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103 lines
3.9 KiB
103 lines
3.9 KiB
/* 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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#include <vector>
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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() == 2 || in_dims.size() == 4,
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"Fully Connected input should be 2-D or 4-D tensor.");
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PADDLE_ENFORCE(w_dims.size() == 2 || w_dims.size() == 4,
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"Fully Connected input should be 2-D or 4-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::kMKLDNN};
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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::kMKLDNN};
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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 FCOpMaker::Make() {
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AddInput("Input", "(Tensor) The input tensor of fully connected operator. ");
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AddInput("W", "(Tensor), The second input tensor of fc op.");
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AddOutput("Out", "(Tensor) The output tensor of fully connected operator. ");
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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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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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The fully connected layer only supports MKLDNN version
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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_OPERATOR(fc, paddle::operators::FCOp, paddle::operators::FCOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(fc_grad, paddle::operators::FCOpGrad);
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