mozga-intel
7 years ago
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/* Copyright (c) 2018 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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#include "paddle/fluid/operators/fc_op.h"
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namespace paddle {
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namespace operators {
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void FCOp::InferShape(framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("Input"),
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"X(Input) of Fully Connected should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Out(Output) of Fully Connected should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("W"),
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"W(Input) of Fully Connected should not be null.");
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auto in_dims = ctx->GetInputDim("Input");
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auto w_dims = ctx->GetInputDim("W");
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std::vector<int64_t> output_shape({in_dims[0], w_dims[1]});
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PADDLE_ENFORCE(in_dims.size() == 4,
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"Fully Connected input should be 4-D tensor.");
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PADDLE_ENFORCE(w_dims.size() == 2,
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"Fully Connected input should be 2-D tensor.");
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ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
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ctx->ShareLoD("Input", "Out");
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}
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framework::OpKernelType FCOp::GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const {
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framework::LibraryType library{framework::LibraryType::kMKLDNN};
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framework::DataLayout layout{framework::DataLayout::kAnyLayout};
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
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layout, library);
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}
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void FCOpGrad::InferShape(framework::InferShapeContext* ctx) const {
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auto in_dims = ctx->GetInputDim("Input");
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auto w_dims = ctx->GetInputDim("W");
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if (ctx->HasOutput(framework::GradVarName("Input"))) {
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ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
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}
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if (ctx->HasOutput(framework::GradVarName("W"))) {
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ctx->SetOutputDim(framework::GradVarName("W"), w_dims);
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}
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}
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framework::OpKernelType FCOpGrad::GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const {
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framework::LibraryType library{framework::LibraryType::kMKLDNN};
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framework::DataLayout layout{framework::DataLayout::kAnyLayout};
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
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layout, library);
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}
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FCOpMaker::FCOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"Input",
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"(Tensor) The input tensor of fully connected operator. "
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"The format of input tensor is NCHW, where N is batch size, C is the "
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"number of channels, H is the height of the feature, "
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"and W is the width of the feature.");
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AddInput("W", "(Tensor), The second input tensor of fc op.");
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AddOutput("Out",
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"(Tensor) The output tensor of fully connected operator. "
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"The format of output tensor is also NCHW, "
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"where N is batch size, C is the number of channels, "
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"H is the height of the feature, "
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"and W is the width of the feature.");
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AddAttr<bool>("use_mkldnn",
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"(bool, default false) Only used in mkldnn kernel")
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.SetDefault(false);
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AddAttr<bool>("bias_attr", "(bool, default false) Only used in mkldnn kernel")
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.SetDefault(false);
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AddComment(R"DOC(
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Fully Connected Operator.
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The fully connected operation calculates the output based on the input, weights and bias attribute.
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The size of each dimension of the parameters checked in the infer-shape.
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Input(Input) is NCHW or NC format. Where N is batch size, C is the number of channels,
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H is the height of the feature, and W is the width of the feature.
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Weights(W) is OIHW or OI format. Where H is the height of the feature, W is the width of the feature,
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O is the height of output, and I is the number of channels.
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Output(Out) is NC format. Where N is batch size, and C is the number of channels.
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The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
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Additional parametrs are use_mkldnn and bias_attr.
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The input(X) size and output(Out) size may be diffrent.
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Example:
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Input:
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Input shape: $(N, C_{in}, H_{in}, W_{in})$
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Weight shape: $(O_{out}, I_{in}, H_{in}, W_{in})$
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Bias shape: $(O_{out})$
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Output:
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Output shape: $(N, C_{out})$
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)DOC");
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}
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} // namespace operators
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} // namespace paddle
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REGISTER_OP(fc, paddle::operators::FCOp, paddle::operators::FCOpMaker, fc_grad,
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paddle::operators::FCOpGrad);
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