wehub-resource-sync
238 lines
9.0 KiB
C++
238 lines
9.0 KiB
C++
/* Copyright 2022 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/slice_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 <limits>
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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 "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 <typename T>
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std::function<T(std::mt19937&)> GetDist() {
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return std::uniform_int_distribution<int32_t>(std::numeric_limits<T>::min(),
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std::numeric_limits<T>::max());
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}
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template <>
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std::function<float(std::mt19937&)> GetDist() {
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return std::uniform_real_distribution<float>();
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}
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template <typename T>
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void SliceTester::Test(Interpreter* default_interpreter,
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Interpreter* delegate_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_distribution = GetDist<T>();
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auto input_rng = std::bind(input_distribution, 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, ComputeSize(InputShape()),
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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, ComputeSize(InputShape()),
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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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for (size_t i = 0; i < ComputeSize(OutputShape()); i++) {
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EXPECT_EQ(default_output_data[i], delegate_output_data[i]);
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}
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}
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void SliceTester::Test(TensorType tensor_type, TfLiteDelegate* delegate) const {
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ASSERT_EQ(InputShape().size(), Offsets().size());
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ASSERT_EQ(InputShape().size(), Sizes().size());
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for (size_t i = 0; i < InputShape().size(); i++) {
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ASSERT_GE(Offsets()[i], 0);
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ASSERT_LT(Offsets()[i], InputShape()[i]);
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if (Sizes()[i] < 0) {
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ASSERT_EQ(Sizes()[i], -1);
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ASSERT_EQ(InputShape()[i] - Offsets()[i], OutputShape()[i]);
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} else {
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ASSERT_GT(Sizes()[i], 0);
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ASSERT_LE(Sizes()[i], InputShape()[i]);
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ASSERT_EQ(Sizes()[i], OutputShape()[i]);
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}
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ASSERT_LE(Offsets()[i] + Sizes()[i], InputShape()[i]);
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}
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const std::vector<char> buffer = CreateTfLiteModel(tensor_type);
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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 (tensor_type) {
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case TensorType_FLOAT32:
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Test<float>(delegate_interpreter.get(), default_interpreter.get());
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break;
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case TensorType_INT8:
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Test<int8_t>(delegate_interpreter.get(), default_interpreter.get());
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break;
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case TensorType_UINT8:
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Test<uint8_t>(delegate_interpreter.get(), default_interpreter.get());
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break;
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default:
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GTEST_FAIL();
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}
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}
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std::vector<char> SliceTester::CreateTfLiteModel(TensorType tensor_type) const {
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flatbuffers::FlatBufferBuilder builder;
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flatbuffers::Offset<OperatorCode> operator_code =
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CreateOperatorCode(builder, BuiltinOperator_SLICE);
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const std::array<flatbuffers::Offset<Buffer>, 3> 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*>(OffsetsData()),
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OffsetsSizeInBytes())),
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CreateBuffer(builder, builder.CreateVector(
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reinterpret_cast<const uint8_t*>(SizesData()),
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SizesSizeInBytes())),
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}};
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flatbuffers::Offset<QuantizationParameters> quantization_params =
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CreateQuantizationParameters(
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builder, /*min=*/0, /*max=*/0,
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builder.CreateVector<float>({/*scale=*/1.0f}),
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builder.CreateVector<int64_t>({/*zero_point=*/0}));
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const int32_t num_dims = Offsets().size();
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TensorType offsets_and_sizes_tensor_type =
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UseInt64OffsetsAndSize() ? TensorType_INT64 : TensorType_INT32;
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const std::array<flatbuffers::Offset<Tensor>, 4> 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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tensor_type, /*buffer=*/0, /*name=*/0, quantization_params),
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CreateTensor(builder, builder.CreateVector<int32_t>({num_dims}),
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offsets_and_sizes_tensor_type, /*buffer=*/1),
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CreateTensor(builder, builder.CreateVector<int32_t>({num_dims}),
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offsets_and_sizes_tensor_type, /*buffer=*/2),
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CreateTensor(builder,
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builder.CreateVector<int32_t>(OutputShape().data(),
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OutputShape().size()),
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tensor_type, /*buffer=*/0, /*name=*/0, quantization_params),
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}};
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const flatbuffers::Offset<Operator> op = CreateOperator(
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builder, /*opcode_index=*/0, builder.CreateVector<int32_t>({0, 1, 2}),
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builder.CreateVector<int32_t>({3}));
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const flatbuffers::Offset<SubGraph> subgraph = CreateSubGraph(
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builder, builder.CreateVector(tensors.data(), tensors.size()),
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builder.CreateVector<int32_t>({0}), builder.CreateVector<int32_t>({3}),
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builder.CreateVector({op}));
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const flatbuffers::Offset<flatbuffers::String> description =
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builder.CreateString("Slice model");
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const 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 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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std::vector<int32_t> RandomOffsets(std::mt19937& rng,
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const std::vector<int32_t>& dims) {
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std::vector<int32_t> offsets(dims.size());
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for (size_t i = 0; i < dims.size(); i++) {
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offsets[i] = std::uniform_int_distribution<int32_t>(0, dims[i] - 1)(rng);
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}
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return offsets;
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}
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std::vector<int32_t> RandomSizes(std::mt19937& rng,
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const std::vector<int32_t>& dims,
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const std::vector<int32_t>& offsets) {
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std::vector<int32_t> sizes(dims.size());
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for (size_t i = 0; i < dims.size(); i++) {
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// Allow -1 as a size (which means select everything).
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std::vector<int32_t> valid_sizes(dims[i] - offsets[i] + 1);
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std::iota(valid_sizes.begin(), valid_sizes.end(), 1);
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valid_sizes.back() = -1;
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sizes[i] = valid_sizes[std::uniform_int_distribution<int32_t>(
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0, valid_sizes.size() - 1)(rng)];
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}
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return sizes;
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}
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} // namespace xnnpack
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} // namespace tflite
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