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CPU Convolution benchmark harness for some popular models (#56455)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/56455 CPU convolution performance is pretty important for inference, so tracking performance for CNNs often boils down to finding shapes that have either regressed or need optimization. This diff adds a benchmark harness that lets you pretty easily add new sets of convolution parameters to benchmark. I've started with an exhaustive list of layers from MobileNetV3, ResNet-18 and ResNet-50, which are fairly popular torchvision models. More to come if these prove useful. I've also added four backend configurations: - native: uses at::conv2d, which applies its own backend selection heuristics - mkldnn_none: uses mkldnn but applies no prepacking; uses the NCHW default - mkldnn_weight: prepacks weights in an mkldnn-friendly format - mkldnn_input: also prepacks the inputs in NCHW16c ghstack-source-id: 127027784 Test Plan: Ran this on my Skylake Xeon Reviewed By: ngimel Differential Revision: D27876139 fbshipit-source-id: 950e1dfa09a33cc3acc7efd579f56df8453af1f2
This commit is contained in:
Bert Maher
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
f84a50109f
commit
461e887d92
2
benchmarks/cpp/CMakeLists.txt
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2
benchmarks/cpp/CMakeLists.txt
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add_executable(convolution_bench convolution.cpp)
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target_link_libraries(convolution_bench PRIVATE torch_library benchmark)
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311
benchmarks/cpp/convolution.cpp
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benchmarks/cpp/convolution.cpp
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#include <ATen/ATen.h>
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#include <ATen/native/mkldnn/MKLDNNCommon.h>
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#include <benchmark/benchmark.h>
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#include <c10/core/InferenceMode.h>
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#include <sstream>
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struct ConvParams {
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std::vector<int64_t> input;
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std::vector<int64_t> weight;
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std::vector<int64_t> bias;
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std::vector<int64_t> stride;
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std::vector<int64_t> padding;
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std::vector<int64_t> dilation;
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int64_t groups;
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};
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struct xs {
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explicit xs(const std::vector<int64_t>& v_) : v(v_) {}
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const std::vector<int64_t>& v;
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};
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std::ostream& operator<<(std::ostream& os, const xs& x) {
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bool first = true;
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for (auto const& xx : x.v) {
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if (!first) {
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os << "x";
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}
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first = false;
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os << xx;
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}
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return os;
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}
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std::ostream& operator<<(std::ostream& os, const ConvParams& params) {
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os << "I" << xs(params.input) << "_W" << xs(params.weight) << "_B"
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<< xs(params.bias) << "_S" << xs(params.stride) << "_P"
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<< xs(params.padding) << "_D" << xs(params.dilation) << "_G"
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<< params.groups;
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return os;
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}
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std::vector<ConvParams> MobileNetV3Params = {
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{{1, 3, 224, 224}, {16, 3, 3, 3}, {16}, {2, 2}, {1, 1}, {1, 1}, 1},
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{{1, 16, 112, 112}, {16, 16, 1, 1}, {16}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 16, 112, 112}, {16, 1, 3, 3}, {16}, {2, 2}, {1, 1}, {1, 1}, 16},
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{{1, 16, 56, 56}, {16, 16, 1, 1}, {16}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 16, 56, 56}, {72, 16, 1, 1}, {72}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 72, 56, 56}, {72, 1, 3, 3}, {72}, {2, 2}, {1, 1}, {1, 1}, 72},
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{{1, 72, 28, 28}, {24, 72, 1, 1}, {24}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 24, 28, 28}, {88, 24, 1, 1}, {88}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 88, 28, 28}, {88, 1, 3, 3}, {88}, {1, 1}, {1, 1}, {1, 1}, 88},
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{{1, 88, 28, 28}, {24, 88, 1, 1}, {24}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 24, 28, 28}, {96, 24, 1, 1}, {96}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 96, 28, 28}, {96, 1, 5, 5}, {96}, {2, 2}, {2, 2}, {1, 1}, 96},
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{{1, 96, 14, 14}, {40, 96, 1, 1}, {40}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 40, 14, 14}, {240, 40, 1, 1}, {240}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 240, 14, 14}, {240, 1, 5, 5}, {240}, {1, 1}, {2, 2}, {1, 1}, 240},
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{{1, 240, 14, 14}, {40, 240, 1, 1}, {40}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 40, 14, 14}, {240, 40, 1, 1}, {240}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 240, 14, 14}, {240, 1, 5, 5}, {240}, {1, 1}, {2, 2}, {1, 1}, 240},
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{{1, 240, 14, 14}, {40, 240, 1, 1}, {40}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 40, 14, 14}, {120, 40, 1, 1}, {120}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 120, 14, 14}, {120, 1, 5, 5}, {120}, {1, 1}, {2, 2}, {1, 1}, 120},
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{{1, 120, 14, 14}, {48, 120, 1, 1}, {48}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 48, 14, 14}, {144, 48, 1, 1}, {144}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 144, 14, 14}, {144, 1, 5, 5}, {144}, {1, 1}, {2, 2}, {1, 1}, 144},
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{{1, 144, 14, 14}, {48, 144, 1, 1}, {48}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 48, 14, 14}, {288, 48, 1, 1}, {288}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 288, 14, 14}, {288, 1, 5, 5}, {288}, {2, 2}, {2, 2}, {1, 1}, 288},
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{{1, 288, 7, 7}, {96, 288, 1, 1}, {96}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 96, 7, 7}, {576, 96, 1, 1}, {576}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 576, 7, 7}, {576, 1, 5, 5}, {576}, {1, 1}, {2, 2}, {1, 1}, 576},
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{{1, 576, 7, 7}, {96, 576, 1, 1}, {96}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 96, 7, 7}, {576, 96, 1, 1}, {576}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 576, 7, 7}, {576, 1, 5, 5}, {576}, {1, 1}, {2, 2}, {1, 1}, 576},
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{{1, 576, 7, 7}, {96, 576, 1, 1}, {96}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 96, 7, 7}, {576, 96, 1, 1}, {576}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 576, 1, 1}, {1280, 576, 1, 1}, {1280}, {1, 1}, {0, 0}, {1, 1}, 1},
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};
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std::vector<ConvParams> ResNet18Params = {
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{{1, 3, 224, 224}, {64, 3, 7, 7}, {}, {2, 2}, {3, 3}, {1, 1}, 1},
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{{1, 64, 56, 56}, {64, 64, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 64, 56, 56}, {64, 64, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 64, 56, 56}, {64, 64, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 64, 56, 56}, {64, 64, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 64, 56, 56}, {128, 64, 3, 3}, {}, {2, 2}, {1, 1}, {1, 1}, 1},
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{{1, 128, 28, 28}, {128, 128, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 64, 56, 56}, {128, 64, 1, 1}, {}, {2, 2}, {0, 0}, {1, 1}, 1},
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{{1, 128, 28, 28}, {128, 128, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 128, 28, 28}, {128, 128, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 128, 28, 28}, {256, 128, 3, 3}, {}, {2, 2}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {256, 256, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 128, 28, 28}, {256, 128, 1, 1}, {}, {2, 2}, {0, 0}, {1, 1}, 1},
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{{1, 256, 14, 14}, {256, 256, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {256, 256, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {512, 256, 3, 3}, {}, {2, 2}, {1, 1}, {1, 1}, 1},
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{{1, 512, 7, 7}, {512, 512, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {512, 256, 1, 1}, {}, {2, 2}, {0, 0}, {1, 1}, 1},
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{{1, 512, 7, 7}, {512, 512, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 512, 7, 7}, {512, 512, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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};
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std::vector<ConvParams> ResNet50Params = {
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{{1, 3, 224, 224}, {64, 3, 7, 7}, {}, {2, 2}, {3, 3}, {1, 1}, 1},
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{{1, 64, 56, 56}, {64, 64, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 64, 56, 56}, {64, 64, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 64, 56, 56}, {256, 64, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 64, 56, 56}, {256, 64, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 56, 56}, {64, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 64, 56, 56}, {64, 64, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 64, 56, 56}, {256, 64, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 56, 56}, {64, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 64, 56, 56}, {64, 64, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 64, 56, 56}, {256, 64, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 56, 56}, {128, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 128, 56, 56}, {128, 128, 3, 3}, {}, {2, 2}, {1, 1}, {1, 1}, 1},
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{{1, 128, 28, 28}, {512, 128, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 56, 56}, {512, 256, 1, 1}, {}, {2, 2}, {0, 0}, {1, 1}, 1},
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{{1, 512, 28, 28}, {128, 512, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 128, 28, 28}, {128, 128, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 128, 28, 28}, {512, 128, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 512, 28, 28}, {128, 512, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 128, 28, 28}, {128, 128, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 128, 28, 28}, {512, 128, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 512, 28, 28}, {128, 512, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 128, 28, 28}, {128, 128, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 128, 28, 28}, {512, 128, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 512, 28, 28}, {256, 512, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 28, 28}, {256, 256, 3, 3}, {}, {2, 2}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {1024, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 512, 28, 28}, {1024, 512, 1, 1}, {}, {2, 2}, {0, 0}, {1, 1}, 1},
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{{1, 1024, 14, 14}, {256, 1024, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 14, 14}, {256, 256, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {1024, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 1024, 14, 14}, {256, 1024, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 14, 14}, {256, 256, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {1024, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 1024, 14, 14}, {256, 1024, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 14, 14}, {256, 256, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {1024, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 1024, 14, 14}, {256, 1024, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 14, 14}, {256, 256, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {1024, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 1024, 14, 14}, {256, 1024, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 256, 14, 14}, {256, 256, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 256, 14, 14}, {1024, 256, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 1024, 14, 14}, {512, 1024, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 512, 14, 14}, {512, 512, 3, 3}, {}, {2, 2}, {1, 1}, {1, 1}, 1},
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{{1, 512, 7, 7}, {2048, 512, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 1024, 14, 14}, {2048, 1024, 1, 1}, {}, {2, 2}, {0, 0}, {1, 1}, 1},
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{{1, 2048, 7, 7}, {512, 2048, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 512, 7, 7}, {512, 512, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 512, 7, 7}, {2048, 512, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 2048, 7, 7}, {512, 2048, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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{{1, 512, 7, 7}, {512, 512, 3, 3}, {}, {1, 1}, {1, 1}, {1, 1}, 1},
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{{1, 512, 7, 7}, {2048, 512, 1, 1}, {}, {1, 1}, {0, 0}, {1, 1}, 1},
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};
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struct EnableMklDnn {
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explicit EnableMklDnn(bool enable)
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: prev_(at::globalContext().userEnabledMkldnn()) {
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at::globalContext().setUserEnabledMkldnn(enable);
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}
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~EnableMklDnn() {
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at::globalContext().setUserEnabledMkldnn(prev_);
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}
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bool prev_;
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};
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template <bool WithMklDnn>
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static void BM_conv2d_native(
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benchmark::State& state,
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const ConvParams& params) {
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EnableMklDnn mkl(WithMklDnn);
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auto input = at::randn(params.input);
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auto weight = at::randn(params.weight);
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auto bias = params.bias.size() > 0 ? at::randn(params.bias) : at::Tensor{};
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auto output = at::conv2d(
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input,
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weight,
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bias,
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params.stride,
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params.padding,
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params.dilation,
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params.groups);
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for (auto _ : state) {
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output = at::conv2d(
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input,
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weight,
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bias,
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params.stride,
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params.padding,
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params.dilation,
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params.groups);
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}
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state.counters["GFLOPS/s"] = benchmark::Counter(
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2.0f * output.numel() * weight.numel() / weight.size(0) *
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state.iterations(),
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benchmark::Counter::kIsRate);
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state.counters["GB/s"] = benchmark::Counter(
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state.iterations() * (input.nbytes() + weight.nbytes() + output.nbytes()),
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benchmark::Counter::kIsRate);
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}
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enum MklDnnReorder {
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None,
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WeightOnly,
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WeightAndInput,
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};
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template <MklDnnReorder Reorder>
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static void BM_conv2d_mkldnn(
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benchmark::State& state,
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const ConvParams& params) {
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auto input = at::randn(params.input);
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auto weight = at::randn(params.weight);
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auto bias = params.bias.size() > 0 ? at::randn(params.bias) : at::Tensor{};
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if (Reorder == WeightAndInput) {
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auto it_input = at::native::itensor_from_mkldnn(input.to_mkldnn());
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auto r = ideep::tensor(
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params.input, ideep::data_type::f32, ideep::format_tag::aBcd16b);
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it_input.reorder_to(r);
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input = at::native::new_with_itensor_mkldnn(
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std::move(r), at::kFloat, at::Device(at::kCPU));
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}
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if (Reorder == WeightOnly || Reorder == WeightAndInput) {
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weight = at::mkldnn_reorder_conv2d_weight(
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weight.to_mkldnn(),
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params.padding,
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params.stride,
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params.dilation,
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params.groups);
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bias = params.bias.size() > 0 ? bias.to_mkldnn() : bias;
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}
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auto output = at::mkldnn_convolution(
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input,
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weight,
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bias,
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params.padding,
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params.stride,
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params.dilation,
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params.groups);
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for (auto _ : state) {
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output = at::mkldnn_convolution(
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input,
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weight,
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bias,
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params.padding,
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params.stride,
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params.dilation,
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params.groups);
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}
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state.counters["GFLOPS/s"] = benchmark::Counter(
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2.0f * output.numel() * weight.numel() / weight.size(0) *
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state.iterations(),
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benchmark::Counter::kIsRate);
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state.counters["GB/s"] = benchmark::Counter(
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state.iterations() * (input.nbytes() + weight.nbytes() + output.nbytes()),
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benchmark::Counter::kIsRate);
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}
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std::string name(
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const char* base,
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const char* suffix,
|
||||
const ConvParams& params) {
|
||||
std::ostringstream os;
|
||||
os << base << "_" << suffix << "_" << params;
|
||||
return os.str();
|
||||
}
|
||||
|
||||
void registerOne(const char* base, const ConvParams& params) {
|
||||
benchmark::RegisterBenchmark(
|
||||
name(base, "native", params).data(), BM_conv2d_native<true>, params);
|
||||
benchmark::RegisterBenchmark(
|
||||
name(base, "native_nomkl", params).data(),
|
||||
BM_conv2d_native<false>,
|
||||
params);
|
||||
benchmark::RegisterBenchmark(
|
||||
name(base, "mkldnn_none", params).data(), BM_conv2d_mkldnn<None>, params);
|
||||
benchmark::RegisterBenchmark(
|
||||
name(base, "mkldnn_weight", params).data(),
|
||||
BM_conv2d_mkldnn<WeightOnly>,
|
||||
params);
|
||||
benchmark::RegisterBenchmark(
|
||||
name(base, "mkldnn_input", params).data(),
|
||||
BM_conv2d_mkldnn<WeightAndInput>,
|
||||
params);
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
c10::InferenceMode guard;
|
||||
|
||||
#define BENCH(x) \
|
||||
for (auto const& params : x##Params) { \
|
||||
registerOne(#x, params); \
|
||||
}
|
||||
BENCH(MobileNetV3);
|
||||
BENCH(ResNet18);
|
||||
BENCH(ResNet50);
|
||||
#undef BENCH
|
||||
|
||||
benchmark::Initialize(&argc, argv);
|
||||
benchmark::RunSpecifiedBenchmarks();
|
||||
}
|
||||
@ -1544,6 +1544,10 @@ if(BUILD_TENSOREXPR_BENCHMARK)
|
||||
add_subdirectory(${TORCH_ROOT}/benchmarks/cpp/tensorexpr ${CMAKE_BINARY_DIR}/tensorexpr_bench)
|
||||
endif()
|
||||
|
||||
if(BUILD_CPP_BENCHMARKS)
|
||||
add_subdirectory(${TORCH_ROOT}/benchmarks/cpp ${PROJECT_BINARY_DIR}/bin)
|
||||
endif()
|
||||
|
||||
if(BUILD_MOBILE_BENCHMARK)
|
||||
foreach(benchmark_src ${ATen_MOBILE_BENCHMARK_SRCS})
|
||||
get_filename_component(benchmark_name ${benchmark_src} NAME_WE)
|
||||
|
||||
Reference in New Issue
Block a user