blob: 1d35df35a18b9a2a23710a778b0e0ea028f5c371 [file] [log] [blame]
// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#pragma once
#include <gtest/gtest.h>
#include <algorithm>
#include <cassert>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <random>
#include <vector>
#include <fp16.h>
#include <xnnpack.h>
class HardSwishOperatorTester {
public:
inline HardSwishOperatorTester& channels(size_t channels) {
assert(channels != 0);
this->channels_ = channels;
return *this;
}
inline size_t channels() const {
return this->channels_;
}
inline HardSwishOperatorTester& input_stride(size_t input_stride) {
assert(input_stride != 0);
this->input_stride_ = input_stride;
return *this;
}
inline size_t input_stride() const {
if (this->input_stride_ == 0) {
return this->channels_;
} else {
assert(this->input_stride_ >= this->channels_);
return this->input_stride_;
}
}
inline HardSwishOperatorTester& output_stride(size_t output_stride) {
assert(output_stride != 0);
this->output_stride_ = output_stride;
return *this;
}
inline size_t output_stride() const {
if (this->output_stride_ == 0) {
return this->channels_;
} else {
assert(this->output_stride_ >= this->channels_);
return this->output_stride_;
}
}
inline HardSwishOperatorTester& batch_size(size_t batch_size) {
assert(batch_size != 0);
this->batch_size_ = batch_size;
return *this;
}
inline size_t batch_size() const {
return this->batch_size_;
}
inline HardSwishOperatorTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void TestF16() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-4.0f, 4.0f), rng);
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) +
(batch_size() - 1) * input_stride() + channels());
std::vector<uint16_t> output((batch_size() - 1) * output_stride() + channels());
std::vector<float> output_ref(batch_size() * channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f16rng));
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t c = 0; c < channels(); c++) {
const float x = fp16_ieee_to_fp32_value(input[i * input_stride() + c]);
const float y = x * std::min(std::max(x + 3.0f, 0.0f), 6.0f) / 6.0f;
output_ref[i * channels() + c] = y;
}
}
// Create, setup, run, and destroy HardSwish operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t hardswish_op = nullptr;
xnn_status status = xnn_create_hardswish_nc_f16(
channels(), input_stride(), output_stride(),
0, &hardswish_op);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_NE(nullptr, hardswish_op);
// Smart pointer to automatically delete hardswish_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_hardswish_op(hardswish_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_hardswish_nc_f16(
hardswish_op,
batch_size(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(hardswish_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t c = 0; c < channels(); c++) {
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))
<< "at position " << i << ", batch size = " << batch_size() << ", channels = " << channels();
}
}
}
}
void TestF32() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-4.0f, 4.0f), rng);
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
(batch_size() - 1) * input_stride() + channels());
std::vector<float> output((batch_size() - 1) * output_stride() + channels());
std::vector<float> output_ref(batch_size() * channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), std::nanf(""));
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t c = 0; c < channels(); c++) {
const float x = input[i * input_stride() + c];
const float y = x * std::min(std::max(x + 3.0f, 0.0f), 6.0f) / 6.0f;
output_ref[i * channels() + c] = y;
}
}
// Create, setup, run, and destroy HardSwish operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t hardswish_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_hardswish_nc_f32(
channels(), input_stride(), output_stride(),
0, &hardswish_op));
ASSERT_NE(nullptr, hardswish_op);
// Smart pointer to automatically delete hardswish_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_hardswish_op(hardswish_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_hardswish_nc_f32(
hardswish_op,
batch_size(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(hardswish_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t c = 0; c < channels(); c++) {
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))
<< "at position " << i << ", batch size = " << batch_size() << ", channels = " << channels();
}
}
}
}
private:
size_t batch_size_{1};
size_t channels_{1};
size_t input_stride_{0};
size_t output_stride_{0};
size_t iterations_{15};
};