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

 // 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 <cstddef>
 #include <cstdlib>
 #include <functional>
 #include <random>
 #include <vector>

 #include <fp16.h>

 #include <xnnpack.h>
 #include <xnnpack/AlignedAllocator.h>
 #include <xnnpack/pack.h>
 #include <xnnpack/params-init.h>
 #include <xnnpack/params.h>


 class VMulCAddCMicrokernelTester {
  public:
   enum class Variant {
     Native,
     Scalar,
   };

   inline VMulCAddCMicrokernelTester& channel_tile(size_t channel_tile) {
     this->channel_tile_ = channel_tile;
     return *this;
   }

   inline size_t channel_tile() const {
     return this->channel_tile_;
   }

   inline VMulCAddCMicrokernelTester& channels(size_t channels) {
     assert(channels != 0);
     this->channels_ = channels;
     return *this;
   }

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

   inline size_t packed_channels() const {
     return channels() % channel_tile() == 0 ? channels() : (channels() / channel_tile() + 1) * channel_tile();
   }

   inline VMulCAddCMicrokernelTester& rows(size_t rows) {
     assert(rows != 0);
     this->rows_ = rows;
     return *this;
   }

   inline size_t rows() const {
     return this->rows_;
   }

   inline VMulCAddCMicrokernelTester& input_stride(size_t input_stride) {
     this->input_stride_ = input_stride;
     return *this;
   }

   inline size_t input_stride() const {
     return this->input_stride_ == 0 ? channels() : this->input_stride_;
   }

   inline VMulCAddCMicrokernelTester& output_stride(size_t output_stride) {
     this->output_stride_ = output_stride;
     return *this;
   }

   inline size_t output_stride() const {
     return this->output_stride_ == 0 ? channels() : this->output_stride_;
   }

   inline VMulCAddCMicrokernelTester& inplace(bool inplace) {
     this->inplace_ = inplace;
     return *this;
   }

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

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

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

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

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

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

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

   void Test(xnn_f16_vmulcaddc_ukernel_function vmulcaddc, Variant variant = Variant::Native) const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
     auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);

     if (inplace()) {
       ASSERT_EQ(input_stride(), output_stride());
     }

     std::vector<uint16_t> x((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
     std::vector<uint16_t> scale(channels());
     std::vector<uint16_t> bias(channels());
     std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> packed_w(packed_channels() * 2);
     std::vector<uint16_t> y((rows() - 1) * output_stride() + channels() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0));
     std::vector<float> y_ref(rows() * channels());

     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(scale.begin(), scale.end(), std::ref(f16rng));
       std::generate(bias.begin(), bias.end(), std::ref(f16rng));
       std::generate(x.begin(), x.end(), std::ref(f16rng));
       if (inplace()) {
         std::copy(x.cbegin(), x.cend(), y.begin());
       } else {
         std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */);
       }
       const uint16_t* x_data = inplace() ? y.data() : x.data();

       std::fill(packed_w.begin(), packed_w.end(), UINT16_C(0x7E00) /* NaN */);
       xnn_pack_f16_vmulcaddc_w(channels(), channel_tile(),
         scale.data(), bias.data(), packed_w.data(), nullptr);

       // Compute reference results.
       for (size_t i = 0; i < rows(); i++) {
         for (size_t j = 0; j < channels(); j++) {
           y_ref[i * channels() + j] = fp16_ieee_to_fp32_value(x_data[i * input_stride() + j]) * fp16_ieee_to_fp32_value(scale[j]) + fp16_ieee_to_fp32_value(bias[j]);
         }
       }
       const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
       const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
       const float accumulated_range = accumulated_max - accumulated_min;
       const float y_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));
       const float y_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));

       for (float& y_value : y_ref) {
         y_value = std::max(std::min(y_value, y_max), y_min);
       }

       // Prepare parameters.
       xnn_f16_minmax_params params = xnn_init_f16_minmax_params(
         fp16_ieee_from_fp32_value(y_min),
         fp16_ieee_from_fp32_value(y_max));

       // Call optimized micro-kernel.
       vmulcaddc(rows(), channels() * sizeof(uint16_t),
         x_data, input_stride() * sizeof(uint16_t),
         packed_w.data(),
         y.data(), output_stride() * sizeof(uint16_t),
         &params);

       // Verify results.
       for (size_t i = 0; i < rows(); i++) {
         for (size_t j = 0; j < channels(); j++) {
           ASSERT_NEAR(fp16_ieee_to_fp32_value(y[i * output_stride() + j]), y_ref[i * channels() + j], std::max(1.0e-4f, std::abs(y_ref[i * channels() + j]) * 1.0e-2f))
             << "at pixel " << i << " / " << rows()
             << ", channel = " << j << " / " << channels();
         }
       }
     }
   }

   void Test(xnn_f32_vmulcaddc_ukernel_function vmulcaddc, Variant variant = Variant::Native) const {
     std::random_device random_device;
     auto rng = std::mt19937(random_device());
     auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);

     if (inplace()) {
       ASSERT_EQ(input_stride(), output_stride());
     }

     std::vector<float> x((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
     std::vector<float> scale(channels());
     std::vector<float> bias(channels());
     std::vector<float, AlignedAllocator<float, 64>> packed_w(packed_channels() * 2);
     std::vector<float> y((rows() - 1) * output_stride() + channels() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
     std::vector<float> y_ref(rows() * channels());
     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(scale.begin(), scale.end(), std::ref(f32rng));
       std::generate(bias.begin(), bias.end(), std::ref(f32rng));
       std::generate(x.begin(), x.end(), std::ref(f32rng));
       if (inplace()) {
         std::copy(x.cbegin(), x.cend(), y.begin());
       } else {
         std::fill(y.begin(), y.end(), nanf(""));
       }
       const float* x_data = inplace() ? y.data() : x.data();

       std::fill(packed_w.begin(), packed_w.end(), nanf(""));
       xnn_pack_f32_vmulcaddc_w(channels(), channel_tile(),
         scale.data(), bias.data(), packed_w.data(), nullptr);

       // Compute reference results.
       for (size_t i = 0; i < rows(); i++) {
         for (size_t j = 0; j < channels(); j++) {
           y_ref[i * channels() + j] = x_data[i * input_stride() + j] * scale[j] + bias[j];
         }
       }
       const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
       const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
       const float accumulated_range = accumulated_max - accumulated_min;
       const float y_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
       const float y_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
       for (float& y_value : y_ref) {
         y_value = std::max<float>(std::min<float>(y_value, y_max), y_min);
       }

       // Prepare parameters.
       xnn_f32_minmax_params params = { };
       switch (variant) {
         case Variant::Native:
           params = xnn_init_f32_minmax_params(y_min, y_max);
           break;
         case Variant::Scalar:
           params = xnn_init_scalar_f32_minmax_params(y_min, y_max);
           break;
       }

       // Call optimized micro-kernel.
       vmulcaddc(rows(), channels() * sizeof(float),
         x_data, input_stride() * sizeof(float),
         packed_w.data(),
         y.data(), output_stride() * sizeof(float),
         &params);

       // Verify results.
       for (size_t i = 0; i < rows(); i++) {
         for (size_t j = 0; j < channels(); j++) {
           ASSERT_NEAR(y[i * output_stride() + j], y_ref[i * channels() + j], std::abs(y_ref[i * channels() + j]) * 1.0e-6f)
             << "at pixel " << i << " / " << rows()
             << ", channel = " << j << " / " << channels();
         }
       }
     }
   }

  private:
   size_t channel_tile_{1};
   size_t channels_{1};
   size_t rows_{1};
   size_t input_stride_{0};
   size_t output_stride_{0};
   bool inplace_{false};
   uint8_t qmin_{0};
   uint8_t qmax_{255};
   size_t iterations_{15};
 };
	// 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 <cstddef>
	#include <cstdlib>
	#include <functional>
	#include <random>
	#include <vector>

	#include <fp16.h>

	#include <xnnpack.h>
	#include <xnnpack/AlignedAllocator.h>
	#include <xnnpack/pack.h>
	#include <xnnpack/params-init.h>
	#include <xnnpack/params.h>


	class VMulCAddCMicrokernelTester {
	public:
	enum class Variant {
	Native,
	Scalar,
	};

	inline VMulCAddCMicrokernelTester& channel_tile(size_t channel_tile) {
	this->channel_tile_ = channel_tile;
	return *this;
	}

	inline size_t channel_tile() const {
	return this->channel_tile_;
	}

	inline VMulCAddCMicrokernelTester& channels(size_t channels) {
	assert(channels != 0);
	this->channels_ = channels;
	return *this;
	}

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

	inline size_t packed_channels() const {
	return channels() % channel_tile() == 0 ? channels() : (channels() / channel_tile() + 1) * channel_tile();
	}

	inline VMulCAddCMicrokernelTester& rows(size_t rows) {
	assert(rows != 0);
	this->rows_ = rows;
	return *this;
	}

	inline size_t rows() const {
	return this->rows_;
	}

	inline VMulCAddCMicrokernelTester& input_stride(size_t input_stride) {
	this->input_stride_ = input_stride;
	return *this;
	}

	inline size_t input_stride() const {
	return this->input_stride_ == 0 ? channels() : this->input_stride_;
	}

	inline VMulCAddCMicrokernelTester& output_stride(size_t output_stride) {
	this->output_stride_ = output_stride;
	return *this;
	}

	inline size_t output_stride() const {
	return this->output_stride_ == 0 ? channels() : this->output_stride_;
	}

	inline VMulCAddCMicrokernelTester& inplace(bool inplace) {
	this->inplace_ = inplace;
	return *this;
	}

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

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

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

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

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

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

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

	void Test(xnn_f16_vmulcaddc_ukernel_function vmulcaddc, Variant variant = Variant::Native) const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
	auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);

	if (inplace()) {
	ASSERT_EQ(input_stride(), output_stride());
	}

	std::vector<uint16_t> x((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
	std::vector<uint16_t> scale(channels());
	std::vector<uint16_t> bias(channels());
	std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> packed_w(packed_channels() * 2);
	std::vector<uint16_t> y((rows() - 1) * output_stride() + channels() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0));
	std::vector<float> y_ref(rows() * channels());

	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(scale.begin(), scale.end(), std::ref(f16rng));
	std::generate(bias.begin(), bias.end(), std::ref(f16rng));
	std::generate(x.begin(), x.end(), std::ref(f16rng));
	if (inplace()) {
	std::copy(x.cbegin(), x.cend(), y.begin());
	} else {
	std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */);
	}
	const uint16_t* x_data = inplace() ? y.data() : x.data();

	std::fill(packed_w.begin(), packed_w.end(), UINT16_C(0x7E00) /* NaN */);
	xnn_pack_f16_vmulcaddc_w(channels(), channel_tile(),
	scale.data(), bias.data(), packed_w.data(), nullptr);

	// Compute reference results.
	for (size_t i = 0; i < rows(); i++) {
	for (size_t j = 0; j < channels(); j++) {
	y_ref[i * channels() + j] = fp16_ieee_to_fp32_value(x_data[i * input_stride() + j]) * fp16_ieee_to_fp32_value(scale[j]) + fp16_ieee_to_fp32_value(bias[j]);
	}
	}
	const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
	const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
	const float accumulated_range = accumulated_max - accumulated_min;
	const float y_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));
	const float y_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));

	for (float& y_value : y_ref) {
	y_value = std::max(std::min(y_value, y_max), y_min);
	}

	// Prepare parameters.
	xnn_f16_minmax_params params = xnn_init_f16_minmax_params(
	fp16_ieee_from_fp32_value(y_min),
	fp16_ieee_from_fp32_value(y_max));

	// Call optimized micro-kernel.
	vmulcaddc(rows(), channels() * sizeof(uint16_t),
	x_data, input_stride() * sizeof(uint16_t),
	packed_w.data(),
	y.data(), output_stride() * sizeof(uint16_t),
	&params);

	// Verify results.
	for (size_t i = 0; i < rows(); i++) {
	for (size_t j = 0; j < channels(); j++) {
	ASSERT_NEAR(fp16_ieee_to_fp32_value(y[i * output_stride() + j]), y_ref[i * channels() + j], std::max(1.0e-4f, std::abs(y_ref[i * channels() + j]) * 1.0e-2f))
	<< "at pixel " << i << " / " << rows()
	<< ", channel = " << j << " / " << channels();
	}
	}
	}
	}

	void Test(xnn_f32_vmulcaddc_ukernel_function vmulcaddc, Variant variant = Variant::Native) const {
	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);

	if (inplace()) {
	ASSERT_EQ(input_stride(), output_stride());
	}

	std::vector<float> x((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
	std::vector<float> scale(channels());
	std::vector<float> bias(channels());
	std::vector<float, AlignedAllocator<float, 64>> packed_w(packed_channels() * 2);
	std::vector<float> y((rows() - 1) * output_stride() + channels() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
	std::vector<float> y_ref(rows() * channels());
	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(scale.begin(), scale.end(), std::ref(f32rng));
	std::generate(bias.begin(), bias.end(), std::ref(f32rng));
	std::generate(x.begin(), x.end(), std::ref(f32rng));
	if (inplace()) {
	std::copy(x.cbegin(), x.cend(), y.begin());
	} else {
	std::fill(y.begin(), y.end(), nanf(""));
	}
	const float* x_data = inplace() ? y.data() : x.data();

	std::fill(packed_w.begin(), packed_w.end(), nanf(""));
	xnn_pack_f32_vmulcaddc_w(channels(), channel_tile(),
	scale.data(), bias.data(), packed_w.data(), nullptr);

	// Compute reference results.
	for (size_t i = 0; i < rows(); i++) {
	for (size_t j = 0; j < channels(); j++) {
	y_ref[i * channels() + j] = x_data[i * input_stride() + j] * scale[j] + bias[j];
	}
	}
	const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
	const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
	const float accumulated_range = accumulated_max - accumulated_min;
	const float y_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
	const float y_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
	for (float& y_value : y_ref) {
	y_value = std::max<float>(std::min<float>(y_value, y_max), y_min);
	}

	// Prepare parameters.
	xnn_f32_minmax_params params = { };
	switch (variant) {
	case Variant::Native:
	params = xnn_init_f32_minmax_params(y_min, y_max);
	break;
	case Variant::Scalar:
	params = xnn_init_scalar_f32_minmax_params(y_min, y_max);
	break;
	}

	// Call optimized micro-kernel.
	vmulcaddc(rows(), channels() * sizeof(float),
	x_data, input_stride() * sizeof(float),
	packed_w.data(),
	y.data(), output_stride() * sizeof(float),
	&params);

	// Verify results.
	for (size_t i = 0; i < rows(); i++) {
	for (size_t j = 0; j < channels(); j++) {
	ASSERT_NEAR(y[i * output_stride() + j], y_ref[i * channels() + j], std::abs(y_ref[i * channels() + j]) * 1.0e-6f)
	<< "at pixel " << i << " / " << rows()
	<< ", channel = " << j << " / " << channels();
	}
	}
	}
	}

	private:
	size_t channel_tile_{1};
	size_t channels_{1};
	size_t rows_{1};
	size_t input_stride_{0};
	size_t output_stride_{0};
	bool inplace_{false};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	size_t iterations_{15};
	};