| // 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. |
| |
| #include <algorithm> |
| #include <cfloat> |
| #include <chrono> |
| #include <cmath> |
| #include <functional> |
| #include <mutex> |
| #include <random> |
| #include <vector> |
| |
| #include <cpuinfo.h> |
| |
| #include <benchmark/benchmark.h> |
| #ifdef BENCHMARK_RUY |
| #include "tensorflow/lite/experimental/ruy/ruy.h" |
| #endif // BENCHMARK_RUY |
| #include "bench/gemm.h" |
| #include "bench/utils.h" |
| #include <xnnpack/AlignedAllocator.h> |
| #include <xnnpack/common.h> |
| #include <xnnpack/gemm.h> |
| #include <xnnpack/pack.h> |
| #include <xnnpack/packx.h> |
| #include <xnnpack/params-init.h> |
| #include <xnnpack/params.h> |
| #include <xnnpack/ppmm.h> |
| |
| |
| static void GEMMBenchmark(benchmark::State& state, |
| xnn_f32_gemm_ukernel_function gemm, |
| size_t mr, size_t nr, size_t kr, size_t sr) |
| { |
| if (!cpuinfo_initialize()) { |
| state.SkipWithError("cpuinfo initialization failed"); |
| return; |
| } |
| |
| const size_t mc = state.range(0); |
| const size_t nc = state.range(1); |
| const size_t kc = state.range(2); |
| |
| const size_t nc_stride = benchmark::utils::RoundUp(nc, nr); |
| const size_t kc_stride = benchmark::utils::RoundUp(kc, kr); |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| |
| std::vector<float> a(mc * kc); |
| std::generate(a.begin(), a.end(), std::ref(f32rng)); |
| std::vector<float> k(nc * kc); |
| std::generate(k.begin(), k.end(), std::ref(f32rng)); |
| std::vector<float> b(nc); |
| std::generate(b.begin(), b.end(), std::ref(f32rng)); |
| |
| const size_t w_elements = nc_stride * kc_stride + nc_stride; |
| const size_t c_elements = mc * nc; |
| const size_t num_buffers = 1 + |
| benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(), |
| sizeof(float) * (w_elements + c_elements)); |
| |
| std::vector<float, AlignedAllocator<float, 32>> w(w_elements * num_buffers); |
| std::fill(w.begin(), w.end(), 0.0f); |
| xnn_pack_f32_gemm_goi_w(1 /* groups */, nc, kc, nr, kr, sr, k.data(), b.data(), w.data()); |
| std::vector<float> c(c_elements * num_buffers); |
| std::fill(c.begin(), c.end(), std::nanf("")); |
| |
| xnn_f32_output_params output_params = |
| xnn_init_f32_output_params(-std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity()); |
| |
| size_t buffer_index = 0; |
| for (auto _ : state) { |
| // Use circular buffers (exceeding cache size) and prefetch to control cache state: |
| // - A is always in L1 cache (if fits, otherwise L2, L3, etc) |
| // - W is not in cache (for any cache level) |
| // - C is not in cache (for any cache level) |
| state.PauseTiming(); |
| benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(float)); |
| buffer_index = (buffer_index + 1) % num_buffers; |
| state.ResumeTiming(); |
| |
| for (uint32_t m = 0; m < mc; m += mr) { |
| const uint32_t mb = min(mc - m, mr); |
| gemm( |
| mb, nc, kc * sizeof(float), |
| a.data() + m * kc, kc * sizeof(float), |
| w.data() + buffer_index * nc_stride * (kc_stride + 1), |
| c.data() + (buffer_index * mc + m) * nc, nc * sizeof(float), nr * sizeof(float), |
| &output_params); |
| } |
| } |
| |
| state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency(); |
| state.counters["FLOPS"] = benchmark::Counter( |
| uint64_t(state.iterations()) * 2 * mc * nc * kc, benchmark::Counter::kIsRate); |
| } |
| |
| static void PPMM1PBenchmark(benchmark::State& state, |
| xnn_f32_ppmm_ukernel_function ppmm, |
| xnn_x32_packx_ukernel_function packx, |
| size_t mr, size_t nr) |
| { |
| if (!cpuinfo_initialize()) { |
| state.SkipWithError("cpuinfo initialization failed"); |
| return; |
| } |
| |
| const size_t mc = state.range(0); |
| const size_t nc = state.range(1); |
| const size_t kc = state.range(2); |
| |
| const size_t nc_stride = benchmark::utils::RoundUp(nc, nr); |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| |
| std::vector<float> a(mc * kc); |
| std::generate(a.begin(), a.end(), std::ref(f32rng)); |
| std::vector<float> k(nc * kc); |
| std::generate(k.begin(), k.end(), std::ref(f32rng)); |
| std::vector<float> b(nc); |
| std::generate(b.begin(), b.end(), std::ref(f32rng)); |
| |
| std::vector<uint32_t, AlignedAllocator<uint32_t, 32>> t(mr * kc); |
| |
| const size_t w_elements = nc_stride * kc + nc_stride; |
| const size_t c_elements = mc * nc; |
| const size_t num_buffers = 1 + |
| benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(), |
| sizeof(float) * (w_elements + c_elements)); |
| |
| std::vector<float, AlignedAllocator<float, 32>> w(w_elements * num_buffers); |
| std::fill(w.begin(), w.end(), 0.0f); |
| xnn_pack_f32_gemm_goi_w(1 /* groups */, nc, kc, nr, 1 /* kr */, 1 /* sr */, k.data(), b.data(), w.data()); |
| std::vector<float> c(c_elements * num_buffers); |
| std::fill(c.begin(), c.end(), std::nanf("")); |
| |
| xnn_f32_output_params output_params = |
| xnn_init_f32_output_params(-std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity()); |
| |
| size_t buffer_index = 0; |
| for (auto _ : state) { |
| // Use circular buffers (exceeding cache size) and prefetch to control cache state: |
| // - A is always in L1 cache (if fits, otherwise L2, L3, etc) |
| // - W is not in cache (for any cache level) |
| // - C is not in cache (for any cache level) |
| state.PauseTiming(); |
| benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(float)); |
| buffer_index = (buffer_index + 1) % num_buffers; |
| state.ResumeTiming(); |
| |
| for (uint32_t m = 0; m < mc; m += mr) { |
| const uint32_t mb = min(mc - m, mr); |
| packx(mb, kc, reinterpret_cast<const uint32_t*>(a.data() + m * kc), kc, t.data()); |
| ppmm( |
| mb, nc, kc * sizeof(float), |
| reinterpret_cast<const float*>(t.data()), |
| w.data() + nc_stride * buffer_index * (kc + 1), |
| c.data() + (mc * buffer_index + m) * nc, nc * sizeof(float), nr * sizeof(float), |
| &output_params); |
| } |
| } |
| |
| state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency(); |
| state.counters["FLOPS"] = benchmark::Counter( |
| uint64_t(state.iterations()) * 2 * mc * nc * kc, benchmark::Counter::kIsRate); |
| } |
| |
| static void PPMM2PBenchmark(benchmark::State& state, |
| xnn_f32_ppmm_ukernel_function ppmm, |
| xnn_x32_packx_ukernel_function packx, |
| size_t mr, size_t nr) |
| { |
| if (!cpuinfo_initialize()) { |
| state.SkipWithError("cpuinfo initialization failed"); |
| return; |
| } |
| |
| const size_t mc = state.range(0); |
| const size_t nc = state.range(1); |
| const size_t kc = state.range(2); |
| |
| const size_t mc_stride = benchmark::utils::RoundUp(mc, mr); |
| const size_t nc_stride = benchmark::utils::RoundUp(nc, nr); |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| |
| std::vector<float> a(mc * kc); |
| std::generate(a.begin(), a.end(), std::ref(f32rng)); |
| std::vector<float> k(nc * kc); |
| std::generate(k.begin(), k.end(), std::ref(f32rng)); |
| std::vector<float> b(nc); |
| std::generate(b.begin(), b.end(), std::ref(f32rng)); |
| |
| std::vector<uint32_t, AlignedAllocator<uint32_t, 32>> t(mc_stride * kc); |
| |
| const size_t w_elements = nc_stride * kc + nc_stride; |
| const size_t c_elements = mc * nc; |
| const size_t num_buffers = 1 + |
| benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(), |
| sizeof(float) * (w_elements + c_elements)); |
| |
| std::vector<float, AlignedAllocator<float, 32>> w(w_elements * num_buffers); |
| std::fill(w.begin(), w.end(), 0.0f); |
| xnn_pack_f32_gemm_goi_w(1 /* groups */, nc, kc, nr, 1 /* kr */, 1 /* sr */, k.data(), b.data(), w.data()); |
| std::vector<float> c(c_elements * num_buffers); |
| std::fill(c.begin(), c.end(), std::nanf("")); |
| |
| xnn_f32_output_params output_params = |
| xnn_init_f32_output_params(-std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity()); |
| |
| size_t buffer_index = 0; |
| for (auto _ : state) { |
| // Use circular buffers (exceeding cache size) and prefetch to control cache state: |
| // - A is always in L1 cache (if fits, otherwise L2, L3, etc) |
| // - W is not in cache (for any cache level) |
| // - C is not in cache (for any cache level) |
| state.PauseTiming(); |
| benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(float)); |
| buffer_index = (buffer_index + 1) % num_buffers; |
| state.ResumeTiming(); |
| |
| for (uint32_t m = 0; m < mc; m += mr) { |
| const uint32_t mb = min(mc - m, mr); |
| packx(mb, kc, reinterpret_cast<const uint32_t*>(a.data() + m * kc), kc, t.data() + m * kc); |
| } |
| for (uint32_t m = 0; m < mc; m += mr) { |
| const uint32_t mb = min(mc - m, mr); |
| ppmm( |
| mb, nc, kc * sizeof(float), |
| reinterpret_cast<const float*>(t.data() + m * kc), |
| w.data() + nc_stride * buffer_index * (kc + 1), |
| c.data() + (mc * buffer_index + m) * nc, nc * sizeof(float), nr * sizeof(float), |
| &output_params); |
| } |
| } |
| |
| state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency(); |
| state.counters["FLOPS"] = benchmark::Counter( |
| uint64_t(state.iterations()) * 2 * mc * nc * kc, benchmark::Counter::kIsRate); |
| } |
| |
| #ifdef BENCHMARK_RUY |
| static void RuyBenchmark(benchmark::State& state, uint32_t threads) |
| { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng); |
| |
| const size_t mc = state.range(0); |
| const size_t nc = state.range(1); |
| const size_t kc = state.range(2); |
| |
| const size_t num_buffers = 1 + |
| benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(), |
| sizeof(float) * (nc * (mc + kc + 1))); |
| |
| std::vector<float> a(mc * kc); |
| std::generate(a.begin(), a.end(), std::ref(f32rng)); |
| std::vector<float> k(num_buffers * nc * kc); |
| std::generate(k.begin(), k.end(), std::ref(f32rng)); |
| std::vector<float> b(num_buffers * nc); |
| std::generate(b.begin(), b.end(), std::ref(f32rng)); |
| std::vector<float> c(num_buffers * nc * mc); |
| std::fill(c.begin(), c.end(), std::nanf("")); |
| |
| // Note: context must be static to avoid the cost of re-creating it for each benchmark. |
| static ruy::Context context; |
| context.max_num_threads = threads; |
| |
| ruy::Matrix<float> ruy_a; |
| ruy::MakeSimpleLayout(nc, kc, ruy::Order::kRowMajor, &ruy_a.layout); |
| ruy::Matrix<float> ruy_b; |
| ruy::MakeSimpleLayout(kc, mc, ruy::Order::kColMajor, &ruy_b.layout); |
| ruy_b.data = a.data(); |
| ruy::Matrix<float> ruy_c; |
| ruy::MakeSimpleLayout(nc, mc, ruy::Order::kColMajor, &ruy_c.layout); |
| |
| ruy::BasicSpec<float, float> spec; |
| |
| // ruy::Context uses deferred initialization, which affects percieved GEMM performance. Initialization happens during |
| // the first GEMM calls, and per Benoit Jacob it takes up to ~250 milliseconds for performance to stabilize. |
| // Thus, on the first benchmark, we compute GEMM for 500 milliseconds (to be safe) without recording performance, and |
| // keep the ruy::Context object initialized (by being static) between subsequent benchmarks. |
| static std::once_flag warmup; |
| std::call_once(warmup, [&](){ |
| auto start = std::chrono::steady_clock::now(); |
| do { |
| ruy_a.data = k.data(); |
| ruy_c.data = c.data(); |
| spec.bias = b.data(); |
| |
| ruy::Mul<ruy::kAllPaths>(ruy_a, ruy_b, spec, &context, &ruy_c); |
| } while (std::chrono::duration<double>(std::chrono::steady_clock::now() - start).count() < 0.5); |
| }); |
| |
| size_t buffer_index = 0; |
| for (auto _ : state) { |
| // Use circular buffers (exceeding cache size) and prefetch to control cache state: |
| // - A is always in L1 cache (if fits, otherwise L2, L3, etc) |
| // - K is not in cache (for any cache level) |
| // - B is not in cache (for any cache level) |
| // - C is not in cache (for any cache level) |
| state.PauseTiming(); |
| benchmark::utils::PrefetchToL1(a.data(), a.size() * sizeof(float)); |
| buffer_index = (buffer_index + 1) % num_buffers; |
| state.ResumeTiming(); |
| |
| ruy_a.data = k.data() + buffer_index * nc * kc; |
| ruy_c.data = c.data() + buffer_index * mc * nc; |
| spec.bias = b.data() + buffer_index * nc; |
| |
| ruy::Mul<ruy::kAllPaths>(ruy_a, ruy_b, spec, &context, &ruy_c); |
| } |
| |
| state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency(); |
| state.counters["FLOPS"] = benchmark::Counter( |
| uint64_t(state.iterations()) * 2 * mc * nc * kc, benchmark::Counter::kIsRate); |
| } |
| |
| static void ruy_st(benchmark::State& state, const char* net) |
| { |
| RuyBenchmark(state, 1); |
| } |
| #endif // BENCHMARK_RUY |
| |
| |
| #if XNN_ARCH_ARM64 && XNN_ENABLE_ASSEMBLY |
| static void sgemm_1x12__aarch64_neonfma_cortex_a53(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x12__aarch64_neonfma_cortex_a53, 1, 12, 1, 1); |
| } |
| static void sgemm_1x8__aarch64_neonfma_cortex_a53(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x8__aarch64_neonfma_cortex_a53, 1, 8, 1, 1); |
| } |
| static void sgemm_1x8__aarch64_neonfma_cortex_a57(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x8__aarch64_neonfma_cortex_a57, 1, 8, 1, 1); |
| } |
| static void sgemm_1x8__aarch64_neonfma_cortex_a75(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x8__aarch64_neonfma_cortex_a75, 1, 8, 1, 1); |
| } |
| static void sgemm_4x12__aarch64_neonfma_cortex_a53(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x12__aarch64_neonfma_cortex_a53, 4, 12, 1, 1); |
| } |
| static void sgemm_4x8__aarch64_neonfma_cortex_a53(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__aarch64_neonfma_cortex_a53, 4, 8, 1, 1); |
| } |
| static void sgemm_4x8__aarch64_neonfma_cortex_a57(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__aarch64_neonfma_cortex_a57, 4, 8, 1, 1); |
| } |
| static void sgemm_4x8__aarch64_neonfma_cortex_a75(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__aarch64_neonfma_cortex_a75, 4, 8, 1, 1); |
| } |
| static void sgemm_4x8__aarch64_neonfma_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__aarch64_neonfma_ld64, 4, 8, 1, 1); |
| } |
| static void sgemm_4x8__aarch64_neonfma_ld128(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__aarch64_neonfma_ld128, 4, 8, 1, 1); |
| } |
| static void sgemm_5x8__aarch64_neonfma_cortex_a75(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_5x8__aarch64_neonfma_cortex_a75, 5, 8, 1, 1); |
| } |
| static void sgemm_6x8__aarch64_neonfma_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__aarch64_neonfma_ld64, 6, 8, 1, 1); |
| } |
| static void sgemm_6x8__aarch64_neonfma_ld128(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__aarch64_neonfma_ld128, 6, 8, 1, 1); |
| } |
| static void sgemm_6x8__aarch64_neonfma_cortex_a53(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__aarch64_neonfma_cortex_a53, 6, 8, 1, 1); |
| } |
| static void sgemm_6x8__aarch64_neonfma_cortex_a57(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__aarch64_neonfma_cortex_a57, 6, 8, 1, 1); |
| } |
| static void sgemm_6x8__aarch64_neonfma_cortex_a73(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__aarch64_neonfma_cortex_a73, 6, 8, 1, 1); |
| } |
| static void sgemm_6x8__aarch64_neonfma_cortex_a75(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__aarch64_neonfma_cortex_a75, 6, 8, 1, 1); |
| } |
| |
| BENCHMARK_GEMM(sgemm_1x12__aarch64_neonfma_cortex_a53) |
| BENCHMARK_GEMM(sgemm_1x8__aarch64_neonfma_cortex_a53) |
| BENCHMARK_GEMM(sgemm_1x8__aarch64_neonfma_cortex_a57) |
| BENCHMARK_GEMM(sgemm_1x8__aarch64_neonfma_cortex_a75) |
| BENCHMARK_GEMM(sgemm_4x12__aarch64_neonfma_cortex_a53) |
| BENCHMARK_GEMM(sgemm_4x8__aarch64_neonfma_cortex_a53) |
| BENCHMARK_GEMM(sgemm_4x8__aarch64_neonfma_cortex_a57) |
| BENCHMARK_GEMM(sgemm_4x8__aarch64_neonfma_cortex_a75) |
| BENCHMARK_GEMM(sgemm_4x8__aarch64_neonfma_ld128) |
| BENCHMARK_GEMM(sgemm_4x8__aarch64_neonfma_ld64) |
| BENCHMARK_GEMM(sgemm_5x8__aarch64_neonfma_cortex_a75) |
| BENCHMARK_GEMM(sgemm_6x8__aarch64_neonfma_cortex_a53) |
| BENCHMARK_GEMM(sgemm_6x8__aarch64_neonfma_cortex_a57) |
| BENCHMARK_GEMM(sgemm_6x8__aarch64_neonfma_cortex_a73) |
| BENCHMARK_GEMM(sgemm_6x8__aarch64_neonfma_cortex_a75) |
| BENCHMARK_GEMM(sgemm_6x8__aarch64_neonfma_ld64) |
| BENCHMARK_GEMM(sgemm_6x8__aarch64_neonfma_ld128) |
| #endif // XNN_ARCH_ARM64 |
| |
| #if XNN_ARCH_ARM || XNN_ARCH_ARM64 |
| static void sgemm_1x8__neon_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x8__neon_ld64, 1, 8, 1, 1); |
| } |
| |
| static void sgemm_1x8__neonfma_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x8__neonfma_ld64, 1, 8, 1, 1); |
| } |
| |
| static void sgemm_4x8__neon_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__neon_ld64, 4, 8, 1, 1); |
| } |
| |
| static void sgemm_4x8__neon_ld128(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__neon_ld128, 4, 8, 1, 1); |
| } |
| |
| static void sgemm_5x8__neon_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_5x8__neon_ld64, 5, 8, 1, 1); |
| } |
| |
| static void sgemm_6x8__neon_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__neon_ld64, 6, 8, 1, 1); |
| } |
| |
| static void sgemm_4x8__neonfma_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__neonfma_ld64, 4, 8, 1, 1); |
| } |
| |
| static void sgemm_4x8__neonfma_ld128(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__neonfma_ld128, 4, 8, 1, 1); |
| } |
| |
| static void sgemm_5x8__neonfma_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_5x8__neonfma_ld64, 5, 8, 1, 1); |
| } |
| |
| static void sgemm_6x8__neonfma_ld64(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__neonfma_ld64, 6, 8, 1, 1); |
| } |
| |
| static void sppmm_4x8_unipass__neonfma(benchmark::State& state, const char* net) { |
| PPMM1PBenchmark(state, xnn_f32_ppmm_ukernel_4x8__neonfma, xnn_x32_packx_ukernel_4x__neon_st4, 4, 8); |
| } |
| |
| static void sppmm_4x8_twopass__neonfma(benchmark::State& state, const char* net) { |
| PPMM2PBenchmark(state, xnn_f32_ppmm_ukernel_4x8__neonfma, xnn_x32_packx_ukernel_4x__neon_st4, 4, 8); |
| } |
| |
| BENCHMARK_GEMM(sgemm_1x8__neon_ld64) |
| BENCHMARK_GEMM(sgemm_1x8__neonfma_ld64) |
| BENCHMARK_GEMM(sgemm_4x8__neon_ld128) |
| BENCHMARK_GEMM(sgemm_4x8__neon_ld64) |
| BENCHMARK_GEMM(sgemm_4x8__neonfma_ld128) |
| BENCHMARK_GEMM(sgemm_4x8__neonfma_ld64) |
| BENCHMARK_GEMM(sgemm_5x8__neon_ld64) |
| BENCHMARK_GEMM(sgemm_5x8__neonfma_ld64) |
| BENCHMARK_GEMM(sgemm_6x8__neon_ld64) |
| BENCHMARK_GEMM(sgemm_6x8__neonfma_ld64) |
| |
| BENCHMARK_GEMM(sppmm_4x8_unipass__neonfma) |
| BENCHMARK_GEMM(sppmm_4x8_twopass__neonfma) |
| #endif // XNN_ARCH_ARM || XNN_ARCH_ARM64 |
| |
| #if XNN_ARCH_X86 || XNN_ARCH_X86_64 |
| static void sgemm_1x8__sse_load1(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x8__sse_load1, 1, 8, 1, 1); |
| } |
| |
| static void sgemm_4x8__sse_load1(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__sse_load1, 4, 8, 1, 1); |
| } |
| |
| static void sgemm_1x8__sse_dup(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x8__sse_dup, 1, 8, 1, 1); |
| } |
| |
| static void sgemm_4x8__sse_dup(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__sse_dup, 4, 8, 1, 1); |
| } |
| |
| static void sgemm_1x8s4__sse(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x8s4__sse, 1, 8, 1, 4); |
| } |
| |
| static void sgemm_4x8s4__sse(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8s4__sse, 4, 8, 1, 4); |
| } |
| |
| static void sppmm_4x8_unipass__sse(benchmark::State& state, const char* net) { |
| PPMM1PBenchmark(state, xnn_f32_ppmm_ukernel_4x8__sse, xnn_x32_packx_ukernel_4x__sse, 4, 8); |
| } |
| |
| static void sppmm_4x8_twopass__sse(benchmark::State& state, const char* net) { |
| PPMM2PBenchmark(state, xnn_f32_ppmm_ukernel_4x8__sse, xnn_x32_packx_ukernel_4x__sse, 4, 8); |
| } |
| |
| BENCHMARK_GEMM(sgemm_1x8__sse_load1) |
| BENCHMARK_GEMM(sgemm_4x8__sse_load1) |
| BENCHMARK_GEMM(sgemm_1x8__sse_dup) |
| BENCHMARK_GEMM(sgemm_4x8__sse_dup) |
| BENCHMARK_GEMM(sgemm_1x8s4__sse) |
| BENCHMARK_GEMM(sgemm_4x8s4__sse) |
| BENCHMARK_GEMM(sppmm_4x8_unipass__sse) |
| BENCHMARK_GEMM(sppmm_4x8_twopass__sse) |
| #endif // XNN_ARCH_X86 || XNN_ARCH_X86_64 |
| |
| #if !XNN_ARCH_WASM && !XNN_ARCH_ASMJS |
| static void sgemm_4x8__psimd_loadsplat(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__psimd_loadsplat, 4, 8, 1, 1); |
| } |
| |
| static void sgemm_6x8__psimd_loadsplat(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__psimd_loadsplat, 6, 8, 1, 1); |
| } |
| |
| static void sgemm_4x8__psimd_splat(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8__psimd_splat, 4, 8, 1, 1); |
| } |
| |
| static void sgemm_6x8__psimd_splat(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8__psimd_splat, 6, 8, 1, 1); |
| } |
| |
| static void sgemm_4x8s4__psimd(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x8s4__psimd, 4, 8, 1, 4); |
| } |
| |
| static void sgemm_6x8s4__psimd(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_6x8s4__psimd, 6, 8, 1, 4); |
| } |
| |
| static void sppmm_4x8_unipass__psimd(benchmark::State& state, const char* net) { |
| PPMM1PBenchmark(state, xnn_f32_ppmm_ukernel_4x8__psimd, xnn_x32_packx_ukernel_4x__psimd, 4, 8); |
| } |
| |
| static void sppmm_4x8_twopass__psimd(benchmark::State& state, const char* net) { |
| PPMM2PBenchmark(state, xnn_f32_ppmm_ukernel_4x8__psimd, xnn_x32_packx_ukernel_4x__psimd, 4, 8); |
| } |
| |
| BENCHMARK_GEMM(sgemm_4x8__psimd_loadsplat) |
| BENCHMARK_GEMM(sgemm_6x8__psimd_loadsplat) |
| BENCHMARK_GEMM(sgemm_4x8__psimd_splat) |
| BENCHMARK_GEMM(sgemm_6x8__psimd_splat) |
| BENCHMARK_GEMM(sgemm_4x8s4__psimd) |
| BENCHMARK_GEMM(sgemm_6x8s4__psimd) |
| BENCHMARK_GEMM(sppmm_4x8_unipass__psimd) |
| BENCHMARK_GEMM(sppmm_4x8_twopass__psimd) |
| #endif // !XNN_ARCH_WASM && !XNN_ARCH_ASMJS |
| |
| static void sgemm_1x4__scalar(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_1x4__scalar, 1, 4, 1, 1); |
| } |
| |
| static void sgemm_2x4__scalar(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_2x4__scalar, 2, 4, 1, 1); |
| } |
| |
| static void sgemm_4x4__scalar(benchmark::State& state, const char* net) { |
| GEMMBenchmark(state, xnn_f32_gemm_ukernel_4x4__scalar, 4, 4, 1, 1); |
| } |
| |
| static void sppmm_2x4_unipass__scalar(benchmark::State& state, const char* net) { |
| PPMM1PBenchmark(state, xnn_f32_ppmm_ukernel_2x4__scalar, xnn_x32_packx_ukernel_2x__scalar, 2, 4); |
| } |
| |
| static void sppmm_4x2_unipass__scalar(benchmark::State& state, const char* net) { |
| PPMM1PBenchmark(state, xnn_f32_ppmm_ukernel_4x2__scalar, xnn_x32_packx_ukernel_4x__scalar, 4, 2); |
| } |
| |
| static void sppmm_4x4_unipass__scalar(benchmark::State& state, const char* net) { |
| PPMM1PBenchmark(state, xnn_f32_ppmm_ukernel_4x4__scalar, xnn_x32_packx_ukernel_4x__scalar, 4, 4); |
| } |
| |
| static void sppmm_3x3_unipass__scalar(benchmark::State& state, const char* net) { |
| PPMM1PBenchmark(state, xnn_f32_ppmm_ukernel_3x3__scalar, xnn_x32_packx_ukernel_3x__scalar, 3, 3); |
| } |
| |
| static void sppmm_2x4_twopass__scalar(benchmark::State& state, const char* net) { |
| PPMM2PBenchmark(state, xnn_f32_ppmm_ukernel_2x4__scalar, xnn_x32_packx_ukernel_2x__scalar, 2, 4); |
| } |
| |
| static void sppmm_4x2_twopass__scalar(benchmark::State& state, const char* net) { |
| PPMM2PBenchmark(state, xnn_f32_ppmm_ukernel_4x2__scalar, xnn_x32_packx_ukernel_4x__scalar, 4, 2); |
| } |
| |
| static void sppmm_4x4_twopass__scalar(benchmark::State& state, const char* net) { |
| PPMM2PBenchmark(state, xnn_f32_ppmm_ukernel_4x4__scalar, xnn_x32_packx_ukernel_4x__scalar, 4, 4); |
| } |
| |
| static void sppmm_3x3_twopass__scalar(benchmark::State& state, const char* net) { |
| PPMM2PBenchmark(state, xnn_f32_ppmm_ukernel_3x3__scalar, xnn_x32_packx_ukernel_3x__scalar, 3, 3); |
| } |
| |
| BENCHMARK_GEMM(sgemm_1x4__scalar) |
| BENCHMARK_GEMM(sgemm_2x4__scalar) |
| BENCHMARK_GEMM(sgemm_4x4__scalar) |
| |
| BENCHMARK_GEMM(sppmm_2x4_unipass__scalar) |
| BENCHMARK_GEMM(sppmm_4x2_unipass__scalar) |
| BENCHMARK_GEMM(sppmm_4x4_unipass__scalar) |
| BENCHMARK_GEMM(sppmm_3x3_unipass__scalar) |
| |
| BENCHMARK_GEMM(sppmm_2x4_twopass__scalar) |
| BENCHMARK_GEMM(sppmm_4x2_twopass__scalar) |
| BENCHMARK_GEMM(sppmm_4x4_twopass__scalar) |
| BENCHMARK_GEMM(sppmm_3x3_twopass__scalar) |
| |
| |
| #ifdef BENCHMARK_RUY |
| BENCHMARK_GEMM(ruy_st) |
| #endif // BENCHMARK_RUY |
| |
| #ifndef XNNPACK_BENCHMARK_NO_MAIN |
| BENCHMARK_MAIN(); |
| #endif |