489 lines
17 KiB
C++
489 lines
17 KiB
C++
// Copyright 2021 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 <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 <fp16.h>
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#include <xnnpack.h>
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class ConvertOperatorTester {
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public:
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inline ConvertOperatorTester& 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 ConvertOperatorTester& 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 ConvertOperatorTester& 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 ConvertOperatorTester& 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 ConvertOperatorTester& scale(float scale) {
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assert(scale >= 0.0f);
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assert(std::isnormal(scale));
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this->scale_ = scale;
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return *this;
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}
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inline float scale() const {
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return this->scale_;
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}
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inline ConvertOperatorTester& zero_point(int16_t zero_point) {
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this->zero_point_ = zero_point;
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return *this;
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}
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inline int16_t zero_point() const {
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return this->zero_point_;
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}
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inline ConvertOperatorTester& 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 ConvertOperatorTester& 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 ConvertOperatorTester& 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 TestF16toF32() 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>(-1.0f, 1.0f), 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<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(f16rng));
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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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output_ref[i * channels() + c] = fp16_ieee_to_fp32_value(input[i * input_stride() + c]);
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}
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}
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// Create, setup, run, and destroy Convert operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t convert_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_convert_nc_f16_f32(
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channels(), input_stride(), output_stride(),
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0, &convert_op));
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ASSERT_NE(nullptr, convert_op);
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// Smart pointer to automatically delete convert op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_convert_nc_f16_f32(
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convert_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(convert_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_EQ(output_ref[i * channels() + c], output[i * output_stride() + c])
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<< "at batch " << 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 TestF32toF16() 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>(-1.0f, 1.0f), 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<uint16_t> output((batch_size() - 1) * output_stride() + channels());
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std::vector<uint16_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(), std::ref(f32rng));
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std::fill(output.begin(), output.end(), UINT16_C(0x7E));
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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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output_ref[i * channels() + c] = fp16_ieee_from_fp32_value(input[i * input_stride() + c]);
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}
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}
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// Create, setup, run, and destroy Convert operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t convert_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_convert_nc_f32_f16(
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channels(), input_stride(), output_stride(),
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0, &convert_op));
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ASSERT_NE(nullptr, convert_op);
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// Smart pointer to automatically delete convert op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_convert_nc_f32_f16(
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convert_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(convert_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_EQ(output_ref[i * channels() + c], output[i * output_stride() + c])
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<< "at batch " << 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 TestF32toQS8() 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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ASSERT_GE(zero_point(), std::numeric_limits<int8_t>::min());
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ASSERT_LE(zero_point(), std::numeric_limits<int8_t>::max());
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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>(-1.0f, 1.0f), 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<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(), std::ref(f32rng));
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std::fill(output.begin(), output.end(), UINT16_C(0x7E));
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// Compute reference results.
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const float inv_scale = 1.0f / scale();
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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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float scaled_input = input[i * input_stride() + c] * inv_scale;
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scaled_input = std::min<float>(scaled_input, float(qmax() - zero_point()));
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scaled_input = std::max<float>(scaled_input, float(qmin() - zero_point()));
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output_ref[i * channels() + c] = int8_t(std::lrintf(scaled_input) + long(zero_point()));
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}
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}
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// Create, setup, run, and destroy Convert operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t convert_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_convert_nc_f32_qs8(
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channels(), input_stride(), output_stride(),
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scale(), int8_t(zero_point()), int8_t(qmin()), int8_t(qmax()),
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0, &convert_op));
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ASSERT_NE(nullptr, convert_op);
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// Smart pointer to automatically delete convert op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_convert_nc_f32_qs8(
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convert_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(convert_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_EQ(int32_t(output_ref[i * channels() + c]), int32_t(output[i * output_stride() + c]))
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<< "at batch " << 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 TestF32toQU8() 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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ASSERT_GE(zero_point(), std::numeric_limits<uint8_t>::min());
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ASSERT_LE(zero_point(), std::numeric_limits<uint8_t>::max());
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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>(-1.0f, 1.0f), 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<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(), std::ref(f32rng));
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std::fill(output.begin(), output.end(), UINT16_C(0x7E));
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// Compute reference results.
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const float inv_scale = 1.0f / scale();
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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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float scaled_input = input[i * input_stride() + c] * inv_scale;
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scaled_input = std::min<float>(scaled_input, float(qmax() - zero_point()));
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scaled_input = std::max<float>(scaled_input, float(qmin() - zero_point()));
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output_ref[i * channels() + c] = uint8_t(std::lrintf(scaled_input) + long(zero_point()));
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}
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}
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// Create, setup, run, and destroy Convert operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t convert_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_convert_nc_f32_qu8(
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channels(), input_stride(), output_stride(),
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scale(), uint8_t(zero_point()), uint8_t(qmin()), uint8_t(qmax()),
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0, &convert_op));
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ASSERT_NE(nullptr, convert_op);
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// Smart pointer to automatically delete convert op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_convert_nc_f32_qu8(
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convert_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(convert_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_EQ(uint32_t(output_ref[i * channels() + c]), uint32_t(output[i * output_stride() + c]))
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<< "at batch " << 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 TestQS8toF32() const {
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ASSERT_GE(zero_point(), std::numeric_limits<int8_t>::min());
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ASSERT_LE(zero_point(), std::numeric_limits<int8_t>::max());
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
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std::ref(rng));
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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<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(i8rng));
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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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output_ref[i * channels() + c] = float(input[i * input_stride() + c] - zero_point()) * scale();
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}
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}
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// Create, setup, run, and destroy Convert operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t convert_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_convert_nc_qs8_f32(
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channels(), input_stride(), output_stride(),
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scale(), int8_t(zero_point()),
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0, &convert_op));
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ASSERT_NE(nullptr, convert_op);
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// Smart pointer to automatically delete convert op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_convert_nc_qs8_f32(
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convert_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(convert_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_EQ(output_ref[i * channels() + c], output[i * output_stride() + c])
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<< "at batch " << 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 TestQU8toF32() const {
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ASSERT_GE(zero_point(), std::numeric_limits<uint8_t>::min());
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ASSERT_LE(zero_point(), std::numeric_limits<uint8_t>::max());
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto u8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()),
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std::ref(rng));
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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<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(u8rng));
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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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output_ref[i * channels() + c] = float(input[i * input_stride() + c] - zero_point()) * scale();
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}
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}
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// Create, setup, run, and destroy Convert operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t convert_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_convert_nc_qu8_f32(
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channels(), input_stride(), output_stride(),
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scale(), uint8_t(zero_point()),
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0, &convert_op));
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ASSERT_NE(nullptr, convert_op);
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// Smart pointer to automatically delete convert op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
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|
|
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ASSERT_EQ(xnn_status_success,
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xnn_setup_convert_nc_qu8_f32(
|
|
convert_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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|
|
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ASSERT_EQ(xnn_status_success,
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|
xnn_run_operator(convert_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_EQ(output_ref[i * channels() + c], output[i * output_stride() + c])
|
|
<< "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
size_t batch_size_{1};
|
|
size_t channels_{1};
|
|
size_t input_stride_{0};
|
|
size_t output_stride_{0};
|
|
float scale_{150.0f};
|
|
int16_t zero_point_{1};
|
|
int16_t qmin_{std::numeric_limits<int16_t>::min()};
|
|
int16_t qmax_{std::numeric_limits<int16_t>::max()};
|
|
size_t iterations_{15};
|
|
};
|