mklml.h 3.3 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
/* 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. */

#pragma once

#include <mkl.h>
#include <mutex>  // NOLINT
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
D
dzhwinter 已提交
20
#include "paddle/fluid/platform/port.h"
21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

namespace paddle {
namespace platform {
namespace dynload {

extern std::once_flag mklml_dso_flag;
extern void* mklml_dso_handle;

/**
 * The following macro definition can generate structs
 * (for each function) to dynamic load mklml routine
 * via operator overloading.
 */
#define DYNAMIC_LOAD_MKLML_WRAP(__name)                                    \
  struct DynLoad__##__name {                                               \
    template <typename... Args>                                            \
    auto operator()(Args... args) -> decltype(__name(args...)) {           \
      using mklmlFunc = decltype(&::__name);                               \
      std::call_once(mklml_dso_flag, []() {                                \
        mklml_dso_handle = paddle::platform::dynload::GetMKLMLDsoHandle(); \
      });                                                                  \
      static void* p_##_name = dlsym(mklml_dso_handle, #__name);           \
      return reinterpret_cast<mklmlFunc>(p_##_name)(args...);              \
    }                                                                      \
  };                                                                       \
  extern DynLoad__##__name __name

#define DECLARE_DYNAMIC_LOAD_MKLML_WRAP(__name) DYNAMIC_LOAD_MKLML_WRAP(__name)

#define MKLML_ROUTINE_EACH(__macro) \
  __macro(cblas_sgemm);             \
  __macro(cblas_dgemm);             \
T
tensor-tang 已提交
53
  __macro(cblas_saxpy);             \
54
  __macro(cblas_daxpy);             \
T
tensor-tang 已提交
55
  __macro(cblas_scopy);             \
56
  __macro(cblas_dcopy);             \
T
tensor-tang 已提交
57
  __macro(cblas_sgemv);             \
58
  __macro(cblas_dgemv);             \
T
tensor-tang 已提交
59 60
  __macro(cblas_sgemm_alloc);       \
  __macro(cblas_dgemm_alloc);       \
T
tensor-tang 已提交
61
  __macro(cblas_sgemm_pack);        \
T
tensor-tang 已提交
62
  __macro(cblas_dgemm_pack);        \
T
tensor-tang 已提交
63
  __macro(cblas_sgemm_compute);     \
T
tensor-tang 已提交
64
  __macro(cblas_dgemm_compute);     \
T
tensor-tang 已提交
65
  __macro(cblas_sgemm_free);        \
T
tensor-tang 已提交
66
  __macro(cblas_dgemm_free);        \
T
tensor-tang 已提交
67 68
  __macro(cblas_sgemm_batch);       \
  __macro(cblas_dgemm_batch);       \
T
tensor-tang 已提交
69 70
  __macro(cblas_sdot);              \
  __macro(cblas_ddot);              \
T
tensor-tang 已提交
71 72
  __macro(cblas_sscal);             \
  __macro(cblas_dscal);             \
T
tensor-tang 已提交
73 74 75 76
  __macro(vsAdd);                   \
  __macro(vdAdd);                   \
  __macro(vsMul);                   \
  __macro(vdMul);                   \
T
tensor-tang 已提交
77 78
  __macro(vsExp);                   \
  __macro(vdExp);                   \
79 80 81 82 83 84 85 86 87
  __macro(MKL_Set_Num_Threads)

MKLML_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_MKLML_WRAP);

#undef DYNAMIC_LOAD_MKLML_WRAP

}  // namespace dynload
}  // namespace platform
}  // namespace paddle