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删除m55库，完善md文档

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QingChuanWS 2021-01-27 00:55:04 +08:00
parent 6bf3533c32
commit d29c1b0446
200 changed files with 1225 additions and 67326 deletions
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4
board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TFlite_Micro_Demo移植参考指南（Keil版）.md
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@ -191,7 +191,7 @@ void task1(void *arg)
### 1. tflite_micro组件加入到keil工程
由于NUCLEO-L496ZG芯片中的内核为ARM Cortex M4，所以本次我们可以直接使用ARM Cortex M4版本的tensorflow_lite_micro.lib库来简化tflite_micro搭建流程。
由于 NUCLEO-L496ZG 芯片中的内核为 ARM Cortex M4，所以本次我们可以直接使用 ARM Cortex M4 版本的tensorflow_lite_micro.lib 库来简化 tflite_micro 搭建流程。
#### 1.1 在project中加入新的文件夹tensorflow
@ -237,7 +237,7 @@ TencentOS-tiny\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow\lite\micro
<img src="./image/tflu_STM32496宏.png" width=80% />
</div>
其中宏`NUCLEO_STM32L496ZG`是指定Nucleo STM32L496的hlpuart1为系统printf函数的输出串口，具体定义在Nucleo STM32L496的BSP文件夹中的`mcu_init.c`中。
其中宏`NUCLEO_STM32L496ZG`是指定 Nucleo STM32L496 的 hlpuart1 为系统 printf 函数的输出串口，具体定义在 Nucleo STM32L496 的 BSP 文件夹中的`mcu_init.c`中。
### 2. 编写Person_Detection 任务函数

23
board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS-tiny Person Detection Demo.md
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@ -1,23 +0,0 @@
## TencentOS-tiny_Person_Detection_Demo
### 1. 目录结构：
- TencentOS-tiny\board\NUCLEO_STM32L496ZG\BSP\Hardware : **外设驱动代码**
- TencentOS-tiny\examples\tflitemicro_person_detection : **Demo任务函数**
- TencentOS-tiny\board\NUCLEO_STM32L496ZG\KEIL\tflitemicro_person_detection : **keil工程**
- TencentOS-tiny\components\tflite_micro\tensorflow : **tflite_micro代码**
### 2. 完成的工作：
- 使用STM32CubeMX，选择与TOS同版本的固件库重新生成外设初始化代码；
- TOS、摄像头和LCD工作都正常工作；
- tflite_micro 以component的形式加到工程
- retarget.c引入工程，并通过宏进行选择
- example中行人检测demo已经可以正常工作
### 3. 未完成的工作：
- 变量名、函数名还没有按照TOS的风格完全统一
- keil移植指南
- tflite_micro用户指南

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board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx Normal file
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49
board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx
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@ -16,8 +16,8 @@
<TargetCommonOption>
<Device>STM32L496ZGTx</Device>
<Vendor>STMicroelectronics</Vendor>
<PackID>Keil.STM32L4xx_DFP.2.4.0</PackID>
<PackURL>http://www.keil.com/pack/</PackURL>
<PackID>Keil.STM32L4xx_DFP.2.5.0</PackID>
<PackURL>https://www.keil.com/pack/</PackURL>
<Cpu>IRAM(0x20000000-0x2004FFFF) IROM(0x8000000-0x80FFFFF) CLOCK(8000000) FPU2 CPUTYPE("Cortex-M4")</Cpu>
<FlashUtilSpec></FlashUtilSpec>
<StartupFile></StartupFile>
@ -137,7 +137,7 @@
<DriverSelection>4107</DriverSelection>
</Flash1>
<bUseTDR>1</bUseTDR>
<Flash2>STLink\ST-LINKIII-KEIL_SWO.dll</Flash2>
<Flash2>BIN\UL2CM3.DLL</Flash2>
<Flash3></Flash3>
<Flash4></Flash4>
<pFcarmOut></pFcarmOut>
@ -779,24 +779,9 @@
<GroupName>tensorflow</GroupName>
<Files>
<File>
<FileName>person_detect_model_data.cc</FileName>
<FileType>8</FileType>
<FilePath>.\tflu_person_detection\person_detect_model_data.cc</FilePath>
</File>
<File>
<FileName>model_settings.cc</FileName>
<FileType>8</FileType>
<FilePath>.\tflu_person_detection\model_settings.cc</FilePath>
</File>
<File>
<FileName>main_functions.cc</FileName>
<FileType>8</FileType>
<FilePath>.\tflu_person_detection\main_functions.cc</FilePath>
</File>
<File>
<FileName>image_provider.cc</FileName>
<FileType>8</FileType>
<FilePath>.\tflu_person_detection\image_provider.cc</FilePath>
<FileName>tensorflow_lite_micro_M4.lib</FileName>
<FileType>4</FileType>
<FilePath>..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro_M4.lib</FilePath>
</File>
<File>
<FileName>detection_responder.cc</FileName>
@ -804,14 +789,24 @@
<FilePath>.\tflu_person_detection\detection_responder.cc</FilePath>
</File>
<File>
<FileName>retarget.c</FileName>
<FileType>1</FileType>
<FilePath>..\..\..\..\components\ai\tflite_micro\KEIL\retarget.c</FilePath>
<FileName>image_provider.cc</FileName>
<FileType>8</FileType>
<FilePath>.\tflu_person_detection\image_provider.cc</FilePath>
</File>
<File>
<FileName>tensorflow_lite_micro_M4.lib</FileName>
<FileType>4</FileType>
<FilePath>..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro_M4.lib</FilePath>
<FileName>main_functions.cc</FileName>
<FileType>8</FileType>
<FilePath>.\tflu_person_detection\main_functions.cc</FilePath>
</File>
<File>
<FileName>model_settings.cc</FileName>
<FileType>8</FileType>
<FilePath>.\tflu_person_detection\model_settings.cc</FilePath>
</File>
<File>
<FileName>person_detect_model_data.cc</FileName>
<FileType>8</FileType>
<FilePath>.\tflu_person_detection\person_detect_model_data.cc</FilePath>
</File>
</Files>
</Group>

203
components/ai/tflite_micro/ARM_CortexM55_lib/LICENSE
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@ -1,203 +0,0 @@
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139
components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/core/public/version.h
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@ -1,139 +0,0 @@
/* Copyright 2015 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.
==============================================================================*/
#ifndef TENSORFLOW_CORE_PUBLIC_VERSION_H_
#define TENSORFLOW_CORE_PUBLIC_VERSION_H_
// TensorFlow uses semantic versioning, see http://semver.org/.
// Also update tensorflow/tensorflow.bzl and
// tensorflow/tools/pip_package/setup.py
#define TF_MAJOR_VERSION 2
#define TF_MINOR_VERSION 4
#define TF_PATCH_VERSION 0
// TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
// "-beta", "-rc", "-rc.1")
#define TF_VERSION_SUFFIX ""
#define TF_STR_HELPER(x) #x
#define TF_STR(x) TF_STR_HELPER(x)
// e.g. "0.5.0" or "0.6.0-alpha".
#define TF_VERSION_STRING \
(TF_STR(TF_MAJOR_VERSION) "." TF_STR(TF_MINOR_VERSION) "." TF_STR( \
TF_PATCH_VERSION) TF_VERSION_SUFFIX)
// GraphDef compatibility versions (the versions field in graph.proto).
//
// Each graph has producer and min_consumer versions, and each
// consumer has its own version and a min_producer. In addition, graphs can
// mark specific consumer versions as bad (to prevent bugs from executing).
// A consumer will execute a graph if the consumer's version is at least the
// graph's min_consumer, the graph's producer version is at least the consumer's
// min_producer, and the consumer version isn't specifically disallowed by the
// graph.
//
// By default, newly created graphs have producer version TF_GRAPH_DEF_VERSION
// min_consumer TF_GRAPH_DEF_MIN_CONSUMER, and no other bad consumer versions.
//
// Version history:
//
// 0. Graphs created before GraphDef versioning
// 1. First real version (2dec2015)
// 2. adjust_contrast only takes float, doesn't perform clamping (11dec2015)
// 3. Remove TileGrad, since it was equivalent to reduce_sum (30dec2015)
// 4. When support for this version is removed, we can safely make AttrValue
// parsing more strict with respect to empty list values (see
// 111635679, 7jan2016).
// 5. Graphs are wholly-validated during Session::Create() (7jan2016).
// 6. TensorFlow is scalar strict within Google (27jan2016).
// 7. Remove TopK in favor of TopKV2 (5feb2016).
// 8. Replace RandomCrop from C++ with pure Python (5feb2016).
// 9. Deprecate batch_norm_with_global_normalization (16feb2016).
// 10. Deprecate conv3d_backprop_{filter,input} (10jun2016).
// 11. Deprecate {batch}_self_adjoint_eig (3aug2016).
// 12. Graph consumers understand the node_def field of FunctionDef (22aug2016).
// 13. Deprecate multiple batch linear algebra ops (9sep2016).
// 14. Deprecate batch_matrix_* ops. (10sep2016).
// 15. Deprecate batch_fft_* ops. (14sep2016).
// 16. Deprecate tensor_array (v1) ops in favor of v2 (10nov2016).
// 17. Deprecate inv (11nov2016).
// 17. Expose reverse_v2 (10nov2016)
// 18. Add VariableV2 (30nov2016)
// 19. Deprecated ops created by models moved out of core SkipGram, NegTrain.
// (08dec2016)
// 20. Catch all version 1.0 changes to Python API generation. SplitV is now
// used for tf.split, ReverseV2 is now used by tf.reverse, ConcatV2 is
// now used by tf.concat. Graphs use flooring
// division and mod semantics. TensorArrayV3. (12dec2016)
// Also considered the version for when it is required for reduction
// ops' indices to be scalar or vector, and not higher rank.
// Some earlier graph def versions allowed this.
// 21. Dropped FunctionDef.Node support, switched to node_def introduced
// in version 12. (11jan2017)
// 22. Placeholder now can specify and enforce scalar and partial
// shapes, particularly when restoring a graph from GraphDef
// produced at version 22 or later. (04/10/2016)
// 23. Remove NonMaxSuppression in favor of NonMaxSuppressionV2.
// 24. Deprecate lookup ops (v1) ops in favor of v2 (30may2017)
// 25. Deprecate stack (v1) ops in favor of v2 (2017/6/15).
// 25. Deprecate RandomPoisson (v1) ops in favor of v2 (2017/10/25).
// 26. Add a bool 'stripped_default_attrs' to MetaInfoDef indicating
// whether default-valued attrs have been stripped from the nodes in the
// GraphDef. (7dec2017)
// 27. Deprecate TensorArray ops v2 in favor of v3 and deprecated io_ops
// deprecated in favor of V2 ops. (2018/01/23)
// 28. Deprecate MatrixExponential op in favor of Python implementation.
// (2018/08/21).
// (2019/02/15). Added `control_ret` field to FunctionDef proto, and
// `control_output` field to OpDef proto.
// 29. Deprecate StatefulStandardNormal op in favor of StatefulStandardNormalV2.
// (2019/03/25).
// (2019/04/17). Added `arg_attr` field to FunctionDefProto.
// 30. (2019/05/09) First date based GraphDef version. GraphDef
// versions advance by 1 each day after this point.
#define TF_GRAPH_DEF_VERSION_MIN_PRODUCER 0
#define TF_GRAPH_DEF_VERSION_MIN_CONSUMER 0
#define TF_GRAPH_DEF_VERSION 485 // Updated: 2020/8/6
// Checkpoint compatibility versions (the versions field in SavedSliceMeta).
//
// The checkpoint versions have the same semantics as GraphDef versions, but the
// numbering scheme is separate. We have no plans to ever deprecate checkpoint
// versions, but it's good to have this in place in case we ever need to.
//
// Version history:
//
// 0. Checkpoints saved before checkpoint versioning.
// 1. First real version (10feb2015).
#define TF_CHECKPOINT_VERSION_MIN_PRODUCER 0
#define TF_CHECKPOINT_VERSION_MIN_CONSUMER 0
#define TF_CHECKPOINT_VERSION 1
/// Version query functions (defined in generated version_info.cc)
// Host compiler version (declared elsewhere to be __VERSION__)
extern const char* tf_compiler_version();
// The git commit designator when tensorflow was built
// If no git repository, this will be "internal".
extern const char* tf_git_version();
// Value of the _GLIBCXX_USE_CXX11_ABI flag, or 0 if it's not set.
extern int tf_cxx11_abi_flag();
// Returns 1 if build is monolithic, or 0 otherwise.
extern int tf_monolithic_build();
#endif // TENSORFLOW_CORE_PUBLIC_VERSION_H_

472
components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/c/builtin_op_data.h
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@ -1,472 +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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_
#define TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_
#include <stdint.h>
#include "tensorflow/lite/c/common.h"
#ifdef __cplusplus
extern "C" {
#endif // __cplusplus
// TfLiteReshapeParams can't have dynamic data so we fix the maximum possible
// number of dimensions.
#define TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT 8
// TODO(aselle): Consider using "if this then that" for testing.
// Useful placeholder to put in otherwise empty structs to avoid size warnings.
typedef struct {
char dummy;
} EmptyStructPlaceholder;
// IMPORTANT: All new members of structs must be added at the end to ensure
// backwards compatibility.
// Possible padding types (for convolutions)
typedef enum {
kTfLitePaddingUnknown = 0,
kTfLitePaddingSame,
kTfLitePaddingValid,
} TfLitePadding;
typedef enum {
kTfLiteMirrorPaddingUnknown = 0,
kTfLiteMirrorPaddingReflect,
kTfLiteMirrorPaddingSymmetric,
} TfLiteMirrorPaddingMode;
// TODO(b/130259536): We should move this out of builtin_op_data.
typedef struct {
int width;
int height;
int width_offset;
int height_offset;
} TfLitePaddingValues;
typedef struct {
TfLiteMirrorPaddingMode mode;
} TfLiteMirrorPaddingParams;
// Possible fused activation functions.
// TODO(aselle): rename to TfLiteActivation
typedef enum {
kTfLiteActNone = 0,
kTfLiteActRelu,
kTfLiteActReluN1To1, // min(max(-1, x), 1)
kTfLiteActRelu1 = kTfLiteActReluN1To1, // kTfLiteActRelu1 will be deprecated.
kTfLiteActRelu6, // min(max(0, x), 6)
kTfLiteActTanh,
kTfLiteActSignBit,
kTfLiteActSigmoid,
} TfLiteFusedActivation;
typedef struct {
// Parameters for CONV_2D version 1.
TfLitePadding padding;
int stride_width;
int stride_height;
TfLiteFusedActivation activation;
// Parameters for CONV_2D version 2.
// Note: Version 2 supports dilation values not equal to 1.
int dilation_width_factor;
int dilation_height_factor;
} TfLiteConvParams;
typedef struct {
TfLitePadding padding;
int stride_width;
int stride_height;
int filter_width;
int filter_height;
TfLiteFusedActivation activation;
struct {
TfLitePaddingValues padding;
} computed;
} TfLitePoolParams;
typedef struct {
// Parameters for DepthwiseConv version 1 or above.
TfLitePadding padding;
int stride_width;
int stride_height;
// `depth_multiplier` is redundant. It's used by CPU kernels in
// TensorFlow 2.0 or below, but ignored in versions above.
//
// The information can be deduced from the shape of input and the shape of
// weights. Since the TFLiteConverter toolchain doesn't support partially
// specified shapes, relying on `depth_multiplier` stops us from supporting
// graphs with dynamic shape tensors.
//
// Note: Some of the delegates (e.g. NNAPI, GPU) are still relying on this
// field.
int depth_multiplier;
TfLiteFusedActivation activation;
// Parameters for DepthwiseConv version 2 or above.
int dilation_width_factor;
int dilation_height_factor;
} TfLiteDepthwiseConvParams;
typedef struct {
int rank;
TfLiteFusedActivation activation;
// Parameter for SVDF version 4.
bool asymmetric_quantize_inputs;
} TfLiteSVDFParams;
typedef struct {
TfLiteFusedActivation activation;
// Parameter for RNN version 3.
bool asymmetric_quantize_inputs;
} TfLiteRNNParams;
typedef struct {
bool time_major;
TfLiteFusedActivation activation;
// Parameter for Sequence RNN version 3.
bool asymmetric_quantize_inputs;
} TfLiteSequenceRNNParams;
typedef struct {
bool time_major;
TfLiteFusedActivation activation;
bool merge_outputs;
// Parameter for Bidirectional RNN verison 3.
bool asymmetric_quantize_inputs;
} TfLiteBidirectionalSequenceRNNParams;
typedef enum {
kTfLiteFullyConnectedWeightsFormatDefault = 0,
kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8 = 1,
} TfLiteFullyConnectedWeightsFormat;
typedef struct {
// Parameters for FullyConnected version 1 or above.
TfLiteFusedActivation activation;
// Parameters for FullyConnected version 2 or above.
TfLiteFullyConnectedWeightsFormat weights_format;
// Parameters for FullyConnected version 5 or above.
// If set to true, then the number of dimensions in the input and the output
// tensors are the same. Furthermore, all but the last dimension of the input
// and output shapes will be equal.
bool keep_num_dims;
// Parameters for FullyConnected version 7 or above.
// If set to true and the weights are quantized, then non constant inputs
// are quantized at evaluation time with asymmetric quantization.
bool asymmetric_quantize_inputs;
} TfLiteFullyConnectedParams;
typedef enum {
kTfLiteLshProjectionUnknown = 0,
kTfLiteLshProjectionSparse = 1,
kTfLiteLshProjectionDense = 2,
} TfLiteLSHProjectionType;
typedef struct {
TfLiteLSHProjectionType type;
} TfLiteLSHProjectionParams;
typedef struct {
float beta;
} TfLiteSoftmaxParams;
typedef struct {
int axis;
TfLiteFusedActivation activation;
} TfLiteConcatenationParams;
typedef struct {
TfLiteFusedActivation activation;
// Parameter added for the version 4.
bool pot_scale_int16;
} TfLiteAddParams;
typedef struct {
EmptyStructPlaceholder placeholder;
} TfLiteSpaceToBatchNDParams;
typedef struct {
EmptyStructPlaceholder placeholder;
} TfLiteBatchToSpaceNDParams;
typedef struct {
bool adj_x;
bool adj_y;
} TfLiteBatchMatMulParams;
typedef struct {
TfLiteFusedActivation activation;
} TfLiteMulParams;
typedef struct {
TfLiteFusedActivation activation;
// Parameter added for the version 5.
bool pot_scale_int16;
} TfLiteSubParams;
typedef struct {
TfLiteFusedActivation activation;
} TfLiteDivParams;
typedef struct {
TfLiteFusedActivation activation;
} TfLiteL2NormParams;
typedef struct {
int radius;
float bias;
float alpha;
float beta;
} TfLiteLocalResponseNormParams;
typedef enum {
kTfLiteLSTMFullKernel = 0,
kTfLiteLSTMBasicKernel
} TfLiteLSTMKernelType;
typedef struct {
// Parameters for LSTM version 1.
TfLiteFusedActivation activation;
float cell_clip;
float proj_clip;
// Parameters for LSTM version 2.
// kTfLiteLSTMBasicKernel is only supported in version 2 or above.
TfLiteLSTMKernelType kernel_type;
// Parameters for LSTM version 4.
bool asymmetric_quantize_inputs;
} TfLiteLSTMParams;
typedef struct {
// Parameters needed for the underlying LSTM.
TfLiteFusedActivation activation;
float cell_clip;
float proj_clip;
// If set to true then the first dimension is time, otherwise batch.
bool time_major;
// Parameter for unidirectional sequence RNN version 3.
bool asymmetric_quantize_inputs;
} TfLiteUnidirectionalSequenceLSTMParams;
typedef struct {
// Parameters supported by version 1:
// Parameters inherited for the LSTM kernel.
TfLiteFusedActivation activation;
float cell_clip;
float proj_clip;
// If true, store the outputs of both directions in the first output.
bool merge_outputs;
// Parameters supported by version 2:
// If set to true then the first dimension is time, otherwise batch.
bool time_major;
// Parameters supported by version 4:
// If set to true, then hybrid ops use asymmetric quantization for inputs.
bool asymmetric_quantize_inputs;
} TfLiteBidirectionalSequenceLSTMParams;
typedef struct {
bool align_corners;
// half_pixel_centers assumes pixels are of half the actual dimensions, and
// yields more accurate resizes. Corresponds to the same argument for the
// original TensorFlow op in TF2.0.
bool half_pixel_centers;
} TfLiteResizeBilinearParams;
typedef struct {
bool align_corners;
bool half_pixel_centers;
} TfLiteResizeNearestNeighborParams;
typedef struct {
EmptyStructPlaceholder placeholder;
} TfLitePadParams;
typedef struct {
EmptyStructPlaceholder placeholder;
} TfLitePadV2Params;
typedef struct {
// TODO(ahentz): We can't have dynamic data in this struct, at least not yet.
// For now we will fix the maximum possible number of dimensions.
int shape[TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT];
int num_dimensions;
} TfLiteReshapeParams;
typedef struct {
int ngram_size;
int max_skip_size;
bool include_all_ngrams;
} TfLiteSkipGramParams;
typedef struct {
int block_size;
} TfLiteSpaceToDepthParams;
typedef struct {
int block_size;
} TfLiteDepthToSpaceParams;
typedef struct {
TfLiteType in_data_type;
TfLiteType out_data_type;
} TfLiteCastParams;
typedef enum {
kTfLiteCombinerTypeSum = 0,
kTfLiteCombinerTypeMean = 1,
kTfLiteCombinerTypeSqrtn = 2,
} TfLiteCombinerType;
typedef struct {
TfLiteCombinerType combiner;
} TfLiteEmbeddingLookupSparseParams;
typedef struct {
int axis;
} TfLiteGatherParams;
typedef struct {
EmptyStructPlaceholder placeholder;
} TfLiteTransposeParams;
typedef struct {
bool keep_dims;
} TfLiteReducerParams;
typedef struct {
int num_splits;
} TfLiteSplitParams;
typedef struct {
int num_splits;
} TfLiteSplitVParams;
typedef struct {
// TODO(ahentz): We can't have dynamic data in this struct, at least not yet.
// For now we will fix the maximum possible number of dimensions.
int squeeze_dims[8];
int num_squeeze_dims;
} TfLiteSqueezeParams;
typedef struct {
int begin_mask;
int end_mask;
int ellipsis_mask;
int new_axis_mask;
int shrink_axis_mask;
} TfLiteStridedSliceParams;
typedef struct {
TfLiteType output_type;
} TfLiteArgMaxParams;
typedef struct {
TfLiteType output_type;
} TfLiteArgMinParams;
typedef struct {
TfLitePadding padding;
int stride_width;
int stride_height;
} TfLiteTransposeConvParams;
typedef struct {
bool validate_indices;
} TfLiteSparseToDenseParams;
typedef struct {
TfLiteType out_type;
} TfLiteShapeParams;
typedef struct {
EmptyStructPlaceholder placeholder;
} TfLiteRankParams;
typedef struct {
// Parameters supported by version 1:
float min;
float max;
int num_bits;
// Parameters supported by version 2:
bool narrow_range;
} TfLiteFakeQuantParams;
typedef struct {
int values_count;
int axis;
} TfLitePackParams;
typedef struct {
int axis;
} TfLiteOneHotParams;
typedef struct {
int num;
int axis;
} TfLiteUnpackParams;
typedef struct {
float alpha;
} TfLiteLeakyReluParams;
typedef struct {
TfLiteType index_out_type;
} TfLiteUniqueParams;
typedef struct {
int seq_dim;
int batch_dim;
} TfLiteReverseSequenceParams;
typedef struct {
EmptyStructPlaceholder placeholder;
} TfLiteMatrixDiagParams;
typedef struct {
EmptyStructPlaceholder placeholder;
} TfLiteMatrixSetDiagParams;
typedef struct {
int then_subgraph_index;
int else_subgraph_index;
} TfLiteIfParams;
typedef struct {
int cond_subgraph_index;
int body_subgraph_index;
} TfLiteWhileParams;
#ifdef __cplusplus
} // extern "C"
#endif // __cplusplus
#endif // TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_

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/* 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.
==============================================================================*/
// This file defines common C types and APIs for implementing operations,
// delegates and other constructs in TensorFlow Lite. The actual operations and
// delegates can be defined using C++, but the interface between the interpreter
// and the operations are C.
//
// Summary of abstractions
// TF_LITE_ENSURE - Self-sufficient error checking
// TfLiteStatus - Status reporting
// TfLiteIntArray - stores tensor shapes (dims),
// TfLiteContext - allows an op to access the tensors
// TfLiteTensor - tensor (a multidimensional array)
// TfLiteNode - a single node or operation
// TfLiteRegistration - the implementation of a conceptual operation.
// TfLiteDelegate - allows delegation of nodes to alternative backends.
//
// Some abstractions in this file are created and managed by Interpreter.
//
// NOTE: The order of values in these structs are "semi-ABI stable". New values
// should be added only to the end of structs and never reordered.
#ifndef TENSORFLOW_LITE_C_COMMON_H_
#define TENSORFLOW_LITE_C_COMMON_H_
#include <stdbool.h>
#include <stddef.h>
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif // __cplusplus
typedef enum TfLiteStatus {
kTfLiteOk = 0,
kTfLiteError = 1,
kTfLiteDelegateError = 2
} TfLiteStatus;
// The list of external context types known to TF Lite. This list exists solely
// to avoid conflicts and to ensure ops can share the external contexts they
// need. Access to the external contexts is controlled by one of the
// corresponding support files.
typedef enum TfLiteExternalContextType {
kTfLiteEigenContext = 0, // include eigen_support.h to use.
kTfLiteGemmLowpContext = 1, // include gemm_support.h to use.
kTfLiteEdgeTpuContext = 2, // Placeholder for Edge TPU support.
kTfLiteCpuBackendContext = 3, // include cpu_backend_context.h to use.
kTfLiteMaxExternalContexts = 4
} TfLiteExternalContextType;
// Forward declare so dependent structs and methods can reference these types
// prior to the struct definitions.
struct TfLiteContext;
struct TfLiteDelegate;
struct TfLiteRegistration;
// An external context is a collection of information unrelated to the TF Lite
// framework, but useful to a subset of the ops. TF Lite knows very little
// about about the actual contexts, but it keeps a list of them, and is able to
// refresh them if configurations like the number of recommended threads
// change.
typedef struct TfLiteExternalContext {
TfLiteExternalContextType type;
TfLiteStatus (*Refresh)(struct TfLiteContext* context);
} TfLiteExternalContext;
#define kTfLiteOptionalTensor (-1)
// Fixed size list of integers. Used for dimensions and inputs/outputs tensor
// indices
typedef struct TfLiteIntArray {
int size;
// gcc 6.1+ have a bug where flexible members aren't properly handled
// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
#if (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
__GNUC_MINOR__ >= 1) || \
defined(HEXAGON)
int data[0];
#else
int data[];
#endif
} TfLiteIntArray;
// Given the size (number of elements) in a TfLiteIntArray, calculate its size
// in bytes.
int TfLiteIntArrayGetSizeInBytes(int size);
#ifndef TF_LITE_STATIC_MEMORY
// Create a array of a given `size` (uninitialized entries).
// This returns a pointer, that you must free using TfLiteIntArrayFree().
TfLiteIntArray* TfLiteIntArrayCreate(int size);
#endif
// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise.
int TfLiteIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b);
// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise.
int TfLiteIntArrayEqualsArray(const TfLiteIntArray* a, int b_size,
const int b_data[]);
#ifndef TF_LITE_STATIC_MEMORY
// Create a copy of an array passed as `src`.
// You are expected to free memory with TfLiteIntArrayFree
TfLiteIntArray* TfLiteIntArrayCopy(const TfLiteIntArray* src);
// Free memory of array `a`.
void TfLiteIntArrayFree(TfLiteIntArray* a);
#endif // TF_LITE_STATIC_MEMORY
// Fixed size list of floats. Used for per-channel quantization.
typedef struct TfLiteFloatArray {
int size;
// gcc 6.1+ have a bug where flexible members aren't properly handled
// https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
// This also applies to the toolchain used for Qualcomm Hexagon DSPs.
#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
__GNUC_MINOR__ >= 1
float data[0];
#else
float data[];
#endif
} TfLiteFloatArray;
// Given the size (number of elements) in a TfLiteFloatArray, calculate its size
// in bytes.
int TfLiteFloatArrayGetSizeInBytes(int size);
#ifndef TF_LITE_STATIC_MEMORY
// Create a array of a given `size` (uninitialized entries).
// This returns a pointer, that you must free using TfLiteFloatArrayFree().
TfLiteFloatArray* TfLiteFloatArrayCreate(int size);
// Free memory of array `a`.
void TfLiteFloatArrayFree(TfLiteFloatArray* a);
#endif // TF_LITE_STATIC_MEMORY
// Since we must not depend on any libraries, define a minimal subset of
// error macros while avoiding names that have pre-conceived meanings like
// assert and check.
// Try to make all reporting calls through TF_LITE_KERNEL_LOG rather than
// calling the context->ReportError function directly, so that message strings
// can be stripped out if the binary size needs to be severely optimized.
#ifndef TF_LITE_STRIP_ERROR_STRINGS
#define TF_LITE_KERNEL_LOG(context, ...) \
do { \
(context)->ReportError((context), __VA_ARGS__); \
} while (false)
#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) \
do { \
if ((context) != nullptr) { \
(context)->ReportError((context), __VA_ARGS__); \
} \
} while (false)
#else // TF_LITE_STRIP_ERROR_STRINGS
#define TF_LITE_KERNEL_LOG(context, ...)
#define TF_LITE_MAYBE_KERNEL_LOG(context, ...)
#endif // TF_LITE_STRIP_ERROR_STRINGS
// Check whether value is true, and if not return kTfLiteError from
// the current function (and report the error string msg).
#define TF_LITE_ENSURE_MSG(context, value, msg) \
do { \
if (!(value)) { \
TF_LITE_KERNEL_LOG((context), __FILE__ " " msg); \
return kTfLiteError; \
} \
} while (0)
// Check whether the value `a` is true, and if not return kTfLiteError from
// the current function, while also reporting the location of the error.
#define TF_LITE_ENSURE(context, a) \
do { \
if (!(a)) { \
TF_LITE_KERNEL_LOG((context), "%s:%d %s was not true.", __FILE__, \
__LINE__, #a); \
return kTfLiteError; \
} \
} while (0)
#define TF_LITE_ENSURE_STATUS(a) \
do { \
const TfLiteStatus s = (a); \
if (s != kTfLiteOk) { \
return s; \
} \
} while (0)
// Check whether the value `a == b` is true, and if not return kTfLiteError from
// the current function, while also reporting the location of the error.
// `a` and `b` may be evaluated more than once, so no side effects or
// extremely expensive computations should be done.
// NOTE: Use TF_LITE_ENSURE_TYPES_EQ if comparing TfLiteTypes.
#define TF_LITE_ENSURE_EQ(context, a, b) \
do { \
if ((a) != (b)) { \
TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%d != %d)", __FILE__, \
__LINE__, #a, #b, (a), (b)); \
return kTfLiteError; \
} \
} while (0)
#define TF_LITE_ENSURE_TYPES_EQ(context, a, b) \
do { \
if ((a) != (b)) { \
TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%s != %s)", __FILE__, \
__LINE__, #a, #b, TfLiteTypeGetName(a), \
TfLiteTypeGetName(b)); \
return kTfLiteError; \
} \
} while (0)
#define TF_LITE_ENSURE_OK(context, status) \
do { \
const TfLiteStatus s = (status); \
if ((s) != kTfLiteOk) { \
return s; \
} \
} while (0)
// Define TFL_CAPI_EXPORT macro to export a function properly with a shared
// library.
#ifdef SWIG
#define TFL_CAPI_EXPORT
#else
#if defined(_WIN32)
#ifdef TFL_COMPILE_LIBRARY
#define TFL_CAPI_EXPORT __declspec(dllexport)
#else
#define TFL_CAPI_EXPORT __declspec(dllimport)
#endif // TFL_COMPILE_LIBRARY
#else
#define TFL_CAPI_EXPORT __attribute__((visibility("default")))
#endif // _WIN32
#endif // SWIG
// Single-precision complex data type compatible with the C99 definition.
typedef struct TfLiteComplex64 {
float re, im; // real and imaginary parts, respectively.
} TfLiteComplex64;
// Double-precision complex data type compatible with the C99 definition.
typedef struct TfLiteComplex128 {
double re, im; // real and imaginary parts, respectively.
} TfLiteComplex128;
// Half precision data type compatible with the C99 definition.
typedef struct TfLiteFloat16 {
uint16_t data;
} TfLiteFloat16;
// Types supported by tensor
typedef enum {
kTfLiteNoType = 0,
kTfLiteFloat32 = 1,
kTfLiteInt32 = 2,
kTfLiteUInt8 = 3,
kTfLiteInt64 = 4,
kTfLiteString = 5,
kTfLiteBool = 6,
kTfLiteInt16 = 7,
kTfLiteComplex64 = 8,
kTfLiteInt8 = 9,
kTfLiteFloat16 = 10,
kTfLiteFloat64 = 11,
kTfLiteComplex128 = 12,
} TfLiteType;
// Return the name of a given type, for error reporting purposes.
const char* TfLiteTypeGetName(TfLiteType type);
// SupportedQuantizationTypes.
typedef enum TfLiteQuantizationType {
// No quantization.
kTfLiteNoQuantization = 0,
// Affine quantization (with support for per-channel quantization).
// Corresponds to TfLiteAffineQuantization.
kTfLiteAffineQuantization = 1,
} TfLiteQuantizationType;
// Structure specifying the quantization used by the tensor, if-any.
typedef struct TfLiteQuantization {
// The type of quantization held by params.
TfLiteQuantizationType type;
// Holds a reference to one of the quantization param structures specified
// below.
void* params;
} TfLiteQuantization;
// Legacy. Will be deprecated in favor of TfLiteAffineQuantization.
// If per-layer quantization is specified this field will still be populated in
// addition to TfLiteAffineQuantization.
// Parameters for asymmetric quantization. Quantized values can be converted
// back to float using:
// real_value = scale * (quantized_value - zero_point)
typedef struct TfLiteQuantizationParams {
float scale;
int32_t zero_point;
} TfLiteQuantizationParams;
// Parameters for asymmetric quantization across a dimension (i.e per output
// channel quantization).
// quantized_dimension specifies which dimension the scales and zero_points
// correspond to.
// For a particular value in quantized_dimension, quantized values can be
// converted back to float using:
// real_value = scale * (quantized_value - zero_point)
typedef struct TfLiteAffineQuantization {
TfLiteFloatArray* scale;
TfLiteIntArray* zero_point;
int32_t quantized_dimension;
} TfLiteAffineQuantization;
/* A union of pointers that points to memory for a given tensor. */
typedef union TfLitePtrUnion {
/* Do not access these members directly, if possible, use
* GetTensorData<TYPE>(tensor) instead, otherwise only access .data, as other
* members are deprecated. */
int32_t* i32;
int64_t* i64;
float* f;
TfLiteFloat16* f16;
double* f64;
char* raw;
const char* raw_const;
uint8_t* uint8;
bool* b;
int16_t* i16;
TfLiteComplex64* c64;
TfLiteComplex128* c128;
int8_t* int8;
/* Only use this member. */
void* data;
} TfLitePtrUnion;
// Memory allocation strategies.
// * kTfLiteMmapRo: Read-only memory-mapped data, or data externally allocated.
// * kTfLiteArenaRw: Arena allocated with no guarantees about persistence,
// and available during eval.
// * kTfLiteArenaRwPersistent: Arena allocated but persistent across eval, and
// only available during eval.
// * kTfLiteDynamic: Allocated during eval, or for string tensors.
// * kTfLitePersistentRo: Allocated and populated during prepare. This is
// useful for tensors that can be computed during prepare and treated
// as constant inputs for downstream ops (also in prepare).
typedef enum TfLiteAllocationType {
kTfLiteMemNone = 0,
kTfLiteMmapRo,
kTfLiteArenaRw,
kTfLiteArenaRwPersistent,
kTfLiteDynamic,
kTfLitePersistentRo,
} TfLiteAllocationType;
// The delegates should use zero or positive integers to represent handles.
// -1 is reserved from unallocated status.
typedef int TfLiteBufferHandle;
enum {
kTfLiteNullBufferHandle = -1,
};
// Storage format of each dimension in a sparse tensor.
typedef enum TfLiteDimensionType {
kTfLiteDimDense = 0,
kTfLiteDimSparseCSR,
} TfLiteDimensionType;
// Metadata to encode each dimension in a sparse tensor.
typedef struct TfLiteDimensionMetadata {
TfLiteDimensionType format;
int dense_size;
TfLiteIntArray* array_segments;
TfLiteIntArray* array_indices;
} TfLiteDimensionMetadata;
// Parameters used to encode a sparse tensor. For detailed explanation of each
// field please refer to lite/schema/schema.fbs.
typedef struct TfLiteSparsity {
TfLiteIntArray* traversal_order;
TfLiteIntArray* block_map;
TfLiteDimensionMetadata* dim_metadata;
int dim_metadata_size;
} TfLiteSparsity;
// An tensor in the interpreter system which is a wrapper around a buffer of
// data including a dimensionality (or NULL if not currently defined).
#ifndef TF_LITE_STATIC_MEMORY
typedef struct TfLiteTensor {
// The data type specification for data stored in `data`. This affects
// what member of `data` union should be used.
TfLiteType type;
// A union of data pointers. The appropriate type should be used for a typed
// tensor based on `type`.
TfLitePtrUnion data;
// A pointer to a structure representing the dimensionality interpretation
// that the buffer should have. NOTE: the product of elements of `dims`
// and the element datatype size should be equal to `bytes` below.
TfLiteIntArray* dims;
// Quantization information.
TfLiteQuantizationParams params;
// How memory is mapped
// kTfLiteMmapRo: Memory mapped read only.
// i.e. weights
// kTfLiteArenaRw: Arena allocated read write memory
// (i.e. temporaries, outputs).
TfLiteAllocationType allocation_type;
// The number of bytes required to store the data of this Tensor. I.e.
// (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if
// type is kTfLiteFloat32 and dims = {3, 2} then
// bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24.
size_t bytes;
// An opaque pointer to a tflite::MMapAllocation
const void* allocation;
// Null-terminated name of this tensor.
const char* name;
// The delegate which knows how to handle `buffer_handle`.
// WARNING: This is an experimental interface that is subject to change.
struct TfLiteDelegate* delegate;
// An integer buffer handle that can be handled by `delegate`.
// The value is valid only when delegate is not null.
// WARNING: This is an experimental interface that is subject to change.
TfLiteBufferHandle buffer_handle;
// If the delegate uses its own buffer (e.g. GPU memory), the delegate is
// responsible to set data_is_stale to true.
// `delegate->CopyFromBufferHandle` can be called to copy the data from
// delegate buffer.
// WARNING: This is an // experimental interface that is subject to change.
bool data_is_stale;
// True if the tensor is a variable.
bool is_variable;
// Quantization information. Replaces params field above.
TfLiteQuantization quantization;
// Parameters used to encode a sparse tensor.
// This is optional. The field is NULL if a tensor is dense.
// WARNING: This is an experimental interface that is subject to change.
TfLiteSparsity* sparsity;
// Optional. Encodes shapes with unknown dimensions with -1. This field is
// only populated when unknown dimensions exist in a read-write tensor (i.e.
// an input or output tensor). (e.g. `dims` contains [1, 1, 1, 3] and
// `dims_signature` contains [1, -1, -1, 3]).
const TfLiteIntArray* dims_signature;
} TfLiteTensor;
// A structure representing an instance of a node.
// This structure only exhibits the inputs, outputs and user defined data, not
// other features like the type.
typedef struct TfLiteNode {
// Inputs to this node expressed as indices into the simulator's tensors.
TfLiteIntArray* inputs;
// Outputs to this node expressed as indices into the simulator's tensors.
TfLiteIntArray* outputs;
// intermediate tensors to this node expressed as indices into the simulator's
// tensors.
TfLiteIntArray* intermediates;
// Temporary tensors uses during the computations. This usually contains no
// tensors, but ops are allowed to change that if they need scratch space of
// any sort.
TfLiteIntArray* temporaries;
// Opaque data provided by the node implementer through `Registration.init`.
void* user_data;
// Opaque data provided to the node if the node is a builtin. This is usually
// a structure defined in builtin_op_data.h
void* builtin_data;
// Custom initial data. This is the opaque data provided in the flatbuffer.
// WARNING: This is an experimental interface that is subject to change.
const void* custom_initial_data;
int custom_initial_data_size;
// The pointer to the delegate. This is non-null only when the node is
// created by calling `interpreter.ModifyGraphWithDelegate`.
// WARNING: This is an experimental interface that is subject to change.
struct TfLiteDelegate* delegate;
} TfLiteNode;
#else // defined(TF_LITE_STATIC_MEMORY)?
// NOTE: This flag is opt-in only at compile time.
//
// Specific reduced TfLiteTensor struct for TF Micro runtime. This struct
// contains only the minimum fields required to initialize and prepare a micro
// inference graph. The fields in this struct have been ordered from
// largest-to-smallest for optimal struct sizeof.
//
// This struct does not use:
// - allocation
// - buffer_handle
// - data_is_stale
// - delegate
// - dims_signature
// - name
// - sparsity
typedef struct TfLiteTensor {
// TODO(b/155784997): Consider consolidating these quantization fields:
// Quantization information. Replaces params field above.
TfLiteQuantization quantization;
// Quantization information.
TfLiteQuantizationParams params;
// A union of data pointers. The appropriate type should be used for a typed
// tensor based on `type`.
TfLitePtrUnion data;
// A pointer to a structure representing the dimensionality interpretation
// that the buffer should have. NOTE: the product of elements of `dims`
// and the element datatype size should be equal to `bytes` below.
TfLiteIntArray* dims;
// The number of bytes required to store the data of this Tensor. I.e.
// (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if
// type is kTfLiteFloat32 and dims = {3, 2} then
// bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24.
size_t bytes;
// The data type specification for data stored in `data`. This affects
// what member of `data` union should be used.
TfLiteType type;
// How memory is mapped
// kTfLiteMmapRo: Memory mapped read only.
// i.e. weights
// kTfLiteArenaRw: Arena allocated read write memory
// (i.e. temporaries, outputs).
TfLiteAllocationType allocation_type;
// True if the tensor is a variable.
bool is_variable;
} TfLiteTensor;
// Specific reduced TfLiteNode struct for TF Micro runtime. This struct contains
// only the minimum fields required to represent a node.
//
// This struct does not use:
// - delegate
// - intermediates
// - temporaries
typedef struct TfLiteNode {
// Inputs to this node expressed as indices into the simulator's tensors.
TfLiteIntArray* inputs;
// Outputs to this node expressed as indices into the simulator's tensors.
TfLiteIntArray* outputs;
// Opaque data provided by the node implementer through `Registration.init`.
void* user_data;
// Opaque data provided to the node if the node is a builtin. This is usually
// a structure defined in builtin_op_data.h
void* builtin_data;
// Custom initial data. This is the opaque data provided in the flatbuffer.
// WARNING: This is an experimental interface that is subject to change.
const void* custom_initial_data;
int custom_initial_data_size;
} TfLiteNode;
#endif // TF_LITE_STATIC_MEMORY
// Light-weight tensor struct for TF Micro runtime. Provides the minimal amount
// of information required for a kernel to run during TfLiteRegistration::Eval.
// TODO(b/160955687): Move this field into TF_LITE_STATIC_MEMORY when TFLM
// builds with this flag by default internally.
typedef struct TfLiteEvalTensor {
// A union of data pointers. The appropriate type should be used for a typed
// tensor based on `type`.
TfLitePtrUnion data;
// A pointer to a structure representing the dimensionality interpretation
// that the buffer should have.
TfLiteIntArray* dims;
// The data type specification for data stored in `data`. This affects
// what member of `data` union should be used.
TfLiteType type;
} TfLiteEvalTensor;
#ifndef TF_LITE_STATIC_MEMORY
// Free data memory of tensor `t`.
void TfLiteTensorDataFree(TfLiteTensor* t);
// Free quantization data.
void TfLiteQuantizationFree(TfLiteQuantization* quantization);
// Free sparsity parameters.
void TfLiteSparsityFree(TfLiteSparsity* sparsity);
// Free memory of tensor `t`.
void TfLiteTensorFree(TfLiteTensor* t);
// Set all of a tensor's fields (and free any previously allocated data).
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);
// Resize the allocated data of a (dynamic) tensor. Tensors with allocation
// types other than kTfLiteDynamic will be ignored.
void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor);
#endif // TF_LITE_STATIC_MEMORY
// WARNING: This is an experimental interface that is subject to change.
//
// Currently, TfLiteDelegateParams has to be allocated in a way that it's
// trivially destructable. It will be stored as `builtin_data` field in
// `TfLiteNode` of the delegate node.
//
// See also the `CreateDelegateParams` function in `interpreter.cc` details.
typedef struct TfLiteDelegateParams {
struct TfLiteDelegate* delegate;
TfLiteIntArray* nodes_to_replace;
TfLiteIntArray* input_tensors;
TfLiteIntArray* output_tensors;
} TfLiteDelegateParams;
typedef struct TfLiteContext {
// Number of tensors in the context.
size_t tensors_size;
// The execution plan contains a list of the node indices in execution
// order. execution_plan->size is the current number of nodes. And,
// execution_plan->data[0] is the first node that needs to be run.
// TfLiteDelegates can traverse the current execution plan by iterating
// through each member of this array and using GetNodeAndRegistration() to
// access details about a node. i.e.
// TfLiteIntArray* execution_plan;
// TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan));
// for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) {
// int node_index = execution_plan->data[exec_index];
// TfLiteNode* node;
// TfLiteRegistration* reg;
// context->GetNodeAndRegistration(context, node_index, &node, &reg);
// }
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context,
TfLiteIntArray** execution_plan);
// An array of tensors in the interpreter context (of length `tensors_size`)
TfLiteTensor* tensors;
// opaque full context ptr (an opaque c++ data structure)
void* impl_;
// Request memory pointer be resized. Updates dimensions on the tensor.
// NOTE: ResizeTensor takes ownership of newSize.
TfLiteStatus (*ResizeTensor)(struct TfLiteContext*, TfLiteTensor* tensor,
TfLiteIntArray* new_size);
// Request that an error be reported with format string msg.
void (*ReportError)(struct TfLiteContext*, const char* msg, ...);
// Add `tensors_to_add` tensors, preserving pre-existing Tensor entries. If
// non-null, the value pointed to by `first_new_tensor_index` will be set to
// the index of the first new tensor.
TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add,
int* first_new_tensor_index);
// Get a Tensor node by node_index.
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*GetNodeAndRegistration)(
struct TfLiteContext*, int node_index, TfLiteNode** node,
struct TfLiteRegistration** registration);
// Replace ops with one or more stub delegate operations. This function
// does not take ownership of `nodes_to_replace`.
TfLiteStatus (*ReplaceNodeSubsetsWithDelegateKernels)(
struct TfLiteContext*, struct TfLiteRegistration registration,
const TfLiteIntArray* nodes_to_replace, struct TfLiteDelegate* delegate);
// Number of threads that are recommended to subsystems like gemmlowp and
// eigen.
int recommended_num_threads;
// Access external contexts by type.
// WARNING: This is an experimental interface that is subject to change.
TfLiteExternalContext* (*GetExternalContext)(struct TfLiteContext*,
TfLiteExternalContextType);
// Set the value of a external context. Does not take ownership of the
// pointer.
// WARNING: This is an experimental interface that is subject to change.
void (*SetExternalContext)(struct TfLiteContext*, TfLiteExternalContextType,
TfLiteExternalContext*);
// Flag for allowing float16 precision for FP32 calculation.
// default: false.
// WARNING: This is an experimental API and subject to change.
bool allow_fp32_relax_to_fp16;
// Pointer to the op-level profiler, if set; nullptr otherwise.
void* profiler;
// Allocate persistent buffer which has the same life time as the interpreter.
// Returns nullptr on failure.
// The memory is allocated from heap for TFL, and from tail in TFLM.
// This method is only available in Init or Prepare stage.
// WARNING: This is an experimental interface that is subject to change.
void* (*AllocatePersistentBuffer)(struct TfLiteContext* ctx, size_t bytes);
// Allocate a buffer which will be deallocated right after invoke phase.
// The memory is allocated from heap in TFL, and from volatile arena in TFLM.
// This method is only available in invoke stage.
// NOTE: If possible use RequestScratchBufferInArena method to avoid memory
// allocation during inference time.
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*AllocateBufferForEval)(struct TfLiteContext* ctx, size_t bytes,
void** ptr);
// Request a scratch buffer in the arena through static memory planning.
// This method is only available in Prepare stage and the buffer is allocated
// by the interpreter between Prepare and Eval stage. In Eval stage,
// GetScratchBuffer API can be used to fetch the address.
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*RequestScratchBufferInArena)(struct TfLiteContext* ctx,
size_t bytes, int* buffer_idx);
// Get the scratch buffer pointer.
// This method is only available in Eval stage.
// WARNING: This is an experimental interface that is subject to change.
void* (*GetScratchBuffer)(struct TfLiteContext* ctx, int buffer_idx);
// Resize the memory pointer of the `tensor`. This method behaves the same as
// `ResizeTensor`, except that it makes a copy of the shape array internally
// so the shape array could be deallocated right afterwards.
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*ResizeTensorExplicit)(struct TfLiteContext* ctx,
TfLiteTensor* tensor, int dims,
const int* shape);
// This method provides a preview of post-delegation partitioning. Each
// TfLiteDelegateParams in the referenced array corresponds to one instance of
// the delegate kernel.
// Example usage:
//
// TfLiteIntArray* nodes_to_replace = ...;
// TfLiteDelegateParams* params_array;
// int num_partitions = 0;
// TF_LITE_ENSURE_STATUS(context->PreviewDelegatePartitioning(
// context, delegate, nodes_to_replace, &params_array, &num_partitions));
// for (int idx = 0; idx < num_partitions; idx++) {
// const auto& partition_params = params_array[idx];
// ...
// }
//
// NOTE: The context owns the memory referenced by partition_params_array. It
// will be cleared with another call to PreviewDelegateParitioning, or after
// TfLiteDelegateParams::Prepare returns.
//
// WARNING: This is an experimental interface that is subject to change.
TfLiteStatus (*PreviewDelegatePartitioning)(
struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
TfLiteDelegateParams** partition_params_array, int* num_partitions);
// Returns a TfLiteTensor struct for a given index.
// WARNING: This is an experimental interface that is subject to change.
// WARNING: This method may not be available on all platforms.
TfLiteTensor* (*GetTensor)(const struct TfLiteContext* context,
int tensor_idx);
// Returns a TfLiteEvalTensor struct for a given index.
// WARNING: This is an experimental interface that is subject to change.
// WARNING: This method may not be available on all platforms.
TfLiteEvalTensor* (*GetEvalTensor)(const struct TfLiteContext* context,
int tensor_idx);
} TfLiteContext;
typedef struct TfLiteRegistration {
// Initializes the op from serialized data.
// If a built-in op:
// `buffer` is the op's params data (TfLiteLSTMParams*).
// `length` is zero.
// If custom op:
// `buffer` is the op's `custom_options`.
// `length` is the size of the buffer.
//
// Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer
// or an instance of a struct).
//
// The returned pointer will be stored with the node in the `user_data` field,
// accessible within prepare and invoke functions below.
// NOTE: if the data is already in the desired format, simply implement this
// function to return `nullptr` and implement the free function to be a no-op.
void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
// The pointer `buffer` is the data previously returned by an init invocation.
void (*free)(TfLiteContext* context, void* buffer);
// prepare is called when the inputs this node depends on have been resized.
// context->ResizeTensor() can be called to request output tensors to be
// resized.
//
// Returns kTfLiteOk on success.
TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);
// Execute the node (should read node->inputs and output to node->outputs).
// Returns kTfLiteOk on success.
TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
// profiling_string is called during summarization of profiling information
// in order to group executions together. Providing a value here will cause a
// given op to appear multiple times is the profiling report. This is
// particularly useful for custom ops that can perform significantly
// different calculations depending on their `user-data`.
const char* (*profiling_string)(const TfLiteContext* context,
const TfLiteNode* node);
// Builtin codes. If this kernel refers to a builtin this is the code
// of the builtin. This is so we can do marshaling to other frameworks like
// NN API.
// Note: It is the responsibility of the registration binder to set this
// properly.
int32_t builtin_code;
// Custom op name. If the op is a builtin, this will be null.
// Note: It is the responsibility of the registration binder to set this
// properly.
// WARNING: This is an experimental interface that is subject to change.
const char* custom_name;
// The version of the op.
// Note: It is the responsibility of the registration binder to set this
// properly.
int version;
} TfLiteRegistration;
// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the
// values should be 1, 2, 4, 8, ...etc.
typedef enum TfLiteDelegateFlags {
kTfLiteDelegateFlagsNone = 0,
// The flag is set if the delegate can handle dynamic sized tensors.
// For example, the output shape of a `Resize` op with non-constant shape
// can only be inferred when the op is invoked.
// In this case, the Delegate is responsible for calling
// `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling
// `ResizeTensor` when invoking the op.
//
// If the delegate isn't capable to handle dynamic tensors, this flag need
// to be set to false.
kTfLiteDelegateFlagsAllowDynamicTensors = 1,
// This flag can be used by delegates (that allow dynamic tensors) to ensure
// applicable tensor shapes are automatically propagated in the case of tensor
// resizing.
// This means that non-dynamic (allocation_type != kTfLiteDynamic) I/O tensors
// of a delegate kernel will have correct shapes before its Prepare() method
// is called. The runtime leverages TFLite builtin ops in the original
// execution plan to propagate shapes.
//
// A few points to note:
// 1. This requires kTfLiteDelegateFlagsAllowDynamicTensors. If that flag is
// false, this one is redundant since the delegate kernels are re-initialized
// every time tensors are resized.
// 2. Enabling this flag adds some overhead to AllocateTensors(), since extra
// work is required to prepare the original execution plan.
// 3. This flag requires that the original execution plan only have ops with
// valid registrations (and not 'dummy' custom ops like with Flex).
// WARNING: This feature is experimental and subject to change.
kTfLiteDelegateFlagsRequirePropagatedShapes = 2
} TfLiteDelegateFlags;
// WARNING: This is an experimental interface that is subject to change.
typedef struct TfLiteDelegate {
// Data that delegate needs to identify itself. This data is owned by the
// delegate. The delegate is owned in the user code, so the delegate is
// responsible for doing this when it is destroyed.
void* data_;
// Invoked by ModifyGraphWithDelegate. This prepare is called, giving the
// delegate a view of the current graph through TfLiteContext*. It typically
// will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels()
// to ask the TensorFlow lite runtime to create macro-nodes to represent
// delegated subgraphs of the original graph.
TfLiteStatus (*Prepare)(TfLiteContext* context,
struct TfLiteDelegate* delegate);
// Copy the data from delegate buffer handle into raw memory of the given
// 'tensor'. Note that the delegate is allowed to allocate the raw bytes as
// long as it follows the rules for kTfLiteDynamic tensors, in which case this
// cannot be null.
TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle buffer_handle,
TfLiteTensor* tensor);
// Copy the data from raw memory of the given 'tensor' to delegate buffer
// handle. This can be null if the delegate doesn't use its own buffer.
TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle buffer_handle,
TfLiteTensor* tensor);
// Free the Delegate Buffer Handle. Note: This only frees the handle, but
// this doesn't release the underlying resource (e.g. textures). The
// resources are either owned by application layer or the delegate.
// This can be null if the delegate doesn't use its own buffer.
void (*FreeBufferHandle)(TfLiteContext* context,
struct TfLiteDelegate* delegate,
TfLiteBufferHandle* handle);
// Bitmask flags. See the comments in `TfLiteDelegateFlags`.
int64_t flags;
} TfLiteDelegate;
// Build a 'null' delegate, with all the fields properly set to their default
// values.
TfLiteDelegate TfLiteDelegateCreate();
#ifdef __cplusplus
} // extern "C"
#endif // __cplusplus
#endif // TENSORFLOW_LITE_C_COMMON_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_
#define TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_
#include <cstdarg>
namespace tflite {
/// A functor that reports error to supporting system. Invoked similar to
/// printf.
///
/// Usage:
/// ErrorReporter foo;
/// foo.Report("test %d", 5);
/// or
/// va_list args;
/// foo.Report("test %d", args); // where args is va_list
///
/// Subclass ErrorReporter to provide another reporting destination.
/// For example, if you have a GUI program, you might redirect to a buffer
/// that drives a GUI error log box.
class ErrorReporter {
public:
virtual ~ErrorReporter() {}
virtual int Report(const char* format, va_list args) = 0;
int Report(const char* format, ...);
int ReportError(void*, const char* format, ...);
};
} // namespace tflite
// You should not make bare calls to the error reporter, instead use the
// TF_LITE_REPORT_ERROR macro, since this allows message strings to be
// stripped when the binary size has to be optimized. If you are looking to
// reduce binary size, define TF_LITE_STRIP_ERROR_STRINGS when compiling and
// every call will be stubbed out, taking no memory.
#ifndef TF_LITE_STRIP_ERROR_STRINGS
#define TF_LITE_REPORT_ERROR(reporter, ...) \
do { \
static_cast<tflite::ErrorReporter*>(reporter)->Report(__VA_ARGS__); \
} while (false)
#else // TF_LITE_STRIP_ERROR_STRINGS
#define TF_LITE_REPORT_ERROR(reporter, ...)
#endif // TF_LITE_STRIP_ERROR_STRINGS
#endif // TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_
#define TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_
// These functions transform codes and data structures that are defined in the
// flatbuffer serialization format into in-memory values that are used by the
// runtime API and interpreter.
#include <cstddef>
#include <new>
#include <type_traits>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
// Interface class for builtin data allocations.
class BuiltinDataAllocator {
public:
virtual void* Allocate(size_t size, size_t alignment_hint) = 0;
virtual void Deallocate(void* data) = 0;
// Allocate a structure, but make sure it is a POD structure that doesn't
// require constructors to run. The reason we do this, is that Interpreter's C
// extension part will take ownership so destructors will not be run during
// deallocation.
template <typename T>
T* AllocatePOD() {
// TODO(b/154346074): Change this to is_trivially_destructible when all
// platform targets support that properly.
static_assert(std::is_pod<T>::value, "Builtin data structure must be POD.");
void* allocated_memory = this->Allocate(sizeof(T), alignof(T));
return new (allocated_memory) T;
}
virtual ~BuiltinDataAllocator() {}
};
// Parse the appropriate data out of the op.
//
// This handles builtin data explicitly as there are flatbuffer schemas.
// If it returns kTfLiteOk, it passes the data out with `builtin_data`. The
// calling function has to pass in an allocator object, and this allocator
// will be called to reserve space for the output data. If the calling
// function's allocator reserves memory on the heap, then it's the calling
// function's responsibility to free it.
// If it returns kTfLiteError, `builtin_data` will be `nullptr`.
TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
// Converts the tensor data type used in the flat buffer to the representation
// used by the runtime.
TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type,
ErrorReporter* error_reporter);
TfLiteStatus ParseAbs(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseAdd(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseArgMax(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseArgMin(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseCeil(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseConcatenation(const Operator* op,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseConv2D(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseCos(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseDepthwiseConv2D(const Operator* op,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseDequantize(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseEqual(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseFloor(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseFullyConnected(const Operator* op,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseGreater(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseGreaterEqual(const Operator* op,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseHardSwish(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseL2Normalization(const Operator* op,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseLess(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseLessEqual(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseLog(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseLogicalAnd(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseLogicalNot(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseLogicalOr(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseLogistic(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseMaximum(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseMinimum(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseMul(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseNeg(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseNotEqual(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParsePack(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParsePad(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParsePadV2(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParsePool(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParsePrelu(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseQuantize(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseReducer(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseRelu(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseRelu6(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseReshape(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseResizeNearestNeighbor(const Operator* op,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseRound(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseRsqrt(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseSin(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseSoftmax(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseSplit(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseSqrt(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseSquare(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseStridedSlice(const Operator* op,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
TfLiteStatus ParseSub(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseSvdf(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseTanh(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
TfLiteStatus ParseUnpack(const Operator* op, ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator, void** builtin_data);
} // namespace tflite
#endif // TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_CORE_API_OP_RESOLVER_H_
#define TENSORFLOW_LITE_CORE_API_OP_RESOLVER_H_
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
/// Abstract interface that returns TfLiteRegistrations given op codes or custom
/// op names. This is the mechanism that ops being referenced in the flatbuffer
/// model are mapped to executable function pointers (TfLiteRegistrations).
class OpResolver {
public:
/// Finds the op registration for a builtin operator by enum code.
virtual const TfLiteRegistration* FindOp(tflite::BuiltinOperator op,
int version) const = 0;
/// Finds the op registration of a custom operator by op name.
virtual const TfLiteRegistration* FindOp(const char* op,
int version) const = 0;
virtual ~OpResolver() {}
};
// Handles the logic for converting between an OperatorCode structure extracted
// from a flatbuffer and information about a registered operator
// implementation.
TfLiteStatus GetRegistrationFromOpCode(const OperatorCode* opcode,
const OpResolver& op_resolver,
ErrorReporter* error_reporter,
const TfLiteRegistration** registration);
} // namespace tflite
#endif // TENSORFLOW_LITE_CORE_API_OP_RESOLVER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_CORE_API_PROFILER_H_
#define TENSORFLOW_LITE_CORE_API_PROFILER_H_
#include <cstdint>
namespace tflite {
// A simple utility for enabling profiled event tracing in TensorFlow Lite.
class Profiler {
public:
// As certain Profiler instance might be only interested in certain event
// types, we define each event type value to allow a Profiler to use
// bitmasking bitwise operations to determine whether an event should be
// recorded or not.
enum class EventType {
// Default event type, the metadata field has no special significance.
DEFAULT = 1,
// The event is an operator invocation and the event_metadata field is the
// index of operator node.
OPERATOR_INVOKE_EVENT = 2,
// The event is an invocation for an internal operator of a TFLite delegate.
// The event_metadata field is the index of operator node that's specific to
// the delegate.
DELEGATE_OPERATOR_INVOKE_EVENT = 4,
// The event is a recording of runtime instrumentation such as the overall
// TFLite runtime status, the TFLite delegate status (if a delegate
// is applied), and the overall model inference latency etc.
// Note, the delegate status and overall status are stored as separate
// event_metadata fields. In particular, the delegate status is encoded
// as DelegateStatus::full_status().
GENERAL_RUNTIME_INSTRUMENTATION_EVENT = 8,
};
virtual ~Profiler() {}
// Signals the beginning of an event and returns a handle to the profile
// event. The `event_metadata1` and `event_metadata2` have different
// interpretations based on the actual Profiler instance and the `event_type`.
// For example, as for the 'SubgraphAwareProfiler' defined in
// lite/core/subgraph.h, when the event_type is OPERATOR_INVOKE_EVENT,
// `event_metadata1` represents the index of a TFLite node, and
// `event_metadata2` represents the index of the subgraph that this event
// comes from.
virtual uint32_t BeginEvent(const char* tag, EventType event_type,
int64_t event_metadata1,
int64_t event_metadata2) = 0;
// Similar w/ the above, but `event_metadata2` defaults to 0.
uint32_t BeginEvent(const char* tag, EventType event_type,
int64_t event_metadata) {
return BeginEvent(tag, event_type, event_metadata, /*event_metadata2*/ 0);
}
// Signals an end to the specified profile event with 'event_metadata's, This
// is useful when 'event_metadata's are not available when the event begins
// or when one wants to overwrite the 'event_metadata's set at the beginning.
virtual void EndEvent(uint32_t event_handle, int64_t event_metadata1,
int64_t event_metadata2) {}
// Signals an end to the specified profile event.
virtual void EndEvent(uint32_t event_handle) = 0;
// Appends an event of type 'event_type' with 'tag' and 'event_metadata'
// which started at 'start' and ended at 'end'
// Note:
// In cases were ProfileSimmarizer and tensorflow::StatsCalculator are used
// they assume the value is in "usec", if in any case subclasses
// didn't put usec, then the values are not meaningful.
// TODO karimnosseir: Revisit and make the function more clear.
void AddEvent(const char* tag, EventType event_type, uint64_t start,
uint64_t end, int64_t event_metadata) {
AddEvent(tag, event_type, start, end, event_metadata,
/*event_metadata2*/ 0);
}
virtual void AddEvent(const char* tag, EventType event_type, uint64_t start,
uint64_t end, int64_t event_metadata1,
int64_t event_metadata2) {}
protected:
friend class ScopedProfile;
};
// Adds a profile event to `profiler` that begins with the construction
// of the object and ends when the object goes out of scope.
// The lifetime of tag should be at least the lifetime of `profiler`.
// `profiler` may be null, in which case nothing is profiled.
class ScopedProfile {
public:
ScopedProfile(Profiler* profiler, const char* tag,
Profiler::EventType event_type = Profiler::EventType::DEFAULT,
int64_t event_metadata = 0)
: profiler_(profiler), event_handle_(0) {
if (profiler) {
event_handle_ = profiler_->BeginEvent(tag, event_type, event_metadata);
}
}
~ScopedProfile() {
if (profiler_) {
profiler_->EndEvent(event_handle_);
}
}
protected:
Profiler* profiler_;
uint32_t event_handle_;
};
class ScopedOperatorProfile : public ScopedProfile {
public:
ScopedOperatorProfile(Profiler* profiler, const char* tag, int node_index)
: ScopedProfile(profiler, tag, Profiler::EventType::OPERATOR_INVOKE_EVENT,
static_cast<uint32_t>(node_index)) {}
};
class ScopedDelegateOperatorProfile : public ScopedProfile {
public:
ScopedDelegateOperatorProfile(Profiler* profiler, const char* tag,
int node_index)
: ScopedProfile(profiler, tag,
Profiler::EventType::DELEGATE_OPERATOR_INVOKE_EVENT,
static_cast<uint32_t>(node_index)) {}
};
class ScopedRuntimeInstrumentationProfile : public ScopedProfile {
public:
ScopedRuntimeInstrumentationProfile(Profiler* profiler, const char* tag)
: ScopedProfile(
profiler, tag,
Profiler::EventType::GENERAL_RUNTIME_INSTRUMENTATION_EVENT, -1) {}
void set_runtime_status(int64_t delegate_status, int64_t interpreter_status) {
if (profiler_) {
delegate_status_ = delegate_status;
interpreter_status_ = interpreter_status;
}
}
~ScopedRuntimeInstrumentationProfile() {
if (profiler_) {
profiler_->EndEvent(event_handle_, delegate_status_, interpreter_status_);
}
}
private:
int64_t delegate_status_;
int64_t interpreter_status_;
};
} // namespace tflite
#define TFLITE_VARNAME_UNIQ_IMPL(name, ctr) name##ctr
#define TFLITE_VARNAME_UNIQ(name, ctr) TFLITE_VARNAME_UNIQ_IMPL(name, ctr)
#define TFLITE_SCOPED_TAGGED_DEFAULT_PROFILE(profiler, tag) \
tflite::ScopedProfile TFLITE_VARNAME_UNIQ(_profile_, __COUNTER__)( \
(profiler), (tag))
#define TFLITE_SCOPED_TAGGED_OPERATOR_PROFILE(profiler, tag, node_index) \
tflite::ScopedOperatorProfile TFLITE_VARNAME_UNIQ(_profile_, __COUNTER__)( \
(profiler), (tag), (node_index))
#define TFLITE_SCOPED_DELEGATE_OPERATOR_PROFILE(profiler, tag, node_index) \
tflite::ScopedDelegateOperatorProfile TFLITE_VARNAME_UNIQ( \
_profile_, __COUNTER__)((profiler), (tag), (node_index))
#define TFLITE_ADD_RUNTIME_INSTRUMENTATION_EVENT( \
profiler, tag, delegate_status, interpreter_status) \
do { \
if (!profiler) { \
const auto handle = profiler->BeginEvent( \
tag, Profiler::EventType::GENERAL_RUNTIME_INSTRUMENTATION_EVENT, \
delegate_status, interpreter_status); \
profiler->EndEvent(handle); \
} \
} while (false);
#endif // TENSORFLOW_LITE_CORE_API_PROFILER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_
#define TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_
#include "tensorflow/lite/c/common.h"
namespace tflite {
// Resets a variable tensor to the default value.
TfLiteStatus ResetVariableTensor(TfLiteTensor* tensor);
} // namespace tflite
#endif // TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_
#ifndef ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK
#ifdef GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK
#define ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK
#endif
#endif
#include <functional>
#include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/optimized/neon_check.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
constexpr int kReverseShift = -1;
inline void GetActivationMinMax(FusedActivationFunctionType ac,
float* output_activation_min,
float* output_activation_max) {
switch (ac) {
case FusedActivationFunctionType::kNone:
*output_activation_min = std::numeric_limits<float>::lowest();
*output_activation_max = std::numeric_limits<float>::max();
break;
case FusedActivationFunctionType::kRelu:
*output_activation_min = 0.f;
*output_activation_max = std::numeric_limits<float>::max();
break;
case FusedActivationFunctionType::kRelu1:
*output_activation_min = -1.f;
*output_activation_max = 1.f;
break;
case FusedActivationFunctionType::kRelu6:
*output_activation_min = 0.f;
*output_activation_max = 6.f;
break;
}
}
template <typename T>
inline T ActivationFunctionWithMinMax(T x, T output_activation_min,
T output_activation_max) {
using std::max;
using std::min;
return min(max(x, output_activation_min), output_activation_max);
}
// Legacy function, left for compatibility only.
template <FusedActivationFunctionType Ac>
float ActivationFunction(float x) {
float output_activation_min, output_activation_max;
GetActivationMinMax(Ac, &output_activation_min, &output_activation_max);
return ActivationFunctionWithMinMax(x, output_activation_min,
output_activation_max);
}
inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size,
const float* bias_data, int array_size,
float* array_data) {
// Note: see b/132215220: in May 2019 we thought it would be OK to replace
// this with the Eigen one-liner:
// return (array.colwise() + bias).cwiseMin(clamp_max).cwiseMin(clamp_max).
// This turned out to severely regress performance: +4ms (i.e. 8%) on
// MobileNet v2 / 1.0 / 224. So we keep custom NEON code for now.
TFLITE_DCHECK_EQ((array_size % bias_size), 0);
#ifdef USE_NEON
float* array_ptr = array_data;
float* array_end_ptr = array_ptr + array_size;
const auto clamp_min_vec = vdupq_n_f32(clamp_min);
const auto clamp_max_vec = vdupq_n_f32(clamp_max);
for (; array_ptr != array_end_ptr; array_ptr += bias_size) {
int i = 0;
for (; i <= bias_size - 16; i += 16) {
auto b0 = vld1q_f32(bias_data + i);
auto b1 = vld1q_f32(bias_data + i + 4);
auto b2 = vld1q_f32(bias_data + i + 8);
auto b3 = vld1q_f32(bias_data + i + 12);
auto a0 = vld1q_f32(array_ptr + i);
auto a1 = vld1q_f32(array_ptr + i + 4);
auto a2 = vld1q_f32(array_ptr + i + 8);
auto a3 = vld1q_f32(array_ptr + i + 12);
auto x0 = vaddq_f32(a0, b0);
auto x1 = vaddq_f32(a1, b1);
auto x2 = vaddq_f32(a2, b2);
auto x3 = vaddq_f32(a3, b3);
x0 = vmaxq_f32(clamp_min_vec, x0);
x1 = vmaxq_f32(clamp_min_vec, x1);
x2 = vmaxq_f32(clamp_min_vec, x2);
x3 = vmaxq_f32(clamp_min_vec, x3);
x0 = vminq_f32(clamp_max_vec, x0);
x1 = vminq_f32(clamp_max_vec, x1);
x2 = vminq_f32(clamp_max_vec, x2);
x3 = vminq_f32(clamp_max_vec, x3);
vst1q_f32(array_ptr + i, x0);
vst1q_f32(array_ptr + i + 4, x1);
vst1q_f32(array_ptr + i + 8, x2);
vst1q_f32(array_ptr + i + 12, x3);
}
for (; i <= bias_size - 4; i += 4) {
auto b = vld1q_f32(bias_data + i);
auto a = vld1q_f32(array_ptr + i);
auto x = vaddq_f32(a, b);
x = vmaxq_f32(clamp_min_vec, x);
x = vminq_f32(clamp_max_vec, x);
vst1q_f32(array_ptr + i, x);
}
for (; i < bias_size; i++) {
array_ptr[i] = ActivationFunctionWithMinMax(array_ptr[i] + bias_data[i],
clamp_min, clamp_max);
}
}
#else // not NEON
for (int array_offset = 0; array_offset < array_size;
array_offset += bias_size) {
for (int i = 0; i < bias_size; i++) {
array_data[array_offset + i] = ActivationFunctionWithMinMax(
array_data[array_offset + i] + bias_data[i], clamp_min, clamp_max);
}
}
#endif
}
inline int32_t MultiplyByQuantizedMultiplierSmallerThanOneExp(
int32_t x, int32_t quantized_multiplier, int left_shift) {
using gemmlowp::RoundingDivideByPOT;
using gemmlowp::SaturatingRoundingDoublingHighMul;
return RoundingDivideByPOT(
SaturatingRoundingDoublingHighMul(x, quantized_multiplier), -left_shift);
}
inline int32_t MultiplyByQuantizedMultiplierGreaterThanOne(
int32_t x, int32_t quantized_multiplier, int left_shift) {
using gemmlowp::SaturatingRoundingDoublingHighMul;
return SaturatingRoundingDoublingHighMul(x * (1 << left_shift),
quantized_multiplier);
}
inline int32_t MultiplyByQuantizedMultiplier(int32_t x,
int32_t quantized_multiplier,
int shift) {
using gemmlowp::RoundingDivideByPOT;
using gemmlowp::SaturatingRoundingDoublingHighMul;
int left_shift = shift > 0 ? shift : 0;
int right_shift = shift > 0 ? 0 : -shift;
return RoundingDivideByPOT(SaturatingRoundingDoublingHighMul(
x * (1 << left_shift), quantized_multiplier),
right_shift);
}
inline int32_t MultiplyByQuantizedMultiplier(int64_t x,
int32_t quantized_multiplier,
int shift) {
// Inputs:
// - quantized_multiplier has fixed point at bit 31
// - shift is -31 to +7 (negative for right shift)
//
// Assumptions: The following input ranges are assumed
// - quantize_scale>=0 (the usual range is (1<<30) to (1>>31)-1)
// - scaling is chosen so final scaled result fits in int32_t
// - input x is in the range -(1<<47) <= x < (1<<47)
assert(quantized_multiplier >= 0);
assert(shift >= -31 && shift < 8);
int32_t reduced_multiplier = (quantized_multiplier + (1 << 15)) >> 16;
int total_shift = 15 - shift;
x = (x * (int64_t)reduced_multiplier) + ((int64_t)1 << (total_shift - 1));
int32_t result = x >> total_shift;
return result;
}
template <typename T>
int CountLeadingZeros(T integer_input) {
static_assert(std::is_unsigned<T>::value,
"Only unsigned integer types handled.");
#if defined(__GNUC__)
return integer_input ? __builtin_clz(integer_input)
: std::numeric_limits<T>::digits;
#else
if (integer_input == 0) {
return std::numeric_limits<T>::digits;
}
const T one_in_leading_positive = static_cast<T>(1)
<< (std::numeric_limits<T>::digits - 1);
int leading_zeros = 0;
while (integer_input < one_in_leading_positive) {
integer_input <<= 1;
++leading_zeros;
}
return leading_zeros;
#endif
}
template <typename T>
inline int CountLeadingSignBits(T integer_input) {
static_assert(std::is_signed<T>::value, "Only signed integer types handled.");
#if defined(__GNUC__) && !defined(__clang__)
return integer_input ? __builtin_clrsb(integer_input)
: std::numeric_limits<T>::digits;
#else
using U = typename std::make_unsigned<T>::type;
return integer_input >= 0
? CountLeadingZeros(static_cast<U>(integer_input)) - 1
: integer_input != std::numeric_limits<T>::min()
? CountLeadingZeros(2 * static_cast<U>(-integer_input) - 1)
: 0;
#endif
}
// Use "count leading zeros" helper functions to do a fast Floor(log_2(x)).
template <typename Integer>
inline Integer FloorLog2(Integer n) {
static_assert(std::is_integral<Integer>::value, "");
static_assert(std::is_signed<Integer>::value, "");
static_assert(sizeof(Integer) == 4 || sizeof(Integer) == 8, "");
TFLITE_CHECK_GT(n, 0);
if (sizeof(Integer) == 4) {
return 30 - CountLeadingSignBits(n);
} else {
return 62 - CountLeadingSignBits(n);
}
}
// generate INT16 LUT for function(), e.g., table exp(x) and 1/(1+x) used in
// softmax
inline void gen_lut(const std::function<double(double)>& func, double min,
double max, int16_t* table, const int num) {
// size of table should equal to num + 1
// last element only for slope calculation
double step = (max - min) / (num - 1);
double half_step = step / 2.0;
for (int i = 0; i < num - 1; i++) {
double sample_val = TfLiteRound(func(min + i * step) * 32768.0);
double midpoint_interp_val =
TfLiteRound((func(min + (i + 1) * step) * 32768.0 +
TfLiteRound(func(min + i * step) * 32768.0)) /
2.0);
double midpoint_val =
TfLiteRound(func(min + i * step + half_step) * 32768.0);
double midpoint_err = midpoint_interp_val - midpoint_val;
double bias = TfLiteRound(midpoint_err / 2.0);
table[i] = std::min(std::max(sample_val - bias, -32768.0), 32767.0);
}
table[num - 1] =
std::min(std::max(TfLiteRound(func(max) * 32768.0), -32768.0), 32767.0);
}
// int16_t func table lookup, e.g., lookup exp() and 1/(1+x) used in softmax
inline int16_t generic_int16_table_lookup(int16_t value, const int16_t* lut) {
// 512 base value, lut[513] only for calculate slope
uint16_t index = static_cast<uint16_t>(256 + (value >> 7));
assert(index < 512 && "LUT index out of range.");
int16_t offset = value & 0x7f;
// base and slope are Q0.15
int16_t base = lut[index];
int16_t slope = lut[index + 1] - lut[index];
// Q0.15 * Q0.7 = Q0.22
// Round and convert from Q0.22 to Q0.15
int32_t delta = (static_cast<int32_t>(slope) * offset + 64) >> 7;
// Q0.15 + Q0.15
return base + delta;
}
// Table of sigmoid(i/24) at 0.16 format - 256 elements.
// We use combined sigmoid and tanh look-up table, since
// tanh(x) = 2*sigmoid(2*x) -1.
// Both functions are symmetric, so the LUT table is only needed
// for the absolute value of the input.
static const uint16_t sigmoid_table_uint16[256] = {
32768, 33451, 34133, 34813, 35493, 36169, 36843, 37513, 38180, 38841, 39498,
40149, 40794, 41432, 42064, 42688, 43304, 43912, 44511, 45102, 45683, 46255,
46817, 47369, 47911, 48443, 48964, 49475, 49975, 50464, 50942, 51409, 51865,
52311, 52745, 53169, 53581, 53983, 54374, 54755, 55125, 55485, 55834, 56174,
56503, 56823, 57133, 57433, 57724, 58007, 58280, 58544, 58800, 59048, 59288,
59519, 59743, 59959, 60168, 60370, 60565, 60753, 60935, 61110, 61279, 61441,
61599, 61750, 61896, 62036, 62172, 62302, 62428, 62549, 62666, 62778, 62886,
62990, 63090, 63186, 63279, 63368, 63454, 63536, 63615, 63691, 63765, 63835,
63903, 63968, 64030, 64090, 64148, 64204, 64257, 64308, 64357, 64405, 64450,
64494, 64536, 64576, 64614, 64652, 64687, 64721, 64754, 64786, 64816, 64845,
64873, 64900, 64926, 64950, 64974, 64997, 65019, 65039, 65060, 65079, 65097,
65115, 65132, 65149, 65164, 65179, 65194, 65208, 65221, 65234, 65246, 65258,
65269, 65280, 65291, 65301, 65310, 65319, 65328, 65337, 65345, 65352, 65360,
65367, 65374, 65381, 65387, 65393, 65399, 65404, 65410, 65415, 65420, 65425,
65429, 65433, 65438, 65442, 65445, 65449, 65453, 65456, 65459, 65462, 65465,
65468, 65471, 65474, 65476, 65479, 65481, 65483, 65485, 65488, 65489, 65491,
65493, 65495, 65497, 65498, 65500, 65501, 65503, 65504, 65505, 65507, 65508,
65509, 65510, 65511, 65512, 65513, 65514, 65515, 65516, 65517, 65517, 65518,
65519, 65520, 65520, 65521, 65522, 65522, 65523, 65523, 65524, 65524, 65525,
65525, 65526, 65526, 65526, 65527, 65527, 65528, 65528, 65528, 65529, 65529,
65529, 65529, 65530, 65530, 65530, 65530, 65531, 65531, 65531, 65531, 65531,
65532, 65532, 65532, 65532, 65532, 65532, 65533, 65533, 65533, 65533, 65533,
65533, 65533, 65533, 65534, 65534, 65534, 65534, 65534, 65534, 65534, 65534,
65534, 65534, 65535};
// TODO(b/77858996): Add these to gemmlowp.
template <typename IntegerType>
IntegerType SaturatingAddNonGemmlowp(IntegerType a, IntegerType b) {
static_assert(std::is_same<IntegerType, void>::value, "unimplemented");
return a;
}
template <>
inline std::int32_t SaturatingAddNonGemmlowp(std::int32_t a, std::int32_t b) {
std::int64_t a64 = a;
std::int64_t b64 = b;
std::int64_t sum = a64 + b64;
return static_cast<std::int32_t>(std::min(
static_cast<std::int64_t>(std::numeric_limits<std::int32_t>::max()),
std::max(
static_cast<std::int64_t>(std::numeric_limits<std::int32_t>::min()),
sum)));
}
template <typename tRawType, int tIntegerBits>
gemmlowp::FixedPoint<tRawType, tIntegerBits> SaturatingAddNonGemmlowp(
gemmlowp::FixedPoint<tRawType, tIntegerBits> a,
gemmlowp::FixedPoint<tRawType, tIntegerBits> b) {
return gemmlowp::FixedPoint<tRawType, tIntegerBits>::FromRaw(
SaturatingAddNonGemmlowp(a.raw(), b.raw()));
}
template <typename IntegerType>
IntegerType SaturatingSub(IntegerType a, IntegerType b) {
static_assert(std::is_same<IntegerType, void>::value, "unimplemented");
return a;
}
template <>
inline std::int16_t SaturatingSub(std::int16_t a, std::int16_t b) {
std::int32_t a32 = a;
std::int32_t b32 = b;
std::int32_t diff = a32 - b32;
return static_cast<std::int16_t>(
std::min(static_cast<int32_t>(32767),
std::max(static_cast<int32_t>(-32768), diff)));
}
template <>
inline std::int32_t SaturatingSub(std::int32_t a, std::int32_t b) {
std::int64_t a64 = a;
std::int64_t b64 = b;
std::int64_t diff = a64 - b64;
return static_cast<std::int32_t>(std::min(
static_cast<std::int64_t>(std::numeric_limits<std::int32_t>::max()),
std::max(
static_cast<std::int64_t>(std::numeric_limits<std::int32_t>::min()),
diff)));
}
template <typename tRawType, int tIntegerBits>
gemmlowp::FixedPoint<tRawType, tIntegerBits> SaturatingSub(
gemmlowp::FixedPoint<tRawType, tIntegerBits> a,
gemmlowp::FixedPoint<tRawType, tIntegerBits> b) {
return gemmlowp::FixedPoint<tRawType, tIntegerBits>::FromRaw(
SaturatingSub(a.raw(), b.raw()));
}
// End section to be moved to gemmlowp.
template <typename IntegerType>
IntegerType SaturatingRoundingMultiplyByPOTParam(IntegerType x, int exponent) {
if (exponent == 0) {
return x;
}
using ScalarIntegerType =
typename gemmlowp::FixedPointRawTypeTraits<IntegerType>::ScalarRawType;
const IntegerType min =
gemmlowp::Dup<IntegerType>(std::numeric_limits<ScalarIntegerType>::min());
const IntegerType max =
gemmlowp::Dup<IntegerType>(std::numeric_limits<ScalarIntegerType>::max());
const int ScalarIntegerTypeBits = 8 * sizeof(ScalarIntegerType);
const std::int32_t threshold =
((1 << (ScalarIntegerTypeBits - 1 - exponent)) - 1);
const IntegerType positive_mask =
gemmlowp::MaskIfGreaterThan(x, gemmlowp::Dup<IntegerType>(threshold));
const IntegerType negative_mask =
gemmlowp::MaskIfLessThan(x, gemmlowp::Dup<IntegerType>(-threshold));
IntegerType result = gemmlowp::ShiftLeft(x, exponent);
result = gemmlowp::SelectUsingMask(positive_mask, max, result);
result = gemmlowp::SelectUsingMask(negative_mask, min, result);
return result;
}
// If we want to leave IntegerBits fixed, then multiplication
// by a power of two has to be saturating/rounding, not exact anymore.
template <typename tRawType, int tIntegerBits>
gemmlowp::FixedPoint<tRawType, tIntegerBits>
SaturatingRoundingMultiplyByPOTParam(
gemmlowp::FixedPoint<tRawType, tIntegerBits> a, int exponent) {
return gemmlowp::FixedPoint<tRawType, tIntegerBits>::FromRaw(
SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent));
}
// Convert int32_t multiplier to int16_t with rounding.
inline void DownScaleInt32ToInt16Multiplier(int32_t multiplier_int32_t,
int16_t* multiplier_int16_t) {
TFLITE_DCHECK_GE(multiplier_int32_t, 0);
static constexpr int32_t kRoundingOffset = 1 << 15;
if (multiplier_int32_t >=
std::numeric_limits<int32_t>::max() - kRoundingOffset) {
*multiplier_int16_t = std::numeric_limits<int16_t>::max();
return;
}
const int32_t result = (multiplier_int32_t + kRoundingOffset) >> 16;
TFLITE_DCHECK_LE(result << 16, multiplier_int32_t + kRoundingOffset);
TFLITE_DCHECK_GT(result << 16, multiplier_int32_t - kRoundingOffset);
*multiplier_int16_t = result;
TFLITE_DCHECK_EQ(*multiplier_int16_t, result);
}
// Minimum output bits to accommodate log of maximum input range. It actually
// does not matter if one considers, say, [-64,64] or [-64,64).
//
// For example, run this through Octave:
// [0:127; ...
// ceil(log(abs( log(2.^(0:127))+1 ))/log(2)); ...
// ceil(log(abs( log(2.^(0:127))+1 ))/log(2))]
constexpr int min_log_x_output_bits(int input_bits) {
return input_bits > 90 ? 7
: input_bits > 44 ? 6
: input_bits > 21 ? 5
: input_bits > 10 ? 4
: input_bits > 4 ? 3
: input_bits > 1 ? 2
: 1;
}
// Although currently the name of this function says that it cannot handle
// values less than 1, in practice it can handle as low as 1/x_max, where
// x_max is the largest representable input. In other words, the output range
// is symmetric.
template <int OutputIntegerBits, int InputIntegerBits>
inline gemmlowp::FixedPoint<int32_t, OutputIntegerBits>
log_x_for_x_greater_than_or_equal_to_1_impl(
gemmlowp::FixedPoint<int32_t, InputIntegerBits> input_val) {
// assert(__builtin_clz(0u) >= std::numeric_limits<uint32_t>::digits - 1);
// assert(__builtin_clz(0u) <= std::numeric_limits<uint32_t>::digits);
using FixedPoint0 = gemmlowp::FixedPoint<int32_t, 0>;
// The reason for accumulating the result with an extra bit of headroom is
// that z_pow_2_adj * log_2 might be saturated, and adding num_scaled *
// recip_denom will otherwise introduce an error.
static constexpr int kAccumIntegerBits = OutputIntegerBits + 1;
using FixedPointAccum = gemmlowp::FixedPoint<int32_t, kAccumIntegerBits>;
const FixedPoint0 log_2 = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(
FixedPoint0, 1488522236, std::log(2.0));
const FixedPoint0 sqrt_sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(
FixedPoint0, 1805811301, std::sqrt(std::sqrt(0.5)));
const FixedPoint0 sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(
FixedPoint0, 1518500250, std::sqrt(0.5));
const FixedPoint0 one_quarter =
GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(FixedPoint0, 536870912, 1.0 / 4.0);
const FixedPoint0 alpha_n = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(
FixedPoint0, 117049297, 11.0 / 240.0 * std::sqrt(std::sqrt(2.0)));
const FixedPoint0 alpha_d = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(
FixedPoint0, 127690142, 1.0 / 20.0 * std::sqrt(std::sqrt(2.0)));
const FixedPoint0 alpha_i = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(
FixedPoint0, 1057819769,
2.0 / std::sqrt(std::sqrt(2.0)) - std::sqrt(std::sqrt(2.0)));
const FixedPoint0 alpha_f = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(
FixedPoint0, 638450708, 1.0 / 4.0 * std::sqrt(std::sqrt(2.0)));
const FixedPointAccum shifted_quarter =
gemmlowp::Rescale<kAccumIntegerBits>(one_quarter);
// Reinterpret the input value as Q0.31, because we will figure out the
// required shift "ourselves" instead of using, say, Rescale.
FixedPoint0 z_a = FixedPoint0::FromRaw(input_val.raw());
// z_a_pow_2 = input_integer_bits - z_a_headroom;
int z_a_headroom_plus_1 = CountLeadingZeros(static_cast<uint32_t>(z_a.raw()));
FixedPoint0 r_a_tmp =
SaturatingRoundingMultiplyByPOTParam(z_a, (z_a_headroom_plus_1 - 1));
const int32_t r_a_raw =
SaturatingRoundingMultiplyByPOTParam((r_a_tmp * sqrt_half).raw(), 1);
// z_pow_2_adj = max(z_pow_2_a - 0.75, z_pow_2_b - 0.25);
// z_pow_2_adj = max(InputIntegerBits - z_a_headroom_plus_1 + 0.25,
// InputIntegerBits - z_b_headroom - 0.25);
const FixedPointAccum z_a_pow_2_adj = SaturatingAddNonGemmlowp(
FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam(
InputIntegerBits - z_a_headroom_plus_1, 31 - kAccumIntegerBits)),
shifted_quarter);
// z_b is treated like z_a, but premultiplying by sqrt(0.5).
FixedPoint0 z_b = z_a * sqrt_half;
int z_b_headroom = CountLeadingZeros(static_cast<uint32_t>(z_b.raw())) - 1;
const int32_t r_b_raw =
SaturatingRoundingMultiplyByPOTParam(z_a.raw(), z_b_headroom);
const FixedPointAccum z_b_pow_2_adj = SaturatingSub(
FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam(
InputIntegerBits - z_b_headroom, 31 - kAccumIntegerBits)),
shifted_quarter);
const FixedPoint0 r = FixedPoint0::FromRaw(std::min(r_a_raw, r_b_raw));
const FixedPointAccum z_pow_2_adj = FixedPointAccum::FromRaw(
std::max(z_a_pow_2_adj.raw(), z_b_pow_2_adj.raw()));
const FixedPoint0 p = gemmlowp::RoundingHalfSum(r, sqrt_sqrt_half);
FixedPoint0 q = r - sqrt_sqrt_half;
q = q + q;
const FixedPoint0 common_sq = q * q;
const FixedPoint0 num = q * r + q * common_sq * alpha_n;
const FixedPoint0 denom_minus_one_0 =
p * (alpha_i + q + alpha_d * common_sq) + alpha_f * q;
const FixedPoint0 recip_denom =
one_over_one_plus_x_for_x_in_0_1(denom_minus_one_0);
const FixedPointAccum num_scaled = gemmlowp::Rescale<kAccumIntegerBits>(num);
return gemmlowp::Rescale<OutputIntegerBits>(z_pow_2_adj * log_2 +
num_scaled * recip_denom);
}
template <int OutputIntegerBits, int InputIntegerBits>
inline gemmlowp::FixedPoint<int32_t, OutputIntegerBits>
log_x_for_x_greater_than_or_equal_to_1(
gemmlowp::FixedPoint<int32_t, InputIntegerBits> input_val) {
static_assert(
OutputIntegerBits >= min_log_x_output_bits(InputIntegerBits),
"Output integer bits must be sufficient to accommodate logs of inputs.");
return log_x_for_x_greater_than_or_equal_to_1_impl<OutputIntegerBits,
InputIntegerBits>(
input_val);
}
inline int32_t GetReciprocal(int32_t x, int x_integer_digits,
int* num_bits_over_unit) {
int headroom_plus_one = CountLeadingZeros(static_cast<uint32_t>(x));
// This is the number of bits to the left of the binary point above 1.0.
// Consider x=1.25. In that case shifted_scale=0.8 and
// no later adjustment will be needed.
*num_bits_over_unit = x_integer_digits - headroom_plus_one;
const int32_t shifted_sum_minus_one =
static_cast<int32_t>((static_cast<uint32_t>(x) << headroom_plus_one) -
(static_cast<uint32_t>(1) << 31));
gemmlowp::FixedPoint<int32_t, 0> shifted_scale =
gemmlowp::one_over_one_plus_x_for_x_in_0_1(
gemmlowp::FixedPoint<int32_t, 0>::FromRaw(shifted_sum_minus_one));
return shifted_scale.raw();
}
inline void GetInvSqrtQuantizedMultiplierExp(int32_t input, int reverse_shift,
int32_t* output_inv_sqrt,
int* output_shift) {
TFLITE_DCHECK_GE(input, 0);
if (input <= 1) {
// Handle the input value 1 separately to avoid overflow in that case
// in the general computation below (b/143972021). Also handle 0 as if it
// were a 1. 0 is an invalid input here (divide by zero) and 1 is a valid
// but rare/unrealistic input value. We can expect both to occur in some
// incompletely trained models, but probably not in fully trained models.
*output_inv_sqrt = std::numeric_limits<std::int32_t>::max();
*output_shift = 0;
return;
}
TFLITE_DCHECK_GT(input, 1);
*output_shift = 11;
while (input >= (1 << 29)) {
input /= 4;
++*output_shift;
}
const unsigned max_left_shift_bits =
CountLeadingZeros(static_cast<uint32_t>(input)) - 1;
const unsigned max_left_shift_bit_pairs = max_left_shift_bits / 2;
const unsigned left_shift_bit_pairs = max_left_shift_bit_pairs - 1;
*output_shift -= left_shift_bit_pairs;
input <<= 2 * left_shift_bit_pairs;
TFLITE_DCHECK_GE(input, (1 << 27));
TFLITE_DCHECK_LT(input, (1 << 29));
using gemmlowp::FixedPoint;
using gemmlowp::Rescale;
using gemmlowp::SaturatingRoundingMultiplyByPOT;
// Using 3 integer bits gives us enough room for the internal arithmetic in
// this Newton-Raphson iteration.
using F3 = FixedPoint<int32_t, 3>;
using F0 = FixedPoint<int32_t, 0>;
const F3 fixedpoint_input = F3::FromRaw(input >> 1);
const F3 fixedpoint_half_input =
SaturatingRoundingMultiplyByPOT<-1>(fixedpoint_input);
const F3 fixedpoint_half_three =
GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F3, (1 << 28) + (1 << 27), 1.5);
// Newton-Raphson iteration
// Naive unoptimized starting guess: x = 1
F3 x = F3::One();
// Naive unoptimized number of iterations: 5
for (int i = 0; i < 5; i++) {
const F3 x3 = Rescale<3>(x * x * x);
x = Rescale<3>(fixedpoint_half_three * x - fixedpoint_half_input * x3);
}
const F0 fixedpoint_half_sqrt_2 =
GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F0, 1518500250, std::sqrt(2.) / 2.);
x = x * fixedpoint_half_sqrt_2;
*output_inv_sqrt = x.raw();
if (*output_shift < 0) {
*output_inv_sqrt <<= -*output_shift;
*output_shift = 0;
}
// Convert right shift (right is positive) to left shift.
*output_shift *= reverse_shift;
}
// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING
// BROADCASTING.
//
// NdArrayDesc<N> describes the shape and memory layout of an N-dimensional
// rectangular array of numbers.
//
// NdArrayDesc<N> is basically identical to Dims<N> defined in types.h.
// However, as Dims<N> is to be deprecated, this class exists as an adaptor
// to enable simple unoptimized implementations of element-wise broadcasting
// operations.
template <int N>
struct NdArrayDesc {
// The "extent" of each dimension. Indices along dimension d must be in the
// half-open interval [0, extents[d]).
int extents[N];
// The number of *elements* (not bytes) between consecutive indices of each
// dimension.
int strides[N];
};
// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING
// BROADCASTING.
//
// Same as Offset(), except takes as NdArrayDesc<N> instead of Dims<N>.
inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2,
int i3) {
TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]);
TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]);
TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]);
TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]);
return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] +
i3 * desc.strides[3];
}
inline int SubscriptToIndex(const NdArrayDesc<5>& desc, int indexes[5]) {
return indexes[0] * desc.strides[0] + indexes[1] * desc.strides[1] +
indexes[2] * desc.strides[2] + indexes[3] * desc.strides[3] +
indexes[4] * desc.strides[4];
}
// Given the dimensions of the operands for an element-wise binary broadcast,
// adjusts them so that they can be directly iterated over with simple loops.
// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and
// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr.
//
// This function assumes that the two input shapes are compatible up to
// broadcasting and the shorter one has already been prepended with 1s to be the
// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64),
// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that
// Dims<N> refer to shapes in reverse order. In this case, input0_dims will be
// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1).
//
// When two shapes are compatible up to broadcasting, for each dimension d,
// the input extents are either equal, or one of them is 1.
//
// This function performs the following for each dimension d:
// - If the extents are equal, then do nothing since the loop that walks over
// both of the input arrays is correct.
// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1
// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows
// array0 to be referenced *at any index* in dimension d and still access the
// same slice.
template <int N>
inline void NdArrayDescsForElementwiseBroadcast(const Dims<N>& input0_dims,
const Dims<N>& input1_dims,
NdArrayDesc<N>* desc0_out,
NdArrayDesc<N>* desc1_out) {
TFLITE_DCHECK(desc0_out != nullptr);
TFLITE_DCHECK(desc1_out != nullptr);
// Copy dims to desc.
for (int i = 0; i < N; ++i) {
desc0_out->extents[i] = input0_dims.sizes[i];
desc0_out->strides[i] = input0_dims.strides[i];
desc1_out->extents[i] = input1_dims.sizes[i];
desc1_out->strides[i] = input1_dims.strides[i];
}
// Walk over each dimension. If the extents are equal do nothing.
// Otherwise, set the desc with extent 1 to have extent equal to the other and
// stride 0.
for (int i = 0; i < N; ++i) {
const int extent0 = ArraySize(input0_dims, i);
const int extent1 = ArraySize(input1_dims, i);
if (extent0 != extent1) {
if (extent0 == 1) {
desc0_out->strides[i] = 0;
desc0_out->extents[i] = extent1;
} else {
TFLITE_DCHECK_EQ(extent1, 1);
desc1_out->strides[i] = 0;
desc1_out->extents[i] = extent0;
}
}
}
}
// Copies dims to desc, calculating strides.
template <int N>
inline void CopyDimsToDesc(const RuntimeShape& input_shape,
NdArrayDesc<N>* desc_out) {
int desc_stride = 1;
for (int i = N - 1; i >= 0; --i) {
desc_out->extents[i] = input_shape.Dims(i);
desc_out->strides[i] = desc_stride;
desc_stride *= input_shape.Dims(i);
}
}
template <int N>
inline void NdArrayDescsForElementwiseBroadcast(
const RuntimeShape& input0_shape, const RuntimeShape& input1_shape,
NdArrayDesc<N>* desc0_out, NdArrayDesc<N>* desc1_out) {
TFLITE_DCHECK(desc0_out != nullptr);
TFLITE_DCHECK(desc1_out != nullptr);
auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape);
auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape);
// Copy dims to desc, calculating strides.
CopyDimsToDesc<N>(extended_input0_shape, desc0_out);
CopyDimsToDesc<N>(extended_input1_shape, desc1_out);
// Walk over each dimension. If the extents are equal do nothing.
// Otherwise, set the desc with extent 1 to have extent equal to the other and
// stride 0.
for (int i = 0; i < N; ++i) {
const int extent0 = extended_input0_shape.Dims(i);
const int extent1 = extended_input1_shape.Dims(i);
if (extent0 != extent1) {
if (extent0 == 1) {
desc0_out->strides[i] = 0;
desc0_out->extents[i] = extent1;
} else {
TFLITE_DCHECK_EQ(extent1, 1);
desc1_out->strides[i] = 0;
desc1_out->extents[i] = extent0;
}
}
}
}
template <int N>
inline void NdArrayDescsForElementwiseBroadcast(
const RuntimeShape& input0_shape, const RuntimeShape& input1_shape,
const RuntimeShape& input2_shape, NdArrayDesc<N>* desc0_out,
NdArrayDesc<N>* desc1_out, NdArrayDesc<N>* desc2_out) {
TFLITE_DCHECK(desc0_out != nullptr);
TFLITE_DCHECK(desc1_out != nullptr);
TFLITE_DCHECK(desc2_out != nullptr);
auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape);
auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape);
auto extended_input2_shape = RuntimeShape::ExtendedShape(N, input2_shape);
// Copy dims to desc, calculating strides.
CopyDimsToDesc<N>(extended_input0_shape, desc0_out);
CopyDimsToDesc<N>(extended_input1_shape, desc1_out);
CopyDimsToDesc<N>(extended_input2_shape, desc2_out);
// Walk over each dimension. If the extents are equal do nothing.
// Otherwise, set the desc with extent 1 to have extent equal to the other and
// stride 0.
for (int i = 0; i < N; ++i) {
const int extent0 = extended_input0_shape.Dims(i);
const int extent1 = extended_input1_shape.Dims(i);
const int extent2 = extended_input2_shape.Dims(i);
int extent = extent0;
if (extent1 != 1) extent = extent1;
if (extent2 != 1) extent = extent2;
TFLITE_DCHECK(extent0 == 1 || extent0 == extent);
TFLITE_DCHECK(extent1 == 1 || extent1 == extent);
TFLITE_DCHECK(extent2 == 1 || extent2 == extent);
if (!(extent0 == extent1 && extent1 == extent2)) {
if (extent0 == 1) {
desc0_out->strides[i] = 0;
desc0_out->extents[i] = extent;
}
if (extent1 == 1) {
desc1_out->strides[i] = 0;
desc1_out->extents[i] = extent;
}
if (extent2 == 1) {
desc2_out->strides[i] = 0;
desc2_out->extents[i] = extent;
}
}
}
}
// Detailed implementation of NDOpsHelper, the indexes must be a zero array.
// This implementation is equivalent to N nested loops. Ex, if N=4, it can be
// re-writen as:
// for (int b = 0; b < output.extents[0]; ++b) {
// for (int y = 0; y < output.extents[1]; ++y) {
// for (int x = 0; x < output.extents[2]; ++x) {
// for (int c = 0; c < output.extents[3]; ++c) {
// calc({b,y,x,c});
// }
// }
// }
// }
template <int N, int DIM, typename Calc>
typename std::enable_if<DIM != N - 1, void>::type NDOpsHelperImpl(
const NdArrayDesc<N>& output, const Calc& calc, int indexes[N]) {
for (indexes[DIM] = 0; indexes[DIM] < output.extents[DIM]; ++indexes[DIM]) {
NDOpsHelperImpl<N, DIM + 1, Calc>(output, calc, indexes);
}
}
template <int N, int DIM, typename Calc>
typename std::enable_if<DIM == N - 1, void>::type NDOpsHelperImpl(
const NdArrayDesc<N>& output, const Calc& calc, int indexes[N]) {
for (indexes[DIM] = 0; indexes[DIM] < output.extents[DIM]; ++indexes[DIM]) {
calc(indexes);
}
}
// Execute the calc function in the innermost iteration based on the shape of
// the output. The calc function should take a single argument of type int[N].
template <int N, typename Calc>
inline void NDOpsHelper(const NdArrayDesc<N>& output, const Calc& calc) {
int indexes[N] = {0};
NDOpsHelperImpl<N, 0, Calc>(output, calc, indexes);
}
// Copied from gemmlowp::RoundDown when we dropped direct dependency on
// gemmlowp.
//
// Returns the runtime argument rounded down to the nearest multiple of
// the fixed Modulus.
template <unsigned Modulus, typename Integer>
Integer RoundDown(Integer i) {
return i - (i % Modulus);
}
// Copied from gemmlowp::RoundUp when we dropped direct dependency on
// gemmlowp.
//
// Returns the runtime argument rounded up to the nearest multiple of
// the fixed Modulus.
template <unsigned Modulus, typename Integer>
Integer RoundUp(Integer i) {
return RoundDown<Modulus>(i + Modulus - 1);
}
// Copied from gemmlowp::CeilQuotient when we dropped direct dependency on
// gemmlowp.
//
// Returns the quotient a / b rounded up ('ceil') to the nearest integer.
template <typename Integer>
Integer CeilQuotient(Integer a, Integer b) {
return (a + b - 1) / b;
}
// This function is a copy of gemmlowp::HowManyThreads, copied when we dropped
// the direct dependency of internal/optimized/ on gemmlowp.
//
// It computes a reasonable number of threads to use for a GEMM of shape
// (rows, cols, depth).
//
// TODO(b/131910176): get rid of this function by switching each call site
// to its own more sensible logic for its own workload.
template <int KernelRows>
inline int LegacyHowManyThreads(int max_num_threads, int rows, int cols,
int depth) {
// Early-exit in the default case where multi-threading is disabled.
if (max_num_threads == 1) {
return 1;
}
// Ensure that each thread has KernelRows rows to process, if at all possible.
int thread_count = std::min(max_num_threads, rows / KernelRows);
// Limit the number of threads according to the overall size of the problem.
if (thread_count > 1) {
// Empirically determined value.
static constexpr std::uint64_t min_cubic_size_per_thread = 64 * 1024;
// We can only multiply two out of three sizes without risking overflow
const std::uint64_t cubic_size =
std::uint64_t(rows) * std::uint64_t(cols) * std::uint64_t(depth);
thread_count = std::min(
thread_count, static_cast<int>(cubic_size / min_cubic_size_per_thread));
}
if (thread_count < 1) {
thread_count = 1;
}
assert(thread_count > 0 && thread_count <= max_num_threads);
return thread_count;
}
template <typename T>
void optimized_ops_preload_l1_stream(const T* ptr) {
#ifdef __GNUC__
// builtin offered by GCC-compatible compilers including clang
__builtin_prefetch(ptr, /* 0 means read */ 0, /* 0 means no locality */ 0);
#else
(void)ptr;
#endif
}
template <typename T>
void optimized_ops_preload_l1_keep(const T* ptr) {
#ifdef __GNUC__
// builtin offered by GCC-compatible compilers including clang
__builtin_prefetch(ptr, /* 0 means read */ 0, /* 3 means high locality */ 3);
#else
(void)ptr;
#endif
}
template <typename T>
void optimized_ops_prefetch_write_l1_keep(const T* ptr) {
#ifdef __GNUC__
// builtin offered by GCC-compatible compilers including clang
__builtin_prefetch(ptr, /* 1 means write */ 1, /* 3 means high locality */ 3);
#else
(void)ptr;
#endif
}
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_
#include <cstdint>
#include "tensorflow/lite/kernels/op_macros.h"
#ifndef TFLITE_DCHECK
#define TFLITE_DCHECK(condition) (condition) ? (void)0 : TFLITE_ASSERT_FALSE
#endif
#ifndef TFLITE_DCHECK_EQ
#define TFLITE_DCHECK_EQ(x, y) ((x) == (y)) ? (void)0 : TFLITE_ASSERT_FALSE
#endif
#ifndef TFLITE_DCHECK_NE
#define TFLITE_DCHECK_NE(x, y) ((x) != (y)) ? (void)0 : TFLITE_ASSERT_FALSE
#endif
#ifndef TFLITE_DCHECK_GE
#define TFLITE_DCHECK_GE(x, y) ((x) >= (y)) ? (void)0 : TFLITE_ASSERT_FALSE
#endif
#ifndef TFLITE_DCHECK_GT
#define TFLITE_DCHECK_GT(x, y) ((x) > (y)) ? (void)0 : TFLITE_ASSERT_FALSE
#endif
#ifndef TFLITE_DCHECK_LE
#define TFLITE_DCHECK_LE(x, y) ((x) <= (y)) ? (void)0 : TFLITE_ASSERT_FALSE
#endif
#ifndef TFLITE_DCHECK_LT
#define TFLITE_DCHECK_LT(x, y) ((x) < (y)) ? (void)0 : TFLITE_ASSERT_FALSE
#endif
// TODO(ahentz): Clean up: We should stick to the DCHECK versions.
#ifndef TFLITE_CHECK
#define TFLITE_CHECK(condition) (condition) ? (void)0 : TFLITE_ABORT
#endif
#ifndef TFLITE_CHECK_EQ
#define TFLITE_CHECK_EQ(x, y) ((x) == (y)) ? (void)0 : TFLITE_ABORT
#endif
#ifndef TFLITE_CHECK_NE
#define TFLITE_CHECK_NE(x, y) ((x) != (y)) ? (void)0 : TFLITE_ABORT
#endif
#ifndef TFLITE_CHECK_GE
#define TFLITE_CHECK_GE(x, y) ((x) >= (y)) ? (void)0 : TFLITE_ABORT
#endif
#ifndef TFLITE_CHECK_GT
#define TFLITE_CHECK_GT(x, y) ((x) > (y)) ? (void)0 : TFLITE_ABORT
#endif
#ifndef TFLITE_CHECK_LE
#define TFLITE_CHECK_LE(x, y) ((x) <= (y)) ? (void)0 : TFLITE_ABORT
#endif
#ifndef TFLITE_CHECK_LT
#define TFLITE_CHECK_LT(x, y) ((x) < (y)) ? (void)0 : TFLITE_ABORT
#endif
#ifndef TF_LITE_STATIC_MEMORY
// TODO(b/162019032): Consider removing these type-aliases.
using int8 = std::int8_t;
using uint8 = std::uint8_t;
using int16 = std::int16_t;
using uint16 = std::uint16_t;
using int32 = std::int32_t;
using uint32 = std::uint32_t;
#endif // !defined(TF_LITE_STATIC_MEMORY)
// TFLITE_DEPRECATED()
//
// Duplicated from absl/base/macros.h to avoid pulling in that library.
// Marks a deprecated class, struct, enum, function, method and variable
// declarations. The macro argument is used as a custom diagnostic message (e.g.
// suggestion of a better alternative).
//
// Example:
//
// class TFLITE_DEPRECATED("Use Bar instead") Foo {...};
// TFLITE_DEPRECATED("Use Baz instead") void Bar() {...}
//
// Every usage of a deprecated entity will trigger a warning when compiled with
// clang's `-Wdeprecated-declarations` option. This option is turned off by
// default, but the warnings will be reported by clang-tidy.
#if defined(__clang__) && __cplusplus >= 201103L
#define TFLITE_DEPRECATED(message) __attribute__((deprecated(message)))
#endif
#ifndef TFLITE_DEPRECATED
#define TFLITE_DEPRECATED(message)
#endif
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_
#include <cmath>
namespace tflite {
#if defined(TF_LITE_USE_GLOBAL_CMATH_FUNCTIONS) || \
(defined(__ANDROID__) && !defined(__NDK_MAJOR__)) || defined(ARDUINO) || \
defined(__ZEPHYR__)
#define TF_LITE_GLOBAL_STD_PREFIX
#else
#define TF_LITE_GLOBAL_STD_PREFIX std
#endif
#define DECLARE_STD_GLOBAL_SWITCH1(tf_name, std_name) \
template <class T> \
inline T tf_name(const T x) { \
return TF_LITE_GLOBAL_STD_PREFIX::std_name(x); \
}
DECLARE_STD_GLOBAL_SWITCH1(TfLiteRound, round);
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_
#include <cmath>
namespace tflite {
#if defined(TF_LITE_USE_GLOBAL_MAX) || defined(__ZEPHYR__)
inline float TfLiteMax(const float& x, const float& y) {
return std::max(x, y);
}
#else
template <class T>
inline T TfLiteMax(const T& x, const T& y) {
return std::fmax(x, y);
}
#endif
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_
#include <cmath>
namespace tflite {
#if defined(TF_LITE_USE_GLOBAL_MIN) || defined(__ZEPHYR__)
inline float TfLiteMin(const float& x, const float& y) {
return std::min(x, y);
}
#else
template <class T>
inline T TfLiteMin(const T& x, const T& y) {
return std::fmin(x, y);
}
#endif
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#define USE_NEON
#include <arm_neon.h>
#endif
#if defined __GNUC__ && defined __SSE4_1__ && !defined TF_LITE_DISABLE_X86_NEON
#define USE_NEON
#include "NEON_2_SSE.h"
#endif
// NEON_OR_PORTABLE(SomeFunc, args) calls NeonSomeFunc(args) if USE_NEON is
// defined, PortableSomeFunc(args) otherwise.
#ifdef USE_NEON
// Always use Neon code
#define NEON_OR_PORTABLE(funcname, ...) Neon##funcname(__VA_ARGS__)
#else
// No NEON available: Use Portable code
#define NEON_OR_PORTABLE(funcname, ...) Portable##funcname(__VA_ARGS__)
#endif // defined(USE_NEON)
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_
#include <cmath>
#include <cstdint>
#include <limits>
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
// Given the min and max values of a float array, return
// reasonable quantization parameters to use for this array.
template <typename T>
QuantizationParams ChooseQuantizationParams(double rmin, double rmax,
bool narrow_range) {
const T qmin = std::numeric_limits<T>::min() + (narrow_range ? 1 : 0);
const T qmax = std::numeric_limits<T>::max();
const double qmin_double = qmin;
const double qmax_double = qmax;
// 0 should always be a representable value. Let's assume that the initial
// min,max range contains 0.
TFLITE_CHECK_LE(rmin, 0.);
TFLITE_CHECK_GE(rmax, 0.);
if (rmin == rmax) {
// Special case where the min,max range is a point. Should be {0}.
TFLITE_CHECK_EQ(rmin, 0.);
TFLITE_CHECK_EQ(rmax, 0.);
QuantizationParams quantization_params;
quantization_params.zero_point = 0;
quantization_params.scale = 0.;
return quantization_params;
}
// General case.
//
// First determine the scale.
const double scale = (rmax - rmin) / (qmax_double - qmin_double);
// Zero-point computation.
// First the initial floating-point computation. The zero-point can be
// determined from solving an affine equation for any known pair
// (real value, corresponding quantized value).
// We know two such pairs: (rmin, qmin) and (rmax, qmax).
// The arithmetic error on the zero point computed from either pair
// will be roughly machine_epsilon * (sum of absolute values of terms)
// so we want to use the variant that adds the smaller terms.
const double zero_point_from_min = qmin_double - rmin / scale;
const double zero_point_from_max = qmax_double - rmax / scale;
const double zero_point_from_min_error =
std::abs(qmin_double) + std::abs(rmin / scale);
const double zero_point_from_max_error =
std::abs(qmax_double) + std::abs(rmax / scale);
const double zero_point_double =
zero_point_from_min_error < zero_point_from_max_error
? zero_point_from_min
: zero_point_from_max;
// Now we need to nudge the zero point to be an integer
// (our zero points are integer, and this is motivated by the requirement
// to be able to represent the real value "0" exactly as a quantized value,
// which is required in multiple places, for example in Im2col with SAME
// padding).
T nudged_zero_point = 0;
if (zero_point_double < qmin_double) {
nudged_zero_point = qmin;
} else if (zero_point_double > qmax_double) {
nudged_zero_point = qmax;
} else {
nudged_zero_point = static_cast<T>(round(zero_point_double));
}
// The zero point should always be in the range of quantized value,
// [qmin, qmax].
TFLITE_CHECK_GE(nudged_zero_point, qmin);
TFLITE_CHECK_LE(nudged_zero_point, qmax);
// Finally, store the result nudged quantization params.
QuantizationParams quantization_params;
quantization_params.zero_point = nudged_zero_point;
quantization_params.scale = scale;
return quantization_params;
}
template <typename T>
QuantizationParams ChooseQuantizationParams(double rmin, double rmax) {
return ChooseQuantizationParams<T>(rmin, rmax, false);
}
// Converts a floating-point number to an integer. For all inputs x where
// static_cast<IntOut>(x) is legal according to the C++ standard, the result
// is identical to that cast (i.e. the result is x with its fractional part
// truncated whenever that is representable as IntOut).
//
// static_cast would cause undefined behavior for the following cases, which
// have well-defined behavior for this function:
//
// 1. If x is NaN, the result is zero.
//
// 2. If the truncated form of x is above the representable range of IntOut,
// the result is std::numeric_limits<IntOut>::max().
//
// 3. If the truncated form of x is below the representable range of IntOut,
// the result is std::numeric_limits<IntOut>::min().
//
// Note that cases #2 and #3 cover infinities as well as finite numbers.
//
// The range of FloatIn must include the range of IntOut, otherwise
// the results are undefined.
// TODO(sfeuz): Replace by absl::SafeCast once available.
template <class IntOut, class FloatIn>
IntOut SafeCast(FloatIn x) {
static_assert(!std::numeric_limits<FloatIn>::is_integer,
"FloatIn is integer");
static_assert(std::numeric_limits<IntOut>::is_integer,
"IntOut is not integer");
static_assert(std::numeric_limits<IntOut>::radix == 2, "IntOut is base 2");
// Special case NaN, for which the logic below doesn't work.
if (std::isnan(x)) {
return 0;
}
// Negative values all clip to zero for unsigned results.
if (!std::numeric_limits<IntOut>::is_signed && x < 0) {
return 0;
}
// Handle infinities.
if (std::isinf(x)) {
return x < 0 ? std::numeric_limits<IntOut>::min()
: std::numeric_limits<IntOut>::max();
}
// Set exp such that x == f * 2^exp for some f with |f| in [0.5, 1.0),
// unless x is zero in which case exp == 0. Note that this implies that the
// magnitude of x is strictly less than 2^exp.
int exp = 0;
std::frexp(x, &exp);
// Let N be the number of non-sign bits in the representation of IntOut. If
// the magnitude of x is strictly less than 2^N, the truncated version of x
// is representable as IntOut. The only representable integer for which this
// is not the case is kMin for signed types (i.e. -2^N), but that is covered
// by the fall-through below.
if (exp <= std::numeric_limits<IntOut>::digits) {
return x;
}
// Handle numbers with magnitude >= 2^N.
return x < 0 ? std::numeric_limits<IntOut>::min()
: std::numeric_limits<IntOut>::max();
}
// Decompose a double multiplier into a Q0.31 int32 representation of its
// significand, and shift representation of NEGATIVE its exponent ---
// this is intended as a RIGHT-shift.
//
// Restricted to the case where the multiplier < 1 (and non-negative).
void QuantizeMultiplierSmallerThanOneExp(double double_multiplier,
int32_t* quantized_multiplier,
int* left_shift);
// Decompose a double multiplier into a Q0.31 int32 representation of its
// significand, and shift representation of its exponent.
//
// Restricted to the case where the multiplier > 1.
void QuantizeMultiplierGreaterThanOne(double double_multiplier,
int32_t* quantized_multiplier,
int* left_shift);
// Decompose a double multiplier into a Q0.31 int32 representation of its
// significand, and shift representation of its exponent.
//
// Handles an arbitrary positive multiplier. The 'shift' output-value is
// basically the 'floating-point exponent' of the multiplier:
// Negative for a right-shift (when the multiplier is <1), positive for a
// left-shift (when the multiplier is >1)
void QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier,
int* shift);
// Splits a double input value into a returned fraction, and a shift value from
// the exponent, using only bitwise and integer operations to support
// microcontrollers and other environments without floating-point support.
//
// This is designed to be a replacement for how std::frexp() is used within the
// QuantizeMultiplier() function, and so has a different signature than the
// standard version, returning a 64-bit integer rather than a double. This
// result has a maximum value of 1<<31, with the fraction expressed as a
// proportion of that maximum.
//
// std::frexp() returns NaNs and infinities unmodified, but since we're
// returning integers that can't represent those values, instead we return
// a shift of std::numeric_limits<int>::max() for all bad numbers, with an int64
// result of 0 for NaNs, std:numeric_limits<int64_t>::max() for +INFINITY, and
// std::numeric_limits<int64_t>::min() for -INFINITY. Denormalized inputs will
// result in return values that end up truncating some bits at the end,
// reflecting the loss of precision inherent in denormalization.
int64_t IntegerFrExp(double input, int* shift);
// Converts an integer fraction in the format produced by IntegerFrExp (where
// 0x40000000 is 1.0) and an exponent shift (between -1022 and +1022) into an
// IEEE binary64 double format result. The implementation uses only integer and
// bitwise operators, so no floating point hardware support or emulation is
// needed. This is here so quantized operations can run non-time-critical
// preparation calculations on microcontrollers and other platforms without
// float support.
double DoubleFromFractionAndShift(int64_t fraction, int shift);
// Performs a multiplication of two numbers in double format, using only integer
// and bitwise instructions. This is aimed at supporting housekeeping functions
// for quantized operations on microcontrollers without floating-point hardware.
double IntegerDoubleMultiply(double a, double b);
// Returns -1 if a is less than b, 0 if a and b are equal, and +1 if a is
// greater than b. It is implemented using only integer and logical instructions
// so that it can be easily run on microcontrollers for quantized operations.
int IntegerDoubleCompare(double a, double b);
// This first creates a multiplier in a double equivalent of
// Q(input_integer_bits).(31-input_integer_bits) representation, with extra
// precision in the double's fractional bits. It then splits the result into
// significand and exponent.
void PreprocessSoftmaxScaling(double beta, double input_scale,
int input_integer_bits,
int32_t* quantized_multiplier, int* left_shift);
// Like PreprocessSoftmaxScaling, but inverse scaling factors also calculated.
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);
// Calculate the largest input that will result in a within-bounds intermediate
// result within MultiplyByQuantizedMultiplierGreaterThanOne. In other words,
// it must not overflow before we reduce the value by multiplication by the
// input multiplier. The negative radius is used as the minimum difference in
// Softmax.
int CalculateInputRadius(int input_integer_bits, int input_left_shift,
int total_signed_bits = 31);
// Nudges a min/max quantization range to ensure zero is zero.
// Gymnastics with nudged zero point is to ensure that real zero maps to
// an integer, which is required for e.g. zero-padding in convolutional layers.
// Outputs nudged_min, nudged_max, nudged_scale.
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);
// Fake quantizes (quantizes and dequantizes) input_data using the scale,
// nudged_min, and nudged_max from NudgeQuantizationRange. This matches the code
// in TensorFlow's FakeQuantizeWithMinMaxVarsFunctor.
void FakeQuantizeArray(const float nudged_scale, const float nudged_min,
const float nudged_max, const float* input_data,
float* output_data, const float size);
// If x is approximately a power of two (with any positive or negative
// exponent), stores that exponent (i.e. log2(x)) in *log2_result, otherwise
// returns false.
bool CheckedLog2(const float x, int* log2_result);
// Decomposes an array of double multipliers into a Q0.31 int32 representation
// of its significand, and shift representation of its exponent.
//
// Handles an arbitrary multiplier. The 'shift' output-value is
// basically the 'floating-point exponent' of the multiplier:
// Negative for a right-shift (when the multiplier is <1), positive for a
// left-shift (when the multiplier is >1)
void QuantizeMultiplierArray(const double* effective_scales, size_t size,
int32_t* effective_scale_significand,
int* effective_shift);
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_
#include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_ops {
template <typename T>
inline void Add(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const T* input1_data,
const RuntimeShape& input2_shape, const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = ActivationFunctionWithMinMax(
input1_data[i] + input2_data[i], params.quantized_activation_min,
params.quantized_activation_max);
}
}
inline void Add(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const float* input1_data,
const RuntimeShape& input2_shape, const float* input2_data,
const RuntimeShape& output_shape, float* output_data) {
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; i++) {
auto x = input1_data[i] + input2_data[i];
output_data[i] = ActivationFunctionWithMinMax(
x, params.float_activation_min, params.float_activation_max);
}
}
// Element-wise add that can often be used for inner loop of broadcast add as
// well as the non-broadcast add.
// This function is used for 8-bit as well as for 16-bit, but the accumulator
// is 32-bit for both cases. The overflow does not happen due to the
// choice of the shift (20 or 15, accordingly - see add.cc for more comments).
template <typename T>
inline void AddElementwise(int size, const ArithmeticParams& params,
const T* input1_data, const T* input2_data,
T* output_data) {
TFLITE_DCHECK_GT(params.input1_offset, -std::numeric_limits<T>::max());
TFLITE_DCHECK_GT(params.input2_offset, -std::numeric_limits<T>::max());
TFLITE_DCHECK_LT(params.input1_offset, std::numeric_limits<T>::max());
TFLITE_DCHECK_LT(params.input2_offset, std::numeric_limits<T>::max());
for (int i = 0; i < size; ++i) {
const int32_t input1_val = params.input1_offset + input1_data[i];
const int32_t input2_val = params.input2_offset + input2_data[i];
const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier, params.input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier, params.input2_shift);
const int32_t raw_sum = scaled_input1_val + scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sum, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[i] = static_cast<T>(clamped_output);
}
}
// Scalar-broadcast add that can be used for inner loop of more general
// broadcast add, so that, for example, scalar-broadcast with batch will still
// be fast.
inline void AddScalarBroadcast(int size, const ArithmeticParams& params,
uint8_t input1_data, const uint8_t* input2_data,
uint8_t* output_data) {
TFLITE_DCHECK_GT(params.input1_offset, -256);
TFLITE_DCHECK_GT(params.input2_offset, -256);
TFLITE_DCHECK_LT(params.input1_offset, 256);
TFLITE_DCHECK_LT(params.input2_offset, 256);
const int32_t input1_val = params.input1_offset + input1_data;
const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier, params.input1_shift);
for (int i = 0; i < size; ++i) {
const int32_t input2_val = params.input2_offset + input2_data[i];
const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier, params.input2_shift);
const int32_t raw_sum = scaled_input1_val + scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sum, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[i] = static_cast<uint8_t>(clamped_output);
}
}
inline void Add(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const uint8_t* input1_data,
const RuntimeShape& input2_shape, const uint8_t* input2_data,
const RuntimeShape& output_shape, uint8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
TFLITE_DCHECK_GT(params.input1_offset, -256);
TFLITE_DCHECK_GT(params.input2_offset, -256);
TFLITE_DCHECK_LT(params.input1_offset, 256);
TFLITE_DCHECK_LT(params.input2_offset, 256);
AddElementwise(flat_size, params, input1_data, input2_data, output_data);
}
inline void AddGeneralParamScale(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int16_t* input1_data,
const RuntimeShape& input2_shape,
const int16_t* input2_data,
const RuntimeShape& output_shape,
int16_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
int max_value = std::numeric_limits<int16_t>::max();
TFLITE_DCHECK_GT(params.input1_offset, -max_value);
TFLITE_DCHECK_GT(params.input2_offset, -max_value);
TFLITE_DCHECK_LT(params.input1_offset, max_value);
TFLITE_DCHECK_LT(params.input2_offset, max_value);
AddElementwise(flat_size, params, input1_data, input2_data, output_data);
}
inline void Add(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const int16_t* input1_data,
const RuntimeShape& input2_shape, const int16_t* input2_data,
const RuntimeShape& output_shape, int16_t* output_data,
bool pot_scale = true) {
if (!pot_scale) {
AddGeneralParamScale(params, input1_shape, input1_data, input2_shape,
input2_data, output_shape, output_data);
return;
}
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
const int input1_shift = params.input1_shift;
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
const int16_t output_activation_min = params.quantized_activation_min;
const int16_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0);
TFLITE_DCHECK_LE(input1_shift, 0);
TFLITE_DCHECK_LE(params.input2_shift, 0);
const int16_t* not_shift_input =
input1_shift == 0 ? input1_data : input2_data;
const int16_t* shift_input = input1_shift == 0 ? input2_data : input1_data;
const int input_right_shift =
input1_shift == 0 ? -params.input2_shift : -input1_shift;
for (int i = 0; i < flat_size; i++) {
// F0 uses 0 integer bits, range [-1, 1].
using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]);
F0 scaled_input = F0::FromRaw(
gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift));
F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled);
const int16_t raw_output = result.raw();
const int16_t clamped_output = std::min(
output_activation_max, std::max(output_activation_min, raw_output));
output_data[i] = clamped_output;
}
}
// TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary
// dimensionality if the runtime code does a single loop over one dimension
// that handles broadcasting as the base case. The code generator would then
// generate max(D1, D2) nested for loops.
// TODO(benoitjacob): BroadcastAdd is intentionally duplicated from
// reference_ops.h. Once an optimized version is implemented and NdArrayDesc<T>
// is no longer referenced in this file, move NdArrayDesc<T> from types.h to
// reference_ops.h.
inline void BroadcastAdd4DSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const float* input1_data,
const RuntimeShape& input2_shape,
const float* input2_data,
const RuntimeShape& output_shape,
float* output_data) {
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
output_data[Offset(extended_output_shape, b, y, x, c)] =
ActivationFunctionWithMinMax(
input1_data[SubscriptToIndex(desc1, b, y, x, c)] +
input2_data[SubscriptToIndex(desc2, b, y, x, c)],
params.float_activation_min, params.float_activation_max);
}
}
}
}
}
inline void BroadcastAdd4DSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int32_t* input1_data,
const RuntimeShape& input2_shape,
const int32_t* input2_data,
const RuntimeShape& output_shape,
int32_t* output_data) {
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
output_data[Offset(extended_output_shape, b, y, x, c)] =
ActivationFunctionWithMinMax(
input1_data[SubscriptToIndex(desc1, b, y, x, c)] +
input2_data[SubscriptToIndex(desc2, b, y, x, c)],
params.quantized_activation_min,
params.quantized_activation_max);
}
}
}
}
}
// This function is used for 8-bit as well as for 16-bit, but the accumulator
// is 32-bit for both cases. The overflow does not happen due to the
// choice of the shift (20 or 15, accordingly - see add.cc for more comments).
template <typename T>
inline void BroadcastAdd4DSlow(
const ArithmeticParams& params, const RuntimeShape& input1_shape,
const T* input1_data, const RuntimeShape& input2_shape,
const T* input2_data, const RuntimeShape& output_shape, T* output_data) {
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
const int32_t input1_val =
params.input1_offset +
input1_data[SubscriptToIndex(desc1, b, y, x, c)];
const int32_t input2_val =
params.input2_offset +
input2_data[SubscriptToIndex(desc2, b, y, x, c)];
const int32_t shifted_input1_val =
input1_val * (1 << params.left_shift);
const int32_t shifted_input2_val =
input2_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier,
params.input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier,
params.input2_shift);
const int32_t raw_sum = scaled_input1_val + scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sum, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[Offset(extended_output_shape, b, y, x, c)] =
static_cast<T>(clamped_output);
}
}
}
}
}
inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params,
const RuntimeShape& unswitched_input1_shape,
const uint8_t* unswitched_input1_data,
const RuntimeShape& unswitched_input2_shape,
const uint8_t* unswitched_input2_data,
const RuntimeShape& output_shape,
uint8_t* output_data) {
ArithmeticParams switched_params = unswitched_params;
switched_params.input1_offset = unswitched_params.input2_offset;
switched_params.input1_multiplier = unswitched_params.input2_multiplier;
switched_params.input1_shift = unswitched_params.input2_shift;
switched_params.input2_offset = unswitched_params.input1_offset;
switched_params.input2_multiplier = unswitched_params.input1_multiplier;
switched_params.input2_shift = unswitched_params.input1_shift;
const bool use_unswitched =
unswitched_params.broadcast_category ==
tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast;
const ArithmeticParams& params =
use_unswitched ? unswitched_params : switched_params;
const uint8_t* input1_data =
use_unswitched ? unswitched_input1_data : unswitched_input2_data;
const uint8_t* input2_data =
use_unswitched ? unswitched_input2_data : unswitched_input1_data;
// Fivefold nested loops. The second input resets its position for each
// iteration of the second loop. The first input resets its position at the
// beginning of the fourth loop. The innermost loop is an elementwise add of
// sections of the arrays.
uint8_t* output_data_ptr = output_data;
const uint8_t* input1_data_ptr = input1_data;
const uint8_t* input2_data_reset = input2_data;
// In the fivefold pattern, y0, y2 and y4 are not broadcast, and so shared
// between input shapes. y3 for input 1 is always broadcast, and so the
// dimension there is 1, whereas optionally y1 might be broadcast for input 2.
// Put another way,
// input1.shape.FlatSize = y0 * y1 * y2 * y4,
// input2.shape.FlatSize = y0 * y2 * y3 * y4.
int y0 = params.broadcast_shape[0];
int y1 = params.broadcast_shape[1];
int y2 = params.broadcast_shape[2];
int y3 = params.broadcast_shape[3];
int y4 = params.broadcast_shape[4];
if (y4 > 1) {
// General fivefold pattern, with y4 > 1 so there is a non-broadcast inner
// dimension.
for (int i0 = 0; i0 < y0; ++i0) {
const uint8_t* input2_data_ptr;
for (int i1 = 0; i1 < y1; ++i1) {
input2_data_ptr = input2_data_reset;
for (int i2 = 0; i2 < y2; ++i2) {
for (int i3 = 0; i3 < y3; ++i3) {
AddElementwise(y4, params, input1_data_ptr, input2_data_ptr,
output_data_ptr);
input2_data_ptr += y4;
output_data_ptr += y4;
}
// We have broadcast y4 of input1 data y3 times, and now move on.
input1_data_ptr += y4;
}
}
// We have broadcast y2*y3*y4 of input2 data y1 times, and now move on.
input2_data_reset = input2_data_ptr;
}
} else {
// Special case of y4 == 1, in which the innermost loop is a single element
// and can be combined with the next (y3) as an inner broadcast.
//
// Note that this handles the case of pure scalar broadcast when
// y0 == y1 == y2 == 1. With low overhead it handles cases such as scalar
// broadcast with batch (as y2 > 1).
//
// NOTE The process is the same as the above general case except simplified
// for y4 == 1 and the loop over y3 is contained within the
// AddScalarBroadcast function.
for (int i0 = 0; i0 < y0; ++i0) {
const uint8_t* input2_data_ptr;
for (int i1 = 0; i1 < y1; ++i1) {
input2_data_ptr = input2_data_reset;
for (int i2 = 0; i2 < y2; ++i2) {
AddScalarBroadcast(y3, params, *input1_data_ptr, input2_data_ptr,
output_data_ptr);
input2_data_ptr += y3;
output_data_ptr += y3;
input1_data_ptr += 1;
}
}
input2_data_reset = input2_data_ptr;
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
template <typename T1, typename T2, typename T3, typename Cmp>
void ArgMinMax(const RuntimeShape& input1_shape, const T1* input1_data,
const T3* input2_data, const RuntimeShape& output_shape,
T2* output_data, const Cmp& cmp) {
TFLITE_DCHECK_GT(input1_shape.DimensionsCount(), 0);
TFLITE_DCHECK_EQ(input1_shape.DimensionsCount() - 1,
output_shape.DimensionsCount());
int axis = input2_data[0];
if (axis < 0) {
axis += input1_shape.DimensionsCount();
}
const int axis_size = input1_shape.Dims(axis);
int outer_size = 1;
for (int i = 0; i < axis; ++i) {
TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i));
outer_size *= input1_shape.Dims(i);
}
int inner_size = 1;
const int dims_count = input1_shape.DimensionsCount();
for (int i = axis + 1; i < dims_count; ++i) {
TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i - 1));
inner_size *= input1_shape.Dims(i);
}
for (int outer = 0; outer < outer_size; ++outer) {
for (int inner = 0; inner < inner_size; ++inner) {
auto min_max_value = input1_data[outer * axis_size * inner_size + inner];
T2 min_max_index = 0;
for (int i = 1; i < axis_size; ++i) {
const auto& curr_value =
input1_data[(outer * axis_size + i) * inner_size + inner];
if (cmp(curr_value, min_max_value)) {
min_max_value = curr_value;
min_max_index = static_cast<T2>(i);
}
}
output_data[outer * inner_size + inner] = min_max_index;
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
// TODO(ycling): Refactoring. Remove BroadcastLogical and use the more
// generalized and efficient BroadcastBinaryFunction.
//
// Also appears to duplicate MinimumMaximum.
//
// R: Result type. T1: Input 1 type. T2: Input 2 type.
template <typename R, typename T1, typename T2>
inline void BroadcastBinaryFunction4DSlow(
const RuntimeShape& unextended_input1_shape, const T1* input1_data,
const RuntimeShape& unextended_input2_shape, const T2* input2_data,
const RuntimeShape& unextended_output_shape, R* output_data,
R (*func)(T1, T2)) {
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] = func(in1_val, in2_val);
}
}
}
}
}
// R: Result type. T1: Input 1 type. T2: Input 2 type.
// TODO(renjieliu): Refactor other binary functions to use this one.
template <typename R, typename T1, typename T2>
inline void BinaryFunction(const RuntimeShape& input1_shape,
const T1* input1_data,
const RuntimeShape& input2_shape,
const T2* input2_data,
const RuntimeShape& output_shape, R* output_data,
R (*func)(T1, T2)) {
const int flat_size =
MatchingFlatSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = func(input1_data[i], input2_data[i]);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_
#include <cmath>
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline void Ceil(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) {
output_data[i] = std::ceil(input_data[i]);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/string_util.h"
namespace tflite {
namespace reference_ops {
template <typename T>
inline bool EqualFn(T lhs, T rhs) {
return lhs == rhs;
}
template <typename T>
inline bool NotEqualFn(T lhs, T rhs) {
return lhs != rhs;
}
template <typename T>
inline bool GreaterFn(T lhs, T rhs) {
return lhs > rhs;
}
template <typename T>
inline bool GreaterEqualFn(T lhs, T rhs) {
return lhs >= rhs;
}
template <typename T>
inline bool LessFn(T lhs, T rhs) {
return lhs < rhs;
}
template <typename T>
inline bool LessEqualFn(T lhs, T rhs) {
return lhs <= rhs;
}
inline bool StringRefEqualFn(const StringRef& lhs, const StringRef& rhs) {
if (lhs.len != rhs.len) return false;
for (int i = 0; i < lhs.len; ++i) {
if (lhs.str[i] != rhs.str[i]) return false;
}
return true;
}
inline bool StringRefNotEqualFn(const StringRef& lhs, const StringRef& rhs) {
return !StringRefEqualFn(lhs, rhs);
}
template <typename T>
using ComparisonFn = bool (*)(T, T);
template <typename T, ComparisonFn<T> F>
inline void ComparisonImpl(
const ComparisonParams& op_params, const RuntimeShape& input1_shape,
const T* input1_data, const RuntimeShape& input2_shape,
const T* input2_data, const RuntimeShape& output_shape, bool* output_data) {
const int64_t flatsize =
MatchingFlatSize(input1_shape, input2_shape, output_shape);
for (int64_t i = 0; i < flatsize; ++i) {
output_data[i] = F(input1_data[i], input2_data[i]);
}
}
inline void ComparisonStringImpl(bool (*F)(const StringRef&, const StringRef&),
const RuntimeShape& input1_shape,
const TfLiteTensor* input1,
const RuntimeShape& input2_shape,
const TfLiteTensor* input2,
const RuntimeShape& output_shape,
bool* output_data) {
const int64_t flatsize =
MatchingFlatSize(input1_shape, input2_shape, output_shape);
for (int64_t i = 0; i < flatsize; ++i) {
const auto lhs = GetString(input1, i);
const auto rhs = GetString(input2, i);
output_data[i] = F(lhs, rhs);
}
}
template <ComparisonFn<float> F>
inline void Comparison(const ComparisonParams& op_params,
const RuntimeShape& input1_shape,
const float* input1_data,
const RuntimeShape& input2_shape,
const float* input2_data,
const RuntimeShape& output_shape, bool* output_data) {
ComparisonImpl<float, F>(op_params, input1_shape, input1_data, input2_shape,
input2_data, output_shape, output_data);
}
template <typename T, ComparisonFn<int32_t> F>
inline void ComparisonWithScaling(
const ComparisonParams& op_params, const RuntimeShape& input1_shape,
const T* input1_data, const RuntimeShape& input2_shape,
const T* input2_data, const RuntimeShape& output_shape, bool* output_data) {
int left_shift = op_params.left_shift;
int32_t input1_offset = op_params.input1_offset;
int32_t input1_multiplier = op_params.input1_multiplier;
int input1_shift = op_params.input1_shift;
int32_t input2_offset = op_params.input2_offset;
int32_t input2_multiplier = op_params.input2_multiplier;
int input2_shift = op_params.input2_shift;
const int64_t flatsize =
MatchingFlatSize(input1_shape, input2_shape, output_shape);
for (int64_t i = 0; i < flatsize; ++i) {
const int32_t input1_val = input1_offset + input1_data[i];
const int32_t input2_val = input2_offset + input2_data[i];
const int32_t shifted_input1_val = input1_val * (1 << left_shift);
const int32_t shifted_input2_val = input2_val * (1 << left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, input1_multiplier, input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, input2_multiplier, input2_shift);
output_data[i] = F(scaled_input1_val, scaled_input2_val);
}
}
struct BroadcastComparison4DSlowCommon {
const RuntimeShape output_shape;
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
};
inline BroadcastComparison4DSlowCommon BroadcastComparison4DSlowPreprocess(
const RuntimeShape& unextended_input1_shape,
const RuntimeShape& unextended_input2_shape,
const RuntimeShape& unextended_output_shape) {
TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
unextended_input2_shape, &desc1, &desc2);
return {RuntimeShape::ExtendedShape(4, unextended_output_shape), desc1,
desc2};
}
template <typename T, ComparisonFn<T> F>
inline void BroadcastComparison4DSlowImpl(
const ComparisonParams& op_params,
const RuntimeShape& unextended_input1_shape, const T* input1_data,
const RuntimeShape& unextended_input2_shape, const T* input2_data,
const RuntimeShape& unextended_output_shape, bool* output_data) {
const BroadcastComparison4DSlowCommon dims =
BroadcastComparison4DSlowPreprocess(unextended_input1_shape,
unextended_input2_shape,
unextended_output_shape);
for (int b = 0; b < dims.output_shape.Dims(0); ++b) {
for (int y = 0; y < dims.output_shape.Dims(1); ++y) {
for (int x = 0; x < dims.output_shape.Dims(2); ++x) {
for (int c = 0; c < dims.output_shape.Dims(3); ++c) {
output_data[Offset(dims.output_shape, b, y, x, c)] =
F(input1_data[SubscriptToIndex(dims.desc1, b, y, x, c)],
input2_data[SubscriptToIndex(dims.desc2, b, y, x, c)]);
}
}
}
}
}
inline void BroadcastComparison4DSlowStringImpl(
bool (*F)(const StringRef&, const StringRef&),
const RuntimeShape& unextended_input1_shape, const TfLiteTensor* input1,
const RuntimeShape& unextended_input2_shape, const TfLiteTensor* input2,
const RuntimeShape& unextended_output_shape, bool* output_data) {
const BroadcastComparison4DSlowCommon dims =
BroadcastComparison4DSlowPreprocess(unextended_input1_shape,
unextended_input2_shape,
unextended_output_shape);
for (int b = 0; b < dims.output_shape.Dims(0); ++b) {
for (int y = 0; y < dims.output_shape.Dims(1); ++y) {
for (int x = 0; x < dims.output_shape.Dims(2); ++x) {
for (int c = 0; c < dims.output_shape.Dims(3); ++c) {
const auto lhs =
GetString(input1, SubscriptToIndex(dims.desc1, b, y, x, c));
const auto rhs =
GetString(input2, SubscriptToIndex(dims.desc2, b, y, x, c));
output_data[Offset(dims.output_shape, b, y, x, c)] = F(lhs, rhs);
}
}
}
}
}
template <ComparisonFn<float> F>
inline void BroadcastComparison4DSlow(const ComparisonParams& op_params,
const RuntimeShape& input1_shape,
const float* input1_data,
const RuntimeShape& input2_shape,
const float* input2_data,
const RuntimeShape& output_shape,
bool* output_data) {
BroadcastComparison4DSlowImpl<float, F>(op_params, input1_shape, input1_data,
input2_shape, input2_data,
output_shape, output_data);
}
template <typename T, ComparisonFn<int32_t> F>
inline void BroadcastComparison4DSlowWithScaling(
const ComparisonParams& op_params,
const RuntimeShape& unextended_input1_shape, const T* input1_data,
const RuntimeShape& unextended_input2_shape, const T* input2_data,
const RuntimeShape& unextended_output_shape, bool* output_data) {
const BroadcastComparison4DSlowCommon dims =
BroadcastComparison4DSlowPreprocess(unextended_input1_shape,
unextended_input2_shape,
unextended_output_shape);
int left_shift = op_params.left_shift;
int32_t input1_offset = op_params.input1_offset;
int32_t input1_multiplier = op_params.input1_multiplier;
int input1_shift = op_params.input1_shift;
int32_t input2_offset = op_params.input2_offset;
int32_t input2_multiplier = op_params.input2_multiplier;
int input2_shift = op_params.input2_shift;
for (int b = 0; b < dims.output_shape.Dims(0); ++b) {
for (int y = 0; y < dims.output_shape.Dims(1); ++y) {
for (int x = 0; x < dims.output_shape.Dims(2); ++x) {
for (int c = 0; c < dims.output_shape.Dims(3); ++c) {
const int32_t input1_val =
input1_offset +
input1_data[SubscriptToIndex(dims.desc1, b, y, x, c)];
const int32_t input2_val =
input2_offset +
input2_data[SubscriptToIndex(dims.desc2, b, y, x, c)];
const int32_t shifted_input1_val = input1_val * (1 << left_shift);
const int32_t shifted_input2_val = input2_val * (1 << left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, input1_multiplier, input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, input2_multiplier, input2_shift);
output_data[Offset(dims.output_shape, b, y, x, c)] =
F(scaled_input1_val, scaled_input2_val);
}
}
}
}
}
#define TFLITE_COMPARISON_OP(name) \
inline void name(const ComparisonParams& op_params, \
const RuntimeShape& input1_shape, const float* input1_data, \
const RuntimeShape& input2_shape, const float* input2_data, \
const RuntimeShape& output_shape, bool* output_data) { \
Comparison<name##Fn>(op_params, input1_shape, input1_data, input2_shape, \
input2_data, output_shape, output_data); \
} \
template <typename T> \
inline void name##NoScaling( \
const ComparisonParams& op_params, const RuntimeShape& input1_shape, \
const T* input1_data, const RuntimeShape& input2_shape, \
const T* input2_data, const RuntimeShape& output_shape, \
bool* output_data) { \
ComparisonImpl<T, name##Fn>(op_params, input1_shape, input1_data, \
input2_shape, input2_data, output_shape, \
output_data); \
} \
template <typename T> \
inline void name##WithScaling( \
const ComparisonParams& op_params, const RuntimeShape& input1_shape, \
const T* input1_data, const RuntimeShape& input2_shape, \
const T* input2_data, const RuntimeShape& output_shape, \
bool* output_data) { \
ComparisonWithScaling<T, name##Fn>(op_params, input1_shape, input1_data, \
input2_shape, input2_data, \
output_shape, output_data); \
} \
template <typename T> \
inline void Broadcast4DSlow##name##NoScaling( \
const ComparisonParams& op_params, const RuntimeShape& input1_shape, \
const T* input1_data, const RuntimeShape& input2_shape, \
const T* input2_data, const RuntimeShape& output_shape, \
bool* output_data) { \
BroadcastComparison4DSlowImpl<T, name##Fn>( \
op_params, input1_shape, input1_data, input2_shape, input2_data, \
output_shape, output_data); \
} \
inline void Broadcast4DSlow##name( \
const ComparisonParams& op_params, const RuntimeShape& input1_shape, \
const float* input1_data, const RuntimeShape& input2_shape, \
const float* input2_data, const RuntimeShape& output_shape, \
bool* output_data) { \
BroadcastComparison4DSlow<name##Fn>(op_params, input1_shape, input1_data, \
input2_shape, input2_data, \
output_shape, output_data); \
} \
template <typename T> \
inline void Broadcast4DSlow##name##WithScaling( \
const ComparisonParams& op_params, const RuntimeShape& input1_shape, \
const T* input1_data, const RuntimeShape& input2_shape, \
const T* input2_data, const RuntimeShape& output_shape, \
bool* output_data) { \
BroadcastComparison4DSlowWithScaling<T, name##Fn>( \
op_params, input1_shape, input1_data, input2_shape, input2_data, \
output_shape, output_data); \
}
TFLITE_COMPARISON_OP(Equal);
TFLITE_COMPARISON_OP(NotEqual);
TFLITE_COMPARISON_OP(Greater);
TFLITE_COMPARISON_OP(GreaterEqual);
TFLITE_COMPARISON_OP(Less);
TFLITE_COMPARISON_OP(LessEqual);
#undef TFLITE_COMPARISON_OP
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
template <typename Scalar>
inline void Concatenation(const ConcatenationParams& params,
const RuntimeShape* const* input_shapes,
const Scalar* const* input_data,
const RuntimeShape& output_shape,
Scalar* output_data) {
int axis = params.axis;
int inputs_count = params.inputs_count;
const int concat_dimensions = output_shape.DimensionsCount();
TFLITE_DCHECK_LT(axis, concat_dimensions);
int64_t concat_size = 0;
for (int i = 0; i < inputs_count; i++) {
TFLITE_DCHECK_EQ(input_shapes[i]->DimensionsCount(), concat_dimensions);
for (int j = 0; j < concat_dimensions; j++) {
if (j != axis) {
MatchingDim(*input_shapes[i], j, output_shape, j);
}
}
concat_size += input_shapes[i]->Dims(axis);
}
TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis));
int64_t outer_size = 1;
for (int i = 0; i < axis; ++i) {
outer_size *= output_shape.Dims(i);
}
// For all input arrays,
// FlatSize() = outer_size * Dims(axis) * base_inner_size;
int64_t base_inner_size = 1;
for (int i = axis + 1; i < concat_dimensions; ++i) {
base_inner_size *= output_shape.Dims(i);
}
Scalar* output_ptr = output_data;
for (int k = 0; k < outer_size; k++) {
for (int i = 0; i < inputs_count; ++i) {
const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size;
const Scalar* input_ptr = input_data[i] + k * copy_size;
memcpy(output_ptr, input_ptr, copy_size * sizeof(Scalar));
output_ptr += copy_size;
}
}
}
// TODO(prabhumk): This is the same as the optimized implementation.
// TODO(prabhumk): The quantized implementation of concatentation isn't fully
// quantized as it takes scale as a floating point value. This should be fixed
// when optimizng this routine further.
inline void ConcatenationWithScaling(const ConcatenationParams& params,
const RuntimeShape* const* input_shapes,
const uint8_t* const* input_data,
const RuntimeShape& output_shape,
uint8_t* output_data) {
int axis = params.axis;
const int32_t* input_zeropoint = params.input_zeropoint;
const float* input_scale = params.input_scale;
int inputs_count = params.inputs_count;
const int32_t output_zeropoint = params.output_zeropoint;
const float output_scale = params.output_scale;
const int concat_dimensions = output_shape.DimensionsCount();
TFLITE_DCHECK_LT(axis, concat_dimensions);
int64_t concat_size = 0;
for (int i = 0; i < inputs_count; i++) {
TFLITE_DCHECK_EQ(input_shapes[i]->DimensionsCount(), concat_dimensions);
for (int j = 0; j < concat_dimensions; j++) {
if (j != axis) {
MatchingDim(*input_shapes[i], j, output_shape, j);
}
}
concat_size += input_shapes[i]->Dims(axis);
}
TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis));
int64_t outer_size = 1;
for (int i = 0; i < axis; ++i) {
outer_size *= output_shape.Dims(i);
}
// For all input arrays,
// FlatSize() = outer_size * Dims(axis) * base_inner_size;
int64_t base_inner_size = 1;
for (int i = axis + 1; i < concat_dimensions; ++i) {
base_inner_size *= output_shape.Dims(i);
}
const float inverse_output_scale = 1.f / output_scale;
uint8_t* output_ptr = output_data;
for (int k = 0; k < outer_size; k++) {
for (int i = 0; i < inputs_count; ++i) {
const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size;
const uint8_t* input_ptr = input_data[i] + k * copy_size;
if (input_zeropoint[i] == output_zeropoint &&
input_scale[i] == output_scale) {
memcpy(output_ptr, input_ptr, copy_size);
} else {
const float scale = input_scale[i] * inverse_output_scale;
const float bias = -input_zeropoint[i] * scale;
for (int j = 0; j < copy_size; ++j) {
const int32_t value = static_cast<int32_t>(tflite::TfLiteRound(
input_ptr[j] * scale + bias)) +
output_zeropoint;
output_ptr[j] = static_cast<uint8_t>(
std::max<int32_t>(std::min<int32_t>(255, value), 0));
}
}
output_ptr += copy_size;
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_ops {
inline void Conv(const ConvParams& params, const RuntimeShape& input_shape,
const float* input_data, const RuntimeShape& filter_shape,
const float* filter_data, const RuntimeShape& bias_shape,
const float* bias_data, const RuntimeShape& output_shape,
float* output_data, const RuntimeShape& im2col_shape,
float* im2col_data) {
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const float output_activation_min = params.float_activation_min;
const float output_activation_max = params.float_activation_max;
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
(void)im2col_data; // only used in optimized code.
(void)im2col_shape; // only used in optimized code.
const int batches = 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 (bias_data) {
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
}
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
float total = 0.f;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// If the location is outside the bounds of the input image,
// use zero as a default value.
if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height)) {
float input_value = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
float filter_value =
filter_data[Offset(filter_shape, out_channel, filter_y,
filter_x, in_channel)];
total += (input_value * filter_value);
}
}
}
}
float bias_value = 0.0f;
if (bias_data) {
bias_value = bias_data[out_channel];
}
output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] =
ActivationFunctionWithMinMax(total + bias_value,
output_activation_min,
output_activation_max);
}
}
}
}
}
inline void Conv(const ConvParams& params, const RuntimeShape& input_shape,
const uint8_t* input_data, const RuntimeShape& filter_shape,
const uint8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
uint8_t* output_data, const RuntimeShape& im2col_shape,
uint8_t* im2col_data, void* cpu_backend_context) {
(void)cpu_backend_context; // only used in optimized code.
(void)im2col_data; // only used in optimized code.
(void)im2col_shape; // only used in optimized code.
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int32_t input_offset = params.input_offset;
const int32_t filter_offset = params.weights_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_multiplier = params.output_multiplier;
const int output_shift = params.output_shift;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_LE(output_activation_min, output_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 batches = 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 (bias_data) {
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
}
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
int32_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// If the location is outside the bounds of the input image,
// use zero as a default value.
if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height)) {
int32_t input_val = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val =
filter_data[Offset(filter_shape, out_channel, filter_y,
filter_x, in_channel)];
acc +=
(filter_val + filter_offset) * (input_val + input_offset);
}
}
}
}
if (bias_data) {
acc += bias_data[out_channel];
}
acc = MultiplyByQuantizedMultiplier(acc, output_multiplier,
output_shift);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] =
static_cast<uint8_t>(acc);
}
}
}
}
}
inline void HybridConvPerChannel(
const ConvParams& params, float* scaling_factors_ptr,
const RuntimeShape& input_shape, const int8_t* input_data,
const RuntimeShape& filter_shape, const int8_t* filter_data,
const RuntimeShape& bias_shape, const float* bias_data,
const RuntimeShape& output_shape, float* output_data,
const RuntimeShape& im2col_shape, int8_t* im2col_data,
const float* per_channel_scale, int32_t* input_offset) {
(void)im2col_data; // only used in optimized code.
(void)im2col_shape; // only used in optimized code.
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const float output_activation_min = params.float_activation_min;
const float output_activation_max = params.float_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 batches = 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 (bias_data) {
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
}
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
int32_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// If the location is outside the bounds of the input image,
// use zero as a default value.
if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height)) {
int32_t input_val = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val =
filter_data[Offset(filter_shape, out_channel, filter_y,
filter_x, in_channel)];
acc += filter_val * (input_val - input_offset[batch]);
}
}
}
}
float acc_float =
acc * per_channel_scale[out_channel] * scaling_factors_ptr[batch];
if (bias_data) {
acc_float += bias_data[out_channel];
}
output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] =
ActivationFunctionWithMinMax(acc_float, output_activation_min,
output_activation_max);
}
}
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline void DepthwiseConv(
const DepthwiseParams& params, const RuntimeShape& input_shape,
const float* input_data, const RuntimeShape& filter_shape,
const float* filter_data, const RuntimeShape& bias_shape,
const float* bias_data, const RuntimeShape& output_shape,
float* output_data) {
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int depth_multiplier = params.depth_multiplier;
const float output_activation_min = params.float_activation_min;
const float output_activation_max = params.float_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 batches = MatchingDim(input_shape, 0, output_shape, 0);
const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
for (int b = 0; b < batches; ++b) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int ic = 0; ic < input_depth; ++ic) {
for (int m = 0; m < depth_multiplier; m++) {
const int oc = m + ic * depth_multiplier;
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
float total = 0.f;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// If the location is outside the bounds of the input image,
// use zero as a default value.
if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height)) {
float input_value =
input_data[Offset(input_shape, b, in_y, in_x, ic)];
float filter_value = filter_data[Offset(
filter_shape, 0, filter_y, filter_x, oc)];
total += (input_value * filter_value);
}
}
}
float bias_value = 0.0f;
if (bias_data) {
bias_value = bias_data[oc];
}
output_data[Offset(output_shape, b, out_y, out_x, oc)] =
ActivationFunctionWithMinMax(total + bias_value,
output_activation_min,
output_activation_max);
}
}
}
}
}
}
} // end namespace reference_ops
} // end namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_
#include <algorithm>
#include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
// Used in tests and template parameters to control which version of depthwise
// convolution is called. Primarily for reference code, and specializations
// forced in tests.
enum class DepthwiseConvImplementation {
// Run all tests against kUseStandardEntry even if also testing another
// kernel, since we need to be sure that the main DepthwiseConv() function in
// optimized_ops.h dispatches to a correctly-executing kernel.
kNone = 0, // The "default" option: use the normal
// DepthwiseConv kernel (entry) function.
kUseGenericKernel, // Forced use of generic kernel.
kUseNeon3x3, // 3x3 kernel that uses NEON when available.
kUseNeon3x3DotProduct, // 3x3 kernel that uses dot-product enabled NEON
// when available.
kUseCModel3x3DotProduct, // 3x3 kernel, reference C model that is intended
// to match overall design NEON code.
kUseUnwound3x3DotProduct, // 3x3 kernel, reference C model with unwound loops
// and some arrays.
kUseIntrinsics3x3DotProduct, // 3x3 kernel using NEON intrinsics.
};
// Category of depthwise convolution output rounding.
enum class DepthwiseConvOutputRounding {
kNone = 0, // Invalid: specific method must be specified.
kAwayFromZero, // Original method: exact halves rounded away from zero.
kUpward, // Halves towards +infinity: adds 0.5 before truncate.
// This is where a future kNearestEven would be placed.
};
// Category of depthwise convolution depth multiplication.
enum class DepthwiseConvDepthMultiplication {
kNoMultiplication = 0, // Depth multiplier = 1.
kUnitInputDepth, // Input depth = 1, output depth = depth multiplier.
};
namespace reference_ops {
namespace depthwise_conv {
template <DepthwiseConvOutputRounding output_rounding>
inline int32_t DepthwiseConvRound(int32_t x, int32_t quantized_multiplier,
int shift) {
TFLITE_DCHECK_NE(output_rounding, DepthwiseConvOutputRounding::kNone);
return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift);
}
template <>
inline int32_t DepthwiseConvRound<DepthwiseConvOutputRounding::kAwayFromZero>(
int32_t x, int32_t quantized_multiplier, int shift) {
return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift);
}
template <>
inline int32_t DepthwiseConvRound<DepthwiseConvOutputRounding::kUpward>(
int32_t x, int32_t quantized_multiplier, int shift) {
using gemmlowp::SaturatingRoundingDoublingHighMul;
const int left_shift = shift > 0 ? shift : 0;
const int right_shift = shift > 0 ? 0 : -shift;
const int rounding_offset = right_shift > 0 ? 1 << (right_shift - 1) : 0;
return (SaturatingRoundingDoublingHighMul(x * (1 << left_shift),
quantized_multiplier) +
rounding_offset) >>
right_shift;
}
template <DepthwiseConvOutputRounding output_rounding>
struct DepthwiseConvBasicKernel {
static inline void Run(
const DepthwiseParams& params, const RuntimeShape& input_shape,
const uint8_t* input_data, const RuntimeShape& filter_shape,
const uint8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
uint8_t* output_data) {
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int depth_multiplier = params.depth_multiplier;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
const int32_t input_offset = params.input_offset;
const int32_t filter_offset = params.weights_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_multiplier = params.output_multiplier;
const int output_shift = params.output_shift;
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
for (int b = 0; b < batches; ++b) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int ic = 0; ic < input_depth; ++ic) {
for (int m = 0; m < depth_multiplier; m++) {
const int oc = m + ic * depth_multiplier;
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
int32_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
const int in_x =
in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// If the location is outside the bounds of the input image,
// use zero as a default value.
if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height)) {
int32_t input_val =
input_data[Offset(input_shape, b, in_y, in_x, ic)];
int32_t filter_val = filter_data[Offset(
filter_shape, 0, filter_y, filter_x, oc)];
acc += (filter_val + filter_offset) *
(input_val + input_offset);
}
}
}
if (bias_data) {
acc += bias_data[oc];
}
acc = DepthwiseConvRound<output_rounding>(acc, output_multiplier,
output_shift);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[Offset(output_shape, b, out_y, out_x, oc)] =
static_cast<uint8_t>(acc);
}
}
}
}
}
}
// TODO(b/148596273): Reconcile reference versions, perhaps with common
// MultiplyByQuantizedMultiplier or DepthwiseConvRound function.
static inline void RunPerChannel(
const DepthwiseParams& params, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
int8_t* output_data) {
// Get parameters.
// TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro.
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int depth_multiplier = params.depth_multiplier;
const int32_t input_offset = params.input_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
const int32_t* output_multiplier = params.output_multiplier_per_channel;
const int32_t* output_shift = params.output_shift_per_channel;
// Check dimensions of the tensors.
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
for (int m = 0; m < depth_multiplier; ++m) {
const int output_channel = m + in_channel * depth_multiplier;
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
int32_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
const int in_x =
in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// Zero padding by omitting the areas outside the image.
const bool is_point_inside_image =
(in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height);
if (is_point_inside_image) {
int32_t input_val = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val = filter_data[Offset(
filter_shape, 0, filter_y, filter_x, output_channel)];
// Accumulate with 32 bits accumulator.
// In the nudging process during model quantization, we
// force real value of 0.0 be represented by a quantized
// value. This guarantees that the input_offset is a int8_t,
// even though it is represented using int32_t. int32_t +=
// int8_t
// * (int8_t - int8_t) so the highest value we can get from
// each accumulation is [-127, 127] * ([-128, 127] -
// [-128, 127]), which is [-32512, 32512]. log2(32512)
// = 14.98, which means we can accumulate at least 2^16
// multiplications without overflow. The accumulator is
// applied to a filter so the accumulation logic will hold
// as long as the filter size (filter_y * filter_x *
// in_channel) does not exceed 2^16, which is the case in
// all the models we have seen so far.
acc += filter_val * (input_val + input_offset);
}
}
}
if (bias_data) {
acc += bias_data[output_channel];
}
acc = DepthwiseConvRound<output_rounding>(
acc, output_multiplier[output_channel],
output_shift[output_channel]);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x,
output_channel)] = static_cast<int8_t>(acc);
}
}
}
}
}
}
};
} // namespace depthwise_conv
inline void DepthwiseConv(
const DepthwiseParams& params, const RuntimeShape& input_shape,
const uint8_t* input_data, const RuntimeShape& filter_shape,
const uint8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
uint8_t* output_data) {
return depthwise_conv::DepthwiseConvBasicKernel<
DepthwiseConvOutputRounding::kAwayFromZero>::Run(params, input_shape,
input_data, filter_shape,
filter_data, bias_shape,
bias_data, output_shape,
output_data);
}
} // namespace reference_ops
} // end namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_
#include <limits.h>
#include <vector>
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
// Dequantizes into a float without rounding.
template <typename InputT, typename OutputT>
inline void Dequantize(const tflite::DequantizationParams& op_params,
const RuntimeShape& input_shape,
const InputT* input_data,
const RuntimeShape& output_shape, OutputT* output_data) {
int32_t zero_point = op_params.zero_point;
const double scale = op_params.scale;
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; i++) {
const int32_t val = input_data[i];
const OutputT result = static_cast<OutputT>(scale * (val - zero_point));
output_data[i] = result;
}
}
// Dequantizes per-channel quantized tensor to float.
template <typename T>
inline void PerChannelDequantize(
const tflite::PerChannelDequantizationParams& op_params,
const RuntimeShape& input_shape, const T* input_data,
const RuntimeShape& output_shape, float* output_data) {
// Ensure flat size is same.
MatchingFlatSize(input_shape, output_shape);
const int32_t* zero_point = op_params.zero_point;
const float* scale = op_params.scale;
const int32_t quantized_dimension = op_params.quantized_dimension;
const int32_t num_dims = input_shape.DimensionsCount();
const int32_t* dims_data = input_shape.DimsData();
std::vector<int> current_dim(num_dims, 0);
do {
size_t offset =
ReducedOutputOffset(num_dims, reinterpret_cast<const int*>(dims_data),
current_dim.data(), 0, nullptr);
const int channel = current_dim[quantized_dimension];
const int32_t val = input_data[offset];
const float result =
static_cast<float>(scale[channel] * (val - zero_point[channel]));
output_data[offset] = result;
} while (NextIndex(num_dims, reinterpret_cast<const int*>(dims_data),
current_dim.data()));
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_
#include <cmath>
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline void Floor(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++) {
int offset = i;
output_data[offset] = std::floor(input_data[offset]);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline void FullyConnected(
const FullyConnectedParams& params, const RuntimeShape& input_shape,
const float* input_data, const RuntimeShape& weights_shape,
const float* weights_data, const RuntimeShape& bias_shape,
const float* bias_data, const RuntimeShape& output_shape,
float* output_data) {
const float output_activation_min = params.float_activation_min;
const float output_activation_max = params.float_activation_max;
// TODO(benoitjacob): This really should be:
// const int batches = ArraySize(output_dims, 1);
// but the current --variable_batch hack consists in overwriting the 3rd
// dimension with the runtime batch size, as we don't keep track for each
// array of which dimension is the batch dimension in it.
const int output_dims_count = output_shape.DimensionsCount();
const int weights_dims_count = weights_shape.DimensionsCount();
const int batches = FlatSizeSkipDim(output_shape, output_dims_count - 1);
const int output_depth = MatchingDim(weights_shape, weights_dims_count - 2,
output_shape, output_dims_count - 1);
const int accum_depth = weights_shape.Dims(weights_dims_count - 1);
for (int b = 0; b < batches; ++b) {
for (int out_c = 0; out_c < output_depth; ++out_c) {
float total = 0.f;
for (int d = 0; d < accum_depth; ++d) {
total += input_data[b * accum_depth + d] *
weights_data[out_c * accum_depth + d];
}
float bias_value = 0.0f;
if (bias_data) {
bias_value = bias_data[out_c];
}
output_data[out_c + output_depth * b] = ActivationFunctionWithMinMax(
total + bias_value, output_activation_min, output_activation_max);
}
}
}
inline void FullyConnected(
const FullyConnectedParams& params, const RuntimeShape& input_shape,
const uint8_t* input_data, const RuntimeShape& filter_shape,
const uint8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
uint8_t* output_data) {
const int32_t input_offset = params.input_offset;
const int32_t filter_offset = params.weights_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_multiplier = params.output_multiplier;
const int output_shift = params.output_shift;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2);
TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
// TODO(benoitjacob): This really should be:
// const int batches = ArraySize(output_dims, 1);
// but the current --variable_batch hack consists in overwriting the 3rd
// dimension with the runtime batch size, as we don't keep track for each
// array of which dimension is the batch dimension in it.
const int output_dim_count = output_shape.DimensionsCount();
const int filter_dim_count = filter_shape.DimensionsCount();
const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1);
const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2,
output_shape, output_dim_count - 1);
const int accum_depth = filter_shape.Dims(filter_dim_count - 1);
for (int b = 0; b < batches; ++b) {
for (int out_c = 0; out_c < output_depth; ++out_c) {
int32_t acc = 0;
for (int d = 0; d < accum_depth; ++d) {
int32_t input_val = input_data[b * accum_depth + d];
int32_t filter_val = filter_data[out_c * accum_depth + d];
acc += (filter_val + filter_offset) * (input_val + input_offset);
}
if (bias_data) {
acc += bias_data[out_c];
}
acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[out_c + output_depth * b] = static_cast<uint8_t>(acc);
}
}
}
inline void FullyConnected(
const FullyConnectedParams& params, const RuntimeShape& input_shape,
const uint8_t* input_data, const RuntimeShape& filter_shape,
const uint8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
int16_t* output_data) {
const int32_t input_offset = params.input_offset;
const int32_t filter_offset = params.weights_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_multiplier = params.output_multiplier;
const int output_shift = params.output_shift;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
TFLITE_DCHECK_EQ(output_offset, 0);
// TODO(benoitjacob): This really should be:
// const int batches = ArraySize(output_dims, 1);
// but the current --variable_batch hack consists in overwriting the 3rd
// dimension with the runtime batch size, as we don't keep track for each
// array of which dimension is the batch dimension in it.
const int output_dim_count = output_shape.DimensionsCount();
const int filter_dim_count = filter_shape.DimensionsCount();
const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1);
const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2,
output_shape, output_dim_count - 1);
const int accum_depth = filter_shape.Dims(filter_dim_count - 1);
for (int b = 0; b < batches; ++b) {
for (int out_c = 0; out_c < output_depth; ++out_c) {
// Internal accumulation.
// Initialize accumulator with the bias-value.
int32_t accum = bias_data[out_c];
// Accumulation loop.
for (int d = 0; d < accum_depth; ++d) {
int16_t input_val = input_data[b * accum_depth + d] + input_offset;
int16_t filter_val =
filter_data[out_c * accum_depth + d] + filter_offset;
accum += filter_val * input_val;
}
// Down-scale the final int32_t accumulator to the scale used by our
// (16-bit, typically 3 integer bits) fixed-point format. The quantized
// multiplier and shift here have been pre-computed offline
// (e.g. by toco).
accum =
MultiplyByQuantizedMultiplier(accum, output_multiplier, output_shift);
// Saturate, cast to int16_t, and store to output array.
accum = std::max(accum, output_activation_min - output_offset);
accum = std::min(accum, output_activation_max - output_offset);
accum += output_offset;
output_data[out_c + output_depth * b] = accum;
}
}
}
inline void ShuffledFullyConnected(
const FullyConnectedParams& params, const RuntimeShape& input_shape,
const uint8_t* input_data, const RuntimeShape& weights_shape,
const uint8_t* shuffled_weights_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
int16_t* output_data, uint8_t* shuffled_input_workspace_data) {
const int32_t output_multiplier = params.output_multiplier;
const int output_shift = params.output_shift;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
TFLITE_DCHECK_GE(input_shape.DimensionsCount(), 1);
TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2);
TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1);
// TODO(benoitjacob): This really should be:
// const int batches = ArraySize(output_dims, 1);
// but the current --variable_batch hack consists in overwriting the 3rd
// dimension with the runtime batch size, as we don't keep track for each
// array of which dimension is the batch dimension in it.
const int output_dim_count = output_shape.DimensionsCount();
const int weights_dim_count = weights_shape.DimensionsCount();
const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1);
const int output_depth = MatchingDim(weights_shape, weights_dim_count - 2,
output_shape, output_dim_count - 1);
const int accum_depth = weights_shape.Dims(weights_dim_count - 1);
TFLITE_DCHECK((accum_depth % 16) == 0);
TFLITE_DCHECK((output_depth % 4) == 0);
// Shuffling and xoring of input activations into the workspace buffer
uint8_t* shuffled_input_workspace_ptr = shuffled_input_workspace_data;
if (batches == 1) {
for (int i = 0; i < accum_depth; i++) {
shuffled_input_workspace_data[i] = input_data[i] ^ 0x80;
}
} else if (batches == 4) {
for (int c = 0; c < accum_depth; c += 16) {
for (int b = 0; b < 4; b++) {
const uint8_t* src_data_ptr = input_data + b * accum_depth + c;
for (int j = 0; j < 16; j++) {
uint8_t src_val = *src_data_ptr++;
// Flip the sign bit, so that the kernel will only need to
// reinterpret these uint8_t values as int8_t, getting for free the
// subtraction of the zero_point value 128.
uint8_t dst_val = src_val ^ 0x80;
*shuffled_input_workspace_ptr++ = dst_val;
}
}
}
} else {
TFLITE_DCHECK(false);
return;
}
// Actual computation
if (batches == 1) {
int16_t* output_ptr = output_data;
// Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd)
// so that just reinterpreting them as int8_t values is equivalent to
// subtracting 128 from them, thus implementing for free the subtraction of
// the zero_point value 128.
const int8_t* shuffled_weights_ptr =
reinterpret_cast<const int8_t*>(shuffled_weights_data);
// Likewise, we preshuffled and pre-xored the input data above.
const int8_t* shuffled_input_data =
reinterpret_cast<const int8_t*>(shuffled_input_workspace_data);
for (int c = 0; c < output_depth; c += 4) {
// Internal accumulation.
// Initialize accumulator with the bias-value.
int32_t accum[4] = {0};
// Accumulation loop.
for (int d = 0; d < accum_depth; d += 16) {
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 16; j++) {
int8_t input_val = shuffled_input_data[d + j];
int8_t weights_val = *shuffled_weights_ptr++;
accum[i] += weights_val * input_val;
}
}
}
for (int i = 0; i < 4; i++) {
// Add bias value
int32_t acc = accum[i] + bias_data[c + i];
// Down-scale the final int32_t accumulator to the scale used by our
// (16-bit, typically 3 integer bits) fixed-point format. The quantized
// multiplier and shift here have been pre-computed offline
// (e.g. by toco).
acc =
MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift);
// Saturate, cast to int16_t, and store to output array.
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_ptr[c + i] = acc;
}
}
} else if (batches == 4) {
int16_t* output_ptr = output_data;
// Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd)
// so that just reinterpreting them as int8_t values is equivalent to
// subtracting 128 from them, thus implementing for free the subtraction of
// the zero_point value 128.
const int8_t* shuffled_weights_ptr =
reinterpret_cast<const int8_t*>(shuffled_weights_data);
// Likewise, we preshuffled and pre-xored the input data above.
const int8_t* shuffled_input_data =
reinterpret_cast<const int8_t*>(shuffled_input_workspace_data);
for (int c = 0; c < output_depth; c += 4) {
const int8_t* shuffled_input_ptr = shuffled_input_data;
// Accumulation loop.
// Internal accumulation.
// Initialize accumulator with the bias-value.
int32_t accum[4][4];
for (int i = 0; i < 4; i++) {
for (int b = 0; b < 4; b++) {
accum[i][b] = 0;
}
}
for (int d = 0; d < accum_depth; d += 16) {
for (int i = 0; i < 4; i++) {
for (int b = 0; b < 4; b++) {
for (int j = 0; j < 16; j++) {
int8_t input_val = shuffled_input_ptr[16 * b + j];
int8_t weights_val = shuffled_weights_ptr[16 * i + j];
accum[i][b] += weights_val * input_val;
}
}
}
shuffled_input_ptr += 64;
shuffled_weights_ptr += 64;
}
for (int i = 0; i < 4; i++) {
for (int b = 0; b < 4; b++) {
// Add bias value
int32_t acc = accum[i][b] + bias_data[c + i];
// Down-scale the final int32_t accumulator to the scale used by our
// (16-bit, typically 3 integer bits) fixed-point format. The
// quantized multiplier and shift here have been pre-computed offline
// (e.g. by toco).
acc = MultiplyByQuantizedMultiplier(acc, output_multiplier,
output_shift);
// Saturate, cast to int16_t, and store to output array.
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_ptr[b * output_depth + c + i] = acc;
}
}
}
} else {
TFLITE_DCHECK(false);
return;
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ACTIVATIONS_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ACTIVATIONS_H_
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline int16_t SaturatingLeftShift(int16_t value, int amount) {
int32_t result = static_cast<int32_t>(value) * (1 << amount);
result = std::min<int32_t>(result, std::numeric_limits<int16_t>::max());
result = std::max<int32_t>(result, std::numeric_limits<int16_t>::min());
return result;
}
// Similar to ARM instruction SQDMULH.
// Similar to gemmlowp::SaturatingRoundingDoublingHighMul except
// rounding to zero instead of to nearest (SQRDMULH).
inline std::int16_t SaturatingDoublingHighMul(std::int16_t a, std::int16_t b) {
bool overflow = a == b && a == std::numeric_limits<std::int16_t>::min();
std::int32_t a_32(a);
std::int32_t b_32(b);
std::int32_t ab_32 = a_32 * b_32;
std::int16_t ab_x2_high16 = static_cast<std::int16_t>((ab_32) / (1 << 15));
return overflow ? std::numeric_limits<std::int16_t>::max() : ab_x2_high16;
}
template <typename T>
inline void HardSwish(const RuntimeShape& input_shape, const T* input_data,
const RuntimeShape& output_shape, T* output_data) {
ruy::profiler::ScopeLabel label("ReferenceHardSwish/Float");
auto matching_size = MatchingFlatSize(input_shape, output_shape);
const T* in_end = input_data + matching_size;
for (; input_data < in_end; input_data++, output_data++) {
const float in = *input_data;
*output_data =
in * std::min(static_cast<T>(6), std::max(static_cast<T>(0), in + 3)) /
6;
}
}
template <typename T>
inline void HardSwish(const HardSwishParams& params,
const RuntimeShape& input_shape, const T* input_data,
const RuntimeShape& output_shape, T* output_data) {
ruy::profiler::ScopeLabel label("ReferenceHardSwish/Quantized");
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; i++) {
const int16_t input_value = input_data[i] - params.input_zero_point;
// Left-shift as much as we can without overflow/saturation to put
// significant bits in the high bits of our 16-bit fixedpoint values, so
// that fixed-point approximate computations below are as accurate as
// possible.
const int16_t input_value_on_hires_input_scale = input_value * (1 << 7);
// Compute the input value on essentially the output scale, just not
// right-shifted yet. This is the value that we'll use in the (x >= +3)
// case, and that in the general case we'll multiply against the "relu-ish"
// fixed-point multiplier in [0, 1].
const int16_t input_value_on_preshift_output_scale =
gemmlowp::SaturatingRoundingDoublingHighMul(
input_value_on_hires_input_scale,
params.output_multiplier_fixedpoint_int16);
// Now compute the "relu-ish multiplier". In the (-3 <= x <= +3) case, that
// is just an affine rescaling of x from [-3, 3] to [0, 1]. In the general
// case, it is just that plus saturation at the boundaries of [-3, 3].
// First, we rescale from [-3, 3] to [-1, 1], saturating.
// That is done by rescaling the input value with a fixed-point multiplier
// (reluish_multiplier_fixedpoint) and bit-shift such that we represent
// that input value on the scale where the real value 3.0f is represented
// by the quantized value 32768. (+32768 is actually not representable as
// int16_t, so this saturates at +32767, and that is seen empirically to be
// a negligible contribution to numerical error/bias).
//
// This code is careful to correctly implement any magnitude of multiplier,
// involving either a right shift or a left shift, with correct saturation
// behavior in the left-shift case. This forces this code to be more
// complicated, but is necessary for real applications: a partially
// trained quantized MobileNet v3-small model that motivated this code
// exhibits some large [min, max] range boundaries, of the order of
// magnitude of 10 or 100 depending on layers.
//
// The next few lines are basically just an ordinary
// MultiplyByQuantizedMultiplier, except that we are more careful here
// about the fine details of saturation when left-shifting, because here
// overflow in left-shift is a common case, not an anomaly as
// MultiplyByQuantizedMultiplier assumes.
int16_t reluish_value = input_value_on_hires_input_scale;
// Shift left, saturating, as much as we can while ensuring that this
// saturation will not contribute to the result. That is, left shift amount
// reduced by 1.
if (params.reluish_multiplier_exponent > 0) {
reluish_value = SaturatingLeftShift(
reluish_value, params.reluish_multiplier_exponent - 1);
}
// Apply the fixed-point multiplier, dividing the value by a divisor
// ranging in [1, 2].
reluish_value = gemmlowp::SaturatingRoundingDoublingHighMul(
reluish_value, params.reluish_multiplier_fixedpoint_int16);
// Apply the last bit of left-shift. Thus, in the left-shifting case, if
// any saturation affects the result, it is happening here --- any
// saturation having occurred above is overwritten here, not affecting the
// result.
if (params.reluish_multiplier_exponent > 0) {
reluish_value = SaturatingLeftShift(reluish_value, 1);
}
// Shift right, in the right-shifting case.
if (params.reluish_multiplier_exponent < 0) {
reluish_value = gemmlowp::RoundingDivideByPOT(
reluish_value, -params.reluish_multiplier_exponent);
}
// At this point we have rescaled the value into a 16bit fixedpoint
// reluish_value in [-1, 1].
// We now convert that to a 16bit fixedpoint value in [0, 1].
reluish_value = (reluish_value + (1 << 15)) >> 1;
// Use of SaturatingDoublingHighMul here is important to cancel the biases
// from the above SaturatingRoundingDoublingHighMul.
//
// On a partially trained MobileNet-v3-small,
//
// | bias on | ImageNet
// | quantized | Top-1
// Operation used here | values | accuracy (50k)
// --------------------------------------+------------+-----------
// SaturatingDoublingHighMul | -0.0024 | 58.920
// SaturatingRoundingDoublingHighMul | -0.0067 | 58.064
//
// In activations_test, this is covered by this testcase:
// QuantizedActivationsOpTest.HardSwishBias
//
const int16_t preshift_output_value = SaturatingDoublingHighMul(
reluish_value, input_value_on_preshift_output_scale);
// We were so far operating on the pre-shift output scale. Now we finally
// apply that output shift, arriving at the final output scale.
int16_t output_value = gemmlowp::RoundingDivideByPOT(
preshift_output_value, -params.output_multiplier_exponent);
output_value += params.output_zero_point;
output_value =
std::min<int16_t>(output_value, std::numeric_limits<T>::max());
output_value =
std::max<int16_t>(output_value, std::numeric_limits<T>::min());
output_data[i] = output_value;
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_
#include <limits>
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_integer_ops {
inline void CheckArithmeticParams(const ArithmeticParams& params) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
// Input offset is negative input zero point. Activation tensors are
// asymmetric quantized so they span the full int8 range.
TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits<int8_t>::min());
TFLITE_DCHECK_GE(-params.input2_offset, std::numeric_limits<int8_t>::min());
TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits<int8_t>::max());
TFLITE_DCHECK_LE(-params.input2_offset, std::numeric_limits<int8_t>::max());
}
// Element-wise add that can often be used for inner loop of broadcast add as
// well as the non-broadcast add.
inline void AddElementwise(int size, const ArithmeticParams& params,
const int8_t* input1_data, const int8_t* input2_data,
int8_t* output_data) {
CheckArithmeticParams(params);
for (int i = 0; i < size; ++i) {
const int32_t input1_val = params.input1_offset + input1_data[i];
const int32_t input2_val = params.input2_offset + input2_data[i];
const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier, params.input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier, params.input2_shift);
const int32_t raw_sum = scaled_input1_val + scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sum, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[i] = static_cast<int8_t>(clamped_output);
}
}
inline void Add(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const int8_t* input1_data,
const RuntimeShape& input2_shape, const int8_t* input2_data,
const RuntimeShape& output_shape, int8_t* output_data) {
CheckArithmeticParams(params);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
AddElementwise(flat_size, params, input1_data, input2_data, output_data);
}
inline void BroadcastAdd4DSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int8_t* input1_data,
const RuntimeShape& input2_shape,
const int8_t* input2_data,
const RuntimeShape& output_shape,
int8_t* output_data) {
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
const int32_t input1_val =
params.input1_offset +
input1_data[SubscriptToIndex(desc1, b, y, x, c)];
const int32_t input2_val =
params.input2_offset +
input2_data[SubscriptToIndex(desc2, b, y, x, c)];
const int32_t shifted_input1_val =
input1_val * (1 << params.left_shift);
const int32_t shifted_input2_val =
input2_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier,
params.input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier,
params.input2_shift);
const int32_t raw_sum = scaled_input1_val + scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sum, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[Offset(extended_output_shape, b, y, x, c)] =
static_cast<int8_t>(clamped_output);
}
}
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
// Fixed-point per-channel-quantization convolution reference kernel.
inline void ConvPerChannel(
const ConvParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
int8_t* output_data) {
// Get parameters.
const int32_t input_offset = params.input_offset; // r = s(q - Z)
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int32_t output_offset = params.output_offset;
// Set min and max value of the output.
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
// Consistency check.
TFLITE_DCHECK_LE(output_activation_min, output_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 batches = 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 (bias_data) {
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
}
// Check dimensions of the tensors.
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
int32_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// Zero padding by omitting the areas outside the image.
const bool is_point_inside_image =
(in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height);
if (is_point_inside_image) {
int32_t input_val = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val =
filter_data[Offset(filter_shape, out_channel, filter_y,
filter_x, in_channel)];
// Accumulate with 32 bits accumulator.
// In the nudging process during model quantization, we force
// real value of 0.0 be represented by a quantized value. This
// guarantees that the input_offset is a int8_t, even though
// it is represented using int32_t. int32_t += int8_t *
// (int8_t - int8_t) so the highest value we can get from each
// accumulation is [-127, 127] * ([-128, 127] -
// [-128, 127]), which is [-32512, 32512]. log2(32512)
// = 14.98, which means we can accumulate at least 2^16
// multiplications without overflow. The accumulator is
// applied to a filter so the accumulation logic will hold as
// long as the filter size (filter_y * filter_x * in_channel)
// does not exceed 2^16, which is the case in all the models
// we have seen so far.
// TODO(jianlijianli): Add a check to make sure the
// accumulator depth is smaller than 2^16.
acc += filter_val * (input_val + input_offset);
}
}
}
}
if (bias_data) {
acc += bias_data[out_channel];
}
acc = MultiplyByQuantizedMultiplier(
acc, output_multiplier[out_channel], output_shift[out_channel]);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] =
static_cast<int8_t>(acc);
}
}
}
}
}
// Fixed-point per-channel-quantization convolution reference kernel.
// 16-bit data and 8-bit filter
inline void ConvPerChannel(
const ConvParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const RuntimeShape& input_shape,
const int16_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const std::int64_t* bias_data, const RuntimeShape& output_shape,
int16_t* output_data) {
// Get parameters.
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
// Set min and max value of the output.
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
// Consistency check.
TFLITE_DCHECK_LE(output_activation_min, output_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 batches = 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 (bias_data) {
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
}
// Check dimensions of the tensors.
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
std::int64_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// Zero padding by omitting the areas outside the image.
const bool is_point_inside_image =
(in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height);
if (is_point_inside_image) {
int32_t input_val = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val =
filter_data[Offset(filter_shape, out_channel, filter_y,
filter_x, in_channel)];
// Accumulate with 64 bits accumulator.
// int64_t += int8_t * int16_t so the highest value we can
// get from each accumulation is [-127, 127] * ([-32768,
// 32767] -
// [-32768, 32767]), which is [-8322945, 8322945].
// log2(8322945) = 22.99.
acc += filter_val * input_val;
}
}
}
}
if (bias_data) {
acc += bias_data[out_channel];
}
int32_t scaled_acc = MultiplyByQuantizedMultiplier(
acc, output_multiplier[out_channel], output_shift[out_channel]);
scaled_acc = std::max(scaled_acc, output_activation_min);
scaled_acc = std::min(scaled_acc, output_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] =
static_cast<int16_t>(scaled_acc);
}
}
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
inline void DepthwiseConvPerChannel(
const DepthwiseParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
int8_t* output_data) {
// Get parameters.
// TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro.
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int depth_multiplier = params.depth_multiplier;
const int32_t input_offset = params.input_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
// Check dimensions of the tensors.
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
for (int m = 0; m < depth_multiplier; ++m) {
const int output_channel = m + in_channel * depth_multiplier;
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
int32_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// Zero padding by omitting the areas outside the image.
const bool is_point_inside_image =
(in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height);
if (is_point_inside_image) {
int32_t input_val = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val = filter_data[Offset(
filter_shape, 0, filter_y, filter_x, output_channel)];
// Accumulate with 32 bits accumulator.
// In the nudging process during model quantization, we force
// real value of 0.0 be represented by a quantized value. This
// guarantees that the input_offset is a int8_t, even though
// it is represented using int32_t. int32_t += int8_t *
// (int8_t - int8_t) so the highest value we can get from each
// accumulation is [-127, 127] * ([-128, 127] -
// [-128, 127]), which is [-32512, 32512]. log2(32512)
// = 14.98, which means we can accumulate at least 2^16
// multiplications without overflow. The accumulator is
// applied to a filter so the accumulation logic will hold as
// long as the filter size (filter_y * filter_x * in_channel)
// does not exceed 2^16, which is the case in all the models
// we have seen so far.
// TODO(jianlijianli): Add a check to make sure the
// accumulator depth is smaller than 2^16.
acc += filter_val * (input_val + input_offset);
}
}
}
if (bias_data) {
acc += bias_data[output_channel];
}
acc = MultiplyByQuantizedMultiplier(
acc, output_multiplier[output_channel],
output_shift[output_channel]);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x,
output_channel)] = static_cast<int8_t>(acc);
}
}
}
}
}
}
inline void DepthwiseConvPerChannel(
const DepthwiseParams& params, const int32_t* output_multiplier,
const int32_t* output_shift, const RuntimeShape& input_shape,
const int16_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const std::int64_t* bias_data, const RuntimeShape& output_shape,
int16_t* output_data) {
// Get parameters.
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int depth_multiplier = params.depth_multiplier;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
// Check dimensions of the tensors.
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
for (int m = 0; m < depth_multiplier; ++m) {
const int output_channel = m + in_channel * depth_multiplier;
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
std::int64_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// Zero padding by omitting the areas outside the image.
const bool is_point_inside_image =
(in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height);
if (is_point_inside_image) {
int32_t input_val = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val = filter_data[Offset(
filter_shape, 0, filter_y, filter_x, output_channel)];
// Accumulate with 64 bits accumulator.
// We assume maximum of 2^16 accumulations as with the 8-bit
// case so actually the value in the accumulator should not
// exceed 40 bits
acc += static_cast<int64_t>(filter_val) *
static_cast<int64_t>(input_val);
}
}
}
if (bias_data) {
acc += bias_data[output_channel];
}
int32_t scaled_acc = MultiplyByQuantizedMultiplier(
acc, output_multiplier[output_channel],
output_shift[output_channel]);
scaled_acc = std::max(scaled_acc, output_activation_min);
scaled_acc = std::min(scaled_acc, output_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x,
output_channel)] =
static_cast<int16_t>(scaled_acc);
}
}
}
}
}
}
inline void DepthwiseConvHybridPerChannel(
const DepthwiseParams& params, float* scaling_factors_ptr,
const RuntimeShape& input_shape, const int8_t* input_data,
const RuntimeShape& filter_shape, const int8_t* filter_data,
const RuntimeShape& bias_shape, const float* bias_data,
const RuntimeShape& output_shape, float* output_data,
const float* per_channel_scale, int32_t* input_offset) {
const int stride_width = params.stride_width;
const int stride_height = params.stride_height;
const int dilation_width_factor = params.dilation_width_factor;
const int dilation_height_factor = params.dilation_height_factor;
const int pad_width = params.padding_values.width;
const int pad_height = params.padding_values.height;
const int depth_multiplier = params.depth_multiplier;
const float output_activation_min = params.float_activation_min;
const float output_activation_max = params.float_activation_max;
// Check dimensions of the tensors.
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int input_depth = input_shape.Dims(3);
const int filter_height = filter_shape.Dims(1);
const int filter_width = filter_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int bias_depth = bias_shape.FlatSize();
TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
TFLITE_DCHECK_EQ(bias_depth, output_depth);
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
for (int m = 0; m < depth_multiplier; ++m) {
const int output_channel = m + in_channel * depth_multiplier;
const int in_x_origin = (out_x * stride_width) - pad_width;
const int in_y_origin = (out_y * stride_height) - pad_height;
int32_t acc = 0;
for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
const int in_x = in_x_origin + dilation_width_factor * filter_x;
const int in_y =
in_y_origin + dilation_height_factor * filter_y;
// Zero padding by omitting the areas outside the image.
const bool is_point_inside_image =
(in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
(in_y < input_height);
if (is_point_inside_image) {
int32_t input_val = input_data[Offset(
input_shape, batch, in_y, in_x, in_channel)];
int32_t filter_val = filter_data[Offset(
filter_shape, 0, filter_y, filter_x, output_channel)];
acc += filter_val * (input_val - input_offset[batch]);
}
}
}
float acc_float = static_cast<float>(acc);
acc_float *=
per_channel_scale[output_channel] * scaling_factors_ptr[batch];
if (bias_data && output_channel < bias_depth) {
acc_float += bias_data[output_channel];
}
output_data[Offset(output_shape, batch, out_y, out_x,
output_channel)] =
ActivationFunctionWithMinMax(acc_float, output_activation_min,
output_activation_max);
}
}
}
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
inline void FullyConnected(
const FullyConnectedParams& params, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const int32_t* bias_data, const RuntimeShape& output_shape,
int8_t* output_data) {
const int32_t input_offset = params.input_offset;
const int32_t filter_offset = params.weights_offset;
const int32_t output_offset = params.output_offset;
const int32_t output_multiplier = params.output_multiplier;
const int output_shift = params.output_shift;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int filter_dim_count = filter_shape.DimensionsCount();
const int batches = output_shape.Dims(0);
const int output_depth = output_shape.Dims(1);
TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2));
const int accum_depth = filter_shape.Dims(filter_dim_count - 1);
for (int b = 0; b < batches; ++b) {
for (int out_c = 0; out_c < output_depth; ++out_c) {
int32_t acc = 0;
for (int d = 0; d < accum_depth; ++d) {
int32_t input_val = input_data[b * accum_depth + d];
int32_t filter_val = filter_data[out_c * accum_depth + d];
acc += (filter_val + filter_offset) * (input_val + input_offset);
}
if (bias_data) {
acc += bias_data[out_c];
}
acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift);
acc += output_offset;
acc = std::max(acc, output_activation_min);
acc = std::min(acc, output_activation_max);
output_data[out_c + output_depth * b] = static_cast<int8_t>(acc);
}
}
}
inline void FullyConnected(
const FullyConnectedParams& params, const RuntimeShape& input_shape,
const int16_t* input_data, const RuntimeShape& filter_shape,
const int8_t* filter_data, const RuntimeShape& bias_shape,
const int64_t* bias_data, const RuntimeShape& output_shape,
int16_t* output_data) {
const int32_t filter_offset = params.weights_offset;
const int32_t output_multiplier = params.output_multiplier;
const int output_shift = params.output_shift;
const int32_t output_activation_min = params.quantized_activation_min;
const int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2);
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int filter_dim_count = filter_shape.DimensionsCount();
const int batches = output_shape.Dims(0);
const int output_depth = output_shape.Dims(1);
TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2));
const int accum_depth = filter_shape.Dims(filter_dim_count - 1);
for (int b = 0; b < batches; ++b) {
for (int out_c = 0; out_c < output_depth; ++out_c) {
int64_t acc = 0;
for (int d = 0; d < accum_depth; ++d) {
int32_t input_val = input_data[b * accum_depth + d];
int32_t filter_val = filter_data[out_c * accum_depth + d];
acc += (filter_val + filter_offset) * input_val;
}
if (bias_data) {
acc += bias_data[out_c];
}
int32_t acc_scaled =
MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift);
acc_scaled = std::max(acc_scaled, output_activation_min);
acc_scaled = std::min(acc_scaled, output_activation_max);
output_data[out_c + output_depth * b] = static_cast<int16_t>(acc_scaled);
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
inline void L2Normalization(int32_t input_zero_point, int32_t outer_size,
int32_t depth, const int8_t* input_data,
int8_t* output_data) {
static constexpr int8_t kMinInt8 = std::numeric_limits<int8_t>::min();
static constexpr int8_t kMaxInt8 = std::numeric_limits<int8_t>::max();
// The output scale must be in sync with Prepare().
// Output is in 1/128 scale so the actual output range is nudged from [-1, 1]
// to [-1, 127/128].
static constexpr int32_t kOutputScale = 7;
for (int outer_index = 0; outer_index < outer_size; ++outer_index) {
// int32_t = (int8_t - int8_t) ^ 2.
// ([-128, 127] - [-128, 127]) ^ 2 = [0, (2^8 - 1)^2] so the accumulator is
// safe from overflowing in at least 2^16 steps.
int32_t acc = 0;
for (int inner_index = 0; inner_index < depth; ++inner_index) {
int32_t input =
input_data[depth * outer_index + inner_index] - input_zero_point;
acc += input * input;
}
int32_t inv_l2norm_multiplier;
int inv_l2norm_shift;
GetInvSqrtQuantizedMultiplierExp(acc, kReverseShift, &inv_l2norm_multiplier,
&inv_l2norm_shift);
for (int inner_index = 0; inner_index < depth; ++inner_index) {
int32_t input =
input_data[depth * outer_index + inner_index] - input_zero_point;
// Rescale and downcast. Rescale is folded into the division.
int32_t output_in_q24 = MultiplyByQuantizedMultiplier(
input, inv_l2norm_multiplier, inv_l2norm_shift + kOutputScale);
output_in_q24 =
std::min(static_cast<int32_t>(kMaxInt8),
std::max(static_cast<int32_t>(kMinInt8), output_in_q24));
output_data[depth * outer_index + inner_index] =
static_cast<int8_t>(output_in_q24);
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_
#include <limits>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
inline void Logistic(int32_t input_zero_point, int32_t input_range_radius,
int32_t input_multiplier, int32_t input_left_shift,
int32_t input_size, const int8_t* input_data,
int8_t* output_data) {
// Integer bits must be in sync with Prepare() function.
static constexpr int32_t kInputIntegerBits = 4;
static constexpr int32_t kOutputIntegerBits = 8;
static constexpr int8_t kMinInt8 = std::numeric_limits<int8_t>::min();
static constexpr int8_t kMaxInt8 = std::numeric_limits<int8_t>::max();
static constexpr int32_t kOutputZeroPoint = -128;
for (int i = 0; i < input_size; ++i) {
const int32_t input =
static_cast<int32_t>(input_data[i]) - input_zero_point;
if (input <= -input_range_radius) {
output_data[i] = kMinInt8;
} else if (input >= input_range_radius) {
output_data[i] = kMaxInt8;
} else {
const int32_t input_in_q4 = MultiplyByQuantizedMultiplier(
input, input_multiplier, input_left_shift);
using FixedPoint4 = gemmlowp::FixedPoint<int32_t, kInputIntegerBits>;
const int32_t output_in_q0 =
gemmlowp::logistic(FixedPoint4::FromRaw(input_in_q4)).raw();
// Rescale and downcast.
using gemmlowp::RoundingDivideByPOT;
int32_t output_in_q23 =
RoundingDivideByPOT(output_in_q0, 31 - kOutputIntegerBits);
output_in_q23 = std::min(std::max(output_in_q23 + kOutputZeroPoint,
static_cast<int32_t>(kMinInt8)),
static_cast<int32_t>(kMaxInt8));
output_data[i] = static_cast<int8_t>(output_in_q23);
}
}
}
inline void Logistic(int32_t input_multiplier, int32_t input_size,
const int16_t* ptr_input_data, int16_t* ptr_output_data) {
// We use the LUT for sigmoid and take into account, that
// tanh(x) = 2*sigmoid(2*x) - 1
int32_t input_data_mul = (input_multiplier > 0) ? input_multiplier : 1;
for (int i = 0; i < input_size; ++i, ptr_input_data++, ptr_output_data++) {
int32_t input_data = (*ptr_input_data) * input_data_mul;
// Scale by 3/4 to expand range [-8,8]->[-10.7,10.7] and
// we do interpolation on unsigned values.
uint32_t abs_input_data = 3 * abs(input_data);
// We divide by 2 power of 9, because
// we need to divide by 2 in power of 7 for
// the input conversion + 1/4 from the scale above.
uint8_t uh = abs_input_data >> 9;
uint32_t ua = sigmoid_table_uint16[uh];
uint32_t ub = sigmoid_table_uint16[uh + 1];
uint32_t ut = abs_input_data & 0x1ff;
// Interpolation is done using the fractional bit.
uint32_t result = (ua << 9) + ut * (ub - ua);
result = (input_data >= 0) ? (result + (1 << 9))
: ((1 << (16 + 9)) - result + (1 << 9) - 1);
// Back to 16-bit.
result >>= 10;
*ptr_output_data = result;
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_
#include "fixedpoint/fixedpoint.h"
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
template <typename T>
inline void MulElementwise(int size, const ArithmeticParams& params,
const T* input1_data, const T* input2_data,
T* output_data) {
for (int i = 0; i < size; ++i) {
const int32_t input1_val = params.input1_offset + input1_data[i];
const int32_t input2_val = params.input2_offset + input2_data[i];
const int32_t unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplier(input1_val * input2_val,
params.output_multiplier,
params.output_shift);
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));
output_data[i] = static_cast<T>(clamped_output);
}
}
template <typename T>
inline void Mul(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const T* input1_data,
const RuntimeShape& input2_shape, const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
ruy::profiler::ScopeLabel label("Mul/8bit");
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
MulElementwise(flat_size, params, input1_data, input2_data, output_data);
}
// Mul with 16 bit inputs and int8_t outputs.
inline void Mul(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const int16_t* input1_data,
const RuntimeShape& input2_shape, const int16_t* input2_data,
const RuntimeShape& output_shape, int8_t* output_data) {
ruy::profiler::ScopeLabel label("Mul/Int16Int8");
int32_t output_offset = params.output_offset;
int32_t output_activation_min = params.quantized_activation_min;
int32_t output_activation_max = params.quantized_activation_max;
TFLITE_DCHECK_LE(output_activation_min, output_activation_max);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; i++) {
// F0 uses 0 integer bits, range [-1, 1].
using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
F0 unclamped_result =
F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]);
int16_t rescaled_result =
gemmlowp::RoundingDivideByPOT(unclamped_result.raw(), 8);
int16_t clamped_result = std::min<int16_t>(
output_activation_max - output_offset, rescaled_result);
clamped_result = std::max<int16_t>(output_activation_min - output_offset,
clamped_result);
output_data[i] = output_offset + clamped_result;
}
}
template <typename T>
inline void BroadcastMul4DSlow(
const ArithmeticParams& params, const RuntimeShape& input1_shape,
const T* input1_data, const RuntimeShape& input2_shape,
const T* input2_data, const RuntimeShape& output_shape, T* output_data) {
ruy::profiler::ScopeLabel label("BroadcastMul4DSlow");
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
// The input shapes are extended as part of NdArrayDesc initialization.
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
const int32_t input1_val =
params.input1_offset +
input1_data[SubscriptToIndex(desc1, b, y, x, c)];
const int32_t input2_val =
params.input2_offset +
input2_data[SubscriptToIndex(desc2, b, y, x, c)];
const int32_t unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplier(input1_val * input2_val,
params.output_multiplier,
params.output_shift);
const int32_t clamped_output = std::min(
params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));
output_data[Offset(extended_output_shape, b, y, x, c)] =
static_cast<T>(clamped_output);
}
}
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_
#include <limits>
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
inline void AveragePool(const PoolParams& params,
const RuntimeShape& input_shape,
const int8_t* input_data,
const RuntimeShape& output_shape, int8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
int32_t acc = 0;
int filter_count = 0;
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
acc +=
input_data[Offset(input_shape, batch, in_y, in_x, channel)];
filter_count++;
}
}
// Round to the closest integer value.
acc = acc > 0 ? (acc + filter_count / 2) / filter_count
: (acc - filter_count / 2) / filter_count;
acc = std::max(acc, params.quantized_activation_min);
acc = std::min(acc, params.quantized_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
static_cast<int8_t>(acc);
}
}
}
}
}
inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape,
const int8_t* input_data, const RuntimeShape& output_shape,
int8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
TFLITE_DCHECK_GE(params.quantized_activation_min,
std::numeric_limits<int8_t>::min());
TFLITE_DCHECK_LE(params.quantized_activation_max,
std::numeric_limits<int8_t>::max());
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
int8_t max = std::numeric_limits<int8_t>::lowest();
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
max = std::max(
max,
input_data[Offset(input_shape, batch, in_y, in_x, channel)]);
}
}
max = std::max<int8_t>(max, params.quantized_activation_min);
max = std::min<int8_t>(max, params.quantized_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
static_cast<int8_t>(max);
}
}
}
}
}
inline void AveragePool(const PoolParams& params,
const RuntimeShape& input_shape,
const int16_t* input_data,
const RuntimeShape& output_shape,
int16_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
int32_t acc = 0;
int filter_count = 0;
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
acc +=
input_data[Offset(input_shape, batch, in_y, in_x, channel)];
filter_count++;
}
}
// Round to the closest integer value.
acc = acc > 0 ? (acc + filter_count / 2) / filter_count
: (acc - filter_count / 2) / filter_count;
acc = std::max(acc, params.quantized_activation_min);
acc = std::min(acc, params.quantized_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
static_cast<int16_t>(acc);
}
}
}
}
}
inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape,
const int16_t* input_data, const RuntimeShape& output_shape,
int16_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
TFLITE_DCHECK_GE(params.quantized_activation_min,
std::numeric_limits<int16_t>::min());
TFLITE_DCHECK_LE(params.quantized_activation_max,
std::numeric_limits<int16_t>::max());
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
int16_t max = std::numeric_limits<int16_t>::lowest();
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
max = std::max(
max,
input_data[Offset(input_shape, batch, in_y, in_x, channel)]);
}
}
max = std::max<int16_t>(max, params.quantized_activation_min);
max = std::min<int16_t>(max, params.quantized_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
static_cast<int16_t>(max);
}
}
}
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_
#include <limits>
#include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_integer_ops {
inline void Tanh(int32_t input_zero_point, int32_t input_range_radius,
int32_t input_multiplier, int32_t input_shift,
int32_t input_size, const int8_t* input_data,
int8_t* output_data) {
// Integer bits must be in sync with Prepare() function.
static constexpr int32_t kInputIntegerBits = 4;
static constexpr int32_t kOutputScale = 7;
static constexpr int32_t kMinInt8 = std::numeric_limits<int8_t>::min();
static constexpr int32_t kMaxInt8 = std::numeric_limits<int8_t>::max();
using F4 = gemmlowp::FixedPoint<int32_t, kInputIntegerBits>;
for (int i = 0; i < input_size; ++i) {
const int32_t input =
static_cast<int32_t>(input_data[i]) - input_zero_point;
if (input <= -input_range_radius) {
output_data[i] = kMinInt8;
} else if (input >= input_range_radius) {
output_data[i] = kMaxInt8;
} else {
const int32_t input_in_q4 =
MultiplyByQuantizedMultiplier(input, input_multiplier, input_shift);
const int32_t output_in_q0 =
gemmlowp::tanh(F4::FromRaw(input_in_q4)).raw();
// Rescale and downcast.
using gemmlowp::RoundingDivideByPOT;
int32_t output_in_q24 =
RoundingDivideByPOT(output_in_q0, 31 - kOutputScale);
output_in_q24 = std::min(std::max(output_in_q24, kMinInt8), kMaxInt8);
output_data[i] = static_cast<int8_t>(output_in_q24);
}
}
}
inline void Tanh(int32_t input_multiplier, int32_t input_left_shift,
int32_t input_size, const int16_t* ptr_input_data,
int16_t* ptr_output_data) {
// We use the LUT for sigmoid and take into account, that
// tanh(x) = 2*sigmoid(2*x) - 1
int32_t input_data_mul = (input_multiplier > 0) ? input_multiplier : 1;
for (int i = 0; i < input_size; ++i, ptr_input_data++, ptr_output_data++) {
int32_t input_data = (*ptr_input_data) * input_data_mul;
if (input_left_shift == 1) {
input_data <<= 1;
}
// Scale by 3/4 to expand range [-8,8]->[-10.7,10.7].
uint32_t abs_input_data = 3 * abs(input_data);
uint32_t uh = abs_input_data >> 8;
int32_t result;
if (uh >= 255) {
// Saturate to maximum.
result = 0xFFFF << 8;
} else {
uint32_t ua = sigmoid_table_uint16[uh];
uint32_t ub = sigmoid_table_uint16[uh + 1];
uint8_t ut = abs_input_data & 0xFF;
result = (ua << 8) + ut * (ub - ua);
}
result = (input_data >= 0)
? (result - (1 << (14 + 9)) + (1 << (9 - 2)))
: (-result + (1 << (14 + 9)) + (1 << (9 - 2)) - 1);
// Convert back to 16-bit.
result >>= (9 - 1);
*ptr_output_data = result;
}
}
} // namespace reference_integer_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/l2normalization.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_
#include <algorithm>
#include <cmath>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline void L2Normalization(const tflite::L2NormalizationParams& op_params,
const RuntimeShape& input_shape,
const float* input_data,
const RuntimeShape& output_shape,
float* output_data, float epsilon = 1e-6) {
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);
for (int i = 0; i < outer_size; ++i) {
float squared_l2_norm = 0;
for (int c = 0; c < depth; ++c) {
const float val = input_data[depth * i + c];
squared_l2_norm += val * val;
}
float l2_norm = std::sqrt(squared_l2_norm);
l2_norm = std::max(l2_norm, epsilon);
for (int c = 0; c < depth; ++c) {
output_data[depth * i + c] = input_data[depth * i + c] / l2_norm;
}
}
}
inline void L2Normalization(const tflite::L2NormalizationParams& op_params,
const RuntimeShape& input_shape,
const uint8_t* input_data,
const RuntimeShape& output_shape,
uint8_t* output_data) {
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);
const int32_t input_zero_point = op_params.input_zero_point;
for (int i = 0; i < outer_size; ++i) {
int32_t square_l2_norm = 0;
for (int c = 0; c < depth; c++) {
int32_t diff = input_data[depth * i + c] - input_zero_point;
square_l2_norm += diff * diff;
}
int32_t inv_l2norm_multiplier;
int inv_l2norm_shift;
GetInvSqrtQuantizedMultiplierExp(square_l2_norm, kReverseShift,
&inv_l2norm_multiplier, &inv_l2norm_shift);
for (int c = 0; c < depth; c++) {
int32_t diff = input_data[depth * i + c] - input_zero_point;
int32_t rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp(
128 * diff, inv_l2norm_multiplier, inv_l2norm_shift);
int32_t unclamped_output_val = 128 + rescaled_diff;
int32_t output_val =
std::min(static_cast<int32_t>(255),
std::max(static_cast<int32_t>(0), unclamped_output_val));
output_data[depth * i + c] = static_cast<uint8_t>(output_val);
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_
#include <cmath>
#include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/op_macros.h"
namespace tflite {
namespace reference_ops {
inline void Logistic(const RuntimeShape& input_shape, const float* input_data,
const RuntimeShape& output_shape, float* output_data) {
const float cutoff_upper = 16.619047164916992188f;
const float cutoff_lower = -9.f;
const int flat_size = MatchingFlatSize(input_shape, output_shape);
// Rational for using approximation in reference kernel.
// 0. This approximation gives enough precision for float.
// 1. This works around an issue on an embedded chipset where exp() does not
// return correctly as expected - exp(x) should return inf when overflown
// not 1.701417 IEEE 754 defines representation for inf.
// 2. This will speed up calculation and is matching the behavior in the
// optimized kernels. (check the definition of scalar_logistic_op<float>)
for (int i = 0; i < flat_size; i++) {
float val = input_data[i];
float result;
if (val > cutoff_upper) {
result = 1.0f;
} else if (val < cutoff_lower) {
result = std::exp(val);
} else {
result = 1.f / (1.f + std::exp(-val));
}
output_data[i] = result;
}
}
// Convenience version that allows, for example, generated-code calls to be
// uniform between data types.
inline void Logistic(const LogisticParams&, const RuntimeShape& input_shape,
const float* input_data, const RuntimeShape& output_shape,
float* output_data) {
// Drop params: not needed.
Logistic(input_shape, input_data, output_shape, output_data);
}
inline void Logistic(const LogisticParams& params,
const RuntimeShape& input_shape, const int16_t* input_data,
const RuntimeShape& output_shape, int16_t* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; i++) {
// F0 uses 0 integer bits, range [-1, 1].
// This is the return type of math functions such as tanh, logistic,
// whose range is in [-1, 1].
using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
// F3 uses 3 integer bits, range [-8, 8], the input range expected here.
using F3 = gemmlowp::FixedPoint<std::int16_t, 3>;
const F3 input = F3::FromRaw(input_data[i]);
F0 output = gemmlowp::logistic(input);
output_data[i] = output.raw();
}
}
// Quantized int8_t logistic activation. Cheats by dequantizing and
// requantizing around the floating point logistic method. This implementation
// is slow on platforms without a floating point unit.
// TODO(b/141211002): Delete this int8_t implementation once we can reuse the
// approach used in TFLite for int8_t Logistic.
inline void Logistic(const RuntimeShape& input_shape, const int8_t* input_data,
float input_scale, int input_zero_point,
const RuntimeShape& output_shape, int8_t* output_data,
float output_scale, int output_zero_point) {
const float cutoff_upper = 16.619047164916992188f;
const float cutoff_lower = -9.f;
const int flat_size = MatchingFlatSize(input_shape, output_shape);
// Rational for using approximation in reference kernel.
// 0. This approximation gives enough precision for float.
// 1. This works around an issue on an embedded chipset where exp() does not
// return correctly as expected - exp(x) should return inf when overflown
// not 1.701417 IEEE 754 defines representation for inf.
// 2. This will speed up calculation and is matching the behavior in the
// optimized kernels. (check the definition of scalar_logistic_op<float>)
for (int i = 0; i < flat_size; i++) {
// Dequantize.
float val =
static_cast<float>((input_data[i] - input_zero_point) * input_scale);
float result;
if (val > cutoff_upper) {
result = 1.0f;
} else if (val < cutoff_lower) {
result = std::exp(val);
} else {
result = 1.f / (1.f + std::exp(-val));
}
// Requantize
int8_t output =
static_cast<int8_t>(result / output_scale + output_zero_point);
output_data[i] = output;
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
template <typename T, typename Op, int N = 5>
void MaximumMinimumBroadcastSlow(const RuntimeShape& unextended_input1_shape,
const T* input1_data,
const RuntimeShape& unextended_input2_shape,
const T* input2_data,
const RuntimeShape& unextended_output_shape,
T* output_data, Op op) {
// Uses element-wise calculation if broadcast is not required.
if (unextended_input1_shape == unextended_input2_shape) {
const int flat_size =
MatchingElementsSize(unextended_input1_shape, unextended_input2_shape,
unextended_output_shape);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = op(input1_data[i], input2_data[i]);
}
} else {
TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N);
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(
unextended_input1_shape, unextended_input2_shape, &desc1, &desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape),
&output_desc);
auto maxmin_func = [&](int indexes[N]) {
output_data[SubscriptToIndex(output_desc, indexes)] =
op(input1_data[SubscriptToIndex(desc1, indexes)],
input2_data[SubscriptToIndex(desc2, indexes)]);
};
NDOpsHelper<N>(output_desc, maxmin_func);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
#include "tensorflow/lite/kernels/internal/common.h"
namespace tflite {
namespace reference_ops {
// Element-wise mul that can often be used for inner loop of broadcast Mul as
// well as the non-broadcast Mul.
inline void MulElementwise(int size, const ArithmeticParams& params,
const uint8_t* input1_data,
const uint8_t* input2_data, uint8_t* output_data) {
for (int i = 0; i < size; ++i) {
const int32_t input1_val = params.input1_offset + input1_data[i];
const int32_t input2_val = params.input2_offset + input2_data[i];
const int32_t unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplier(input1_val * input2_val,
params.output_multiplier,
params.output_shift);
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));
output_data[i] = static_cast<uint8_t>(clamped_output);
}
}
template <typename T>
inline void Mul(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const T* input1_data,
const RuntimeShape& input2_shape, const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
T output_activation_min;
T output_activation_max;
GetActivationParams(params, &output_activation_min, &output_activation_max);
const int flat_size =
MatchingFlatSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = ActivationFunctionWithMinMax(
input1_data[i] * input2_data[i], output_activation_min,
output_activation_max);
}
}
inline void Mul(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const uint8_t* input1_data,
const RuntimeShape& input2_shape, const uint8_t* input2_data,
const RuntimeShape& output_shape, uint8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
const int flat_size =
MatchingFlatSize(input1_shape, input2_shape, output_shape);
MulElementwise(flat_size, params, input1_data, input2_data, output_data);
}
inline void BroadcastMul4DSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const uint8_t* input1_data,
const RuntimeShape& input2_shape,
const uint8_t* input2_data,
const RuntimeShape& output_shape,
uint8_t* output_data) {
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
const int32_t input1_val =
params.input1_offset +
input1_data[SubscriptToIndex(desc1, b, y, x, c)];
const int32_t input2_val =
params.input2_offset +
input2_data[SubscriptToIndex(desc2, b, y, x, c)];
const int32_t unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplier(input1_val * input2_val,
params.output_multiplier,
params.output_shift);
const int32_t clamped_output = std::min(
params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));
output_data[Offset(extended_output_shape, b, y, x, c)] =
static_cast<uint8_t>(clamped_output);
}
}
}
}
}
template <typename T>
void BroadcastMul4DSlow(const ArithmeticParams& params,
const RuntimeShape& unextended_input1_shape,
const T* input1_data,
const RuntimeShape& unextended_input2_shape,
const T* input2_data,
const RuntimeShape& unextended_output_shape,
T* output_data) {
T output_activation_min;
T output_activation_max;
GetActivationParams(params, &output_activation_min, &output_activation_max);
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);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
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) {
output_data[Offset(output_shape, b, y, x, c)] =
ActivationFunctionWithMinMax(
input1_data[SubscriptToIndex(desc1, b, y, x, c)] *
input2_data[SubscriptToIndex(desc2, b, y, x, c)],
output_activation_min, output_activation_max);
}
}
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
template <typename T>
inline void Negate(const RuntimeShape& input_shape, const T* input_data,
const RuntimeShape& output_shape, T* output_data) {
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = -input_data[i];
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_
#include <vector>
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
// TFLite Pad supports activation tensors with up to 4 dimensions.
constexpr int PadKernelMaxDimensionCount() { return 4; }
// There are two versions of pad: Pad and PadV2. In PadV2 there is a second
// scalar input that provides the padding value. Therefore pad_value_ptr can be
// equivalent to a simple input1_data. For Pad, it should point to a zero
// value.
//
// Note that two typenames are required, so that T=P=int32_t is considered a
// specialization distinct from P=int32_t.
template <typename T, typename P>
inline void PadImpl(const tflite::PadParams& op_params,
const RuntimeShape& input_shape, const T* input_data,
const P* pad_value_ptr, const RuntimeShape& output_shape,
T* output_data) {
const RuntimeShape ext_input_shape =
RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), input_shape);
const RuntimeShape ext_output_shape =
RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), output_shape);
TFLITE_DCHECK_LE(op_params.left_padding_count, PadKernelMaxDimensionCount());
TFLITE_DCHECK_LE(op_params.right_padding_count, PadKernelMaxDimensionCount());
// Runtime calls are currently fixed at 4 dimensions. Copy inputs so we can
// pad them to 4 dims (yes, we are "padding the padding").
int left_padding_copy[PadKernelMaxDimensionCount()];
for (int i = 0; i < PadKernelMaxDimensionCount(); i++) {
left_padding_copy[i] = 0;
}
for (int i = 0; i < op_params.left_padding_count; ++i) {
left_padding_copy[i + PadKernelMaxDimensionCount() -
op_params.left_padding_count] = op_params.left_padding[i];
}
int right_padding_copy[PadKernelMaxDimensionCount()];
for (int i = 0; i < PadKernelMaxDimensionCount(); i++) {
right_padding_copy[i] = 0;
}
for (int i = 0; i < op_params.right_padding_count; ++i) {
right_padding_copy[i + PadKernelMaxDimensionCount() -
op_params.right_padding_count] =
op_params.right_padding[i];
}
const int output_batch = ext_output_shape.Dims(0);
const int output_height = ext_output_shape.Dims(1);
const int output_width = ext_output_shape.Dims(2);
const int output_depth = ext_output_shape.Dims(3);
const int left_b_padding = left_padding_copy[0];
const int left_h_padding = left_padding_copy[1];
const int left_w_padding = left_padding_copy[2];
const int left_d_padding = left_padding_copy[3];
const int right_b_padding = right_padding_copy[0];
const int right_h_padding = right_padding_copy[1];
const int right_w_padding = right_padding_copy[2];
const int right_d_padding = right_padding_copy[3];
const T pad_value = *pad_value_ptr;
const T* in_ptr = input_data;
T* out_ptr = output_data;
for (int out_b = 0; out_b < output_batch; ++out_b) {
for (int out_h = 0; out_h < output_height; ++out_h) {
for (int out_w = 0; out_w < output_width; ++out_w) {
for (int out_d = 0; out_d < output_depth; ++out_d) {
if (out_b < left_b_padding ||
out_b >= output_batch - right_b_padding ||
out_h < left_h_padding ||
out_h >= output_height - right_h_padding ||
out_w < left_w_padding ||
out_w >= output_width - right_w_padding ||
out_d < left_d_padding ||
out_d >= output_depth - right_d_padding) {
*out_ptr++ = pad_value;
} else {
*out_ptr++ = *in_ptr++;
}
}
}
}
}
}
template <typename T, typename P>
inline void Pad(const tflite::PadParams& op_params,
const RuntimeShape& input_shape, const T* input_data,
const P* pad_value_ptr, const RuntimeShape& output_shape,
T* output_data) {
PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape,
output_data);
}
// The second (pad-value) input can be int32_t when, say, the first is uint8_t.
template <typename T>
inline void Pad(const tflite::PadParams& op_params,
const RuntimeShape& input_shape, const T* input_data,
const int32_t* pad_value_ptr, const RuntimeShape& output_shape,
T* output_data) {
const T converted_pad_value = static_cast<T>(*pad_value_ptr);
PadImpl(op_params, input_shape, input_data, &converted_pad_value,
output_shape, output_data);
}
// This version avoids conflicting template matching.
template <>
inline void Pad(const tflite::PadParams& op_params,
const RuntimeShape& input_shape, const int32_t* input_data,
const int32_t* pad_value_ptr, const RuntimeShape& output_shape,
int32_t* output_data) {
PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape,
output_data);
}
template <typename T, typename P>
inline void PadImageStyle(const tflite::PadParams& op_params,
const RuntimeShape& input_shape, const T* input_data,
const P* pad_value_ptr,
const RuntimeShape& output_shape, T* output_data) {
Pad(op_params, input_shape, input_data, pad_value_ptr, output_shape,
output_data);
}
template <typename P>
inline void PadImageStyle(const tflite::PadParams& op_params,
const RuntimeShape& input_shape,
const float* input_data, const P* pad_value_ptr,
const RuntimeShape& output_shape,
float* output_data) {
Pad(op_params, input_shape, input_data, pad_value_ptr, output_shape,
output_data);
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline void AveragePool(const PoolParams& params,
const RuntimeShape& input_shape,
const float* input_data,
const RuntimeShape& output_shape, float* output_data) {
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
float total = 0.f;
float filter_count = 0;
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
total +=
input_data[Offset(input_shape, batch, in_y, in_x, channel)];
filter_count++;
}
}
const float average = total / filter_count;
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
ActivationFunctionWithMinMax(average, params.float_activation_min,
params.float_activation_max);
}
}
}
}
}
inline void AveragePool(const PoolParams& params,
const RuntimeShape& input_shape,
const uint8_t* input_data,
const RuntimeShape& output_shape,
uint8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
int32_t acc = 0;
int filter_count = 0;
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
acc +=
input_data[Offset(input_shape, batch, in_y, in_x, channel)];
filter_count++;
}
}
acc = (acc + filter_count / 2) / filter_count;
acc = std::max(acc, params.quantized_activation_min);
acc = std::min(acc, params.quantized_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
static_cast<uint8_t>(acc);
}
}
}
}
}
inline void L2Pool(const PoolParams& params, const RuntimeShape& input_shape,
const float* input_data, const RuntimeShape& output_shape,
float* output_data) {
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
float sum_squares = 0.f;
int filter_count = 0;
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
const float val =
input_data[Offset(input_shape, batch, in_y, in_x, channel)];
sum_squares += val * val;
filter_count++;
}
}
const float l2pool_result = std::sqrt(sum_squares / filter_count);
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
ActivationFunctionWithMinMax(l2pool_result,
params.float_activation_min,
params.float_activation_max);
}
}
}
}
}
inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape,
const float* input_data, const RuntimeShape& output_shape,
float* output_data) {
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
float max = std::numeric_limits<float>::lowest();
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
max = std::max(
max,
input_data[Offset(input_shape, batch, in_y, in_x, channel)]);
}
}
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
ActivationFunctionWithMinMax(max, params.float_activation_min,
params.float_activation_max);
}
}
}
}
}
inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape,
const uint8_t* input_data, const RuntimeShape& output_shape,
uint8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
TFLITE_DCHECK_GE(params.quantized_activation_min, 0);
TFLITE_DCHECK_LE(params.quantized_activation_max, 255);
TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
const int batches = MatchingDim(input_shape, 0, output_shape, 0);
const int depth = MatchingDim(input_shape, 3, output_shape, 3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int stride_height = params.stride_height;
const int stride_width = params.stride_width;
for (int batch = 0; batch < batches; ++batch) {
for (int out_y = 0; out_y < output_height; ++out_y) {
for (int out_x = 0; out_x < output_width; ++out_x) {
for (int channel = 0; channel < depth; ++channel) {
const int in_x_origin =
(out_x * stride_width) - params.padding_values.width;
const int in_y_origin =
(out_y * stride_height) - params.padding_values.height;
// Compute the boundaries of the filter region clamped so as to
// ensure that the filter window fits in the input array.
const int filter_x_start = std::max(0, -in_x_origin);
const int filter_x_end =
std::min(params.filter_width, input_width - in_x_origin);
const int filter_y_start = std::max(0, -in_y_origin);
const int filter_y_end =
std::min(params.filter_height, input_height - in_y_origin);
uint8_t max = 0;
for (int filter_y = filter_y_start; filter_y < filter_y_end;
++filter_y) {
for (int filter_x = filter_x_start; filter_x < filter_x_end;
++filter_x) {
const int in_x = in_x_origin + filter_x;
const int in_y = in_y_origin + filter_y;
max = std::max(
max,
input_data[Offset(input_shape, batch, in_y, in_x, channel)]);
}
}
max = std::max<uint8_t>(max, params.quantized_activation_min);
max = std::min<uint8_t>(max, params.quantized_activation_max);
output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
static_cast<uint8_t>(max);
}
}
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
// Broadcast prelu to output_shape for quantized uint8_t/int8_t data.
template <typename T>
inline void BroadcastPrelu4DSlow(
const PreluParams& params, const RuntimeShape& input_shape,
const T* input_data, const RuntimeShape& alpha_shape, const T* alpha_data,
const RuntimeShape& output_shape, T* output_data) {
TFLITE_DCHECK_LE(input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(alpha_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 4);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input_shape, alpha_shape, &desc1, &desc2);
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
int output_index = Offset(extended_output_shape, b, y, x, c);
int input_index = SubscriptToIndex(desc1, b, y, x, c);
const int32_t input_value =
params.input_offset + input_data[input_index];
int32_t output_value;
if (input_value >= 0) {
output_value = MultiplyByQuantizedMultiplier(
input_value, params.output_multiplier_1, params.output_shift_1);
} else {
auto alpha_index = SubscriptToIndex(desc2, b, y, x, c);
const int32_t alpha_value =
params.alpha_offset + alpha_data[alpha_index];
output_value = MultiplyByQuantizedMultiplier(
input_value * alpha_value, params.output_multiplier_2,
params.output_shift_2);
}
output_value += params.output_offset;
const int32_t quantized_min = std::numeric_limits<T>::min();
const int32_t quantized_max = std::numeric_limits<T>::max();
const int32_t clamped_output =
std::min(quantized_max, std::max(quantized_min, output_value));
output_data[output_index] = static_cast<T>(clamped_output);
}
}
}
}
}
template <typename T>
inline void Prelu(const PreluParams& params, const RuntimeShape& input_shape,
const T* input_data, const RuntimeShape& alpha_shape,
const T* alpha_data, const RuntimeShape& output_shape,
T* output_data) {
const int32_t quantized_min = std::numeric_limits<T>::min();
const int32_t quantized_max = std::numeric_limits<T>::max();
const int flat_size =
MatchingElementsSize(input_shape, alpha_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
const int32_t input_value = params.input_offset + input_data[i];
int32_t output_value;
if (input_value >= 0) {
output_value = MultiplyByQuantizedMultiplier(
input_value, params.output_multiplier_1, params.output_shift_1);
} else {
const int32_t alpha_value = params.alpha_offset + alpha_data[i];
output_value = MultiplyByQuantizedMultiplier(input_value * alpha_value,
params.output_multiplier_2,
params.output_shift_2);
}
output_value += params.output_offset;
const int32_t clamped_output =
std::min(quantized_max, std::max(quantized_min, output_value));
output_data[i] = static_cast<T>(clamped_output);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
// Consolidates dimensions in broadcast inputs, checks for five-fold pattern.
//
// For example, if sequence of dimensions of one input is
// ..., 1, 3, 1, 7, 9, 5,... and the other is ..., 2, 3, 1, 7, 1, 1, ...
// we can consolidate these as
// ..., 1, 3*7, 9*5, ... and 2, 3*7, 1.
//
// The category is updated in the less-frequent case of shapes that are
// not suited to a fivefold-loop broadcast.
//
// Falls back to generic pattern when it does not know how to process properly.
//
// Returns true iff there is some sort of broadcast, which includes five-fold
// patterns and falling back to generic broadcast.
inline bool ProcessBroadcastShapes(const RuntimeShape& shape0,
const RuntimeShape& shape1,
tflite::ArithmeticParams* params) {
const int dims_count =
std::max(shape0.DimensionsCount(), shape1.DimensionsCount());
params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast;
RuntimeShape scalar_shape(dims_count, 1);
auto extended_shape0 = RuntimeShape::ExtendedShape(dims_count, shape0);
auto extended_shape1 = RuntimeShape::ExtendedShape(dims_count, shape1);
// Check for "exact" match, implicitly accepting any scalar shapes.
if (extended_shape0 == extended_shape1) {
params->broadcast_category = BroadcastableOpCategory::kNonBroadcast;
return false;
}
for (int i = dims_count - 1; i >= 0; --i) {
if (extended_shape0.Dims(i) == extended_shape1.Dims(i)) {
continue;
} else if (extended_shape0.Dims(i) == 1) {
params->broadcast_category =
BroadcastableOpCategory::kFirstInputBroadcastsFast;
break;
} else if (extended_shape1.Dims(i) == 1) {
params->broadcast_category =
BroadcastableOpCategory::kSecondInputBroadcastsFast;
break;
} else {
// This case is erroneous: there is a dimension that does not match and
// is not a broadcast from one shape to the other.
params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast;
return true;
}
}
if (params->broadcast_category !=
BroadcastableOpCategory::kFirstInputBroadcastsFast &&
params->broadcast_category !=
BroadcastableOpCategory::kSecondInputBroadcastsFast) {
// This is unreachable because at least one else clause in the above loop
// must be reached.
TFLITE_DCHECK(false);
params->broadcast_category = BroadcastableOpCategory::kNonBroadcast;
return false;
}
// From this point it is assumed contractually that corresponding dimensions
// in shape0 and shape1 are either (a) equal or (b) one or other equals 1.
const bool swap_inputs = params->broadcast_category ==
BroadcastableOpCategory::kSecondInputBroadcastsFast;
const RuntimeShape* shape_a =
swap_inputs ? &extended_shape1 : &extended_shape0;
const RuntimeShape* shape_b =
swap_inputs ? &extended_shape0 : &extended_shape1;
int i = dims_count - 1;
params->broadcast_shape[0] = 1;
params->broadcast_shape[1] = 1;
params->broadcast_shape[2] = 1;
params->broadcast_shape[3] = 1;
params->broadcast_shape[4] = 1;
// y_0 is greedy: include dims if both or neither equal 1: in other words,
// test for equality rather than (shape_a->Dims(i) != 1).
while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) {
params->broadcast_shape[4] *= shape_b->Dims(i);
--i;
}
// Here either input_a or input_b has dim of 1 (if i >= 0). If it is input_b
// that has the unit dimension, the next two loops are not entered.
while (i >= 0 && shape_a->Dims(i) == 1) {
params->broadcast_shape[3] *= shape_b->Dims(i);
--i;
}
while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) {
params->broadcast_shape[2] *= shape_a->Dims(i);
--i;
}
// Here either input_a or input_b has dim of 1 (if i >= 0).
while (i >= 0 && shape_b->Dims(i) == 1) {
params->broadcast_shape[1] *= shape_a->Dims(i);
--i;
}
while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) {
params->broadcast_shape[0] *= shape_b->Dims(i);
--i;
}
// Rarer case is when the broadcast dimensions cannot be handled by a fivefold
// loop.
if (i >= 0) {
params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast;
}
return true;
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_

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components/ai/tflite_micro/ARM_CortexM55_lib/tensorflow/lite/kernels/internal/reference/quantize.h
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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_
#include <algorithm>
#include <limits>
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
template <typename InputT, typename OutputT>
inline void AffineQuantize(const tflite::QuantizationParams& op_params,
const RuntimeShape& input_shape,
const InputT* input_data,
const RuntimeShape& output_shape,
OutputT* output_data) {
const int32_t zero_point = op_params.zero_point;
const double scale = op_params.scale;
const int flat_size = MatchingFlatSize(input_shape, output_shape);
static constexpr int32_t min_val = std::numeric_limits<OutputT>::min();
static constexpr int32_t max_val = std::numeric_limits<OutputT>::max();
for (int i = 0; i < flat_size; i++) {
const InputT val = input_data[i];
int32_t unclamped =
static_cast<int32_t>(TfLiteRound(val / static_cast<float>(scale))) +
zero_point;
int32_t clamped = std::min(std::max(unclamped, min_val), max_val);
output_data[i] = clamped;
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/max.h"
#include "tensorflow/lite/kernels/internal/min.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
// A generic reduce method that can be used for reduce_sum, reduce_mean, etc.
// This method iterates through input data and reduce elements along the
// dimensions given in axis.
template <typename In, typename Out>
inline bool Reduce(const In* input_data, const int* input_dims,
const int* output_dims, const int input_num_dims,
const int output_num_dims, const int* axis,
const int num_axis, int* input_iter,
Out reducer(const Out current, const In in),
Out* output_data) {
// Reset input iterator.
for (int idx = 0; idx < input_num_dims; ++idx) {
input_iter[idx] = 0;
}
// Iterate through input_data.
do {
size_t input_offset =
ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr);
size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims,
input_iter, num_axis, axis);
output_data[output_offset] =
reducer(output_data[output_offset], input_data[input_offset]);
} while (NextIndex(input_num_dims, input_dims, input_iter));
return true;
}
// This method parses the input 'axis' to remove duplicates and handle negative
// values, and returns a valid 'out_axis'
inline bool ResolveAxis(const int num_dims, const int* axis,
const int64_t num_axis, int* out_axis,
int* out_num_axis) {
*out_num_axis = 0; // Just in case.
// Short-circuit axis resolution for scalars; the axis will go unused.
if (num_dims == 0) {
return true;
}
// o(n^2) is fine since out_num_axis should be really small, mostly <= 4
for (int64_t idx = 0; idx < num_axis; ++idx) {
// Handle negative index. A positive index 'p_idx' can be represented as a
// negative index 'n_idx' as: n_idx = p_idx-num_dims
// eg: For num_dims=3, [0, 1, 2] is the same as [-3, -2, -1] */
int current = axis[idx] < 0 ? (axis[idx] + num_dims) : axis[idx];
TFLITE_DCHECK(current >= 0 && current < num_dims);
bool is_dup = false;
for (int j = 0; j < *out_num_axis; ++j) {
if (out_axis[j] == current) {
is_dup = true;
break;
}
}
if (!is_dup) {
out_axis[*out_num_axis] = current;
*out_num_axis += 1;
}
}
return true;
}
// This method expects that output_data has been initialized.
template <typename In, typename Out>
inline bool ReduceSumImpl(const In* input_data, const int* input_dims,
const int* output_dims, const int input_num_dims,
const int output_num_dims, const int* axis,
const int num_axis, int* input_iter,
Out* output_data) {
auto reducer = [](const Out current, const In in) -> Out {
const Out actual_in = static_cast<Out>(in);
return current + actual_in;
};
return Reduce<In, Out>(input_data, input_dims, output_dims, input_num_dims,
output_num_dims, axis, num_axis, input_iter, reducer,
output_data);
}
template <typename T>
inline bool InitTensorDataForReduce(const int* dims, const int num_dims,
const T init_value, T* data) {
size_t num_elements = 1;
for (int idx = 0; idx < num_dims; ++idx) {
size_t current = static_cast<size_t>(dims[idx]);
// Overflow prevention.
if (num_elements > std::numeric_limits<size_t>::max() / current) {
return false;
}
num_elements *= current;
}
for (size_t idx = 0; idx < num_elements; ++idx) {
data[idx] = init_value;
}
return true;
}
// Computes the generic value (i.e., sum/max/min/prod) of elements across
// dimensions given in axis. It needs to pass in init_value and reducer.
template <typename T>
inline bool ReduceGeneric(const T* input_data, const int* input_dims,
const int input_num_dims, T* output_data,
const int* output_dims, const int output_num_dims,
const int* axis, const int64_t num_axis_dimensions,
bool keep_dims, int* temp_index, int* resolved_axis,
T init_value,
T reducer(const T current, const T in)) {
// Reset output data.
if (!InitTensorDataForReduce(output_dims, output_num_dims, init_value,
output_data)) {
return false;
}
// Resolve axis.
int num_resolved_axis = 0;
if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis,
&num_resolved_axis)) {
return false;
}
return Reduce<T, T>(input_data, input_dims, output_dims, input_num_dims,
output_num_dims, resolved_axis, num_resolved_axis,
temp_index, reducer, output_data);
}
// Computes the mean of elements across dimensions given in axis.
// It does so in two stages, first calculates the sum of elements along the axis
// then divides it by the number of element in axis.
template <typename T, typename U>
inline bool Mean(const T* input_data, const int* input_dims,
const int input_num_dims, T* output_data,
const int* output_dims, const int output_num_dims,
const int* axis, const int num_axis_dimensions, bool keep_dims,
int* temp_index, int* resolved_axis, U* temp_sum) {
ruy::profiler::ScopeLabel label("Mean");
// Reset output data.
size_t num_outputs = 1;
for (int idx = 0; idx < output_num_dims; ++idx) {
size_t current = static_cast<size_t>(output_dims[idx]);
// Overflow prevention.
if (num_outputs > std::numeric_limits<size_t>::max() / current) {
return false;
}
num_outputs *= current;
}
for (size_t idx = 0; idx < num_outputs; ++idx) {
output_data[idx] = T();
temp_sum[idx] = U();
}
// Resolve axis.
int num_resolved_axis = 0;
if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis,
&num_resolved_axis)) {
return false;
}
if (!ReduceSumImpl<T, U>(input_data, input_dims, output_dims, input_num_dims,
output_num_dims, resolved_axis, num_resolved_axis,
temp_index, temp_sum)) {
return false;
}
// Calculate mean by dividing output_data by num of aggregated element.
U num_elements_in_axis = 1;
for (int idx = 0; idx < num_resolved_axis; ++idx) {
size_t current = static_cast<size_t>(input_dims[resolved_axis[idx]]);
// Overflow prevention.
if (current > (std::numeric_limits<U>::max() / num_elements_in_axis)) {
return false;
}
num_elements_in_axis *= current;
}
if (num_elements_in_axis > 0) {
for (size_t idx = 0; idx < num_outputs; ++idx) {
output_data[idx] =
static_cast<T>(temp_sum[idx] / static_cast<U>(num_elements_in_axis));
}
}
return true;
}
template <typename T>
inline void Mean(const tflite::MeanParams& op_params,
const RuntimeShape& unextended_input_shape,
const T* input_data,
const RuntimeShape& unextended_output_shape, T* output_data) {
ruy::profiler::ScopeLabel label("Mean4D");
// Current implementation only supports dimension equals 4 and simultaneous
// reduction over width and height.
TFLITE_CHECK_EQ(unextended_input_shape.DimensionsCount(), 4);
TFLITE_CHECK_LE(unextended_output_shape.DimensionsCount(), 4);
const RuntimeShape input_shape =
RuntimeShape::ExtendedShape(4, unextended_input_shape);
const RuntimeShape output_shape =
RuntimeShape::ExtendedShape(4, unextended_output_shape);
const int output_batch = output_shape.Dims(0);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int output_depth = output_shape.Dims(3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
TFLITE_CHECK_EQ(op_params.axis_count, 2);
TFLITE_CHECK((op_params.axis[0] == 1 && op_params.axis[1] == 2) ||
(op_params.axis[0] == 2 && op_params.axis[1] == 1));
TFLITE_CHECK_EQ(output_height, 1);
TFLITE_CHECK_EQ(output_width, 1);
for (int out_b = 0; out_b < output_batch; ++out_b) {
for (int out_d = 0; out_d < output_depth; ++out_d) {
float value = 0;
for (int in_h = 0; in_h < input_height; ++in_h) {
for (int in_w = 0; in_w < input_width; ++in_w) {
value += input_data[Offset(input_shape, out_b, in_h, in_w, out_d)];
}
}
output_data[Offset(output_shape, out_b, 0, 0, out_d)] =
value / (input_width * input_height);
}
}
}
inline void Mean(const tflite::MeanParams& op_params,
const RuntimeShape& unextended_input_shape,
const uint8_t* input_data, int32_t input_zero_point,
float input_scale, const RuntimeShape& unextended_output_shape,
uint8_t* output_data, int32_t output_zero_point,
float output_scale) {
ruy::profiler::ScopeLabel label("Mean4D/Uint8");
// Current implementation only supports dimension equals 4 and simultaneous
// reduction over width and height.
TFLITE_CHECK_EQ(unextended_input_shape.DimensionsCount(), 4);
TFLITE_CHECK_LE(unextended_output_shape.DimensionsCount(), 4);
const RuntimeShape input_shape =
RuntimeShape::ExtendedShape(4, unextended_input_shape);
const RuntimeShape output_shape =
RuntimeShape::ExtendedShape(4, unextended_output_shape);
const int output_batch = output_shape.Dims(0);
const int output_height = output_shape.Dims(1);
const int output_width = output_shape.Dims(2);
const int output_depth = output_shape.Dims(3);
const int input_height = input_shape.Dims(1);
const int input_width = input_shape.Dims(2);
const float num_elements_in_axis = input_width * input_height;
TFLITE_CHECK_EQ(op_params.axis_count, 2);
TFLITE_CHECK((op_params.axis[0] == 1 && op_params.axis[1] == 2) ||
(op_params.axis[0] == 2 && op_params.axis[1] == 1));
TFLITE_CHECK_EQ(output_height, 1);
TFLITE_CHECK_EQ(output_width, 1);
constexpr int32_t kMinValue = std::numeric_limits<uint8_t>::min();
constexpr int32_t kMaxValue = std::numeric_limits<uint8_t>::max();
int32_t bias =
output_zero_point -
static_cast<int32_t>(input_zero_point * input_scale / output_scale);
double real_scale =
static_cast<double>(input_scale / (num_elements_in_axis * output_scale));
int32_t multiplier;
int shift;
QuantizeMultiplier(real_scale, &multiplier, &shift);
for (int out_b = 0; out_b < output_batch; ++out_b) {
for (int out_d = 0; out_d < output_depth; ++out_d) {
int32_t acc = 0;
for (int in_h = 0; in_h < input_height; ++in_h) {
for (int in_w = 0; in_w < input_width; ++in_w) {
acc += input_data[Offset(input_shape, out_b, in_h, in_w, out_d)];
}
}
acc = MultiplyByQuantizedMultiplier(acc, multiplier, shift);
acc += bias;
acc = std::min(std::max(acc, kMinValue), kMaxValue);
output_data[Offset(output_shape, out_b, 0, 0, out_d)] =
static_cast<uint8_t>(acc);
}
}
}
// Computes the mean of elements across dimensions given in axis.
// It does so in two stages, first calculates the sum of elements along the axis
// then divides it by the number of element in axis for quantized values.
template <typename T, typename U>
inline bool QuantizedMeanOrSum(const T* input_data, int32_t input_zero_point,
float input_scale, const int* input_dims,
const int input_num_dims, T* output_data,
int32_t output_zero_point, float output_scale,
const int* output_dims,
const int output_num_dims, const int* axis,
const int num_axis_dimensions, bool keep_dims,
int* temp_index, int* resolved_axis, U* temp_sum,
bool compute_sum) {
const bool uint8_case = std::is_same<T, uint8_t>::value;
const bool int16_case = std::is_same<T, int16_t>::value;
if (uint8_case) {
ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Uint8" : "Mean/Uint8");
} else if (int16_case) {
ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Int16" : "Mean/Int16");
} else {
ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Int8" : "Mean/Int8");
}
// Reset output data.
size_t num_outputs = 1;
for (int idx = 0; idx < output_num_dims; ++idx) {
size_t current = static_cast<size_t>(output_dims[idx]);
// Overflow prevention.
if (num_outputs > std::numeric_limits<size_t>::max() / current) {
return false;
}
num_outputs *= current;
}
for (size_t idx = 0; idx < num_outputs; ++idx) {
output_data[idx] = T();
temp_sum[idx] = U();
}
// Resolve axis.
int num_resolved_axis = 0;
if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis,
&num_resolved_axis)) {
return false;
}
if (!ReduceSumImpl<T, U>(input_data, input_dims, output_dims, input_num_dims,
output_num_dims, resolved_axis, num_resolved_axis,
temp_index, temp_sum)) {
return false;
}
// Calculate mean by dividing output_data by num of aggregated element.
U num_elements_in_axis = 1;
for (int idx = 0; idx < num_resolved_axis; ++idx) {
size_t current = static_cast<size_t>(input_dims[resolved_axis[idx]]);
// Overflow prevention.
if (current > (std::numeric_limits<U>::max() / num_elements_in_axis)) {
return false;
}
num_elements_in_axis *= current;
}
if (num_elements_in_axis > 0) {
const float scale = input_scale / output_scale;
if (compute_sum) {
// TODO(b/116341117): Eliminate float and do this completely in 8bit.
const float bias =
-input_zero_point * scale * num_elements_in_axis + 0.5f;
for (size_t idx = 0; idx < num_outputs; ++idx) {
const U value =
static_cast<U>(TfLiteRound(temp_sum[idx] * scale + bias)) +
output_zero_point;
output_data[idx] = static_cast<T>(value);
}
} else {
const float bias = -input_zero_point * scale + 0.5f;
for (size_t idx = 0; idx < num_outputs; ++idx) {
float float_mean = static_cast<float>(temp_sum[idx]) /
static_cast<float>(num_elements_in_axis);
float result = TfLiteMin(
TfLiteRound(float_mean * scale + bias) + output_zero_point,
static_cast<float>(std::numeric_limits<T>::max()));
result = TfLiteMax(result,
static_cast<float>(std::numeric_limits<T>::min()));
output_data[idx] = static_cast<T>(result);
}
}
}
return true;
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
template <typename input_type, typename output_type>
inline void Requantize(const input_type* input_data, int32_t size,
int32_t effective_scale_multiplier,
int32_t effective_scale_shift, int32_t input_zeropoint,
int32_t output_zeropoint, output_type* output_data) {
ruy::profiler::ScopeLabel label("Requantize");
const bool same_scale =
(effective_scale_multiplier == 1 << 30 && effective_scale_shift == 1);
if (same_scale) {
const bool mixed_type_int8_uint8 =
std::is_same<input_type, int8_t>::value &&
std::is_same<output_type, uint8_t>::value;
const bool mixed_type_uint8_int8 =
std::is_same<input_type, uint8_t>::value &&
std::is_same<output_type, int8_t>::value;
const int32_t zero_point_diff = input_zeropoint - output_zeropoint;
// Fast path to do requantization for the case when just a shift of 128 is
// needed.
if ((mixed_type_int8_uint8 && zero_point_diff == -128) ||
(mixed_type_uint8_int8 && zero_point_diff == 128)) {
for (int i = 0; i < size; ++i) {
output_data[i] = input_data[i] ^ 0x80;
}
}
}
static constexpr int32_t kMinOutput = std::numeric_limits<output_type>::min();
static constexpr int32_t kMaxOutput = std::numeric_limits<output_type>::max();
for (int i = 0; i < size; ++i) {
const int32_t input = input_data[i] - input_zeropoint;
const int32_t output =
MultiplyByQuantizedMultiplier(input, effective_scale_multiplier,
effective_scale_shift) +
output_zeropoint;
const int32_t clamped_output =
std::max(std::min(output, kMaxOutput), kMinOutput);
output_data[i] = static_cast<output_type>(clamped_output);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_
#include <cmath>
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline int32_t GetNearestNeighbor(const int input_value,
const int32_t input_size,
const int32_t output_size,
const bool align_corners,
const bool half_pixel_centers) {
const float scale =
(align_corners && output_size > 1)
? (input_size - 1) / static_cast<float>(output_size - 1)
: input_size / static_cast<float>(output_size);
const float offset = half_pixel_centers ? 0.5f : 0.0f;
int32_t output_value = std::min(
align_corners
? static_cast<int32_t>(TfLiteRound((input_value + offset) * scale))
: static_cast<int32_t>(std::floor((input_value + offset) * scale)),
input_size - 1);
if (half_pixel_centers) {
output_value = std::max(static_cast<int32_t>(0), output_value);
}
return output_value;
}
template <typename T>
inline void ResizeNearestNeighbor(
const tflite::ResizeNearestNeighborParams& op_params,
const RuntimeShape& unextended_input_shape, const T* input_data,
const RuntimeShape& output_size_shape, const int32_t* output_size_data,
const RuntimeShape& unextended_output_shape, T* output_data) {
TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4);
TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
const RuntimeShape input_shape =
RuntimeShape::ExtendedShape(4, unextended_input_shape);
const RuntimeShape output_shape =
RuntimeShape::ExtendedShape(4, unextended_output_shape);
int32_t batches = MatchingDim(input_shape, 0, output_shape, 0);
int32_t input_height = input_shape.Dims(1);
int32_t input_width = input_shape.Dims(2);
int32_t depth = MatchingDim(input_shape, 3, output_shape, 3);
// The Tensorflow version of this op allows resize on the width and height
// axis only.
TFLITE_DCHECK_EQ(output_size_shape.FlatSize(), 2);
int32_t output_height = output_size_data[0];
int32_t output_width = output_size_data[1];
const int col_offset = input_shape.Dims(3);
const int row_offset = input_shape.Dims(2) * col_offset;
const int batch_offset = input_shape.Dims(1) * row_offset;
const T* input_ptr = input_data;
T* output_ptr = output_data;
for (int b = 0; b < batches; ++b) {
for (int y = 0; y < output_height; ++y) {
int32_t in_y = GetNearestNeighbor(y, input_height, output_height,
op_params.align_corners,
op_params.half_pixel_centers);
const T* y_input_ptr = input_ptr + in_y * row_offset;
for (int x = 0; x < output_width; ++x) {
int32_t in_x = GetNearestNeighbor(x, input_width, output_width,
op_params.align_corners,
op_params.half_pixel_centers);
const T* x_input_ptr = y_input_ptr + in_x * col_offset;
memcpy(output_ptr, x_input_ptr, depth * sizeof(T));
output_ptr += depth;
}
}
input_ptr += batch_offset;
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_
#include <cmath>
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline float RoundToNearest(float value) {
auto floor_val = std::floor(value);
auto diff = value - floor_val;
if ((diff < 0.5f) ||
((diff == 0.5f) && (static_cast<int>(floor_val) % 2 == 0))) {
return floor_val;
} else {
return floor_val = floor_val + 1.0f;
}
}
inline void Round(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) {
// Note that this implementation matches that of tensorFlow tf.round
// and corresponds to the bankers rounding method.
// cfenv (for fesetround) is not yet supported universally on Android, so
// using a work around.
output_data[i] = RoundToNearest(input_data[i]);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_
#include <limits>
#include <vector>
#include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/op_macros.h"
namespace tflite {
namespace reference_ops {
inline void Softmax(const SoftmaxParams& params,
const RuntimeShape& input_shape, const float* input_data,
const RuntimeShape& output_shape, float* output_data) {
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);
for (int i = 0; i < outer_size; ++i) {
// Find max element value which we'll use to ensure numerical stability
// taking advantage of the following equality:
// exp(x[i])/sum(exp(x[i])) == exp(x[i]+C)/sum(exp(x[i]+C))
float max = std::numeric_limits<float>::lowest();
for (int c = 0; c < depth; ++c) {
max = std::max(max, input_data[i * depth + c]);
}
// Compute sum.
float sum = 0.f;
for (int c = 0; c < depth; ++c) {
sum += std::exp((input_data[i * depth + c] - max) *
static_cast<float>(params.beta));
}
// Compute result.
for (int c = 0; c < depth; ++c) {
output_data[i * depth + c] = std::exp((input_data[i * depth + c] - max) *
static_cast<float>(params.beta)) /
sum;
}
}
}
// Quantized softmax with int8_t/uint8_t input and int8_t/uint8_t/int16_t
// output.
template <typename InputT, typename OutputT>
inline void Softmax(const SoftmaxParams& params,
const RuntimeShape& input_shape, const InputT* input_data,
const RuntimeShape& output_shape, OutputT* output_data) {
const int32_t input_beta_multiplier = params.input_multiplier;
const int32_t input_beta_left_shift = params.input_left_shift;
const int diff_min = params.diff_min;
// The representation chosen for the input to the exp() function is Q5.26.
// We need to leave extra space since values that we skip might be as large as
// -32 before multiplying by input_beta_multiplier, and therefore as large as
// -16 afterwards. Note that exp(-8) is definitely not insignificant to
// accumulation, but exp(-16) definitely is.
static const int kScaledDiffIntegerBits = 5;
static const int kAccumulationIntegerBits = 12;
using FixedPointScaledDiff =
gemmlowp::FixedPoint<int32_t, kScaledDiffIntegerBits>;
using FixedPointAccum =
gemmlowp::FixedPoint<int32_t, kAccumulationIntegerBits>;
using FixedPoint0 = gemmlowp::FixedPoint<int32_t, 0>;
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);
for (int i = 0; i < outer_size; ++i) {
InputT max_in_row = std::numeric_limits<InputT>::min();
for (int c = 0; c < depth; ++c) {
max_in_row = std::max(max_in_row, input_data[i * depth + c]);
}
FixedPointAccum sum_of_exps = FixedPointAccum::Zero();
for (int c = 0; c < depth; ++c) {
int32_t input_diff =
static_cast<int32_t>(input_data[i * depth + c]) - max_in_row;
if (input_diff >= diff_min) {
const int32_t input_diff_rescaled =
MultiplyByQuantizedMultiplierGreaterThanOne(
input_diff, input_beta_multiplier, input_beta_left_shift);
const FixedPointScaledDiff scaled_diff_f8 =
FixedPointScaledDiff::FromRaw(input_diff_rescaled);
sum_of_exps = sum_of_exps + gemmlowp::Rescale<kAccumulationIntegerBits>(
exp_on_negative_values(scaled_diff_f8));
}
}
int num_bits_over_unit;
FixedPoint0 shifted_scale = FixedPoint0::FromRaw(GetReciprocal(
sum_of_exps.raw(), kAccumulationIntegerBits, &num_bits_over_unit));
for (int c = 0; c < depth; ++c) {
int32_t input_diff =
static_cast<int32_t>(input_data[i * depth + c]) - max_in_row;
if (input_diff >= diff_min) {
const int32_t input_diff_rescaled =
MultiplyByQuantizedMultiplierGreaterThanOne(
input_diff, input_beta_multiplier, input_beta_left_shift);
const FixedPointScaledDiff scaled_diff_f8 =
FixedPointScaledDiff::FromRaw(input_diff_rescaled);
FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8);
int32_t unsat_output = gemmlowp::RoundingDivideByPOT(
(shifted_scale * exp_in_0).raw(),
num_bits_over_unit + 31 - (sizeof(OutputT) * 8));
const int32_t shifted_output =
unsat_output +
static_cast<int32_t>(std::numeric_limits<OutputT>::min());
output_data[i * depth + c] = static_cast<OutputT>(std::max(
std::min(shifted_output,
static_cast<int32_t>(std::numeric_limits<OutputT>::max())),
static_cast<int32_t>(std::numeric_limits<OutputT>::min())));
} else {
output_data[i * depth + c] = std::numeric_limits<OutputT>::min();
}
}
}
}
// Quantized softmax with int16_t input and int16_t output.
inline void SoftmaxInt16(const SoftmaxParams& params,
const RuntimeShape& input_shape,
const int16_t* input_data,
const RuntimeShape& output_shape,
int16_t* output_data) {
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);
for (int i = 0; i < outer_size; ++i) {
// Find the largest element
int16_t max_in_row = std::numeric_limits<int16_t>::min();
for (int c = 0; c < depth; ++c) {
max_in_row = std::max(max_in_row, input_data[i * depth + c]);
}
// Compute exp(input - max_input)
std::vector<int16_t> exp_result_Q015(depth);
for (int c = 0; c < depth; ++c) {
int32_t input_diff = input_data[i * depth + c] - max_in_row;
// scale the input_diff such that [-65535, 0] correspond to [-10.0, 0.0]
int32_t scaled_diff = MultiplyByQuantizedMultiplier(
input_diff, params.input_multiplier, params.input_left_shift);
// recenter to [-32768, 32767]
int32_t sym_scaled_diff = scaled_diff + 32767;
int16_t sat_sym_scaled_diff =
std::min(std::max(sym_scaled_diff, static_cast<int32_t>(-32768)),
static_cast<int32_t>(32767));
// apply the exp() LUT activation function
exp_result_Q015[c] =
generic_int16_table_lookup(sat_sym_scaled_diff, params.exp_lut);
}
// sum_of_exps is a Q16.15 fixed point format.
int32_t sum_of_exps = 0;
for (int c = 0; c < depth; ++c) {
// Q16.15 + Q0.15
sum_of_exps += exp_result_Q015[c];
}
// Compute the reciprocal 1/sum_of_exps
uint8_t headroom_plus_one =
CountLeadingZeros(static_cast<uint32_t>(sum_of_exps));
int32_t shifted_sum =
((static_cast<int64_t>(sum_of_exps) << (headroom_plus_one - 1)) +
(1 << 13)) >>
14;
// since the LUT computes 1/(1 + x) we need to first compute x = (sum - 1).
// also, the LUT expects a symmetrical input, so we must also recenter x
// from [0, 65535] to [-32768, 32767].
int32_t sym_shifted_sum = shifted_sum + (-((1 << 15) + (1 << 16)));
int16_t sat_sym_shifted_sum = static_cast<int16_t>(
std::min(std::max(sym_shifted_sum, static_cast<int32_t>(-32768)),
static_cast<int32_t>(32767)));
// apply 1/(1 + x) LUT activation function
int16_t reciprocal_scale_Q015 = generic_int16_table_lookup(
sat_sym_shifted_sum, params.one_over_one_plus_x_lut);
// Rescale the exp_result with reciprocal
// range of output is [0, 32767] correspond to [0.0, 1.0]
for (int c = 0; c < depth; ++c) {
uint8_t right_shift = 31 - headroom_plus_one;
int64_t round = 1 << (right_shift - 1);
int32_t result = (static_cast<int64_t>(exp_result_Q015[c]) *
static_cast<int64_t>(reciprocal_scale_Q015) +
round) >>
right_shift;
output_data[i * depth + c] = static_cast<int16_t>(
std::min(std::max(result, static_cast<int32_t>(0)),
static_cast<int32_t>(32767)));
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/strided_slice_logic.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
template <typename T>
inline void StridedSlice(const tflite::StridedSliceParams& op_params,
const RuntimeShape& unextended_input_shape,
const T* input_data,
const RuntimeShape& unextended_output_shape,
T* output_data) {
using strided_slice::LoopCondition;
using strided_slice::StartForAxis;
using strided_slice::StopForAxis;
// Note that the output_shape is not used herein.
tflite::StridedSliceParams params_copy = op_params;
TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 5);
TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 5);
const RuntimeShape input_shape =
RuntimeShape::ExtendedShape(5, unextended_input_shape);
const RuntimeShape output_shape =
RuntimeShape::ExtendedShape(5, unextended_output_shape);
// Reverse and pad to 5 dimensions because that is what the runtime code
// requires (ie. all shapes must be 5D and are given backwards).
strided_slice::StridedSlicePadIndices(&params_copy, 5);
const int start_0 = StartForAxis(params_copy, input_shape, 0);
const int stop_0 = StopForAxis(params_copy, input_shape, 0, start_0);
const int start_1 = StartForAxis(params_copy, input_shape, 1);
const int stop_1 = StopForAxis(params_copy, input_shape, 1, start_1);
const int start_2 = StartForAxis(params_copy, input_shape, 2);
const int stop_2 = StopForAxis(params_copy, input_shape, 2, start_2);
const int start_3 = StartForAxis(params_copy, input_shape, 3);
const int stop_3 = StopForAxis(params_copy, input_shape, 3, start_3);
const int start_4 = StartForAxis(params_copy, input_shape, 4);
const int stop_4 = StopForAxis(params_copy, input_shape, 4, start_4);
T* out_ptr = output_data;
for (int offset_0 = start_0 * input_shape.Dims(1),
end_0 = stop_0 * input_shape.Dims(1),
step_0 = params_copy.strides[0] * input_shape.Dims(1);
!LoopCondition(offset_0, end_0, params_copy.strides[0]);
offset_0 += step_0) {
for (int offset_1 = (offset_0 + start_1) * input_shape.Dims(2),
end_1 = (offset_0 + stop_1) * input_shape.Dims(2),
step_1 = params_copy.strides[1] * input_shape.Dims(2);
!LoopCondition(offset_1, end_1, params_copy.strides[1]);
offset_1 += step_1) {
for (int offset_2 = (offset_1 + start_2) * input_shape.Dims(3),
end_2 = (offset_1 + stop_2) * input_shape.Dims(3),
step_2 = params_copy.strides[2] * input_shape.Dims(3);
!LoopCondition(offset_2, end_2, params_copy.strides[2]);
offset_2 += step_2) {
for (int offset_3 = (offset_2 + start_3) * input_shape.Dims(4),
end_3 = (offset_2 + stop_3) * input_shape.Dims(4),
step_3 = params_copy.strides[3] * input_shape.Dims(4);
!LoopCondition(offset_3, end_3, params_copy.strides[3]);
offset_3 += step_3) {
for (int offset_4 = offset_3 + start_4, end_4 = offset_3 + stop_4;
!LoopCondition(offset_4, end_4, params_copy.strides[4]);
offset_4 += params_copy.strides[4]) {
*out_ptr++ = input_data[offset_4];
}
}
}
}
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_
#include <stdint.h>
#include <algorithm>
#include <limits>
#include "ruy/profiler/instrumentation.h" // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace reference_ops {
inline void SubNonBroadcast(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const float* input1_data,
const RuntimeShape& input2_shape,
const float* input2_data,
const RuntimeShape& output_shape,
float* output_data) {
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = ActivationFunctionWithMinMax(
input1_data[i] - input2_data[i], params.float_activation_min,
params.float_activation_max);
}
}
inline void SubNonBroadcast(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int32_t* input1_data,
const RuntimeShape& input2_shape,
const int32_t* input2_data,
const RuntimeShape& output_shape,
int32_t* output_data) {
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = ActivationFunctionWithMinMax(
input1_data[i] - input2_data[i], params.quantized_activation_min,
params.quantized_activation_max);
}
}
// TODO(b/151345304): We can implement BroadcastSub on buffers of arbitrary
// dimensionality if the runtime code does a single loop over one dimension
// that handles broadcasting as the base case. The code generator would then
// generate max(D1, D2) nested for loops.
// TODO(b/151345101): BroadcastSub is intentionally duplicated from
// reference_ops.h. Once an optimized version is implemented and NdArrayDesc<T>
// is no longer referenced in this file, move NdArrayDesc<T> from types.h to
// reference_ops.h.
template <int N = 5>
inline void BroadcastSubSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const float* input1_data,
const RuntimeShape& input2_shape,
const float* input2_data,
const RuntimeShape& output_shape,
float* output_data) {
ruy::profiler::ScopeLabel label("BroadcastSubSlow/float");
TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
auto sub_func = [&](int indexes[N]) {
output_data[SubscriptToIndex(output_desc, indexes)] =
ActivationFunctionWithMinMax(
input1_data[SubscriptToIndex(desc1, indexes)] -
input2_data[SubscriptToIndex(desc2, indexes)],
params.float_activation_min, params.float_activation_max);
};
NDOpsHelper<N>(output_desc, sub_func);
}
template <int N = 5>
inline void BroadcastSubSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const uint8_t* input1_data,
const RuntimeShape& input2_shape,
const uint8_t* input2_data,
const RuntimeShape& output_shape,
uint8_t* output_data) {
ruy::profiler::ScopeLabel label("BroadcastSubSlow/uint8_t");
TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
auto sub_func = [&](int indexes[N]) {
const int32_t input1_val =
params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)];
const int32_t input2_val =
params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)];
const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier, params.input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier, params.input2_shift);
const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sub, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[SubscriptToIndex(output_desc, indexes)] =
static_cast<uint8_t>(clamped_output);
};
NDOpsHelper<N>(output_desc, sub_func);
}
template <int N = 5>
inline void BroadcastSubSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int32_t* input1_data,
const RuntimeShape& input2_shape,
const int32_t* input2_data,
const RuntimeShape& output_shape,
int32_t* output_data) {
ruy::profiler::ScopeLabel label("BroadcastSubSlow/int32_t");
TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
auto sub_func = [&](int indexes[N]) {
output_data[SubscriptToIndex(output_desc, indexes)] =
ActivationFunctionWithMinMax(
input1_data[SubscriptToIndex(desc1, indexes)] -
input2_data[SubscriptToIndex(desc2, indexes)],
params.quantized_activation_min, params.quantized_activation_max);
};
NDOpsHelper<N>(output_desc, sub_func);
}
template <int N = 5>
inline void BroadcastSubSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int8_t* input1_data,
const RuntimeShape& input2_shape,
const int8_t* input2_data,
const RuntimeShape& output_shape,
int8_t* output_data) {
ruy::profiler::ScopeLabel label("BroadcastSubSlow/int8_t");
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
auto sub_func = [&](int indexes[N]) {
const int32_t input1_val =
params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)];
const int32_t input2_val =
params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)];
const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier, params.input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier, params.input2_shift);
const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sub, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[SubscriptToIndex(output_desc, indexes)] =
static_cast<int8_t>(clamped_output);
};
NDOpsHelper<N>(output_desc, sub_func);
}
template <int N = 5>
void BroadcastSubSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape,
const int64_t* input1_data,
const RuntimeShape& input2_shape,
const int64_t* input2_data,
const RuntimeShape& output_shape, int64_t* output_data) {
ruy::profiler::ScopeLabel label("BroadcastSubSlow/int64_t");
TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
auto sub_func = [&](int indexes[N]) {
output_data[SubscriptToIndex(output_desc, indexes)] =
ActivationFunctionWithMinMax(
input1_data[SubscriptToIndex(desc1, indexes)] -
input2_data[SubscriptToIndex(desc2, indexes)],
params.int64_activation_min, params.int64_activation_max);
};
NDOpsHelper<N>(output_desc, sub_func);
}
template <typename T, int N = 5>
void BroadcastSubSlow(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const T* input1_data,
const RuntimeShape& input2_shape, const T* input2_data,
const RuntimeShape& output_shape, T* output_data) {
ruy::profiler::ScopeLabel label("BroadcastSubSlow/templated");
TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N);
TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N);
NdArrayDesc<N> desc1;
NdArrayDesc<N> desc2;
NdArrayDesc<N> output_desc;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
auto sub_func = [&](int indexes[N]) {
output_data[SubscriptToIndex(output_desc, indexes)] =
ActivationFunctionWithMinMax(
input1_data[SubscriptToIndex(desc1, indexes)] -
input2_data[SubscriptToIndex(desc2, indexes)],
params.quantized_activation_min, params.quantized_activation_max);
};
NDOpsHelper<N>(output_desc, sub_func);
}
// Element-wise Sub that can often be used for inner loop of broadcast sub as
// well as the non-broadcast sub.
inline void SubElementwise(int size, const ArithmeticParams& params,
const uint8_t* input1_data,
const uint8_t* input2_data, uint8_t* output_data) {
TFLITE_DCHECK_GT(params.input1_offset, -256);
TFLITE_DCHECK_GT(params.input2_offset, -256);
TFLITE_DCHECK_LT(params.input1_offset, 256);
TFLITE_DCHECK_LT(params.input2_offset, 256);
for (int i = 0; i < size; ++i) {
const int32_t input1_val = params.input1_offset + input1_data[i];
const int32_t input2_val = params.input2_offset + input2_data[i];
const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier, params.input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier, params.input2_shift);
const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sub, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[i] = static_cast<uint8_t>(clamped_output);
}
}
// Element-wise add that can often be used for inner loop of broadcast add as
// well as the non-broadcast add.
inline void SubElementwise(int size, const ArithmeticParams& params,
const int8_t* input1_data, const int8_t* input2_data,
int8_t* output_data) {
const int32_t int8_max_value = std::numeric_limits<int8_t>::max();
TFLITE_DCHECK_GE(params.input1_offset, -1 * int8_max_value);
TFLITE_DCHECK_GE(params.input2_offset, -1 * int8_max_value);
TFLITE_DCHECK_LE(params.input1_offset, int8_max_value);
TFLITE_DCHECK_LE(params.input2_offset, int8_max_value);
for (int i = 0; i < size; ++i) {
const int32_t input1_val = params.input1_offset + input1_data[i];
const int32_t input2_val = params.input2_offset + input2_data[i];
const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
const int32_t scaled_input1_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input1_val, params.input1_multiplier, params.input1_shift);
const int32_t scaled_input2_val =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
shifted_input2_val, params.input2_multiplier, params.input2_shift);
const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
const int32_t raw_output =
MultiplyByQuantizedMultiplierSmallerThanOneExp(
raw_sub, params.output_multiplier, params.output_shift) +
params.output_offset;
const int32_t clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, raw_output));
output_data[i] = static_cast<int8_t>(clamped_output);
}
}
inline void Sub(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const uint8_t* input1_data,
const RuntimeShape& input2_shape, const uint8_t* input2_data,
const RuntimeShape& output_shape, uint8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
TFLITE_DCHECK_GT(params.input1_offset, -256);
TFLITE_DCHECK_GT(params.input2_offset, -256);
TFLITE_DCHECK_LT(params.input1_offset, 256);
TFLITE_DCHECK_LT(params.input2_offset, 256);
SubElementwise(flat_size, params, input1_data, input2_data, output_data);
}
inline void Sub(const ArithmeticParams& params,
const RuntimeShape& input1_shape, const int8_t* input1_data,
const RuntimeShape& input2_shape, const int8_t* input2_data,
const RuntimeShape& output_shape, int8_t* output_data) {
TFLITE_DCHECK_LE(params.quantized_activation_min,
params.quantized_activation_max);
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
const int32_t int8_max_value = std::numeric_limits<int8_t>::max();
TFLITE_DCHECK_GE(params.input1_offset, -1 * int8_max_value);
TFLITE_DCHECK_GE(params.input2_offset, -1 * int8_max_value);
TFLITE_DCHECK_LE(params.input1_offset, int8_max_value);
TFLITE_DCHECK_LE(params.input2_offset, int8_max_value);
SubElementwise(flat_size, params, input1_data, input2_data, output_data);
}
template <typename T>
void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape,
const T* input1_data, const RuntimeShape& input2_shape,
const T* input2_data, const RuntimeShape& output_shape,
T* output_data) {
NdArrayDesc<4> desc1;
NdArrayDesc<4> desc2;
NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
&desc2);
const RuntimeShape extended_output_shape =
RuntimeShape::ExtendedShape(4, output_shape);
// In Tensorflow, the dimensions are canonically named (batch_number, row,
// col, channel), with extents (batches, height, width, depth), with the
// trailing dimension changing most rapidly (channels has the smallest stride,
// typically 1 element).
//
// In generated C code, we store arrays with the dimensions reversed. The
// first dimension has smallest stride.
//
// We name our variables by their Tensorflow convention, but generate C code
// nesting loops such that the innermost loop has the smallest stride for the
// best cache behavior.
for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
output_data[Offset(extended_output_shape, b, y, x, c)] =
input1_data[SubscriptToIndex(desc1, b, y, x, c)] -
input2_data[SubscriptToIndex(desc2, b, y, x, c)];
}
}
}
}
}
inline void SetActivationMinMax(const ArithmeticParams& params,
int32_t* activation_min,
int32_t* activation_max) {
*activation_min = params.quantized_activation_min;
*activation_max = params.quantized_activation_max;
}
inline void SetActivationMinMax(const ArithmeticParams& params,
float* activation_min, float* activation_max) {
*activation_min = params.float_activation_min;
*activation_max = params.float_activation_max;
}
inline void SetActivationMinMax(const ArithmeticParams& params,
int64_t* activation_min,
int64_t* activation_max) {
*activation_min = params.int64_activation_min;
*activation_max = params.int64_activation_max;
}
template <typename T>
inline void SubWithActivation(
const ArithmeticParams& params, const RuntimeShape& input1_shape,
const T* input1_data, const RuntimeShape& input2_shape,
const T* input2_data, const RuntimeShape& output_shape, T* output_data) {
ruy::profiler::ScopeLabel label("SubWithActivation");
const int flat_size =
MatchingElementsSize(input1_shape, input2_shape, output_shape);
T activation_min, activation_max;
SetActivationMinMax(params, &activation_min, &activation_max);
for (int i = 0; i < flat_size; ++i) {
output_data[i] = ActivationFunctionWithMinMax(
input1_data[i] - input2_data[i], activation_min, activation_max);
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_
#include <cmath>
#include "fixedpoint/fixedpoint.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/op_macros.h"
namespace tflite {
namespace reference_ops {
inline void Tanh(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++) {
float val = input_data[i];
float result = std::tanh(val);
output_data[i] = result;
}
}
// Convenience version that allows, for example, generated-code calls to be
// uniform between data types.
inline void Tanh(const TanhParams&, const RuntimeShape& input_shape,
const float* input_data, const RuntimeShape& output_shape,
float* output_data) {
// Drop params: not needed.
Tanh(input_shape, input_data, output_shape, output_data);
}
inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape,
const int16_t* input_data, const RuntimeShape& output_shape,
int16_t* output_data) {
const int input_left_shift = params.input_left_shift;
// Support for shifts is limited until we have a parameterized version of
// SaturatingRoundingMultiplyByPOT().
TFLITE_DCHECK_GE(input_left_shift, 0);
TFLITE_DCHECK_LE(input_left_shift, 1);
const int flat_size = MatchingFlatSize(input_shape, output_shape);
// F0 uses 0 integer bits, range [-1, 1].
// This is the return type of math functions such as tanh, logistic,
// whose range is in [-1, 1].
using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
// F3 uses 3 integer bits, range [-8, 8], the input range expected here.
using F3 = gemmlowp::FixedPoint<std::int16_t, 3>;
if (input_left_shift == 0) {
for (int i = 0; i < flat_size; i++) {
F3 input = F3::FromRaw(input_data[i]);
F0 output = gemmlowp::tanh(input);
output_data[i] = output.raw();
}
} else {
for (int i = 0; i < flat_size; i++) {
F3 input = F3::FromRaw(
gemmlowp::SaturatingRoundingMultiplyByPOT<1>(input_data[i]));
F0 output = gemmlowp::tanh(input);
output_data[i] = output.raw();
}
}
}
inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape,
const uint8_t* input_data, const RuntimeShape& output_shape,
uint8_t* output_data) {
const int32_t input_zero_point = params.input_zero_point;
const int32_t input_range_radius = params.input_range_radius;
const int32_t input_multiplier = params.input_multiplier;
const int input_left_shift = params.input_left_shift;
const int32_t output_zero_point = 128;
const int flat_size = MatchingFlatSize(input_shape, output_shape);
for (int i = 0; i < flat_size; i++) {
const uint8_t input_val_u8 = input_data[i];
const int32_t input_val_centered =
static_cast<int32_t>(input_val_u8) - input_zero_point;
uint8_t output_val;
if (input_val_centered <= -input_range_radius) {
output_val = 0;
} else if (input_val_centered >= input_range_radius) {
output_val = 255;
} else {
const int32_t input_val_rescaled =
MultiplyByQuantizedMultiplierGreaterThanOne(
input_val_centered, input_multiplier, input_left_shift);
using FixedPoint4 = gemmlowp::FixedPoint<int32_t, 4>;
using FixedPoint0 = gemmlowp::FixedPoint<int32_t, 0>;
const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled);
const FixedPoint0 output_val_f0 = gemmlowp::tanh(input_val_f4);
// Convert from Q0.31 to Q24.7.
using gemmlowp::RoundingDivideByPOT;
int32_t output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24);
output_val_s32 += output_zero_point;
if (output_val_s32 == 256) {
output_val_s32 = 255;
}
// Reinterpret as Q0.7, encoded in uint8_t.
TFLITE_DCHECK_GE(output_val_s32, 0);
TFLITE_DCHECK_LE(output_val_s32, 255);
output_val = static_cast<uint8_t>(output_val_s32);
}
output_data[i] = output_val;
}
}
} // namespace reference_ops
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_
#include <limits>
#include <vector>
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace strided_slice {
// Use until std::clamp() is available from C++17.
inline int Clamp(const int v, const int lo, const int hi) {
TFLITE_DCHECK(!(hi < lo));
if (hi < v) return hi;
if (v < lo) return lo;
return v;
}
inline void StridedSlicePadIndices(tflite::StridedSliceParams* p,
int dim_count) {
// Add indices and mask bits to fully include extra dimensions
TFLITE_CHECK_LE(dim_count, 5);
TFLITE_CHECK_GE(dim_count, p->start_indices_count);
TFLITE_CHECK_EQ(p->start_indices_count, p->stop_indices_count);
TFLITE_CHECK_EQ(p->stop_indices_count, p->strides_count);
const int pad_count = dim_count - p->start_indices_count;
// Pad indices at start, so move arrays by pad_count.
for (int i = p->start_indices_count - 1; i >= 0; --i) {
p->strides[i + pad_count] = p->strides[i];
p->start_indices[i + pad_count] = p->start_indices[i];
p->stop_indices[i + pad_count] = p->stop_indices[i];
}
for (int i = 0; i < pad_count; ++i) {
p->start_indices[i] = 0;
p->stop_indices[i] = 1;
p->strides[i] = 1;
}
// Pad masks with 0s or 1s as required.
p->shrink_axis_mask <<= pad_count;
p->ellipsis_mask <<= pad_count;
p->new_axis_mask <<= pad_count;
p->begin_mask <<= pad_count;
p->end_mask <<= pad_count;
p->begin_mask |= (1 << pad_count) - 1;
p->end_mask |= (1 << pad_count) - 1;
p->start_indices_count = dim_count;
p->stop_indices_count = dim_count;
p->strides_count = dim_count;
}
// Return the index for the first element along that axis. This index will be a
// positive integer between [0, axis_size - 1] that can be used to index
// directly into the data.
inline int StartForAxis(const tflite::StridedSliceParams& params,
const RuntimeShape& input_shape, int axis) {
const auto begin_mask = params.begin_mask;
const auto* start_indices = params.start_indices;
const auto* strides = params.strides;
const int axis_size = input_shape.Dims(axis);
if (axis_size == 0) {
return 0;
}
// Begin with the specified index.
int start = start_indices[axis];
// begin_mask override
if (begin_mask & 1 << axis) {
if (strides[axis] > 0) {
// Forward iteration - use the first element. These values will get
// clamped below (Note: We could have set them to 0 and axis_size-1, but
// use lowest() and max() to maintain symmetry with StopForAxis())
start = std::numeric_limits<int>::lowest();
} else {
// Backward iteration - use the last element.
start = std::numeric_limits<int>::max();
}
}
// Handle negative indices
if (start < 0) {
start += axis_size;
}
// Clamping
start = Clamp(start, 0, axis_size - 1);
return start;
}
// Return the "real" index for the end of iteration along that axis. This is an
// "end" in the traditional C sense, in that it points to one past the last
// element. ie. So if you were iterating through all elements of a 1D array of
// size 4, this function would return 4 as the stop, because it is one past the
// "real" indices of 0, 1, 2 & 3.
inline int StopForAxis(const tflite::StridedSliceParams& params,
const RuntimeShape& input_shape, int axis,
int start_for_axis) {
const auto end_mask = params.end_mask;
const auto shrink_axis_mask = params.shrink_axis_mask;
const auto* stop_indices = params.stop_indices;
const auto* strides = params.strides;
const int axis_size = input_shape.Dims(axis);
if (axis_size == 0) {
return 0;
}
// Begin with the specified index
const bool shrink_axis = shrink_axis_mask & (1 << axis);
int stop = stop_indices[axis];
// When shrinking an axis, the end position does not matter (and can be
// incorrect when negative indexing is used, see Issue #19260). Always use
// start_for_axis + 1 to generate a length 1 slice, since start_for_axis has
// already been adjusted for negative indices.
if (shrink_axis) {
stop = start_for_axis + 1;
}
// end_mask override
if (end_mask & (1 << axis)) {
if (strides[axis] > 0) {
// Forward iteration - use the last element. These values will get
// clamped below
stop = std::numeric_limits<int>::max();
} else {
// Backward iteration - use the first element.
stop = std::numeric_limits<int>::lowest();
}
}
// Handle negative indices
if (stop < 0) {
stop += axis_size;
}
// Clamping
// Because the end index points one past the last element, we need slightly
// different clamping ranges depending on the direction.
if (strides[axis] > 0) {
// Forward iteration
stop = Clamp(stop, 0, axis_size);
} else {
// Backward iteration
stop = Clamp(stop, -1, axis_size - 1);
}
return stop;
}
inline bool LoopCondition(int index, int stop, int stride) {
// True when we have reached the end of an axis and should loop.
return stride > 0 ? index >= stop : index <= stop;
}
inline tflite::StridedSliceParams BuildStridedSliceParams(
int begin_mask, int end_mask, int shrink_axis_mask,
const std::vector<int>& start_indices, const std::vector<int>& stop_indices,
const std::vector<int>& strides) {
tflite::StridedSliceParams op_params;
const int dims_count = start_indices.size();
op_params.start_indices_count = dims_count;
op_params.stop_indices_count = dims_count;
op_params.strides_count = dims_count;
for (int i = 0; i < dims_count; ++i) {
op_params.start_indices[i] = start_indices[i];
op_params.stop_indices[i] = stop_indices[i];
op_params.strides[i] = strides[i];
}
op_params.begin_mask = begin_mask;
op_params.ellipsis_mask = 0;
op_params.end_mask = end_mask;
op_params.new_axis_mask = 0;
op_params.shrink_axis_mask = shrink_axis_mask;
return op_params;
}
} // namespace strided_slice
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_H_
#include <complex>
#include <vector>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/string_util.h"
namespace tflite {
inline RuntimeShape GetTensorShape(std::vector<int32_t> data) {
return RuntimeShape(data.size(), data.data());
}
// A list of tensors in a format that can be used by kernels like split and
// concatenation.
template <typename T>
class VectorOfTensors {
public:
// Build with the tensors in 'tensor_list'.
VectorOfTensors(const TfLiteContext& context,
const TfLiteIntArray& tensor_list) {
int num_tensors = tensor_list.size;
all_data_.reserve(num_tensors);
all_shape_.reserve(num_tensors);
all_shape_ptr_.reserve(num_tensors);
for (int i = 0; i < num_tensors; ++i) {
TfLiteTensor* t = &context.tensors[tensor_list.data[i]];
all_data_.push_back(GetTensorData<T>(t));
all_shape_.push_back(GetTensorShape(t));
}
// Taking the pointer from inside a std::vector is only OK if the vector is
// never modified, so we populate all_shape in the previous loop and then we
// are free to grab iterators here.
for (int i = 0; i < num_tensors; ++i) {
all_shape_ptr_.push_back(&all_shape_[i]);
}
}
// Return a pointer to the data pointers of all tensors in the list. For
// example:
// float* const* f = v.data();
// f[0][1] is the second element of the first tensor.
T* const* data() const { return all_data_.data(); }
// Return a pointer the shape pointers of all tensors in the list. For
// example:
// const RuntimeShape* const* d = v.dims();
// dims[1] are the dimensions of the second tensor in the list.
const RuntimeShape* const* shapes() const { return all_shape_ptr_.data(); }
private:
std::vector<T*> all_data_;
std::vector<RuntimeShape> all_shape_;
std::vector<RuntimeShape*> all_shape_ptr_;
};
// A list of quantized tensors in a format that can be used by kernels like
// split and concatenation.
class VectorOfQuantizedTensors : public VectorOfTensors<uint8_t> {
public:
// Build with the tensors in 'tensor_list'.
VectorOfQuantizedTensors(const TfLiteContext& context,
const TfLiteIntArray& tensor_list)
: VectorOfTensors<uint8_t>(context, tensor_list) {
for (int i = 0; i < tensor_list.size; ++i) {
TfLiteTensor* t = &context.tensors[tensor_list.data[i]];
zero_point_.push_back(t->params.zero_point);
scale_.push_back(t->params.scale);
}
}
const float* scale() const { return scale_.data(); }
const int32_t* zero_point() const { return zero_point_.data(); }
private:
std::vector<int32_t> zero_point_;
std::vector<float> scale_;
};
// Writes randomly accessed values from `input` sequentially into `output`.
template <typename T>
class SequentialTensorWriter {
public:
SequentialTensorWriter(const TfLiteTensor* input, TfLiteTensor* output) {
input_data_ = GetTensorData<T>(input);
output_ptr_ = GetTensorData<T>(output);
}
SequentialTensorWriter(const T* input_data, T* output_data)
: input_data_(input_data), output_ptr_(output_data) {}
void Write(int position) { *output_ptr_++ = input_data_[position]; }
void WriteN(int position, int len) {
memcpy(output_ptr_, &input_data_[position], sizeof(T) * len);
output_ptr_ += len;
}
private:
const T* input_data_;
T* output_ptr_;
};
// String ops are not yet supported on platforms w/ static memory.
#ifndef TF_LITE_STATIC_MEMORY
template <>
class SequentialTensorWriter<string> {
public:
SequentialTensorWriter(const TfLiteTensor* input, TfLiteTensor* output)
: input_(input), output_(output) {}
~SequentialTensorWriter() { buffer_.WriteToTensor(output_, nullptr); }
void Write(int position) { this->WriteN(position, 1); }
void WriteN(int position, int len) {
for (int i = 0; i < len; i++) {
buffer_.AddString(GetString(input_, position + i));
}
}
private:
const TfLiteTensor* input_;
TfLiteTensor* output_;
DynamicBuffer buffer_;
};
#endif // TF_LITE_STATIC_MEMORY
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
template <typename T>
inline T* GetTensorData(TfLiteTensor* tensor) {
return tensor != nullptr ? reinterpret_cast<T*>(tensor->data.raw) : nullptr;
}
template <typename T>
inline const T* GetTensorData(const TfLiteTensor* tensor) {
return tensor != nullptr ? reinterpret_cast<const T*>(tensor->data.raw)
: nullptr;
}
inline RuntimeShape GetTensorShape(const TfLiteTensor* tensor) {
if (tensor == nullptr) {
return RuntimeShape();
}
TfLiteIntArray* dims = tensor->dims;
const int dims_size = dims->size;
const int32_t* dims_data = reinterpret_cast<const int32_t*>(dims->data);
return RuntimeShape(dims_size, dims_data);
}
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_
#define TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_
#include <stdint.h>
#include <limits>
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
namespace tflite {
// A fair number of functions in this header have historically been inline.
// It is ok to change functions to not be inline if the latency with
// benchmark_model for MobileNet + MobileBERT is unaffected. If such a change is
// made, move the newly non-inlined function declarations to the top of this
// header file.
const TfLiteTensor* GetInput(const TfLiteContext* context,
const TfLiteNode* node, int index);
// Note: You must check if result is not null:
// TfLiteTensor* my_tensor = GetVariableInput(context, node, kMyTensorIdx);
// TF_LITE_ENSURE(context, my_tensor != nullptr);
TfLiteTensor* GetVariableInput(TfLiteContext* context, const TfLiteNode* node,
int index);
TfLiteTensor* GetOutput(TfLiteContext* context, const TfLiteNode* node,
int index);
const TfLiteTensor* GetOptionalInputTensor(const TfLiteContext* context,
const TfLiteNode* node, int index);
inline int NumDimensions(const TfLiteTensor* t) { return t->dims->size; }
inline int SizeOfDimension(const TfLiteTensor* t, int dim) {
return t->dims->data[dim];
}
#ifndef TF_LITE_STATIC_MEMORY
inline TfLiteTensor* GetTemporary(TfLiteContext* context,
const TfLiteNode* node, int index) {
return &context->tensors[node->temporaries->data[index]];
}
inline const TfLiteTensor* GetIntermediates(TfLiteContext* context,
const TfLiteNode* node, int index) {
return &context->tensors[node->intermediates->data[index]];
}
inline int NumIntermediates(const TfLiteNode* node) {
return node->intermediates->size;
}
#endif // TF_LITE_STATIC_MEMORY
inline int NumInputs(const TfLiteNode* node) { return node->inputs->size; }
inline int NumOutputs(const TfLiteNode* node) { return node->outputs->size; }
inline int64_t NumElements(const TfLiteIntArray* dims) {
int64_t count = 1;
for (int i = 0; i < dims->size; ++i) {
count *= dims->data[i];
}
return count;
}
inline int64_t NumElements(const TfLiteTensor* t) {
return NumElements(t->dims);
}
// Determines whether tensor is constant.
// TODO(b/138199592): Introduce new query which checks for constant OR
// persistent-read-only, which would be useful for most tensor kernels that
// are potentially dynamic based on the input tensor value availability at the
// time of prepare.
inline bool IsConstantTensor(const TfLiteTensor* tensor) {
return tensor->allocation_type == kTfLiteMmapRo;
}
// Determines whether tensor is dynamic. Note that a tensor can be non-const and
// not dynamic. This function specifically checks for a dynamic tensor.
inline bool IsDynamicTensor(const TfLiteTensor* tensor) {
return tensor->allocation_type == kTfLiteDynamic;
}
// Sets tensor to dynamic.
inline void SetTensorToDynamic(TfLiteTensor* tensor) {
if (tensor->allocation_type != kTfLiteDynamic) {
tensor->allocation_type = kTfLiteDynamic;
tensor->data.raw = nullptr;
}
}
// Sets tensor to persistent and read-only.
inline void SetTensorToPersistentRo(TfLiteTensor* tensor) {
if (tensor->allocation_type != kTfLitePersistentRo) {
tensor->allocation_type = kTfLitePersistentRo;
tensor->data.raw = nullptr;
}
}
// Determines whether it is a hybrid op - one that has float inputs and
// quantized weights.
inline bool IsHybridOp(const TfLiteTensor* input, const TfLiteTensor* weight) {
return ((weight->type == kTfLiteUInt8 || weight->type == kTfLiteInt8) &&
input->type == kTfLiteFloat32);
}
// Check dimensionality match and populate OpData for Conv and DepthwiseConv.
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);
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);
// Calculates the multiplication factor for a quantized convolution (or
// quantized depthwise convolution) involving the given tensors. Returns an
// error if the scales of the tensors are not compatible.
TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context,
const TfLiteTensor* input,
const TfLiteTensor* filter,
const TfLiteTensor* bias,
TfLiteTensor* output,
double* multiplier);
TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context,
const TfLiteTensor* input,
const TfLiteTensor* filter,
TfLiteTensor* output,
double* multiplier);
// Calculates the useful quantized range of an activation layer given its
// activation tensor.
TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context,
TfLiteFusedActivation activation,
TfLiteTensor* output,
int32_t* act_min,
int32_t* act_max);
// Calculates the useful range of an activation layer given its activation
// tensor.a
template <typename T>
void CalculateActivationRange(TfLiteFusedActivation activation,
T* activation_min, T* activation_max) {
if (activation == kTfLiteActRelu) {
*activation_min = 0;
*activation_max = std::numeric_limits<T>::max();
} else if (activation == kTfLiteActRelu6) {
*activation_min = 0;
*activation_max = 6;
} else if (activation == kTfLiteActReluN1To1) {
*activation_min = -1;
*activation_max = 1;
} else {
*activation_min = std::numeric_limits<T>::lowest();
*activation_max = std::numeric_limits<T>::max();
}
}
// Return true if the given tensors have the same shape.
bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2);
// Calculates the output_shape that is necessary for element-wise operations
// with broadcasting involving the two input tensors.
TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context,
const TfLiteTensor* input1,
const TfLiteTensor* input2,
TfLiteIntArray** output_shape);
// Calculates the output_shape that is necessary for element-wise operations
// with broadcasting involving the three input tensors.
TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context,
const TfLiteTensor* input1,
const TfLiteTensor* input2,
const TfLiteTensor* input3,
TfLiteIntArray** output_shape);
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_OP_MACROS_H_
#define TENSORFLOW_LITE_KERNELS_OP_MACROS_H_
// If we're on a platform without standard IO functions, fall back to a
// non-portable function.
#ifdef TF_LITE_MCU_DEBUG_LOG
#include "tensorflow/lite/micro/debug_log.h"
#define DEBUG_LOG(x) \
do { \
DebugLog(x); \
} while (0)
inline void InfiniteLoop() {
DEBUG_LOG("HALTED\n");
while (1) {
}
}
#define TFLITE_ABORT InfiniteLoop();
#else // TF_LITE_MCU_DEBUG_LOG
#include <stdio.h>
#include <cstdlib>
#define DEBUG_LOG(x) \
do { \
printf("%s", (x)); \
} while (0)
// Report Error for unsupported type by op 'op_name' and returns kTfLiteError.
#define TF_LITE_UNSUPPORTED_TYPE(context, type, op_name) \
do { \
TF_LITE_KERNEL_LOG((context), "%s:%d Type %s is unsupported by op %s.", \
__FILE__, __LINE__, TfLiteTypeGetName(type), \
(op_name)); \
return kTfLiteError; \
} while (0)
#define TFLITE_ABORT abort()
#endif // TF_LITE_MCU_DEBUG_LOG
#ifdef NDEBUG
#define TFLITE_ASSERT_FALSE (static_cast<void>(0))
#else
#define TFLITE_ASSERT_FALSE TFLITE_ABORT
#endif
#define TF_LITE_FATAL(msg) \
do { \
DEBUG_LOG(msg); \
DEBUG_LOG("\nFATAL\n"); \
TFLITE_ABORT; \
} while (0)
#define TF_LITE_ASSERT(x) \
do { \
if (!(x)) TF_LITE_FATAL(#x); \
} while (0)
#define TF_LITE_ASSERT_EQ(x, y) \
do { \
if ((x) != (y)) TF_LITE_FATAL(#x " didn't equal " #y); \
} while (0)
#endif // TENSORFLOW_LITE_KERNELS_OP_MACROS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_PADDING_H_
#define TENSORFLOW_LITE_KERNELS_PADDING_H_
#include "tensorflow/lite/c/builtin_op_data.h"
namespace tflite {
// TODO(renjieliu): Migrate others to use ComputePaddingWithLeftover.
inline int ComputePadding(int stride, int dilation_rate, int in_size,
int filter_size, int out_size) {
int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
int padding = ((out_size - 1) * stride + effective_filter_size - in_size) / 2;
return padding > 0 ? padding : 0;
}
// It's not guaranteed that padding is symmetric. It's important to keep
// offset for algorithms need all paddings.
inline int ComputePaddingWithOffset(int stride, int dilation_rate, int in_size,
int filter_size, int out_size,
int* offset) {
int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
int total_padding =
((out_size - 1) * stride + effective_filter_size - in_size);
total_padding = total_padding > 0 ? total_padding : 0;
*offset = total_padding % 2;
return total_padding / 2;
}
// Matching GetWindowedOutputSize in TensorFlow.
inline int ComputeOutSize(TfLitePadding padding, int image_size,
int filter_size, int stride, int dilation_rate = 1) {
int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
switch (padding) {
case kTfLitePaddingSame:
return (image_size + stride - 1) / stride;
case kTfLitePaddingValid:
return (image_size + stride - effective_filter_size) / stride;
default:
return 0;
}
}
inline TfLitePaddingValues ComputePaddingHeightWidth(
int stride_height, int stride_width, int dilation_rate_height,
int dilation_rate_width, int in_height, int in_width, int filter_height,
int filter_width, TfLitePadding padding, int* out_height, int* out_width) {
*out_width = ComputeOutSize(padding, in_width, filter_width, stride_width,
dilation_rate_width);
*out_height = ComputeOutSize(padding, in_height, filter_height, stride_height,
dilation_rate_height);
TfLitePaddingValues padding_values;
int offset = 0;
padding_values.height =
ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height,
filter_height, *out_height, &offset);
padding_values.height_offset = offset;
padding_values.width =
ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width,
filter_width, *out_width, &offset);
padding_values.width_offset = offset;
return padding_values;
}
} // namespace tflite
#endif // TENSORFLOW_LITE_KERNELS_PADDING_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_ALL_OPS_RESOLVER_H_
#define TENSORFLOW_LITE_MICRO_ALL_OPS_RESOLVER_H_
#include "tensorflow/lite/micro/compatibility.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
namespace tflite {
// The magic number in the template parameter is the maximum number of ops that
// can be added to AllOpsResolver. It can be increased if needed. And most
// applications that care about the memory footprint will want to directly use
// MicroMutableOpResolver and have an application specific template parameter.
// The examples directory has sample code for this.
class AllOpsResolver : public MicroMutableOpResolver<128> {
public:
AllOpsResolver();
private:
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_ALL_OPS_RESOLVER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_BENCHMARKS_KEYWORD_SCRAMBLED_MODEL_DATA_H_
#define TENSORFLOW_LITE_MICRO_BENCHMARKS_KEYWORD_SCRAMBLED_MODEL_DATA_H_
extern const unsigned char g_keyword_scrambled_model_data[];
extern const unsigned int g_keyword_scrambled_model_data_length;
#endif // TENSORFLOW_LITE_MICRO_BENCHMARKS_KEYWORD_SCRAMBLED_MODEL_DATA_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_
#define TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_
// C++ will automatically create class-specific delete operators for virtual
// objects, which by default call the global delete function. For embedded
// applications we want to avoid this, and won't be calling new/delete on these
// objects, so we need to override the default implementation with one that does
// nothing to avoid linking in ::delete().
// This macro needs to be included in all subclasses of a virtual base class in
// the private section.
#ifdef TF_LITE_STATIC_MEMORY
#define TF_LITE_REMOVE_VIRTUAL_DELETE \
void operator delete(void* p) {}
#else
#define TF_LITE_REMOVE_VIRTUAL_DELETE
#endif
#endif // TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_
#define TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_
// This function should be implemented by each target platform, and provide a
// way for strings to be output to some text stream. For more information, see
// tensorflow/lite/micro/debug_log.cc.
extern "C" void DebugLog(const char* s);
#endif // TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_

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/* 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.
==============================================================================*/
// Provides an interface to take an action based on the output from the person
// detection model.
#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
// Called every time the results of a person detection run are available. The
// `person_score` has the numerical confidence that the captured image contains
// a person, and `no_person_score` has the numerical confidence that the image
// does not contain a person. Typically if person_score > no person score, the
// image is considered to contain a person. This threshold may be adjusted for
// particular applications.
void RespondToDetection(tflite::ErrorReporter* error_reporter,
int8_t person_score, int8_t no_person_score);
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
// This is an abstraction around an image source like a camera, and is
// expected to return 8-bit sample data. The assumption is that this will be
// called in a low duty-cycle fashion in a low-power application. In these
// cases, the imaging sensor need not be run in a streaming mode, but rather can
// be idled in a relatively low-power mode between calls to GetImage(). The
// assumption is that the overhead and time of bringing the low-power sensor out
// of this standby mode is commensurate with the expected duty cycle of the
// application. The underlying sensor may actually be put into a streaming
// configuration, but the image buffer provided to GetImage should not be
// overwritten by the driver code until the next call to GetImage();
//
// The reference implementation can have no platform-specific dependencies, so
// it just returns a static image. For real applications, you should
// ensure there's a specialized implementation that accesses hardware APIs.
TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
int image_height, int channels, int8_t* image_data,
uint8_t * hardware_input);
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
#include "tensorflow/lite/c/common.h"
// Initializes all data needed for the example. The name is important, and needs
// to be setup() for Arduino compatibility.
extern "C" void person_detect_init();
// Runs one iteration of data gathering and inference. This should be called
// repeatedly from the application code. The name needs to be loop() for Arduino
// compatibility.
extern "C" int person_detect(uint8_t * hardware_input);
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
// Keeping these as constant expressions allow us to allocate fixed-sized arrays
// on the stack for our working memory.
// All of these values are derived from the values used during model training,
// if you change your model you'll need to update these constants.
constexpr int kNumCols = 96;
constexpr int kNumRows = 96;
constexpr int kNumChannels = 1;
constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels;
constexpr int kCategoryCount = 2;
constexpr int kPersonIndex = 1;
constexpr int kNotAPersonIndex = 0;
extern const char* kCategoryLabels[kCategoryCount];
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_

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/* 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.
==============================================================================*/
// This is a standard TensorFlow Lite model file that has been converted into a
// C data array, so it can be easily compiled into a binary for devices that
// don't have a file system. It was created using the command:
// xxd -i person_detect.tflite > person_detect_model_data.cc
#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
extern const unsigned char g_person_detect_model_data[];
extern const int g_person_detect_model_data_len;
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_
#define TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_
#include <algorithm>
#include <cmath>
#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/kernels/internal/cppmath.h"
#include "tensorflow/lite/kernels/internal/max.h"
#include "tensorflow/lite/kernels/internal/min.h"
namespace tflite {
namespace ops {
namespace micro {
// Returns the floating point value for a fused activation:
inline float ActivationValFloat(TfLiteFusedActivation act, float a) {
switch (act) {
case kTfLiteActNone:
return a;
case kTfLiteActRelu:
return TfLiteMax(0.0f, a);
case kTfLiteActReluN1To1:
return TfLiteMax(-1.0f, TfLiteMin(a, 1.0f));
case kTfLiteActRelu6:
return TfLiteMax(0.0f, TfLiteMin(a, 6.0f));
case kTfLiteActTanh:
return std::tanh(a);
case kTfLiteActSignBit:
return std::signbit(a);
case kTfLiteActSigmoid:
return 1.0f / (1.0f + std::exp(-a));
}
return 0.0f; // To indicate an unsupported activation (i.e. when a new fused
// activation is added to the enum and not handled here).
}
} // namespace micro
} // namespace ops
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_
#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/micro/simple_memory_allocator.h"
namespace tflite {
namespace micro {
// Helper class to perform a simulated kernel (i.e. TfLiteRegistration) lifecyle
// (init, prepare, invoke). All internal allocations are handled by this class.
// Simply pass in the registration, list of required tensors, inputs array,
// outputs array, and any pre-builtin data. Calling Invoke() will automatically
// walk the kernl and outputs will be ready on the the TfLiteTensor output
// provided during construction.
class KernelRunner {
public:
KernelRunner(const TfLiteRegistration& registration, TfLiteTensor* tensors,
int tensors_size, TfLiteIntArray* inputs,
TfLiteIntArray* outputs, void* builtin_data,
ErrorReporter* error_reporter);
// Calls init and prepare on the kernel (i.e. TfLiteRegistration) struct. Any
// exceptions will be reported through the error_reporter and returned as a
// status code here.
TfLiteStatus InitAndPrepare(const char* init_data = nullptr);
// Calls init, prepare, and invoke on a given TfLiteRegistration pointer.
// After successful invoke, results will be available in the output tensor as
// passed into the constructor of this class.
TfLiteStatus Invoke();
protected:
static TfLiteTensor* GetTensor(const struct TfLiteContext* context,
int tensor_index);
static TfLiteEvalTensor* GetEvalTensor(const struct TfLiteContext* context,
int tensor_index);
static void* AllocatePersistentBuffer(TfLiteContext* context, size_t bytes);
static TfLiteStatus RequestScratchBufferInArena(TfLiteContext* context,
size_t bytes,
int* buffer_index);
static void* GetScratchBuffer(TfLiteContext* context, int buffer_index);
static void ReportOpError(struct TfLiteContext* context, const char* format,
...);
private:
static constexpr int kNumScratchBuffers_ = 5;
static constexpr int kKernelRunnerBufferSize_ = 10000;
static uint8_t kKernelRunnerBuffer_[kKernelRunnerBufferSize_];
SimpleMemoryAllocator* allocator_ = nullptr;
const TfLiteRegistration& registration_;
TfLiteTensor* tensors_ = nullptr;
ErrorReporter* error_reporter_ = nullptr;
TfLiteContext context_ = {};
TfLiteNode node_ = {};
int scratch_buffer_count_ = 0;
uint8_t* scratch_buffers_[kNumScratchBuffers_];
};
} // namespace micro
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_
#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_
#include <cstdint>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/types.h"
namespace tflite {
namespace micro {
// Returns a mutable tensor for a given input index. is_variable must be checked
// during prepare when the full TfLiteTensor is available.
inline TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context,
const TfLiteNode* node,
int index) {
TFLITE_DCHECK(context != nullptr);
TFLITE_DCHECK(node != nullptr);
return context->GetEvalTensor(context, node->inputs->data[index]);
}
// Returns the TfLiteEvalTensor struct for a given input index in a node.
inline const TfLiteEvalTensor* GetEvalInput(const TfLiteContext* context,
const TfLiteNode* node, int index) {
return GetMutableEvalInput(context, node, index);
}
// Returns the TfLiteEvalTensor struct for a given output index in a node.
inline TfLiteEvalTensor* GetEvalOutput(const TfLiteContext* context,
const TfLiteNode* node, int index) {
TFLITE_DCHECK(context != nullptr);
TFLITE_DCHECK(node != nullptr);
return context->GetEvalTensor(context, node->outputs->data[index]);
}
// Returns data for a TfLiteEvalTensor struct.
template <typename T>
T* GetTensorData(TfLiteEvalTensor* tensor) {
return tensor != nullptr ? reinterpret_cast<T*>(tensor->data.raw) : nullptr;
}
// Returns const data for a TfLiteEvalTensor struct.
template <typename T>
const T* GetTensorData(const TfLiteEvalTensor* tensor) {
TFLITE_DCHECK(tensor != nullptr);
return reinterpret_cast<const T*>(tensor->data.raw);
}
// Returns the shape of a TfLiteEvalTensor struct.
inline const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor) {
if (tensor == nullptr) {
return RuntimeShape();
}
TfLiteIntArray* dims = tensor->dims;
const int dims_size = dims->size;
const int32_t* dims_data = reinterpret_cast<const int32_t*>(dims->data);
return RuntimeShape(dims_size, dims_data);
}
// Return true if the given tensors have the same shape.
bool HaveSameShapes(const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2);
} // namespace micro
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_
#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_
#include "tensorflow/lite/c/common.h"
namespace tflite {
namespace ops {
namespace micro {
// Forward declaration of all micro op kernel registration methods. These
// registrations are included with the standard `BuiltinOpResolver`.
//
// This header is particularly useful in cases where only a subset of ops are
// needed. In such cases, the client can selectively add only the registrations
// their model requires, using a custom `(Micro)MutableOpResolver`. Selective
// registration in turn allows the linker to strip unused kernels.
TfLiteRegistration Register_ABS();
TfLiteRegistration Register_ADD();
TfLiteRegistration Register_ARG_MAX();
TfLiteRegistration Register_ARG_MIN();
TfLiteRegistration Register_AVERAGE_POOL_2D();
TfLiteRegistration Register_CEIL();
// TODO(b/160234179): Change custom OPs to also return by value.
TfLiteRegistration* Register_CIRCULAR_BUFFER();
TfLiteRegistration Register_CONV_2D();
TfLiteRegistration Register_CONCATENATION();
TfLiteRegistration Register_COS();
TfLiteRegistration Register_DEPTHWISE_CONV_2D();
TfLiteRegistration Register_DEQUANTIZE();
TfLiteRegistration Register_EQUAL();
TfLiteRegistration Register_FLOOR();
TfLiteRegistration Register_FULLY_CONNECTED();
TfLiteRegistration Register_GREATER();
TfLiteRegistration Register_GREATER_EQUAL();
TfLiteRegistration Register_HARD_SWISH();
TfLiteRegistration Register_LESS();
TfLiteRegistration Register_LESS_EQUAL();
TfLiteRegistration Register_LOG();
TfLiteRegistration Register_LOGICAL_AND();
TfLiteRegistration Register_LOGICAL_NOT();
TfLiteRegistration Register_LOGICAL_OR();
TfLiteRegistration Register_LOGISTIC();
TfLiteRegistration Register_MAXIMUM();
TfLiteRegistration Register_MAX_POOL_2D();
TfLiteRegistration Register_MEAN();
TfLiteRegistration Register_MINIMUM();
TfLiteRegistration Register_MUL();
TfLiteRegistration Register_NEG();
TfLiteRegistration Register_NOT_EQUAL();
TfLiteRegistration Register_PACK();
TfLiteRegistration Register_PAD();
TfLiteRegistration Register_PADV2();
TfLiteRegistration Register_PRELU();
TfLiteRegistration Register_QUANTIZE();
TfLiteRegistration Register_RELU();
TfLiteRegistration Register_RELU6();
TfLiteRegistration Register_RESHAPE();
TfLiteRegistration Register_RESIZE_NEAREST_NEIGHBOR();
TfLiteRegistration Register_ROUND();
TfLiteRegistration Register_RSQRT();
TfLiteRegistration Register_SIN();
TfLiteRegistration Register_SOFTMAX();
TfLiteRegistration Register_SPLIT();
TfLiteRegistration Register_SQRT();
TfLiteRegistration Register_SQUARE();
TfLiteRegistration Register_STRIDED_SLICE();
TfLiteRegistration Register_SUB();
TfLiteRegistration Register_SVDF();
TfLiteRegistration Register_UNPACK();
TfLiteRegistration Register_L2_NORMALIZATION();
TfLiteRegistration Register_TANH();
} // namespace micro
} // namespace ops
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_UTILS_H_
#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_UTILS_H_
namespace tflite {
namespace ops {
namespace micro {
// Same as gtl::Greater but defined here to reduce dependencies and
// binary size for micro environment.
struct Greater {
template <typename T>
bool operator()(const T& x, const T& y) const {
return x > y;
}
};
struct Less {
template <typename T>
bool operator()(const T& x, const T& y) const {
return x < y;
}
};
} // namespace micro
} // namespace ops
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_UTILS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_
#define TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_
#include <cstddef>
#include <cstdint>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
// Returns the next pointer address aligned to the given alignment.
uint8_t* AlignPointerUp(uint8_t* data, size_t alignment);
// Returns the previous pointer address aligned to the given alignment.
uint8_t* AlignPointerDown(uint8_t* data, size_t alignment);
// Returns an increased size that's a multiple of alignment.
size_t AlignSizeUp(size_t size, size_t alignment);
// Returns size in bytes for a given TfLiteType.
TfLiteStatus TfLiteTypeSizeOf(TfLiteType type, size_t* size);
// How many bytes are needed to hold a tensor's contents.
TfLiteStatus BytesRequiredForTensor(const tflite::Tensor& flatbuffer_tensor,
size_t* bytes, size_t* type_size,
ErrorReporter* error_reporter);
// How many bytes are used in a TfLiteEvalTensor instance. The byte length is
// returned in out_bytes.
TfLiteStatus TfLiteEvalTensorByteLength(const TfLiteEvalTensor* eval_tensor,
size_t* out_bytes);
// Deduce output dimensions from input and allocate given size.
// Useful for operators with two inputs where the largest input should equal the
// output dimension.
TfLiteStatus AllocateOutputDimensionsFromInput(TfLiteContext* context,
const TfLiteTensor* input1,
const TfLiteTensor* input2,
TfLiteTensor* output);
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_
#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_
#include "tensorflow/lite/micro/compatibility.h"
#include "tensorflow/lite/micro/memory_planner/memory_planner.h"
namespace tflite {
constexpr int kOnlinePlannedBuffer = -1;
// A memory planner that uses a greedy algorithm to arrange buffers in memory
// to minimize the overall arena size needed.
//
// The algorithm works like this:
// - The client enters the buffer information through AddBuffer().
// - When a function like GetOffsetForBuffer() is called, the
// CalculateOffsetsIfNeeded() method is invoked.
// - If an up to date plan is not already present, one will be calculated.
// - The buffers are sorted in descending order of size.
// - The largest buffer is placed at offset zero.
// - The rest of the buffers are looped through in descending size order.
// - The other buffers that need to be in memory at the same time are found.
// - The first gap between simultaneously active buffers that the current
// buffer fits into will be used.
// - If no large-enough gap is found, the current buffer is placed after the
// last buffer that's simultaneously active.
// - This continues until all buffers are placed, and the offsets stored.
//
// This is not guaranteed to produce the best placement, since that's an
// NP-Complete problem, but in practice it should produce one that's decent.
class GreedyMemoryPlanner : public MemoryPlanner {
public:
// You need to pass in an area of memory to be used for planning. This memory
// needs to have a lifetime as long as the planner, but isn't owned by this
// object, so management should be handled by the client. This is so it can be
// stack or globally allocated if necessary on devices without dynamic memory
// allocation. How many buffers can be planned for will depend on the size of
// this scratch memory, so you should enlarge it if you see an error when
// calling AddBuffer(). The memory can be reused once you're done with the
// planner, as long as you copy the calculated offsets to another location.
// Each buffer requires about 36 bytes of scratch.
GreedyMemoryPlanner(unsigned char* scratch_buffer, int scratch_buffer_size);
~GreedyMemoryPlanner() override;
// Record details of a buffer we want to place.
TfLiteStatus AddBuffer(ErrorReporter* error_reporter, int size,
int first_time_used, int last_time_used) override;
// Record details of an offline planned buffer offset we want to place.
// offline_offset is the buffer offset from the start of the arena.
TfLiteStatus AddBuffer(ErrorReporter* error_reporter, int size,
int first_time_used, int last_time_used,
int offline_offset);
// Returns the high-water mark of used memory. This is the minimum size of a
// memory arena you'd need to allocate to hold these buffers.
size_t GetMaximumMemorySize() override;
// How many buffers have been recorded.
int GetBufferCount() override;
// Where a given buffer should be placed in the memory arena.
// This information is stored in the memory arena itself, so once the arena
// is used for inference, it will be overwritten.
TfLiteStatus GetOffsetForBuffer(ErrorReporter* error_reporter,
int buffer_index, int* offset) override;
// Prints an ascii-art diagram of the buffer layout plan.
void PrintMemoryPlan(ErrorReporter* error_reporter);
// Debug method to check whether any buffer allocations are overlapping. This
// is an O(N^2) complexity operation, so only use for testing.
bool DoAnyBuffersOverlap(ErrorReporter* error_reporter);
// Used to store a list of buffers ordered by their offset.
struct ListEntry {
int offset;
int requirements_index;
int next_entry_index;
};
// Number of bytes required in order to plan a buffer.
static size_t per_buffer_size() {
const int per_buffer_size =
sizeof(BufferRequirements) + // requirements_
sizeof(int) + // buffer_sizes_sorted_
sizeof(int) + // buffer_ids_sorted_
sizeof(ListEntry) + // buffers_sorted_by_offset_
sizeof(int); // buffer_offsets_;
return per_buffer_size;
}
private:
// Whether a buffer is active in a given time range.
bool DoesEntryOverlapInTime(const ListEntry* entry, const int first_time_used,
const int last_time_used) const;
// Walks the list to return the next buffer that is active in a given time
// range, or a null pointer if there are none.
ListEntry* NextSimultaneouslyActiveBuffer(const ListEntry* start,
const int first_time_used,
const int last_time_used);
// If there isn't an up to date plan, calculate a new one.
void CalculateOffsetsIfNeeded();
// How many buffers we can plan for, based on the arena size we're given in
// the constructor.
int max_buffer_count_;
// The number of buffers added so far.
int buffer_count_;
// Records the client-provided information about each buffer.
struct BufferRequirements {
int size;
int offline_offset;
int first_time_used;
int last_time_used;
};
// Working arrays used during the layout algorithm.
BufferRequirements* requirements_;
// buffer_sizes_sorted_ and buffer_ids_sorted_ are sorted according to:
// {
// offline planned buffers,
// online planned buffers sorted by size
// }
int* buffer_sizes_sorted_;
int* buffer_ids_sorted_;
ListEntry* buffers_sorted_by_offset_;
int next_free_entry_; // Index of the next free entry of
// buffers_sorted_by_offset_
int first_entry_index_; // Index of the first entry (smallest offset) of
// buffers_sorted_by_offset_
// Stores the outcome of the plan, the location of each buffer in the arena.
int* buffer_offsets_;
// Whether buffers have been added since the last plan was calculated.
bool need_to_calculate_offsets_;
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_
#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_
#include "tensorflow/lite/micro/compatibility.h"
#include "tensorflow/lite/micro/memory_planner/memory_planner.h"
namespace tflite {
// The simplest possible memory planner that just lays out all buffers at
// increasing offsets without trying to reuse memory.
class LinearMemoryPlanner : public MemoryPlanner {
public:
LinearMemoryPlanner();
~LinearMemoryPlanner() override;
TfLiteStatus AddBuffer(tflite::ErrorReporter* error_reporter, int size,
int first_time_used, int last_time_used) override;
size_t GetMaximumMemorySize() override;
int GetBufferCount() override;
TfLiteStatus GetOffsetForBuffer(tflite::ErrorReporter* error_reporter,
int buffer_index, int* offset) override;
private:
static constexpr int kMaxBufferCount = 1024;
size_t buffer_offsets_[kMaxBufferCount];
int current_buffer_count_;
size_t next_free_offset_;
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_
#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/core/api/error_reporter.h"
namespace tflite {
// Interface class for planning the layout of memory buffers during the
// execution of a graph.
// It's designed to be used by a client that iterates in any order through the
// buffers it wants to lay out, and then calls the getter functions for
// information about the calculated layout. For example:
//
// SomeMemoryPlanner planner;
// planner.AddBuffer(reporter, 100, 0, 1); // Buffer 0
// planner.AddBuffer(reporter, 50, 2, 3); // Buffer 1
// planner.AddBuffer(reporter, 50, 2, 3); // Buffer 2
//
// int offset0;
// TF_EXPECT_OK(planner.GetOffsetForBuffer(reporter, 0, &offset0));
// int offset1;
// TF_EXPECT_OK(planner.GetOffsetForBuffer(reporter, 1, &offset1));
// int offset2;
// TF_EXPECT_OK(planner.GetOffsetForBuffer(reporter, 2, &offset2));
// const int arena_size_needed = planner.GetMaximumMemorySize();
//
// The goal is for applications to be able to experiment with different layout
// strategies without changing their client code, by swapping out classes that
// implement this interface.=
class MemoryPlanner {
public:
MemoryPlanner() {}
virtual ~MemoryPlanner() {}
// Pass information about a buffer's size and lifetime to the layout
// algorithm. The order this is called implicitly assigns an index to the
// result, so the buffer information that's passed into the N-th call of
// this method will be used as the buffer_index argument to
// GetOffsetForBuffer().
virtual TfLiteStatus AddBuffer(tflite::ErrorReporter* error_reporter,
int size, int first_time_used,
int last_time_used) = 0;
// The largest contiguous block of memory that's needed to hold the layout.
virtual size_t GetMaximumMemorySize() = 0;
// How many buffers have been added to the planner.
virtual int GetBufferCount() = 0;
// Calculated layout offset for the N-th buffer added to the planner.
virtual TfLiteStatus GetOffsetForBuffer(tflite::ErrorReporter* error_reporter,
int buffer_index, int* offset) = 0;
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_

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/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
b/160894903
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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_
#define TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_
#include <cstddef>
#include <cstdint>
#include "flatbuffers/flatbuffers.h" // from @flatbuffers
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/micro/compatibility.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 for unittests.
namespace internal {
// Sets up all of the data structure members for a TfLiteTensor based on the
// contents of a serialized tensor in the flatbuffer.
// TODO(b/160894903): Once all kernels have been updated to the new
// TfLiteEvalTensor API - drop the allocate_temp flag. This enables internal
// flatbuffer quantization or dimension allocations to take place in either the
// temp or tail section of the arena.
TfLiteStatus InitializeTfLiteTensorFromFlatbuffer(
SimpleMemoryAllocator* allocator, bool allocate_temp,
const tflite::Tensor& flatbuffer_tensor,
const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
ErrorReporter* error_reporter, TfLiteTensor* result);
// A handle tracking scratch buffer allocation. This handle is created by
// `RequestScratchBufferInArena`. `data` field is populated in
// `FinishModelAllocation` after static memory planning.
// TODO(b/150257460) As a future optimization, this struct could be replaced by
// a union, since once `data` is populated, `bytes` and `node_idx` is not
// needed.
typedef struct {
// Pointer to the scratch buffer.
uint8_t* data;
// Number of bytes required by the buffer. The actual allocated size might be
// greater than `bytes` due to buffer alignment.
size_t bytes;
// Node where the buffer is allocated for. This provides useful information to
// determine the lifetime of the buffer. In AllocationInfo, this buffer will
// have `before` = node_idx and `after` = node_idx.
int node_idx;
} ScratchBufferHandle;
} // namespace internal
typedef struct {
TfLiteNode node;
const TfLiteRegistration* registration;
} NodeAndRegistration;
// Allocator responsible for allocating memory for all intermediate tensors
// necessary to invoke a model.
//
// The lifetime of the model, tensor arena and error reporter must be at
// least as long as that of the allocator object, since the allocator needs
// them to be accessible during its entire lifetime.
//
// The MicroAllocator simply plans out additional allocations that are required
// to standup a model for inference in TF Micro. This class currently relies on
// an additional allocator - SimpleMemoryAllocator - for all allocations from an
// arena. These allocations are divided into head (non-persistent) and tail
// (persistent) regions:
//
// Memory layout to help understand how it works
// This information could change in the future version.
// ************** .memory_allocator->GetBuffer()
// Tensors/Scratch buffers (head)
// ************** .head_watermark
// unused memory
// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize()
// - ->GetDataSize()
// persistent area (tail)
// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize()
class MicroAllocator {
public:
// Creates a MicroAllocator instance from a given tensor arena. This arena
// will be managed by the created instance.
// Note: Please use __declspec(align(16)) to make sure tensor_arena is 16
// bytes aligned, otherwise some head room will be wasted.
// TODO(b/157615197): Cleanup constructor + factory usage.
static MicroAllocator* Create(uint8_t* tensor_arena, size_t arena_size,
ErrorReporter* error_reporter);
// Creates a MicroAllocator instance using the provided SimpleMemoryAllocator
// intance. This allocator instance will use the SimpleMemoryAllocator
// instance to manage allocations internally.
static MicroAllocator* Create(SimpleMemoryAllocator* memory_allocator,
ErrorReporter* error_reporter);
// Begin allocating internal resources required for model inference.
// This method will run through the flatbuffer data supplied in the model to
// properly allocate tensor, node, and op registration data. This method is
// expected to be followed with a call to FinishModelAllocation() before
// resuming allocation with another model. All persistent tensor buffers are
// stored in the out-param eval_tensors. This value is allocated from the
// persistent memory arena and will be used to host runtime tensor buffers.
TfLiteStatus StartModelAllocation(
const Model* model, const MicroOpResolver& op_resolver,
NodeAndRegistration** node_and_registrations,
TfLiteEvalTensor** eval_tensors);
// Finish allocating internal resources required for model inference.
// This method will plan non-persistent buffers and commit a memory plan to
// the 'head' section of the memory arena. All variable tensor data will also
// be allocated. This method should be called after assigning model resources
// in StartModelAllocation(). The eval_tensors pointer should be the value
// passed into this class during StartModelAllocation().
TfLiteStatus FinishModelAllocation(const Model* model,
TfLiteEvalTensor* eval_tensors);
// Allocates a TfLiteTensor struct and populates the returned value with
// properties from the model flatbuffer. This struct is allocated from
// persistent arena memory is only guaranteed for the lifetime of the
// application. The eval_tensors pointer should be the value passed into this
// class during StartModelAllocation() and contains the source-of-truth for
// buffers.
virtual TfLiteTensor* AllocatePersistentTfLiteTensor(
const Model* model, TfLiteEvalTensor* eval_tensors, int tensor_index);
// Allocates a TfLiteTensor struct and populates the returned value with
// properties from the model flatbuffer. This struct is allocated from
// temporary arena memory is only guaranteed until a call is made to
// ResetTempAllocations(). The eval_tensors pointer should be the value passed
// into this class during StartModelAllocation() and contains the
// source-of-truth for buffers.
virtual TfLiteTensor* AllocateTempTfLiteTensor(const Model* model,
TfLiteEvalTensor* eval_tensors,
int tensor_index);
// Resets all temporary allocations. This method should be called after a
// chain of temp allocations (e.g. chain of TfLiteTensor objects via
// AllocateTfLiteTensor()).
virtual void ResetTempAllocations();
// Allocates persistent buffer which has the same life time as the allocator.
// The memory is immediately available and is allocated from the tail of the
// arena.
void* AllocatePersistentBuffer(size_t bytes);
// Register a scratch buffer of size `bytes` for Node with `node_id`.
// This method only allocates a BufferHandle holding information for memory
// planning. The buffer ptr is ready after `FinishModelAllocation` and can
// be retrieved by `GetScratchBuffer` method using the returned buffer_idx.
// Note that there should be no tail allocation between two consecutive
// `RequestScratchBufferInArena` calls.
TfLiteStatus RequestScratchBufferInArena(int node_id, size_t bytes,
int* buffer_idx);
// Returns the pointer to the planned scratch buffer.
void* GetScratchBuffer(int buffer_idx) const;
// Returns the arena usage in bytes, only available after
// `FinishModelAllocation`. Otherwise, it will return 0.
size_t used_bytes() const;
protected:
MicroAllocator(SimpleMemoryAllocator* memory_allocator,
ErrorReporter* error_reporter);
virtual ~MicroAllocator();
// Allocates an array in the arena to hold pointers to the node and
// registration pointers required to represent the inference graph of the
// model.
virtual TfLiteStatus AllocateNodeAndRegistrations(
const Model* model, NodeAndRegistration** node_and_registrations);
// Populates node and registration pointers representing the inference graph
// of the model from values inside the flatbuffer (loaded from the TfLiteModel
// instance). Persistent data (e.g. operator data) is allocated from the
// arena.
virtual TfLiteStatus PrepareNodeAndRegistrationDataFromFlatbuffer(
const Model* model, const MicroOpResolver& op_resolver,
NodeAndRegistration* node_and_registrations);
// Allocates the list of persistent TfLiteEvalTensors that are used for the
// "eval" phase of model inference. These structs will be the source of truth
// for all tensor buffers. Allocation results are stored in the out-param
// eval_tensors.
virtual TfLiteStatus AllocateTfLiteEvalTensors(
const Model* model, TfLiteEvalTensor** eval_tensors);
// Allocates persistent tensor buffers for variable tensors in the subgraph.
virtual TfLiteStatus AllocateVariables(const SubGraph* subgraph,
TfLiteEvalTensor* eval_tensors);
// TODO(b/160894903): Once all kernels have been updated to the new API drop
// this method. It is only used to record TfLiteTensor persistent allocations.
virtual TfLiteTensor* AllocatePersistentTfLiteTensorInternal(
const Model* model, TfLiteEvalTensor* eval_tensors, int tensor_index);
// Populates a TfLiteTensor struct with data from the model flatbuffer. Any
// quantization data is allocated from either the tail (persistent) or temp
// sections of the arena based on the allocation flag.
// TODO(b/160894903): Once all kernels have been updated to the new API drop
// this function since all allocations for quantized data will take place in
// the temp section.
virtual TfLiteStatus PopulateTfLiteTensorFromFlatbuffer(
const Model* model, const SubGraph* subgraph, TfLiteTensor* tensor,
int tensor_index, bool allocate_temp);
ErrorReporter* error_reporter() const;
// Returns the first subgraph from the model.
const SubGraph* GetSubGraphFromModel(const Model* model);
private:
// Commits a memory plan for all non-persistent buffer allocations in the
// 'head' section of the memory arena. The eval_tensors pointer is the list of
// pre-allocated TfLiteEvalTensor structs that will point to the buffers that
// will be allocated into the head section in this function call.
virtual TfLiteStatus CommitStaticMemoryPlan(const Model* model,
const SubGraph* subgraph,
TfLiteEvalTensor* eval_tensors);
// A simple memory allocator that always allocate from the arena tail or head.
SimpleMemoryAllocator* memory_allocator_;
ErrorReporter* error_reporter_;
bool model_is_allocating_;
// In reverse order for efficiency.
// i.e. scratch_buffer_handles_[0] is the handle for the last buffer,
// corresponding to the last RequestScratchBufferInArena call.
internal::ScratchBufferHandle* scratch_buffer_handles_ = nullptr;
// How many scratch buffers have been allocated.
size_t scratch_buffer_count_ = 0;
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_ERROR_REPORTER_H_
#define TENSORFLOW_LITE_MICRO_MICRO_ERROR_REPORTER_H_
#include <cstdarg>
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/micro/compatibility.h"
namespace tflite {
class MicroErrorReporter : public ErrorReporter {
public:
~MicroErrorReporter() override {}
int Report(const char* format, va_list args) override;
private:
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_ERROR_REPORTER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_
#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_
#include <cstddef>
#include <cstdint>
#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/profiler.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/micro/micro_allocator.h"
#include "tensorflow/lite/micro/micro_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/type_to_tflitetype.h"
namespace tflite {
namespace internal {
// A helper class to encapsulate the implementation of APIs in Context.
// context->impl_ points to an instance of this class.
// Check tensorflow/lite/c/common.h for detailed descriptions.
// TODO(b/16157777): Consider rolling this class into MicroInterpreter.
class ContextHelper {
public:
explicit ContextHelper(ErrorReporter* error_reporter,
MicroAllocator* allocator, const Model* model);
// Functions that will be assigned to function pointers on TfLiteContext:
static void* AllocatePersistentBuffer(TfLiteContext* ctx, size_t bytes);
static TfLiteStatus RequestScratchBufferInArena(TfLiteContext* ctx,
size_t bytes,
int* buffer_idx);
static void* GetScratchBuffer(TfLiteContext* ctx, int buffer_idx);
static void ReportOpError(struct TfLiteContext* context, const char* format,
...);
static TfLiteTensor* GetTensor(const struct TfLiteContext* context,
int tensor_idx);
static TfLiteEvalTensor* GetEvalTensor(const struct TfLiteContext* context,
int tensor_idx);
// Sets the current node index to assist with scratch buffer allocations:
void SetNodeIndex(int idx);
// Sets the pointer to a list of TfLiteEvalTensor instances.
void SetTfLiteEvalTensors(TfLiteEvalTensor* eval_tensors);
private:
MicroAllocator* allocator_;
ErrorReporter* error_reporter_;
const Model* model_;
TfLiteEvalTensor* eval_tensors_;
int current_node_idx_ = -1;
};
} // namespace internal
class MicroInterpreter {
public:
// The lifetime of the model, op resolver, tensor arena, error reporter and
// profiler must be at least as long as that of the interpreter object, since
// the interpreter may need to access them at any time. This means that you
// should usually create them with the same scope as each other, for example
// having them all allocated on the stack as local variables through a
// top-level function. The interpreter doesn't do any deallocation of any of
// the pointed-to objects, ownership remains with the caller.
MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver,
uint8_t* tensor_arena, size_t tensor_arena_size,
ErrorReporter* error_reporter,
tflite::Profiler* profiler = nullptr);
// Create an interpreter instance using an existing MicroAllocator instance.
// This constructor should be used when creating an allocator that needs to
// have allocation handled in more than one interpreter or for recording
// allocations inside the interpreter. The lifetime of the allocator must be
// as long as that of the interpreter object.
MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver,
MicroAllocator* allocator, ErrorReporter* error_reporter,
tflite::Profiler* profiler = nullptr);
~MicroInterpreter();
// Runs through the model and allocates all necessary input, output and
// intermediate tensors.
TfLiteStatus AllocateTensors();
// In order to support partial graph runs for strided models, this can return
// values other than kTfLiteOk and kTfLiteError.
// TODO(b/149795762): Add this to the TfLiteStatus enum.
TfLiteStatus Invoke();
size_t tensors_size() const { return context_.tensors_size; }
TfLiteTensor* tensor(size_t tensor_index);
template <class T>
T* typed_tensor(int tensor_index) {
if (TfLiteTensor* tensor_ptr = tensor(tensor_index)) {
if (tensor_ptr->type == typeToTfLiteType<T>()) {
return GetTensorData<T>(tensor_ptr);
}
}
return nullptr;
}
TfLiteTensor* input(size_t index);
size_t inputs_size() const { return subgraph_->inputs()->Length(); }
const flatbuffers::Vector<int32_t>& inputs() const {
return *subgraph_->inputs();
}
TfLiteTensor* input_tensor(size_t index) { return input(index); }
template <class T>
T* typed_input_tensor(int tensor_index) {
if (TfLiteTensor* tensor_ptr = input_tensor(tensor_index)) {
if (tensor_ptr->type == typeToTfLiteType<T>()) {
return GetTensorData<T>(tensor_ptr);
}
}
return nullptr;
}
TfLiteTensor* output(size_t index);
size_t outputs_size() const { return subgraph_->outputs()->Length(); }
const flatbuffers::Vector<int32_t>& outputs() const {
return *subgraph_->outputs();
}
TfLiteTensor* output_tensor(size_t index) { return output(index); }
template <class T>
T* typed_output_tensor(int tensor_index) {
if (TfLiteTensor* tensor_ptr = output_tensor(tensor_index)) {
if (tensor_ptr->type == typeToTfLiteType<T>()) {
return GetTensorData<T>(tensor_ptr);
}
}
return nullptr;
}
// Reset all variable tensors to the default value.
TfLiteStatus ResetVariableTensors();
TfLiteStatus initialization_status() const { return initialization_status_; }
size_t operators_size() const { return subgraph_->operators()->size(); }
// For debugging only.
const NodeAndRegistration node_and_registration(int node_index) const {
return node_and_registrations_[node_index];
}
// For debugging only.
// Returns the actual used arena in bytes. This method gives the optimal arena
// size. It's only available after `AllocateTensors` has been called.
// Note that normally `tensor_arena` requires 16 bytes alignment to fully
// utilize the space. If it's not the case, the optimial arena size would be
// arena_used_bytes() + 16.
size_t arena_used_bytes() const { return allocator_.used_bytes(); }
protected:
const MicroAllocator& allocator() const { return allocator_; }
const TfLiteContext& context() const { return context_; }
private:
// TODO(b/158263161): Consider switching to Create() function to enable better
// error reporting during initialization.
void Init(tflite::Profiler* profiler);
void CorrectTensorEndianness(TfLiteEvalTensor* tensorCorr);
template <class T>
void CorrectTensorDataEndianness(T* data, int32_t size);
NodeAndRegistration* node_and_registrations_ = nullptr;
const Model* model_;
const MicroOpResolver& op_resolver_;
ErrorReporter* error_reporter_;
TfLiteContext context_ = {};
MicroAllocator& allocator_;
bool tensors_allocated_;
TfLiteStatus initialization_status_;
const SubGraph* subgraph_;
TfLiteEvalTensor* eval_tensors_;
internal::ContextHelper context_helper_;
// TODO(b/160894903): Clean these pointers up when all APIs are updated to new
// TfLiteEvalTensor buffers.
TfLiteTensor* input_tensor_;
TfLiteTensor* output_tensor_;
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_
#define TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_
#include <stdio.h>
#include <cstring>
#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/compatibility.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/compatibility.h"
#include "tensorflow/lite/micro/kernels/micro_ops.h"
#include "tensorflow/lite/micro/micro_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
template <unsigned int tOpCount>
class MicroMutableOpResolver : public MicroOpResolver {
public:
explicit MicroMutableOpResolver(ErrorReporter* error_reporter = nullptr)
: error_reporter_(error_reporter) {}
const TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const override {
if (op == BuiltinOperator_CUSTOM) return nullptr;
for (unsigned int i = 0; i < registrations_len_; ++i) {
const TfLiteRegistration& registration = registrations_[i];
if (registration.builtin_code == op) {
return &registration;
}
}
return nullptr;
}
const TfLiteRegistration* FindOp(const char* op) const override {
for (unsigned int i = 0; i < registrations_len_; ++i) {
const TfLiteRegistration& registration = registrations_[i];
if ((registration.builtin_code == BuiltinOperator_CUSTOM) &&
(strcmp(registration.custom_name, op) == 0)) {
return &registration;
}
}
return nullptr;
}
MicroOpResolver::BuiltinParseFunction GetOpDataParser(
BuiltinOperator op) const override {
TFLITE_DCHECK(num_buitin_ops_ <= tOpCount);
for (unsigned int i = 0; i < num_buitin_ops_; ++i) {
if (builtin_codes_[i] == op) return builtin_parsers_[i];
}
return nullptr;
}
// Registers a Custom Operator with the MicroOpResolver.
//
// Only the first call for a given name will be successful. i.e. if this
// function is called again for a previously added Custom Operator, the
// MicroOpResolver will be unchanged and this function will return
// kTfLiteError.
TfLiteStatus AddCustom(const char* name, TfLiteRegistration* registration) {
if (registrations_len_ >= tOpCount) {
if (error_reporter_) {
TF_LITE_REPORT_ERROR(
error_reporter_,
"Couldn't register custom op '%s', resolver size is too small (%d)",
name, tOpCount);
}
return kTfLiteError;
}
if (FindOp(name) != nullptr) {
if (error_reporter_ != nullptr) {
TF_LITE_REPORT_ERROR(error_reporter_,
"Calling AddCustom for the same op more than once "
"is not supported (Op: %s).",
name);
}
return kTfLiteError;
}
TfLiteRegistration* new_registration = &registrations_[registrations_len_];
registrations_len_ += 1;
*new_registration = *registration;
new_registration->builtin_code = BuiltinOperator_CUSTOM;
new_registration->custom_name = name;
return kTfLiteOk;
}
// The Add* functions below add the various Builtin operators to the
// MicroMutableOpResolver object.
TfLiteStatus AddAbs() {
return AddBuiltin(BuiltinOperator_ABS, tflite::ops::micro::Register_ABS(),
ParseAbs);
}
TfLiteStatus AddAdd() {
return AddBuiltin(BuiltinOperator_ADD, tflite::ops::micro::Register_ADD(),
ParseAdd);
}
TfLiteStatus AddArgMax() {
return AddBuiltin(BuiltinOperator_ARG_MAX,
tflite::ops::micro::Register_ARG_MAX(), ParseArgMax);
}
TfLiteStatus AddArgMin() {
return AddBuiltin(BuiltinOperator_ARG_MIN,
tflite::ops::micro::Register_ARG_MIN(), ParseArgMin);
}
TfLiteStatus AddAveragePool2D() {
return AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D,
tflite::ops::micro::Register_AVERAGE_POOL_2D(),
ParsePool);
}
TfLiteStatus AddCeil() {
return AddBuiltin(BuiltinOperator_CEIL, tflite::ops::micro::Register_CEIL(),
ParseCeil);
}
TfLiteStatus AddCircularBuffer() {
return AddCustom("CIRCULAR_BUFFER",
tflite::ops::micro::Register_CIRCULAR_BUFFER());
}
TfLiteStatus AddConcatenation() {
return AddBuiltin(BuiltinOperator_CONCATENATION,
tflite::ops::micro::Register_CONCATENATION(),
ParseConcatenation);
}
TfLiteStatus AddConv2D() {
return AddBuiltin(BuiltinOperator_CONV_2D,
tflite::ops::micro::Register_CONV_2D(), ParseConv2D);
}
TfLiteStatus AddCos() {
return AddBuiltin(BuiltinOperator_COS, tflite::ops::micro::Register_COS(),
ParseCos);
}
TfLiteStatus AddDepthwiseConv2D() {
return AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D,
tflite::ops::micro::Register_DEPTHWISE_CONV_2D(),
ParseDepthwiseConv2D);
}
TfLiteStatus AddDequantize() {
return AddBuiltin(BuiltinOperator_DEQUANTIZE,
tflite::ops::micro::Register_DEQUANTIZE(),
ParseDequantize);
}
TfLiteStatus AddEqual() {
return AddBuiltin(BuiltinOperator_EQUAL,
tflite::ops::micro::Register_EQUAL(), ParseEqual);
}
TfLiteStatus AddFloor() {
return AddBuiltin(BuiltinOperator_FLOOR,
tflite::ops::micro::Register_FLOOR(), ParseFloor);
}
TfLiteStatus AddFullyConnected() {
return AddBuiltin(BuiltinOperator_FULLY_CONNECTED,
tflite::ops::micro::Register_FULLY_CONNECTED(),
ParseFullyConnected);
}
TfLiteStatus AddGreater() {
return AddBuiltin(BuiltinOperator_GREATER,
tflite::ops::micro::Register_GREATER(), ParseGreater);
}
TfLiteStatus AddGreaterEqual() {
return AddBuiltin(BuiltinOperator_GREATER_EQUAL,
tflite::ops::micro::Register_GREATER_EQUAL(),
ParseGreaterEqual);
}
TfLiteStatus AddHardSwish() {
return AddBuiltin(BuiltinOperator_HARD_SWISH,
tflite::ops::micro::Register_HARD_SWISH(),
ParseHardSwish);
}
TfLiteStatus AddL2Normalization() {
return AddBuiltin(BuiltinOperator_L2_NORMALIZATION,
tflite::ops::micro::Register_L2_NORMALIZATION(),
ParseL2Normalization);
}
TfLiteStatus AddLess() {
return AddBuiltin(BuiltinOperator_LESS, tflite::ops::micro::Register_LESS(),
ParseLess);
}
TfLiteStatus AddLessEqual() {
return AddBuiltin(BuiltinOperator_LESS_EQUAL,
tflite::ops::micro::Register_LESS_EQUAL(),
ParseLessEqual);
}
TfLiteStatus AddLog() {
return AddBuiltin(BuiltinOperator_LOG, tflite::ops::micro::Register_LOG(),
ParseLog);
}
TfLiteStatus AddLogicalAnd() {
return AddBuiltin(BuiltinOperator_LOGICAL_AND,
tflite::ops::micro::Register_LOGICAL_AND(),
ParseLogicalAnd);
}
TfLiteStatus AddLogicalNot() {
return AddBuiltin(BuiltinOperator_LOGICAL_NOT,
tflite::ops::micro::Register_LOGICAL_NOT(),
ParseLogicalNot);
}
TfLiteStatus AddLogicalOr() {
return AddBuiltin(BuiltinOperator_LOGICAL_OR,
tflite::ops::micro::Register_LOGICAL_OR(),
ParseLogicalOr);
}
TfLiteStatus AddLogistic() {
return AddBuiltin(BuiltinOperator_LOGISTIC,
tflite::ops::micro::Register_LOGISTIC(), ParseLogistic);
}
TfLiteStatus AddMaximum() {
return AddBuiltin(BuiltinOperator_MAXIMUM,
tflite::ops::micro::Register_MAXIMUM(), ParseMaximum);
}
TfLiteStatus AddMaxPool2D() {
return AddBuiltin(BuiltinOperator_MAX_POOL_2D,
tflite::ops::micro::Register_MAX_POOL_2D(), ParsePool);
}
TfLiteStatus AddMean() {
return AddBuiltin(BuiltinOperator_MEAN, tflite::ops::micro::Register_MEAN(),
ParseReducer);
}
TfLiteStatus AddMinimum() {
return AddBuiltin(BuiltinOperator_MINIMUM,
tflite::ops::micro::Register_MINIMUM(), ParseMinimum);
}
TfLiteStatus AddMul() {
return AddBuiltin(BuiltinOperator_MUL, tflite::ops::micro::Register_MUL(),
ParseMul);
}
TfLiteStatus AddNeg() {
return AddBuiltin(BuiltinOperator_NEG, tflite::ops::micro::Register_NEG(),
ParseNeg);
}
TfLiteStatus AddNotEqual() {
return AddBuiltin(BuiltinOperator_NOT_EQUAL,
tflite::ops::micro::Register_NOT_EQUAL(), ParseNotEqual);
}
TfLiteStatus AddPack() {
return AddBuiltin(BuiltinOperator_PACK, tflite::ops::micro::Register_PACK(),
ParsePack);
}
TfLiteStatus AddPad() {
return AddBuiltin(BuiltinOperator_PAD, tflite::ops::micro::Register_PAD(),
ParsePad);
}
TfLiteStatus AddPadV2() {
return AddBuiltin(BuiltinOperator_PADV2,
tflite::ops::micro::Register_PADV2(), ParsePadV2);
}
TfLiteStatus AddPrelu() {
return AddBuiltin(BuiltinOperator_PRELU,
tflite::ops::micro::Register_PRELU(), ParsePrelu);
}
TfLiteStatus AddQuantize() {
return AddBuiltin(BuiltinOperator_QUANTIZE,
tflite::ops::micro::Register_QUANTIZE(), ParseQuantize);
}
TfLiteStatus AddRelu() {
return AddBuiltin(BuiltinOperator_RELU, tflite::ops::micro::Register_RELU(),
ParseRelu);
}
TfLiteStatus AddRelu6() {
return AddBuiltin(BuiltinOperator_RELU6,
tflite::ops::micro::Register_RELU6(), ParseRelu6);
}
TfLiteStatus AddReshape() {
return AddBuiltin(BuiltinOperator_RESHAPE,
tflite::ops::micro::Register_RESHAPE(), ParseReshape);
}
TfLiteStatus AddResizeNearestNeighbor() {
return AddBuiltin(BuiltinOperator_RESIZE_NEAREST_NEIGHBOR,
tflite::ops::micro::Register_RESIZE_NEAREST_NEIGHBOR(),
ParseResizeNearestNeighbor);
}
TfLiteStatus AddRound() {
return AddBuiltin(BuiltinOperator_ROUND,
tflite::ops::micro::Register_ROUND(), ParseRound);
}
TfLiteStatus AddRsqrt() {
return AddBuiltin(BuiltinOperator_RSQRT,
tflite::ops::micro::Register_RSQRT(), ParseRsqrt);
}
TfLiteStatus AddSin() {
return AddBuiltin(BuiltinOperator_SIN, tflite::ops::micro::Register_SIN(),
ParseSin);
}
TfLiteStatus AddSoftmax() {
return AddBuiltin(BuiltinOperator_SOFTMAX,
tflite::ops::micro::Register_SOFTMAX(), ParseSoftmax);
}
TfLiteStatus AddSplit() {
return AddBuiltin(BuiltinOperator_SPLIT,
tflite::ops::micro::Register_SPLIT(), ParseSplit);
}
TfLiteStatus AddSqrt() {
return AddBuiltin(BuiltinOperator_SQRT, tflite::ops::micro::Register_SQRT(),
ParseSqrt);
}
TfLiteStatus AddSquare() {
return AddBuiltin(BuiltinOperator_SQUARE,
tflite::ops::micro::Register_SQUARE(), ParseSquare);
}
TfLiteStatus AddStridedSlice() {
return AddBuiltin(BuiltinOperator_STRIDED_SLICE,
tflite::ops::micro::Register_STRIDED_SLICE(),
ParseStridedSlice);
}
TfLiteStatus AddSub() {
return AddBuiltin(BuiltinOperator_SUB, tflite::ops::micro::Register_SUB(),
ParseSub);
}
TfLiteStatus AddSvdf() {
return AddBuiltin(BuiltinOperator_SVDF, tflite::ops::micro::Register_SVDF(),
ParseSvdf);
}
TfLiteStatus AddTanh() {
return AddBuiltin(BuiltinOperator_TANH, tflite::ops::micro::Register_TANH(),
ParseTanh);
}
TfLiteStatus AddUnpack() {
return AddBuiltin(BuiltinOperator_UNPACK,
tflite::ops::micro::Register_UNPACK(), ParseUnpack);
}
unsigned int GetRegistrationLength() { return registrations_len_; }
private:
TfLiteStatus AddBuiltin(tflite::BuiltinOperator op,
const TfLiteRegistration& registration,
MicroOpResolver::BuiltinParseFunction parser) {
if (op == BuiltinOperator_CUSTOM) {
if (error_reporter_ != nullptr) {
TF_LITE_REPORT_ERROR(error_reporter_,
"Invalid parameter BuiltinOperator_CUSTOM to the "
"AddBuiltin function.");
}
return kTfLiteError;
}
if (FindOp(op) != nullptr) {
if (error_reporter_ != nullptr) {
TF_LITE_REPORT_ERROR(error_reporter_,
"Calling AddBuiltin with the same op more than "
"once is not supported (Op: #%d).",
op);
}
return kTfLiteError;
}
if (registrations_len_ >= tOpCount) {
if (error_reporter_) {
TF_LITE_REPORT_ERROR(error_reporter_,
"Couldn't register builtin op #%d, resolver size "
"is too small (%d).",
op, tOpCount);
}
return kTfLiteError;
}
registrations_[registrations_len_] = registration;
// Strictly speaking, the builtin_code is not necessary for TFLM but filling
// it in regardless.
registrations_[registrations_len_].builtin_code = op;
registrations_len_++;
builtin_codes_[num_buitin_ops_] = op;
builtin_parsers_[num_buitin_ops_] = parser;
num_buitin_ops_++;
return kTfLiteOk;
}
TfLiteRegistration registrations_[tOpCount];
unsigned int registrations_len_ = 0;
// Arrays (and counter) to store the builtin codes and their corresponding
// parse functions as these are registered with the Op Resolver.
BuiltinOperator builtin_codes_[tOpCount];
MicroOpResolver::BuiltinParseFunction builtin_parsers_[tOpCount];
unsigned int num_buitin_ops_ = 0;
ErrorReporter* error_reporter_;
TF_LITE_REMOVE_VIRTUAL_DELETE
};
}; // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_
#define TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_
#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/schema/schema_generated.h"
namespace tflite {
// This is an interface for the OpResolver for TFLiteMicro. The differences from
// the TFLite OpResolver base class are to:
// * explicitly remove support for Op versions
// * allow for finer grained registration of the Builtin Ops to reduce code
// size for TFLiteMicro.
//
// We need an interface class instead of directly using MicroMutableOpResolver
// because MicroMutableOpResolver is a class template with the number of
// registered Ops as the template parameter.
class MicroOpResolver : public OpResolver {
public:
typedef TfLiteStatus (*BuiltinParseFunction)(const Operator* op,
ErrorReporter* error_reporter,
BuiltinDataAllocator* allocator,
void** builtin_data);
// Returns the Op registration struct corresponding to the enum code from the
// flatbuffer schema. Returns nullptr if the op is not found or if op ==
// BuiltinOperator_CUSTOM.
virtual const TfLiteRegistration* FindOp(BuiltinOperator op) const = 0;
// Returns the Op registration struct corresponding to the custom operator by
// name.
virtual const TfLiteRegistration* FindOp(const char* op) const = 0;
// This implementation exists for compatibility with the OpResolver base class
// and disregards the version parameter.
const TfLiteRegistration* FindOp(BuiltinOperator op,
int version) const final {
return FindOp(op);
}
// This implementation exists for compatibility with the OpResolver base class
// and disregards the version parameter.
const TfLiteRegistration* FindOp(const char* op, int version) const final {
return FindOp(op);
}
// Returns the operator specific parsing function for the OpData for a
// BuiltinOperator (if registered), else nullptr.
virtual BuiltinParseFunction GetOpDataParser(BuiltinOperator op) const = 0;
~MicroOpResolver() override {}
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_

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/* 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.
==============================================================================*/
// Optional debugging functionality. For small sized binaries, these are not
// needed.
#ifndef TENSORFLOW_LITE_MICRO_MICRO_OPTIONAL_DEBUG_TOOLS_H_
#define TENSORFLOW_LITE_MICRO_MICRO_OPTIONAL_DEBUG_TOOLS_H_
#include "tensorflow/lite/micro/micro_interpreter.h"
namespace tflite {
// 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);
// Prints a dump of what tensors and what nodes are in the interpreter.
void PrintInterpreterState(MicroInterpreter* interpreter);
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_OPTIONAL_DEBUG_TOOLS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_
#define TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/core/api/profiler.h"
#include "tensorflow/lite/micro/compatibility.h"
namespace tflite {
// MicroProfiler creates a common way to gain fine-grained insight into runtime
// performance. Bottleck operators can be identified along with slow code
// sections. This can be used in conjunction with running the relevant micro
// benchmark to evaluate end-to-end performance.
//
// Usage example:
// MicroProfiler profiler(error_reporter);
// {
// ScopedProfile scoped_profile(profiler, tag);
// work_to_profile();
// }
//
// This will call the following methods in order:
// int event_handle = profiler->BeginEvent(op_name, EventType::DEFAULT, 0)
// work_to_profile();
// profiler->EndEvent(event_handle)
class MicroProfiler : public tflite::Profiler {
public:
explicit MicroProfiler(tflite::ErrorReporter* reporter);
~MicroProfiler() override = default;
// AddEvent is unused for Tf Micro.
void AddEvent(const char* tag, EventType event_type, uint64_t start,
uint64_t end, int64_t event_metadata1,
int64_t event_metadata2) override{};
// BeginEvent followed by code followed by EndEvent will profile the code
// enclosed. Multiple concurrent events are unsupported, so the return value
// is always 0. Event_metadata1 and event_metadata2 are unused. The tag
// pointer must be valid until EndEvent is called.
uint32_t BeginEvent(const char* tag, EventType event_type,
int64_t event_metadata1,
int64_t event_metadata2) override;
// Event_handle is ignored since TF Micro does not support concurrent events.
void EndEvent(uint32_t event_handle) override;
private:
tflite::ErrorReporter* reporter_;
int32_t start_time_;
const char* event_tag_;
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_STRING_H_
#define TENSORFLOW_LITE_MICRO_MICRO_STRING_H_
#include <cstdarg>
// Implements simple string formatting for numeric types. Returns the number of
// bytes written to output.
extern "C" {
// Functionally equivalent to vsnprintf, trimmed down for TFLite Micro.
// MicroSnprintf() is implemented using MicroVsnprintf().
int MicroVsnprintf(char* output, int len, const char* format, va_list args);
// Functionally equavalent to snprintf, trimmed down for TFLite Micro.
// For example, MicroSnprintf(buffer, 10, "int %d", 10) will put the string
// "int 10" in the buffer.
// Floating point values are logged in exponent notation (1.XXX*2^N).
int MicroSnprintf(char* output, int len, const char* format, ...);
}
#endif // TENSORFLOW_LITE_MICRO_MICRO_STRING_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_TIME_H_
#define TENSORFLOW_LITE_MICRO_MICRO_TIME_H_
#include <stdint.h>
namespace tflite {
// These functions should be implemented by each target platform, and provide an
// accurate tick count along with how many ticks there are per second.
int32_t ticks_per_second();
// Return time in ticks. The meaning of a tick varies per platform.
int32_t GetCurrentTimeTicks();
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_TIME_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_
#define TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_
#include <stdint.h>
#include "tensorflow/lite/c/common.h"
namespace tflite {
// Returns number of elements in the shape array.
int ElementCount(const TfLiteIntArray& dims);
uint8_t FloatToAsymmetricQuantizedUInt8(const float value, const float scale,
const int zero_point);
uint8_t FloatToSymmetricQuantizedUInt8(const float value, const float scale);
int8_t FloatToAsymmetricQuantizedInt8(const float value, const float scale,
const int zero_point);
int16_t FloatToAsymmetricQuantizedInt16(const float value, const float scale,
const int zero_point);
int8_t FloatToSymmetricQuantizedInt8(const float value, const float scale);
// Converts a float value into a signed thirty-two-bit quantized value. Note
// that values close to max int and min int may see significant error due to
// a lack of floating point granularity for large values.
int32_t FloatToSymmetricQuantizedInt32(const float value, const float scale);
// Helper methods to quantize arrays of floats to the desired format.
//
// There are several key flavors of quantization in TfLite:
// asymmetric symmetric per channel
// int8_t | X | X | X |
// uint8_t | X | X | |
// int16_t | X | | |
// int32_t | | X | X |
//
// The per-op quantization spec can be found here:
// https://www.tensorflow.org/lite/performance/quantization_spec
void AsymmetricQuantize(const float* input, int8_t* output, int num_elements,
float scale, int zero_point = 0);
void AsymmetricQuantize(const float* input, uint8_t* output, int num_elements,
float scale, int zero_point = 128);
void AsymmetricQuantize(const float* input, int16_t* output, int num_elements,
float scale, int zero_point = 0);
void SymmetricQuantize(const float* input, int32_t* output, int num_elements,
float scale);
void SymmetricPerChannelQuantize(const float* input, int32_t* output,
int num_elements, int num_channels,
float* scales);
void SignedSymmetricPerChannelQuantize(const float* values,
TfLiteIntArray* dims,
int quantized_dimension,
int8_t* quantized_values,
float* scaling_factor);
void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims,
int8_t* quantized_values, float* scaling_factor);
void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims,
int16_t* quantized_values, float* scaling_factor);
void SignedSymmetricQuantize(const float* values, TfLiteIntArray* dims,
int32_t* quantized_values, float* scaling_factor);
void SymmetricQuantize(const float* values, TfLiteIntArray* dims,
uint8_t* quantized_values, float* scaling_factor);
void SymmetricDequantize(const int8_t* values, const int size,
const float dequantization_scale,
float* dequantized_values);
template <typename T>
void AsymmetricDequantize(const T* values, const int size,
const float dequantization_scale,
int dequantization_zero_point,
float* dequantized_values) {
for (int i = 0; i < size; ++i) {
dequantized_values[i] =
(values[i] - dequantization_zero_point) * dequantization_scale;
}
}
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_
#define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_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 {
// List of buckets currently recorded by this class. Each type keeps a list of
// allocated information during model initialization.
enum class RecordedAllocationType {
kTfLiteEvalTensorData,
kPersistentTfLiteTensorData,
kPersistentTfLiteTensorQuantizationData,
kTfLiteTensorVariableBufferData,
kNodeAndRegistrationArray,
kOpData,
};
// Container for holding information about allocation recordings by a given
// type. Each recording contains the number of bytes requested, the actual bytes
// allocated (can defer from requested by alignment), and the number of items
// allocated.
struct RecordedAllocation {
size_t requested_bytes;
size_t used_bytes;
size_t count;
};
// Utility subclass of MicroAllocator that records all allocations
// inside the arena. A summary of allocations can be logged through the
// ErrorReporter by invoking LogAllocations(). This special allocator requires
// an instance of RecordingSimpleMemoryAllocator to capture allocations in the
// head and tail. Arena allocation recording can be retrieved by type through
// the GetRecordedAllocation() function. This class should only be used for
// auditing memory usage or integration testing.
class RecordingMicroAllocator : public MicroAllocator {
public:
static RecordingMicroAllocator* Create(uint8_t* tensor_arena,
size_t arena_size,
ErrorReporter* error_reporter);
// Returns the recorded allocations information for a given allocation type.
RecordedAllocation GetRecordedAllocation(
RecordedAllocationType allocation_type) const;
const RecordingSimpleMemoryAllocator* GetSimpleMemoryAllocator() const;
// Logs out through the ErrorReporter all allocation recordings by type
// defined in RecordedAllocationType.
void PrintAllocations() const;
protected:
TfLiteStatus AllocateNodeAndRegistrations(
const Model* model,
NodeAndRegistration** node_and_registrations) override;
TfLiteStatus PrepareNodeAndRegistrationDataFromFlatbuffer(
const Model* model, const MicroOpResolver& op_resolver,
NodeAndRegistration* node_and_registrations) override;
TfLiteStatus AllocateTfLiteEvalTensors(
const Model* model, TfLiteEvalTensor** eval_tensors) override;
TfLiteStatus AllocateVariables(const SubGraph* subgraph,
TfLiteEvalTensor* eval_tensors) override;
// TODO(b/160894903): Once all kernels have been updated to the new API drop
// this method. It is only used to record TfLiteTensor persistent allocations.
TfLiteTensor* AllocatePersistentTfLiteTensorInternal(
const Model* model, TfLiteEvalTensor* eval_tensors,
int tensor_index) override;
// TODO(b/160894903): Once all kernels have been updated to the new API drop
// this function since all allocations for quantized data will take place in
// the temp section.
TfLiteStatus PopulateTfLiteTensorFromFlatbuffer(const Model* model,
const SubGraph* subgraph,
TfLiteTensor* tensor,
int tensor_index,
bool allocate_temp) override;
private:
RecordingMicroAllocator(RecordingSimpleMemoryAllocator* memory_allocator,
ErrorReporter* error_reporter);
void PrintRecordedAllocation(RecordedAllocationType allocation_type,
const char* allocation_name,
const char* allocation_description) const;
RecordedAllocation SnapshotAllocationUsage() const;
void RecordAllocationUsage(const RecordedAllocation& snapshotted_allocation,
RecordedAllocation& recorded_allocation);
const RecordingSimpleMemoryAllocator* recording_memory_allocator_;
RecordedAllocation recorded_tflite_eval_tensor_data_ = {};
RecordedAllocation recorded_persistent_tflite_tensor_data_ = {};
RecordedAllocation recorded_persistent_tflite_tensor_quantization_data_ = {};
RecordedAllocation recorded_tflite_tensor_variable_buffer_data_ = {};
RecordedAllocation recorded_node_and_registration_array_data_ = {};
RecordedAllocation recorded_op_data_ = {};
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_
#define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/recording_micro_allocator.h"
namespace tflite {
// Utility subclass that enables internal recordings of the MicroInterpreter.
// This class should be used to audit and analyze memory arena usage for a given
// model and interpreter.
//
// After construction and the first Invoke() or AllocateTensors() call - the
// memory usage is recorded and available through the GetMicroAllocator()
// function. See RecordingMicroAlloctor for more details on what is currently
// recorded from arena allocations.
//
// It is recommended for users to increase the tensor arena size by at least 1kb
// to ensure enough additional memory is available for internal recordings.
class RecordingMicroInterpreter : public MicroInterpreter {
public:
RecordingMicroInterpreter(const Model* model,
const MicroOpResolver& op_resolver,
uint8_t* tensor_arena, size_t tensor_arena_size,
ErrorReporter* error_reporter)
: MicroInterpreter(model, op_resolver,
RecordingMicroAllocator::Create(
tensor_arena, tensor_arena_size, error_reporter),
error_reporter),
recording_micro_allocator_(
static_cast<const RecordingMicroAllocator&>(allocator())) {}
RecordingMicroInterpreter(const Model* model,
const MicroOpResolver& op_resolver,
RecordingMicroAllocator* allocator,
ErrorReporter* error_reporter)
: MicroInterpreter(model, op_resolver, allocator, error_reporter),
recording_micro_allocator_(*allocator) {}
const RecordingMicroAllocator& GetMicroAllocator() const {
return recording_micro_allocator_;
}
private:
const RecordingMicroAllocator& recording_micro_allocator_;
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_
#define TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_
#include "tensorflow/lite/micro/compatibility.h"
#include "tensorflow/lite/micro/simple_memory_allocator.h"
namespace tflite {
// Utility class used to log allocations of a SimpleMemoryAllocator. Should only
// be used in debug/evaluation settings or unit tests to evaluate allocation
// usage.
class RecordingSimpleMemoryAllocator : public SimpleMemoryAllocator {
public:
RecordingSimpleMemoryAllocator(ErrorReporter* error_reporter,
uint8_t* buffer_head, size_t buffer_size);
// TODO(b/157615197): Cleanup constructors/destructor and use factory
// functions.
~RecordingSimpleMemoryAllocator() override;
static RecordingSimpleMemoryAllocator* Create(ErrorReporter* error_reporter,
uint8_t* buffer_head,
size_t buffer_size);
// Returns the number of bytes requested from the head or tail.
size_t GetRequestedBytes() const;
// Returns the number of bytes actually allocated from the head or tail. This
// value will be >= to the number of requested bytes due to padding and
// alignment.
size_t GetUsedBytes() const;
// Returns the number of alloc calls from the head or tail.
size_t GetAllocatedCount() const;
TfLiteStatus EnsureHeadSize(size_t size, size_t alignment) override;
uint8_t* AllocateFromTail(size_t size, size_t alignment) override;
private:
size_t requested_head_bytes_;
size_t requested_tail_bytes_;
size_t used_bytes_;
size_t alloc_count_;
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_
#define TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_
#include <cstddef>
#include <cstdint>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/core/api/error_reporter.h"
#include "tensorflow/lite/micro/compatibility.h"
namespace tflite {
// TODO(petewarden): This allocator never frees up or reuses any memory, even
// though we have enough information about lifetimes of the tensors to do so.
// This makes it pretty wasteful, so we should use a more intelligent method.
class SimpleMemoryAllocator {
public:
// TODO(b/157615197): Cleanup constructors/destructor and use factory
// functions.
SimpleMemoryAllocator(ErrorReporter* error_reporter, uint8_t* buffer_head,
uint8_t* buffer_tail);
SimpleMemoryAllocator(ErrorReporter* error_reporter, uint8_t* buffer,
size_t buffer_size);
virtual ~SimpleMemoryAllocator();
// Creates a new SimpleMemoryAllocator from a given buffer head and size.
static SimpleMemoryAllocator* Create(ErrorReporter* error_reporter,
uint8_t* buffer_head,
size_t buffer_size);
// Ensure that the head (lowest address and moving upwards) memory allocation
// is at least a given size. This function will only increase the head size if
// the passed in value is larger than the current head size. Calls to this
// method will also invalidate all temporary allocation values. This call will
// fail if a chain of allocations through AllocateTemp() have not been cleaned
// up with a call to ResetTempAllocations().
virtual TfLiteStatus EnsureHeadSize(size_t size, size_t alignment);
// Allocates memory starting at the tail of the arena (highest address and
// moving downwards).
virtual uint8_t* AllocateFromTail(size_t size, size_t alignment);
// Allocates a temporary buffer from the head of the arena (lowest address and
// moving upwards) but does not update the actual head allocation size or
// position. The returned buffer is guaranteed until either
// ResetTempAllocations() is called or another call to AllocateFromHead().
// Repeat calls to this function will create a chain of temp allocations. All
// calls to AllocateTemp() must end with a call to ResetTempAllocations(). If
// AllocateFromHead() is called before a call to ResetTempAllocations(), it
// will fail with an error message.
virtual uint8_t* AllocateTemp(size_t size, size_t alignment);
// Resets a chain of temporary allocations back to the current head of the
// arena (lowest address).
virtual void ResetTempAllocations();
uint8_t* GetHead() const;
uint8_t* GetBufferHead() const;
uint8_t* GetTail() const;
size_t GetHeadUsedBytes() const;
size_t GetTailUsedBytes() const;
// Returns the number of bytes available with a given alignment.
size_t GetAvailableMemory(size_t alignment) const;
size_t GetUsedBytes() const;
private:
size_t GetBufferSize() const;
ErrorReporter* error_reporter_;
uint8_t* buffer_head_;
uint8_t* buffer_tail_;
uint8_t* head_;
uint8_t* tail_;
uint8_t* temp_;
TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_

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/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_
#define TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_
// Useful functions for writing tests.
#include <cstdint>
#include "flatbuffers/flatbuffers.h" // from @flatbuffers
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.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 {
constexpr int kOfflinePlannerHeaderSize = 3;
struct NodeConnection_ {
std::initializer_list<int32_t> input;
std::initializer_list<int32_t> output;
};
typedef struct NodeConnection_ NodeConnection;
// A simple operator that returns the median of the input with the number of
// times the kernel was invoked. The implementation below is deliberately
// complicated, just to demonstrate how kernel memory planning works.
class SimpleStatefulOp {
static constexpr int kBufferNotAllocated = 0;
// Inputs:
static constexpr int kInputTensor = 0;
// Outputs:
static constexpr int kMedianTensor = 0;
static constexpr int kInvokeCount = 1;
struct OpData {
int invoke_count = 0;
int sorting_buffer = kBufferNotAllocated;
};
public:
static const TfLiteRegistration* getRegistration();
static TfLiteRegistration* GetMutableRegistration();
static void* Init(TfLiteContext* context, const char* buffer, size_t length);
static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node);
static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node);
};
class MockCustom {
public:
static const TfLiteRegistration* getRegistration();
static TfLiteRegistration* GetMutableRegistration();
static void* Init(TfLiteContext* context, const char* buffer, size_t length);
static void Free(TfLiteContext* context, void* buffer);
static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node);
static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node);
static bool freed_;
};
// Returns an Op Resolver that can be used in the testing code.
AllOpsResolver GetOpResolver();
// Returns a simple example flatbuffer TensorFlow Lite model. Contains 1 input,
// 1 layer of weights, 1 output Tensor, and 1 operator.
const Model* GetSimpleMockModel();
// Returns a flatbuffer TensorFlow Lite model with more inputs, variable
// tensors, and operators.
const Model* GetComplexMockModel();
// Returns a simple flatbuffer model with two branches.
const Model* GetSimpleModelWithBranch();
// Returns a simple flatbuffer model with offline planned tensors
const Model* GetModelWithOfflinePlanning(int num_tensors,
const int32_t* metadata_buffer,
NodeConnection* node_conn,
int num_conns);
// Returns a flatbuffer model with `simple_stateful_op`
const Model* GetSimpleStatefulModel();
// Builds a one-dimensional flatbuffer tensor of the given size.
const Tensor* Create1dFlatbufferTensor(int size, bool is_variable = false);
// Builds a one-dimensional flatbuffer tensor of the given size with
// quantization metadata.
const Tensor* CreateQuantizedFlatbufferTensor(int size);
// Creates a one-dimensional tensor with no quantization metadata.
const Tensor* CreateMissingQuantizationFlatbufferTensor(int size);
// Creates a vector of flatbuffer buffers.
const flatbuffers::Vector<flatbuffers::Offset<Buffer>>*
CreateFlatbufferBuffers();
// Performs a simple string comparison without requiring standard C library.
int TestStrcmp(const char* a, const char* b);
// Wrapper to forward kernel errors to the interpreter's error reporter.
void ReportOpError(struct TfLiteContext* context, const char* format, ...);
void PopulateContext(TfLiteTensor* tensors, int tensors_size,
TfLiteContext* context);
// 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);
// 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);
TfLiteTensor CreateFloatTensor(const float* data, TfLiteIntArray* dims,
bool is_variable = false);
void PopulateFloatTensor(TfLiteTensor* tensor, float* begin, float* end);
TfLiteTensor CreateBoolTensor(const bool* data, TfLiteIntArray* dims,
bool is_variable = false);
TfLiteTensor CreateInt32Tensor(const int32_t*, TfLiteIntArray* dims,
bool is_variable = false);
TfLiteTensor CreateQuantizedTensor(const uint8_t* data, TfLiteIntArray* dims,
float scale, int zero_point,
bool is_variable = false);
TfLiteTensor CreateQuantizedTensor(const int8_t* data, TfLiteIntArray* dims,
float scale, int zero_point,
bool is_variable = false);
TfLiteTensor CreateQuantizedTensor(const int16_t* data, TfLiteIntArray* dims,
float scale, int zero_point,
bool is_variable = false);
template <typename T>
TfLiteTensor CreateQuantizedTensor(const float* input, T* quantized,
TfLiteIntArray* dims, float scale,
int zero_point, bool is_variable = false) {
int input_size = ElementCount(*dims);
tflite::AsymmetricQuantize(input, quantized, input_size, scale, zero_point);
return CreateQuantizedTensor(quantized, dims, scale, zero_point, is_variable);
}
TfLiteTensor CreateQuantizedBiasTensor(const float* data, int32_t* quantized,
TfLiteIntArray* dims, float input_scale,
float weights_scale,
bool is_variable = false);
// 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 = false);
TfLiteTensor CreateSymmetricPerChannelQuantizedTensor(
const float* input, int8_t* quantized, TfLiteIntArray* dims, float* scales,
int* zero_points, TfLiteAffineQuantization* affine_quant,
int quantized_dimension, bool is_variable = false);
// Returns the number of tensors in the default subgraph for a tflite::Model.
size_t GetModelTensorCount(const Model* model);
} // namespace testing
} // namespace tflite
#endif // TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_

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