - 0x0000, 0x07fd, 0x0feb, 0x17b9, 0x1f59, 0x26bf, 0x2ddf, 0x34ae, - 0x3b27, 0x4142, 0x46fd, 0x4c56, 0x514d, 0x55e2, 0x5a1a, 0x5df6, - 0x617c, 0x64b0, 0x6797, 0x6a37, 0x6c95, 0x6eb5, 0x709e, 0x7254, - 0x73dc, 0x753a, 0x7672, 0x7788, 0x787f, 0x795b, 0x7a1e, 0x7acb, - 0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f, - 0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48, - 0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc, - 0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7, - 0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7, - 0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd, - 0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, - 0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003, - 0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007, - 0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013, - 0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035, - 0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f, - 0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183, - 0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412, - 0x849b, 0x8535, 0x85e2, 0x86a5, 0x8781, 0x8878, 0x898e, 0x8ac6, - 0x8c24, 0x8dac, 0x8f62, 0x914b, 0x936b, 0x95c9, 0x9869, 0x9b50, - 0x9e84, 0xa20a, 0xa5e6, 0xaa1e, 0xaeb3, 0xb3aa, 0xb903, 0xbebe, - 0xc4d9, 0xcb52, 0xd221, 0xd941, 0xe0a7, 0xe847, 0xf015, 0xf803, -}; - -const q15_t tanhLTable_q15[128] = { - 0x0000, 0x0400, 0x07fd, 0x0bf7, 0x0feb, 0x13d7, 0x17b9, 0x1b90, - 0x1f59, 0x2314, 0x26bf, 0x2a58, 0x2ddf, 0x3151, 0x34ae, 0x37f6, - 0x3b27, 0x3e40, 0x4142, 0x442c, 0x46fd, 0x49b6, 0x4c56, 0x4edd, - 0x514d, 0x53a3, 0x55e2, 0x580a, 0x5a1a, 0x5c13, 0x5df6, 0x5fc4, - 0x617c, 0x6320, 0x64b0, 0x662d, 0x6797, 0x68f0, 0x6a37, 0x6b6e, - 0x6c95, 0x6dac, 0x6eb5, 0x6fb0, 0x709e, 0x717f, 0x7254, 0x731e, - 0x73dc, 0x7490, 0x753a, 0x75da, 0x7672, 0x7701, 0x7788, 0x7807, - 0x787f, 0x78f0, 0x795b, 0x79bf, 0x7a1e, 0x7a77, 0x7acb, 0x7b1b, - 0x849b, 0x84e5, 0x8535, 0x8589, 0x85e2, 0x8641, 0x86a5, 0x8710, - 0x8781, 0x87f9, 0x8878, 0x88ff, 0x898e, 0x8a26, 0x8ac6, 0x8b70, - 0x8c24, 0x8ce2, 0x8dac, 0x8e81, 0x8f62, 0x9050, 0x914b, 0x9254, - 0x936b, 0x9492, 0x95c9, 0x9710, 0x9869, 0x99d3, 0x9b50, 0x9ce0, - 0x9e84, 0xa03c, 0xa20a, 0xa3ed, 0xa5e6, 0xa7f6, 0xaa1e, 0xac5d, - 0xaeb3, 0xb123, 0xb3aa, 0xb64a, 0xb903, 0xbbd4, 0xbebe, 0xc1c0, - 0xc4d9, 0xc80a, 0xcb52, 0xceaf, 0xd221, 0xd5a8, 0xd941, 0xdcec, - 0xe0a7, 0xe470, 0xe847, 0xec29, 0xf015, 0xf409, 0xf803, 0xfc00, -}; - -const q15_t tanhHTable_q15[192] = { - 0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f, - 0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48, - 0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc, - 0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7, - 0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7, - 0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd, - 0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, - 0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, - 0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003, - 0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007, - 0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013, - 0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035, - 0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f, - 0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183, - 0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412, -}; diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c deleted file mode 100644 index 5cdac6ce..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c +++ /dev/null @@ -1,122 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_q7_to_q15_no_shift.c - * Description: Converts the elements of the Q7 vector to Q15 vector without left-shift - * - * $Date: May 29, 2020 - * $Revision: V.1.0.2 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup nndata_convert - * @{ - */ - -/** - * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift - * @param[in] *pSrc points to the Q7 input vector - * @param[out] *pDst points to the Q15 output vector - * @param[in] blockSize length of the input vector - * - * \par Description: - * - * The equation used for the conversion process is: - * - * 

- * 	pDst[n] = (q15_t) pSrc[n];   0 <= n < blockSize.
- *
- * - */ - -void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize) -{ - const q7_t *pIn = pSrc; - uint32_t blkCnt; - -#if defined(ARM_MATH_DSP) - q31_t in; - q31_t in1, in2; - q31_t out1, out2; - - /*loop Unrolling */ - blkCnt = blockSize >> 2u; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. */ - while (blkCnt > 0u) - { - in = arm_nn_read_q7x4_ia(&pIn); - - /* rotatate in by 8 and extend two q7_t values to q15_t values */ - in1 = __SXTB16(__ROR((uint32_t)in, 8)); - - /* extend remaining two q7_t values to q15_t values */ - in2 = __SXTB16(in); - -#ifndef ARM_MATH_BIG_ENDIAN - out2 = (int32_t)__PKHTB(in1, in2, 16); - out1 = (int32_t)__PKHBT(in2, in1, 16); -#else - out1 = (int32_t)__PKHTB(in1, in2, 16); - out2 = (int32_t)__PKHBT(in2, in1, 16); -#endif - write_q15x2_ia(&pDst, out1); - write_q15x2_ia(&pDst, out2); - - /* Decrement the loop counter */ - blkCnt--; - } - - /* If the blockSize is not a multiple of 4, compute any remaining output samples here. - ** No loop unrolling is used. */ - blkCnt = blockSize % 0x4u; - -#else - - /* Run the below code for Cortex-M0 */ - - /* Loop over blockSize number of values */ - blkCnt = blockSize; - -#endif /* #ifndef ARM_MATH_CM0_FAMILY */ - - while (blkCnt > 0u) - { - /* convert from q7 to q15 and then store the results in the destination buffer */ - *pDst++ = (q15_t)*pIn++; - - /* Decrement the loop counter */ - blkCnt--; - } - -} - -/** - * @} end of nndata_convert group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c deleted file mode 100644 index 8128aaa2..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c +++ /dev/null @@ -1,144 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_q7_to_q15_reordered_no_shift.c - * Description: Converts the elements of the Q7 vector to reordered Q15 vector without left-shift - * - * $Date: May 29, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup nndata_convert - * @{ - */ - -/** - * @brief Converts the elements of the Q7 vector to reordered Q15 vector without left-shift - * @param[in] *pSrc points to the Q7 input vector - * @param[out] *pDst points to the Q15 output vector - * @param[in] blockSize length of the input vector - * - * @details - * - * This function does the q7 to q15 expansion with re-ordering - * - * 
- *                          |   A1   |   A2   |   A3   |   A4   |
- *
- *                           0      7 8     15 16    23 24    31
- *
- * - * is converted into: - * - * 
- *  |       A1       |       A3       |   and  |       A2       |       A4       |
- *
- *   0             15 16            31          0             15 16            31
- *
- * - * - * This looks strange but is natural considering how sign-extension is done at - * assembly level. - * - * The expansion of other other oprand will follow the same rule so that the end - * results are the same. - * - * The tail (i.e., last (N % 4) elements) will still be in original order. - * - */ - -void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize) -{ - const q7_t *pIn = pSrc; /* Src pointer */ - uint32_t blkCnt; /* loop counter */ - -#ifndef ARM_MATH_CM0_FAMILY - q31_t in; - q31_t in1, in2; - - /* Run the below code for Cortex-M4 and Cortex-M3 */ - - /*loop Unrolling */ - blkCnt = blockSize >> 2u; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. - ** a second loop below computes the remaining 1 to 3 samples. */ - while (blkCnt > 0u) - { - /* C = (q15_t) A << 8 */ - /* convert from q7 to q15 and then store the results in the destination buffer */ - in = arm_nn_read_q7x4_ia(&pIn); - - /* rotatate in by 8 and extend two q7_t values to q15_t values */ - in1 = __SXTB16(__ROR((uint32_t)in, 8)); - - /* extend remainig two q7_t values to q15_t values */ - in2 = __SXTB16(in); - -#ifndef ARM_MATH_BIG_ENDIAN - *__SIMD32(pDst)++ = in2; - *__SIMD32(pDst)++ = in1; -#else - *__SIMD32(pDst)++ = in1; - *__SIMD32(pDst)++ = in2; -#endif - - /* Decrement the loop counter */ - blkCnt--; - } - - /* If the blockSize is not a multiple of 4, compute any remaining output samples here. - ** No loop unrolling is used. */ - blkCnt = blockSize % 0x4u; - -#else - - /* Run the below code for Cortex-M0 */ - - /* Loop over blockSize number of values */ - blkCnt = blockSize; - -#endif /* #ifndef ARM_MATH_CM0_FAMILY */ - - while (blkCnt > 0u) - { - /* C = (q15_t) A << 8 */ - /* convert from q7 to q15 and then store the results in the destination buffer */ - *pDst++ = (q15_t) * pIn++; - - /* Decrement the loop counter */ - blkCnt--; - } - -} - -/** - * @} end of q7_to_x group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_with_offset.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_with_offset.c deleted file mode 100644 index 34e13ca1..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_with_offset.c +++ /dev/null @@ -1,100 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_q7_to_q15_reordered_with_offset.c - * Description: Converts the elements of the Q7 vector to a reordered Q15 vector with an added offset. The re-ordering - * is a signature of sign extension intrinsic(DSP extension). - * - * $Date: May 29, 2020 - * $Revision: V.2.0.3 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup nndata_convert - * @{ - */ - -/** - * @brief Converts the elements of the Q7 vector to a reordered Q15 vector with an added offset. - * - * @note Refer header file for details. - * - */ - -void arm_q7_to_q15_reordered_with_offset(const q7_t *src, q15_t *dst, uint32_t block_size, q15_t offset) -{ - -#if defined(ARM_MATH_DSP) - uint32_t block_cnt; - /* Run the below code for cores that support SIMD instructions */ - q31_t in_q7x4; - q31_t out_q15x2_1; - q31_t out_q15x2_2; - - /*loop unrolling */ - block_cnt = block_size >> 2u; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. */ - const q31_t offset_q15x2 = (q31_t)__PKHBT(offset, offset, 16); - while (block_cnt > 0u) - { - /* convert from q7 to q15 and then store the results in the destination buffer */ - in_q7x4 = arm_nn_read_q7x4_ia(&src); - - /* Extract and sign extend each of the four q7 values to q15 */ - out_q15x2_1 = __SXTAB16(offset_q15x2, __ROR((uint32_t)in_q7x4, 8)); - out_q15x2_2 = __SXTAB16(offset_q15x2, in_q7x4); - - write_q15x2_ia(&dst, out_q15x2_2); - write_q15x2_ia(&dst, out_q15x2_1); - - block_cnt--; - } - /* Handle left over samples */ - block_cnt = block_size % 0x4u; - - while (block_cnt > 0u) - { - *dst++ = (q15_t)*src++ + offset; - - /* Decrement the loop counter */ - block_cnt--; - } -#else - (void)src; - (void)dst; - (void)block_size; - (void)offset; - /* Not available */ -#endif -} - -/** - * @} end of nndata_convert group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_with_offset.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_with_offset.c deleted file mode 100644 index 4b69f556..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_with_offset.c +++ /dev/null @@ -1,117 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in_q7x4 compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in_q7x4 writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_q7_to_q15_with_offset.c - * Description: Converts the elements of the Q7 vector to Q15 vector with an added offset - * - * $Date: March 3, 2020 - * $Revision: V.2.0.2 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -/** - * @ingroup groupSupport - */ - -/** - * @addtogroup nndata_convert - * @{ - */ - -void arm_q7_to_q15_with_offset(const q7_t *src, - q15_t *dst, - uint32_t block_size, - q15_t offset) -{ - int block_cnt; - -#if defined(ARM_MATH_MVEI) - - int16x8_t source; - const int16x8_t source_offset = vdupq_n_s16(offset); - block_cnt = block_size / 8; - - while (block_cnt > 0) - { - source = vldrbq_s16(src); - source = vaddq_s16(source, source_offset); - vstrhq_s16(dst, source); - dst += 8; - src += 8; - block_cnt--; - } - - block_cnt = block_size & 0x7; - -#elif defined(ARM_MATH_DSP) - /* Run the below code for cores that support SIMD instructions */ - q31_t in_q7x4; - q31_t in_q15x2_1; - q31_t in_q15x2_2; - q31_t out_q15x2_1; - q31_t out_q15x2_2; - - /*loop unrolling */ - block_cnt = block_size >> 2; - - /* First part of the processing with loop unrolling. Compute 4 outputs at a time. */ - const q31_t offset_q15x2 = __PKHBT(offset, offset, 16); - while (block_cnt > 0) - { - /* convert from q7 to q15 and then store the results in the destination buffer */ - in_q7x4 = arm_nn_read_q7x4_ia(&src); - - /* Extract and sign extend each of the four q7 values to q15 */ - in_q15x2_1 = __SXTAB16(offset_q15x2, __ROR(in_q7x4, 8)); - in_q15x2_2 = __SXTAB16(offset_q15x2, in_q7x4); - - out_q15x2_2 = __PKHTB(in_q15x2_1, in_q15x2_2, 16); - out_q15x2_1 = __PKHBT(in_q15x2_2, in_q15x2_1, 16); - - write_q15x2_ia(&dst, out_q15x2_1); - write_q15x2_ia(&dst, out_q15x2_2); - - block_cnt--; - } - /* Handle left over samples */ - block_cnt = block_size % 0x4; - -#else - /* Run the below code for Cortex-M0 */ - /* Loop over block_size number of values */ - block_cnt = block_size; -#endif - - while (block_cnt > 0) - { - *dst++ = (q15_t)*src++ + offset; - - /* Decrement the loop counter */ - block_cnt--; - } -} - -/** - * @} end of nndata_convert group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_avgpool_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_avgpool_s8.c deleted file mode 100644 index fbcfdf6c..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_avgpool_s8.c +++ /dev/null @@ -1,361 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_avgpool_s8.c - * Description: Pooling function implementations - * - * $Date: May 29,2020 - * $Revision: V.2.0.1 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - - -#if defined (ARM_MATH_DSP) && !defined (ARM_MATH_MVEI) - -static void buffer_scale_back_q15_to_q7_and_clamp(q15_t *buffer, q7_t *target, uint16_t length, - uint16_t count,const int act_min, const int act_max) -{ - int i; - int sum; - - for (i = 0; i < length; i++) - { - sum = buffer[i] > 0 ? (buffer[i] + count / 2) / count : (buffer[i] - count / 2) / count; - - sum = MAX(sum, act_min); - sum = MIN(sum, act_max); - - target[i] = (q7_t) (sum); - } -} -#endif - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Pooling - * @{ - */ - -/* - * s8 average pooling function - * - * Refer to header file for details. - * - */ - -#if defined(ARM_MATH_MVEI) - -arm_status arm_avgpool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *src, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *dst) -{ - (void)ctx; - const int32_t dim_src_height = input_dims->h; - const int32_t dim_src_width = input_dims->w; - const int32_t dim_dst_height = output_dims->h; - const int32_t dim_dst_width = output_dims->w; - const int32_t stride_height = pool_params->stride.h; - const int32_t stride_width = pool_params->stride.w; - const int32_t dim_kernel_height = filter_dims->h; - const int32_t dim_kernel_width = filter_dims->w; - const int32_t padding_height = pool_params->padding.h; - const int32_t padding_width = pool_params->padding.w; - const int32_t act_min = pool_params->activation.min; - const int32_t act_max = pool_params->activation.max; - const int32_t ch_src = input_dims->c; - - int32_t i_x, i_y; - int32_t k_x, k_y; - - for (i_y = 0; i_y < dim_dst_height; i_y++) - { - for (i_x = 0; i_x < dim_dst_width; i_x++) - { - - int32_t k_y_start,k_y_end; - int32_t k_x_start,k_x_end; - int32_t chCnt; - const int8_t *pTmp, *pTmpInner; - int8_t *pDst; - - k_y_start = MAX(0, i_y * stride_height - padding_height); - k_y_end = MIN(i_y * stride_height - padding_height + dim_kernel_height,dim_src_height); - - k_x_start = MAX(0,i_x * stride_width - padding_width); - k_x_end = MIN(i_x * stride_width - padding_width + dim_kernel_width, dim_src_width); - - - pTmp = src; - pDst = &dst[ch_src * (i_x + i_y * dim_dst_width)]; - - chCnt = ch_src >> 4; - while(chCnt > 0) - { - int32x4_t sumV1,sumV2,sumV3,sumV4; - - int8x16_t tempV; - int16x8_t tempVLO, tempVHI; - int32x4_t tempVLOLO, tempVLOHI, tempVHILO, tempVHIHI; - int32_t count = 0; - - sumV1 = vdupq_n_s32(0); - sumV2 = vdupq_n_s32(0); - sumV3 = vdupq_n_s32(0); - sumV4 = vdupq_n_s32(0); - - for (k_y = k_y_start; k_y < k_y_end; k_y++) - { - for (k_x = k_x_start; k_x < k_x_end; k_x++) - { - pTmpInner = pTmp + (ch_src * (k_x + k_y * dim_src_width)); - tempV = vldrbq_s8 (pTmpInner); - - tempVLO = vmovlbq_s8(tempV); - tempVHI = vmovltq_s8(tempV); - - tempVLOLO = vmovlbq_s16(tempVLO); - tempVLOHI = vmovltq_s16(tempVLO); - - tempVHILO = vmovlbq_s16(tempVHI); - tempVHIHI = vmovltq_s16(tempVHI); - - sumV1 = vaddq_s32(sumV1,tempVLOLO); - sumV2 = vaddq_s32(sumV2,tempVLOHI); - sumV3 = vaddq_s32(sumV3,tempVHILO); - sumV4 = vaddq_s32(sumV4,tempVHIHI); - - count++; - } - } - - - sumV1[0] = sumV1[0] > 0 ? (sumV1[0] + count / 2) / count : (sumV1[0] - count / 2) / count; - sumV1[1] = sumV1[1] > 0 ? (sumV1[1] + count / 2) / count : (sumV1[1] - count / 2) / count; - sumV1[2] = sumV1[2] > 0 ? (sumV1[2] + count / 2) / count : (sumV1[2] - count / 2) / count; - sumV1[3] = sumV1[3] > 0 ? (sumV1[3] + count / 2) / count : (sumV1[3] - count / 2) / count; - - sumV2[0] = sumV2[0] > 0 ? (sumV2[0] + count / 2) / count : (sumV2[0] - count / 2) / count; - sumV2[1] = sumV2[1] > 0 ? (sumV2[1] + count / 2) / count : (sumV2[1] - count / 2) / count; - sumV2[2] = sumV2[2] > 0 ? (sumV2[2] + count / 2) / count : (sumV2[2] - count / 2) / count; - sumV2[3] = sumV2[3] > 0 ? (sumV2[3] + count / 2) / count : (sumV2[3] - count / 2) / count; - - sumV3[0] = sumV3[0] > 0 ? (sumV3[0] + count / 2) / count : (sumV3[0] - count / 2) / count; - sumV3[1] = sumV3[1] > 0 ? (sumV3[1] + count / 2) / count : (sumV3[1] - count / 2) / count; - sumV3[2] = sumV3[2] > 0 ? (sumV3[2] + count / 2) / count : (sumV3[2] - count / 2) / count; - sumV3[3] = sumV3[3] > 0 ? (sumV3[3] + count / 2) / count : (sumV3[3] - count / 2) / count; - - sumV4[0] = sumV4[0] > 0 ? (sumV4[0] + count / 2) / count : (sumV4[0] - count / 2) / count; - sumV4[1] = sumV4[1] > 0 ? (sumV4[1] + count / 2) / count : (sumV4[1] - count / 2) / count; - sumV4[2] = sumV4[2] > 0 ? (sumV4[2] + count / 2) / count : (sumV4[2] - count / 2) / count; - sumV4[3] = sumV4[3] > 0 ? (sumV4[3] + count / 2) / count : (sumV4[3] - count / 2) / count; - - sumV1 = vmaxq_s32(sumV1, vdupq_n_s32(act_min)); - sumV1 = vminq_s32(sumV1, vdupq_n_s32(act_max)); - - sumV2 = vmaxq_s32(sumV2, vdupq_n_s32(act_min)); - sumV2 = vminq_s32(sumV2, vdupq_n_s32(act_max)); - - sumV3 = vmaxq_s32(sumV3, vdupq_n_s32(act_min)); - sumV3 = vminq_s32(sumV3, vdupq_n_s32(act_max)); - - sumV4 = vmaxq_s32(sumV4, vdupq_n_s32(act_min)); - sumV4 = vminq_s32(sumV4, vdupq_n_s32(act_max)); - - tempVLO = vmovnbq_s32(tempVLO,sumV1); - tempVLO = vmovntq_s32(tempVLO,sumV2); - - tempVHI = vmovnbq_s32(tempVHI,sumV3); - tempVHI = vmovntq_s32(tempVHI,sumV4); - - - tempV = vmovnbq_s16(tempV,tempVLO); - tempV = vmovntq_s16(tempV,tempVHI); - - vstrbq_s8(pDst,tempV); - pDst += 16; - - chCnt --; - pTmp += 16; - } - - chCnt = ch_src & 0xF; - while(chCnt > 0) - { - int32_t sum = 0; - int32_t count = 0; - - for (k_y = k_y_start; k_y < k_y_end; k_y++) - { - for (k_x = k_x_start; k_x < k_x_end; k_x++) - { - sum += pTmp[ch_src * (k_x + k_y * dim_src_width)]; - count++; - } - } - sum = sum > 0 ? (sum + count / 2) / count : (sum - count / 2) / count; - sum = MAX(sum, act_min); - sum = MIN(sum, act_max); - - *pDst++ = sum; - - chCnt --; - pTmp++; - } - } - } - return ARM_MATH_SUCCESS; -} - -#else -arm_status arm_avgpool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *src, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *dst) -{ - const int32_t dim_src_height = input_dims->h; - const int32_t dim_src_width = input_dims->w; - const int32_t dim_dst_height = output_dims->h; - const int32_t dim_dst_width = output_dims->w; - const int32_t stride_height = pool_params->stride.h; - const int32_t stride_width = pool_params->stride.w; - const int32_t dim_kernel_height = filter_dims->h; - const int32_t dim_kernel_width = filter_dims->w; - const int32_t padding_height = pool_params->padding.h; - const int32_t padding_width = pool_params->padding.w; - const int32_t act_min = pool_params->activation.min; - const int32_t act_max = pool_params->activation.max; - const int32_t ch_src = input_dims->c; - q15_t *bufferA = (q15_t *)ctx->buf; - -#if defined (ARM_MATH_DSP) - - /* Run the following code for Cortex-M4 and Cortex-M7 - */ - int32_t k_x, k_y, i_x, i_y; - - for (i_y = 0; i_y < dim_dst_height; i_y++) - { - for (i_x = 0; i_x < dim_dst_width; i_x++) - { - /* Condition for kernel start dimension: (base_idx_ + kernel__start) >= 0 */ - const int32_t base_idx_y = (i_y * stride_height) - padding_height; - const int32_t base_idx_x = (i_x * stride_width) - padding_width; - const int32_t kernel_y_start = MAX(0, -base_idx_y); - const int32_t kernel_x_start = MAX(0, -base_idx_x); - - /* Condition for kernel end dimension: (base_idx_ + kernel__end) < dim_src_ */ - const int32_t kernel_y_end = MIN(dim_kernel_height, dim_src_height - base_idx_y); - const int32_t kernel_x_end = MIN(dim_kernel_width, dim_src_width - base_idx_x); - - int count = 0; - - for (k_y = kernel_y_start; k_y < kernel_y_end; k_y++) - { - for (k_x = kernel_x_start; k_x < kernel_x_end; k_x++) - { - const q7_t *start = src + ch_src * (k_x + base_idx_x + (k_y + base_idx_y) * dim_src_width); - - if (count == 0) - { - arm_q7_to_q15_no_shift(start, bufferA, ch_src); - } - else - { - arm_nn_accumulate_q7_to_q15(bufferA, start, ch_src); - } - count++; - } - } - buffer_scale_back_q15_to_q7_and_clamp(bufferA, dst, ch_src, count, act_min, act_max); - dst += ch_src; - } - } -#else - - /* Reference C code adapted from CMSIS-NN arm_avepool_q7_HWC. - */ - (void)bufferA; - int16_t i_ch_in, i_x, i_y; - int16_t k_x, k_y; - - for (i_y = 0; i_y < dim_dst_height; i_y++) - { - for (i_x = 0; i_x < dim_dst_width; i_x++) - { - for (i_ch_in = 0; i_ch_in < ch_src; i_ch_in++) - { - int sum = 0; - int count = 0; - for (k_y = i_y * stride_height - padding_height; k_y < i_y * stride_height - padding_height + dim_kernel_height; k_y++) - { - for (k_x = i_x * stride_width - padding_width; k_x < i_x * stride_width - padding_width + dim_kernel_width; k_x++) - { - if (k_y >= 0 && k_x >= 0 && k_y < dim_src_height && k_x < dim_src_width) - { - sum += src[i_ch_in + ch_src * (k_x + k_y * dim_src_width)]; - count++; - } - } - } - sum = sum > 0 ? (sum + count / 2) / count : (sum - count / 2) / count; - sum = MAX(sum, act_min); - sum = MIN(sum, act_max); - - dst[i_ch_in + ch_src * (i_x + i_y * dim_dst_width)] = sum; - } - } - } - -#endif - return ARM_MATH_SUCCESS; -} - -#endif /* ARM_MATH_MVEI */ - -int32_t arm_avgpool_s8_get_buffer_size(const int dim_dst_width, - const int ch_src) -{ - (void)dim_dst_width; - -#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI) - return (ch_src * sizeof(int16_t)); -#else - (void)ch_src; - return 0; -#endif -} -/** - * @} end of Pooling group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_max_pool_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_max_pool_s8.c deleted file mode 100644 index df87ad98..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_max_pool_s8.c +++ /dev/null @@ -1,235 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_max_pool_s8.c - * Description: Pooling function implementations - * - * $Date: June 11, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -static void compare_and_replace_if_larger_q7(q7_t *base, - const q7_t *target, - int32_t length) -{ -#if defined(ARM_MATH_MVEI) - int32_t loop_count = (length + 15) / 16; - for (int i = 0; i < loop_count; i++) - { - mve_pred16_t p = vctp16q((uint32_t)length); - const int8x16_t op_1 = vldrbq_z_s8(base, p); - const int8x16_t op_2 = vldrbq_z_s8(target, p); - const int8x16_t max = vmaxq_m_s8(vuninitializedq_s8(), op_1, op_2, p); - vstrbq_p_s8(base, max, p); - base += 16; - target += 16; - length -= 16; - } -#else - q7_t *dst = base; - const q7_t *src = target; - union arm_nnword ref_max; - union arm_nnword comp_max; - int32_t cnt = length >> 2; - - while (cnt > 0l) - { - ref_max.word = arm_nn_read_q7x4(dst); - comp_max.word = arm_nn_read_q7x4_ia(&src); - - if (comp_max.bytes[0] > ref_max.bytes[0]) - { - ref_max.bytes[0] = comp_max.bytes[0]; - } - if (comp_max.bytes[1] > ref_max.bytes[1]) - { - ref_max.bytes[1] = comp_max.bytes[1]; - } - if (comp_max.bytes[2] > ref_max.bytes[2]) - { - ref_max.bytes[2] = comp_max.bytes[2]; - } - if (comp_max.bytes[3] > ref_max.bytes[3]) - { - ref_max.bytes[3] = comp_max.bytes[3]; - } - - write_q7x4_ia(&dst, ref_max.word); - - cnt--; - } - - cnt = length & 0x3; - while (cnt > 0l) - { - if (*src > *dst) - { - *dst = *src; - } - dst++; - src++; - cnt--; - } -#endif -} - -static void -clamp_output(q7_t *source, int32_t length, const int32_t act_min, const int32_t act_max) -{ -#if defined(ARM_MATH_MVEI) - int32_t - loop_count = (length + 15) / 16; - for (int i = 0; i < loop_count; i++) - { - mve_pred16_t p = vctp16q((uint32_t)length); - length -= 16; - const int8x16_t src = vldrbq_z_s8(source, p); - const int8x16_t predicated_min = vdupq_m_n_s8(vuninitializedq_s8(), (int8_t)act_min, p); - const int8x16_t predicated_max = vdupq_m_n_s8(vuninitializedq_s8(), (int8_t)act_max, p); - int8x16_t - res = vmaxq_m_s8(vuninitializedq_s8(), src, predicated_min, p); - res = vminq_m_s8(vuninitializedq_s8(), src, predicated_max, p); - vstrbq_p_s8(source, res, p); - source += 16; - } -#else - union arm_nnword in; - int32_t cnt = length >> 2; - - while (cnt > 0l) - { - in.word = arm_nn_read_q7x4(source); - - in.bytes[0] = MAX(in.bytes[0], act_min); - in.bytes[0] = MIN(in.bytes[0], act_max); - in.bytes[1] = MAX(in.bytes[1], act_min); - in.bytes[1] = MIN(in.bytes[1], act_max); - in.bytes[2] = MAX(in.bytes[2], act_min); - in.bytes[2] = MIN(in.bytes[2], act_max); - in.bytes[3] = MAX(in.bytes[3], act_min); - in.bytes[3] = MIN(in.bytes[3], act_max); - - write_q7x4_ia(&source, in.word); - cnt--; - } - - cnt = length & 0x3; - while (cnt > 0l) - { - int32_t comp = *source; - comp = MAX(comp, act_min); - comp = MIN(comp, act_max); - *source++ = (int8_t)comp; - cnt--; - } -#endif -} - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Pooling - * @{ - */ - -/* - * Optimized s8 max pooling function - * - * Refer to header file for details. - * - */ - -arm_status -arm_max_pool_s8(const cmsis_nn_context *ctx, - const cmsis_nn_pool_params *pool_params, - const cmsis_nn_dims *input_dims, - const q7_t *src, - const cmsis_nn_dims *filter_dims, - const cmsis_nn_dims *output_dims, - q7_t *dst) -{ - const int32_t input_y = input_dims->h; - const int32_t input_x = input_dims->w; - const int32_t output_y = output_dims->h; - const int32_t output_x = output_dims->w; - const int32_t stride_y = pool_params->stride.h; - const int32_t stride_x = pool_params->stride.w; - const int32_t kernel_y = filter_dims->h; - const int32_t kernel_x = filter_dims->w; - const int32_t pad_y = pool_params->padding.h; - const int32_t pad_x = pool_params->padding.w; - const int32_t act_min = pool_params->activation.min; - const int32_t act_max = pool_params->activation.max; - const int32_t channel_in = input_dims->c; - (void)ctx; - q7_t *dst_base = dst; - - for (int i_y = 0, base_idx_y = -pad_y; i_y < output_y; base_idx_y += stride_y, i_y++) - { - for (int i_x = 0, base_idx_x = -pad_x; i_x < output_x; base_idx_x += stride_x, i_x++) - { - /* Condition for kernel start dimension: (base_idx_ + kernel__start) >= 0 */ - const int32_t ker_y_start = MAX(0, -base_idx_y); - const int32_t ker_x_start = MAX(0, -base_idx_x); - - /* Condition for kernel end dimension: (base_idx_ + kernel__end) < dim_src_ */ - const int32_t kernel_y_end = MIN(kernel_y, input_y - base_idx_y); - const int32_t kernel_x_end = MIN(kernel_x, input_x - base_idx_x); - - int count = 0; - - for (int k_y = ker_y_start; k_y < kernel_y_end; k_y++) - { - for (int k_x = ker_x_start; k_x < kernel_x_end; k_x++) - { - const q7_t *start = src + channel_in * (k_x + base_idx_x + (k_y + base_idx_y) * input_x); - - if (count == 0) - { - memcpy(dst, start, channel_in); - count++; - } - else - { - compare_and_replace_if_larger_q7(dst, start, channel_in); - } - } - } - /* 'count' is expected to be non-zero here. */ - dst += channel_in; - } - } - - clamp_output(dst_base, output_x * output_y * channel_in, act_min, act_max); - - return ARM_MATH_SUCCESS; -} - -/** - * @} end of Pooling group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c deleted file mode 100644 index 4f1e2a51..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c +++ /dev/null @@ -1,454 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_pool_q7_HWC.c - * Description: Pooling function implementations - * - * $Date: 17. January 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -#if defined (ARM_MATH_DSP) - -/** - * @brief A few utility functions used by pooling functions - * - * - */ - -static void buffer_scale_back_q15_to_q7(q15_t * buffer, q7_t * target, uint16_t length, uint16_t scale) -{ - int i; - - for (i = 0; i < length; i++) - { - target[i] = (q7_t) (buffer[i] / scale); - } -} - -static void compare_and_replace_if_larger_q7(q7_t * base, // base data - const q7_t * target, // compare target - const uint16_t length // data length - ) -{ - q7_t *pIn = base; - const q7_t *pCom = target; - union arm_nnword in; - union arm_nnword com; - uint16_t cnt = length >> 2; - - while (cnt > 0u) - { - in.word = arm_nn_read_q7x4((const q7_t*)pIn); - com.word = arm_nn_read_q7x4_ia((const q7_t**)&pCom); - - // if version - if (com.bytes[0] > in.bytes[0]) - in.bytes[0] = com.bytes[0]; - if (com.bytes[1] > in.bytes[1]) - in.bytes[1] = com.bytes[1]; - if (com.bytes[2] > in.bytes[2]) - in.bytes[2] = com.bytes[2]; - if (com.bytes[3] > in.bytes[3]) - in.bytes[3] = com.bytes[3]; - - *__SIMD32(pIn)++ = in.word; - - cnt--; - } - - cnt = length & 0x3; - while (cnt > 0u) - { - if (*pCom > *pIn) - { - *pIn = *pCom; - } - pIn++; - pCom++; - cnt--; - } -} - -static void accumulate_q7_to_q15(q15_t * base, q7_t * target, const uint16_t length) -{ - q15_t *pCnt = base; - q7_t *pV = target; - q31_t v1, v2, vo1, vo2; - uint16_t cnt = length >> 2; - q31_t in; - - while (cnt > 0u) - { - q31_t value = arm_nn_read_q7x4_ia((const q7_t**)&pV); - v1 = __SXTB16(__ROR(value, 8)); - v2 = __SXTB16(value); -#ifndef ARM_MATH_BIG_ENDIAN - - vo2 = __PKHTB(v1, v2, 16); - vo1 = __PKHBT(v2, v1, 16); - -#else - - vo1 = __PKHTB(v1, v2, 16); - vo2 = __PKHBT(v2, v1, 16); - -#endif - - in = arm_nn_read_q15x2(pCnt); - *__SIMD32(pCnt)++ = __QADD16(vo1, in); - - in = arm_nn_read_q15x2(pCnt); - *__SIMD32(pCnt)++ = __QADD16(vo2, in); - - cnt--; - } - cnt = length & 0x3; - while (cnt > 0u) - { - *pCnt++ += *pV++; - cnt--; - } -} - -#endif // ARM_MATH_DSP - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Pooling - * @{ - */ - - /** - * @brief Q7 max pooling function - * @param[in, out] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA Not used - * @param[in,out] Im_out pointer to output tensor - * - * @details - * - * The pooling function is implemented as split x-pooling then - * y-pooling. - * - * This pooling function is input-destructive. Input data is undefined - * after calling this function. - * - */ - -void -arm_maxpool_q7_HWC(q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out) -{ - (void)bufferA; -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - int16_t i_x, i_y; - - /* first does the pooling along x axis */ - for (i_y = 0; i_y < dim_im_in; i_y++) - { - - for (i_x = 0; i_x < dim_im_out; i_x++) - { - /* for each output pixel */ - q7_t *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in; - q7_t *win_start; - q7_t *win_stop; - if (i_x * stride - padding < 0) - { - win_start = target; - } else - { - win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in; - } - - if (i_x * stride - padding + dim_kernel >= dim_im_in) - { - win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in; - } else - { - win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in; - } - - /* first step is to copy over initial data */ - /* arm_copy_q7(win_start, target, ch_im_in); */ - memmove(target, win_start, ch_im_in); - - /* start the max operation from the second part */ - win_start += ch_im_in; - for (; win_start < win_stop; win_start += ch_im_in) - { - compare_and_replace_if_larger_q7(target, win_start, ch_im_in); - } - } - } - - /* then does the pooling along y axis */ - for (i_y = 0; i_y < dim_im_out; i_y++) - { - - /* for each output row */ - q7_t *target = Im_out + i_y * dim_im_out * ch_im_in; - q7_t *row_start; - q7_t *row_end; - /* setting the starting row */ - if (i_y * stride - padding < 0) - { - row_start = Im_in; - } else - { - row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in; - } - /* setting the stopping row */ - if (i_y * stride - padding + dim_kernel >= dim_im_in) - { - row_end = Im_in + dim_im_in * dim_im_in * ch_im_in; - } else - { - row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in; - } - - /* copy over the first row */ - /* arm_copy_q7(row_start, target, dim_im_out * ch_im_in); */ - memmove(target, row_start, dim_im_out * ch_im_in); - - /* move over to next row */ - row_start += ch_im_in * dim_im_in; - - for (; row_start < row_end; row_start += dim_im_in * ch_im_in) - { - compare_and_replace_if_larger_q7(target, row_start, dim_im_out * ch_im_in); - } - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - int16_t i_ch_in, i_x, i_y; - int16_t k_x, k_y; - - for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++) - { - for (i_y = 0; i_y < dim_im_out; i_y++) - { - for (i_x = 0; i_x < dim_im_out; i_x++) - { - int max = -129; - for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++) - { - for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++) - { - if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in) - { - if (Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)] > max) - { - max = Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)]; - } - } - } - } - Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = max; - } - } - } - -#endif /* ARM_MATH_DSP */ - -} - - /** - * @brief Q7 average pooling function - * @param[in,out] Im_in pointer to input tensor - * @param[in] dim_im_in input tensor dimention - * @param[in] ch_im_in number of input tensor channels - * @param[in] dim_kernel filter kernel size - * @param[in] padding padding sizes - * @param[in] stride convolution stride - * @param[in] dim_im_out output tensor dimension - * @param[in,out] bufferA pointer to buffer space for input - * @param[in,out] Im_out pointer to output tensor - * - * @details - * - * Buffer size: - * - * bufferA size: 2*dim_im_out*ch_im_in - * - * The pooling function is implemented as split x-pooling then - * y-pooling. - * - * This pooling function is input-destructive. Input data is undefined - * after calling this function. - * - */ - -void -arm_avepool_q7_HWC(q7_t * Im_in, - const uint16_t dim_im_in, - const uint16_t ch_im_in, - const uint16_t dim_kernel, - const uint16_t padding, - const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out) -{ - -#if defined (ARM_MATH_DSP) - /* Run the following code for Cortex-M4 and Cortex-M7 */ - - q15_t *buffer = (q15_t *) bufferA; - int16_t i_x, i_y; - int16_t count = 0; - - /* first does the pooling along x axis */ - for (i_y = 0; i_y < dim_im_in; i_y++) - { - - for (i_x = 0; i_x < dim_im_out; i_x++) - { - /* for each output pixel */ - q7_t *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in; - q7_t *win_start; - q7_t *win_stop; - if (i_x * stride - padding < 0) - { - win_start = target; - } else - { - win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in; - } - - if (i_x * stride - padding + dim_kernel >= dim_im_in) - { - win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in; - } else - { - win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in; - } - - /* first step is to copy over initial data */ - arm_q7_to_q15_no_shift(win_start, buffer, ch_im_in); - count = 1; - - /* start the max operation from the second part */ - win_start += ch_im_in; - for (; win_start < win_stop; win_start += ch_im_in) - { - accumulate_q7_to_q15(buffer, win_start, ch_im_in); - count++; - } - buffer_scale_back_q15_to_q7(buffer, target, ch_im_in, count); - } - } - - /* then does the pooling along y axis */ - for (i_y = 0; i_y < dim_im_out; i_y++) - { - /* for each output row */ - q7_t *target = Im_out + i_y * dim_im_out * ch_im_in; - q7_t *row_start; - q7_t *row_end; - /* setting the starting row */ - if (i_y * stride - padding < 0) - { - row_start = Im_in; - } else - { - row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in; - } - /* setting the stopping row */ - if (i_y * stride - padding + dim_kernel >= dim_im_in) - { - row_end = Im_in + dim_im_in * dim_im_in * ch_im_in; - } else - { - row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in; - } - - /* copy over the first row */ - arm_q7_to_q15_no_shift(row_start, buffer, dim_im_out * ch_im_in); - count = 1; - - /* move over to next row */ - row_start += ch_im_in * dim_im_in; - - for (; row_start < row_end; row_start += dim_im_in * ch_im_in) - { - accumulate_q7_to_q15(buffer, row_start, dim_im_out * ch_im_in); - count++; - } - buffer_scale_back_q15_to_q7(buffer, target, dim_im_out * ch_im_in, count); - } - -#else - /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ - - (void)bufferA; - int16_t i_ch_in, i_x, i_y; - int16_t k_x, k_y; - - for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++) - { - for (i_y = 0; i_y < dim_im_out; i_y++) - { - for (i_x = 0; i_x < dim_im_out; i_x++) - { - int sum = 0; - int count = 0; - for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++) - { - for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++) - { - if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in) - { - sum += Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)]; - count++; - } - } - } - Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = sum / count; - } - } - } - -#endif /* ARM_MATH_DSP */ - -} - -/** - * @} end of Pooling group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ReshapeFunctions/arm_reshape_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ReshapeFunctions/arm_reshape_s8.c deleted file mode 100644 index f943976c..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ReshapeFunctions/arm_reshape_s8.c +++ /dev/null @@ -1,58 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_reshape_s8.c - * Description: Reshape a s8 vector - * - * $Date: September 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Reshape - * @{ - */ - -/** - * Basic s8 reshape function. - * - * Refer header file for details. - * - */ - -void arm_reshape_s8(const int8_t *input, - int8_t *output, - const uint32_t total_size) -{ - memcpy(output, input, total_size); -} - -/** - * @} end of Reshape group - */ \ No newline at end of file diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c deleted file mode 100644 index 30000431..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c +++ /dev/null @@ -1,119 +0,0 @@ -/* - * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_q15.c - * Description: Q15 softmax function - * - * $Date: 20. February 2018 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ - - /** - * @brief Q15 softmax function - * @param[in] vec_in pointer to input vector - * @param[in] dim_vec input vector dimention - * @param[out] p_out pointer to output vector - * - * @details - * - * Here, instead of typical e based softmax, we use - * 2-based softmax, i.e.,: - * - * y_i = 2^(x_i) / sum(2^x_j) - * - * The relative output will be different here. - * But mathematically, the gradient will be the same - * with a log(2) scaling factor. - * - */ - -void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out) -{ - q31_t sum; - int16_t i; - uint8_t shift; - q31_t base; - base = -1 * 0x100000; - for (i = 0; i < dim_vec; i++) - { - if (vec_in[i] > base) - { - base = vec_in[i]; - } - } - - /* we ignore really small values - * anyway, they will be 0 after shrinking - * to q15_t - */ - base = base - 16; - - sum = 0; - - for (i = 0; i < dim_vec; i++) - { - if (vec_in[i] > base) - { - shift = (uint8_t)__USAT(vec_in[i] - base, 5); - sum += 0x1 << shift; - } - } - - /* This is effectively (0x1 << 32) / sum */ - int64_t div_base = 0x100000000LL; - int output_base = (int32_t)(div_base / sum); - - /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) ) - * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16 - * and vec_in[i]-base = 16 - */ - for (i = 0; i < dim_vec; i++) - { - if (vec_in[i] > base) - { - /* Here minimum value of 17+base-vec[i] will be 1 */ - shift = (uint8_t)__USAT(17+base-vec_in[i], 5); - p_out[i] = (q15_t) __SSAT((output_base >> shift), 16); - } else - { - p_out[i] = 0; - } - } - -} - -/** - * @} end of Softmax group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c deleted file mode 100644 index c2032271..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c +++ /dev/null @@ -1,108 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_q7.c - * Description: Q7 softmax function - * - * $Date: June 8, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ - - /** - * @brief Q7 softmax function - * @param[in] vec_in pointer to input vector - * @param[in] dim_vec input vector dimention - * @param[out] p_out pointer to output vector - * - * @details - * - * Here, instead of typical natural logarithm e based softmax, we use - * 2-based softmax here, i.e.,: - * - * y_i = 2^(x_i) / sum(2^x_j) - * - * The relative output will be different here. - * But mathematically, the gradient will be the same - * with a log(2) scaling factor. - * - */ - -void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out ) -{ - q31_t sum; - int16_t i; - uint8_t shift; - q15_t base; - base = -128; - - /* We first search for the maximum */ - for (i = 0; i < dim_vec; i++) - { - if (vec_in[i] > base) - { - base = vec_in[i]; - } - } - - /* - * So the base is set to max-8, meaning - * that we ignore really small values. - * anyway, they will be 0 after shrinking to q7_t. - */ - base = base - (1 << 3); - - sum = 0; - - for (i = 0; i < dim_vec; i++) - { - shift = (uint8_t)__USAT(vec_in[i] - base, 3); - sum += 0x1 << shift; - } - - /* This is effectively (0x1 << 20) / sum */ - int output_base = (1 << 20) / sum; - - for (i = 0; i < dim_vec; i++) - { - - /* Here minimum value of 13+base-vec_in[i] will be 5 */ - shift = (uint8_t)__USAT(13 + base - vec_in[i], 5); - p_out[i] = (q7_t)__SSAT((output_base >> shift), 8); - } -} - -/** - * @} end of Softmax group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_s8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_s8.c deleted file mode 100644 index 8639f706..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_s8.c +++ /dev/null @@ -1,257 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_s8.c - * Description: S8 softmax function - * - * $Date: April 6, 2020 - * $Revision: V.2.0.0 - * - * Target Processor: Cortex-M cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnsupportfunctions.h" - -#define ACCUM_BITS 12 - -#ifdef ARM_MATH_MVEI -static int32x4_t arm_exp_on_negative_values_mve_32x4(int32x4_t val) -{ -#define SHIFT_START (24) - int32_t shift = SHIFT_START; - int32x4_t mask; - - const int32x4_t val_mod_minus_quarter = vandq_s32(val, vdupq_n_s32((1 << SHIFT_START) - 1)) - vdupq_n_s32(1 << SHIFT_START); - const int32x4_t remainder = vsubq_s32(val_mod_minus_quarter, val); - const int32x4_t x = vaddq_n_s32(val_mod_minus_quarter << 5, 1 << 28); - const int32x4_t x2 = MUL_SAT_MVE(x, x); - const int32x4_t op_1 = DIV_POW2_MVE(MUL_SAT_MVE(x2, x2), 2) + MUL_SAT_MVE(x2, x); - const int32x4_t op_2 = x + DIV_POW2_MVE(MUL_SAT_MVE(op_1, vdupq_n_s32(715827883)) + x2, 1); - int32x4_t result = vdupq_n_s32(1895147668) + MUL_SAT_MVE(vdupq_n_s32(1895147668), op_2); - -#define SELECT_IF_NON_ZERO(x) \ - { \ - mve_pred16_t p = vcmpneq_n_s32(remainder & vdupq_n_s32(1 << shift++), 0); \ - mask = vmvnq_m_s32(vdupq_n_s32(0), vdupq_n_s32(0), p); \ - result = SELECT_USING_MASK(mask, MUL_SAT_MVE(result, vdupq_n_s32(x)), result); \ - } - - SELECT_IF_NON_ZERO(1672461947) - SELECT_IF_NON_ZERO(1302514674) - SELECT_IF_NON_ZERO(790015084) - SELECT_IF_NON_ZERO(290630308) - SELECT_IF_NON_ZERO(39332535) - SELECT_IF_NON_ZERO(720401) - SELECT_IF_NON_ZERO(242) - -#undef SELECT_IF_NON_ZERO - - mve_pred16_t p = vcmpeqq_n_s32(val, 0); - mask = vmvnq_m_s32(vdupq_n_s32(0), vdupq_n_s32(0), p); - - result = SELECT_USING_MASK(mask, vdupq_n_s32(Q31_MAX), result); - return result; -} -#endif - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ - -void arm_softmax_s8(const int8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - int8_t *output) -{ -#ifdef ARM_MATH_MVEI - -#define ACT_MIN ((int8_t)Q7_MIN) -#define ACT_MAX ((int8_t)Q7_MAX) - - const int32_t mask = (1 << shift); - - for (int i_num_rows = 0; i_num_rows < num_rows; ++i_num_rows) - { - int8_t max = ACT_MIN; - - int32_t vec_count = (row_size + 15) / 16; - uint32_t r_count = (uint32_t)row_size; - for (int i = 0; i < vec_count; i++) - { - mve_pred16_t p = vctp8q(r_count); - const int8x16_t ip = vldrbq_z_s8(&input[i * 16], p); - max = vmaxvq_p_s8(max, ip, p); - r_count -= 16; - } - - vec_count = row_size / 4; - int32_t idx = 0; - int32_t sum = 0; - - while (vec_count) - { - int32x4_t ip = vldrbq_s32(&input[idx * 4]); - ip = vsubq_n_s32(ip, max); - mve_pred16_t p = vcmpgeq_n_s32(ip, diff_min); - if (p != 0) - { - ip = vmulq_n_s32(ip, mask); - - int32x4_t res = MUL_SAT_MVE(ip, vdupq_n_s32(mult)); - - res = arm_exp_on_negative_values_mve_32x4(res); - res = DIV_POW2_MVE(res, ACCUM_BITS); - res = vpselq_s32(res, vdupq_n_s32(0), p); - sum += vaddvq_s32(res); - } - - vec_count--; - idx++; - } - - const int32_t tail_idx = row_size & ~3; - for (int i = 0; i < (row_size & 3); i++) - { - const int32_t diff = input[tail_idx + i] - max; - if (diff >= diff_min) - { - sum += DIV_POW2(EXP_ON_NEG(MUL_SAT(diff * mask, mult)), ACCUM_BITS); - } - } - - const int32_t headroom = __CLZ((uint32_t)sum); - const int32_t bits_over_unit = ACCUM_BITS - headroom + 23; - const int32_t shifted_scale = ONE_OVER1((sum << headroom) - (1 << 31)); - - vec_count = row_size / 4; - idx = 0; - - while (vec_count) - { - int32x4_t ip = vldrbq_s32(&input[idx]); - ip = vsubq_n_s32(ip, max); - - mve_pred16_t p = vcmpgeq_n_s32(ip, diff_min); - - int32x4_t tmp_res; - - if (p != 0) - { - ip = vmulq_n_s32(ip, mask); - - tmp_res = MUL_SAT_MVE(ip, vdupq_n_s32(mult)); - tmp_res = arm_exp_on_negative_values_mve_32x4(tmp_res); - tmp_res = MUL_SAT_MVE(vdupq_n_s32(shifted_scale), tmp_res); - tmp_res = DIV_POW2_MVE(tmp_res, bits_over_unit); - tmp_res += vdupq_n_s32(ACT_MIN); - - tmp_res = vmaxq_s32(tmp_res, vdupq_n_s32(ACT_MIN)); - tmp_res = vminq_s32(tmp_res, vdupq_n_s32(ACT_MAX)); - tmp_res = vpselq_s32(tmp_res, vdupq_n_s32(ACT_MIN), p); - } - else - { - tmp_res = vdupq_n_s32(ACT_MIN); - } - vstrbq_s32(&output[idx], tmp_res); - vec_count--; - idx += 4; - } - - for (int i = 0; i < (row_size & 3); i++) - { - int32_t diff = input[tail_idx + i] - max; - if (diff >= diff_min) - { - const int32_t res = DIV_POW2(MUL_SAT(shifted_scale, EXP_ON_NEG(MUL_SAT(diff * mask, mult))), bits_over_unit) - 128; - output[tail_idx + i] = (int8_t)CLAMP(res, (int32_t)ACT_MAX, (int32_t)ACT_MIN); - } - else - { - output[tail_idx + i] = ACT_MIN; - } - } - - input += row_size; - output += row_size; - } -#else - const int32_t mask = (1 << shift); - - int32_t col = 0; - int32_t row_idx; - - for (row_idx = 0; row_idx < num_rows; ++row_idx) - { - // Find the maximum value in order to ensure numerical stability - int8_t max = *input; - - for (col = 1; col < row_size; ++col) - { - max = MAX(max, input[col]); - } - - int32_t diff = 0; - int32_t sum = 0; - - for (col = 0; col < row_size; ++col) - { - diff = input[col] - max; - if (diff >= diff_min) - { - sum += DIV_POW2(EXP_ON_NEG(MUL_SAT(diff * mask, mult)), ACCUM_BITS); - } - } - - const int32_t headroom = __CLZ(sum); - const int32_t bits_over_unit = ACCUM_BITS - headroom + 23; - const int32_t shifted_scale = ONE_OVER1((sum << headroom) - (1 << 31)); - - for (col = 0; col < row_size; ++col) - { - diff = input[col] - max; - if (diff >= diff_min) - { - const int32_t res = DIV_POW2(MUL_SAT(shifted_scale, EXP_ON_NEG(MUL_SAT(diff * mask, mult))), bits_over_unit) - 128; - output[col] = (int8_t)CLAMP(res, (int32_t)127, (int32_t)-128); - } - else - { - output[col] = -128; - } - } - input += row_size; - output += row_size; - } - -#endif -} -/** - * @} end of Softmax group - */ diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_u8.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_u8.c deleted file mode 100644 index 2820af64..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_u8.c +++ /dev/null @@ -1,101 +0,0 @@ -/* - * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_u8.c - * Description: U8 softmax function - * - * $Date: May 29, 2020 - * $Revision: V.1.0.1 - * - * Target Processor: Cortex-M CPUs - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -#define ACCUM_BITS 12 - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ -void arm_softmax_u8(const uint8_t *input, - const int32_t num_rows, - const int32_t row_size, - const int32_t mult, - const int32_t shift, - const int32_t diff_min, - uint8_t *output) -{ - const int32_t mask = (1 << shift); - - int32_t col = 0; - int32_t row_idx; - - for(row_idx = 0; row_idx < num_rows; ++row_idx) - { - // Find the maximum value in order to ensure numerical stability - uint8_t max = *input; - - for (col = 1; col < row_size; ++col) - { - max = MAX(max, input[col]); - } - - int32_t diff = 0; - int32_t sum = 0; - - for (col = 0; col < row_size; ++col) - { - diff = input[col] - max; - if(diff >= diff_min) - { - sum += DIV_POW2(EXP_ON_NEG(MUL_SAT(diff * mask, mult)), ACCUM_BITS); - } - } - - const int32_t headroom = __CLZ((uint32_t)sum); - const int32_t bits_over_unit = ACCUM_BITS - headroom + 23; - const int32_t shifted_scale = ONE_OVER1((sum << headroom) - (1 << 31)); - - for (col = 0; col < row_size; ++col) - { - diff = input[col] - max; - if (diff >= diff_min) - { - const int32_t res = DIV_POW2(MUL_SAT(shifted_scale, EXP_ON_NEG(MUL_SAT(diff * mask, mult))), bits_over_unit); - output[col] = (uint8_t) CLAMP(res, (int32_t)255, (int32_t)0); - } - else - { - output[col] = 0; - } - } - input += row_size; - output += row_size; - } -} -/** - * @} end of Softmax group - */ \ No newline at end of file diff --git a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_with_batch_q7.c b/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_with_batch_q7.c deleted file mode 100644 index e4a634d6..00000000 --- a/components/tflite_micro/Source/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_with_batch_q7.c +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved. - * - * SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the License); you may - * not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, WITHOUT - * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* ---------------------------------------------------------------------- - * Project: CMSIS NN Library - * Title: arm_softmax_with_batch_q7.c - * Description: Q7 softmax function - * - * $Date: 05. August 2019 - * $Revision: V.1.0.0 - * - * Target Processor: Cortex-M and Cortex-A cores - * - * -------------------------------------------------------------------- */ - -#include "cmsis/CMSIS/DSP/Include/arm_math.h" -#include "cmsis/CMSIS/NN/Include/arm_nnfunctions.h" - -/** - * @ingroup groupNN - */ - -/** - * @addtogroup Softmax - * @{ - */ - - /** - * @brief Q7 softmax function with batch parameter - * @param[in] vec_in pointer to input vector - * @param[in] nb_batches number of batches - * @param[in] dim_vec input vector dimention - * @param[out] p_out pointer to output vector - * - * @details - * - * Here, instead of typical natural logarithm e based softmax, we use - * 2-based softmax here, i.e.,: - * - * y_i = 2^(x_i) / sum(2^x_j) - * - * The relative output will be different here. - * But mathematically, the gradient will be the same - * with a log(2) scaling factor. - * - */ - -void arm_softmax_with_batch_q7(const q7_t * vec_in, const uint16_t nb_batches,const uint16_t dim_vec, q7_t * p_out ) -{ - for(int i=0; i

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