wehub-resource-sync
252 lines
9.3 KiB
C++
252 lines
9.3 KiB
C++
/* Copyright 2021 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/reduce_tester.h"
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#include <array>
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#include <cstddef>
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#include <cstdint>
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#include <cstdlib>
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#include <functional>
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#include <limits>
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#include <memory>
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#include <numeric>
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#include <random>
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#include <string>
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#include <type_traits>
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#include <vector>
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#include <gtest/gtest.h>
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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/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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template <class T>
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struct UniformDistribution {
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static std::uniform_int_distribution<int32_t> Get() {
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return std::uniform_int_distribution<int32_t>(
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std::numeric_limits<T>::min(), std::numeric_limits<T>::max());
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}
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};
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template <>
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struct UniformDistribution<float> {
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static std::uniform_real_distribution<float> Get() { return {}; }
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};
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template <class T>
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void ReduceTester::Test(Interpreter* delegate_interpreter,
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Interpreter* default_interpreter) 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 input_rng = std::bind(UniformDistribution<T>::Get(), std::ref(rng));
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T* default_input_data = default_interpreter->typed_input_tensor<T>(0);
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std::generate_n(default_input_data, InputSize(), std::ref(input_rng));
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T* delegate_input_data = delegate_interpreter->typed_input_tensor<T>(0);
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std::copy_n(default_input_data, InputSize(), delegate_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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T* default_output_data = default_interpreter->typed_output_tensor<T>(0);
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T* delegate_output_data = delegate_interpreter->typed_output_tensor<T>(0);
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const int32_t output_size = OutputSize();
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if constexpr (std::is_floating_point_v<T>) {
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for (size_t i = 0; i < output_size; i++) {
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ASSERT_NEAR(
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default_output_data[i], delegate_output_data[i],
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std::numeric_limits<float>::epsilon() *
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std::max(std::abs(default_output_data[i]) * RelativeTolerance(),
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1.0f));
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}
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} else {
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for (size_t i = 0; i < output_size; i++) {
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ASSERT_LE(std::abs(default_output_data[i] - delegate_output_data[i]), 1)
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<< "default " << +default_output_data[i] << ", delegate "
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<< +delegate_output_data[i] << " at index " << i << " / "
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<< output_size;
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}
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}
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}
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void ReduceTester::Test(tflite::BuiltinOperator reduce_op,
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TfLiteDelegate* delegate) const {
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std::vector<char> buffer = CreateTfLiteModel(reduce_op);
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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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switch (Quantization()) {
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case Quantization::None:
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Test<float>(delegate_interpreter.get(), default_interpreter.get());
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break;
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case Quantization::Signed:
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Test<int8_t>(delegate_interpreter.get(), default_interpreter.get());
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break;
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case Quantization::Unsigned:
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Test<uint8_t>(delegate_interpreter.get(), default_interpreter.get());
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break;
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}
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}
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namespace {
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TensorType GetTensorType(enum ReduceTester::Quantization q) {
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switch (q) {
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case ReduceTester::Quantization::None:
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return TensorType_FLOAT32;
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case ReduceTester::Quantization::Signed:
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return TensorType_INT8;
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case ReduceTester::Quantization::Unsigned:
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return TensorType_UINT8;
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}
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}
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} // namespace
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std::vector<char> ReduceTester::CreateTfLiteModel(
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tflite::BuiltinOperator reduce_op) const {
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flatbuffers::FlatBufferBuilder builder;
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flatbuffers::Offset<OperatorCode> operator_code =
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CreateOperatorCode(builder, reduce_op);
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const std::array<flatbuffers::Offset<Buffer>, 2> buffers{{
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CreateBuffer(builder, builder.CreateVector({})),
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CreateBuffer(builder, builder.CreateVector(
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reinterpret_cast<const uint8_t*>(Axes().data()),
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sizeof(int32_t) * Axes().size())),
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}};
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const std::vector<int32_t> output_shape = OutputShape();
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const std::array<int32_t, 1> axes_shape{
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{static_cast<int32_t>(Axes().size())}};
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const flatbuffers::Offset<QuantizationParameters> input_quantization =
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Quantization() == Quantization::None
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? 0
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: CreateQuantizationParameters(
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builder, /*min=*/0, /*max=*/0,
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builder.CreateVector<float>({InputScale()}),
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builder.CreateVector<int64_t>({InputZeroPoint()}));
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const flatbuffers::Offset<QuantizationParameters> output_quantization =
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Quantization() == Quantization::None
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? 0
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: CreateQuantizationParameters(
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builder, /*min=*/0, /*max=*/0,
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builder.CreateVector<float>({OutputScale()}),
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builder.CreateVector<int64_t>({OutputZeroPoint()}));
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const std::array<flatbuffers::Offset<Tensor>, 3> tensors{{
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CreateTensor(builder,
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builder.CreateVector<int32_t>(InputShape().data(),
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InputShape().size()),
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GetTensorType(Quantization()), /*buffer=*/0, /*name=*/0,
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input_quantization),
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CreateTensor(
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builder,
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builder.CreateVector<int32_t>(axes_shape.data(), axes_shape.size()),
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TensorType_INT32, /*buffer=*/1),
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CreateTensor(builder,
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builder.CreateVector<int32_t>(output_shape.data(),
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output_shape.size()),
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GetTensorType(Quantization()), /*buffer=*/0, /*name=*/0,
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output_quantization),
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}};
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const flatbuffers::Offset<ReducerOptions> reducer_options =
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CreateReducerOptions(builder, KeepDims());
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const std::array<int32_t, 2> op_inputs{{0, 1}};
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const std::array<int32_t, 1> op_outputs{{2}};
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flatbuffers::Offset<Operator> op = 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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tflite::BuiltinOptions_ReducerOptions, reducer_options.Union());
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const std::array<int32_t, 1> subgraph_inputs{{0}};
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const std::array<int32_t, 1> subgraph_outputs{{2}};
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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(&op, 1));
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std::string model_description = "Reduce model";
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if (Quantization() != Quantization::None) {
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model_description = "Quantized reduce model";
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}
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flatbuffers::Offset<flatbuffers::String> description =
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builder.CreateString(model_description);
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flatbuffers::Offset<Model> model_buffer = CreateModel(
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builder, TFLITE_SCHEMA_VERSION, builder.CreateVector(&operator_code, 1),
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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 ReduceTester::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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