bench/f32-dwconv-spchw.cc - 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.

 #include <algorithm>
 #include <cfloat>
 #include <cmath>
 #include <functional>
 #include <random>
 #include <vector>

 #include <cpuinfo.h>

 #include <benchmark/benchmark.h>
 #include "bench/dwconv.h"
 #include "bench/utils.h"
 #include <xnnpack/AlignedAllocator.h>
 #include <xnnpack/common.h>
 #include <xnnpack/dwconv.h>
 #include <xnnpack/indirection.h>
 #include <xnnpack/operator.h>
 #include <xnnpack/pack.h>
 #include <xnnpack/params-init.h>
 #include <xnnpack/params.h>


 static void DWConvCHWBenchmark(benchmark::State& state,
   xnn_f32_dwconv_spchw_ukernel_function dwconv,
   uint32_t it, uint32_t ot, uint32_t kh, uint32_t kw, uint32_t pw, uint32_t s)
 {
   if (!cpuinfo_initialize()) {
     state.SkipWithError("cpuinfo initialization failed");
     return;
   }

   const size_t input_height = state.range(0);
   const size_t input_width = state.range(1);
   const size_t kernel_height = state.range(2);
   const size_t kernel_width = state.range(3);
   const size_t padding_height = state.range(4);
   const size_t padding_width = state.range(5);
   const size_t subsampling = state.range(6);
   const size_t dilation = state.range(7);
   const size_t channels = state.range(8);

   if (kernel_height != kh) {
     state.SkipWithError("kernel height mismatch");
     return;
   }

   if (kernel_width != kw) {
     state.SkipWithError("kernel width mismatch");
     return;
   }

   if (subsampling != s) {
     state.SkipWithError("subsampling mismatch");
     return;
   }

   if (padding_width % 2 != 0 || padding_width / 2 != pw) {
     state.SkipWithError("padding width mismatch");
     return;
   }

   if (dilation != 1) {
     state.SkipWithError("unsupported dilation");
     return;
   }

   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);

   const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
   const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
   const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
   const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;

   const size_t inputSize = (input_height + padding_height) * input_width;
   const size_t kernel_size = kernel_height * kernel_width;
   const size_t output_size = output_height * output_width;

   std::vector<float> input(inputSize * channels + 2 * it);
   std::generate(input.begin(), input.end(), std::ref(f32rng));
   std::vector<float> bias(channels);
   std::generate(bias.begin(), bias.end(), std::ref(f32rng));
   std::vector<float> kernel(channels * kernel_size);
   std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));

   const size_t w_elements = (kernel_size + 1) * channels;
   const size_t o_elements = output_size * channels;
   const size_t num_buffers = 1 +
     benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
       sizeof(float) * (w_elements + o_elements));

   std::vector<float, AlignedAllocator<float, 32>> packed_weights(w_elements * num_buffers);
   std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
   for (size_t c = 0; c < channels; c++) {
     packed_weights[c * kernel_size + c] = bias[c];
     for (size_t i = 0; i < kernel_size; i++) {
       packed_weights[c * kernel_size + c + 1 + i] = kernel[c * kernel_size + i];
     }
   }
   for (size_t n = 1; n < num_buffers; n++) {
     std::copy(packed_weights.cbegin(), packed_weights.cbegin() + w_elements, packed_weights.begin() + n * w_elements);
   }

   std::vector<float> output(o_elements * num_buffers);
   std::fill(output.begin(), output.end(), std::nanf(""));

   xnn_f32_spchw_params spchw_params =
     xnn_init_f32_spchw_params(input_width, -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity());

   size_t buffer_index = 0;
   for (auto _ : state) {
     state.PauseTiming();
     benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float));
     buffer_index = (buffer_index + 1) % num_buffers;
     state.ResumeTiming();

     for (uint32_t channel = 0; channel < channels; channel++) {
       dwconv(
         output_height, input_width,
         input.data() + channel * inputSize,
         packed_weights.data() + channel * (kernel_size + 1) + buffer_index * w_elements,
         output.data() + channel * output_size + buffer_index * o_elements,
         it * sizeof(float), ot * sizeof(float),
         input_width * sizeof(float), output_width * sizeof(float),
         &spchw_params);
     }
   }

   state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
   state.counters["FLOPS"] = benchmark::Counter(
     uint64_t(state.iterations()) * 2 * output_size * channels * kernel_size,
     benchmark::Counter::kIsRate);

   state.counters["BYTES"] = benchmark::Counter(
     uint64_t(state.iterations()) * (output_size + inputSize + kernel_size + 1 /* bias */) * channels * sizeof(float),
     benchmark::Counter::kIsRate);
 }

 static void DWConvHWoTCTBenchmark(benchmark::State& state,
   xnn_f32_dwconv_spchw_ukernel_function dwconv,
   uint32_t it, uint32_t ot, uint32_t kh, uint32_t kw, uint32_t pw, uint32_t s)
 {
   if (!cpuinfo_initialize()) {
     state.SkipWithError("cpuinfo initialization failed");
     return;
   }

   const size_t input_height = state.range(0);
   const size_t input_width = state.range(1);
   const size_t kernel_height = state.range(2);
   const size_t kernel_width = state.range(3);
   const size_t padding_height = state.range(4);
   const size_t padding_width = state.range(5);
   const size_t subsampling = state.range(6);
   const size_t dilation = state.range(7);
   const size_t channels = state.range(8);

   if (kernel_height != kh) {
     state.SkipWithError("kernel height mismatch");
     return;
   }

   if (kernel_width != kw) {
     state.SkipWithError("kernel width mismatch");
     return;
   }

   if (subsampling != s) {
     state.SkipWithError("subsampling mismatch");
     return;
   }

   if (padding_width % 2 != 0 || padding_width / 2 != pw) {
     state.SkipWithError("padding width mismatch");
     return;
   }

   if (dilation != 1) {
     state.SkipWithError("unsupported dilation");
     return;
   }

   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);

   const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
   const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
   const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
   const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;

   const size_t inputSize = (input_height + padding_height) * input_width;
   const size_t kernel_size = kernel_height * kernel_width;
   const size_t output_size = output_height * output_width;

   std::vector<float> input(input_height * benchmark::utils::RoundUp<size_t>(input_width, it) * channels);
   std::generate(input.begin(), input.end(), std::ref(f32rng));
   std::vector<float> bias(channels);
   std::generate(bias.begin(), bias.end(), std::ref(f32rng));
   std::vector<float> kernel(channels * kernel_size);
   std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));

   const size_t w_elements = (kernel_size + 1) * channels;
   const size_t o_elements = output_height * benchmark::utils::RoundUp<size_t>(output_width, ot) * channels;
   const size_t num_buffers = 1 +
     benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
       sizeof(float) * (w_elements + o_elements));

   std::vector<float, AlignedAllocator<float, 32>> packed_weights(w_elements * num_buffers);
   std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
   for (size_t c = 0; c < channels; c++) {
     packed_weights[c * kernel_size + c] = bias[c];
     for (size_t i = 0; i < kernel_size; i++) {
       packed_weights[c * kernel_size + c + 1 + i] = kernel[c * kernel_size + i];
     }
   }
   for (size_t n = 1; n < num_buffers; n++) {
     std::copy(packed_weights.cbegin(), packed_weights.cbegin() + w_elements, packed_weights.begin() + n * w_elements);
   }

   std::vector<float> output(o_elements * num_buffers);
   std::fill(output.begin(), output.end(), std::nanf(""));

   xnn_f32_spchw_params spchw_params =
     xnn_init_f32_spchw_params(input_width, -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity());

   size_t buffer_index = 0;
   for (auto _ : state) {
     state.PauseTiming();
     benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float));
     buffer_index = (buffer_index + 1) % num_buffers;
     state.ResumeTiming();

     for (uint32_t channel = 0; channel < channels; channel++) {
       dwconv(
         output_height, input_width,
         input.data() + channel * it,
         packed_weights.data() + channel * (kernel_size + 1) + buffer_index * w_elements,
         output.data() + channel * ot + buffer_index * o_elements,
         it * channels * sizeof(float), ot * channels * sizeof(float),
         benchmark::utils::RoundUp<size_t>(input_width, it) * channels * sizeof(float),
         benchmark::utils::RoundUp<size_t>(output_width, ot) * channels * sizeof(float),
         &spchw_params);
     }
   }

   state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
   state.counters["FLOPS"] = benchmark::Counter(
     uint64_t(state.iterations()) * 2 * output_size * channels * kernel_size,
     benchmark::Counter::kIsRate);

   state.counters["BYTES"] = benchmark::Counter(
     uint64_t(state.iterations()) * (output_size + inputSize + kernel_size + 1 /* bias */) * channels * sizeof(float),
     benchmark::Counter::kIsRate);
 }

 #if XNN_ARCH_ARM64
   static void CHW_3x3p1__neonfma(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__neonfma, 4, 4, 3, 3, 1, 1);
   }

   static void CHW_5x5p2__neonfma(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__neonfma, 4, 4, 5, 5, 2, 1);
   }

   static void CHW_3x3s2p1__neonfma(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__neonfma, 4, 4, 3, 3, 1, 2);
   }

   static void CHW_5x5s2p2__neonfma(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__neonfma, 4, 4, 5, 5, 2, 2);
   }

   static void HWo4C4_3x3p1__neonfma(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__neonfma, 4, 4, 3, 3, 1, 1);
   }

   static void HWo4C4_5x5p2__neonfma(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__neonfma, 4, 4, 5, 5, 2, 1);
   }

   static void HWo4C4_3x3s2p1__neonfma(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__neonfma, 4, 4, 3, 3, 1, 2);
   }

   static void HWo4C4_5x5s2p2__neonfma(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__neonfma, 4, 4, 5, 5, 2, 2);
   }

   BENCHMARK_DWCONV(CHW_3x3p1__neonfma)
   BENCHMARK_DWCONV(CHW_5x5p2__neonfma)
   BENCHMARK_DWCONV(CHW_3x3s2p1__neonfma)
   BENCHMARK_DWCONV(CHW_5x5s2p2__neonfma)
   BENCHMARK_DWCONV(HWo4C4_3x3p1__neonfma)
   BENCHMARK_DWCONV(HWo4C4_5x5p2__neonfma)
   BENCHMARK_DWCONV(HWo4C4_3x3s2p1__neonfma)
   BENCHMARK_DWCONV(HWo4C4_5x5s2p2__neonfma)
 #endif  // XNN_ARCH_ARM64


 #if XNN_ARCH_X86 || XNN_ARCH_X86_64
   static void CHW_3x3p1__sse(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__sse, 4, 4, 3, 3, 1, 1);
   }

   static void CHW_3x3s2p1__sse(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__sse, 4, 4, 3, 3, 1, 2);
   }

   static void HWo4C4_3x3p1__sse(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__sse, 4, 4, 3, 3, 1, 1);
   }

   static void HWo4C4_3x3s2p1__sse(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__sse, 4, 4, 3, 3, 1, 2);
   }

   BENCHMARK_DWCONV(CHW_3x3p1__sse)
   BENCHMARK_DWCONV(CHW_3x3s2p1__sse)
   BENCHMARK_DWCONV(HWo4C4_3x3p1__sse)
   BENCHMARK_DWCONV(HWo4C4_3x3s2p1__sse)
 #endif  // XNN_ARCH_X86 || XNN_ARCH_X86_64

   static void CHW_3x3p1__scalar(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__scalar, 1, 1, 3, 3, 1, 1);
   }

   static void CHW_5x5p2__scalar(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__scalar, 1, 1, 5, 5, 2, 1);
   }

   static void CHW_3x3s2p1__scalar(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__scalar, 1, 1, 3, 3, 1, 2);
   }

   static void CHW_5x5s2p2__scalar(benchmark::State& state, const char* net) {
     DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__scalar, 1, 1, 5, 5, 2, 2);
   }

   static void HWC_3x3p1__scalar(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__scalar, 1, 1, 3, 3, 1, 1);
   }

   static void HWC_5x5p2__scalar(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__scalar, 1, 1, 5, 5, 2, 1);
   }

   static void HWC_3x3s2p1__scalar(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__scalar, 1, 1, 3, 3, 1, 2);
   }

   static void HWC_5x5s2p2__scalar(benchmark::State& state, const char* net) {
     DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__scalar, 1, 1, 5, 5, 2, 2);
   }


   BENCHMARK_DWCONV(CHW_3x3p1__scalar)
   BENCHMARK_DWCONV(CHW_5x5p2__scalar)
   BENCHMARK_DWCONV(CHW_3x3s2p1__scalar)
   BENCHMARK_DWCONV(CHW_5x5s2p2__scalar)
   BENCHMARK_DWCONV(HWC_3x3p1__scalar)
   BENCHMARK_DWCONV(HWC_5x5p2__scalar)
   BENCHMARK_DWCONV(HWC_3x3s2p1__scalar)
   BENCHMARK_DWCONV(HWC_5x5s2p2__scalar)

 #ifndef XNNPACK_BENCHMARK_NO_MAIN
 BENCHMARK_MAIN();
 #endif
	// 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.

	#include <algorithm>
	#include <cfloat>
	#include <cmath>
	#include <functional>
	#include <random>
	#include <vector>

	#include <cpuinfo.h>

	#include <benchmark/benchmark.h>
	#include "bench/dwconv.h"
	#include "bench/utils.h"
	#include <xnnpack/AlignedAllocator.h>
	#include <xnnpack/common.h>
	#include <xnnpack/dwconv.h>
	#include <xnnpack/indirection.h>
	#include <xnnpack/operator.h>
	#include <xnnpack/pack.h>
	#include <xnnpack/params-init.h>
	#include <xnnpack/params.h>


	static void DWConvCHWBenchmark(benchmark::State& state,
	xnn_f32_dwconv_spchw_ukernel_function dwconv,
	uint32_t it, uint32_t ot, uint32_t kh, uint32_t kw, uint32_t pw, uint32_t s)
	{
	if (!cpuinfo_initialize()) {
	state.SkipWithError("cpuinfo initialization failed");
	return;
	}

	const size_t input_height = state.range(0);
	const size_t input_width = state.range(1);
	const size_t kernel_height = state.range(2);
	const size_t kernel_width = state.range(3);
	const size_t padding_height = state.range(4);
	const size_t padding_width = state.range(5);
	const size_t subsampling = state.range(6);
	const size_t dilation = state.range(7);
	const size_t channels = state.range(8);

	if (kernel_height != kh) {
	state.SkipWithError("kernel height mismatch");
	return;
	}

	if (kernel_width != kw) {
	state.SkipWithError("kernel width mismatch");
	return;
	}

	if (subsampling != s) {
	state.SkipWithError("subsampling mismatch");
	return;
	}

	if (padding_width % 2 != 0 \|\| padding_width / 2 != pw) {
	state.SkipWithError("padding width mismatch");
	return;
	}

	if (dilation != 1) {
	state.SkipWithError("unsupported dilation");
	return;
	}

	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);

	const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
	const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
	const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
	const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;

	const size_t inputSize = (input_height + padding_height) * input_width;
	const size_t kernel_size = kernel_height * kernel_width;
	const size_t output_size = output_height * output_width;

	std::vector<float> input(inputSize * channels + 2 * it);
	std::generate(input.begin(), input.end(), std::ref(f32rng));
	std::vector<float> bias(channels);
	std::generate(bias.begin(), bias.end(), std::ref(f32rng));
	std::vector<float> kernel(channels * kernel_size);
	std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));

	const size_t w_elements = (kernel_size + 1) * channels;
	const size_t o_elements = output_size * channels;
	const size_t num_buffers = 1 +
	benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
	sizeof(float) * (w_elements + o_elements));

	std::vector<float, AlignedAllocator<float, 32>> packed_weights(w_elements * num_buffers);
	std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
	for (size_t c = 0; c < channels; c++) {
	packed_weights[c * kernel_size + c] = bias[c];
	for (size_t i = 0; i < kernel_size; i++) {
	packed_weights[c * kernel_size + c + 1 + i] = kernel[c * kernel_size + i];
	}
	}
	for (size_t n = 1; n < num_buffers; n++) {
	std::copy(packed_weights.cbegin(), packed_weights.cbegin() + w_elements, packed_weights.begin() + n * w_elements);
	}

	std::vector<float> output(o_elements * num_buffers);
	std::fill(output.begin(), output.end(), std::nanf(""));

	xnn_f32_spchw_params spchw_params =
	xnn_init_f32_spchw_params(input_width, -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity());

	size_t buffer_index = 0;
	for (auto _ : state) {
	state.PauseTiming();
	benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float));
	buffer_index = (buffer_index + 1) % num_buffers;
	state.ResumeTiming();

	for (uint32_t channel = 0; channel < channels; channel++) {
	dwconv(
	output_height, input_width,
	input.data() + channel * inputSize,
	packed_weights.data() + channel * (kernel_size + 1) + buffer_index * w_elements,
	output.data() + channel * output_size + buffer_index * o_elements,
	it * sizeof(float), ot * sizeof(float),
	input_width * sizeof(float), output_width * sizeof(float),
	&spchw_params);
	}
	}

	state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
	state.counters["FLOPS"] = benchmark::Counter(
	uint64_t(state.iterations()) * 2 * output_size * channels * kernel_size,
	benchmark::Counter::kIsRate);

	state.counters["BYTES"] = benchmark::Counter(
	uint64_t(state.iterations()) * (output_size + inputSize + kernel_size + 1 /* bias /) channels * sizeof(float),
	benchmark::Counter::kIsRate);
	}

	static void DWConvHWoTCTBenchmark(benchmark::State& state,
	xnn_f32_dwconv_spchw_ukernel_function dwconv,
	uint32_t it, uint32_t ot, uint32_t kh, uint32_t kw, uint32_t pw, uint32_t s)
	{
	if (!cpuinfo_initialize()) {
	state.SkipWithError("cpuinfo initialization failed");
	return;
	}

	const size_t input_height = state.range(0);
	const size_t input_width = state.range(1);
	const size_t kernel_height = state.range(2);
	const size_t kernel_width = state.range(3);
	const size_t padding_height = state.range(4);
	const size_t padding_width = state.range(5);
	const size_t subsampling = state.range(6);
	const size_t dilation = state.range(7);
	const size_t channels = state.range(8);

	if (kernel_height != kh) {
	state.SkipWithError("kernel height mismatch");
	return;
	}

	if (kernel_width != kw) {
	state.SkipWithError("kernel width mismatch");
	return;
	}

	if (subsampling != s) {
	state.SkipWithError("subsampling mismatch");
	return;
	}

	if (padding_width % 2 != 0 \|\| padding_width / 2 != pw) {
	state.SkipWithError("padding width mismatch");
	return;
	}

	if (dilation != 1) {
	state.SkipWithError("unsupported dilation");
	return;
	}

	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);

	const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1;
	const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1;
	const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1;
	const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1;

	const size_t inputSize = (input_height + padding_height) * input_width;
	const size_t kernel_size = kernel_height * kernel_width;
	const size_t output_size = output_height * output_width;

	std::vector<float> input(input_height * benchmark::utils::RoundUp<size_t>(input_width, it) * channels);
	std::generate(input.begin(), input.end(), std::ref(f32rng));
	std::vector<float> bias(channels);
	std::generate(bias.begin(), bias.end(), std::ref(f32rng));
	std::vector<float> kernel(channels * kernel_size);
	std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));

	const size_t w_elements = (kernel_size + 1) * channels;
	const size_t o_elements = output_height * benchmark::utils::RoundUp<size_t>(output_width, ot) * channels;
	const size_t num_buffers = 1 +
	benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
	sizeof(float) * (w_elements + o_elements));

	std::vector<float, AlignedAllocator<float, 32>> packed_weights(w_elements * num_buffers);
	std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
	for (size_t c = 0; c < channels; c++) {
	packed_weights[c * kernel_size + c] = bias[c];
	for (size_t i = 0; i < kernel_size; i++) {
	packed_weights[c * kernel_size + c + 1 + i] = kernel[c * kernel_size + i];
	}
	}
	for (size_t n = 1; n < num_buffers; n++) {
	std::copy(packed_weights.cbegin(), packed_weights.cbegin() + w_elements, packed_weights.begin() + n * w_elements);
	}

	std::vector<float> output(o_elements * num_buffers);
	std::fill(output.begin(), output.end(), std::nanf(""));

	xnn_f32_spchw_params spchw_params =
	xnn_init_f32_spchw_params(input_width, -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity());

	size_t buffer_index = 0;
	for (auto _ : state) {
	state.PauseTiming();
	benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float));
	buffer_index = (buffer_index + 1) % num_buffers;
	state.ResumeTiming();

	for (uint32_t channel = 0; channel < channels; channel++) {
	dwconv(
	output_height, input_width,
	input.data() + channel * it,
	packed_weights.data() + channel * (kernel_size + 1) + buffer_index * w_elements,
	output.data() + channel * ot + buffer_index * o_elements,
	it * channels * sizeof(float), ot * channels * sizeof(float),
	benchmark::utils::RoundUp<size_t>(input_width, it) * channels * sizeof(float),
	benchmark::utils::RoundUp<size_t>(output_width, ot) * channels * sizeof(float),
	&spchw_params);
	}
	}

	state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
	state.counters["FLOPS"] = benchmark::Counter(
	uint64_t(state.iterations()) * 2 * output_size * channels * kernel_size,
	benchmark::Counter::kIsRate);

	state.counters["BYTES"] = benchmark::Counter(
	uint64_t(state.iterations()) * (output_size + inputSize + kernel_size + 1 /* bias /) channels * sizeof(float),
	benchmark::Counter::kIsRate);
	}

	#if XNN_ARCH_ARM64
	static void CHW_3x3p1__neonfma(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__neonfma, 4, 4, 3, 3, 1, 1);
	}

	static void CHW_5x5p2__neonfma(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__neonfma, 4, 4, 5, 5, 2, 1);
	}

	static void CHW_3x3s2p1__neonfma(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__neonfma, 4, 4, 3, 3, 1, 2);
	}

	static void CHW_5x5s2p2__neonfma(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__neonfma, 4, 4, 5, 5, 2, 2);
	}

	static void HWo4C4_3x3p1__neonfma(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__neonfma, 4, 4, 3, 3, 1, 1);
	}

	static void HWo4C4_5x5p2__neonfma(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__neonfma, 4, 4, 5, 5, 2, 1);
	}

	static void HWo4C4_3x3s2p1__neonfma(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__neonfma, 4, 4, 3, 3, 1, 2);
	}

	static void HWo4C4_5x5s2p2__neonfma(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__neonfma, 4, 4, 5, 5, 2, 2);
	}

	BENCHMARK_DWCONV(CHW_3x3p1__neonfma)
	BENCHMARK_DWCONV(CHW_5x5p2__neonfma)
	BENCHMARK_DWCONV(CHW_3x3s2p1__neonfma)
	BENCHMARK_DWCONV(CHW_5x5s2p2__neonfma)
	BENCHMARK_DWCONV(HWo4C4_3x3p1__neonfma)
	BENCHMARK_DWCONV(HWo4C4_5x5p2__neonfma)
	BENCHMARK_DWCONV(HWo4C4_3x3s2p1__neonfma)
	BENCHMARK_DWCONV(HWo4C4_5x5s2p2__neonfma)
	#endif // XNN_ARCH_ARM64


	#if XNN_ARCH_X86 \|\| XNN_ARCH_X86_64
	static void CHW_3x3p1__sse(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__sse, 4, 4, 3, 3, 1, 1);
	}

	static void CHW_3x3s2p1__sse(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__sse, 4, 4, 3, 3, 1, 2);
	}

	static void HWo4C4_3x3p1__sse(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__sse, 4, 4, 3, 3, 1, 1);
	}

	static void HWo4C4_3x3s2p1__sse(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__sse, 4, 4, 3, 3, 1, 2);
	}

	BENCHMARK_DWCONV(CHW_3x3p1__sse)
	BENCHMARK_DWCONV(CHW_3x3s2p1__sse)
	BENCHMARK_DWCONV(HWo4C4_3x3p1__sse)
	BENCHMARK_DWCONV(HWo4C4_3x3s2p1__sse)
	#endif // XNN_ARCH_X86 \|\| XNN_ARCH_X86_64

	static void CHW_3x3p1__scalar(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__scalar, 1, 1, 3, 3, 1, 1);
	}

	static void CHW_5x5p2__scalar(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__scalar, 1, 1, 5, 5, 2, 1);
	}

	static void CHW_3x3s2p1__scalar(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__scalar, 1, 1, 3, 3, 1, 2);
	}

	static void CHW_5x5s2p2__scalar(benchmark::State& state, const char* net) {
	DWConvCHWBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__scalar, 1, 1, 5, 5, 2, 2);
	}

	static void HWC_3x3p1__scalar(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3p1__scalar, 1, 1, 3, 3, 1, 1);
	}

	static void HWC_5x5p2__scalar(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5p2__scalar, 1, 1, 5, 5, 2, 1);
	}

	static void HWC_3x3s2p1__scalar(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_3x3s2p1__scalar, 1, 1, 3, 3, 1, 2);
	}

	static void HWC_5x5s2p2__scalar(benchmark::State& state, const char* net) {
	DWConvHWoTCTBenchmark(state, xnn_f32_dwconv_spchw_ukernel_5x5s2p2__scalar, 1, 1, 5, 5, 2, 2);
	}


	BENCHMARK_DWCONV(CHW_3x3p1__scalar)
	BENCHMARK_DWCONV(CHW_5x5p2__scalar)
	BENCHMARK_DWCONV(CHW_3x3s2p1__scalar)
	BENCHMARK_DWCONV(CHW_5x5s2p2__scalar)
	BENCHMARK_DWCONV(HWC_3x3p1__scalar)
	BENCHMARK_DWCONV(HWC_5x5p2__scalar)
	BENCHMARK_DWCONV(HWC_3x3s2p1__scalar)
	BENCHMARK_DWCONV(HWC_5x5s2p2__scalar)

	#ifndef XNNPACK_BENCHMARK_NO_MAIN
	BENCHMARK_MAIN();
	#endif