mkldnn_helper.h 8.7 KB
Newer Older
1
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved.
T
tensor-tang 已提交
2 3 4 5 6 7 8 9 10 11 12 13 14 15

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. */
#pragma once

16
#include <mkldnn.h>
17
#include <vector>
18
#include "paddle/fluid/framework/operator.h"
M
mozga-intel 已提交
19
#include "paddle/fluid/platform/place.h"
20

T
tensor-tang 已提交
21 22 23 24 25 26
namespace paddle {
namespace platform {

using MKLDNNStream = mkldnn::stream;
using MKLDNNEngine = mkldnn::engine;
using MKLDNNMemory = mkldnn::memory;
27
using MKLDNNMemoryDescriptor = mkldnn::memory::desc;
T
tensor-tang 已提交
28 29 30
using MKLDNNPrimitive = mkldnn::primitive;
using MKLDNNPrimitiveDesc = mkldnn::handle<mkldnn_primitive_desc_t>;

31 32 33 34 35
typedef std::unique_ptr<MKLDNNStream> MKLDNNStreamPtr;
typedef std::unique_ptr<MKLDNNEngine> MKLDNNEnginePtr;
typedef std::unique_ptr<MKLDNNMemory> MKLDNNMemoryPtr;
typedef std::unique_ptr<MKLDNNPrimitive> MKLDNNPrimitivePtr;
typedef std::unique_ptr<MKLDNNPrimitiveDesc> MKLDNNPrimitiveDescPtr;
T
tensor-tang 已提交
36

37 38 39 40 41
template <typename Type>
void* to_void_cast(const Type* t) {
  return static_cast<void*>(const_cast<Type*>(t));
}

K
Krzysztof Binias 已提交
42 43 44 45 46
template <typename Type>
void* to_void_reinterpret_cast(const Type* t) {
  return reinterpret_cast<void*>(const_cast<Type*>(t));
}

47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67
template <class Type>
using tf_desc = typename Type::desc;

template <class Type>
using tf_pd = typename Type::primitive_desc;

template <typename Type, typename Engine, typename... Args>
std::shared_ptr<tf_pd<Type>> MKLDNNFwdPrimitiveDesc(const Engine& e,
                                                    Args&&... args) {
  auto desc = tf_desc<Type>(mkldnn::prop_kind::forward, (args)...);
  auto pd = new tf_pd<Type>(desc, e);
  return std::shared_ptr<tf_pd<Type>>(pd);
}

template <typename Type, typename Engine, typename Primitive, typename... Args>
tf_pd<Type> MKLDNNBwdPrimitiveDesc(const Engine& e, const Primitive& p,
                                   Args&&... args) {
  auto desc = tf_desc<Type>(args...);
  return tf_pd<Type>(desc, e, p);
}

68 69 70 71 72 73 74 75 76 77 78 79
inline mkldnn::memory::desc MKLDNNMemDesc(const std::vector<int>& dims,
                                          mkldnn::memory::data_type data_type,
                                          mkldnn::memory::format format) {
  mkldnn::memory::dims tz = dims;
  return mkldnn::memory::desc({tz}, data_type, format);
}

inline bool CanMKLDNNBeUsed(const framework::ExecutionContext& ctx) {
  bool use_mkldnn = ctx.Attr<bool>("use_mkldnn");
  return use_mkldnn && platform::is_cpu_place(ctx.GetPlace());
}

80 81 82 83 84 85 86 87 88 89
template <typename Type>
mkldnn::memory::data_type MKLDNNGetDataType() {
  return mkldnn::memory::data_undef;
}

template <>
inline mkldnn::memory::data_type MKLDNNGetDataType<float>() {
  return mkldnn::memory::f32;
}

M
mozga-intel 已提交
90 91 92 93 94 95 96 97 98 99 100 101
inline void Reorder(const mkldnn::memory& src, const mkldnn::memory& dst) {
  auto reorder_prim = mkldnn::reorder(src, dst);
  std::vector<mkldnn::primitive> pipeline;
  pipeline.push_back(reorder_prim);
  mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}

inline mkldnn::memory::format GetMKLDNNFormat(const mkldnn::memory memory) {
  return static_cast<mkldnn::memory::format>(
      memory.get_primitive_desc().desc().data.format);
}

102 103 104 105 106 107
inline mkldnn::memory::format GetMKLDNNFormat(
    const mkldnn::sum::primitive_desc& memory) {
  return static_cast<mkldnn::memory::format>(
      memory.dst_primitive_desc().desc().data.format);
}

J
Jacek Czaja 已提交
108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230
class MKLDNNHandler {
 public:
  MKLDNNHandler(const MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
                const std::string& base_key)
      : dev_ctx_(dev_ctx),
        engine_(engine),
        key_(base_key),
        is_reusing_(false) {}

  std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
      const mkldnn::memory::desc& md, void* ptr) {
    return this->AcquireMemory(md, ptr, "@user_src_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireWeightsMemory(
      const mkldnn::memory::desc& md, void* ptr) {
    return this->AcquireMemory(md, ptr, "@user_weights_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireDstMemory(
      const mkldnn::memory::desc& md, void* ptr) {
    return this->AcquireMemory(md, ptr, "@user_dst_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireDiffDstMemory(
      const mkldnn::memory::desc& md, void* ptr) {
    return this->AcquireMemory(md, ptr, "@user_diff_dst_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemory(
      const mkldnn::memory::desc& md, void* ptr) {
    return this->AcquireMemory(md, ptr, "@user_diff_src_mem_p");
  }

  std::shared_ptr<mkldnn::memory> AcquireMemoryFromPrimitive(
      mkldnn::memory::primitive_desc mdp, void* ptr,
      const std::string& suffix) {
    auto local_key = key_ + suffix;
    auto mem_p =
        std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
    PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false),
                   "Fail to find mem primitive in device context");
    if (mem_p == nullptr) {
      mem_p = std::make_shared<mkldnn::memory>(mdp, ptr);
      dev_ctx_.SetBlob(local_key, mem_p);
    } else {
      mem_p->set_data_handle(ptr);
      // Mark that reusing happenned. All primitives from operator instance
      // should be reused or none of them. So we check consistency
      is_reusing_ = true;
    }
    return mem_p;
  }

  std::shared_ptr<mkldnn::memory> AcquireMemory(const mkldnn::memory::desc& md,
                                                void* ptr,
                                                const std::string& suffix) {
    /*Generate key*/
    auto local_key = key_ + suffix;
    auto mem_p =
        std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
    PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false),
                   "Fail to find mem primitive in device context");
    if (mem_p == nullptr) {
      mem_p = std::make_shared<mkldnn::memory>(
          mkldnn::memory::primitive_desc{md, engine_}, ptr);
      dev_ctx_.SetBlob(local_key, mem_p);
    } else {
      mem_p->set_data_handle(ptr);
      // Mark that reusing happenned. All primitives from operator instance
      // should be reused or none of them. So we check consistency
      is_reusing_ = true;
    }
    return mem_p;
  }

  std::shared_ptr<mkldnn::memory> AcquireMemory(
      mkldnn::memory::primitive_desc& mpd,
      mkldnn::memory::primitive_desc& user_mpd,
      const std::shared_ptr<mkldnn::memory> user_memory_p,
      const std::string& suffix, std::vector<mkldnn::primitive>& pipeline) {
    // create reorder primitive if the input format is not the preferred one
    auto local_key = key_ + suffix;
    auto key_reorder_p = key_ + suffix + "reorder_p";

    auto target_memory_p =
        std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
    PADDLE_ENFORCE((target_memory_p != nullptr) || (is_reusing_ == false),
                   "Fail to find mem primitive in device context");
    if (target_memory_p == nullptr) {
      target_memory_p = user_memory_p;
      std::shared_ptr<mkldnn::primitive> reorder_p;
      if (mpd != user_mpd) {
        target_memory_p = std::make_shared<mkldnn::memory>(mpd);

        auto reorder_p =
            std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
        dev_ctx_.SetBlob(key_reorder_p, reorder_p);
        pipeline.push_back(*reorder_p);
      }
      dev_ctx_.SetBlob(local_key, target_memory_p);
    } else {
      // Make reorder if needed
      auto reorder_p = std::static_pointer_cast<mkldnn::reorder>(
          dev_ctx_.GetBlob(key_reorder_p));
      if (reorder_p != nullptr) {
        pipeline.push_back(*reorder_p);
      }
      is_reusing_ = true;
    }
    return target_memory_p;
  }

  static std::string GetHash(mkldnn::memory::dims& operand_dims,
                             const std::string& suffix) {
    auto dims2str = [](const mkldnn::memory::dims& operand_dims) {
      std::string dstr = "";
      for (size_t i = 0; i < operand_dims.size(); ++i) {
        dstr += std::to_string(operand_dims[i]) + "-";
      }
      return dstr;
    };
    return dims2str(operand_dims) + suffix;
231
  }
J
Jacek Czaja 已提交
232 233 234 235 236 237 238 239

 protected:
  const MKLDNNDeviceContext& dev_ctx_;
  mkldnn::engine engine_;
  std::string key_;
  bool is_reusing_;
};

240 241 242 243 244 245 246 247 248 249
inline mkldnn::memory::format MKLDNNFormatForSize(
    size_t dims_size, mkldnn::memory::format data_format) {
  if (dims_size == 1) {
    return mkldnn::memory::format::x;
  } else if (dims_size == 2) {
    return mkldnn::memory::format::nc;
  }
  return data_format;
}

T
tensor-tang 已提交
250 251
}  // namespace platform
}  // namespace paddle