test/dwconv-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 <limits>
 #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>
 #include <xnnpack/requantization.h>


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

   inline DWConvMicrokernelTester& width(uint32_t width) {
     assert(width >= 1);
     this->width_ = width;
     return *this;
   }

   inline uint32_t width() const {
     return this->width_;
   }

   inline DWConvMicrokernelTester& step(uint32_t step) {
     assert(step >= 1);
     this->step_ = step;
     return *this;
   }

   inline uint32_t step() const {
     return this->step_;
   }

   inline DWConvMicrokernelTester& channels(uint32_t channels) {
     assert(channels >= 1);
     this->channels_ = channels;
     return *this;
   }

   inline uint32_t channels() const {
     return this->channels_;
   }

   inline DWConvMicrokernelTester& cr(uint32_t cr) {
     assert(cr != 0);
     assert((cr & (cr - 1)) == 0);
     this->cr_ = cr;
     return *this;
   }

   inline uint32_t cr() const {
     return this->cr_;
   }

   inline DWConvMicrokernelTester& kr(uint32_t kr) {
     assert(kr != 0);
     this->kr_ = kr;
     return *this;
   }

   inline uint32_t kr() const {
     return this->kr_;
   }

   inline uint32_t packed_channels() const {
     return (channels() / cr() + !!(channels() % cr())) * cr();
   }

   inline DWConvMicrokernelTester& output_stride(uint32_t output_stride) {
     assert(output_stride != 0);
     this->output_stride_ = output_stride;
     return *this;
   }

   inline uint32_t output_stride() const {
     if (this->output_stride_ == 0) {
       return channels();
     } else {
       assert(this->output_stride_ >= channels());
       return this->output_stride_;
     }
   }

   inline DWConvMicrokernelTester& input_zero_point(uint8_t input_zero_point) {
     this->input_zero_point_ = input_zero_point;
     return *this;
   }

   inline uint8_t input_zero_point() const {
     return this->input_zero_point_;
   }

   inline DWConvMicrokernelTester& kernel_zero_point(uint8_t kernel_zero_point) {
     this->kernel_zero_point_ = kernel_zero_point;
     return *this;
   }

   inline uint8_t kernel_zero_point() const {
     return this->kernel_zero_point_;
   }

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

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

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

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

   inline DWConvMicrokernelTester& input_offset(size_t input_offset) {
     this->input_offset_ = input_offset;
     return *this;
   }

   inline size_t input_offset() const {
     return this->input_offset_;
   }

   inline DWConvMicrokernelTester& zero_index(size_t zero_index) {
     this->zero_index_ = zero_index;
     return *this;
   }

   inline size_t zero_index() const {
     return this->zero_index_;
   }

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

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

   void Test(xnn_qu8_dwconv_minmax_unipass_ukernel_function dwconv_minmax, Variant variant = Variant::Native) 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<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);

     std::vector<const uint8_t*> indirection((width() - 1) * step() + kr());
     std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + indirection.size() * channels());
     std::vector<uint8_t> kernel(channels() * kr());
     std::vector<int32_t> bias(channels());
     std::vector<uint8_t, AlignedAllocator<uint8_t, 64>> packed_weights((kr() + sizeof(int32_t) / sizeof(uint8_t)) * packed_channels());
     std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
     std::vector<uint8_t> output((width() - 1) * output_stride() + channels());
     std::vector<int32_t> accumulators(width() * channels());
     std::vector<uint8_t> output_ref(width() * channels());

     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       do {
         std::generate(input.begin(), input.end(), std::ref(u8rng));
       } while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend()));
       do {
         std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
       } while (kernel.size() > 1 && *std::max_element(kernel.cbegin(), kernel.cend()) == *std::min_element(kernel.cbegin(), kernel.cend()));
       std::generate(bias.begin(), bias.end(), std::ref(s32rng));
       std::fill(zero.begin(), zero.end(), input_zero_point());
       std::fill(output.begin(), output.end(), 0xA5);

       std::fill(packed_weights.begin(), packed_weights.end(), 0);
       const xnn_qu8_packing_params packing_params = { input_zero_point(), kernel_zero_point() };
       xnn_pack_qu8_dwconv_ghw_w(
         kr(), 1, channels(), cr(),
         kernel.data(), bias.data(), packed_weights.data(), &packing_params);
       for (size_t i = 0; i < indirection.size(); i++) {
         indirection[i] = input.data() + i * channels() - input_offset();
       }
       std::shuffle(indirection.begin(), indirection.end(), rng);
       if (zero_index() != SIZE_MAX) {
         for (size_t i = 0; i < indirection.size(); i += kr()) {
           indirection[i + zero_index()] = zero.data();
         }
       }

       // Compute reference results, without renormalization.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           float acc = bias[c];
           for (size_t k = 0; k < kr(); k++) {
             if (indirection[x * step() + k] != zero.data()) {
               acc +=
                 (int32_t(indirection[x * step() + k][c + input_offset()]) - int32_t(input_zero_point())) *
                 (int32_t(kernel[c * kr() + k]) - int32_t(kernel_zero_point()));
             }
           }
           accumulators[x * channels() + c] = acc;
         }
       }

       // 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 uint32_t accumulated_range = uint32_t(accumulated_max) - uint32_t(accumulated_min);
       const double output_scale = accumulated_range >= 256 ? double(accumulated_range) / 255.0 : 1.00001;
       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())));

       // Prepare parameters.
       const float requantization_scale = 1.0f / float(output_scale);
       union xnn_qu8_gemm_params quantization_params = { };
       switch (variant) {
         case Variant::Native:
           quantization_params = xnn_init_qu8_gemm_params(
             input_zero_point(), kernel_zero_point(),
             requantization_scale, output_zero_point, qmin(), qmax());
           break;
         case Variant::Scalar:
           quantization_params = xnn_init_scalar_qu8_gemm_params(
             input_zero_point(), kernel_zero_point(),
             requantization_scale, output_zero_point, qmin(), qmax());
           break;
       }
       const union xnn_q31_requantization_params scalar_requantization_params =
         xnn_init_scalar_requantization_params(
           requantization_scale, output_zero_point, qmin(), qmax());

       // Renormalize reference results.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           output_ref[x * channels() + c] = xnn_q31_requantize(accumulators[x * channels() + c], scalar_requantization_params);
         }
       }

       // Call optimized micro-kernel.
       dwconv_minmax(
         channels(), width(),
         indirection.data(), packed_weights.data(), output.data(),
         step() * sizeof(void*),
         (output_stride() - channels()) * sizeof(uint8_t),
         input_offset() * sizeof(uint8_t), zero.data(),
         &quantization_params);

       // Verify results.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_GE(uint32_t(output[x * output_stride() + c]), uint32_t(qmin()))
             << "x = " << x << ", channel = " << c;
           ASSERT_LE(uint32_t(output[x * output_stride() + c]), uint32_t(qmax()))
             << "x = " << x << ", channel = " << c;
           ASSERT_EQ(uint32_t(output[x * output_stride() + c]), uint32_t(output_ref[x * channels() + c]))
             << "x = " << x << ", channel = " << c << ", accumulator = " << accumulators[x * channels() + c];
         }
       }
     }
   }

   void Test(xnn_f16_dwconv_minmax_unipass_ukernel_function dwconv_minmax, 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>(0.1f, 1.0f), rng);
     auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);

     std::vector<const uint16_t*> indirection((width() - 1) * step() + kr());
     std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + indirection.size() * channels());
     std::vector<uint16_t> kernel(channels() * kr());
     std::vector<uint16_t> bias(channels());
     std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> packed_weights((kr() + 1) * packed_channels());
     std::vector<uint16_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
     std::vector<uint16_t> output((width() - 1) * output_stride() + channels());
     std::vector<float> output_ref(width() * channels());

     for (size_t iteration = 0; iteration < iterations(); iteration++) {
       std::generate(input.begin(), input.end(), std::ref(f16rng));
       std::generate(kernel.begin(), kernel.end(), std::ref(f16rng));
       std::generate(bias.begin(), bias.end(), std::ref(f16rng));
       std::fill(zero.begin(), zero.end(), 0);
       std::fill(output_ref.begin(), output_ref.end(), 0.0f);
       std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);

       std::fill(packed_weights.begin(), packed_weights.end(), 0);
       xnn_pack_f16_dwconv_ghw_w(
         kr(), 1, channels(), cr(),
         kernel.data(), bias.data(), packed_weights.data(), nullptr);
       for (size_t i = 0; i < indirection.size(); i++) {
         indirection[i] = input.data() + i * channels() - input_offset();
       }
       std::shuffle(indirection.begin(), indirection.end(), rng);
       if (zero_index() != SIZE_MAX) {
         for (size_t i = 0; i < indirection.size(); i += kr()) {
           indirection[i + zero_index()] = zero.data();
         }
       }

       // Compute reference results, without clamping.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           float acc = fp16_ieee_to_fp32_value(bias[c]);
           for (size_t k = 0; k < kr(); k++) {
             if (indirection[x * step() + k] != zero.data()) {
               acc += fp16_ieee_to_fp32_value(indirection[x * step() + k][c + input_offset()]) * fp16_ieee_to_fp32_value(kernel[c * kr() + k]);
             }
           }
           output_ref[x * channels() + c] = 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 = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));
       const float output_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));

       // Prepare parameters.
       xnn_f16_minmax_params params = xnn_init_f16_minmax_params(
         fp16_ieee_from_fp32_value(output_min),
         fp16_ieee_from_fp32_value(output_max));

       // 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_minmax(
         channels(), width(),
         reinterpret_cast<const void**>(indirection.data()), packed_weights.data(), output.data(),
         step() * sizeof(void*),
         (output_stride() - channels()) * sizeof(uint16_t),
         input_offset() * sizeof(uint16_t), zero.data(),
         &params);

       // Verify results.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_GE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_min)
             << "x = " << x << ", channel = " << c;
           ASSERT_LE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_max)
             << "x = " << x << ", channel = " << c;
           ASSERT_NEAR(
               output_ref[x * channels() + c],
               fp16_ieee_to_fp32_value(output[x * output_stride() + c]),
               std::abs(output_ref[x * channels() + c]) * 1.0e-2)
             << "x = " << x << ", channel = " << c;
         }
       }
     }
   }

   void Test(xnn_f32_dwconv_unipass_ukernel_function dwconv) 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);

     std::vector<const float*> indirection((width() - 1) * step() + kr());
     std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
     std::vector<float> kernel(channels() * kr());
     std::vector<float> bias(channels());
     std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
     std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
     std::vector<float> output((width() - 1) * output_stride() + channels());
     std::vector<float> output_ref(width() * 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(zero.begin(), zero.end(), 0.0f);
       std::fill(output_ref.begin(), output_ref.end(), nanf(""));
       std::fill(output.begin(), output.end(), nanf(""));

       std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
       xnn_pack_f32_dwconv_ghw_w(
         kr(), 1, channels(), cr(),
         kernel.data(), bias.data(), packed_weights.data(), nullptr);
       for (size_t i = 0; i < indirection.size(); i++) {
         indirection[i] = input.data() + i * channels() - input_offset();
       }
       std::shuffle(indirection.begin(), indirection.end(), rng);
       if (zero_index() != SIZE_MAX) {
         for (size_t i = 0; i < indirection.size(); i += kr()) {
           indirection[i + zero_index()] = zero.data();
         }
       }

       // Compute reference results, without clamping.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           float acc = bias[c];
           for (size_t k = 0; k < kr(); k++) {
             if (indirection[x * step() + k] != zero.data()) {
               acc += indirection[x * step() + k][c + input_offset()] * kernel[c * kr() + k];
             }
           }
           output_ref[x * channels() + c] = acc;
         }
       }

       // Call optimized micro-kernel.
       dwconv(
         channels(), width(),
         indirection.data(), packed_weights.data(), output.data(),
         step() * sizeof(void*),
         (output_stride() - channels()) * sizeof(float),
         input_offset() * sizeof(float), zero.data(),
         nullptr);

       // Verify results.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_NEAR(
               output_ref[x * channels() + c],
               output[x * output_stride() + c],
               std::abs(output_ref[x * channels() + c]) * 1.0e-5)
             << "x = " << x << ", channel = " << c;
         }
       }
     }
   }

   void Test(xnn_f32_dwconv_minmax_unipass_ukernel_function dwconv_minmax, 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);

     std::vector<const float*> indirection((width() - 1) * step() + kr());
     std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
     std::vector<float> kernel(channels() * kr());
     std::vector<float> bias(channels());
     std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
     std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
     std::vector<float> output((width() - 1) * output_stride() + channels());
     std::vector<float> output_ref(width() * 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(zero.begin(), zero.end(), 0.0f);
       std::fill(output_ref.begin(), output_ref.end(), nanf(""));
       std::fill(output.begin(), output.end(), nanf(""));

       std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
       xnn_pack_f32_dwconv_ghw_w(
         kr(), 1, channels(), cr(),
         kernel.data(), bias.data(), packed_weights.data(), nullptr);
       for (size_t i = 0; i < indirection.size(); i++) {
         indirection[i] = input.data() + i * channels() - input_offset();
       }
       std::shuffle(indirection.begin(), indirection.end(), rng);
       if (zero_index() != SIZE_MAX) {
         for (size_t i = 0; i < indirection.size(); i += kr()) {
           indirection[i + zero_index()] = zero.data();
         }
       }

       // Compute reference results, without clamping.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           float acc = bias[c];
           for (size_t k = 0; k < kr(); k++) {
             if (indirection[x * step() + k] != zero.data()) {
               acc += indirection[x * step() + k][c + input_offset()] * kernel[c * kr() + k];
             }
           }
           output_ref[x * channels() + c] = 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_minmax_params params = { };
       switch (variant) {
         case Variant::Native:
           params = xnn_init_f32_minmax_params(output_min, output_max);
           break;
         case Variant::Scalar:
           params = xnn_init_scalar_f32_minmax_params(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_minmax(
         channels(), width(),
         indirection.data(), packed_weights.data(), output.data(),
         step() * sizeof(void*),
         (output_stride() - channels()) * sizeof(float),
         input_offset() * sizeof(float), zero.data(),
         &params);

       // Verify results.
       for (size_t x = 0; x < width(); x++) {
         for (size_t c = 0; c < channels(); c++) {
           ASSERT_GE(output[x * output_stride() + c], output_min)
             << "x = " << x << ", channel = " << c;
           ASSERT_LE(output[x * output_stride() + c], output_max)
             << "x = " << x << ", channel = " << c;
           ASSERT_NEAR(
               output_ref[x * channels() + c],
               output[x * output_stride() + c],
               std::abs(output_ref[x * channels() + c]) * 1.0e-5)
             << "x = " << x << ", channel = " << c;
         }
       }
     }
   }

  private:
   uint32_t channels_{1};
   uint32_t cr_{1};
   uint32_t kr_{1};
   uint32_t width_{1};
   uint32_t step_{1};
   uint32_t output_stride_{0};
   uint8_t input_zero_point_{127};
   uint8_t kernel_zero_point_{127};
   uint8_t qmin_{0};
   uint8_t qmax_{255};
   size_t input_offset_{0};
   size_t zero_index_{SIZE_MAX};
   size_t iterations_{3};
 };
	// 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 <limits>
	#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>
	#include <xnnpack/requantization.h>


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

	inline DWConvMicrokernelTester& width(uint32_t width) {
	assert(width >= 1);
	this->width_ = width;
	return *this;
	}

	inline uint32_t width() const {
	return this->width_;
	}

	inline DWConvMicrokernelTester& step(uint32_t step) {
	assert(step >= 1);
	this->step_ = step;
	return *this;
	}

	inline uint32_t step() const {
	return this->step_;
	}

	inline DWConvMicrokernelTester& channels(uint32_t channels) {
	assert(channels >= 1);
	this->channels_ = channels;
	return *this;
	}

	inline uint32_t channels() const {
	return this->channels_;
	}

	inline DWConvMicrokernelTester& cr(uint32_t cr) {
	assert(cr != 0);
	assert((cr & (cr - 1)) == 0);
	this->cr_ = cr;
	return *this;
	}

	inline uint32_t cr() const {
	return this->cr_;
	}

	inline DWConvMicrokernelTester& kr(uint32_t kr) {
	assert(kr != 0);
	this->kr_ = kr;
	return *this;
	}

	inline uint32_t kr() const {
	return this->kr_;
	}

	inline uint32_t packed_channels() const {
	return (channels() / cr() + !!(channels() % cr())) * cr();
	}

	inline DWConvMicrokernelTester& output_stride(uint32_t output_stride) {
	assert(output_stride != 0);
	this->output_stride_ = output_stride;
	return *this;
	}

	inline uint32_t output_stride() const {
	if (this->output_stride_ == 0) {
	return channels();
	} else {
	assert(this->output_stride_ >= channels());
	return this->output_stride_;
	}
	}

	inline DWConvMicrokernelTester& input_zero_point(uint8_t input_zero_point) {
	this->input_zero_point_ = input_zero_point;
	return *this;
	}

	inline uint8_t input_zero_point() const {
	return this->input_zero_point_;
	}

	inline DWConvMicrokernelTester& kernel_zero_point(uint8_t kernel_zero_point) {
	this->kernel_zero_point_ = kernel_zero_point;
	return *this;
	}

	inline uint8_t kernel_zero_point() const {
	return this->kernel_zero_point_;
	}

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

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

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

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

	inline DWConvMicrokernelTester& input_offset(size_t input_offset) {
	this->input_offset_ = input_offset;
	return *this;
	}

	inline size_t input_offset() const {
	return this->input_offset_;
	}

	inline DWConvMicrokernelTester& zero_index(size_t zero_index) {
	this->zero_index_ = zero_index;
	return *this;
	}

	inline size_t zero_index() const {
	return this->zero_index_;
	}

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

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

	void Test(xnn_qu8_dwconv_minmax_unipass_ukernel_function dwconv_minmax, Variant variant = Variant::Native) 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<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);

	std::vector<const uint8_t> indirection((width() - 1) step() + kr());
	std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + indirection.size() * channels());
	std::vector<uint8_t> kernel(channels() * kr());
	std::vector<int32_t> bias(channels());
	std::vector<uint8_t, AlignedAllocator<uint8_t, 64>> packed_weights((kr() + sizeof(int32_t) / sizeof(uint8_t)) * packed_channels());
	std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
	std::vector<uint8_t> output((width() - 1) * output_stride() + channels());
	std::vector<int32_t> accumulators(width() * channels());
	std::vector<uint8_t> output_ref(width() * channels());

	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	do {
	std::generate(input.begin(), input.end(), std::ref(u8rng));
	} while (input.size() > 1 && std::max_element(input.cbegin(), input.cend()) == std::min_element(input.cbegin(), input.cend()));
	do {
	std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
	} while (kernel.size() > 1 && std::max_element(kernel.cbegin(), kernel.cend()) == std::min_element(kernel.cbegin(), kernel.cend()));
	std::generate(bias.begin(), bias.end(), std::ref(s32rng));
	std::fill(zero.begin(), zero.end(), input_zero_point());
	std::fill(output.begin(), output.end(), 0xA5);

	std::fill(packed_weights.begin(), packed_weights.end(), 0);
	const xnn_qu8_packing_params packing_params = { input_zero_point(), kernel_zero_point() };
	xnn_pack_qu8_dwconv_ghw_w(
	kr(), 1, channels(), cr(),
	kernel.data(), bias.data(), packed_weights.data(), &packing_params);
	for (size_t i = 0; i < indirection.size(); i++) {
	indirection[i] = input.data() + i * channels() - input_offset();
	}
	std::shuffle(indirection.begin(), indirection.end(), rng);
	if (zero_index() != SIZE_MAX) {
	for (size_t i = 0; i < indirection.size(); i += kr()) {
	indirection[i + zero_index()] = zero.data();
	}
	}

	// Compute reference results, without renormalization.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	float acc = bias[c];
	for (size_t k = 0; k < kr(); k++) {
	if (indirection[x * step() + k] != zero.data()) {
	acc +=
	(int32_t(indirection[x * step() + k][c + input_offset()]) - int32_t(input_zero_point())) *
	(int32_t(kernel[c * kr() + k]) - int32_t(kernel_zero_point()));
	}
	}
	accumulators[x * channels() + c] = acc;
	}
	}

	// 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 uint32_t accumulated_range = uint32_t(accumulated_max) - uint32_t(accumulated_min);
	const double output_scale = accumulated_range >= 256 ? double(accumulated_range) / 255.0 : 1.00001;
	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())));

	// Prepare parameters.
	const float requantization_scale = 1.0f / float(output_scale);
	union xnn_qu8_gemm_params quantization_params = { };
	switch (variant) {
	case Variant::Native:
	quantization_params = xnn_init_qu8_gemm_params(
	input_zero_point(), kernel_zero_point(),
	requantization_scale, output_zero_point, qmin(), qmax());
	break;
	case Variant::Scalar:
	quantization_params = xnn_init_scalar_qu8_gemm_params(
	input_zero_point(), kernel_zero_point(),
	requantization_scale, output_zero_point, qmin(), qmax());
	break;
	}
	const union xnn_q31_requantization_params scalar_requantization_params =
	xnn_init_scalar_requantization_params(
	requantization_scale, output_zero_point, qmin(), qmax());

	// Renormalize reference results.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	output_ref[x * channels() + c] = xnn_q31_requantize(accumulators[x * channels() + c], scalar_requantization_params);
	}
	}

	// Call optimized micro-kernel.
	dwconv_minmax(
	channels(), width(),
	indirection.data(), packed_weights.data(), output.data(),
	step() * sizeof(void*),
	(output_stride() - channels()) * sizeof(uint8_t),
	input_offset() * sizeof(uint8_t), zero.data(),
	&quantization_params);

	// Verify results.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_GE(uint32_t(output[x * output_stride() + c]), uint32_t(qmin()))
	<< "x = " << x << ", channel = " << c;
	ASSERT_LE(uint32_t(output[x * output_stride() + c]), uint32_t(qmax()))
	<< "x = " << x << ", channel = " << c;
	ASSERT_EQ(uint32_t(output[x * output_stride() + c]), uint32_t(output_ref[x * channels() + c]))
	<< "x = " << x << ", channel = " << c << ", accumulator = " << accumulators[x * channels() + c];
	}
	}
	}
	}

	void Test(xnn_f16_dwconv_minmax_unipass_ukernel_function dwconv_minmax, 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>(0.1f, 1.0f), rng);
	auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);

	std::vector<const uint16_t> indirection((width() - 1) step() + kr());
	std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + indirection.size() * channels());
	std::vector<uint16_t> kernel(channels() * kr());
	std::vector<uint16_t> bias(channels());
	std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> packed_weights((kr() + 1) * packed_channels());
	std::vector<uint16_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
	std::vector<uint16_t> output((width() - 1) * output_stride() + channels());
	std::vector<float> output_ref(width() * channels());

	for (size_t iteration = 0; iteration < iterations(); iteration++) {
	std::generate(input.begin(), input.end(), std::ref(f16rng));
	std::generate(kernel.begin(), kernel.end(), std::ref(f16rng));
	std::generate(bias.begin(), bias.end(), std::ref(f16rng));
	std::fill(zero.begin(), zero.end(), 0);
	std::fill(output_ref.begin(), output_ref.end(), 0.0f);
	std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);

	std::fill(packed_weights.begin(), packed_weights.end(), 0);
	xnn_pack_f16_dwconv_ghw_w(
	kr(), 1, channels(), cr(),
	kernel.data(), bias.data(), packed_weights.data(), nullptr);
	for (size_t i = 0; i < indirection.size(); i++) {
	indirection[i] = input.data() + i * channels() - input_offset();
	}
	std::shuffle(indirection.begin(), indirection.end(), rng);
	if (zero_index() != SIZE_MAX) {
	for (size_t i = 0; i < indirection.size(); i += kr()) {
	indirection[i + zero_index()] = zero.data();
	}
	}

	// Compute reference results, without clamping.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	float acc = fp16_ieee_to_fp32_value(bias[c]);
	for (size_t k = 0; k < kr(); k++) {
	if (indirection[x * step() + k] != zero.data()) {
	acc += fp16_ieee_to_fp32_value(indirection[x * step() + k][c + input_offset()]) * fp16_ieee_to_fp32_value(kernel[c * kr() + k]);
	}
	}
	output_ref[x * channels() + c] = 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 = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));
	const float output_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));

	// Prepare parameters.
	xnn_f16_minmax_params params = xnn_init_f16_minmax_params(
	fp16_ieee_from_fp32_value(output_min),
	fp16_ieee_from_fp32_value(output_max));

	// 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_minmax(
	channels(), width(),
	reinterpret_cast<const void**>(indirection.data()), packed_weights.data(), output.data(),
	step() * sizeof(void*),
	(output_stride() - channels()) * sizeof(uint16_t),
	input_offset() * sizeof(uint16_t), zero.data(),
	&params);

	// Verify results.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_GE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_min)
	<< "x = " << x << ", channel = " << c;
	ASSERT_LE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_max)
	<< "x = " << x << ", channel = " << c;
	ASSERT_NEAR(
	output_ref[x * channels() + c],
	fp16_ieee_to_fp32_value(output[x * output_stride() + c]),
	std::abs(output_ref[x * channels() + c]) * 1.0e-2)
	<< "x = " << x << ", channel = " << c;
	}
	}
	}
	}

	void Test(xnn_f32_dwconv_unipass_ukernel_function dwconv) 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);

	std::vector<const float> indirection((width() - 1) step() + kr());
	std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
	std::vector<float> kernel(channels() * kr());
	std::vector<float> bias(channels());
	std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
	std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
	std::vector<float> output((width() - 1) * output_stride() + channels());
	std::vector<float> output_ref(width() * 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(zero.begin(), zero.end(), 0.0f);
	std::fill(output_ref.begin(), output_ref.end(), nanf(""));
	std::fill(output.begin(), output.end(), nanf(""));

	std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
	xnn_pack_f32_dwconv_ghw_w(
	kr(), 1, channels(), cr(),
	kernel.data(), bias.data(), packed_weights.data(), nullptr);
	for (size_t i = 0; i < indirection.size(); i++) {
	indirection[i] = input.data() + i * channels() - input_offset();
	}
	std::shuffle(indirection.begin(), indirection.end(), rng);
	if (zero_index() != SIZE_MAX) {
	for (size_t i = 0; i < indirection.size(); i += kr()) {
	indirection[i + zero_index()] = zero.data();
	}
	}

	// Compute reference results, without clamping.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	float acc = bias[c];
	for (size_t k = 0; k < kr(); k++) {
	if (indirection[x * step() + k] != zero.data()) {
	acc += indirection[x * step() + k][c + input_offset()] * kernel[c * kr() + k];
	}
	}
	output_ref[x * channels() + c] = acc;
	}
	}

	// Call optimized micro-kernel.
	dwconv(
	channels(), width(),
	indirection.data(), packed_weights.data(), output.data(),
	step() * sizeof(void*),
	(output_stride() - channels()) * sizeof(float),
	input_offset() * sizeof(float), zero.data(),
	nullptr);

	// Verify results.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_NEAR(
	output_ref[x * channels() + c],
	output[x * output_stride() + c],
	std::abs(output_ref[x * channels() + c]) * 1.0e-5)
	<< "x = " << x << ", channel = " << c;
	}
	}
	}
	}

	void Test(xnn_f32_dwconv_minmax_unipass_ukernel_function dwconv_minmax, 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);

	std::vector<const float> indirection((width() - 1) step() + kr());
	std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
	std::vector<float> kernel(channels() * kr());
	std::vector<float> bias(channels());
	std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
	std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
	std::vector<float> output((width() - 1) * output_stride() + channels());
	std::vector<float> output_ref(width() * 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(zero.begin(), zero.end(), 0.0f);
	std::fill(output_ref.begin(), output_ref.end(), nanf(""));
	std::fill(output.begin(), output.end(), nanf(""));

	std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
	xnn_pack_f32_dwconv_ghw_w(
	kr(), 1, channels(), cr(),
	kernel.data(), bias.data(), packed_weights.data(), nullptr);
	for (size_t i = 0; i < indirection.size(); i++) {
	indirection[i] = input.data() + i * channels() - input_offset();
	}
	std::shuffle(indirection.begin(), indirection.end(), rng);
	if (zero_index() != SIZE_MAX) {
	for (size_t i = 0; i < indirection.size(); i += kr()) {
	indirection[i + zero_index()] = zero.data();
	}
	}

	// Compute reference results, without clamping.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	float acc = bias[c];
	for (size_t k = 0; k < kr(); k++) {
	if (indirection[x * step() + k] != zero.data()) {
	acc += indirection[x * step() + k][c + input_offset()] * kernel[c * kr() + k];
	}
	}
	output_ref[x * channels() + c] = 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_minmax_params params = { };
	switch (variant) {
	case Variant::Native:
	params = xnn_init_f32_minmax_params(output_min, output_max);
	break;
	case Variant::Scalar:
	params = xnn_init_scalar_f32_minmax_params(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_minmax(
	channels(), width(),
	indirection.data(), packed_weights.data(), output.data(),
	step() * sizeof(void*),
	(output_stride() - channels()) * sizeof(float),
	input_offset() * sizeof(float), zero.data(),
	&params);

	// Verify results.
	for (size_t x = 0; x < width(); x++) {
	for (size_t c = 0; c < channels(); c++) {
	ASSERT_GE(output[x * output_stride() + c], output_min)
	<< "x = " << x << ", channel = " << c;
	ASSERT_LE(output[x * output_stride() + c], output_max)
	<< "x = " << x << ", channel = " << c;
	ASSERT_NEAR(
	output_ref[x * channels() + c],
	output[x * output_stride() + c],
	std::abs(output_ref[x * channels() + c]) * 1.0e-5)
	<< "x = " << x << ", channel = " << c;
	}
	}
	}
	}

	private:
	uint32_t channels_{1};
	uint32_t cr_{1};
	uint32_t kr_{1};
	uint32_t width_{1};
	uint32_t step_{1};
	uint32_t output_stride_{0};
	uint8_t input_zero_point_{127};
	uint8_t kernel_zero_point_{127};
	uint8_t qmin_{0};
	uint8_t qmax_{255};
	size_t input_offset_{0};
	size_t zero_index_{SIZE_MAX};
	size_t iterations_{3};
	};