5f8e3e8d54
min/max to the op. PiperOrigin-RevId: 323644369 Change-Id: I96cfe9cb506662111c7545194f998306b2b293fd
126 lines
5.3 KiB
C++
126 lines
5.3 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/delegates/hexagon/builders/arithmetic_builder.h"
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#include <stdint.h>
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#include <limits>
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#include "hexagon/hexagon_nn_ops.h"
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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/delegates/hexagon/hexagon_nn/hexagon_nn.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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namespace tflite {
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namespace delegates {
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namespace hexagon {
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TfLiteStatus ArithmeticOpBuilder::PopulateSubGraph(
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const TfLiteIntArray* inputs, const TfLiteIntArray* outputs,
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TfLiteContext* context) {
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// First input data tensor.
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int tensor_id = inputs->data[0];
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const auto& input1_tensor = context->tensors[tensor_id];
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AddInput(graph_builder_->GetHexagonTensorId(tensor_id));
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// Second input data tensor.
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tensor_id = inputs->data[1];
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const auto& input2_tensor = context->tensors[tensor_id];
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AddInput(graph_builder_->GetHexagonTensorId(tensor_id));
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// Inputs min/max
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TF_LITE_ENSURE_STATUS(ComputeAndAddMinAndMax(context, input1_tensor));
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TF_LITE_ENSURE_STATUS(ComputeAndAddMinAndMax(context, input2_tensor));
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// Output details.
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TF_LITE_ENSURE_STATUS(ComputeMinAndMaxQuantValues(
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context->tensors[outputs->data[0]], &output_min_, &output_max_));
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auto* output_min_const = graph_builder_->AddConstNodeWithData(
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kScalarShape, reinterpret_cast<char*>(&output_min_), sizeof(output_min_));
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auto* output_max_const = graph_builder_->AddConstNodeWithData(
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kScalarShape, reinterpret_cast<char*>(&output_max_), sizeof(output_max_));
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int output_batch_size, output_height_size, output_width_size,
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output_depth_size;
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GetDims(&output_batch_size, &output_height_size, &output_width_size,
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&output_depth_size, context->tensors[outputs->data[0]].dims);
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if (op_node_.op_type == OP_QuantizedAdd_8p8to8 && output_max_ != 0) {
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// Hexagon's QuantizedAdd supports output min/max as input.
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AddInput(TensorID(output_min_const->GetID(), 0));
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AddInput(TensorID(output_max_const->GetID(), 0));
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}
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if (op_node_.op_type == OP_QuantizedMul_8x8to32) {
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const auto& math_out = AddOutput(sizeof(int), 4,
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{output_batch_size, output_height_size,
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output_width_size, output_depth_size});
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const auto& math_out_min = AddOutput(sizeof(float), 4, kScalarShape);
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const auto& math_out_max = AddOutput(sizeof(float), 4, kScalarShape);
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auto* requantize_op = graph_builder_->AddNode(GetTFLiteNodeID());
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requantize_op->SetOpType(OP_Requantize_32to8);
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requantize_op->AddInput(math_out);
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requantize_op->AddInput(math_out_min);
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requantize_op->AddInput(math_out_max);
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requantize_op->AddInput(TensorID(output_min_const->GetID(), 0));
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requantize_op->AddInput(TensorID(output_max_const->GetID(), 0));
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node_output_ =
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requantize_op->AddOutput(sizeof(uint8_t), 4,
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{output_batch_size, output_height_size,
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output_width_size, output_depth_size});
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requantize_op->AddOutput(sizeof(float), 4, kScalarShape);
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requantize_op->AddOutput(sizeof(float), 4, kScalarShape);
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} else {
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auto result_out = AddOutput(sizeof(uint8_t), 4,
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{output_batch_size, output_height_size,
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output_width_size, output_depth_size});
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auto result_min = AddOutput(sizeof(float), 4, kScalarShape);
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auto result_max = AddOutput(sizeof(float), 4, kScalarShape);
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auto* requantize_op = graph_builder_->AddNode(GetTFLiteNodeID());
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requantize_op->SetOpType(OP_Requantize_8to8);
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requantize_op->AddInput(result_out);
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requantize_op->AddInput(result_min);
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requantize_op->AddInput(result_max);
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requantize_op->AddInput(TensorID(output_min_const->GetID(), 0));
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requantize_op->AddInput(TensorID(output_max_const->GetID(), 0));
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node_output_ =
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requantize_op->AddOutput(sizeof(uint8_t), 4,
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{output_batch_size, output_height_size,
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output_width_size, output_depth_size});
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requantize_op->AddOutput(sizeof(float), 4, kScalarShape);
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requantize_op->AddOutput(sizeof(float), 4, kScalarShape);
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}
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return kTfLiteOk;
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}
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TfLiteStatus ArithmeticOpBuilder::RegisterOutputs(const TfLiteIntArray* outputs,
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TfLiteContext* context) {
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// Should be only 1 output.
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graph_builder_->AddTensorWithID(outputs->data[0], node_output_.first,
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node_output_.second);
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return kTfLiteOk;
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}
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ArithmeticOpBuilder::~ArithmeticOpBuilder() {}
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OpBuilder* CreateArithmeticBuilder(GraphBuilder* graph_builder, int op_type) {
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return new ArithmeticOpBuilder(graph_builder, op_type);
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}
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} // namespace hexagon
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} // namespace delegates
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} // namespace tflite
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