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145 lines
4.2 KiB
145 lines
4.2 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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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#include "paddle/fluid/operators/jit/helper.h"
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#include <algorithm> // tolower
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#include <numeric>
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#include <string>
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace operators {
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namespace jit {
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std::map<size_t, std::shared_ptr<void>>& GetFuncCacheMap() {
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static thread_local std::map<size_t, std::shared_ptr<void>> g_func_cache_map;
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return g_func_cache_map;
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}
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#define ONE_CASE(key) \
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case key: \
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return #key
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const char* to_string(KernelType kt) {
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switch (kt) {
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ONE_CASE(kNone);
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ONE_CASE(kVMul);
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ONE_CASE(kVAdd);
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ONE_CASE(kVAddRelu);
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ONE_CASE(kVSub);
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ONE_CASE(kVScal);
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ONE_CASE(kStrideScal);
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ONE_CASE(kVAddBias);
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ONE_CASE(kVRelu);
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ONE_CASE(kVBroadcast);
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ONE_CASE(kVCopy);
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ONE_CASE(kVIdentity);
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ONE_CASE(kVExp);
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ONE_CASE(kVSquare);
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ONE_CASE(kVSigmoid);
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ONE_CASE(kVTanh);
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ONE_CASE(kLSTMCtHt);
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ONE_CASE(kLSTMC1H1);
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ONE_CASE(kGRUH1);
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ONE_CASE(kGRUHtPart1);
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ONE_CASE(kGRUHtPart2);
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ONE_CASE(kCRFDecoding);
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ONE_CASE(kLayerNorm);
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ONE_CASE(kNCHW16CMulNC);
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ONE_CASE(kSeqPool);
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ONE_CASE(kMatMul);
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ONE_CASE(kHMax);
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ONE_CASE(kHSum);
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ONE_CASE(kStrideASum);
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ONE_CASE(kSoftmax);
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ONE_CASE(kEmbSeqPool);
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ONE_CASE(kSgd);
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default:
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PADDLE_THROW("Not support type: %d, or forget to add it.", kt);
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return "NOT JITKernel";
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}
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return nullptr;
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}
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const char* to_string(SeqPoolType tp) {
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switch (tp) {
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ONE_CASE(kNonePoolType);
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ONE_CASE(kSum);
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ONE_CASE(kAvg);
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ONE_CASE(kSqrt);
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default:
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PADDLE_THROW("Not support type: %d, or forget to add it.", tp);
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return "NOT PoolType";
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}
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return nullptr;
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}
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#undef ONE_CASE
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KernelType to_kerneltype(const std::string& act) {
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std::string lower = act;
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std::transform(lower.begin(), lower.end(), lower.begin(), ::tolower);
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if (lower == "relu" || lower == "vrelu") {
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return kVRelu;
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} else if (lower == "identity" || lower == "videntity" || lower == "") {
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return kVIdentity;
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} else if (lower == "exp" || lower == "vexp") {
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return kVExp;
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} else if (lower == "sigmoid" || lower == "vsigmoid") {
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return kVSigmoid;
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} else if (lower == "tanh" || lower == "vtanh") {
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return kVTanh;
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}
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PADDLE_THROW("Not support type: %s, or forget to add this case", act);
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return kNone;
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}
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template <>
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void pack_weights<float>(const float* src, float* dst, int n, int k) {
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int block, rest;
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const auto groups = packed_groups(n, k, &block, &rest);
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std::for_each(groups.begin(), groups.end(), [&](int i) {
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PADDLE_ENFORCE_GT(i, 0, "each element of groups should be larger than 0.");
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});
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int sum = std::accumulate(groups.begin(), groups.end(), 0);
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std::memset(dst, 0, k * sum * block * sizeof(float));
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PADDLE_ENFORCE_GE(sum * block, n,
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"The packed n should be equal to or larger than n");
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const int block_len = sizeof(float) * block;
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int n_offset = 0;
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for (size_t g = 0; g < groups.size(); ++g) {
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const float* from = src + n_offset;
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for (int j = 0; j < k; ++j) {
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size_t copy_sz = groups[g] * block_len;
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if (g == groups.size() - 1 && rest != 0) {
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copy_sz = (groups[g] - 1) * block_len + rest * sizeof(float);
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}
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std::memcpy(dst, from + j * n, copy_sz);
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dst += groups[g] * block;
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}
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n_offset += groups[g] * block;
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}
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}
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template <typename T>
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typename std::enable_if<!std::is_same<T, float>::value>::type pack_weights(
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const T* src, T* dst, int n, int k) {
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PADDLE_THROW("Only support pack with float type.");
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}
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} // namespace jit
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} // namespace operators
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} // namespace paddle
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