575 lines
20 KiB
C++
575 lines
20 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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#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 <cstdint>
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#include <cstdlib>
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#include <limits>
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#include <memory>
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#include <random>
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#include <vector>
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#include <gtest/gtest.h>
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#include "xnnpack.h"
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#include "xnnpack/config-types.h"
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#include "xnnpack/config.h"
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#include "xnnpack/internal.h"
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#include "xnnpack/math.h"
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#include "xnnpack/packq.h"
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#include "xnnpack/buffer.h"
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#include "replicable_random_device.h"
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class ConvertOperatorTester {
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public:
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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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size_t channels() const {
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return this->channels_;
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}
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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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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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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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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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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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size_t batch_size() const {
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return this->batch_size_;
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}
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ConvertOperatorTester& input_scale(float input_scale) {
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assert(input_scale >= 0.0f);
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assert(std::isnormal(input_scale));
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this->input_scale_ = input_scale;
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return *this;
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}
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float input_scale() const {
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return this->input_scale_;
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}
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ConvertOperatorTester& output_scale(float output_scale) {
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assert(output_scale >= 0.0f);
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assert(std::isnormal(output_scale));
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this->output_scale_ = output_scale;
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return *this;
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}
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float output_scale() const {
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return this->output_scale_;
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}
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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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int16_t zero_point() const {
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return this->zero_point_;
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}
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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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size_t iterations() const {
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return this->iterations_;
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}
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void TestF16toQD8() const {
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xnnpack::ReplicableRandomDevice rng;
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xnnpack::Buffer<float> input_float((batch_size() - 1) * input_stride() +
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channels());
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xnnpack::Buffer<xnn_float16> input(XNN_EXTRA_BYTES / sizeof(xnn_float16) +
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(batch_size() - 1) * input_stride() +
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channels());
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xnnpack::Buffer<int8_t> output((batch_size() - 1) * output_stride() +
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channels());
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xnnpack::Buffer<xnn_quantization_params> quantization_params(
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batch_size() + XNN_EXTRA_QUANTIZATION_PARAMS);
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std::uniform_real_distribution<float> range_dist(-10, 10);
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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const float min_val = std::min(range_dist(rng), range_dist(rng));
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const float max_val = std::uniform_real_distribution<float>(
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min_val *
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(1.0f + std::numeric_limits<uint8_t>::max() * 6.103515625e-5f),
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10.0f)(rng);
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std::uniform_real_distribution<float> f32dist(min_val, max_val);
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std::generate(input_float.begin(), input_float.end(),
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[&]() { return f32dist(rng); });
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std::copy(input_float.begin(), input_float.end(), input.begin());
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std::copy(input.begin(), input.begin() + channels(),
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input_float.begin());
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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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xnn_status status = xnn_create_convert_nc_f16_qd8(0, &convert_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, 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)>
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auto_convert_op(convert_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_reshape_convert_nc_f16_qd8(
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convert_op, batch_size(), channels(), input_stride(),
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output_stride(), /*threadpool=*/nullptr));
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ASSERT_EQ(xnn_status_success, xnn_setup_convert_nc_f16_qd8(
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convert_op, input.data(), output.data(),
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quantization_params.data()));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(convert_op, /*threadpool=*/nullptr));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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const float* input_ptr = &input_float[i * input_stride()];
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const auto minmax =
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std::minmax_element(input_ptr, input_ptr + channels());
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const float rmin = math_min_f32(0.0f, *minmax.first);
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const float rmax = math_max_f32(0.0f, *minmax.second);
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const float max_acceptable_error =
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0.8f * (rmax - rmin) / std::numeric_limits<uint8_t>::max();
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for (size_t c = 0; c < channels(); c++) {
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float expected = input_float[i * input_stride() + c];
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int8_t quantized_val = (int)output[i * output_stride() + c];
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float dequantized_val =
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static_cast<float>(quantized_val -
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quantization_params[i].zero_point) *
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quantization_params[i].scale;
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ASSERT_NEAR(expected, dequantized_val, max_acceptable_error)
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<< "at batch " << i << " / " << batch_size() << ", channel " << c
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<< " / " << channels() << ", rmin=" << rmin << ", rmax=" << rmax
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<< ", quantization_params={zero_point="
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<< quantization_params[i].zero_point
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<< ", scale=" << quantization_params[i].scale << "}";
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}
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}
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}
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}
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void TestF32toQD8() const {
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xnnpack::ReplicableRandomDevice rng;
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xnnpack::Buffer<float> input(XNN_EXTRA_BYTES / sizeof(float) +
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(batch_size() - 1) * input_stride() + channels());
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xnnpack::Buffer<int8_t> output((batch_size() - 1) * output_stride() + channels());
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xnnpack::Buffer<xnn_quantization_params> quantization_params(batch_size() + XNN_EXTRA_QUANTIZATION_PARAMS);
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std::uniform_real_distribution<float> range_dist(-100000, 100000);
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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const float first_val = range_dist(rng);
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const float second_val = range_dist(rng);
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std::uniform_real_distribution<float> f32dist(std::min(first_val, second_val), std::max(first_val, second_val));
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
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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_qd8(
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/*flags=*/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, xnn_reshape_convert_nc_f32_qd8(convert_op, batch_size(),
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channels(), input_stride(), output_stride(), /*threadpool=*/nullptr));
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ASSERT_EQ(xnn_status_success, xnn_setup_convert_nc_f32_qd8(convert_op, input.data(), output.data(), quantization_params.data()));
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ASSERT_EQ(xnn_status_success, xnn_run_operator(convert_op, /*threadpool=*/nullptr));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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const float* input_ptr = &input[i * input_stride()];
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const auto minmax = std::minmax_element(input_ptr, input_ptr + channels());
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const float rmin = math_min_f32(0.0f, *minmax.first);
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const float rmax = math_max_f32(0.0f, *minmax.second);
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const float max_acceptable_error = 0.5001f * (rmax - rmin) / std::numeric_limits<uint8_t>::max();
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for (size_t c = 0; c < channels(); c++) {
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float expected = input[i * input_stride() + c];
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int8_t quantized_val = output[i * output_stride() + c];
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float dequantized_val =
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float((int)quantized_val - quantization_params[i].zero_point) *
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quantization_params[i].scale;
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EXPECT_NEAR(expected, dequantized_val, max_acceptable_error)
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<< "at batch " << i << " / " << batch_size() << ", channel " << c
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<< " / " << channels() << " scale "
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<< quantization_params[i].scale << " zp "
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<< quantization_params[i].zero_point << " int "
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<< (int)quantized_val;
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}
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}
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}
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}
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void TestF32toQDU8() const {
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xnnpack::ReplicableRandomDevice rng;
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xnnpack::Buffer<float> input(XNN_EXTRA_BYTES / sizeof(float) +
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(batch_size() - 1) * input_stride() +
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channels());
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xnnpack::Buffer<uint8_t> output((batch_size() - 1) * output_stride() +
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channels());
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xnnpack::Buffer<xnn_quantization_params> quantization_params(
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batch_size() + XNN_EXTRA_QUANTIZATION_PARAMS);
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// std::uniform_real_distribution<float> range_dist(-100000, 100000);
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// std::uniform_real_distribution<float> range_dist(-1, 1);
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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// const float first_val = range_dist(rng);
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// const float second_val = range_dist(rng);
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// std::uniform_real_distribution<float> f32dist(std::min(first_val,
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// second_val), std::max(first_val, second_val));
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std::uniform_real_distribution<float> f32dist(-1.f, 1.f);
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
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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, xnn_create_convert_nc_f32_qdu8(
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/*flags=*/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)>
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auto_convert_op(convert_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_reshape_convert_nc_f32_qdu8(
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convert_op, batch_size(), channels(), input_stride(),
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output_stride(), /*threadpool=*/nullptr));
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ASSERT_EQ(xnn_status_success, xnn_setup_convert_nc_f32_qdu8(
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convert_op, input.data(), output.data(),
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quantization_params.data()));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(convert_op, /*threadpool=*/nullptr));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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const float* input_ptr = &input[i * input_stride()];
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const auto minmax =
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std::minmax_element(input_ptr, input_ptr + channels());
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const float rmin = math_min_f32(0.0f, *minmax.first);
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const float rmax = math_max_f32(0.0f, *minmax.second);
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const float max_acceptable_error =
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0.5001f * (rmax - rmin) / std::numeric_limits<uint8_t>::max();
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for (size_t c = 0; c < channels(); c++) {
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float expected = input[i * input_stride() + c];
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uint8_t quantized_val = output[i * output_stride() + c];
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float dequantized_val = float(quantized_val - quantization_params[i].zero_point) * quantization_params[i].scale;
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EXPECT_NEAR(expected, dequantized_val, max_acceptable_error)
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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 TestF32toQP8() const {
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xnnpack::ReplicableRandomDevice rng;
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// The parameters of the GEMM config are used as packing parameters.
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const struct xnn_gemm_config* gemm_config = xnn_init_f32_gemm_nr2_config();
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xnnpack::Buffer<float> input(XNN_EXTRA_BYTES / sizeof(float) +
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(batch_size() - 1) * input_stride() + channels());
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xnnpack::Buffer<int8_t> output(xnn_x8_packq_f32qp8_packed_size(
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batch_size(), channels(), gemm_config->mr, 1 << gemm_config->log2_kr,
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1 << gemm_config->log2_sr));
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std::uniform_real_distribution<float> range_dist(-100000, 100000);
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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const float first_val = range_dist(rng);
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const float second_val = range_dist(rng);
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std::uniform_real_distribution<float> f32dist(
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std::min(first_val, second_val), std::max(first_val, second_val));
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
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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_qp8(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)>
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auto_convert_op(convert_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_reshape_convert_nc_f32_qp8(convert_op, batch_size(),
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channels(), input_stride(),
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/*threadpool=*/nullptr));
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ASSERT_EQ(xnn_status_success,
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xnn_setup_convert_nc_f32_qp8(convert_op, input.data(),
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output.data()));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(convert_op, /*threadpool=*/nullptr));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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// const float* input_ptr = &input[i * input_stride()];
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// const auto minmax =
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// std::minmax_element(input_ptr, input_ptr + channels());
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// const float rmin = math_min_f32(0.0f, *minmax.first);
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// const float rmax = math_max_f32(0.0f, *minmax.second);
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// const float max_acceptable_error =
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// 0.5001f * (rmax - rmin) / std::numeric_limits<uint8_t>::max();
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// TODO(b/340399245) - Find a way to extract individual quantized values
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// from the packing?
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ASSERT_TRUE(true);
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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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float input_scale_{150.0f};
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float output_scale_{3.0f};
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int16_t zero_point_{1};
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size_t iterations_{15};
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};
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TEST(CONVERT_NC_F16_QD8, unit_batch) {
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for (size_t channels = 1; channels < 100; channels++) {
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ConvertOperatorTester()
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.batch_size(1)
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.channels(channels)
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.iterations(3)
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.TestF16toQD8();
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}
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}
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TEST(CONVERT_NC_F16_QD8, small_batch) {
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for (size_t channels = 1; channels < 100; channels++) {
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ConvertOperatorTester()
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.batch_size(3)
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.channels(channels)
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.iterations(3)
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.TestF16toQD8();
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}
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}
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TEST(CONVERT_NC_F16_QD8, small_batch_with_input_stride) {
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for (size_t channels = 10; channels < 11; channels += 15) {
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ConvertOperatorTester()
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.batch_size(3)
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.channels(channels)
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.input_stride(129)
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.iterations(3)
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.TestF16toQD8();
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}
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}
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TEST(CONVERT_NC_F16_QD8, small_batch_with_output_stride) {
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for (size_t channels = 1; channels < 100; channels += 15) {
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ConvertOperatorTester()
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.batch_size(3)
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.channels(channels)
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.output_stride(117)
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.iterations(3)
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.TestF16toQD8();
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}
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}
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TEST(CONVERT_NC_F16_QD8, small_batch_with_input_and_output_stride) {
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for (size_t channels = 1; channels < 100; channels += 15) {
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ConvertOperatorTester()
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.batch_size(3)
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.channels(channels)
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.input_stride(129)
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.output_stride(117)
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.iterations(3)
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.TestF16toQD8();
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}
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}
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TEST(CONVERT_NC_F32_QD8, unit_batch) {
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for (size_t channels = 1; channels < 100; channels++) {
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ConvertOperatorTester()
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.batch_size(1)
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.channels(channels)
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.iterations(3)
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.TestF32toQD8();
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}
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}
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TEST(CONVERT_NC_F32_QD8, small_batch) {
|
|
for (size_t channels = 1; channels < 100; channels++) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.iterations(3)
|
|
.TestF32toQD8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QD8, small_batch_with_input_stride) {
|
|
for (size_t channels = 10; channels < 11; channels += 15) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.input_stride(129)
|
|
.iterations(3)
|
|
.TestF32toQD8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QD8, small_batch_with_output_stride) {
|
|
for (size_t channels = 1; channels < 100; channels += 15) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.output_stride(117)
|
|
.iterations(3)
|
|
.TestF32toQD8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QD8, small_batch_with_input_and_output_stride) {
|
|
for (size_t channels = 1; channels < 100; channels += 15) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.input_stride(129)
|
|
.output_stride(117)
|
|
.iterations(3)
|
|
.TestF32toQD8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QP8, unit_batch) {
|
|
for (size_t channels = 1; channels < 100; channels++) {
|
|
ConvertOperatorTester()
|
|
.batch_size(1)
|
|
.channels(channels)
|
|
.iterations(3)
|
|
.TestF32toQD8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QP8, small_batch) {
|
|
for (size_t channels = 1; channels < 100; channels++) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.iterations(3)
|
|
.TestF32toQD8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QP8, small_batch_with_input_stride) {
|
|
for (size_t channels = 10; channels < 11; channels += 15) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.input_stride(129)
|
|
.iterations(3)
|
|
.TestF32toQD8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QDU8, unit_batch) {
|
|
for (size_t channels = 1; channels < 100; channels++) {
|
|
ConvertOperatorTester()
|
|
.batch_size(1)
|
|
.channels(channels)
|
|
.iterations(3)
|
|
.TestF32toQDU8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QDU8, small_batch) {
|
|
for (size_t channels = 1; channels < 100; channels++) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.iterations(3)
|
|
.TestF32toQDU8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QDU8, small_batch_with_input_stride) {
|
|
for (size_t channels = 10; channels < 11; channels += 15) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.input_stride(129)
|
|
.iterations(3)
|
|
.TestF32toQDU8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QDU8, small_batch_with_output_stride) {
|
|
for (size_t channels = 1; channels < 100; channels += 15) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.output_stride(117)
|
|
.iterations(3)
|
|
.TestF32toQDU8();
|
|
}
|
|
}
|
|
|
|
TEST(CONVERT_NC_F32_QDU8, small_batch_with_input_and_output_stride) {
|
|
for (size_t channels = 1; channels < 100; channels += 15) {
|
|
ConvertOperatorTester()
|
|
.batch_size(3)
|
|
.channels(channels)
|
|
.input_stride(129)
|
|
.output_stride(117)
|
|
.iterations(3)
|
|
.TestF32toQDU8();
|
|
}
|
|
}
|