test/dwconv-chw-microkernel-tester.h - platform/external/XNNPACK - Gitiles

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

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


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

   inline DWConvCHWMicrokernelTester& input_tuple_size(uint32_t input_tuple_size) {
     this->input_tuple_size_ = input_tuple_size;
     return *this;
   }

   inline uint32_t input_tuple_size() const {
     return this->input_tuple_size_;
   }

   inline DWConvCHWMicrokernelTester& output_tuple_size(uint32_t output_tuple_size) {
     this->output_tuple_size_ = output_tuple_size;
     return *this;
   }

   inline uint32_t output_tuple_size() const {
     return this->output_tuple_size_;
   }

   inline DWConvCHWMicrokernelTester& padding_left(uint32_t padding_left) {
     this->padding_left_ = padding_left;
     return *this;
   }

   inline uint32_t padding_left() const {
     return this->padding_left_;
   }

   inline DWConvCHWMicrokernelTester& padding_right(uint32_t padding_right) {
     this->padding_right_ = padding_right;
     return *this;
   }

   inline uint32_t padding_right() const {
     return this->padding_right_;
   }

   inline DWConvCHWMicrokernelTester& padding_top(uint32_t padding_top) {
     this->padding_top_ = padding_top;
     return *this;
   }

   inline uint32_t padding_top() const {
     return this->padding_top_;
   }


   inline DWConvCHWMicrokernelTester& padding_bottom(uint32_t padding_bottom) {
     this->padding_bottom_ = padding_bottom;
     return *this;
   }
   inline uint32_t padding_bottom() const {
     return this->padding_bottom_;
   }

   inline uint32_t input_height() const {
     return (output_height() - 1) * subsampling() + kernel_height() - padding_top() - padding_bottom();
   }

   inline DWConvCHWMicrokernelTester& input_width(uint32_t input_width) {
     assert(input_width >= 1);
     this->input_width_ = input_width;
     return *this;
   }

   inline uint32_t input_width() const {
     return this->input_width_;
   }

   inline DWConvCHWMicrokernelTester& subsampling(uint32_t subsampling) {
     assert(subsampling >= 1);
     this->subsampling_ = subsampling;
     return *this;
   }

   inline uint32_t subsampling() const {
     return this->subsampling_;
   }

   inline DWConvCHWMicrokernelTester& kernel_height(uint32_t kernel_height) {
     assert(kernel_height != 0);
     this->kernel_height_ = kernel_height;
     return *this;
   }

   inline uint32_t kernel_height() const {
     return this->kernel_height_;
   }

   inline DWConvCHWMicrokernelTester& kernel_width(uint32_t kernel_width) {
     assert(kernel_width != 0);
     this->kernel_width_ = kernel_width;
     return *this;
   }

   inline uint32_t kernel_width() const {
     return this->kernel_width_;
   }

   inline uint32_t kernel_size() const {
     return kernel_height() * kernel_width();
   }

   inline DWConvCHWMicrokernelTester& output_height(uint32_t output_height) {
     assert(output_height >= 1);
     this->output_height_ = output_height;
     return *this;
   }

   inline uint32_t output_height() const {
     return this->output_height_;
   }

   inline uint32_t output_width() const {
     const uint32_t padded_input_width = padding_left() + input_width() + padding_right();
     if (padded_input_width <= kernel_width()) {
       return 1;
     } else {
       return (padded_input_width - kernel_width()) / subsampling() + 1;
     }
   }

   inline DWConvCHWMicrokernelTester& input_tuple_stride(uint32_t input_tuple_stride) {
     assert(input_tuple_stride != 0);
     this->input_tuple_stride_ = input_tuple_stride;
     return *this;
   }

   inline uint32_t input_tuple_stride() const {
     if (this->input_tuple_stride_ == 0) {
       return this->input_tuple_size();
     } else {
       return this->input_tuple_stride_;
     }
   }

   inline DWConvCHWMicrokernelTester& output_tuple_stride(uint32_t output_tuple_stride) {
     assert(output_tuple_stride != 0);
     this->output_tuple_stride_ = output_tuple_stride;
     return *this;
   }

   inline uint32_t output_tuple_stride() const {
     if (this->output_tuple_stride_ == 0) {
       return this->output_tuple_size();
     } else {
       return this->output_tuple_stride_;
     }
   }

   inline DWConvCHWMicrokernelTester& input_width_stride(uint32_t input_width_stride) {
     assert(input_width_stride != 0);
     this->input_width_stride_ = input_width_stride;
     return *this;
   }

   inline uint32_t input_width_stride() const {
     if (this->input_width_stride_ == 0) {
       return (this->input_width() + input_tuple_size() - 1) / input_tuple_size() * input_tuple_size();
     } else {
       return this->input_width_stride_;
     }
   }

   inline DWConvCHWMicrokernelTester& output_width_stride(uint32_t output_width_stride) {
     assert(output_width_stride != 0);
     this->output_width_stride_ = output_width_stride;
     return *this;
   }

   inline uint32_t output_width_stride() const {
     if (this->output_width_stride_ == 0) {
       return (this->output_width() + output_tuple_size() - 1) / output_tuple_size() * output_tuple_size();
     } else {
       return this->output_width_stride_;
     }
   }

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

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

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

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

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

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

   void Test(xnn_f32_dwconv_chw_ukernel_function dwconv, Variant variant = Variant::Native) const {
     ASSERT_EQ(0, input_tuple_stride() % input_tuple_size());
     ASSERT_EQ(0, output_tuple_stride() % output_tuple_size());

     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);

     std::vector<float, AlignedAllocator<float, 64>> input((input_height() - 1) * input_width_stride() +
       (input_width() - 1) / input_tuple_size() * input_tuple_stride() + input_tuple_stride() + input_tuple_size());
     std::vector<float> zero((input_width() - 1) / input_tuple_size() * input_tuple_stride() + input_tuple_stride() + input_tuple_size());
     std::vector<float> packed_weights(kernel_size() + 1);
     std::vector<float, AlignedAllocator<float, 64>> output((output_height() - 1) * output_width_stride() +
       (output_width() - 1) / output_tuple_size() * output_tuple_stride() + output_tuple_size());
     std::vector<float> output_ref(output_height() * output_width());

     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(f32rng));
       std::generate(packed_weights.begin(), packed_weights.end(), std::ref(f32rng));
       std::fill(output.begin(), output.end(), nanf(""));

       for (size_t oy = 0; oy < output_height(); oy++) {
         for (size_t ox = 0; ox < output_width(); ox++) {
           float acc = packed_weights[0];
           for (size_t ky = 0; ky < kernel_height(); ky++) {
             const size_t iy = oy * subsampling() + ky - padding_top();
             for (size_t kx = 0; kx < kernel_width(); kx++) {
               const size_t ix = ox * subsampling() + kx - padding_left();
               if (ix < input_width() && iy <= input_height() - 1) {
                 float input_val = input[ iy * input_width_stride() + ix / input_tuple_size() * input_tuple_stride() + ix % input_tuple_size()];
                 float kernel_val = packed_weights[1 + ky * kernel_width() + kx];
                 acc += input_val * kernel_val;
               }
             }
           }
           output_ref[oy * output_width() + ox] = acc;
         }
       }

       // 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 accumulated_range = accumulated_max - accumulated_min;
       const float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
       const float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());

       // Prepare parameters.
       xnn_f32_chw_params chw_params = { };
       switch (variant) {
         case Variant::Native:
           chw_params = xnn_init_f32_chw_params(input_width(), output_min, output_max);
           break;
         case Variant::Scalar:
           chw_params = xnn_init_scalar_f32_chw_params(input_width(), output_min, output_max);
           break;
       }

       // Clamp reference results.
       for (float& output_val : output_ref) {
         output_val = std::max(std::min(output_val, output_max), output_min);
       }

       // Call optimized micro-kernel.
       dwconv(
         input_height(), input_width(),
         input.data(), packed_weights.data(), zero.data(), output.data(),
         padding_top(),
         input_tuple_stride() * sizeof(float), output_tuple_stride() * sizeof(float),
         input_width_stride() * sizeof(float), output_width_stride() * sizeof(float),
         &chw_params);

       // Verify results.
       for (size_t y = 0; y < output_height(); y++) {
         for (size_t x = 0; x < output_width(); x++) {
           ASSERT_NEAR(
               output_ref[y * output_width() + x],
               output[y * output_width_stride() + x / output_tuple_size() * output_tuple_stride() + x % output_tuple_size()],
               std::abs(output_ref[y * output_width() + x]) * 1.0e-5)
             << "x = " << x << ", y = " << y;
         }
       }

       // Verify that remainder of the last tile left unchanged.
       if (output_width() % output_tuple_size() != 0) {
         for (size_t i = output.size() - output_tuple_size() + output_width() % output_tuple_size(); i < output.size(); i++) {
           ASSERT_TRUE(std::isnan(output[i]))
             << "i = " << i << ", output = " << output[i];
         }
       }
     }
   }

  private:
   uint32_t input_tuple_size_{1};
   uint32_t output_tuple_size_{1};
   uint32_t padding_left_{0};
   uint32_t padding_right_{0};
   uint32_t padding_top_{0};
   uint32_t padding_bottom_{0};
   uint32_t output_height_{1};
   uint32_t input_width_{1};
   uint32_t subsampling_{1};
   uint32_t kernel_height_{1};
   uint32_t kernel_width_{1};
   uint32_t input_tuple_stride_{0};
   uint32_t output_tuple_stride_{0};
   uint32_t input_width_stride_{0};
   uint32_t output_width_stride_{0};
   uint8_t qmin_{0};
   uint8_t qmax_{255};
   size_t iterations_{1};
 };
	// 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 <functional>
	#include <random>
	#include <vector>

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


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

	inline DWConvCHWMicrokernelTester& input_tuple_size(uint32_t input_tuple_size) {
	this->input_tuple_size_ = input_tuple_size;
	return *this;
	}

	inline uint32_t input_tuple_size() const {
	return this->input_tuple_size_;
	}

	inline DWConvCHWMicrokernelTester& output_tuple_size(uint32_t output_tuple_size) {
	this->output_tuple_size_ = output_tuple_size;
	return *this;
	}

	inline uint32_t output_tuple_size() const {
	return this->output_tuple_size_;
	}

	inline DWConvCHWMicrokernelTester& padding_left(uint32_t padding_left) {
	this->padding_left_ = padding_left;
	return *this;
	}

	inline uint32_t padding_left() const {
	return this->padding_left_;
	}

	inline DWConvCHWMicrokernelTester& padding_right(uint32_t padding_right) {
	this->padding_right_ = padding_right;
	return *this;
	}

	inline uint32_t padding_right() const {
	return this->padding_right_;
	}

	inline DWConvCHWMicrokernelTester& padding_top(uint32_t padding_top) {
	this->padding_top_ = padding_top;
	return *this;
	}

	inline uint32_t padding_top() const {
	return this->padding_top_;
	}


	inline DWConvCHWMicrokernelTester& padding_bottom(uint32_t padding_bottom) {
	this->padding_bottom_ = padding_bottom;
	return *this;
	}
	inline uint32_t padding_bottom() const {
	return this->padding_bottom_;
	}

	inline uint32_t input_height() const {
	return (output_height() - 1) * subsampling() + kernel_height() - padding_top() - padding_bottom();
	}

	inline DWConvCHWMicrokernelTester& input_width(uint32_t input_width) {
	assert(input_width >= 1);
	this->input_width_ = input_width;
	return *this;
	}

	inline uint32_t input_width() const {
	return this->input_width_;
	}

	inline DWConvCHWMicrokernelTester& subsampling(uint32_t subsampling) {
	assert(subsampling >= 1);
	this->subsampling_ = subsampling;
	return *this;
	}

	inline uint32_t subsampling() const {
	return this->subsampling_;
	}

	inline DWConvCHWMicrokernelTester& kernel_height(uint32_t kernel_height) {
	assert(kernel_height != 0);
	this->kernel_height_ = kernel_height;
	return *this;
	}

	inline uint32_t kernel_height() const {
	return this->kernel_height_;
	}

	inline DWConvCHWMicrokernelTester& kernel_width(uint32_t kernel_width) {
	assert(kernel_width != 0);
	this->kernel_width_ = kernel_width;
	return *this;
	}

	inline uint32_t kernel_width() const {
	return this->kernel_width_;
	}

	inline uint32_t kernel_size() const {
	return kernel_height() * kernel_width();
	}

	inline DWConvCHWMicrokernelTester& output_height(uint32_t output_height) {
	assert(output_height >= 1);
	this->output_height_ = output_height;
	return *this;
	}

	inline uint32_t output_height() const {
	return this->output_height_;
	}

	inline uint32_t output_width() const {
	const uint32_t padded_input_width = padding_left() + input_width() + padding_right();
	if (padded_input_width <= kernel_width()) {
	return 1;
	} else {
	return (padded_input_width - kernel_width()) / subsampling() + 1;
	}
	}

	inline DWConvCHWMicrokernelTester& input_tuple_stride(uint32_t input_tuple_stride) {
	assert(input_tuple_stride != 0);
	this->input_tuple_stride_ = input_tuple_stride;
	return *this;
	}

	inline uint32_t input_tuple_stride() const {
	if (this->input_tuple_stride_ == 0) {
	return this->input_tuple_size();
	} else {
	return this->input_tuple_stride_;
	}
	}

	inline DWConvCHWMicrokernelTester& output_tuple_stride(uint32_t output_tuple_stride) {
	assert(output_tuple_stride != 0);
	this->output_tuple_stride_ = output_tuple_stride;
	return *this;
	}

	inline uint32_t output_tuple_stride() const {
	if (this->output_tuple_stride_ == 0) {
	return this->output_tuple_size();
	} else {
	return this->output_tuple_stride_;
	}
	}

	inline DWConvCHWMicrokernelTester& input_width_stride(uint32_t input_width_stride) {
	assert(input_width_stride != 0);
	this->input_width_stride_ = input_width_stride;
	return *this;
	}

	inline uint32_t input_width_stride() const {
	if (this->input_width_stride_ == 0) {
	return (this->input_width() + input_tuple_size() - 1) / input_tuple_size() * input_tuple_size();
	} else {
	return this->input_width_stride_;
	}
	}

	inline DWConvCHWMicrokernelTester& output_width_stride(uint32_t output_width_stride) {
	assert(output_width_stride != 0);
	this->output_width_stride_ = output_width_stride;
	return *this;
	}

	inline uint32_t output_width_stride() const {
	if (this->output_width_stride_ == 0) {
	return (this->output_width() + output_tuple_size() - 1) / output_tuple_size() * output_tuple_size();
	} else {
	return this->output_width_stride_;
	}
	}

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

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

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

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

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

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

	void Test(xnn_f32_dwconv_chw_ukernel_function dwconv, Variant variant = Variant::Native) const {
	ASSERT_EQ(0, input_tuple_stride() % input_tuple_size());
	ASSERT_EQ(0, output_tuple_stride() % output_tuple_size());

	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);

	std::vector<float, AlignedAllocator<float, 64>> input((input_height() - 1) * input_width_stride() +
	(input_width() - 1) / input_tuple_size() * input_tuple_stride() + input_tuple_stride() + input_tuple_size());
	std::vector<float> zero((input_width() - 1) / input_tuple_size() * input_tuple_stride() + input_tuple_stride() + input_tuple_size());
	std::vector<float> packed_weights(kernel_size() + 1);
	std::vector<float, AlignedAllocator<float, 64>> output((output_height() - 1) * output_width_stride() +
	(output_width() - 1) / output_tuple_size() * output_tuple_stride() + output_tuple_size());
	std::vector<float> output_ref(output_height() * output_width());

	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(f32rng));
	std::generate(packed_weights.begin(), packed_weights.end(), std::ref(f32rng));
	std::fill(output.begin(), output.end(), nanf(""));

	for (size_t oy = 0; oy < output_height(); oy++) {
	for (size_t ox = 0; ox < output_width(); ox++) {
	float acc = packed_weights[0];
	for (size_t ky = 0; ky < kernel_height(); ky++) {
	const size_t iy = oy * subsampling() + ky - padding_top();
	for (size_t kx = 0; kx < kernel_width(); kx++) {
	const size_t ix = ox * subsampling() + kx - padding_left();
	if (ix < input_width() && iy <= input_height() - 1) {
	float input_val = input[ iy * input_width_stride() + ix / input_tuple_size() * input_tuple_stride() + ix % input_tuple_size()];
	float kernel_val = packed_weights[1 + ky * kernel_width() + kx];
	acc += input_val * kernel_val;
	}
	}
	}
	output_ref[oy * output_width() + ox] = acc;
	}
	}

	// 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 accumulated_range = accumulated_max - accumulated_min;
	const float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
	const float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());

	// Prepare parameters.
	xnn_f32_chw_params chw_params = { };
	switch (variant) {
	case Variant::Native:
	chw_params = xnn_init_f32_chw_params(input_width(), output_min, output_max);
	break;
	case Variant::Scalar:
	chw_params = xnn_init_scalar_f32_chw_params(input_width(), output_min, output_max);
	break;
	}

	// Clamp reference results.
	for (float& output_val : output_ref) {
	output_val = std::max(std::min(output_val, output_max), output_min);
	}

	// Call optimized micro-kernel.
	dwconv(
	input_height(), input_width(),
	input.data(), packed_weights.data(), zero.data(), output.data(),
	padding_top(),
	input_tuple_stride() * sizeof(float), output_tuple_stride() * sizeof(float),
	input_width_stride() * sizeof(float), output_width_stride() * sizeof(float),
	&chw_params);

	// Verify results.
	for (size_t y = 0; y < output_height(); y++) {
	for (size_t x = 0; x < output_width(); x++) {
	ASSERT_NEAR(
	output_ref[y * output_width() + x],
	output[y * output_width_stride() + x / output_tuple_size() * output_tuple_stride() + x % output_tuple_size()],
	std::abs(output_ref[y * output_width() + x]) * 1.0e-5)
	<< "x = " << x << ", y = " << y;
	}
	}

	// Verify that remainder of the last tile left unchanged.
	if (output_width() % output_tuple_size() != 0) {
	for (size_t i = output.size() - output_tuple_size() + output_width() % output_tuple_size(); i < output.size(); i++) {
	ASSERT_TRUE(std::isnan(output[i]))
	<< "i = " << i << ", output = " << output[i];
	}
	}
	}
	}

	private:
	uint32_t input_tuple_size_{1};
	uint32_t output_tuple_size_{1};
	uint32_t padding_left_{0};
	uint32_t padding_right_{0};
	uint32_t padding_top_{0};
	uint32_t padding_bottom_{0};
	uint32_t output_height_{1};
	uint32_t input_width_{1};
	uint32_t subsampling_{1};
	uint32_t kernel_height_{1};
	uint32_t kernel_width_{1};
	uint32_t input_tuple_stride_{0};
	uint32_t output_tuple_stride_{0};
	uint32_t input_width_stride_{0};
	uint32_t output_width_stride_{0};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	size_t iterations_{1};
	};