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https://github.com/saymrwulf/onnxruntime.git
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Add mlas_bench tools. Starting with sconv bench and sgemm bench. (#7139)
* Add mlas_bench tools. Starting with sconv bench and sgemm bench. * Some update with build related.
This commit is contained in:
Zhang Lei
GitHub
parent
56b22c1c6b
commit
dbcfc4bee6
6 changed files with 462 additions and 0 deletions
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@ -768,6 +768,16 @@ if(onnxruntime_BUILD_BENCHMARKS)
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target_link_libraries(onnxruntime_benchmark PRIVATE onnx_test_runner_common benchmark::benchmark ${onnx_test_libs})
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add_dependencies(onnxruntime_benchmark ${onnxruntime_EXTERNAL_DEPENDENCIES})
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set_target_properties(onnxruntime_benchmark PROPERTIES FOLDER "ONNXRuntimeTest")
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SET(MLAS_BENCH_DIR ${TEST_SRC_DIR}/mlas/bench)
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file(GLOB_RECURSE MLAS_BENCH_SOURCE_FILES "${MLAS_BENCH_DIR}/*.cpp" "${MLAS_BENCH_DIR}/*.h")
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onnxruntime_add_executable(onnxruntime_mlas_benchmark ${MLAS_BENCH_SOURCE_FILES})
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target_include_directories(onnxruntime_mlas_benchmark PRIVATE ${ONNXRUNTIME_ROOT}/core/mlas/inc)
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target_link_libraries(onnxruntime_mlas_benchmark PRIVATE benchmark::benchmark onnxruntime_mlas onnxruntime_common)
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if(NOT WIN32)
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target_link_libraries(onnxruntime_mlas_benchmark PRIVATE nsync_cpp)
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endif()
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set_target_properties(onnxruntime_mlas_benchmark PROPERTIES FOLDER "ONNXRuntimeTest")
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endif()
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if(WIN32)
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6
onnxruntime/test/mlas/bench/bench_main.cpp
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6
onnxruntime/test/mlas/bench/bench_main.cpp
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@ -0,0 +1,6 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include <benchmark/benchmark.h>
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BENCHMARK_MAIN();
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247
onnxruntime/test/mlas/bench/bench_sconv.cpp
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247
onnxruntime/test/mlas/bench/bench_sconv.cpp
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@ -0,0 +1,247 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "mlas.h"
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#include "bench_util.h"
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#include <stdexcept>
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#include <numeric>
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static std::vector<std::string> BuildArgNamesForConv(size_t rank) {
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std::vector<std::string> names = {"Rank", "N", "G", "Cpg", "Fpg"};
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size_t arg_position = names.size();
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names.resize(arg_position + rank * 6, std::string(""));
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names[arg_position] = "I";
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arg_position += rank;
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names[arg_position] = "K";
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arg_position += rank;
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names[arg_position] = "P";
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arg_position += rank * 2;
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names[arg_position] = "S";
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arg_position += rank;
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names[arg_position] = "D";
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return names;
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}
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static const std::vector<std::string>& ArgNamesForConv(size_t rank) {
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static std::map<size_t, std::vector<std::string>> rank_to_args_name;
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if (rank_to_args_name.find(rank) == rank_to_args_name.end()) {
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rank_to_args_name.emplace(std::make_pair(rank, BuildArgNamesForConv(rank)));
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}
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return rank_to_args_name[rank];
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}
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// dummy for some strange build error when using Bench capture
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void SCONV_NCHW(benchmark::State& state, const char* /*dummy*/) {
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const int64_t rank = state.range(0); // Rank
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const int64_t batch_size = state.range(1); // N
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const int64_t groups = state.range(2); // G
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const int64_t input_channels_per_group = state.range(3); // Cpg
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const int64_t output_channels_per_group = state.range(4); // Fpg
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if (rank <= 0) throw std::invalid_argument("Kernel rank must greater than 0!");
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if (batch_size <= 0) throw std::invalid_argument("Batch size must greater than 0!");
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if (groups <= 0) throw std::invalid_argument("Group count must greater than 0!");
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if (input_channels_per_group <= 0) throw std::invalid_argument("input_channels_per_group must greater than 0!");
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if (output_channels_per_group <= 0) throw std::invalid_argument("output_channels_per_group must greater than 0!");
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size_t arg_position = 5;
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const auto input_shape = BenchArgsVector(state, arg_position, rank);
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const auto kernel_shape = BenchArgsVector(state, arg_position, rank);
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const auto paddings = BenchArgsVector(state, arg_position, rank * 2);
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const auto strides = BenchArgsVector(state, arg_position, rank);
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const auto dilations = BenchArgsVector(state, arg_position, rank);
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// do not check the size of each vector as they are forced from args.
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if (std::any_of(input_shape.begin(), input_shape.end(), [](const int64_t& dim) { return dim <= 0; })) {
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throw std::invalid_argument("all input image dim must > 0");
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}
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if (std::any_of(kernel_shape.begin(), kernel_shape.end(), [](const int64_t& dim) { return dim <= 0; })) {
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throw std::invalid_argument("all kernel dim must > 0");
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}
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if (std::any_of(strides.begin(), strides.end(), [](const int64_t& dim) { return dim <= 0; })) {
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throw std::invalid_argument("all strides dim must > 0");
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}
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if (std::any_of(dilations.begin(), dilations.end(), [](const int64_t& dim) { return dim <= 0; })) {
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throw std::invalid_argument("all dilations dim must > 0");
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}
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const int64_t GC = groups * input_channels_per_group;
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const int64_t GF = groups * output_channels_per_group;
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std::vector<int64_t> x_shape = {batch_size, GC};
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x_shape.insert(x_shape.end(), input_shape.begin(), input_shape.end());
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std::vector<int64_t> f_shape = {GF, input_channels_per_group};
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f_shape.insert(f_shape.end(), kernel_shape.begin(), kernel_shape.end());
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std::vector<int64_t> output_shape((size_t)rank);
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for (int64_t i = 0; i < rank; ++i) {
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auto km = 1 + dilations[i] * (kernel_shape[i] - 1);
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output_shape[i] = (paddings[i] + paddings[i + rank] + input_shape[i] - km) / strides[i] + 1;
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}
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std::vector<int64_t> y_shape = {batch_size, GF};
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y_shape.insert(y_shape.end(), output_shape.begin(), output_shape.end());
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MLAS_ACTIVATION activation;
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activation.ActivationKind = MlasIdentityActivation;
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MLAS_CONV_PARAMETERS Parameters;
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size_t WorkingBufferSize = 0;
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MlasConvPrepare(&Parameters,
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static_cast<size_t>(rank),
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static_cast<size_t>(batch_size),
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static_cast<size_t>(groups),
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static_cast<size_t>(input_channels_per_group),
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input_shape.data(),
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kernel_shape.data(),
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dilations.data(),
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paddings.data(),
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strides.data(),
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output_shape.data(),
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static_cast<size_t>(output_channels_per_group),
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&activation,
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&WorkingBufferSize,
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nullptr);
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auto X = RandomVectorUniform(x_shape, -2.0, 2.0);
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auto F = RandomVectorUniform(f_shape, -1.0, 1.0);
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int64_t y_size = std::accumulate(y_shape.begin(), y_shape.end(), 1LL, std::multiplies<int64_t>());
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std::vector<float> Y(static_cast<size_t>(y_size));
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std::vector<float> working_buffer(WorkingBufferSize);
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// warm up first round.
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MlasConv(&Parameters,
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X.data(),
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F.data(),
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nullptr,
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working_buffer.data(),
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Y.data(),
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nullptr);
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for (auto _ : state) {
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MlasConv(&Parameters,
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X.data(),
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F.data(),
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nullptr,
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working_buffer.data(),
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Y.data(),
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nullptr);
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}
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}
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static void ResNet50(benchmark::internal::Benchmark* b) {
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b->ArgNames(ArgNamesForConv(2));
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//************************* Conv 1 *************************
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// Rank, N, G,Cpg,Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1, 3, 64,224,224, 7,7, 3,3,3,3, 2,2, 1,1});
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//************************ Conv 2.1 ************************
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// Rank, N, G,Cpg,Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1, 64, 64, 56, 56, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1, 64, 64, 56, 56, 3,3, 1,1,1,1, 1,1, 1,1});
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b->Args({2, 1, 1, 64,256, 56, 56, 1,1, 0,0,0,0, 1,1, 1,1});
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//b->Args({2, 1, 1, 64,256, 56, 56, 1,1, 0,0,0,0, 1,1, 1,1});
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//************************ Conv 2.X ************************
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// Rank, N, G,Cpg,Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1,256, 64, 56, 56, 1,1, 0,0,0,0, 1,1, 1,1});
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//b->Args({2, 1, 1, 64, 64, 56, 56, 3,3, 1,1,1,1, 1,1, 1,1});
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//b->Args({2, 1, 1, 64,256, 56, 56, 1,1, 0,0,0,0, 1,1, 1,1});
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/************************ Conv 3.1 ************************/
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// Rank, N, G,Cpg,Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1,256,128, 56, 56, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1,128,128, 56, 56, 3,3, 1,1,1,1, 2,2, 1,1});
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b->Args({2, 1, 1,128,512, 28, 28, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1,256,512, 56, 56, 1,1, 0,0,0,0, 2,2, 1,1});
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/************************ Conv 3.X ************************/
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// Rank, N, G,Cpg,Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1,512,128, 28, 28, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1,128,128, 28, 28, 3,3, 1,1,1,1, 1,1, 1,1});
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//b->Args({2, 1, 1,128,512, 28, 28, 1,1, 0,0,0,0, 1,1, 1,1});
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/************************ Conv 4.1 ************************/
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// Rank, N, G,Cpg,Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1,512,256, 28, 28, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1,256,256, 28, 28, 3,3, 1,1,1,1, 2,2, 1,1});
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b->Args({2, 1, 1,256,1024,14, 14, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1,512,1024,28, 28, 1,1, 0,0,0,0, 2,2, 1,1});
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/************************ Conv 4.X ************************/
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// Rank, N, G, Cpg, Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1, 1024, 256, 14, 14, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1, 256, 256, 14, 14, 3,3, 1,1,1,1, 1,1, 1,1});
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//b->Args({2, 1, 1, 256, 1024, 14, 14, 1,1, 0,0,0,0, 1,1, 1,1});
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/************************ Conv 5.1 ************************/
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// Rank, N, G, Cpg, Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1, 1024, 512, 14, 14, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1, 512, 512, 14, 14, 3,3, 1,1,1,1, 2,2, 1,1});
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b->Args({2, 1, 1, 512,2048, 7, 7, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1, 1024,2048, 14, 14, 1,1, 0,0,0,0, 2,2, 1,1});
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/************************ Conv 5.X ************************/
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// Rank, N, G, Cpg, Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1, 2048, 512, 7, 7, 1,1, 0,0,0,0, 1,1, 1,1});
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b->Args({2, 1, 1, 512, 512, 7, 7, 3,3, 1,1,1,1, 1,1, 1,1});
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//b->Args({2, 1, 1, 512,2048, 7, 7, 1,1, 0,0,0,0, 1,1, 1,1});
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}
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BENCHMARK_CAPTURE(SCONV_NCHW, ResNet50, "")->Apply(ResNet50)->UseRealTime();
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static void TeamsModel(benchmark::internal::Benchmark* b) {
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b->ArgNames(ArgNamesForConv(2));
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// Rank, N, G, Cpg, Fpg, I, , K, , P, , , , S, , D, ,
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b->Args({2, 1, 1, 40, 24, 24, 40, 3,3, 1,1,1,1, 1,1, 1,1}); // fused conv_349 => 24x40
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b->Args({2, 1, 1, 24, 24, 24, 40, 3,3, 1,1,1,1, 1,1, 1,1}); // fused Conv_367 => 24x40
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b->Args({2, 1, 1, 4, 24, 96,160, 3,3, 0,0,1,1, 2,2, 1,1}); // fused Conv_15 => 48x80
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b->Args({2, 1, 1, 12, 72, 48, 80, 1,1, 0,0,0,0, 1,1, 1,1}); // fused Conv_38 => 48x80
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b->Args({2, 1, 1, 12, 8, 48, 80, 3,3, 1,1,1,1, 1,1, 1,1}); // fused Conv_395 => 48x80
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b->Args({2, 1,24, 1, 1, 48, 80, 3,3, 1,1,1,1, 1,1, 1,1}); // fused Conv_33 => 48x80
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b->Args({2, 1, 1, 8, 8, 48, 80, 3,3, 1,1,1,1, 1,1, 1,1}); // fused Conv_413 => 48x80
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b->Args({2, 1,72, 1, 1, 48, 80, 3,3, 0,0,1,1, 2,2, 1,1}); // fused Conv_56 => 24x40
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b->Args({2, 1,72, 1, 1, 24, 40, 3,3, 1,1,1,1, 2,2, 1,1}); // fused Conv_79 => 24x40
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b->Args({2, 1, 1, 24, 12, 48, 80, 1,1, 0,0,0,0, 1,1, 1,1}); // Conv_36 => 48x80
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b->Args({2, 1, 1, 12, 72, 24, 40, 1,1, 0,0,0,0, 1,1, 1,1}); // fused Conv_61/85 => 24x40
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b->Args({2, 1, 1, 24, 144, 12, 20, 1,1, 0,0,0,0, 1,1, 1,1}); // fused Conv_108/132 => 12x20
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b->Args({2, 1, 1, 12, 12, 48, 80, 1,1, 0,0,0,0, 1,1, 1,1}); // fused Conv_376 => 48x80
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b->Args({2, 1, 1, 12, 72, 48, 80, 1,1, 0,0,0,0, 1,1, 1,1}); // Conv_59 => 24x40
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}
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BENCHMARK_CAPTURE(SCONV_NCHW, TeamsModel, "")->Apply(TeamsModel)->UseRealTime();
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static void General_Conv2d(benchmark::internal::Benchmark* b) {
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b->ArgNames(ArgNamesForConv(2));
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ArgsProduct(
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b,
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{{2}, // Rank,
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{1}, // N
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{1, 2}, // Groups
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{3, 12}, // Cpg
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{6}, // Fpg
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{24, 72}, // Input Image Shape
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{36},
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{3}, // kernel shape
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{3},
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{0}, // paddings
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{0},
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{0},
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{0},
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{1}, // strides
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{1},
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{1}, // dilations
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{1}});
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}
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BENCHMARK_CAPTURE(SCONV_NCHW, 2d, "")->Apply(General_Conv2d)->UseRealTime();
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120
onnxruntime/test/mlas/bench/bench_sgemm.cpp
Normal file
120
onnxruntime/test/mlas/bench/bench_sgemm.cpp
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@ -0,0 +1,120 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "mlas.h"
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#include "bench_util.h"
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#include <stdexcept>
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#include <numeric>
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static const std::vector<std::string> sgemm_bench_arg_names = {"M", "N", "K"};
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void SGEMM(benchmark::State& state, bool pack_b, bool trans_a, bool trans_b, float alpha = 1.0f, float beta = 0.0f) {
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const int64_t M = state.range(0);
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const int64_t N = state.range(1);
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const int64_t K = state.range(2);
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if (M <= 0) throw std::invalid_argument("M must greater than 0!");
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if (N <= 0) throw std::invalid_argument("N must greater than 0!");
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if (K <= 0) throw std::invalid_argument("K must greater than 0!");
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auto A = RandomVectorUniform(static_cast<size_t>(M * K), -1.0, 1.0);
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auto B = RandomVectorUniform(static_cast<size_t>(N * K), -1.0, 1.0);
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std::vector<float> C(static_cast<size_t>(M * N));
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if (pack_b) {
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size_t pack_b_size = MlasGemmPackBSize(N, K);
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std::vector<float> B_packed(pack_b_size);
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MlasGemmPackB(CblasNoTrans, N, K, B.data(), N, B_packed.data());
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MlasGemm(
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trans_a ? CblasTrans : CblasNoTrans,
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static_cast<size_t>(M),
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static_cast<size_t>(N),
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static_cast<size_t>(K),
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alpha,
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A.data(),
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trans_a ? M : K,
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B_packed.data(),
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beta,
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C.data(),
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N,
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nullptr);
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for (auto _ : state) {
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MlasGemm(
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trans_a ? CblasTrans : CblasNoTrans,
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static_cast<size_t>(M),
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static_cast<size_t>(N),
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static_cast<size_t>(K),
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alpha,
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A.data(),
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trans_a ? M : K,
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B_packed.data(),
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beta,
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C.data(),
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N,
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nullptr);
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}
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} else {
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MlasGemm(
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trans_a ? CblasTrans : CblasNoTrans,
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trans_b ? CblasTrans : CblasNoTrans,
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static_cast<size_t>(M),
|
||||
static_cast<size_t>(N),
|
||||
static_cast<size_t>(K),
|
||||
alpha,
|
||||
A.data(),
|
||||
trans_a ? M : K,
|
||||
B.data(),
|
||||
trans_b ? K : N,
|
||||
beta,
|
||||
C.data(),
|
||||
N,
|
||||
nullptr);
|
||||
|
||||
for (auto _ : state) {
|
||||
MlasGemm(
|
||||
trans_a ? CblasTrans : CblasNoTrans,
|
||||
trans_b ? CblasTrans : CblasNoTrans,
|
||||
static_cast<size_t>(M),
|
||||
static_cast<size_t>(N),
|
||||
static_cast<size_t>(K),
|
||||
alpha,
|
||||
A.data(),
|
||||
trans_a ? M : K,
|
||||
B.data(),
|
||||
trans_b ? K : N,
|
||||
beta,
|
||||
C.data(),
|
||||
N,
|
||||
nullptr);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void GemmSizeWithOne(benchmark::internal::Benchmark* b) {
|
||||
b->ArgNames(sgemm_bench_arg_names);
|
||||
ArgsProduct(b, {{1}, {63, 255, 1023}, {63, 255, 1023}});
|
||||
ArgsProduct(b, {{63, 255, 1023}, {1}, {63, 255, 1023}});
|
||||
ArgsProduct(b, {{63, 255, 1023}, {63, 255, 1023}, {1}});
|
||||
}
|
||||
|
||||
static void GemmSizeProducts(benchmark::internal::Benchmark* b) {
|
||||
b->ArgNames(sgemm_bench_arg_names);
|
||||
ArgsProduct(b, {{63, 255, 1023}, {63, 255, 1023}, {63, 255, 1023}});
|
||||
}
|
||||
|
||||
BENCHMARK_CAPTURE(SGEMM, NORMAL_NoTrans, false, false, false)->Apply(GemmSizeProducts)->UseRealTime();
|
||||
BENCHMARK_CAPTURE(SGEMM, NORMAL_TransA, false, true, false)->Apply(GemmSizeProducts)->UseRealTime();
|
||||
BENCHMARK_CAPTURE(SGEMM, NORMAL_TransB, false, false, true)->Apply(GemmSizeProducts)->UseRealTime();
|
||||
BENCHMARK_CAPTURE(SGEMM, NORMAL_ABTrans, false, true, true)->Apply(GemmSizeProducts)->UseRealTime();
|
||||
|
||||
BENCHMARK_CAPTURE(SGEMM, GEMV_NoTrans, false, false, false)->Apply(GemmSizeWithOne)->UseRealTime();
|
||||
BENCHMARK_CAPTURE(SGEMM, GEMV_TransA, false, true, false)->Apply(GemmSizeWithOne)->UseRealTime();
|
||||
BENCHMARK_CAPTURE(SGEMM, GEMV_TransB, false, false, true)->Apply(GemmSizeWithOne)->UseRealTime();
|
||||
BENCHMARK_CAPTURE(SGEMM, GEMV_ABTrans, false, true, true)->Apply(GemmSizeWithOne)->UseRealTime();
|
||||
|
||||
BENCHMARK_CAPTURE(SGEMM, PACKB_NoTransA, true, false, false)->Apply(GemmSizeProducts)->UseRealTime();
|
||||
BENCHMARK_CAPTURE(SGEMM, PACKB_TransA, true, true, false)->Apply(GemmSizeProducts)->UseRealTime();
|
||||
64
onnxruntime/test/mlas/bench/bench_util.cpp
Normal file
64
onnxruntime/test/mlas/bench/bench_util.cpp
Normal file
|
|
@ -0,0 +1,64 @@
|
|||
// Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
// Licensed under the MIT License.
|
||||
|
||||
#include "bench_util.h"
|
||||
#include <numeric>
|
||||
#include <random>
|
||||
#include <stdexcept>
|
||||
|
||||
std::vector<int64_t> BenchArgsVector(benchmark::State& state, size_t& start, size_t count) {
|
||||
std::vector<int64_t> shape;
|
||||
shape.reserve(count);
|
||||
for (size_t axis = 0; axis < count; ++axis) {
|
||||
shape.emplace_back(state.range(start + axis));
|
||||
}
|
||||
start += count;
|
||||
return shape;
|
||||
}
|
||||
|
||||
std::vector<float> RandomVectorUniform(size_t N, float min_value, float max_value) {
|
||||
if (min_value >= max_value) {
|
||||
return std::vector<float>(N, min_value);
|
||||
}
|
||||
std::default_random_engine generator(static_cast<unsigned>(N));
|
||||
std::uniform_real_distribution<float> distribution(min_value, max_value);
|
||||
std::vector<float> r(N);
|
||||
for (size_t i = 0; i < N; i++) {
|
||||
r[i] = distribution(generator);
|
||||
}
|
||||
return r;
|
||||
}
|
||||
|
||||
std::vector<float> RandomVectorUniform(std::vector<int64_t> shape, float min_value, float max_value) {
|
||||
int64_t sz = std::accumulate(shape.begin(), shape.end(), 1LL, std::multiplies<int64_t>());
|
||||
if (sz <= 0) {
|
||||
throw std::invalid_argument("shape gives size must greater than 0!");
|
||||
}
|
||||
return RandomVectorUniform(static_cast<size_t>(sz), min_value, max_value);
|
||||
}
|
||||
|
||||
// The Benchmark used here do not contains this as in newer version.
|
||||
// Use the code from newer version.
|
||||
void ArgsProduct(benchmark::internal::Benchmark* bench,
|
||||
const std::vector<std::vector<int64_t>>& arglists) {
|
||||
std::vector<std::size_t> indices(arglists.size(), 0);
|
||||
const std::size_t total = std::accumulate(
|
||||
std::begin(arglists), std::end(arglists), std::size_t{1},
|
||||
[](const std::size_t res, const std::vector<int64_t>& arglist) {
|
||||
return res * arglist.size();
|
||||
});
|
||||
std::vector<int64_t> args;
|
||||
args.reserve(arglists.size());
|
||||
for (std::size_t i = 0; i < total; i++) {
|
||||
for (std::size_t arg = 0; arg < arglists.size(); arg++) {
|
||||
args.push_back(arglists[arg][indices[arg]]);
|
||||
}
|
||||
bench->Args(args);
|
||||
args.clear();
|
||||
|
||||
std::size_t arg = 0;
|
||||
do {
|
||||
indices[arg] = (indices[arg] + 1) % arglists[arg].size();
|
||||
} while (indices[arg++] == 0 && arg < arglists.size());
|
||||
}
|
||||
}
|
||||
15
onnxruntime/test/mlas/bench/bench_util.h
Normal file
15
onnxruntime/test/mlas/bench/bench_util.h
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
// Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
// Licensed under the MIT License.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <benchmark/benchmark.h>
|
||||
|
||||
void ArgsProduct(benchmark::internal::Benchmark* bench,
|
||||
const std::vector<std::vector<int64_t>>& arglists);
|
||||
|
||||
std::vector<float> RandomVectorUniform(size_t N, float min_value, float max_value);
|
||||
|
||||
std::vector<float> RandomVectorUniform(std::vector<int64_t> shape, float min_value, float max_value);
|
||||
|
||||
std::vector<int64_t> BenchArgsVector(benchmark::State& state, size_t& start, size_t count);
|
||||
Loading…
Reference in a new issue