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1433160a36
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742 Modified loops in files under fbsource/fbcode/caffe2/ from the format `for(TYPE var=x0;var<x_max;x++)` to the format `for(const auto var: irange(xmax))` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: malfet Differential Revision: D31705366 fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
55 lines
2.1 KiB
C++
55 lines
2.1 KiB
C++
#ifndef CAFFE2_OPERATORS_CUDNN_OP_UTILS_H_
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#define CAFFE2_OPERATORS_CUDNN_OP_UTILS_H_
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#include "caffe2/core/cudnn_wrappers.h"
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namespace caffe2 {
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// Earlier in the days Caffe sets the default cudnn workspace to 8MB. We bump
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// it up to 64MB in Caffe2, as this enables the use of Winograd in many cases,
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// something very beneficial to more recent CNN models.
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static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES = 64 * 1024 * 1024;
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// Manually specified number of algorithms implemented in CuDNN.
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// This does not have any performance implications, as we will always find the
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// fastest algorithm; setting them to the right number of algorithms will enable
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// us to best report the statistics when doing an exhaustive search, though.
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#if CUDNN_VERSION_MIN(7, 0, 0)
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// Note: Double each of these due to potential
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// tensorcore + non-tensorcore versions
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// which are treated as separate returned algos
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static constexpr size_t kNUM_CUDNN_FWD_ALGS =
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2 * CUDNN_CONVOLUTION_FWD_ALGO_COUNT;
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static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS =
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2 * CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT;
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static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS =
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2 * CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT;
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#else
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static constexpr size_t kNUM_CUDNN_FWD_ALGS = 7;
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static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS = 4;
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static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS = 5;
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#endif
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namespace {
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template <typename ArrayOfcudnnConvolutionAlgoPerf_t>
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inline void LogCuDNNPerfStats(
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const ArrayOfcudnnConvolutionAlgoPerf_t& perf_stat,
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int returned_algo_count) {
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VLOG(1) << "Perf result: (algo: stat, time, memory)";
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for (const auto i : c10::irange(returned_algo_count)) {
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const auto& stat = perf_stat[i];
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VLOG(1) << stat.algo << ": " << stat.status << " " << stat.time << " "
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<< stat.memory;
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}
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}
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} // namespace
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// Easier indexing into force_algo_ vector,
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// shared by CudnnConvTransposeOpBase and CudnnConvOpBase to force
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// usage of a particular algorithm instead of searching
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enum { ALGO_FWD = 0, ALGO_WGRAD = 1, ALGO_DGRAD = 2 };
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_CUDNN_OP_UTILS_H_
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