wehub-resource-sync
241 lines
9.0 KiB
C++
241 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/concatenation_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 "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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std::vector<int32_t> SameShapeDifferentAxis(std::vector<int32_t> shape,
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int axis, int32_t size) {
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std::vector<int32_t> new_shape{shape};
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new_shape[axis < 0 ? axis + shape.size() : axis] = size;
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return new_shape;
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}
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template <class T>
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void ConcatenationTester::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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std::uniform_int_distribution<int32_t> input_distribution(
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std::numeric_limits<T>::min(), std::numeric_limits<T>::max());
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auto input_rng = std::bind(input_distribution, std::ref(rng));
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for (size_t i = 0; i < NumInputs(); i++) {
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T *default_input_data = default_interpreter->typed_input_tensor<T>(i);
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std::generate_n(default_input_data, ComputeSize(InputShape(i)),
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std::ref(input_rng));
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T *xnnpack_input_data = delegate_interpreter->typed_input_tensor<T>(i);
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std::copy_n(default_input_data, ComputeSize(InputShape(i)),
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xnnpack_input_data);
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}
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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 *xnnpack_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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ASSERT_EQ(static_cast<int32_t>(default_output_data[i]),
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static_cast<int32_t>(xnnpack_output_data[i]));
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}
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}
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template <>
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void ConcatenationTester::Test<float>(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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std::uniform_real_distribution<float> input_distribution(-25.0f, 25.0f);
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auto input_rng = std::bind(input_distribution, std::ref(rng));
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for (size_t i = 0; i < NumInputs(); i++) {
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float *default_input_data =
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default_interpreter->typed_input_tensor<float>(i);
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std::generate_n(default_input_data, ComputeSize(InputShape(i)),
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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>(i);
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std::copy_n(default_input_data, ComputeSize(InputShape(i)),
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xnnpack_input_data);
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}
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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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void ConcatenationTester::Test(TensorType tensor_type,
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TfLiteDelegate *delegate) const {
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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(), NumInputs());
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ASSERT_EQ(default_interpreter->inputs().size(), NumInputs());
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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> ConcatenationTester::CreateTfLiteModel(
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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_CONCATENATION, 0);
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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<flatbuffers::Offset<Tensor>> tensors;
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tensors.reserve(NumInputs());
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for (size_t i = 0; i < NumInputs(); i++) {
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tensors.push_back(CreateTensor(
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builder,
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builder.CreateVector<int32_t>(InputShape(i).data(),
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InputShape(i).size()),
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tensor_type,
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/*buffer=*/0, /*name=*/0,
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CreateQuantizationParameters(
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builder, /*min=*/0, /*max=*/0,
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builder.CreateVector<float>({input_scales_[i]}),
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builder.CreateVector<int64_t>({input_zero_points_[i]}))));
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}
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tensors.push_back(CreateTensor(
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builder,
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builder.CreateVector<int32_t>(OutputShape().data(), OutputShape().size()),
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tensor_type,
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/*buffer=*/0, /*name=*/0,
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CreateQuantizationParameters(
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builder, /*min=*/0, /*max=*/0,
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builder.CreateVector<float>({output_scale_}),
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builder.CreateVector<int64_t>({output_zero_point_}))));
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std::vector<int32_t> op_inputs;
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op_inputs.reserve(NumInputs());
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for (size_t i = 0; i < NumInputs(); i++) {
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op_inputs.push_back(static_cast<int32_t>(i));
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}
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const std::array<int32_t, 1> op_outputs{static_cast<int32_t>(NumInputs())};
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BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE;
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flatbuffers::Offset<void> builtin_options = 0;
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builtin_options_type = tflite::BuiltinOptions_ConcatenationOptions;
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builtin_options = CreateConcatenationOptions(builder, Axis()).Union();
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const 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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builtin_options_type, builtin_options);
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const std::vector<int32_t> subgraph_inputs = op_inputs;
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const std::array<int32_t, 1> subgraph_outputs = op_outputs;
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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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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),
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builder.CreateString("Concatenation model"),
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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 ConcatenationTester::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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