312 lines
13 KiB
C++
312 lines
13 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
==============================================================================*/
|
|
|
|
#include <limits>
|
|
|
|
#include "tensorflow/lite/c/common.h"
|
|
#include "tensorflow/lite/kernels/internal/quantization_util.h"
|
|
#include "tensorflow/lite/kernels/internal/reference/quantize.h"
|
|
#include "tensorflow/lite/kernels/internal/reference/requantize.h"
|
|
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
|
|
#include "tensorflow/lite/kernels/kernel_util.h"
|
|
#include "tensorflow/lite/micro/kernels/kernel_util.h"
|
|
#include "tensorflow/lite/micro/kernels/quantize.h"
|
|
#include "tensorflow/lite/micro/micro_log.h"
|
|
#include "tensorflow/lite/micro/micro_utils.h"
|
|
|
|
namespace tflite {
|
|
|
|
TfLiteStatus PrepareQuantizeReference(TfLiteContext* context,
|
|
TfLiteNode* node) {
|
|
TFLITE_DCHECK(node->user_data != nullptr);
|
|
auto* data = static_cast<OpDataQuantizeReference*>(node->user_data);
|
|
|
|
TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
|
|
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
|
|
|
|
MicroContext* micro_context = GetMicroContext(context);
|
|
|
|
TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0);
|
|
TF_LITE_ENSURE(context, input != nullptr);
|
|
TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
|
|
TF_LITE_ENSURE(context, output != nullptr);
|
|
|
|
// TODO(b/128934713): Add support for fixed-point per-channel quantization.
|
|
// Currently this only support affine per-layer quantization.
|
|
TF_LITE_ENSURE_EQ(context, output->quantization.type,
|
|
kTfLiteAffineQuantization);
|
|
const auto* affine_quantization =
|
|
reinterpret_cast<TfLiteAffineQuantization*>(output->quantization.params);
|
|
TF_LITE_ENSURE(context, affine_quantization);
|
|
TF_LITE_ENSURE(context, affine_quantization->scale);
|
|
TF_LITE_ENSURE(context, affine_quantization->scale->size == 1);
|
|
|
|
TF_LITE_ENSURE(
|
|
context, input->type == kTfLiteFloat32 || input->type == kTfLiteInt32 ||
|
|
input->type == kTfLiteInt16 || input->type == kTfLiteInt8 ||
|
|
input->type == kTfLiteUInt8);
|
|
TF_LITE_ENSURE(context, output->type == kTfLiteInt8 ||
|
|
output->type == kTfLiteInt16 ||
|
|
output->type == kTfLiteInt32 ||
|
|
output->type == kTfLiteUInt8);
|
|
|
|
if ((input->type == kTfLiteInt16 && output->type == kTfLiteInt8) ||
|
|
(input->type == kTfLiteInt8 && output->type == kTfLiteInt8) ||
|
|
(input->type == kTfLiteInt8 && output->type == kTfLiteUInt8) ||
|
|
(input->type == kTfLiteUInt8 && output->type == kTfLiteInt8) ||
|
|
(input->type == kTfLiteInt8 && output->type == kTfLiteInt16) ||
|
|
(input->type == kTfLiteInt8 && output->type == kTfLiteInt32) ||
|
|
(input->type == kTfLiteInt16 && output->type == kTfLiteInt16) ||
|
|
(input->type == kTfLiteInt16 && output->type == kTfLiteInt32) ||
|
|
(input->type == kTfLiteInt32 && output->type == kTfLiteInt8) ||
|
|
(input->type == kTfLiteInt32 && output->type == kTfLiteInt16)) {
|
|
double effective_scale = static_cast<double>(input->params.scale) /
|
|
static_cast<double>(output->params.scale);
|
|
|
|
QuantizeMultiplier(effective_scale, &data->requantize_output_multiplier,
|
|
&data->requantize_output_shift);
|
|
}
|
|
|
|
data->quantization_params.zero_point = output->params.zero_point;
|
|
data->quantization_params.scale = static_cast<double>(output->params.scale);
|
|
|
|
data->input_zero_point = input->params.zero_point;
|
|
|
|
micro_context->DeallocateTempTfLiteTensor(input);
|
|
micro_context->DeallocateTempTfLiteTensor(output);
|
|
return kTfLiteOk;
|
|
}
|
|
|
|
TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node) {
|
|
TFLITE_DCHECK(node->user_data != nullptr);
|
|
auto* data = static_cast<OpDataQuantizeReference*>(node->user_data);
|
|
|
|
const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
|
|
TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
|
|
|
|
if (input->type == kTfLiteFloat32) {
|
|
switch (output->type) {
|
|
case kTfLiteInt8:
|
|
reference_ops::AffineQuantize(
|
|
data->quantization_params, tflite::micro::GetTensorShape(input),
|
|
tflite::micro::GetTensorData<float>(input),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<int8_t>(output));
|
|
break;
|
|
case kTfLiteInt16:
|
|
reference_ops::AffineQuantize(
|
|
data->quantization_params, tflite::micro::GetTensorShape(input),
|
|
tflite::micro::GetTensorData<float>(input),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<int16_t>(output));
|
|
return kTfLiteOk;
|
|
default:
|
|
MicroPrintf("Input %s, output %s not supported.",
|
|
TfLiteTypeGetName(input->type),
|
|
TfLiteTypeGetName(output->type));
|
|
return kTfLiteError;
|
|
}
|
|
} else if (input->type == kTfLiteInt32) {
|
|
size_t size = ElementCount(*input->dims);
|
|
switch (output->type) {
|
|
case kTfLiteInt8:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int32_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int8_t>(output));
|
|
break;
|
|
case kTfLiteInt16:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int32_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int16_t>(output));
|
|
break;
|
|
default:
|
|
MicroPrintf("Input %s, output %s not supported.",
|
|
TfLiteTypeGetName(input->type),
|
|
TfLiteTypeGetName(output->type));
|
|
return kTfLiteError;
|
|
}
|
|
} else if (input->type == kTfLiteInt16) {
|
|
size_t size = ElementCount(*input->dims);
|
|
switch (output->type) {
|
|
case kTfLiteInt8:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int16_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int8_t>(output));
|
|
break;
|
|
case kTfLiteInt16:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int16_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int16_t>(output));
|
|
return kTfLiteOk;
|
|
case kTfLiteInt32:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int16_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int32_t>(output));
|
|
return kTfLiteOk;
|
|
default:
|
|
MicroPrintf("Input %s, output %s not supported.",
|
|
TfLiteTypeGetName(input->type),
|
|
TfLiteTypeGetName(output->type));
|
|
return kTfLiteError;
|
|
}
|
|
} else if (input->type == kTfLiteInt8) {
|
|
// Int8 to Int8 requantization, required if the input and output tensors
|
|
// have different scales and/or zero points.
|
|
size_t size = ElementCount(*input->dims);
|
|
switch (output->type) {
|
|
case kTfLiteInt8:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int8_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int8_t>(output));
|
|
break;
|
|
case kTfLiteUInt8:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int8_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<uint8_t>(output));
|
|
break;
|
|
case kTfLiteInt16:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int8_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int16_t>(output));
|
|
break;
|
|
case kTfLiteInt32:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<int8_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int32_t>(output));
|
|
break;
|
|
default:
|
|
MicroPrintf("Input %s, output %s not supported.",
|
|
TfLiteTypeGetName(input->type),
|
|
TfLiteTypeGetName(output->type));
|
|
return kTfLiteError;
|
|
}
|
|
} else if (input->type == kTfLiteUInt8) {
|
|
size_t size = ElementCount(*input->dims);
|
|
switch (output->type) {
|
|
case kTfLiteInt8:
|
|
reference_ops::Requantize(
|
|
tflite::micro::GetTensorData<uint8_t>(input), size,
|
|
data->requantize_output_multiplier, data->requantize_output_shift,
|
|
data->input_zero_point, data->quantization_params.zero_point,
|
|
tflite::micro::GetTensorData<int8_t>(output));
|
|
break;
|
|
default:
|
|
MicroPrintf("Input %s, output %s not supported.",
|
|
TfLiteTypeGetName(input->type),
|
|
TfLiteTypeGetName(output->type));
|
|
return kTfLiteError;
|
|
}
|
|
} else {
|
|
MicroPrintf("Input %s, output %s not supported.",
|
|
TfLiteTypeGetName(input->type),
|
|
TfLiteTypeGetName(output->type));
|
|
return kTfLiteError;
|
|
}
|
|
|
|
return kTfLiteOk;
|
|
}
|
|
|
|
TfLiteStatus EvalQuantizeReferenceFloat32ToInt8(TfLiteContext* context, TfLiteNode* node) {
|
|
TFLITE_DCHECK(node->user_data != nullptr);
|
|
auto* data = static_cast<OpDataQuantizeReference*>(node->user_data);
|
|
|
|
const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
|
|
TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
|
|
|
|
TFLITE_DCHECK(input->type == kTfLiteFloat32 && output->type == kTfLiteInt8);
|
|
reference_ops::AffineQuantize(
|
|
data->quantization_params, tflite::micro::GetTensorShape(input),
|
|
tflite::micro::GetTensorData<float>(input),
|
|
tflite::micro::GetTensorShape(output),
|
|
tflite::micro::GetTensorData<int8_t>(output));
|
|
return kTfLiteOk;
|
|
}
|
|
|
|
#ifdef TFLITE_MODEL_COMPILER
|
|
TfLiteStatus CompileQuantizeReferenceFloat32ToInt8(TfLiteContext* context, TfLiteNode* node,
|
|
TfLiteCompileStep step, std::ofstream& ofs) {
|
|
switch (step) {
|
|
case kTfLiteCompileStepInclude:
|
|
ofs << "#include \"tensorflow/lite/micro/kernels/quantize.h\"" << std::endl
|
|
<< "#include \"tensorflow/lite/kernels/internal/reference/quantize.h\""
|
|
<< std::endl;
|
|
break;
|
|
|
|
case kTfLiteCompileStepEval: {
|
|
TFLITE_DCHECK(node->user_data != nullptr);
|
|
auto* data = static_cast<OpDataQuantizeReference*>(node->user_data);
|
|
|
|
const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
|
|
TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
|
|
TFLITE_DCHECK(input->type == kTfLiteFloat32 && output->type == kTfLiteInt8);
|
|
|
|
ofs << "{ // quantize float to int8" << std::endl;
|
|
|
|
tflite::micro::CompileAddress(ofs, "input_data", input->data.data);
|
|
tflite::micro::CompileAddress(ofs, "output_data", output->data.data);
|
|
|
|
ofs << "static const tflite::QuantizationParams op_params = {"
|
|
<< ".zero_point = " << data->quantization_params.zero_point
|
|
<< ", .scale = " << data->quantization_params.scale << "};"
|
|
<< std::endl;
|
|
|
|
tflite::micro::CompileArray(ofs, "const int32_t", "input_dims_data",
|
|
input->dims->data, input->dims->size);
|
|
|
|
tflite::micro::CompileArray(ofs, "const int32_t", "output_dims_data",
|
|
output->dims->data, output->dims->size);
|
|
|
|
ofs << "tflite::reference_ops::AffineQuantize(op_params, "
|
|
"tflite::RuntimeShape("
|
|
<< input->dims->size
|
|
<< ", input_dims_data), "
|
|
"reinterpret_cast<float*>(input_data), "
|
|
"tflite::RuntimeShape("
|
|
<< output->dims->size
|
|
<< ", output_dims_data), "
|
|
"reinterpret_cast<int8_t*>(output_data));"
|
|
<< std::endl;
|
|
|
|
ofs << "}" << std::endl;
|
|
} break;
|
|
|
|
default:
|
|
return kTfLiteError;
|
|
}
|
|
|
|
return kTfLiteOk;
|
|
}
|
|
#endif
|
|
|
|
} // namespace tflite
|