| // Copyright (c) Facebook, Inc. and its affiliates. |
| // All rights reserved. |
| // |
| // Copyright 2019 Google LLC |
| // |
| // This source code is licensed under the BSD-style license found in the |
| // LICENSE file in the root directory of this source tree. |
| |
| #pragma once |
| |
| #include <gtest/gtest.h> |
| |
| #include <cstddef> |
| #include <cstdlib> |
| #include <algorithm> |
| #include <cmath> |
| #include <functional> |
| #include <random> |
| #include <vector> |
| |
| #include <xnnpack.h> |
| |
| |
| class FullyConnectedOperatorTester { |
| public: |
| inline FullyConnectedOperatorTester& input_channels(size_t input_channels) { |
| assert(input_channels >= 1); |
| this->input_channels_ = input_channels; |
| return *this; |
| } |
| |
| inline size_t input_channels() const { |
| return this->input_channels_; |
| } |
| |
| inline FullyConnectedOperatorTester& output_channels(size_t output_channels) { |
| assert(output_channels >= 1); |
| this->output_channels_ = output_channels; |
| return *this; |
| } |
| |
| inline size_t output_channels() const { |
| return this->output_channels_; |
| } |
| |
| inline FullyConnectedOperatorTester& batch_size(size_t batch_size) { |
| assert(batch_size >= 1); |
| this->batch_size_ = batch_size; |
| return *this; |
| } |
| |
| inline size_t batch_size() const { |
| return this->batch_size_; |
| } |
| |
| inline FullyConnectedOperatorTester& input_stride(size_t input_stride) { |
| assert(input_stride >= 1); |
| this->input_stride_ = input_stride; |
| return *this; |
| } |
| |
| inline size_t input_stride() const { |
| if (this->input_stride_ == 0) { |
| return input_channels(); |
| } else { |
| assert(this->input_stride_ >= input_channels()); |
| return this->input_stride_; |
| } |
| } |
| |
| inline FullyConnectedOperatorTester& output_stride(size_t output_stride) { |
| assert(output_stride >= 1); |
| this->output_stride_ = output_stride; |
| return *this; |
| } |
| |
| inline size_t output_stride() const { |
| if (this->output_stride_ == 0) { |
| return output_channels(); |
| } else { |
| assert(this->output_stride_ >= output_channels()); |
| return this->output_stride_; |
| } |
| } |
| |
| inline FullyConnectedOperatorTester& qmin(uint8_t qmin) { |
| this->qmin_ = qmin; |
| return *this; |
| } |
| |
| inline uint8_t qmin() const { |
| return this->qmin_; |
| } |
| |
| inline FullyConnectedOperatorTester& qmax(uint8_t qmax) { |
| this->qmax_ = qmax; |
| return *this; |
| } |
| |
| inline uint8_t qmax() const { |
| return this->qmax_; |
| } |
| |
| inline FullyConnectedOperatorTester& iterations(size_t iterations) { |
| this->iterations_ = iterations; |
| return *this; |
| } |
| |
| inline size_t iterations() const { |
| return this->iterations_; |
| } |
| |
| void TestQ8() const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng); |
| auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng); |
| |
| std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + |
| (batch_size() - 1) * input_stride() + input_channels()); |
| std::vector<uint8_t> kernel(output_channels() * input_channels()); |
| std::vector<int32_t> bias(output_channels()); |
| std::vector<uint8_t> output((batch_size() - 1) * output_stride() + output_channels()); |
| std::vector<int32_t> accumulators(batch_size() * output_channels()); |
| std::vector<double> output_ref(batch_size() * output_channels()); |
| |
| const uint8_t input_zero_point = 127; |
| const uint8_t kernel_zero_point = 127; |
| |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| std::generate(kernel.begin(), kernel.end(), std::ref(u8rng)); |
| std::generate(bias.begin(), bias.end(), std::ref(s32rng)); |
| std::fill(output.begin(), output.end(), 0xA5); |
| std::fill(accumulators.begin(), accumulators.end(), 0); |
| |
| // Compute reference results, without renormalization. |
| for (size_t i = 0; i < batch_size(); i++) { |
| for (size_t oc = 0; oc < output_channels(); oc++) { |
| accumulators[i * output_channels() + oc] = bias[oc]; |
| } |
| } |
| for (size_t i = 0; i < batch_size(); i++) { |
| for (size_t oc = 0; oc < output_channels(); oc++) { |
| for (size_t ic = 0; ic < input_channels(); ic++) { |
| accumulators[i * output_channels() + oc] += |
| (int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) * |
| (int32_t(kernel[oc * input_channels() + ic]) - int32_t(kernel_zero_point)); |
| } |
| } |
| } |
| |
| // Compute renormalization parameters. |
| const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend()); |
| const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend()); |
| |
| const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0; |
| const uint8_t output_zero_point = uint8_t(std::max(std::min( |
| lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale), |
| long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min()))); |
| |
| // Renormalize reference results. |
| std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(), |
| [this, output_scale, output_zero_point](int32_t x) -> double { |
| return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point); |
| }); |
| |
| // Create, setup, run, and destroy Fully Connected operator. |
| ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| xnn_operator_t fully_connected_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_fully_connected_nc_q8( |
| input_channels(), output_channels(), |
| input_stride(), output_stride(), |
| input_zero_point, 1.0f /* input scale */, |
| kernel_zero_point, 1.0f /* kernel scale */, |
| kernel.data(), bias.data(), |
| output_zero_point, output_scale, qmin(), qmax(), |
| 0, &fully_connected_op)); |
| |
| // Smart pointer to automatically delete fully_connected_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_fully_connected_op(fully_connected_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_fully_connected_nc_q8( |
| fully_connected_op, |
| batch_size(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(fully_connected_op, nullptr /* thread pool */)); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| for (size_t c = 0; c < output_channels(); c++) { |
| ASSERT_LE(int32_t(output[i * output_stride() + c]), int32_t(qmax())) |
| << "batch index = " << i << ", channel = " << c; |
| ASSERT_GE(int32_t(output[i * output_stride() + c]), int32_t(qmin())) |
| << "batch index = " << i << ", channel = " << c; |
| ASSERT_NEAR( |
| output_ref[i * output_channels() + c], |
| double(output[i * output_stride() + c]) - double(output_zero_point), |
| 0.9) |
| << "batch index = " << i << ", channel = " << c; |
| } |
| } |
| } |
| } |
| |
| void TestF32() const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng); |
| |
| std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
| (batch_size() - 1) * input_stride() + input_channels()); |
| std::vector<float> kernel(output_channels() * input_channels()); |
| std::vector<float> bias(output_channels()); |
| std::vector<float> output((batch_size() - 1) * output_stride() + output_channels()); |
| std::vector<float> output_ref(batch_size() * output_channels()); |
| |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::generate(kernel.begin(), kernel.end(), std::ref(f32rng)); |
| std::generate(bias.begin(), bias.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), nanf("")); |
| |
| // Compute reference results, without renormalization. |
| for (size_t i = 0; i < batch_size(); i++) { |
| for (size_t oc = 0; oc < output_channels(); oc++) { |
| output_ref[i * output_channels() + oc] = bias[oc]; |
| } |
| } |
| for (size_t i = 0; i < batch_size(); i++) { |
| for (size_t oc = 0; oc < output_channels(); oc++) { |
| for (size_t ic = 0; ic < input_channels(); ic++) { |
| output_ref[i * output_channels() + oc] += |
| input[i * input_stride() + ic] * kernel[oc * input_channels() + ic]; |
| } |
| } |
| } |
| |
| // Compute clamping parameters. |
| const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
| const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
| |
| const float output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin()); |
| const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 255.0f * float(255 - qmax()); |
| |
| // Clamp reference results. |
| for (float& value : output_ref) { |
| value = std::max(std::min(value, output_max), output_min); |
| } |
| |
| // Create, setup, run, and destroy Fully Connected operator. |
| ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| xnn_operator_t fully_connected_op = nullptr; |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_create_fully_connected_nc_f32( |
| input_channels(), output_channels(), |
| input_stride(), output_stride(), |
| kernel.data(), bias.data(), |
| output_min, output_max, |
| 0, &fully_connected_op)); |
| |
| // Smart pointer to automatically delete fully_connected_op. |
| std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_fully_connected_op(fully_connected_op, xnn_delete_operator); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_setup_fully_connected_nc_f32( |
| fully_connected_op, |
| batch_size(), |
| input.data(), output.data(), |
| nullptr /* thread pool */)); |
| |
| ASSERT_EQ(xnn_status_success, |
| xnn_run_operator(fully_connected_op, nullptr /* thread pool */)); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| for (size_t c = 0; c < output_channels(); c++) { |
| ASSERT_LE(output[i * output_stride() + c], output_max) |
| << "batch index = " << i << ", channel = " << c; |
| ASSERT_GE(output[i * output_stride() + c], output_min) |
| << "batch index = " << i << ", channel = " << c; |
| ASSERT_NEAR( |
| output_ref[i * output_channels() + c], |
| output[i * output_stride() + c], |
| 1.0e-4 * std::abs(output_ref[i * output_channels() + c])) |
| << "batch index = " << i << ", channel = " << c; |
| } |
| } |
| } |
| } |
| |
| private: |
| size_t input_channels_{1}; |
| size_t input_stride_{0}; |
| size_t output_channels_{1}; |
| size_t output_stride_{0}; |
| size_t batch_size_{1}; |
| uint8_t qmin_{0}; |
| uint8_t qmax_{255}; |
| size_t iterations_{1}; |
| }; |