test/leaky-relu-operator-tester.h - platform/external/XNNPACK - Git at Google

 // 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 <algorithm>
 #include <cassert>
 #include <cmath>
 #include <cstddef>
 #include <cstdlib>
 #include <limits>
 #include <random>
 #include <vector>

 #include <fp16.h>

 #include <xnnpack.h>

 static uint16_t flush_fp16_denormal_to_zero(uint16_t v) {
   return (v & UINT16_C(0x7C00)) == 0 ? v & UINT16_C(0x8000) : v;
 };


 class LeakyReLUOperatorTester {
  public:
   inline LeakyReLUOperatorTester& channels(size_t channels) {
     assert(channels != 0);
     this->channels_ = channels;
     return *this;
   }

   inline size_t channels() const {
     return this->channels_;
   }

   inline LeakyReLUOperatorTester& input_stride(size_t input_stride) {
     assert(input_stride != 0);
     this->input_stride_ = input_stride;
     return *this;
   }

   inline size_t input_stride() const {
     if (this->input_stride_ == 0) {
       return this->channels_;
     } else {
       assert(this->input_stride_ >= this->channels_);
       return this->input_stride_;
     }
   }

   inline LeakyReLUOperatorTester& output_stride(size_t output_stride) {
     assert(output_stride != 0);
     this->output_stride_ = output_stride;
     return *this;
   }

   inline size_t output_stride() const {
     if (this->output_stride_ == 0) {
       return this->channels_;
     } else {
       assert(this->output_stride_ >= this->channels_);
       return this->output_stride_;
     }
   }

   inline LeakyReLUOperatorTester& batch_size(size_t batch_size) {
     assert(batch_size != 0);
     this->batch_size_ = batch_size;
     return *this;
   }

   inline size_t batch_size() const {
     return this->batch_size_;
   }

   inline LeakyReLUOperatorTester& negative_slope(float negative_slope) {
     assert(std::isnormal(negative_slope));
     this->negative_slope_ = negative_slope;
     return *this;
   }

   inline float negative_slope() const {
     return this->negative_slope_;
   }

   inline LeakyReLUOperatorTester& input_scale(float input_scale) {
     assert(input_scale > 0.0f);
     assert(std::isnormal(input_scale));
     this->input_scale_ = input_scale;
     return *this;
   }

   inline float input_scale() const {
     return this->input_scale_;
   }

   inline LeakyReLUOperatorTester& input_zero_point(int16_t input_zero_point) {
     this->input_zero_point_ = input_zero_point;
     return *this;
   }

   inline int16_t input_zero_point() const {
     return this->input_zero_point_;
   }

   inline LeakyReLUOperatorTester& output_scale(float output_scale) {
     assert(output_scale > 0.0f);
     assert(std::isnormal(output_scale));
     this->output_scale_ = output_scale;
     return *this;
   }

   inline float output_scale() const {
     return this->output_scale_;
   }

   inline LeakyReLUOperatorTester& output_zero_point(int16_t output_zero_point) {
     this->output_zero_point_ = output_zero_point;
     return *this;
   }

   inline int16_t output_zero_point() const {
     return this->output_zero_point_;
   }

   inline LeakyReLUOperatorTester& iterations(size_t iterations) {
     this->iterations_ = iterations;
     return *this;
   }

   inline size_t iterations() const {
     return this->iterations_;
   }

   void TestF16() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     std::uniform_real_distribution<float> f32dist(-1.0f, 1.0f);

     std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + (batch_size() - 1) * input_stride() + channels());
     std::vector<uint16_t> output((batch_size() - 1) * output_stride() + channels());
     std::vector<float> output_ref(batch_size() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), [&]() {
         return flush_fp16_denormal_to_zero(fp16_ieee_from_fp32_value(f32dist(rng)));
       });
       std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
       const uint16_t negative_slope_as_half = fp16_ieee_from_fp32_value(negative_slope());
       const float negative_slope_as_float = fp16_ieee_to_fp32_value(negative_slope_as_half);

       // Compute reference results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           const float x = fp16_ieee_to_fp32_value(input[i * input_stride() + c]);
           const float y = std::signbit(x) ? x * negative_slope_as_float : x;
           output_ref[i * channels() + c] = y;
         }
       }

       // Create, setup, run, and destroy Leaky ReLU operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t leaky_relu_op = nullptr;

       const xnn_status status = xnn_create_leaky_relu_nc_f16(
           channels(), input_stride(), output_stride(),
           negative_slope(),
           0, &leaky_relu_op);
       if (status == xnn_status_unsupported_hardware) {
         GTEST_SKIP();
       }
       ASSERT_EQ(xnn_status_success, status);
       ASSERT_NE(nullptr, leaky_relu_op);

       // Smart pointer to automatically delete leaky_relu_op.
       std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);

       ASSERT_EQ(xnn_status_success,
         xnn_setup_leaky_relu_nc_f16(
           leaky_relu_op,
           batch_size(),
           input.data(), output.data(),
           nullptr /* thread pool */));

       ASSERT_EQ(xnn_status_success,
         xnn_run_operator(leaky_relu_op, nullptr /* thread pool */));

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_NEAR(
               fp16_ieee_to_fp32_value(output[i * output_stride() + c]),
               output_ref[i * channels() + c],
               std::max(2.0e-4f, std::abs(output_ref[i * channels() + c]) * 1.0e-3f))
             << "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
         }
       }
     }
   }

   void TestF32() const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     std::uniform_real_distribution<float> f32dist(-1.0f, 1.0f);

     std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + (batch_size() - 1) * input_stride() + channels());
     std::vector<float> output((batch_size() - 1) * output_stride() + channels());
     std::vector<float> output_ref(batch_size() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
       std::fill(output.begin(), output.end(), std::nanf(""));

       // Compute reference results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           const float x = input[i * input_stride() + c];
           const float y = std::signbit(x) ? x * negative_slope() : x;
           output_ref[i * channels() + c] = y;
         }
       }

       // Create, setup, run, and destroy Leaky ReLU operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t leaky_relu_op = nullptr;

       ASSERT_EQ(xnn_status_success,
         xnn_create_leaky_relu_nc_f32(
           channels(), input_stride(), output_stride(),
           negative_slope(),
           0, &leaky_relu_op));
       ASSERT_NE(nullptr, leaky_relu_op);

       // Smart pointer to automatically delete leaky_relu_op.
       std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);

       ASSERT_EQ(xnn_status_success,
         xnn_setup_leaky_relu_nc_f32(
           leaky_relu_op,
           batch_size(),
           input.data(), output.data(),
           nullptr /* thread pool */));

       ASSERT_EQ(xnn_status_success,
         xnn_run_operator(leaky_relu_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[i * output_stride() + c], output_ref[i * channels() + c])
             << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
             << ", input " << input[i * input_stride() + c] << ", negative slope " << negative_slope();
         }
       }
     }
   }

   void TestQS8() const {
     ASSERT_GE(input_zero_point(), std::numeric_limits<int8_t>::min());
     ASSERT_LE(input_zero_point(), std::numeric_limits<int8_t>::max());
     ASSERT_GE(output_zero_point(), std::numeric_limits<int8_t>::min());
     ASSERT_LE(output_zero_point(), std::numeric_limits<int8_t>::max());

     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     std::uniform_int_distribution<int32_t> i8dist(
       std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());

     std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) + (batch_size() - 1) * input_stride() + channels());
     std::vector<int8_t> output((batch_size() - 1) * output_stride() + channels());
     std::vector<float> output_ref(batch_size() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });
       std::fill(output.begin(), output.end(), INT8_C(0xA5));

       // Compute reference results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           const float x = input_scale() * (int32_t(input[i * input_stride() + c]) - input_zero_point());
           float y = (x < 0.0f ? x * negative_slope() : x) / output_scale() + float(output_zero_point());
           y = std::max<float>(y, std::numeric_limits<int8_t>::min());
           y = std::min<float>(y, std::numeric_limits<int8_t>::max());
           output_ref[i * channels() + c] = y;
         }
       }

       // Create, setup, run, and destroy Leaky ReLU operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t leaky_relu_op = nullptr;

       ASSERT_EQ(xnn_status_success,
         xnn_create_leaky_relu_nc_qs8(
           channels(), input_stride(), output_stride(),
           negative_slope(),
           input_zero_point(), input_scale(),
           output_zero_point(), output_scale(),
           0, &leaky_relu_op));
       ASSERT_NE(nullptr, leaky_relu_op);

       // Smart pointer to automatically delete leaky_relu_op.
       std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);

       ASSERT_EQ(xnn_status_success,
         xnn_setup_leaky_relu_nc_qs8(
           leaky_relu_op,
           batch_size(),
           input.data(), output.data(),
           nullptr /* thread pool */));

       ASSERT_EQ(xnn_status_success,
         xnn_run_operator(leaky_relu_op, nullptr /* thread pool */));

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.9f)
             << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
             << ", input " << int32_t(input[i * input_stride() + c])
             << ", input zero point " << input_zero_point() << ", output zero point " << output_zero_point()
             << ", positive input-to-output ratio " << (input_scale() / output_scale())
             << ", negative input-to-output ratio " << (input_scale() / output_scale() * negative_slope());
         }
       }
     }
   }

   void TestQU8() const {
     ASSERT_GE(input_zero_point(), std::numeric_limits<uint8_t>::min());
     ASSERT_LE(input_zero_point(), std::numeric_limits<uint8_t>::max());
     ASSERT_GE(output_zero_point(), std::numeric_limits<uint8_t>::min());
     ASSERT_LE(output_zero_point(), std::numeric_limits<uint8_t>::max());

     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     std::uniform_int_distribution<int32_t> u8dist(
       std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());

     std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + (batch_size() - 1) * input_stride() + channels());
     std::vector<uint8_t> output((batch_size() - 1) * output_stride() + channels());
     std::vector<float> output_ref(batch_size() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); });
       std::fill(output.begin(), output.end(), UINT8_C(0xA5));

       // Compute reference results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           const float x = input_scale() * (int32_t(input[i * input_stride() + c]) - input_zero_point());
           float y = (x < 0.0f ? x * negative_slope() : x) / output_scale() + float(output_zero_point());
           y = std::max<float>(y, std::numeric_limits<uint8_t>::min());
           y = std::min<float>(y, std::numeric_limits<uint8_t>::max());
           output_ref[i * channels() + c] = y;
         }
       }

       // Create, setup, run, and destroy Leaky ReLU operator.
       ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
       xnn_operator_t leaky_relu_op = nullptr;

       ASSERT_EQ(xnn_status_success,
         xnn_create_leaky_relu_nc_qu8(
           channels(), input_stride(), output_stride(),
           negative_slope(),
           input_zero_point(), input_scale(),
           output_zero_point(), output_scale(),
           0, &leaky_relu_op));
       ASSERT_NE(nullptr, leaky_relu_op);

       // Smart pointer to automatically delete leaky_relu_op.
       std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);

       ASSERT_EQ(xnn_status_success,
         xnn_setup_leaky_relu_nc_qu8(
           leaky_relu_op,
           batch_size(),
           input.data(), output.data(),
           nullptr /* thread pool */));

       ASSERT_EQ(xnn_status_success,
         xnn_run_operator(leaky_relu_op, nullptr /* thread pool */));

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.9f)
             << "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
             << ", input " << int32_t(input[i * input_stride() + c])
             << ", input zero point " << input_zero_point() << ", output zero point " << output_zero_point()
             << ", positive input-to-output ratio " << (input_scale() / output_scale())
             << ", negative input-to-output ratio " << (input_scale() / output_scale() * negative_slope());
         }
       }
     }
   }

  private:
   size_t batch_size_{1};
   size_t channels_{1};
   size_t input_stride_{0};
   size_t output_stride_{0};
   float negative_slope_{0.3f};
   float output_scale_{0.75f};
   int16_t output_zero_point_{53};
   float input_scale_{1.25f};
   int16_t input_zero_point_{41};
   size_t iterations_{15};
 };
	// 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 <algorithm>
	#include <cassert>
	#include <cmath>
	#include <cstddef>
	#include <cstdlib>
	#include <limits>
	#include <random>
	#include <vector>

	#include <fp16.h>

	#include <xnnpack.h>

	static uint16_t flush_fp16_denormal_to_zero(uint16_t v) {
	return (v & UINT16_C(0x7C00)) == 0 ? v & UINT16_C(0x8000) : v;
	};


	class LeakyReLUOperatorTester {
	public:
	inline LeakyReLUOperatorTester& channels(size_t channels) {
	assert(channels != 0);
	this->channels_ = channels;
	return *this;
	}

	inline size_t channels() const {
	return this->channels_;
	}

	inline LeakyReLUOperatorTester& input_stride(size_t input_stride) {
	assert(input_stride != 0);
	this->input_stride_ = input_stride;
	return *this;
	}

	inline size_t input_stride() const {
	if (this->input_stride_ == 0) {
	return this->channels_;
	} else {
	assert(this->input_stride_ >= this->channels_);
	return this->input_stride_;
	}
	}

	inline LeakyReLUOperatorTester& output_stride(size_t output_stride) {
	assert(output_stride != 0);
	this->output_stride_ = output_stride;
	return *this;
	}

	inline size_t output_stride() const {
	if (this->output_stride_ == 0) {
	return this->channels_;
	} else {
	assert(this->output_stride_ >= this->channels_);
	return this->output_stride_;
	}
	}

	inline LeakyReLUOperatorTester& batch_size(size_t batch_size) {
	assert(batch_size != 0);
	this->batch_size_ = batch_size;
	return *this;
	}

	inline size_t batch_size() const {
	return this->batch_size_;
	}

	inline LeakyReLUOperatorTester& negative_slope(float negative_slope) {
	assert(std::isnormal(negative_slope));
	this->negative_slope_ = negative_slope;
	return *this;
	}

	inline float negative_slope() const {
	return this->negative_slope_;
	}

	inline LeakyReLUOperatorTester& input_scale(float input_scale) {
	assert(input_scale > 0.0f);
	assert(std::isnormal(input_scale));
	this->input_scale_ = input_scale;
	return *this;
	}

	inline float input_scale() const {
	return this->input_scale_;
	}

	inline LeakyReLUOperatorTester& input_zero_point(int16_t input_zero_point) {
	this->input_zero_point_ = input_zero_point;
	return *this;
	}

	inline int16_t input_zero_point() const {
	return this->input_zero_point_;
	}

	inline LeakyReLUOperatorTester& output_scale(float output_scale) {
	assert(output_scale > 0.0f);
	assert(std::isnormal(output_scale));
	this->output_scale_ = output_scale;
	return *this;
	}

	inline float output_scale() const {
	return this->output_scale_;
	}

	inline LeakyReLUOperatorTester& output_zero_point(int16_t output_zero_point) {
	this->output_zero_point_ = output_zero_point;
	return *this;
	}

	inline int16_t output_zero_point() const {
	return this->output_zero_point_;
	}

	inline LeakyReLUOperatorTester& iterations(size_t iterations) {
	this->iterations_ = iterations;
	return *this;
	}

	inline size_t iterations() const {
	return this->iterations_;
	}

	void TestF16() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	std::uniform_real_distribution<float> f32dist(-1.0f, 1.0f);

	std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + (batch_size() - 1) * input_stride() + channels());
	std::vector<uint16_t> output((batch_size() - 1) * output_stride() + channels());
	std::vector<float> output_ref(batch_size() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), [&]() {
	return flush_fp16_denormal_to_zero(fp16_ieee_from_fp32_value(f32dist(rng)));
	});
	std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
	const uint16_t negative_slope_as_half = fp16_ieee_from_fp32_value(negative_slope());
	const float negative_slope_as_float = fp16_ieee_to_fp32_value(negative_slope_as_half);

	// Compute reference results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	const float x = fp16_ieee_to_fp32_value(input[i * input_stride() + c]);
	const float y = std::signbit(x) ? x * negative_slope_as_float : x;
	output_ref[i * channels() + c] = y;
	}
	}

	// Create, setup, run, and destroy Leaky ReLU operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t leaky_relu_op = nullptr;

	const xnn_status status = xnn_create_leaky_relu_nc_f16(
	channels(), input_stride(), output_stride(),
	negative_slope(),
	0, &leaky_relu_op);
	if (status == xnn_status_unsupported_hardware) {
	GTEST_SKIP();
	}
	ASSERT_EQ(xnn_status_success, status);
	ASSERT_NE(nullptr, leaky_relu_op);

	// Smart pointer to automatically delete leaky_relu_op.
	std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);

	ASSERT_EQ(xnn_status_success,
	xnn_setup_leaky_relu_nc_f16(
	leaky_relu_op,
	batch_size(),
	input.data(), output.data(),
	nullptr /* thread pool */));

	ASSERT_EQ(xnn_status_success,
	xnn_run_operator(leaky_relu_op, nullptr /* thread pool */));

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_NEAR(
	fp16_ieee_to_fp32_value(output[i * output_stride() + c]),
	output_ref[i * channels() + c],
	std::max(2.0e-4f, std::abs(output_ref[i * channels() + c]) * 1.0e-3f))
	<< "at position " << i << " / " << batch_size() << ", channel " << c << " / " << channels();
	}
	}
	}
	}

	void TestF32() const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	std::uniform_real_distribution<float> f32dist(-1.0f, 1.0f);

	std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + (batch_size() - 1) * input_stride() + channels());
	std::vector<float> output((batch_size() - 1) * output_stride() + channels());
	std::vector<float> output_ref(batch_size() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
	std::fill(output.begin(), output.end(), std::nanf(""));

	// Compute reference results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	const float x = input[i * input_stride() + c];
	const float y = std::signbit(x) ? x * negative_slope() : x;
	output_ref[i * channels() + c] = y;
	}
	}

	// Create, setup, run, and destroy Leaky ReLU operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t leaky_relu_op = nullptr;

	ASSERT_EQ(xnn_status_success,
	xnn_create_leaky_relu_nc_f32(
	channels(), input_stride(), output_stride(),
	negative_slope(),
	0, &leaky_relu_op));
	ASSERT_NE(nullptr, leaky_relu_op);

	// Smart pointer to automatically delete leaky_relu_op.
	std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);

	ASSERT_EQ(xnn_status_success,
	xnn_setup_leaky_relu_nc_f32(
	leaky_relu_op,
	batch_size(),
	input.data(), output.data(),
	nullptr /* thread pool */));

	ASSERT_EQ(xnn_status_success,
	xnn_run_operator(leaky_relu_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[i * output_stride() + c], output_ref[i * channels() + c])
	<< "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
	<< ", input " << input[i * input_stride() + c] << ", negative slope " << negative_slope();
	}
	}
	}
	}

	void TestQS8() const {
	ASSERT_GE(input_zero_point(), std::numeric_limits<int8_t>::min());
	ASSERT_LE(input_zero_point(), std::numeric_limits<int8_t>::max());
	ASSERT_GE(output_zero_point(), std::numeric_limits<int8_t>::min());
	ASSERT_LE(output_zero_point(), std::numeric_limits<int8_t>::max());

	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	std::uniform_int_distribution<int32_t> i8dist(
	std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());

	std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) + (batch_size() - 1) * input_stride() + channels());
	std::vector<int8_t> output((batch_size() - 1) * output_stride() + channels());
	std::vector<float> output_ref(batch_size() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });
	std::fill(output.begin(), output.end(), INT8_C(0xA5));

	// Compute reference results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	const float x = input_scale() * (int32_t(input[i * input_stride() + c]) - input_zero_point());
	float y = (x < 0.0f ? x * negative_slope() : x) / output_scale() + float(output_zero_point());
	y = std::max<float>(y, std::numeric_limits<int8_t>::min());
	y = std::min<float>(y, std::numeric_limits<int8_t>::max());
	output_ref[i * channels() + c] = y;
	}
	}

	// Create, setup, run, and destroy Leaky ReLU operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t leaky_relu_op = nullptr;

	ASSERT_EQ(xnn_status_success,
	xnn_create_leaky_relu_nc_qs8(
	channels(), input_stride(), output_stride(),
	negative_slope(),
	input_zero_point(), input_scale(),
	output_zero_point(), output_scale(),
	0, &leaky_relu_op));
	ASSERT_NE(nullptr, leaky_relu_op);

	// Smart pointer to automatically delete leaky_relu_op.
	std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);

	ASSERT_EQ(xnn_status_success,
	xnn_setup_leaky_relu_nc_qs8(
	leaky_relu_op,
	batch_size(),
	input.data(), output.data(),
	nullptr /* thread pool */));

	ASSERT_EQ(xnn_status_success,
	xnn_run_operator(leaky_relu_op, nullptr /* thread pool */));

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.9f)
	<< "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
	<< ", input " << int32_t(input[i * input_stride() + c])
	<< ", input zero point " << input_zero_point() << ", output zero point " << output_zero_point()
	<< ", positive input-to-output ratio " << (input_scale() / output_scale())
	<< ", negative input-to-output ratio " << (input_scale() / output_scale() * negative_slope());
	}
	}
	}
	}

	void TestQU8() const {
	ASSERT_GE(input_zero_point(), std::numeric_limits<uint8_t>::min());
	ASSERT_LE(input_zero_point(), std::numeric_limits<uint8_t>::max());
	ASSERT_GE(output_zero_point(), std::numeric_limits<uint8_t>::min());
	ASSERT_LE(output_zero_point(), std::numeric_limits<uint8_t>::max());

	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	std::uniform_int_distribution<int32_t> u8dist(
	std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());

	std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + (batch_size() - 1) * input_stride() + channels());
	std::vector<uint8_t> output((batch_size() - 1) * output_stride() + channels());
	std::vector<float> output_ref(batch_size() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); });
	std::fill(output.begin(), output.end(), UINT8_C(0xA5));

	// Compute reference results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	const float x = input_scale() * (int32_t(input[i * input_stride() + c]) - input_zero_point());
	float y = (x < 0.0f ? x * negative_slope() : x) / output_scale() + float(output_zero_point());
	y = std::max<float>(y, std::numeric_limits<uint8_t>::min());
	y = std::min<float>(y, std::numeric_limits<uint8_t>::max());
	output_ref[i * channels() + c] = y;
	}
	}

	// Create, setup, run, and destroy Leaky ReLU operator.
	ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
	xnn_operator_t leaky_relu_op = nullptr;

	ASSERT_EQ(xnn_status_success,
	xnn_create_leaky_relu_nc_qu8(
	channels(), input_stride(), output_stride(),
	negative_slope(),
	input_zero_point(), input_scale(),
	output_zero_point(), output_scale(),
	0, &leaky_relu_op));
	ASSERT_NE(nullptr, leaky_relu_op);

	// Smart pointer to automatically delete leaky_relu_op.
	std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);

	ASSERT_EQ(xnn_status_success,
	xnn_setup_leaky_relu_nc_qu8(
	leaky_relu_op,
	batch_size(),
	input.data(), output.data(),
	nullptr /* thread pool */));

	ASSERT_EQ(xnn_status_success,
	xnn_run_operator(leaky_relu_op, nullptr /* thread pool */));

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.9f)
	<< "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
	<< ", input " << int32_t(input[i * input_stride() + c])
	<< ", input zero point " << input_zero_point() << ", output zero point " << output_zero_point()
	<< ", positive input-to-output ratio " << (input_scale() / output_scale())
	<< ", negative input-to-output ratio " << (input_scale() / output_scale() * negative_slope());
	}
	}
	}
	}

	private:
	size_t batch_size_{1};
	size_t channels_{1};
	size_t input_stride_{0};
	size_t output_stride_{0};
	float negative_slope_{0.3f};
	float output_scale_{0.75f};
	int16_t output_zero_point_{53};
	float input_scale_{1.25f};
	int16_t input_zero_point_{41};
	size_t iterations_{15};
	};