mul_op.cc 8.0 KB
Newer Older
1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
2

3 4 5
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
6

7
    http://www.apache.org/licenses/LICENSE-2.0
8

9 10 11 12 13
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. */
14

Y
Yi Wang 已提交
15
#include "paddle/fluid/operators/mul_op.h"
16
#include <string>
17
#include <vector>
18

19 20 21 22
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif

23 24 25
namespace paddle {
namespace operators {

26
using framework::OpKernelType;
D
dongzhihong 已提交
27 28
using framework::Tensor;

29
class MulOp : public framework::OperatorWithKernel {
Y
Yu Yang 已提交
30
 public:
31 32 33
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
Q
Qiao Longfei 已提交
34 35 36 37 38 39 40
    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MulOp should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of MulOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Out"),
                   "Output(Out) of MulOp should not be null.");

    auto x_dims = ctx->GetInputDim("X");
    auto y_dims = ctx->GetInputDim("Y");
Y
Yu Yang 已提交
41

Q
Qiao Longfei 已提交
42 43
    int x_num_col_dims = ctx->Attrs().Get<int>("x_num_col_dims");
    int y_num_col_dims = ctx->Attrs().Get<int>("y_num_col_dims");
F
WIP  
fengjiayi 已提交
44

Y
Yu Yang 已提交
45 46 47 48
    VLOG(3) << "mul operator x.shape=" << x_dims << " y.shape=" << y_dims
            << " x_num_col_dims=" << x_num_col_dims
            << " y_num_col_dims=" << y_num_col_dims;

49 50 51 52 53 54 55 56
    PADDLE_ENFORCE_GT(
        x_dims.size(), x_num_col_dims,
        "The input tensor X's rank of MulOp should be larger than "
        "x_num_col_dims.");
    PADDLE_ENFORCE_GT(
        y_dims.size(), y_num_col_dims,
        "The input tensor Y's rank of MulOp should be larger than "
        "y_num_col_dims.");
57

F
fengjiayi 已提交
58 59
    auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
    auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
60

Y
Yan Chunwei 已提交
61
    PADDLE_ENFORCE_EQ(
62
        x_mat_dims[1], y_mat_dims[0],
63
        "First matrix's width must be equal with second matrix's height.");
Y
Yu Yang 已提交
64 65 66 67 68 69 70 71 72 73 74 75 76
    std::vector<int64_t> output_dims;
    output_dims.reserve(
        static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));

    for (int i = 0; i < x_num_col_dims; ++i) {
      output_dims.push_back(x_dims[i]);
    }

    for (int i = y_num_col_dims; i < y_dims.size(); ++i) {
      output_dims.push_back(y_dims[i]);
    }

    ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
Q
Qiao Longfei 已提交
77
    ctx->ShareLoD("X", /*->*/ "Out");
78
  }
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94

 private:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    framework::LibraryType library{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_MKLDNN
    if (library == framework::LibraryType::kPlain &&
        platform::CanMKLDNNBeUsed(ctx)) {
      library = framework::LibraryType::kMKLDNN;
    }
#endif
    framework::DataLayout layout{framework::DataLayout::kAnyLayout};
    return framework::OpKernelType(
        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
        layout, library);
  }
95 96
};

D
dongzhihong 已提交
97
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
98
 public:
99
  MulOpMaker(OpProto* proto, OpAttrChecker* op_checker)
100
      : OpProtoAndCheckerMaker(proto, op_checker) {
C
caoying03 已提交
101 102 103
    AddInput("X", "(Tensor), The first input tensor of mul op.");
    AddInput("Y", "(Tensor), The second input tensor of mul op.");
    AddOutput("Out", "(Tensor), The output tensor of mul op.");
F
WIP  
fengjiayi 已提交
104
    AddAttr<int>(
F
fengjiayi 已提交
105
        "x_num_col_dims",
C
caoying03 已提交
106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
        R"DOC((int, default 1), The mul_op can take tensors with more than two
              dimensions as its inputs. If the input $X$ is a tensor with more
              than two dimensions, $X$ will be flattened into a two-dimensional
              matrix first. The flattening rule is: the first `num_col_dims`
              will be flattened to form the first dimension of the final matrix
              (the height of the matrix), and the rest `rank(X) - num_col_dims`
              dimensions are flattened to form the second dimension of the final
              matrix (the width of the matrix). As a result, height of the
              flattened matrix is equal to the product of $X$'s first
              `x_num_col_dims` dimensions' sizes, and width of the flattened
              matrix is equal to the product of $X$'s last `rank(x) - num_col_dims`
              dimensions' size. For example, suppose $X$ is a 6-dimensional
              tensor with the shape [2, 3, 4, 5, 6], and `x_num_col_dims` = 3.
              Thus, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] =
              [24, 30].
F
fengjiayi 已提交
121
        )DOC")
F
WIP  
fengjiayi 已提交
122
        .SetDefault(1)
F
fengjiayi 已提交
123
        .EqualGreaterThan(1);
124 125 126
    AddAttr<bool>("use_mkldnn",
                  "(bool, default false) Only used in mkldnn kernel")
        .SetDefault(false);
F
WIP  
fengjiayi 已提交
127
    AddAttr<int>(
F
fengjiayi 已提交
128
        "y_num_col_dims",
C
caoying03 已提交
129 130 131 132
        R"DOC((int, default 1), The mul_op can take tensors with more than two,
              dimensions as its inputs. If the input $Y$ is a tensor with more
              than two dimensions, $Y$ will be flattened into a two-dimensional
              matrix first. The attribute `y_num_col_dims` determines how $Y$ is
C
caoying03 已提交
133
              flattened. See comments of `x_num_col_dims` for more details.
F
fengjiayi 已提交
134
        )DOC")
F
WIP  
fengjiayi 已提交
135
        .SetDefault(1)
F
fengjiayi 已提交
136
        .EqualGreaterThan(1);
137
    AddComment(R"DOC(
C
caoying03 已提交
138
Mul Operator.
K
kexinzhao 已提交
139

C
caoying03 已提交
140
This operator is used to perform matrix multiplication for input $X$ and $Y$.
141

142 143
The equation is:

C
caoying03 已提交
144
$$Out = X * Y$$
145

C
caoying03 已提交
146 147
Both the input $X$ and $Y$ can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input $X$.
K
kexinzhao 已提交
148

149 150 151 152
)DOC");
  }
};

153
class MulGradOp : public framework::OperatorWithKernel {
Y
Yu Yang 已提交
154 155 156
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

157
  void InferShape(framework::InferShapeContext* ctx) const override {
Q
Qiao Longfei 已提交
158 159 160 161 162 163 164
    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                   "Input(Out@GRAD) should not be null");
    auto x_dims = ctx->GetInputDim("X");
    auto y_dims = ctx->GetInputDim("Y");
    auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
165

Q
Qiao Longfei 已提交
166 167 168 169
    auto x_mat_dims = framework::flatten_to_2d(
        x_dims, ctx->Attrs().Get<int>("x_num_col_dims"));
    auto y_mat_dims = framework::flatten_to_2d(
        y_dims, ctx->Attrs().Get<int>("y_num_col_dims"));
170

Q
Qiao Longfei 已提交
171 172 173 174 175 176 177 178 179
    auto x_grad_name = framework::GradVarName("X");
    auto y_grad_name = framework::GradVarName("Y");

    if (ctx->HasOutput(x_grad_name)) {
      ctx->SetOutputDim(x_grad_name, x_dims);
    }
    if (ctx->HasOutput(y_grad_name)) {
      ctx->SetOutputDim(y_grad_name, y_dims);
    }
D
dongzhihong 已提交
180
  }
181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196

 private:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    framework::LibraryType library{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_MKLDNN
    if (library == framework::LibraryType::kPlain &&
        platform::CanMKLDNNBeUsed(ctx)) {
      library = framework::LibraryType::kMKLDNN;
    }
#endif
    framework::DataLayout layout{framework::DataLayout::kAnyLayout};
    return framework::OpKernelType(
        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
        layout, library);
  }
D
dongzhihong 已提交
197 198
};

199 200 201
}  // namespace operators
}  // namespace paddle

D
dongzhihong 已提交
202
namespace ops = paddle::operators;
Y
Yang Yang 已提交
203
REGISTER_OPERATOR(mul, ops::MulOp, ops::MulOpMaker,
204 205
                  paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(mul_grad, ops::MulGradOp);
Q
QI JUN 已提交
206
REGISTER_OP_CPU_KERNEL(
D
dzhwinter 已提交
207 208
    mul, ops::MulKernel<paddle::platform::CPUDeviceContext, float>,
    ops::MulKernel<paddle::platform::CPUDeviceContext, double>);
Q
QI JUN 已提交
209
REGISTER_OP_CPU_KERNEL(
D
dzhwinter 已提交
210 211
    mul_grad, ops::MulGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::MulGradKernel<paddle::platform::CPUDeviceContext, double>);