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11a4ca741d
* Fuse op conv Add and activation from two branch * simplify code Co-authored-by: Jicheng Wen <jicwen@microsoft.com>
248 lines
10 KiB
C++
248 lines
10 KiB
C++
// 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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0.0f,
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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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