179 lines
7.1 KiB
C++
179 lines
7.1 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/kernels/internal/reference/quantize.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.h"
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#include "tensorflow/lite/kernels/internal/reference/requantize.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/micro_utils.h"
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namespace tflite {
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namespace ops {
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namespace micro {
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namespace quantize {
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struct OpData {
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tflite::QuantizationParams quantization_params;
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// The scaling factor from input to output (aka the 'real multiplier') can
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// be represented as a fixed point multiplier plus a left shift.
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int32_t output_multiplier;
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int output_shift;
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int32_t input_zero_point;
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};
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
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return context->AllocatePersistentBuffer(context, sizeof(OpData));
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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OpData* data = static_cast<OpData*>(node->user_data);
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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const TfLiteTensor* input = GetInput(context, node, 0);
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TfLiteTensor* output = GetOutput(context, node, 0);
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// TODO(b/128934713): Add support for fixed-point per-channel quantization.
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// Currently this only support affine per-layer quantization.
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TF_LITE_ENSURE_EQ(context, output->quantization.type,
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kTfLiteAffineQuantization);
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const auto* affine_quantization =
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reinterpret_cast<TfLiteAffineQuantization*>(output->quantization.params);
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TF_LITE_ENSURE(context, affine_quantization);
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TF_LITE_ENSURE(context, affine_quantization->scale);
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TF_LITE_ENSURE(context, affine_quantization->scale->size == 1);
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TF_LITE_ENSURE(context, input->type == kTfLiteFloat32 ||
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input->type == kTfLiteInt16 ||
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input->type == kTfLiteInt8);
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TF_LITE_ENSURE(context,
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output->type == kTfLiteUInt8 || output->type == kTfLiteInt8);
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if ((input->type == kTfLiteInt16 || input->type == kTfLiteInt8) &&
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output->type == kTfLiteInt8) {
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double effective_scale =
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static_cast<double>(input->params.scale / output->params.scale);
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QuantizeMultiplier(effective_scale, &data->output_multiplier,
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&data->output_shift);
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}
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data->quantization_params.zero_point = output->params.zero_point;
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data->quantization_params.scale = static_cast<double>(output->params.scale);
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data->input_zero_point = input->params.zero_point;
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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OpData* data = static_cast<OpData*>(node->user_data);
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const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
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TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
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if (input->type == kTfLiteFloat32) {
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switch (output->type) {
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case kTfLiteInt8:
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reference_ops::AffineQuantize(
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data->quantization_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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case kTfLiteUInt8:
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reference_ops::AffineQuantize(
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data->quantization_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output));
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
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TfLiteTypeGetName(input->type),
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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} else if (input->type == kTfLiteInt16) {
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size_t size = ElementCount(*input->dims);
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switch (output->type) {
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case kTfLiteInt8:
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reference_ops::Requantize(tflite::micro::GetTensorData<int16_t>(input),
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size, data->output_multiplier,
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data->output_shift, data->input_zero_point,
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data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
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TfLiteTypeGetName(input->type),
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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} else if (input->type == kTfLiteInt8) {
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// Int8 to Int8 requantization, required if the input and output tensors
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// have different scales and/or zero points.
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size_t size = ElementCount(*input->dims);
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switch (output->type) {
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case kTfLiteInt8:
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reference_ops::Requantize(tflite::micro::GetTensorData<int8_t>(input),
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size, data->output_multiplier,
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data->output_shift, data->input_zero_point,
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data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
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TfLiteTypeGetName(input->type),
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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} else {
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TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
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TfLiteTypeGetName(input->type),
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace quantize
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// This Op (QUANTIZE) quantizes the input and produces quantized output.
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// AffineQuantize takes scale and zero point and quantizes the float value to
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// quantized output, in int8_t or uint8_t format.
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TfLiteRegistration Register_QUANTIZE() {
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return {/*init=*/quantize::Init,
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/*free=*/nullptr,
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/*prepare=*/quantize::Prepare,
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/*invoke=*/quantize::Eval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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