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Merge pull request PaddlePaddle#15844 from panyx0718/infer
add per kernel config and remove const_cast.
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,118 @@ | ||
| /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
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| #pragma once | ||
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| #include <algorithm> | ||
| #include <unordered_map> | ||
| #include <vector> | ||
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| namespace paddle { | ||
| namespace framework { | ||
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| // Not thread-safe. Should be owned per-kernel. | ||
| template <typename TAlgorithm> | ||
| class AlgorithmsCache { | ||
| public: | ||
| AlgorithmsCache() : search_times_(0) { hash_.clear(); } | ||
| // Caches the best algorithm for a given | ||
| // combination of tensor dimensions & compute data type. | ||
| TAlgorithm GetAlgorithm( | ||
| const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2, | ||
| const std::vector<int>& strides, const std::vector<int>& paddings, | ||
| const std::vector<int>& dilations, | ||
| int algorithmFlags, // can set for different data type | ||
| std::function<TAlgorithm()> gen_func); | ||
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| TAlgorithm GetAlgorithm(int64_t area, int search_times, int algorithmFlags, | ||
| std::function<TAlgorithm()> gen_func); | ||
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| private: | ||
| std::unordered_map<int64_t, TAlgorithm> hash_; | ||
| int search_times_; | ||
| }; | ||
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| template <typename TAlgorithm> | ||
| TAlgorithm framework::AlgorithmsCache<TAlgorithm>::GetAlgorithm( | ||
| const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2, | ||
| const std::vector<int>& strides, const std::vector<int>& paddings, | ||
| const std::vector<int>& dilations, int algorithmFlags, | ||
| std::function<TAlgorithm()> gen_func) { | ||
| int64_t seed = 0; | ||
| // Hash all of the inputs, use to try and look up a previously | ||
| // discovered algorithm, or fall back to generating a new one. | ||
| std::hash<int64_t> hashFn; | ||
| // do hash like boost | ||
| // https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x | ||
| for (const auto num : dims1) { | ||
| seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2); | ||
| } | ||
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| for (const auto num : dims2) { | ||
| seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2) + 1; | ||
| } | ||
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| for (const auto num : strides) { | ||
| seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) + | ||
| (seed >> 2) + 2; | ||
| } | ||
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| for (const auto num : paddings) { | ||
| seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) + | ||
| (seed >> 2) + 3; | ||
| } | ||
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| for (const auto num : dilations) { | ||
| seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) + | ||
| (seed >> 2) + 4; | ||
| } | ||
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| seed ^= hashFn(static_cast<int64_t>(algorithmFlags)) + 0x9e3779b9 + | ||
| (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()) { | ||
| TAlgorithm value = gen_func(); | ||
| hash_[seed] = value; | ||
| } | ||
| return hash_[seed]; | ||
| } | ||
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| template <typename TAlgorithm> | ||
| TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm( | ||
| int64_t area, int search_times, int algorithmFlags, | ||
| std::function<TAlgorithm()> gen_func) { | ||
| if (hash_.find(area) != hash_.end()) { | ||
| return hash_[area]; | ||
| } | ||
| if (search_times_ < search_times) { | ||
| auto algo = gen_func(); | ||
| hash_[area] = algo; | ||
| ++search_times_; | ||
| return algo; | ||
| } | ||
| TAlgorithm algo; | ||
| int64_t min = static_cast<uint64_t>(INT_MAX); | ||
| for (const auto& m : hash_) { | ||
| if (m.first < min) { | ||
| min = m.first; | ||
| algo = m.second; | ||
| } | ||
| } | ||
| return algo; | ||
| } | ||
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| } // namespace framework | ||
| } // namespace paddle |
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