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121 lines
4.1 KiB
121 lines
4.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/nce_op.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class NCEOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"));
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PADDLE_ENFORCE(ctx->HasInput("Label"));
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PADDLE_ENFORCE(ctx->HasInput("W"));
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PADDLE_ENFORCE(ctx->HasOutput("Out"));
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PADDLE_ENFORCE(ctx->HasOutput("SampleLogits"));
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PADDLE_ENFORCE(ctx->HasOutput("SampleLabels"));
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0]);
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if (ctx->HasInput("B")) {
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("W")[0], ctx->GetInputDim("B")[0]);
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}
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int num_sampled_classes = ctx->Attrs().Get<int>("num_sampled_classes");
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int num_classes = ctx->Attrs().Get<int>("num_classes");
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PADDLE_ENFORCE_EQ(num_classes, ctx->GetInputDim("W")[0]);
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PADDLE_ENFORCE_LT(num_sampled_classes, num_classes);
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// set dims of output(Out)
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std::vector<int64_t> out_dims(1);
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out_dims.push_back(x_dims[0]);
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ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
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// set dims of output(SampleOut)
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std::vector<int64_t> sample_out_dims(2);
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sample_out_dims.push_back(x_dims[0]);
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sample_out_dims.push_back(num_sampled_classes + 1);
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ctx->SetOutputDim("SampleLogits", framework::make_ddim(sample_out_dims));
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ctx->SetOutputDim("SampleLabels", framework::make_ddim(sample_out_dims));
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}
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};
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class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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NCEOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "");
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AddInput("Label", "");
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AddInput("W", "");
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AddInput("B", "");
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AddInput("SampleWeight", "");
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AddOutput("Out", "");
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AddOutput("SampleLogits", "");
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AddOutput("SampleLabels", "");
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AddAttr<int>("num_classes", "");
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AddAttr<int>("num_sampled_classes", "").SetDefault(10);
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AddComment(R"DOC(
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Expand input(X) according to LOD of input(Y).
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)DOC");
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}
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};
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class NCEOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"));
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PADDLE_ENFORCE(ctx->HasInput("W"));
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PADDLE_ENFORCE(ctx->HasInput("Out"));
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"The input(Out@GRAD) should not be null");
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auto x_dims = ctx->GetInputDim("X");
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auto x_grad_name = framework::GradVarName("X");
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if (ctx->HasOutput(x_grad_name)) {
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ctx->SetOutputDim(x_grad_name, x_dims);
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}
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auto w_dims = ctx->GetInputDim("W");
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auto w_grad_name = framework::GradVarName("W");
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if (ctx->HasOutput(w_grad_name)) {
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ctx->SetOutputDim(w_grad_name, w_dims);
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}
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auto bias_grad_name = framework::GradVarName("B");
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if (ctx->HasOutput(bias_grad_name)) {
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auto bias_dims = ctx->GetInputDim("B");
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ctx->SetOutputDim(bias_grad_name, bias_dims);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(nce, ops::NCEOp, ops::NCEOpMaker, nce_grad, ops::NCEOpGrad);
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REGISTER_OP_CPU_KERNEL(nce, ops::NCEKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(nce_grad,
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ops::NCEGradKernel<paddle::platform::CPUPlace, float>);
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