wehub-resource-sync
237 lines
8.9 KiB
C++
237 lines
8.9 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/quantize_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 <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 <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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void QuantizeTester::PopulateInput(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<int> 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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T* default_input_data = default_interpreter->typed_input_tensor<T>(0);
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std::generate_n(default_input_data, ComputeSize(Shape()),
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std::ref(input_rng));
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T* xnnpack_input_data = delegate_interpreter->typed_input_tensor<T>(0);
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std::copy_n(default_input_data, ComputeSize(Shape()), xnnpack_input_data);
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}
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template <>
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void QuantizeTester::PopulateInput<float>(
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Interpreter* delegate_interpreter, 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(-1.0f, 1.0f);
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auto input_rng = std::bind(input_distribution, std::ref(rng));
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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(Shape()),
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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(Shape()), xnnpack_input_data);
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}
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template <class T>
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void QuantizeTester::InvokeAndCheckOutput(
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Interpreter* delegate_interpreter, Interpreter* default_interpreter) const {
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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(Shape()); i++) {
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ASSERT_LE(std::abs(static_cast<int32_t>(default_output_data[i]) -
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static_cast<int32_t>(delegate_output_data[i])),
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1)
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<< "default " << static_cast<int32_t>(default_output_data[i])
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<< ", delegate " << static_cast<int32_t>(delegate_output_data[i])
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<< " at index " << i << " / " << ComputeSize(Shape());
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}
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}
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void QuantizeTester::Test(TensorType input_type, TensorType output_type,
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TfLiteDelegate* delegate) const {
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std::vector<char> buffer = CreateTfLiteModel(input_type, output_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 (input_type) {
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case TensorType_FLOAT32:
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PopulateInput<float>(delegate_interpreter.get(),
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default_interpreter.get());
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break;
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case TensorType_INT8:
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PopulateInput<int8_t>(delegate_interpreter.get(),
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default_interpreter.get());
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break;
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case TensorType_UINT8:
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PopulateInput<uint8_t>(delegate_interpreter.get(),
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default_interpreter.get());
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break;
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default:
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GTEST_FAIL() << "unsupported input type "
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<< EnumNameTensorType(input_type);
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}
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switch (output_type) {
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case TensorType_INT8:
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InvokeAndCheckOutput<int8_t>(delegate_interpreter.get(),
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default_interpreter.get());
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break;
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case TensorType_UINT8:
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InvokeAndCheckOutput<uint8_t>(delegate_interpreter.get(),
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default_interpreter.get());
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break;
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default:
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GTEST_FAIL() << "unsupported output type "
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<< EnumNameTensorType(output_type);
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}
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}
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std::vector<char> QuantizeTester::CreateTfLiteModel(
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TensorType input_type, TensorType output_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_QUANTIZE);
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const std::array<flatbuffers::Offset<Buffer>, 1> buffers{{
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CreateBuffer(builder, builder.CreateVector({})),
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}};
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flatbuffers::Offset<QuantizationParameters> input_quantization = 0;
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if (input_type != TensorType_FLOAT32) {
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input_quantization = 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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}
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const std::array<flatbuffers::Offset<Tensor>, 2> tensors{{
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CreateTensor(
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builder,
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builder.CreateVector<int32_t>(Shape().data(), Shape().size()),
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input_type,
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/*buffer=*/0, /*name=*/0, input_quantization),
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CreateTensor(
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builder,
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builder.CreateVector<int32_t>(Shape().data(), Shape().size()),
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output_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>({OutputScale()}),
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builder.CreateVector<int64_t>({OutputZeroPoint()}))),
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}};
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const std::array<int32_t, 1> op_inputs{{0}};
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const std::array<int32_t, 1> op_outputs{{1}};
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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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const std::array<int32_t, 1> subgraph_inputs{{0}};
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const std::array<int32_t, 1> subgraph_outputs{{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(&op, 1));
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flatbuffers::Offset<flatbuffers::String> description =
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builder.CreateString("Quantize operator model");
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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 QuantizeTester::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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