249 lines
7.8 KiB
C++
249 lines
7.8 KiB
C++
// Copyright 2019 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#pragma once
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#include <gtest/gtest.h>
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#include <fp16.h>
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#include <algorithm>
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#include <cmath>
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#include <cstddef>
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#include <cstdlib>
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#include <functional>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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class PReLUOperatorTester {
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public:
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enum class WeightsType {
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Default,
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FP32,
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};
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inline PReLUOperatorTester& batch_size(size_t batch_size) {
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assert(batch_size != 0);
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this->batch_size_ = batch_size;
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return *this;
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}
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inline size_t batch_size() const {
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return this->batch_size_;
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}
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inline PReLUOperatorTester& channels(size_t channels) {
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assert(channels != 0);
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this->channels_ = channels;
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return *this;
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}
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inline size_t channels() const {
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return this->channels_;
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}
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inline PReLUOperatorTester& x_stride(size_t x_stride) {
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assert(x_stride != 0);
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this->x_stride_ = x_stride;
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return *this;
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}
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inline size_t x_stride() const {
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if (this->x_stride_ == 0) {
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return this->channels_;
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} else {
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assert(this->x_stride_ >= this->channels_);
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return this->x_stride_;
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}
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}
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inline PReLUOperatorTester& y_stride(size_t y_stride) {
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assert(y_stride != 0);
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this->y_stride_ = y_stride;
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return *this;
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}
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inline size_t y_stride() const {
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if (this->y_stride_ == 0) {
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return this->channels_;
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} else {
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assert(this->y_stride_ >= this->channels_);
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return this->y_stride_;
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}
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}
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inline PReLUOperatorTester& weights_type(WeightsType weights_type) {
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this->weights_type_ = weights_type;
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return *this;
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}
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inline WeightsType weights_type() const {
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return this->weights_type_;
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}
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inline PReLUOperatorTester& iterations(size_t iterations) {
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this->iterations_ = iterations;
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return *this;
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}
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inline size_t iterations() const {
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return this->iterations_;
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}
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void TestF16() const {
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switch (weights_type()) {
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case WeightsType::Default:
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break;
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case WeightsType::FP32:
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break;
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default:
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GTEST_FAIL() << "unexpected weights type";
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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 f32irng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), rng);
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auto f16irng = std::bind(fp16_ieee_from_fp32_value, f32irng);
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auto f32wrng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.75f), rng);
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auto f16wrng = std::bind(fp16_ieee_from_fp32_value, f32wrng);
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std::vector<uint16_t> x((batch_size() - 1) * x_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
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std::vector<uint16_t> w(channels());
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std::vector<float> w_as_float(channels());
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std::vector<uint16_t> y((batch_size() - 1) * y_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
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std::vector<float> y_ref(batch_size() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(x.begin(), x.end(), std::ref(f16irng));
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std::generate(w.begin(), w.end(), std::ref(f16wrng));
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std::transform(w.cbegin(), w.cend(), w_as_float.begin(), fp16_ieee_to_fp32_value);
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std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */);
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// Compute reference results, without clamping.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < channels(); c++) {
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const float x_value = fp16_ieee_to_fp32_value(x[i * x_stride() + c]);
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const float w_value = w_as_float[c];
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y_ref[i * channels() + c] = signbit(x_value) ? x_value * w_value : x_value;
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}
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}
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// Create, setup, run, and destroy PReLU operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t prelu_op = nullptr;
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const void* negative_slope_data = w.data();
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if (weights_type() == WeightsType::FP32) {
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negative_slope_data = w_as_float.data();
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}
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uint32_t flags = 0;
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if (weights_type() == WeightsType::FP32) {
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flags |= XNN_FLAG_FP32_STATIC_WEIGHTS;
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}
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ASSERT_EQ(xnn_status_success,
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xnn_create_prelu_nc_f16(
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channels(), x_stride(), y_stride(),
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negative_slope_data,
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flags, &prelu_op));
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ASSERT_NE(nullptr, prelu_op);
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// Smart pointer to automatically delete prelu_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_prelu_op(prelu_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_prelu_nc_f16(
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prelu_op,
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batch_size(),
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x.data(), y.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(prelu_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_NEAR(
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fp16_ieee_to_fp32_value(y[i * y_stride() + c]),
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y_ref[i * channels() + c],
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std::max(1.0e-4f, std::abs(y_ref[i * channels() + c]) * 1.0e-4f))
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
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}
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}
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}
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}
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void TestF32() const {
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ASSERT_EQ(weights_type(), WeightsType::Default);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32irng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), rng);
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auto f32wrng = std::bind(std::uniform_real_distribution<float>(0.25f, 0.75f), rng);
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std::vector<float> x((batch_size() - 1) * x_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> w(channels());
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std::vector<float> y((batch_size() - 1) * y_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> y_ref(batch_size() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(x.begin(), x.end(), std::ref(f32irng));
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std::generate(w.begin(), w.end(), std::ref(f32wrng));
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std::fill(y.begin(), y.end(), nanf(""));
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// Compute reference results, without clamping.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < channels(); c++) {
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y_ref[i * channels() + c] = std::signbit(x[i * x_stride() + c]) ? x[i * x_stride() + c] * w[c] : x[i * x_stride() + c];
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}
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}
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// Create, setup, run, and destroy PReLU operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t prelu_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_prelu_nc_f32(
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channels(), x_stride(), y_stride(),
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w.data(),
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0, &prelu_op));
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ASSERT_NE(nullptr, prelu_op);
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// Smart pointer to automatically delete prelu_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_prelu_op(prelu_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_prelu_nc_f32(
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prelu_op,
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batch_size(),
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x.data(), y.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(prelu_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_NEAR(
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y[i * y_stride() + c],
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y_ref[i * channels() + c],
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std::max(1.0e-6f, std::abs(y_ref[i * channels() + c]) * 1.0e-6f))
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
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}
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}
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}
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}
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private:
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size_t batch_size_{1};
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size_t channels_{1};
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size_t x_stride_{0};
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size_t y_stride_{0};
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WeightsType weights_type_{WeightsType::Default};
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size_t iterations_{15};
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};
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