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212 lines
6.8 KiB
212 lines
6.8 KiB
/* Copyright (c) 2017 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 <mkldnn.h>
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#include <algorithm>
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/platform/place.h"
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namespace paddle {
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#ifdef PADDLE_WITH_MKLDNN
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using MKLDNNMemoryFormat = mkldnn::memory::format;
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#endif
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namespace platform {
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using MKLDNNStream = mkldnn::stream;
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using MKLDNNEngine = mkldnn::engine;
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using MKLDNNMemory = mkldnn::memory;
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using MKLDNNMemoryDescriptor = mkldnn::memory::desc;
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using MKLDNNPrimitive = mkldnn::primitive;
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using MKLDNNPrimitiveDesc = mkldnn::handle<mkldnn_primitive_desc_t>;
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typedef std::unique_ptr<MKLDNNStream> MKLDNNStreamPtr;
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typedef std::unique_ptr<MKLDNNEngine> MKLDNNEnginePtr;
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typedef std::unique_ptr<MKLDNNMemory> MKLDNNMemoryPtr;
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typedef std::unique_ptr<MKLDNNPrimitive> MKLDNNPrimitivePtr;
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typedef std::unique_ptr<MKLDNNPrimitiveDesc> MKLDNNPrimitiveDescPtr;
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template <typename Type>
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void* to_void_cast(const Type* t) {
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return static_cast<void*>(const_cast<Type*>(t));
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}
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template <typename Type>
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void* to_void_reinterpret_cast(const Type* t) {
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return reinterpret_cast<void*>(const_cast<Type*>(t));
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}
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template <class Type>
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using tf_desc = typename Type::desc;
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template <class Type>
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using tf_pd = typename Type::primitive_desc;
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template <typename Type, typename Engine, typename... Args>
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std::shared_ptr<tf_pd<Type>> MKLDNNFwdPrimitiveDesc(const Engine& e,
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Args&&... args) {
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auto desc = tf_desc<Type>(mkldnn::prop_kind::forward, (args)...);
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auto pd = new tf_pd<Type>(desc, e);
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return std::shared_ptr<tf_pd<Type>>(pd);
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}
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template <typename Type, typename Engine, typename Primitive, typename... Args>
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tf_pd<Type> MKLDNNBwdPrimitiveDesc(const Engine& e, const Primitive& p,
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Args&&... args) {
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auto desc = tf_desc<Type>(args...);
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return tf_pd<Type>(desc, e, p);
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}
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inline mkldnn::memory::desc MKLDNNMemDesc(const std::vector<int>& dims,
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mkldnn::memory::data_type data_type,
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MKLDNNMemoryFormat format) {
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mkldnn::memory::dims tz = dims;
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return mkldnn::memory::desc({tz}, data_type, format);
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}
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inline bool CanMKLDNNBeUsed(const framework::ExecutionContext& ctx) {
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bool use_mkldnn = ctx.Attr<bool>("use_mkldnn");
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return use_mkldnn && platform::is_cpu_place(ctx.GetPlace());
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}
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template <typename Type>
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mkldnn::memory::data_type MKLDNNGetDataType() {
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return mkldnn::memory::data_type::data_undef;
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}
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template <>
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inline mkldnn::memory::data_type MKLDNNGetDataType<float>() {
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return mkldnn::memory::data_type::f32;
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}
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template <>
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inline mkldnn::memory::data_type MKLDNNGetDataType<int32_t>() {
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return mkldnn::memory::data_type::s32;
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}
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template <>
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inline mkldnn::memory::data_type MKLDNNGetDataType<int8_t>() {
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return mkldnn::memory::data_type::s8;
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}
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template <>
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inline mkldnn::memory::data_type MKLDNNGetDataType<uint8_t>() {
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return mkldnn::memory::data_type::u8;
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}
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inline void Reorder(const mkldnn::memory& src, const mkldnn::memory& dst) {
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auto reorder_prim = mkldnn::reorder(src, dst);
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std::vector<mkldnn::primitive> pipeline;
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pipeline.push_back(reorder_prim);
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mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
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}
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inline MKLDNNMemoryFormat GetMKLDNNFormat(const mkldnn::memory memory) {
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return static_cast<MKLDNNMemoryFormat>(
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memory.get_primitive_desc().desc().data.format);
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}
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inline MKLDNNMemoryFormat GetMKLDNNFormat(
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const mkldnn::sum::primitive_desc& memory) {
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return static_cast<MKLDNNMemoryFormat>(
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memory.dst_primitive_desc().desc().data.format);
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}
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inline MKLDNNMemoryFormat MKLDNNFormatForSize(size_t dims_size,
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MKLDNNMemoryFormat data_format) {
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if (dims_size == 1) {
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return MKLDNNMemoryFormat::x;
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} else if (dims_size == 2) {
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return MKLDNNMemoryFormat::nc;
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} else if (dims_size == 3) {
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if (data_format == MKLDNNMemoryFormat::nchw) {
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return MKLDNNMemoryFormat::ncw;
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} else if (data_format == MKLDNNMemoryFormat::nhwc) {
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return MKLDNNMemoryFormat::nwc;
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}
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} else if (dims_size == 4) {
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if (data_format == MKLDNNMemoryFormat::goihw) {
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return MKLDNNMemoryFormat::oihw;
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}
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} else if (dims_size == 5) {
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if (data_format == MKLDNNMemoryFormat::goidhw) {
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return MKLDNNMemoryFormat::oidhw;
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}
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if (data_format == MKLDNNMemoryFormat::nchw) {
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return MKLDNNMemoryFormat::ncdhw;
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} else if (data_format == MKLDNNMemoryFormat::nhwc) {
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return MKLDNNMemoryFormat::ndhwc;
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}
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}
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return data_format;
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}
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inline MKLDNNMemoryFormat data_format_to_memory_format(
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const std::string& data_format) {
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switch (framework::StringToDataLayout(data_format)) {
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case framework::DataLayout::kNHWC:
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return MKLDNNMemoryFormat::nhwc;
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case framework::DataLayout::kNCHW:
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return MKLDNNMemoryFormat::nchw;
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default:
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return MKLDNNMemoryFormat::any;
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}
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}
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inline MKLDNNMemoryFormat StringToMKLDNNFormat(std::string* format) {
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std::transform(format->begin(), format->end(), format->begin(), ::tolower);
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if (!format->compare("nchw")) {
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return MKLDNNMemoryFormat::nchw;
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} else if (!format->compare("nchw16c")) {
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return MKLDNNMemoryFormat::nChw16c;
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} else if (!format->compare("nchw8c")) {
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return MKLDNNMemoryFormat::nChw8c;
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} else if (!format->compare("nhwc")) {
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return MKLDNNMemoryFormat::nhwc;
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} else {
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return MKLDNNMemoryFormat::any;
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}
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}
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inline std::string ThreadIDasStr(void) {
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return std::to_string(
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std::hash<std::thread::id>()(std::this_thread::get_id()));
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}
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inline std::string dims2str(const mkldnn::memory::dims& operand_dims) {
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std::string dstr = "";
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for (size_t i = 0; i < operand_dims.size(); ++i) {
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dstr += std::to_string(operand_dims[i]) + "-";
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}
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return dstr;
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}
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inline void AppendKey(std::string* key, const std::string& s) {
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key->append(s);
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}
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inline std::string GetHash(const mkldnn::memory::dims& operand_dims,
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const std::string& suffix) {
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return dims2str(operand_dims) + suffix;
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}
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inline void AppendKeyDims(std::string* key, const mkldnn::memory::dims& dims) {
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for (unsigned int i = 0; i < dims.size(); i++) {
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AppendKey(key, std::to_string(dims[i]));
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}
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}
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} // namespace platform
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} // namespace paddle
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