wehub-resource-sync
303 lines
12 KiB
C++
303 lines
12 KiB
C++
/* Copyright 2020 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/xnnpack/prelu_tester.h"
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#include <algorithm>
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#include <array>
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#include <cstddef>
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#include <cstdint>
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#include <functional>
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#include <memory>
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#include <numeric>
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#include <random>
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#include <vector>
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#include <gtest/gtest.h>
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#include "fp16.h" // from @FP16
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#include "flatbuffers/buffer.h" // from @flatbuffers
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#include "flatbuffers/flatbuffer_builder.h" // from @flatbuffers
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#include "flatbuffers/string.h" // from @flatbuffers
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#include "tensorflow/compiler/mlir/lite/schema/schema_conversion_utils.h"
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#include "tensorflow/lite/core/interpreter_builder.h"
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#include "tensorflow/lite/core/kernels/register.h"
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#include "tensorflow/lite/delegates/xnnpack/test_util.h"
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#include "tensorflow/lite/delegates/xnnpack/xnnpack_delegate.h"
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/version.h"
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namespace tflite {
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namespace xnnpack {
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void PreluTester::Test(TfLiteDelegate* delegate) const {
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if (INT8ChannelWiseWeights()) {
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ASSERT_FALSE(SlopeShape().empty());
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}
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto input_rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f),
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std::ref(rng));
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std::vector<char> buffer = CreateTfLiteModel();
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const Model* model = GetModel(buffer.data());
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std::unique_ptr<Interpreter> delegate_interpreter;
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ASSERT_EQ(
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InterpreterBuilder(
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model,
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::tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates())(
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&delegate_interpreter),
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kTfLiteOk);
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std::unique_ptr<Interpreter> default_interpreter;
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ASSERT_EQ(
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InterpreterBuilder(
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model,
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::tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates())(
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&default_interpreter),
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kTfLiteOk);
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ASSERT_TRUE(delegate_interpreter);
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ASSERT_TRUE(default_interpreter);
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ASSERT_EQ(delegate_interpreter->inputs().size(), 1);
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ASSERT_EQ(default_interpreter->inputs().size(), 1);
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ASSERT_EQ(delegate_interpreter->outputs().size(), 1);
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ASSERT_EQ(default_interpreter->outputs().size(), 1);
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ASSERT_EQ(delegate_interpreter->AllocateTensors(), kTfLiteOk);
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ASSERT_EQ(default_interpreter->AllocateTensors(), kTfLiteOk);
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ASSERT_EQ(delegate_interpreter->ModifyGraphWithDelegate(delegate), kTfLiteOk);
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if (weights_cache_ != nullptr) {
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TfLiteXNNPackDelegateWeightsCacheFinalizeHard(weights_cache_);
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}
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float* default_input_data = default_interpreter->typed_input_tensor<float>(0);
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std::generate_n(default_input_data, ComputeSize(InputShape()),
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std::ref(input_rng));
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float* xnnpack_input_data =
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delegate_interpreter->typed_input_tensor<float>(0);
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std::copy_n(default_input_data, ComputeSize(InputShape()),
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xnnpack_input_data);
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ASSERT_EQ(default_interpreter->Invoke(), kTfLiteOk);
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ASSERT_EQ(delegate_interpreter->Invoke(), kTfLiteOk);
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float* default_output_data =
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default_interpreter->typed_output_tensor<float>(0);
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float* xnnpack_output_data =
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delegate_interpreter->typed_output_tensor<float>(0);
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for (size_t i = 0; i < ComputeSize(OutputShape()); i++) {
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ASSERT_EQ(default_output_data[i], xnnpack_output_data[i]);
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}
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}
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std::vector<char> PreluTester::CreateTfLiteModel() const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto slope_rng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.5f),
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std::ref(rng));
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flatbuffers::FlatBufferBuilder builder;
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std::vector<flatbuffers::Offset<OperatorCode>> operator_codes{
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{CreateOperatorCode(builder, BuiltinOperator_PRELU)}};
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if (FP16Weights() || INT8Weights() || INT8ChannelWiseWeights()) {
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operator_codes.emplace_back(
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CreateOperatorCode(builder, BuiltinOperator_DEQUANTIZE));
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} else if (SparseWeights()) {
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operator_codes.emplace_back(
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CreateOperatorCode(builder, BuiltinOperator_DENSIFY));
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}
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std::vector<flatbuffers::Offset<Buffer>> buffers{{
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CreateBuffer(builder, builder.CreateVector({})),
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}};
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std::vector<float> slope_scales;
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std::vector<int64_t> slope_zero_points;
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int32_t slope_quantized_dimension = 0;
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if (FP16Weights()) {
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std::vector<uint16_t> slope_data(ComputeSize(SlopeShape()));
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std::generate(slope_data.begin(), slope_data.end(),
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std::bind(fp16_ieee_from_fp32_value, slope_rng));
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buffers.push_back(CreateBuffer(
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builder, builder.CreateVector(
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reinterpret_cast<const uint8_t*>(slope_data.data()),
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sizeof(uint16_t) * slope_data.size())));
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} else {
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std::vector<float> slope_data(ComputeSize(SlopeShape()));
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std::generate(slope_data.begin(), slope_data.end(), slope_rng);
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if (INT8Weights()) {
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std::vector<int8_t> quantized_slope_data(slope_data.size());
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slope_scales.resize(1, GetInt8QuantizationScale(slope_data));
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slope_zero_points.resize(1, 0);
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std::transform(
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slope_data.begin(), slope_data.end(), quantized_slope_data.begin(),
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std::bind(QuantizeInt8, std::placeholders::_1, 0, slope_scales[0]));
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buffers.push_back(CreateBuffer(
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builder,
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builder.CreateVector(
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reinterpret_cast<const uint8_t*>(quantized_slope_data.data()),
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sizeof(int8_t) * quantized_slope_data.size())));
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} else if (INT8ChannelWiseWeights()) {
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std::vector<int8_t> quantized_slope_data(slope_data.size());
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slope_quantized_dimension = static_cast<int32_t>(SlopeShape().size()) - 1;
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const int32_t num_scales = SlopeShape()[slope_quantized_dimension];
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slope_scales = GetInt8QuantizationScalePerChannel(
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slope_data.data(), slope_quantized_dimension, SlopeShape());
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slope_zero_points.resize(num_scales, 0);
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QuantizeInt8PerChannel(slope_scales.data(), slope_zero_points.data(),
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slope_quantized_dimension, slope_data.data(),
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quantized_slope_data.data(), SlopeShape());
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buffers.push_back(CreateBuffer(
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builder,
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builder.CreateVector(
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reinterpret_cast<const uint8_t*>(quantized_slope_data.data()),
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sizeof(int8_t) * quantized_slope_data.size())));
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} else {
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buffers.push_back(CreateBuffer(
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builder, builder.CreateVector(
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reinterpret_cast<const uint8_t*>(slope_data.data()),
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sizeof(float) * slope_data.size())));
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}
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}
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std::vector<flatbuffers::Offset<Tensor>> tensors;
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std::vector<flatbuffers::Offset<Operator>> operators;
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if (FP16Weights()) {
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tensors.emplace_back(CreateTensor(
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builder,
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builder.CreateVector<int32_t>(SlopeShape().data(), SlopeShape().size()),
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TensorType_FLOAT16, /*buffer=*/1));
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} else if (INT8Weights() || INT8ChannelWiseWeights()) {
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tensors.emplace_back(CreateTensor(
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builder,
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builder.CreateVector<int32_t>(SlopeShape().data(), SlopeShape().size()),
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TensorType_INT8, /*buffer=*/1, /*name=*/0,
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CreateQuantizationParameters(
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builder, /*min=*/0, /*max=*/0,
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builder.CreateVector<float>(slope_scales),
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builder.CreateVector<int64_t>(slope_zero_points),
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/*details_type=*/QuantizationDetails_NONE,
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/*details=*/0, slope_quantized_dimension)));
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} else if (SparseWeights()) {
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const int dims_count = SlopeShape().size();
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std::vector<flatbuffers::Offset<DimensionMetadata>> dim_metadata(
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dims_count);
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std::vector<int> traversal_order(dims_count);
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for (int i = 0; i < dims_count; i++) {
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traversal_order[i] = i;
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dim_metadata[i] = CreateDimensionMetadata(builder, DimensionType_DENSE,
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SlopeShape()[i]);
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}
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const flatbuffers::Offset<SparsityParameters> sparsity_param =
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CreateSparsityParameters(builder, builder.CreateVector(traversal_order),
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0, builder.CreateVector(dim_metadata));
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tensors.emplace_back(CreateTensor(
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builder,
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builder.CreateVector<int32_t>(SlopeShape().data(), SlopeShape().size()),
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TensorType_FLOAT32, /*buffer=*/1, /*name=*/0, /*quantization=*/0,
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/*is_variable=*/false, /*sparsity=*/sparsity_param));
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}
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if (FP16Weights() || INT8Weights() || INT8ChannelWiseWeights()) {
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const std::array<int32_t, 1> dequantize_inputs{{0}};
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const std::array<int32_t, 1> dequantize_outputs{{2}};
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operators.emplace_back(CreateOperator(
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builder, /*opcode_index=*/1,
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builder.CreateVector<int32_t>(dequantize_inputs.data(),
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dequantize_inputs.size()),
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builder.CreateVector<int32_t>(dequantize_outputs.data(),
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dequantize_outputs.size())));
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} else if (SparseWeights()) {
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const std::array<int32_t, 1> densify_inputs{{0}};
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const std::array<int32_t, 1> densify_outputs{{2}};
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operators.emplace_back(
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CreateOperator(builder, /*opcode_index=*/1,
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builder.CreateVector<int32_t>(densify_inputs.data(),
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densify_inputs.size()),
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builder.CreateVector<int32_t>(densify_outputs.data(),
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densify_outputs.size())));
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}
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tensors.emplace_back(CreateTensor(
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builder,
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builder.CreateVector<int32_t>(InputShape().data(), InputShape().size()),
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TensorType_FLOAT32));
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tensors.emplace_back(CreateTensor(
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builder,
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builder.CreateVector<int32_t>(SlopeShape().data(), SlopeShape().size()),
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TensorType_FLOAT32,
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/*buffer=*/
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(FP16Weights() || INT8Weights() || INT8ChannelWiseWeights() ||
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SparseWeights())
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? 0
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: 1));
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tensors.emplace_back(CreateTensor(
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builder,
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builder.CreateVector<int32_t>(OutputShape().data(), OutputShape().size()),
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TensorType_FLOAT32));
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const std::array<int32_t, 2> op_inputs{
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{static_cast<int>(tensors.size()) - 3,
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static_cast<int>(tensors.size()) - 2}};
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const std::array<int32_t, 1> op_outputs{
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{static_cast<int>(tensors.size()) - 1}};
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operators.emplace_back(CreateOperator(
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builder, /*opcode_index=*/0,
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builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
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builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())));
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const std::array<int32_t, 1> subgraph_inputs{
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{static_cast<int32_t>(tensors.size() - 3)}};
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const std::array<int32_t, 1> subgraph_outputs{
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{static_cast<int32_t>(tensors.size()) - 1}};
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flatbuffers::Offset<SubGraph> subgraph = CreateSubGraph(
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builder, builder.CreateVector(tensors.data(), tensors.size()),
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builder.CreateVector<int32_t>(subgraph_inputs.data(),
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subgraph_inputs.size()),
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builder.CreateVector<int32_t>(subgraph_outputs.data(),
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subgraph_outputs.size()),
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builder.CreateVector(operators.data(), operators.size()));
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flatbuffers::Offset<flatbuffers::String> description =
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builder.CreateString("PReLU model");
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flatbuffers::Offset<Model> model_buffer = CreateModel(
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builder, TFLITE_SCHEMA_VERSION,
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builder.CreateVector(operator_codes.data(), operator_codes.size()),
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builder.CreateVector(&subgraph, 1), description,
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builder.CreateVector(buffers.data(), buffers.size()));
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builder.Finish(model_buffer);
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return std::vector<char>(builder.GetBufferPointer(),
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builder.GetBufferPointer() + builder.GetSize());
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}
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int32_t PreluTester::ComputeSize(const std::vector<int32_t>& shape) {
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return std::accumulate(shape.cbegin(), shape.cend(), 1,
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std::multiplies<int32_t>());
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}
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} // namespace xnnpack
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} // namespace tflite
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