tensorflow/lite/delegates/hexagon/builders/conv_2d_helpers.cc - platform/external/tensorflow - Git at Google

 /* 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 <stdint.h>

 #include <algorithm>
 #include <cmath>
 #include <limits>
 #include <vector>

 #include "tensorflow/lite/c/builtin_op_data.h"
 #include "tensorflow/lite/c/common.h"
 #include "tensorflow/lite/delegates/hexagon/builders/conv_2d_builder.h"
 #include "tensorflow/lite/delegates/hexagon/hexagon_nn/hexagon_nn.h"
 #include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
 #include "tensorflow/lite/kernels/kernel_util.h"

 namespace tflite {
 namespace delegates {
 namespace hexagon {
 namespace {

 constexpr uint8_t k8BitSignFlipConstant = 0x80;
 // 1/1024 ~ 0.0009766 is a restriction set by Hexagon's kernels.
 // TODO(b/151103818): Figure out a way to retrieve this constant reliably.
 constexpr float kHexagonMinRelativeScale = 0.0009766f;

 }  // namespace

 TfLiteStatus ProcessPerChannelQuantizedWeights(
     const TfLiteIntArray* inputs, const TfLiteIntArray* outputs,
     TfLiteContext* context, float* weights_min, float* weights_max,
     GraphBuilder* graph_builder, PerChannelQuantData* per_channel_quant) {
   if (!per_channel_quant) return kTfLiteError;
   const auto& weights_tensor = context->tensors[inputs->data[1]];
   TfLiteAffineQuantization* weights_quant_params =
       reinterpret_cast<TfLiteAffineQuantization*>(
           weights_tensor.quantization.params);

   // Retrieve channel scales.
   per_channel_quant->num_scale_values = weights_quant_params->scale->size;
   // Normalize the scales as expected by Hexagon.
   per_channel_quant->scales_data = weights_quant_params->scale->data;
   std::vector<float> normalized_scales;
   normalized_scales.reserve(per_channel_quant->num_scale_values);
   float scale_max = 0.0;
   for (int i = 0; i < per_channel_quant->num_scale_values; ++i) {
     normalized_scales.push_back(per_channel_quant->scales_data[i]);
     if (per_channel_quant->scales_data[i] > scale_max) {
       scale_max = per_channel_quant->scales_data[i];
     }
   }
   if (scale_max == 0.0) {
     TF_LITE_KERNEL_LOG(context, "Scale max is zero for: %s",
                        weights_tensor.name);
     return kTfLiteError;
   }
   for (int i = 0; i < per_channel_quant->num_scale_values; ++i) {
     normalized_scales[i] =
         std::max(normalized_scales[i] / scale_max, kHexagonMinRelativeScale);
   }
   // Add node for channel scales data.
   const std::vector<int> scales_shape = {1, 1, 1,
                                          per_channel_quant->num_scale_values};
   per_channel_quant->channel_scales_node = graph_builder->AddConstNodeWithData(
       scales_shape.data(), reinterpret_cast<char*>(normalized_scales.data()),
       normalized_scales.size() * sizeof(normalized_scales[0]));
   *weights_min = -128 * scale_max;
   *weights_max = 127 * scale_max;
   return kTfLiteOk;
 }

 TfLiteStatus ProcessPerChannelQuantizedBias(
     const TfLiteIntArray* inputs, const TfLiteIntArray* outputs,
     TfLiteContext* context, float* bias_min, float* bias_max,
     GraphBuilder* graph_builder, PerChannelQuantData* per_channel_quant,
     OpBuilder** bias_const_node) {
   const auto& bias_tensor = context->tensors[inputs->data[2]];

   const TfLiteAffineQuantization* input_quant_params =
       static_cast<const TfLiteAffineQuantization*>(
           context->tensors[inputs->data[0]].quantization.params);
   const float input_scale = input_quant_params->scale->data[0];
   // Now dequantize bias values to float first, to adjust for the
   // normalization of channel scales.
   auto* bias_data = bias_tensor.data.i32;
   const int bias_size = NumElements(&bias_tensor);
   if (bias_size != per_channel_quant->num_scale_values) {
     TF_LITE_KERNEL_LOG(
         context, "Bias/channel scales number mismatch for bias tensor: %s",
         bias_tensor.name);
     return kTfLiteError;
   }
   std::vector<float> dequantized_bias;
   dequantized_bias.reserve(bias_size);
   for (int i = 0; i < bias_size; ++i) {
     const float dequantized_value =
         bias_data[i] * input_scale * per_channel_quant->scales_data[i];
     const float abs_dequantized_value = std::abs(dequantized_value);
     if (abs_dequantized_value > *bias_max) {
       *bias_max = abs_dequantized_value;
     }
     dequantized_bias.push_back(dequantized_value);
   }
   *bias_max = *bias_max * 8;
   *bias_min = -1 * *bias_max;
   // Now requantize the bias values to the new min/max values.
   std::vector<int> preprocessed_bias_data;
   preprocessed_bias_data.reserve(per_channel_quant->num_scale_values);
   for (int i = 0; i < bias_size; ++i) {
     preprocessed_bias_data.push_back(static_cast<int>(
         std::round(std::pow(2, 31) * (dequantized_bias[i] / *bias_max))));
   }
   // Add nodes for bias.
   const std::vector<int> bias_shape = {1, 1, 1, bias_size};
   auto* bias_data_node = graph_builder->AddConstNodeWithData(
       bias_shape.data(), reinterpret_cast<char*>(preprocessed_bias_data.data()),
       preprocessed_bias_data.size() * sizeof(preprocessed_bias_data[0]));
   if (bias_const_node) {
     *bias_const_node = bias_data_node;
   }
   graph_builder->AddTensorWithID(inputs->data[2], bias_data_node->GetID(), 0,
                                  /*overwrite=*/true);
   return kTfLiteOk;
 }

 TfLiteStatus Conv2dOpBuilder::InitializeWeightsNodes(
     const TfLiteIntArray* inputs, const TfLiteIntArray* outputs,
     TfLiteContext* context, const int input_depth) {
   const std::vector<int> quant_bound_shape = {1, 1, 1, 1};

   const auto& weights_tensor = context->tensors[inputs->data[1]];
   if (weights_tensor.allocation_type != kTfLiteMmapRo) {
     TF_LITE_KERNEL_LOG(
         context, "Weights tensor doesn't have correct allocation type: %s",
         weights_tensor.name);
     return kTfLiteError;
   }
   int weights_batch_size, weights_height_size, weights_width_size,
       weights_depth_size;
   // Hexagon lib expects the weight tensor in HWCN, TFLite uses NHWC.
   // Transpose NHWC -> HWCN
   GetDims(&weights_batch_size, &weights_height_size, &weights_width_size,
           &weights_depth_size, weights_tensor.dims);

   // Weights tensor could be int8 even for per-tensor quantization.
   // Therefore, we look at the number of scale values to check if it is
   // per-channel quantized.
   TfLiteAffineQuantization* weights_quant_params =
       reinterpret_cast<TfLiteAffineQuantization*>(
           weights_tensor.quantization.params);
   const bool is_per_channel_quant = weights_quant_params->scale->size > 1;

   // WEIGHTS DATA.
   OpBuilder* weights_data_node = nullptr;
   if (op_node_.op_type == OP_Supernode_8x8p32to8) {
     // Hexagon lib expects the weight tensor in HWCN, TFLite uses NHWC.
     // Transpose NHWC -> HWCN
     weight_shape_ = {weights_height_size, weights_width_size,
                      weights_depth_size, weights_batch_size};
     RuntimeShape nhwc_shape({weights_batch_size, weights_height_size,
                              weights_width_size, weights_depth_size});
     RuntimeShape hwcn_shape({weights_height_size, weights_width_size,
                              weights_depth_size, weights_batch_size});
     std::vector<uint8_t> hwcn(NumElements(&weights_tensor));
     TransposeParams transpose_params;
     transpose_params.perm_count = 4;
     transpose_params.perm[0] = 1;
     transpose_params.perm[1] = 2;
     transpose_params.perm[2] = 3;
     transpose_params.perm[3] = 0;
     // TODO(b/151103818): Try merging Transpose & bit flip.
     if (weights_tensor.type == kTfLiteInt8) {
       optimized_ops::Transpose<int8_t>(transpose_params, nhwc_shape,
                                        weights_tensor.data.int8, hwcn_shape,
                                        reinterpret_cast<int8_t*>(hwcn.data()));
       // Flip bits on the weight values so that the int8 values are treated
       // as uint8.
       for (int i = 0; i < hwcn.size(); ++i) {
         hwcn[i] = hwcn[i] ^ k8BitSignFlipConstant;
       }
     } else {
       optimized_ops::Transpose<uint8_t>(transpose_params, nhwc_shape,
                                         weights_tensor.data.uint8, hwcn_shape,
                                         hwcn.data());
     }
     weights_data_node = graph_builder_->AddConstNodeWithData(
         weight_shape_.data(), reinterpret_cast<char*>(hwcn.data()),
         hwcn.size() * sizeof(hwcn[0]));
   } else if (op_node_.op_type == OP_DepthwiseSupernode_8x8p32to8) {
     // Hexagon treats depthwise conv like tf.nn.depthwise_conv2d, where the
     // expected filter shape is [fh,fw,din,dmul].
     // The data itself will remain the same, since TFLite's representation is
     // just a 'flattening' of Hexagon's version.
     const int channel_multiplier = weights_depth_size / input_depth;
     weight_shape_ = {weights_height_size, weights_width_size, input_depth,
                      channel_multiplier};

     if (weights_tensor.type == kTfLiteInt8) {
       // Flip bits on the weight values so that the int8 values are treated
       // as uint8.
       std::vector<uint8_t> converted_data(NumElements(&weights_tensor));
       for (int i = 0; i < converted_data.size(); ++i) {
         converted_data[i] = weights_tensor.data.int8[i] ^ k8BitSignFlipConstant;
       }
       weights_data_node = graph_builder_->AddConstNodeWithData(
           weight_shape_.data(), reinterpret_cast<char*>(converted_data.data()),
           converted_data.size() * sizeof(converted_data[0]));
     } else {
       weights_data_node = graph_builder_->AddConstNodeWithData(
           weight_shape_.data(), weights_tensor.data.raw,
           NumElements(&weights_tensor) * sizeof(weights_tensor.data.uint8[0]));
     }
   }
   graph_builder_->AddTensorWithID(inputs->data[1], weights_data_node->GetID(),
                                   0, /*overwrite=*/true);

   // WEIGHTS QUANTIZATION.
   float weights_min = 0;
   float weights_max = 0;
   if (is_per_channel_quant) {
     ProcessPerChannelQuantizedWeights(inputs, outputs, context, &weights_min,
                                       &weights_max, graph_builder_,
                                       &per_channel_quant_);
   } else {
     TF_LITE_ENSURE_STATUS(ComputeMinAndMaxQuantValues(
         weights_tensor, &weights_min, &weights_max));
   }
   weights_min_node_ = graph_builder_->AddConstNodeWithData(
       quant_bound_shape.data(), reinterpret_cast<char*>(&weights_min),
       sizeof(weights_min));
   weights_max_node_ = graph_builder_->AddConstNodeWithData(
       quant_bound_shape.data(), reinterpret_cast<char*>(&weights_max),
       sizeof(weights_max));

   return kTfLiteOk;
 }

 TfLiteStatus Conv2dOpBuilder::InitializeBiasNodes(const TfLiteIntArray* inputs,
                                                   const TfLiteIntArray* outputs,
                                                   TfLiteContext* context) {
   const std::vector<int> quant_bound_shape = {1, 1, 1, 1};

   const auto& bias_tensor = context->tensors[inputs->data[2]];

   float bias_min = 0;
   float bias_max = 0;
   if (per_channel_quant_.channel_scales_node != nullptr) {
     ProcessPerChannelQuantizedBias(inputs, outputs, context, &bias_min,
                                    &bias_max, graph_builder_,
                                    &per_channel_quant_);
   } else {
     auto* bias_data_node =
         graph_builder_->AddConstNodeWithData(inputs->data[2], bias_tensor);
     graph_builder_->AddTensorWithID(inputs->data[2], bias_data_node->GetID(), 0,
                                     /*overwrite=*/true);
     TF_LITE_ENSURE_STATUS(
         ComputeMinAndMaxQuantValues(bias_tensor, &bias_min, &bias_max));
   }

   bias_min_node_ = graph_builder_->AddConstNodeWithData(
       quant_bound_shape.data(), reinterpret_cast<char*>(&bias_min),
       sizeof(bias_min));
   bias_max_node_ = graph_builder_->AddConstNodeWithData(
       quant_bound_shape.data(), reinterpret_cast<char*>(&bias_max),
       sizeof(bias_max));

   return kTfLiteOk;
 }

 }  // namespace hexagon
 }  // namespace delegates
 }  // namespace tflite
	/* 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 <stdint.h>

	#include <algorithm>
	#include <cmath>
	#include <limits>
	#include <vector>

	#include "tensorflow/lite/c/builtin_op_data.h"
	#include "tensorflow/lite/c/common.h"
	#include "tensorflow/lite/delegates/hexagon/builders/conv_2d_builder.h"
	#include "tensorflow/lite/delegates/hexagon/hexagon_nn/hexagon_nn.h"
	#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
	#include "tensorflow/lite/kernels/kernel_util.h"

	namespace tflite {
	namespace delegates {
	namespace hexagon {
	namespace {

	constexpr uint8_t k8BitSignFlipConstant = 0x80;
	// 1/1024 ~ 0.0009766 is a restriction set by Hexagon's kernels.
	// TODO(b/151103818): Figure out a way to retrieve this constant reliably.
	constexpr float kHexagonMinRelativeScale = 0.0009766f;

	} // namespace

	TfLiteStatus ProcessPerChannelQuantizedWeights(
	const TfLiteIntArray* inputs, const TfLiteIntArray* outputs,
	TfLiteContext* context, float* weights_min, float* weights_max,
	GraphBuilder* graph_builder, PerChannelQuantData* per_channel_quant) {
	if (!per_channel_quant) return kTfLiteError;
	const auto& weights_tensor = context->tensors[inputs->data[1]];
	TfLiteAffineQuantization* weights_quant_params =
	reinterpret_cast<TfLiteAffineQuantization*>(
	weights_tensor.quantization.params);

	// Retrieve channel scales.
	per_channel_quant->num_scale_values = weights_quant_params->scale->size;
	// Normalize the scales as expected by Hexagon.
	per_channel_quant->scales_data = weights_quant_params->scale->data;
	std::vector<float> normalized_scales;
	normalized_scales.reserve(per_channel_quant->num_scale_values);
	float scale_max = 0.0;
	for (int i = 0; i < per_channel_quant->num_scale_values; ++i) {
	normalized_scales.push_back(per_channel_quant->scales_data[i]);
	if (per_channel_quant->scales_data[i] > scale_max) {
	scale_max = per_channel_quant->scales_data[i];
	}
	}
	if (scale_max == 0.0) {
	TF_LITE_KERNEL_LOG(context, "Scale max is zero for: %s",
	weights_tensor.name);
	return kTfLiteError;
	}
	for (int i = 0; i < per_channel_quant->num_scale_values; ++i) {
	normalized_scales[i] =
	std::max(normalized_scales[i] / scale_max, kHexagonMinRelativeScale);
	}
	// Add node for channel scales data.
	const std::vector<int> scales_shape = {1, 1, 1,
	per_channel_quant->num_scale_values};
	per_channel_quant->channel_scales_node = graph_builder->AddConstNodeWithData(
	scales_shape.data(), reinterpret_cast<char*>(normalized_scales.data()),
	normalized_scales.size() * sizeof(normalized_scales[0]));
	weights_min = -128 scale_max;
	weights_max = 127 scale_max;
	return kTfLiteOk;
	}

	TfLiteStatus ProcessPerChannelQuantizedBias(
	const TfLiteIntArray* inputs, const TfLiteIntArray* outputs,
	TfLiteContext* context, float* bias_min, float* bias_max,
	GraphBuilder* graph_builder, PerChannelQuantData* per_channel_quant,
	OpBuilder** bias_const_node) {
	const auto& bias_tensor = context->tensors[inputs->data[2]];

	const TfLiteAffineQuantization* input_quant_params =
	static_cast<const TfLiteAffineQuantization*>(
	context->tensors[inputs->data[0]].quantization.params);
	const float input_scale = input_quant_params->scale->data[0];
	// Now dequantize bias values to float first, to adjust for the
	// normalization of channel scales.
	auto* bias_data = bias_tensor.data.i32;
	const int bias_size = NumElements(&bias_tensor);
	if (bias_size != per_channel_quant->num_scale_values) {
	TF_LITE_KERNEL_LOG(
	context, "Bias/channel scales number mismatch for bias tensor: %s",
	bias_tensor.name);
	return kTfLiteError;
	}
	std::vector<float> dequantized_bias;
	dequantized_bias.reserve(bias_size);
	for (int i = 0; i < bias_size; ++i) {
	const float dequantized_value =
	bias_data[i] * input_scale * per_channel_quant->scales_data[i];
	const float abs_dequantized_value = std::abs(dequantized_value);
	if (abs_dequantized_value > *bias_max) {
	*bias_max = abs_dequantized_value;
	}
	dequantized_bias.push_back(dequantized_value);
	}
	bias_max = bias_max * 8;
	bias_min = -1 *bias_max;
	// Now requantize the bias values to the new min/max values.
	std::vector<int> preprocessed_bias_data;
	preprocessed_bias_data.reserve(per_channel_quant->num_scale_values);
	for (int i = 0; i < bias_size; ++i) {
	preprocessed_bias_data.push_back(static_cast<int>(
	std::round(std::pow(2, 31) * (dequantized_bias[i] / *bias_max))));
	}
	// Add nodes for bias.
	const std::vector<int> bias_shape = {1, 1, 1, bias_size};
	auto* bias_data_node = graph_builder->AddConstNodeWithData(
	bias_shape.data(), reinterpret_cast<char*>(preprocessed_bias_data.data()),
	preprocessed_bias_data.size() * sizeof(preprocessed_bias_data[0]));
	if (bias_const_node) {
	*bias_const_node = bias_data_node;
	}
	graph_builder->AddTensorWithID(inputs->data[2], bias_data_node->GetID(), 0,
	/overwrite=/true);
	return kTfLiteOk;
	}

	TfLiteStatus Conv2dOpBuilder::InitializeWeightsNodes(
	const TfLiteIntArray* inputs, const TfLiteIntArray* outputs,
	TfLiteContext* context, const int input_depth) {
	const std::vector<int> quant_bound_shape = {1, 1, 1, 1};

	const auto& weights_tensor = context->tensors[inputs->data[1]];
	if (weights_tensor.allocation_type != kTfLiteMmapRo) {
	TF_LITE_KERNEL_LOG(
	context, "Weights tensor doesn't have correct allocation type: %s",
	weights_tensor.name);
	return kTfLiteError;
	}
	int weights_batch_size, weights_height_size, weights_width_size,
	weights_depth_size;
	// Hexagon lib expects the weight tensor in HWCN, TFLite uses NHWC.
	// Transpose NHWC -> HWCN
	GetDims(&weights_batch_size, &weights_height_size, &weights_width_size,
	&weights_depth_size, weights_tensor.dims);

	// Weights tensor could be int8 even for per-tensor quantization.
	// Therefore, we look at the number of scale values to check if it is
	// per-channel quantized.
	TfLiteAffineQuantization* weights_quant_params =
	reinterpret_cast<TfLiteAffineQuantization*>(
	weights_tensor.quantization.params);
	const bool is_per_channel_quant = weights_quant_params->scale->size > 1;

	// WEIGHTS DATA.
	OpBuilder* weights_data_node = nullptr;
	if (op_node_.op_type == OP_Supernode_8x8p32to8) {
	// Hexagon lib expects the weight tensor in HWCN, TFLite uses NHWC.
	// Transpose NHWC -> HWCN
	weight_shape_ = {weights_height_size, weights_width_size,
	weights_depth_size, weights_batch_size};
	RuntimeShape nhwc_shape({weights_batch_size, weights_height_size,
	weights_width_size, weights_depth_size});
	RuntimeShape hwcn_shape({weights_height_size, weights_width_size,
	weights_depth_size, weights_batch_size});
	std::vector<uint8_t> hwcn(NumElements(&weights_tensor));
	TransposeParams transpose_params;
	transpose_params.perm_count = 4;
	transpose_params.perm[0] = 1;
	transpose_params.perm[1] = 2;
	transpose_params.perm[2] = 3;
	transpose_params.perm[3] = 0;
	// TODO(b/151103818): Try merging Transpose & bit flip.
	if (weights_tensor.type == kTfLiteInt8) {
	optimized_ops::Transpose<int8_t>(transpose_params, nhwc_shape,
	weights_tensor.data.int8, hwcn_shape,
	reinterpret_cast<int8_t*>(hwcn.data()));
	// Flip bits on the weight values so that the int8 values are treated
	// as uint8.
	for (int i = 0; i < hwcn.size(); ++i) {
	hwcn[i] = hwcn[i] ^ k8BitSignFlipConstant;
	}
	} else {
	optimized_ops::Transpose<uint8_t>(transpose_params, nhwc_shape,
	weights_tensor.data.uint8, hwcn_shape,
	hwcn.data());
	}
	weights_data_node = graph_builder_->AddConstNodeWithData(
	weight_shape_.data(), reinterpret_cast<char*>(hwcn.data()),
	hwcn.size() * sizeof(hwcn[0]));
	} else if (op_node_.op_type == OP_DepthwiseSupernode_8x8p32to8) {
	// Hexagon treats depthwise conv like tf.nn.depthwise_conv2d, where the
	// expected filter shape is [fh,fw,din,dmul].
	// The data itself will remain the same, since TFLite's representation is
	// just a 'flattening' of Hexagon's version.
	const int channel_multiplier = weights_depth_size / input_depth;
	weight_shape_ = {weights_height_size, weights_width_size, input_depth,
	channel_multiplier};

	if (weights_tensor.type == kTfLiteInt8) {
	// Flip bits on the weight values so that the int8 values are treated
	// as uint8.
	std::vector<uint8_t> converted_data(NumElements(&weights_tensor));
	for (int i = 0; i < converted_data.size(); ++i) {
	converted_data[i] = weights_tensor.data.int8[i] ^ k8BitSignFlipConstant;
	}
	weights_data_node = graph_builder_->AddConstNodeWithData(
	weight_shape_.data(), reinterpret_cast<char*>(converted_data.data()),
	converted_data.size() * sizeof(converted_data[0]));
	} else {
	weights_data_node = graph_builder_->AddConstNodeWithData(
	weight_shape_.data(), weights_tensor.data.raw,
	NumElements(&weights_tensor) * sizeof(weights_tensor.data.uint8[0]));
	}
	}
	graph_builder_->AddTensorWithID(inputs->data[1], weights_data_node->GetID(),
	0, /overwrite=/true);

	// WEIGHTS QUANTIZATION.
	float weights_min = 0;
	float weights_max = 0;
	if (is_per_channel_quant) {
	ProcessPerChannelQuantizedWeights(inputs, outputs, context, &weights_min,
	&weights_max, graph_builder_,
	&per_channel_quant_);
	} else {
	TF_LITE_ENSURE_STATUS(ComputeMinAndMaxQuantValues(
	weights_tensor, &weights_min, &weights_max));
	}
	weights_min_node_ = graph_builder_->AddConstNodeWithData(
	quant_bound_shape.data(), reinterpret_cast<char*>(&weights_min),
	sizeof(weights_min));
	weights_max_node_ = graph_builder_->AddConstNodeWithData(
	quant_bound_shape.data(), reinterpret_cast<char*>(&weights_max),
	sizeof(weights_max));

	return kTfLiteOk;
	}

	TfLiteStatus Conv2dOpBuilder::InitializeBiasNodes(const TfLiteIntArray* inputs,
	const TfLiteIntArray* outputs,
	TfLiteContext* context) {
	const std::vector<int> quant_bound_shape = {1, 1, 1, 1};

	const auto& bias_tensor = context->tensors[inputs->data[2]];

	float bias_min = 0;
	float bias_max = 0;
	if (per_channel_quant_.channel_scales_node != nullptr) {
	ProcessPerChannelQuantizedBias(inputs, outputs, context, &bias_min,
	&bias_max, graph_builder_,
	&per_channel_quant_);
	} else {
	auto* bias_data_node =
	graph_builder_->AddConstNodeWithData(inputs->data[2], bias_tensor);
	graph_builder_->AddTensorWithID(inputs->data[2], bias_data_node->GetID(), 0,
	/overwrite=/true);
	TF_LITE_ENSURE_STATUS(
	ComputeMinAndMaxQuantValues(bias_tensor, &bias_min, &bias_max));
	}

	bias_min_node_ = graph_builder_->AddConstNodeWithData(
	quant_bound_shape.data(), reinterpret_cast<char*>(&bias_min),
	sizeof(bias_min));
	bias_max_node_ = graph_builder_->AddConstNodeWithData(
	quant_bound_shape.data(), reinterpret_cast<char*>(&bias_max),
	sizeof(bias_max));

	return kTfLiteOk;
	}

	} // namespace hexagon
	} // namespace delegates
	} // namespace tflite