Initial open-source release
PiperOrigin-RevId: 271685289
diff --git a/test/fully-connected-operator-tester.h b/test/fully-connected-operator-tester.h
new file mode 100644
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+++ b/test/fully-connected-operator-tester.h
@@ -0,0 +1,308 @@
+// 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 int32_t accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
+ const int32_t 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};
+};