203 lines
6.6 KiB
C++
203 lines
6.6 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 <algorithm>
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#include <cassert>
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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 <fp16.h>
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#include <xnnpack.h>
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class HardSwishOperatorTester {
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public:
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inline HardSwishOperatorTester& 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 HardSwishOperatorTester& input_stride(size_t input_stride) {
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assert(input_stride != 0);
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this->input_stride_ = input_stride;
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return *this;
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}
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inline size_t input_stride() const {
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if (this->input_stride_ == 0) {
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return this->channels_;
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} else {
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assert(this->input_stride_ >= this->channels_);
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return this->input_stride_;
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}
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}
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inline HardSwishOperatorTester& output_stride(size_t output_stride) {
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assert(output_stride != 0);
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this->output_stride_ = output_stride;
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return *this;
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}
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inline size_t output_stride() const {
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if (this->output_stride_ == 0) {
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return this->channels_;
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} else {
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assert(this->output_stride_ >= this->channels_);
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return this->output_stride_;
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}
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}
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inline HardSwishOperatorTester& 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 HardSwishOperatorTester& 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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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng = std::bind(std::uniform_real_distribution<float>(-4.0f, 4.0f), rng);
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auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
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std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) +
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(batch_size() - 1) * input_stride() + channels());
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std::vector<uint16_t> output((batch_size() - 1) * output_stride() + channels());
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std::vector<float> output_ref(batch_size() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(f16rng));
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std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
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// Compute reference 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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const float x = fp16_ieee_to_fp32_value(input[i * input_stride() + c]);
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const float y = x * std::min(std::max(x + 3.0f, 0.0f), 6.0f) / 6.0f;
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output_ref[i * channels() + c] = y;
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}
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}
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// Create, setup, run, and destroy HardSwish operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t hardswish_op = nullptr;
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xnn_status status = xnn_create_hardswish_nc_f16(
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channels(), input_stride(), output_stride(),
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0, &hardswish_op);
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if (status == xnn_status_unsupported_hardware) {
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GTEST_SKIP();
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}
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ASSERT_NE(nullptr, hardswish_op);
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// Smart pointer to automatically delete hardswish_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_hardswish_op(hardswish_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_hardswish_nc_f16(
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hardswish_op,
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batch_size(),
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input.data(), output.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(hardswish_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(fp16_ieee_to_fp32_value(output[i * output_stride() + c]), output_ref[i * channels() + c], std::max(1.0e-3f, std::abs(output_ref[i * channels() + c]) * 1.0e-2f))
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<< "at position " << i << ", batch size = " << batch_size() << ", channels = " << 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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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng = std::bind(std::uniform_real_distribution<float>(-4.0f, 4.0f), rng);
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std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
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(batch_size() - 1) * input_stride() + channels());
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std::vector<float> output((batch_size() - 1) * output_stride() + channels());
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std::vector<float> output_ref(batch_size() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(f32rng));
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std::fill(output.begin(), output.end(), std::nanf(""));
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// Compute reference 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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const float x = input[i * input_stride() + c];
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const float y = x * std::min(std::max(x + 3.0f, 0.0f), 6.0f) / 6.0f;
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output_ref[i * channels() + c] = y;
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}
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}
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// Create, setup, run, and destroy HardSwish operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t hardswish_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_hardswish_nc_f32(
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channels(), input_stride(), output_stride(),
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0, &hardswish_op));
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ASSERT_NE(nullptr, hardswish_op);
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// Smart pointer to automatically delete hardswish_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_hardswish_op(hardswish_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_hardswish_nc_f32(
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hardswish_op,
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batch_size(),
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input.data(), output.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(hardswish_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(output_ref[i * channels() + c], output[i * output_stride() + c], std::max(1.0e-7f, std::abs(output[i * output_stride() + c]) * 1.0e-6f))
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<< "at position " << i << ", batch size = " << batch_size() << ", channels = " << 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 input_stride_{0};
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size_t output_stride_{0};
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size_t iterations_{15};
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};
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