Merge pull request #14284 from PaddlePaddle/revert-14043-conv_cudnn_cache
Revert " Exhaustive search for cuDNN conv."revert-14324-fix_vlog
Xin Pan
6 years ago
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <functional>
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#include <unordered_map>
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#include <vector>
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namespace paddle {
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namespace operators {
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template <typename TAlgorithm>
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class AlgorithmsCache {
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public:
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// Caches the best algorithm for a given
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// combination of tensor dimensions & compute data type.
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TAlgorithm GetAlgorithm(
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const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
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const std::vector<int>& strides, const std::vector<int>& paddings,
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const std::vector<int>& dilations,
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int algorithmFlags, // can set for different data type
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std::function<TAlgorithm()> gen_func);
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private:
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std::unordered_map<int64_t, TAlgorithm> hash_;
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std::mutex mutex_;
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};
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template <typename TAlgorithm>
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TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm(
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const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
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const std::vector<int>& strides, const std::vector<int>& paddings,
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const std::vector<int>& dilations, int algorithmFlags,
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std::function<TAlgorithm()> gen_func) {
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std::lock_guard<std::mutex> lock(mutex_);
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int64_t seed = 0;
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// Hash all of the inputs, use to try and look up a previously
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// discovered algorithm, or fall back to generating a new one.
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std::hash<int64_t> hashFn;
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// do hash like boost
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// https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
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for (const auto num : dims1) {
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seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
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}
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for (const auto num : dims2) {
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seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2) + 1;
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}
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for (const auto num : strides) {
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seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
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(seed >> 2) + 2;
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}
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for (const auto num : paddings) {
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seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
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(seed >> 2) + 3;
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}
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for (const auto num : dilations) {
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seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
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(seed >> 2) + 4;
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}
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seed ^= hashFn(static_cast<int64_t>(algorithmFlags)) + 0x9e3779b9 +
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(seed << 6) + (seed >> 2) + 5;
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if (seed == 0) return gen_func();
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if (hash_.find(seed) == hash_.end()) {
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TAlgorithm value = gen_func();
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hash_[seed] = value;
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}
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return hash_[seed];
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}
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} // namespace operators
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} // namespace paddle
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