393 lines
14 KiB
C
393 lines
14 KiB
C
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// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// 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 <limits>
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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 ClampOperatorTester {
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public:
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inline ClampOperatorTester& 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 ClampOperatorTester& 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 ClampOperatorTester& 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 ClampOperatorTester& 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 ClampOperatorTester& qmin(int16_t qmin) {
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this->qmin_ = qmin;
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return *this;
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}
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inline int16_t qmin() const {
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return this->qmin_;
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}
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inline ClampOperatorTester& qmax(int16_t qmax) {
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this->qmax_ = qmax;
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return *this;
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}
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inline int16_t qmax() const {
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return this->qmax_;
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}
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inline ClampOperatorTester& relu_activation(bool relu_activation) {
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this->relu_activation_ = relu_activation;
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return *this;
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}
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inline bool relu_activation() const {
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return this->relu_activation_;
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}
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inline ClampOperatorTester& 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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ASSERT_LT(qmin(), qmax());
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ASSERT_FALSE(relu_activation());
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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> f32dist(
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std::numeric_limits<int16_t>::min(), std::numeric_limits<int16_t>::max());
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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(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); });
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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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const float output_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(float(qmin())));
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const float output_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(float(qmax())));
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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 = relu_activation() ? std::max(x, 0.f) : std::min(std::max(x, output_min), output_max);
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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 Clamp operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t clamp_op = nullptr;
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const xnn_status status = xnn_create_clamp_nc_f16(
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channels(), input_stride(), output_stride(),
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output_min, output_max,
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0, &clamp_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_EQ(xnn_status_success, status);
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ASSERT_NE(nullptr, clamp_op);
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// Smart pointer to automatically delete clamp_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_clamp_op(clamp_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_clamp_nc_f16(
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clamp_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(clamp_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_LE(fp16_ieee_to_fp32_value(output[i * output_stride() + c]), output_max)
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
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ASSERT_GE(fp16_ieee_to_fp32_value(output[i * output_stride() + c]), output_min)
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
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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-4f, std::abs(output_ref[i * channels() + c]) * 1.0e-2f))
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
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<< ", min " << output_min << ", max " << output_max;
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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_LT(qmin(), qmax());
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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> f32dist(
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std::numeric_limits<int16_t>::min(), std::numeric_limits<int16_t>::max());
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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(), [&]() { return f32dist(rng); });
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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 = relu_activation() ? std::max(x, 0.f) :
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std::min(std::max(x, float(qmin())), float(qmax()));
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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 Clamp operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t clamp_op = nullptr;
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const float output_min = relu_activation() ? 0.0f : float(qmin());
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const float output_max = relu_activation() ? std::numeric_limits<float>::infinity() : float(qmax());
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ASSERT_EQ(xnn_status_success,
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xnn_create_clamp_nc_f32(
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channels(), input_stride(), output_stride(),
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output_min, output_max,
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0, &clamp_op));
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ASSERT_NE(nullptr, clamp_op);
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// Smart pointer to automatically delete clamp_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_clamp_op(clamp_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_clamp_nc_f32(
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clamp_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(clamp_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_LE(output[i * output_stride() + c], output_max)
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
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ASSERT_GE(output[i * output_stride() + c], output_min)
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
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ASSERT_EQ(output_ref[i * channels() + c], output[i * output_stride() + c])
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
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<< ", min " << output_min << ", max " << output_max;
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}
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}
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}
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}
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void TestS8() const {
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ASSERT_GE(qmin(), std::numeric_limits<int8_t>::min());
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ASSERT_LE(qmax(), std::numeric_limits<int8_t>::max());
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ASSERT_LT(qmin(), qmax());
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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> i8dist(
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std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
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std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) +
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(batch_size() - 1) * input_stride() + channels());
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std::vector<int8_t> output((batch_size() - 1) * output_stride() + channels());
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std::vector<int8_t> 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(), [&]() { return i8dist(rng); });
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std::fill(output.begin(), output.end(), INT8_C(0xA5));
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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 int8_t x = input[i * input_stride() + c];
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const int8_t y = std::min(std::max(x, int8_t(qmin())), int8_t(qmax()));
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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 Clamp operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t clamp_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_clamp_nc_s8(
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channels(), input_stride(), output_stride(),
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int8_t(qmin()), int8_t(qmax()),
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0, &clamp_op));
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ASSERT_NE(nullptr, clamp_op);
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// Smart pointer to automatically delete clamp_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_clamp_op(clamp_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_clamp_nc_s8(
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clamp_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(clamp_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_LE(int16_t(output[i * output_stride() + c]), qmax())
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
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ASSERT_GE(int16_t(output[i * output_stride() + c]), qmin())
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
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ASSERT_EQ(int16_t(output[i * output_stride() + c]), int16_t(output_ref[i * channels() + c]))
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<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
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<< ", min " << qmin() << ", max " << qmax();
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}
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}
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}
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}
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void TestU8() const {
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ASSERT_GE(qmin(), std::numeric_limits<uint8_t>::min());
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ASSERT_LE(qmax(), std::numeric_limits<uint8_t>::max());
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ASSERT_LT(qmin(), qmax());
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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> u8dist(
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std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
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std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
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(batch_size() - 1) * input_stride() + channels());
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std::vector<uint8_t> output((batch_size() - 1) * output_stride() + channels());
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std::vector<uint8_t> 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(), [&]() { return u8dist(rng); });
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std::fill(output.begin(), output.end(), UINT8_C(0xA5));
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// Compute reference results.
|
||
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
||
|
|
for (size_t c = 0; c < channels(); c++) {
|
||
|
|
const uint8_t x = input[i * input_stride() + c];
|
||
|
|
const uint8_t y = std::min(std::max(x, uint8_t(qmin())), uint8_t(qmax()));
|
||
|
|
output_ref[i * channels() + c] = y;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
// Create, setup, run, and destroy Clamp operator.
|
||
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
||
|
|
xnn_operator_t clamp_op = nullptr;
|
||
|
|
|
||
|
|
ASSERT_EQ(xnn_status_success,
|
||
|
|
xnn_create_clamp_nc_u8(
|
||
|
|
channels(), input_stride(), output_stride(),
|
||
|
|
uint8_t(qmin()), uint8_t(qmax()),
|
||
|
|
0, &clamp_op));
|
||
|
|
ASSERT_NE(nullptr, clamp_op);
|
||
|
|
|
||
|
|
// Smart pointer to automatically delete clamp_op.
|
||
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_clamp_op(clamp_op, xnn_delete_operator);
|
||
|
|
|
||
|
|
ASSERT_EQ(xnn_status_success,
|
||
|
|
xnn_setup_clamp_nc_u8(
|
||
|
|
clamp_op,
|
||
|
|
batch_size(),
|
||
|
|
input.data(), output.data(),
|
||
|
|
nullptr /* thread pool */));
|
||
|
|
|
||
|
|
ASSERT_EQ(xnn_status_success,
|
||
|
|
xnn_run_operator(clamp_op, nullptr /* thread pool */));
|
||
|
|
|
||
|
|
// Verify results .
|
||
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
||
|
|
for (size_t c = 0; c < channels(); c++) {
|
||
|
|
ASSERT_LE(int16_t(output[i * output_stride() + c]), qmax())
|
||
|
|
<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
|
||
|
|
ASSERT_GE(int16_t(output[i * output_stride() + c]), qmin())
|
||
|
|
<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
|
||
|
|
ASSERT_EQ(int16_t(output[i * output_stride() + c]), int16_t(output_ref[i * channels() + c]))
|
||
|
|
<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
|
||
|
|
<< ", min " << qmin() << ", max " << qmax();
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
private:
|
||
|
|
size_t batch_size_{1};
|
||
|
|
size_t channels_{1};
|
||
|
|
size_t input_stride_{0};
|
||
|
|
size_t output_stride_{0};
|
||
|
|
int16_t qmin_{std::numeric_limits<int16_t>::min()};
|
||
|
|
int16_t qmax_{std::numeric_limits<int16_t>::max()};
|
||
|
|
bool relu_activation_{false};
|
||
|
|
size_t iterations_{15};
|
||
|
|
};
|