test/vmulc-microkernel-tester.h - platform/external/XNNPACK - Gitiles

 // Copyright 2021 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 <cstddef>
 #include <cstdlib>
 #include <functional>
 #include <limits>
 #include <random>
 #include <vector>

 #include <xnnpack.h>
 #include <xnnpack/params-init.h>
 #include <xnnpack/params.h>
 #include <xnnpack/requantization.h>


 class VMulCMicrokernelTester {
  public:
   inline VMulCMicrokernelTester& 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 VMulCMicrokernelTester& inplace(bool inplace) {
     this->inplace_ = inplace;
     return *this;
   }

   inline bool inplace() const {
     return this->inplace_;
   }

   inline VMulCMicrokernelTester& a_scale(float a_scale) {
     assert(a_scale > 0.0f);
     assert(std::isnormal(a_scale));
     this->a_scale_ = a_scale;
     return *this;
   }

   inline float a_scale() const {
     return this->a_scale_;
   }

   inline VMulCMicrokernelTester& a_zero_point(uint8_t a_zero_point) {
     this->a_zero_point_ = a_zero_point;
     return *this;
   }

   inline uint8_t a_zero_point() const {
     return this->a_zero_point_;
   }

   inline VMulCMicrokernelTester& b_scale(float b_scale) {
     assert(b_scale > 0.0f);
     assert(std::isnormal(b_scale));
     this->b_scale_ = b_scale;
     return *this;
   }

   inline float b_scale() const {
     return this->b_scale_;
   }

   inline VMulCMicrokernelTester& b_zero_point(uint8_t b_zero_point) {
     this->b_zero_point_ = b_zero_point;
     return *this;
   }

   inline uint8_t b_zero_point() const {
     return this->b_zero_point_;
   }

   inline VMulCMicrokernelTester& y_scale(float y_scale) {
     assert(y_scale > 0.0f);
     assert(std::isnormal(y_scale));
     this->y_scale_ = y_scale;
     return *this;
   }

   inline float y_scale() const {
     return this->y_scale_;
   }

   inline VMulCMicrokernelTester& y_zero_point(uint8_t y_zero_point) {
     this->y_zero_point_ = y_zero_point;
     return *this;
   }

   inline uint8_t y_zero_point() const {
     return this->y_zero_point_;
   }

   inline VMulCMicrokernelTester& qmin(uint8_t qmin) {
     this->qmin_ = qmin;
     return *this;
   }

   inline uint8_t qmin() const {
     return this->qmin_;
   }

   inline VMulCMicrokernelTester& qmax(uint8_t qmax) {
     this->qmax_ = qmax;
     return *this;
   }

   inline uint8_t qmax() const {
     return this->qmax_;
   }

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

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

   void Test(
       xnn_qu8_vmul_minmax_ukernel_function vmul_minmax,
       xnn_init_qu8_mul_minmax_params_fn init_params,
       xnn_init_qu8_requantization_params_fn init_requantization_params,
       xnn_qu8_requantize_fn requantize) const
   {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);

     std::vector<uint8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t));
     std::vector<uint8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint8_t) : 0));
     std::vector<float> y_fp(batch_size());
     std::vector<uint8_t> y_ref(batch_size());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(a.begin(), a.end(), std::ref(u8rng));
       const uint8_t b = u8rng();
       if (inplace()) {
         std::generate(y.begin(), y.end(), std::ref(u8rng));
       } else {
         std::fill(y.begin(), y.end(), 0xA5);
       }
       const uint8_t* a_data = inplace() ? y.data() : a.data();

       // Prepare parameters.
       const float product_scale = a_scale() * b_scale();
       const float product_output_scale = product_scale / y_scale();
       xnn_qu8_mul_minmax_params quantization_params;
       init_params(
         &quantization_params,
         a_zero_point(), b_zero_point(), y_zero_point(),
         product_output_scale, qmin(), qmax());
       union xnn_qu8_requantization_params requantization_params;
       init_requantization_params(&requantization_params,
         product_output_scale, y_zero_point(), qmin(), qmax());

       // Compute reference results.
       for (size_t i = 0; i < batch_size(); i++) {
         const int32_t acc =
           (int32_t(a_data[i]) - int32_t(a_zero_point())) * (int32_t(b) - int32_t(b_zero_point()));
         y_fp[i] = float(y_zero_point()) + product_output_scale * float(acc);
         y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax())));
         y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin())));
         y_ref[i] = requantize(acc, &requantization_params);
       }

       // Call optimized micro-kernel.
       vmul_minmax(batch_size(), a_data, &b, y.data(), &quantization_params);

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         ASSERT_LE(uint32_t(y[i]), uint32_t(qmax()))
           << "at element " << i << " / " << batch_size();
         ASSERT_GE(uint32_t(y[i]), uint32_t(qmin()))
           << "at element " << i << " / " << batch_size();
         ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
           << "at element " << i << " / " << batch_size();
         ASSERT_EQ(uint32_t(y[i]), uint32_t(y_ref[i]))
           << "at element " << i << " / " << batch_size();
       }
     }
   }

   void Test(
       xnn_qs8_vmul_minmax_ukernel_function vmul_minmax,
       xnn_init_qs8_mul_minmax_params_fn init_params,
       xnn_init_qs8_requantization_params_fn init_requantization_params,
       xnn_qs8_requantize_fn requantize) const
   {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto i8rng = std::bind(
       std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
       rng);

     std::vector<int8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t));
     std::vector<int8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(int8_t) : 0));
     std::vector<float> y_fp(batch_size());
     std::vector<int8_t> y_ref(batch_size());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(a.begin(), a.end(), std::ref(i8rng));
       const int8_t b = i8rng();
       if (inplace()) {
         std::generate(y.begin(), y.end(), std::ref(i8rng));
       } else {
         std::fill(y.begin(), y.end(), 0xA5);
       }
       const int8_t* a_data = inplace() ? y.data() : a.data();

       // Prepare parameters.
       const float product_scale = a_scale() * b_scale();
       const float product_output_scale = product_scale / y_scale();
       EXPECT_GE(product_output_scale, 0x1.0p-32f);
       xnn_qs8_mul_minmax_params quantization_params;
       init_params(
         &quantization_params,
         int8_t(a_zero_point() - 0x80), int8_t(b_zero_point() - 0x80), int8_t(y_zero_point() - 0x80),
         product_output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
       union xnn_qs8_requantization_params requantization_params;
       init_requantization_params(&requantization_params,
         product_output_scale, int8_t(y_zero_point() - 0x80), int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));

       // Compute reference results.
       for (size_t i = 0; i < batch_size(); i++) {
         const int32_t acc =
           (int32_t(a_data[i]) - int32_t(a_zero_point() - 0x80)) * (int32_t(b) - int32_t(b_zero_point() - 0x80));
         y_fp[i] = float(y_zero_point() - 0x80) + product_output_scale * float(acc);
         y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax() - 0x80)));
         y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin() - 0x80)));
         y_ref[i] = requantize(acc, &requantization_params);
       }

       // Call optimized micro-kernel.
       vmul_minmax(batch_size(), a_data, &b, y.data(), &quantization_params);

       // Verify results.
       for (size_t i = 0; i < batch_size(); i++) {
         ASSERT_LE(int32_t(y[i]), int32_t(qmax() - 0x80))
           << "at element " << i << " / " << batch_size();
         ASSERT_GE(int32_t(y[i]), int32_t(qmin() - 0x80))
           << "at element " << i << " / " << batch_size();
         ASSERT_EQ(int32_t(y_ref[i]), int32_t(y[i]))
           << "at element " << i << " / " << batch_size();
         ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
           << "at element " << i << " / " << batch_size();
       }
     }
   }

  private:
   size_t batch_size_{1};
   bool inplace_{false};
   float a_scale_{0.75f};
   float b_scale_{1.25f};
   float y_scale_{0.96875f};
   uint8_t a_zero_point_{121};
   uint8_t b_zero_point_{127};
   uint8_t y_zero_point_{133};
   uint8_t qmin_{0};
   uint8_t qmax_{255};
   size_t iterations_{15};
 };
	// Copyright 2021 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 <cstddef>
	#include <cstdlib>
	#include <functional>
	#include <limits>
	#include <random>
	#include <vector>

	#include <xnnpack.h>
	#include <xnnpack/params-init.h>
	#include <xnnpack/params.h>
	#include <xnnpack/requantization.h>


	class VMulCMicrokernelTester {
	public:
	inline VMulCMicrokernelTester& 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 VMulCMicrokernelTester& inplace(bool inplace) {
	this->inplace_ = inplace;
	return *this;
	}

	inline bool inplace() const {
	return this->inplace_;
	}

	inline VMulCMicrokernelTester& a_scale(float a_scale) {
	assert(a_scale > 0.0f);
	assert(std::isnormal(a_scale));
	this->a_scale_ = a_scale;
	return *this;
	}

	inline float a_scale() const {
	return this->a_scale_;
	}

	inline VMulCMicrokernelTester& a_zero_point(uint8_t a_zero_point) {
	this->a_zero_point_ = a_zero_point;
	return *this;
	}

	inline uint8_t a_zero_point() const {
	return this->a_zero_point_;
	}

	inline VMulCMicrokernelTester& b_scale(float b_scale) {
	assert(b_scale > 0.0f);
	assert(std::isnormal(b_scale));
	this->b_scale_ = b_scale;
	return *this;
	}

	inline float b_scale() const {
	return this->b_scale_;
	}

	inline VMulCMicrokernelTester& b_zero_point(uint8_t b_zero_point) {
	this->b_zero_point_ = b_zero_point;
	return *this;
	}

	inline uint8_t b_zero_point() const {
	return this->b_zero_point_;
	}

	inline VMulCMicrokernelTester& y_scale(float y_scale) {
	assert(y_scale > 0.0f);
	assert(std::isnormal(y_scale));
	this->y_scale_ = y_scale;
	return *this;
	}

	inline float y_scale() const {
	return this->y_scale_;
	}

	inline VMulCMicrokernelTester& y_zero_point(uint8_t y_zero_point) {
	this->y_zero_point_ = y_zero_point;
	return *this;
	}

	inline uint8_t y_zero_point() const {
	return this->y_zero_point_;
	}

	inline VMulCMicrokernelTester& qmin(uint8_t qmin) {
	this->qmin_ = qmin;
	return *this;
	}

	inline uint8_t qmin() const {
	return this->qmin_;
	}

	inline VMulCMicrokernelTester& qmax(uint8_t qmax) {
	this->qmax_ = qmax;
	return *this;
	}

	inline uint8_t qmax() const {
	return this->qmax_;
	}

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

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

	void Test(
	xnn_qu8_vmul_minmax_ukernel_function vmul_minmax,
	xnn_init_qu8_mul_minmax_params_fn init_params,
	xnn_init_qu8_requantization_params_fn init_requantization_params,
	xnn_qu8_requantize_fn requantize) const
	{
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);

	std::vector<uint8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t));
	std::vector<uint8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint8_t) : 0));
	std::vector<float> y_fp(batch_size());
	std::vector<uint8_t> y_ref(batch_size());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(a.begin(), a.end(), std::ref(u8rng));
	const uint8_t b = u8rng();
	if (inplace()) {
	std::generate(y.begin(), y.end(), std::ref(u8rng));
	} else {
	std::fill(y.begin(), y.end(), 0xA5);
	}
	const uint8_t* a_data = inplace() ? y.data() : a.data();

	// Prepare parameters.
	const float product_scale = a_scale() * b_scale();
	const float product_output_scale = product_scale / y_scale();
	xnn_qu8_mul_minmax_params quantization_params;
	init_params(
	&quantization_params,
	a_zero_point(), b_zero_point(), y_zero_point(),
	product_output_scale, qmin(), qmax());
	union xnn_qu8_requantization_params requantization_params;
	init_requantization_params(&requantization_params,
	product_output_scale, y_zero_point(), qmin(), qmax());

	// Compute reference results.
	for (size_t i = 0; i < batch_size(); i++) {
	const int32_t acc =
	(int32_t(a_data[i]) - int32_t(a_zero_point())) * (int32_t(b) - int32_t(b_zero_point()));
	y_fp[i] = float(y_zero_point()) + product_output_scale * float(acc);
	y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax())));
	y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin())));
	y_ref[i] = requantize(acc, &requantization_params);
	}

	// Call optimized micro-kernel.
	vmul_minmax(batch_size(), a_data, &b, y.data(), &quantization_params);

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	ASSERT_LE(uint32_t(y[i]), uint32_t(qmax()))
	<< "at element " << i << " / " << batch_size();
	ASSERT_GE(uint32_t(y[i]), uint32_t(qmin()))
	<< "at element " << i << " / " << batch_size();
	ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
	<< "at element " << i << " / " << batch_size();
	ASSERT_EQ(uint32_t(y[i]), uint32_t(y_ref[i]))
	<< "at element " << i << " / " << batch_size();
	}
	}
	}

	void Test(
	xnn_qs8_vmul_minmax_ukernel_function vmul_minmax,
	xnn_init_qs8_mul_minmax_params_fn init_params,
	xnn_init_qs8_requantization_params_fn init_requantization_params,
	xnn_qs8_requantize_fn requantize) const
	{
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto i8rng = std::bind(
	std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
	rng);

	std::vector<int8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t));
	std::vector<int8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(int8_t) : 0));
	std::vector<float> y_fp(batch_size());
	std::vector<int8_t> y_ref(batch_size());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(a.begin(), a.end(), std::ref(i8rng));
	const int8_t b = i8rng();
	if (inplace()) {
	std::generate(y.begin(), y.end(), std::ref(i8rng));
	} else {
	std::fill(y.begin(), y.end(), 0xA5);
	}
	const int8_t* a_data = inplace() ? y.data() : a.data();

	// Prepare parameters.
	const float product_scale = a_scale() * b_scale();
	const float product_output_scale = product_scale / y_scale();
	EXPECT_GE(product_output_scale, 0x1.0p-32f);
	xnn_qs8_mul_minmax_params quantization_params;
	init_params(
	&quantization_params,
	int8_t(a_zero_point() - 0x80), int8_t(b_zero_point() - 0x80), int8_t(y_zero_point() - 0x80),
	product_output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
	union xnn_qs8_requantization_params requantization_params;
	init_requantization_params(&requantization_params,
	product_output_scale, int8_t(y_zero_point() - 0x80), int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));

	// Compute reference results.
	for (size_t i = 0; i < batch_size(); i++) {
	const int32_t acc =
	(int32_t(a_data[i]) - int32_t(a_zero_point() - 0x80)) * (int32_t(b) - int32_t(b_zero_point() - 0x80));
	y_fp[i] = float(y_zero_point() - 0x80) + product_output_scale * float(acc);
	y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax() - 0x80)));
	y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin() - 0x80)));
	y_ref[i] = requantize(acc, &requantization_params);
	}

	// Call optimized micro-kernel.
	vmul_minmax(batch_size(), a_data, &b, y.data(), &quantization_params);

	// Verify results.
	for (size_t i = 0; i < batch_size(); i++) {
	ASSERT_LE(int32_t(y[i]), int32_t(qmax() - 0x80))
	<< "at element " << i << " / " << batch_size();
	ASSERT_GE(int32_t(y[i]), int32_t(qmin() - 0x80))
	<< "at element " << i << " / " << batch_size();
	ASSERT_EQ(int32_t(y_ref[i]), int32_t(y[i]))
	<< "at element " << i << " / " << batch_size();
	ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
	<< "at element " << i << " / " << batch_size();
	}
	}
	}

	private:
	size_t batch_size_{1};
	bool inplace_{false};
	float a_scale_{0.75f};
	float b_scale_{1.25f};
	float y_scale_{0.96875f};
	uint8_t a_zero_point_{121};
	uint8_t b_zero_point_{127};
	uint8_t y_zero_point_{133};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	size_t iterations_{15};
	};