// 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 #include #include #include #include #include #include #include #include #include #include #include #include "bench/utils.h" #include #include #include struct ComputeErrorContext { const float* input; const float* output; float* error; }; static void ComputeError( struct ComputeErrorContext* context, size_t start, size_t range) { const float* input = context->input; const float* output = context->output; float* error = context->error; for (size_t i = start; i < start + range; i++) { const double output_ref = std::exp(double(input[i])); const double abs_error = std::abs(output_ref - double(output[i])); const float output_abs = std::abs(output_ref); const float output_ulp = fp32_from_bits(fp32_to_bits(output_abs) + 1) - output_abs; error[i] = float(abs_error / output_ulp); } } static void ExpError( benchmark::State& state, xnn_f32_unary_math_function exp, benchmark::utils::IsaCheckFunction isa_check = nullptr) { if (!cpuinfo_initialize()) { state.SkipWithError("failed cpuinfo init"); return; } if (isa_check && !isa_check(state)) { return; } // The smallest x for which expf(x) is non-zero (-0x1.9FE368p+6f). const uint32_t min_input = 0xC2CFF1B4; // The largest x for which expf(x) is finite (0x1.62E42Ep6f). const uint32_t max_input = 0x42B17217; // Number of elements in one block of inputs/outputs. // Combining multiple elements in a block reduce function call overhead. const size_t block_size = 16384; // Number of elements in one parallelization tile. Worker threads process this many elements in each task. const size_t tile_size = 64; uint32_t num_threads = cpuinfo_get_cores_count(); #if XNN_ARCH_ARM || XNN_ARCH_ARM64 // Use all cores except for the least performant cluster if (cpuinfo_get_clusters_count() > 1) { num_threads -= cpuinfo_get_cluster(cpuinfo_get_clusters_count() - 1)->core_count; } #endif // XNN_ARCH_ARM || XNN_ARCH_ARM64 std::unique_ptr threadpool( pthreadpool_create(num_threads), pthreadpool_destroy); std::vector> x(block_size); std::vector> y(block_size); std::vector ulp_error(block_size); float max_ulp_error = 0.0f; ComputeErrorContext context; context.input = x.data(); context.output = y.data(); context.error = ulp_error.data(); for (auto _ : state) { for (uint32_t n = min_input; int32_t(n) < 0; n -= block_size) { for (uint32_t i = 0; i < block_size; i++) { x[i] = fp32_from_bits(std::max(n - i, 0x80000000)); } std::fill(y.begin(), y.end(), std::nanf("")); exp(block_size * sizeof(float), x.data(), y.data()); pthreadpool_parallelize_1d_tile_1d( threadpool.get(), reinterpret_cast(ComputeError), static_cast(&context), block_size, tile_size, 0 /* flags */); max_ulp_error = std::accumulate(ulp_error.cbegin(), ulp_error.cend(), max_ulp_error, static_cast(std::max)); } for (uint32_t n = 0; n < max_input; n += block_size) { for (uint32_t i = 0; i < block_size; i++) { x[i] = fp32_from_bits(std::min(n + i, max_input)); } std::fill(y.begin(), y.end(), std::nanf("")); exp(block_size * sizeof(float), x.data(), y.data()); pthreadpool_parallelize_1d_tile_1d( threadpool.get(), reinterpret_cast(ComputeError), static_cast(&context), block_size, tile_size, 0 /* flags */); max_ulp_error = std::accumulate(ulp_error.cbegin(), ulp_error.cend(), max_ulp_error, static_cast(std::max)); } } state.counters["ULPERROR"] = benchmark::Counter(max_ulp_error); } #if XNN_ARCH_ARM || XNN_ARCH_ARM64 BENCHMARK_CAPTURE(ExpError, neonfma_rr2_lut64_p2, xnn_math_f32_exp__neonfma_rr2_lut64_p2, benchmark::utils::CheckNEONFMA) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, neonfma_rr2_p5, xnn_math_f32_exp__neonfma_rr2_p5, benchmark::utils::CheckNEONFMA) ->Unit(benchmark::kMillisecond) ->Iterations(1); #endif // XNN_ARCH_ARM || XNN_ARCH_ARM64 #if XNN_ARCH_X86 || XNN_ARCH_X86_64 BENCHMARK_CAPTURE(ExpError, avx512f_rr2_lut16_p3_perm, xnn_math_f32_exp__avx512f_rr2_lut16_p3_perm, benchmark::utils::CheckAVX512F) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx512f_rr2_lut16_p3_perm_scalef, xnn_math_f32_exp__avx512f_rr2_lut16_p3_perm_scalef, benchmark::utils::CheckAVX512F) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx512f_rr2_lut32_p2_perm2, xnn_math_f32_exp__avx512f_rr2_lut32_p2_perm2, benchmark::utils::CheckAVX512F) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx512f_rr2_lut32_p2_perm2_scalef, xnn_math_f32_exp__avx512f_rr2_lut32_p2_perm2_scalef, benchmark::utils::CheckAVX512F) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx512f_rr2_p5, xnn_math_f32_exp__avx512f_rr2_p5, benchmark::utils::CheckAVX512F) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx512f_rr2_p5_scalef, xnn_math_f32_exp__avx512f_rr2_p5_scalef, benchmark::utils::CheckAVX512F) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx2_rr2_lut8_p3_perm, xnn_math_f32_exp__avx2_rr2_lut8_p3_perm, benchmark::utils::CheckAVX2) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx2_rr2_lut8_p4_perm, xnn_math_f32_exp__avx2_rr2_lut8_p4_perm, benchmark::utils::CheckAVX2) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx2_rr2_p5, xnn_math_f32_exp__avx2_rr2_p5, benchmark::utils::CheckAVX2) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, avx_rr2_p5, xnn_math_f32_exp__avx_rr2_p5, benchmark::utils::CheckAVX) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, sse2_rr2_lut64_p2, xnn_math_f32_exp__sse2_rr2_lut64_p2) ->Unit(benchmark::kMillisecond) ->Iterations(1); BENCHMARK_CAPTURE(ExpError, sse2_rr2_p5, xnn_math_f32_exp__sse2_rr2_p5) ->Unit(benchmark::kMillisecond) ->Iterations(1); #endif // XNN_ARCH_X86 || XNN_ARCH_X86_64 #ifndef XNNPACK_BENCHMARK_NO_MAIN BENCHMARK_MAIN(); #endif