bench/f16-igemm.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 <fp16/fp16.h>
 #include "bench/conv.h"
 #include "bench/utils.h"
 #include <xnnpack/AlignedAllocator.h>
 #include <xnnpack/common.h>
 #include <xnnpack/igemm.h>
 #include <xnnpack/indirection.h>
 #include <xnnpack/operator.h>
 #include <xnnpack/pack.h>
 #include <xnnpack/params-init.h>
 #include <xnnpack/params.h>


 static void IGEMMBenchmark(benchmark::State& state,
   xnn_f16_igemm_minmax_ukernel_function f16_igemm,
   uint32_t mr, uint32_t nr, uint32_t kr, uint32_t sr)
 {
   if (!cpuinfo_initialize()) {
     state.SkipWithError("cpuinfo initialization failed");
   }
   if (!benchmark::utils::CheckNEONFP16ARITH(state)) {
     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 kernel_size = kernel_height * kernel_width;
   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 group_input_channels = state.range(8);
   const size_t group_output_channels = state.range(9);

   std::random_device random_device;
   auto rng = std::mt19937(random_device());
   auto f32rng = std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
   auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);

   const size_t output_pixel_stride = group_output_channels;
   const size_t input_pixel_stride = group_input_channels;
   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 padding_left = padding_width / 2;
   const size_t padding_top = padding_height / 2;
   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 output_size = output_height * output_width;

   const size_t mc_stride = benchmark::utils::RoundUp<size_t>(output_size, mr);
   const size_t nc_stride = benchmark::utils::RoundUp<size_t>(group_output_channels, nr);
   const size_t kc_stride = benchmark::utils::RoundUp<size_t>(group_input_channels, kr);

   std::vector<uint16_t> a(input_height * input_width * input_pixel_stride);
   std::generate(a.begin(), a.end(), std::ref(f16rng));
   std::vector<uint16_t> k(group_output_channels * kernel_height * kernel_width * group_input_channels);
   std::generate(k.begin(), k.end(), std::ref(f16rng));
   std::vector<uint16_t> b(group_output_channels);
   std::generate(b.begin(), b.end(), std::ref(f16rng));

   std::vector<uint16_t> z(group_input_channels);

   const size_t w_elements = (kernel_size * kc_stride + 1) * nc_stride;
   const size_t i_elements = mc_stride * kernel_size;
   const size_t c_elements = output_height * output_width * output_pixel_stride;
   const size_t num_buffers = 1 +
     benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
       sizeof(uint16_t) * (w_elements + c_elements) + sizeof(void*) * i_elements);

   std::vector<uint16_t, AlignedAllocator<uint16_t, 32>> w(w_elements * num_buffers);
   std::fill(w.begin(), w.end(), 0);
   xnn_pack_f16_conv_goki_w(
     1 /* groups */, group_output_channels, kernel_size, group_input_channels,
     nr, kr, sr, k.data(), b.data(), w.data(), 0 /* extra bytes */, nullptr);
   for (size_t n = 1; n < num_buffers; n++) {
     std::copy(w.cbegin(), w.cbegin() + w_elements, w.begin() + n * w_elements);
   }

   std::vector<const uint16_t*> i(i_elements * num_buffers);
   xnn_operator convolution_op = { };
   convolution_op.indirection_buffer   = reinterpret_cast<const void**>(i.data());
   convolution_op.input                = a.data();
   convolution_op.input_pixel_stride   = input_pixel_stride;
   convolution_op.zero_buffer          = z.data();
   convolution_op.groups               = 1;
   convolution_op.group_input_channels = group_input_channels;
   convolution_op.batch_size           = 1;
   convolution_op.input_height         = input_height;
   convolution_op.input_width          = input_width;
   convolution_op.output_height        = output_height;
   convolution_op.output_width         = output_width;
   convolution_op.kernel_height        = kernel_height;
   convolution_op.kernel_width         = kernel_width;
   convolution_op.stride_height        = subsampling;
   convolution_op.stride_width         = subsampling;
   convolution_op.dilation_height      = dilation;
   convolution_op.dilation_width       = dilation;
   convolution_op.padding_top          = padding_top;
   convolution_op.padding_left         = padding_left;
   xnn_indirection_init_conv2d(&convolution_op, mr, 1 /* log2(sizeof(uint16_t)) */);
   for (size_t n = 1; n < num_buffers; n++) {
     std::copy(i.cbegin(), i.cbegin() + i_elements, i.begin() + n * i_elements);
   }

   std::vector<uint16_t> c(c_elements * num_buffers);
   std::fill(c.begin(), c.end(), std::nanf(""));

   // Prepare minmax parameters.
   xnn_f16_scaleminmax_params params;
   xnn_init_f16_scaleminmax_params(
     &params,
     UINT16_C(0x3C00),  /* 1.0 */
     UINT16_C(0x7C00),  /* inf */
     UINT16_C(0xFC00)); /* -inf */

   size_t buffer_index = 0;
   for (auto _ : state) {
     state.PauseTiming();
     benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(uint16_t));
     buffer_index = (buffer_index + 1) % num_buffers;
     state.ResumeTiming();

     for (uint32_t m = 0; m < output_size; m += mr) {
       const uint32_t mb = min(output_size - m, mr);
       for (uint32_t n = 0; n < group_output_channels; n += nr) {
         const uint32_t nb = min(group_output_channels - n, nr);
         f16_igemm(
           mb, nb, group_input_channels * sizeof(uint16_t), kernel_size * mr * sizeof(void*),
           reinterpret_cast<const void**>(i.data()) + buffer_index * i_elements + m,
           w.data() + buffer_index * w_elements + n * (kc_stride * kernel_size + 1),
           c.data() + buffer_index * c_elements + m * group_output_channels + n, group_output_channels * sizeof(uint16_t), nr * sizeof(uint16_t),
           0, z.data(), &params);
       }
     }
   }

   const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
   if (cpu_frequency != 0) {
     state.counters["cpufreq"] = cpu_frequency;
   }

   state.counters["FLOPS"] = benchmark::Counter(
     uint64_t(state.iterations()) * 2 *
       output_height * output_width *
       group_input_channels * group_output_channels *
       kernel_height * kernel_width,
     benchmark::Counter::kIsRate);
 }

 #if XNN_ARCH_ARM64
   static void f16_igemm_1x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
     IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_1x8__neonfp16arith_ld64, 1, 8, 1, 1);
   }

   static void f16_igemm_4x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
     IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_4x8__neonfp16arith_ld64, 4, 8, 1, 1);
   }

   static void f16_igemm_6x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
     IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_6x8__neonfp16arith_ld64, 6, 8, 1, 1);
   }

   static void f16_igemm_8x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
     IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_8x8__neonfp16arith_ld64, 8, 8, 1, 1);
   }

   static void f16_igemm_1x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
     IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_1x16__neonfp16arith_ld64, 1, 16, 1, 1);
   }

   static void f16_igemm_4x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
     IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_4x16__neonfp16arith_ld64, 4, 16, 1, 1);
   }

   static void f16_igemm_6x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
     IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_6x16__neonfp16arith_ld64, 6, 16, 1, 1);
   }

   static void f16_igemm_8x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
     IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_8x16__neonfp16arith_ld64, 8, 16, 1, 1);
   }

   BENCHMARK_CONV(f16_igemm_1x8__neonfp16arith_ld64)
   BENCHMARK_CONV(f16_igemm_4x8__neonfp16arith_ld64)
   BENCHMARK_CONV(f16_igemm_6x8__neonfp16arith_ld64)
   BENCHMARK_CONV(f16_igemm_8x8__neonfp16arith_ld64)

   BENCHMARK_CONV(f16_igemm_1x16__neonfp16arith_ld64)
   BENCHMARK_CONV(f16_igemm_4x16__neonfp16arith_ld64)
   BENCHMARK_CONV(f16_igemm_6x16__neonfp16arith_ld64)
   BENCHMARK_CONV(f16_igemm_8x16__neonfp16arith_ld64)
 #endif  /* XNN_ARCH_ARM64 */

 #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 <fp16/fp16.h>
	#include "bench/conv.h"
	#include "bench/utils.h"
	#include <xnnpack/AlignedAllocator.h>
	#include <xnnpack/common.h>
	#include <xnnpack/igemm.h>
	#include <xnnpack/indirection.h>
	#include <xnnpack/operator.h>
	#include <xnnpack/pack.h>
	#include <xnnpack/params-init.h>
	#include <xnnpack/params.h>


	static void IGEMMBenchmark(benchmark::State& state,
	xnn_f16_igemm_minmax_ukernel_function f16_igemm,
	uint32_t mr, uint32_t nr, uint32_t kr, uint32_t sr)
	{
	if (!cpuinfo_initialize()) {
	state.SkipWithError("cpuinfo initialization failed");
	}
	if (!benchmark::utils::CheckNEONFP16ARITH(state)) {
	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 kernel_size = kernel_height * kernel_width;
	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 group_input_channels = state.range(8);
	const size_t group_output_channels = state.range(9);

	std::random_device random_device;
	auto rng = std::mt19937(random_device());
	auto f32rng = std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
	auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);

	const size_t output_pixel_stride = group_output_channels;
	const size_t input_pixel_stride = group_input_channels;
	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 padding_left = padding_width / 2;
	const size_t padding_top = padding_height / 2;
	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 output_size = output_height * output_width;

	const size_t mc_stride = benchmark::utils::RoundUp<size_t>(output_size, mr);
	const size_t nc_stride = benchmark::utils::RoundUp<size_t>(group_output_channels, nr);
	const size_t kc_stride = benchmark::utils::RoundUp<size_t>(group_input_channels, kr);

	std::vector<uint16_t> a(input_height * input_width * input_pixel_stride);
	std::generate(a.begin(), a.end(), std::ref(f16rng));
	std::vector<uint16_t> k(group_output_channels * kernel_height * kernel_width * group_input_channels);
	std::generate(k.begin(), k.end(), std::ref(f16rng));
	std::vector<uint16_t> b(group_output_channels);
	std::generate(b.begin(), b.end(), std::ref(f16rng));

	std::vector<uint16_t> z(group_input_channels);

	const size_t w_elements = (kernel_size * kc_stride + 1) * nc_stride;
	const size_t i_elements = mc_stride * kernel_size;
	const size_t c_elements = output_height * output_width * output_pixel_stride;
	const size_t num_buffers = 1 +
	benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(),
	sizeof(uint16_t) * (w_elements + c_elements) + sizeof(void) i_elements);

	std::vector<uint16_t, AlignedAllocator<uint16_t, 32>> w(w_elements * num_buffers);
	std::fill(w.begin(), w.end(), 0);
	xnn_pack_f16_conv_goki_w(
	1 /* groups */, group_output_channels, kernel_size, group_input_channels,
	nr, kr, sr, k.data(), b.data(), w.data(), 0 /* extra bytes */, nullptr);
	for (size_t n = 1; n < num_buffers; n++) {
	std::copy(w.cbegin(), w.cbegin() + w_elements, w.begin() + n * w_elements);
	}

	std::vector<const uint16_t> i(i_elements num_buffers);
	xnn_operator convolution_op = { };
	convolution_op.indirection_buffer = reinterpret_cast<const void**>(i.data());
	convolution_op.input = a.data();
	convolution_op.input_pixel_stride = input_pixel_stride;
	convolution_op.zero_buffer = z.data();
	convolution_op.groups = 1;
	convolution_op.group_input_channels = group_input_channels;
	convolution_op.batch_size = 1;
	convolution_op.input_height = input_height;
	convolution_op.input_width = input_width;
	convolution_op.output_height = output_height;
	convolution_op.output_width = output_width;
	convolution_op.kernel_height = kernel_height;
	convolution_op.kernel_width = kernel_width;
	convolution_op.stride_height = subsampling;
	convolution_op.stride_width = subsampling;
	convolution_op.dilation_height = dilation;
	convolution_op.dilation_width = dilation;
	convolution_op.padding_top = padding_top;
	convolution_op.padding_left = padding_left;
	xnn_indirection_init_conv2d(&convolution_op, mr, 1 /* log2(sizeof(uint16_t)) */);
	for (size_t n = 1; n < num_buffers; n++) {
	std::copy(i.cbegin(), i.cbegin() + i_elements, i.begin() + n * i_elements);
	}

	std::vector<uint16_t> c(c_elements * num_buffers);
	std::fill(c.begin(), c.end(), std::nanf(""));

	// Prepare minmax parameters.
	xnn_f16_scaleminmax_params params;
	xnn_init_f16_scaleminmax_params(
	&params,
	UINT16_C(0x3C00), /* 1.0 */
	UINT16_C(0x7C00), /* inf */
	UINT16_C(0xFC00)); /* -inf */

	size_t buffer_index = 0;
	for (auto _ : state) {
	state.PauseTiming();
	benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(uint16_t));
	buffer_index = (buffer_index + 1) % num_buffers;
	state.ResumeTiming();

	for (uint32_t m = 0; m < output_size; m += mr) {
	const uint32_t mb = min(output_size - m, mr);
	for (uint32_t n = 0; n < group_output_channels; n += nr) {
	const uint32_t nb = min(group_output_channels - n, nr);
	f16_igemm(
	mb, nb, group_input_channels * sizeof(uint16_t), kernel_size * mr * sizeof(void*),
	reinterpret_cast<const void*>(i.data()) + buffer_index i_elements + m,
	w.data() + buffer_index * w_elements + n * (kc_stride * kernel_size + 1),
	c.data() + buffer_index * c_elements + m * group_output_channels + n, group_output_channels * sizeof(uint16_t), nr * sizeof(uint16_t),
	0, z.data(), &params);
	}
	}
	}

	const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
	if (cpu_frequency != 0) {
	state.counters["cpufreq"] = cpu_frequency;
	}

	state.counters["FLOPS"] = benchmark::Counter(
	uint64_t(state.iterations()) * 2 *
	output_height * output_width *
	group_input_channels * group_output_channels *
	kernel_height * kernel_width,
	benchmark::Counter::kIsRate);
	}

	#if XNN_ARCH_ARM64
	static void f16_igemm_1x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
	IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_1x8__neonfp16arith_ld64, 1, 8, 1, 1);
	}

	static void f16_igemm_4x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
	IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_4x8__neonfp16arith_ld64, 4, 8, 1, 1);
	}

	static void f16_igemm_6x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
	IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_6x8__neonfp16arith_ld64, 6, 8, 1, 1);
	}

	static void f16_igemm_8x8__neonfp16arith_ld64(benchmark::State& state, const char* net) {
	IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_8x8__neonfp16arith_ld64, 8, 8, 1, 1);
	}

	static void f16_igemm_1x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
	IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_1x16__neonfp16arith_ld64, 1, 16, 1, 1);
	}

	static void f16_igemm_4x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
	IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_4x16__neonfp16arith_ld64, 4, 16, 1, 1);
	}

	static void f16_igemm_6x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
	IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_6x16__neonfp16arith_ld64, 6, 16, 1, 1);
	}

	static void f16_igemm_8x16__neonfp16arith_ld64(benchmark::State& state, const char* net) {
	IGEMMBenchmark(state, xnn_f16_igemm_minmax_ukernel_8x16__neonfp16arith_ld64, 8, 16, 1, 1);
	}

	BENCHMARK_CONV(f16_igemm_1x8__neonfp16arith_ld64)
	BENCHMARK_CONV(f16_igemm_4x8__neonfp16arith_ld64)
	BENCHMARK_CONV(f16_igemm_6x8__neonfp16arith_ld64)
	BENCHMARK_CONV(f16_igemm_8x8__neonfp16arith_ld64)

	BENCHMARK_CONV(f16_igemm_1x16__neonfp16arith_ld64)
	BENCHMARK_CONV(f16_igemm_4x16__neonfp16arith_ld64)
	BENCHMARK_CONV(f16_igemm_6x16__neonfp16arith_ld64)
	BENCHMARK_CONV(f16_igemm_8x16__neonfp16arith_ld64)
	#endif /* XNN_ARCH_ARM64 */

	#ifndef XNNPACK_BENCHMARK_NO_MAIN
	BENCHMARK_MAIN();
	#endif