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237 lines
9.2 KiB
237 lines
9.2 KiB
/* Copyright (c) 2016 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/addmm_op.h"
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#include <memory>
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#include <string>
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#include <unordered_map>
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#include <vector>
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#ifdef PADDLE_WITH_MKLDNN
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#include "paddle/fluid/platform/mkldnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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using framework::OpKernelType;
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using framework::Tensor;
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class AddMMOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
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platform::errors::NotFound(
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"Input(Input) of AddMMOp should not be null."));
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("X"), true,
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platform::errors::NotFound("Input(X) of AddMMOp should not be null."));
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("Y"), true,
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platform::errors::NotFound("Input(Y) of AddMMOp should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
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platform::errors::NotFound(
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"Output(Out) of AddMMOp should not be null."));
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auto input_dims = ctx->GetInputDim("Input");
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auto x_dims = ctx->GetInputDim("X");
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auto y_dims = ctx->GetInputDim("Y");
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auto ndim_input = input_dims.size();
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auto ndim_x = x_dims.size();
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auto ndim_y = y_dims.size();
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float alpha = ctx->Attrs().Get<float>("Alpha");
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float beta = ctx->Attrs().Get<float>("Beta");
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VLOG(3) << "addmm operator input.shape=" << input_dims
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<< " x.shape=" << x_dims << " y.shape=" << y_dims
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<< " beta=" << beta << " alpha=" << alpha
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<< " ndim_input=" << ndim_input << " ndim_x=" << ndim_x
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<< " ndim_y=" << ndim_y;
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PADDLE_ENFORCE_NE(framework::product(input_dims), 0,
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platform::errors::PreconditionNotMet(
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"The Input variable Input(%s) has not "
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"been initialized. You may need to confirm "
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"if you put exe.run(startup_program) "
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"after optimizer.minimize function.",
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ctx->Inputs("Input").front()));
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PADDLE_ENFORCE_NE(framework::product(x_dims), 0,
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platform::errors::PreconditionNotMet(
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"The Input variable X(%s) has not "
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"been initialized. You may need to confirm "
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"if you put exe.run(startup_program) "
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"after optimizer.minimize function.",
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ctx->Inputs("X").front()));
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PADDLE_ENFORCE_NE(framework::product(y_dims), 0,
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platform::errors::PreconditionNotMet(
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"The Input variable Y(%s) has not "
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"been initialized. You may need to confirm "
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"if you put exe.run(startup_program) "
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"after optimizer.minimize function.",
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ctx->Inputs("Y").front()));
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// dim check
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PADDLE_ENFORCE_EQ(ndim_input, 2,
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platform::errors::InvalidArgument(
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"The input tensor input's dimension must be 2. "
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"But received input's dimension = [%s].",
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ndim_input));
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PADDLE_ENFORCE_EQ(ndim_x, 2,
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platform::errors::InvalidArgument(
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"The input tensor x's dimension must be 2. "
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"But received x's dimension = [%s].",
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ndim_x));
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PADDLE_ENFORCE_EQ(ndim_y, 2,
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platform::errors::InvalidArgument(
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"The input tensor y's dimension must be 2. "
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"But received y's dimension = [%s].",
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ndim_y));
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std::vector<int64_t> output_dims;
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output_dims.push_back(x_dims[0]);
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output_dims.push_back(y_dims[1]);
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ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
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ctx->ShareLoD("Input", /*->*/ "Out");
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const {
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framework::LibraryType library = framework::LibraryType::kPlain;
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framework::DataLayout layout = framework::DataLayout::kAnyLayout;
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int customized_type_value =
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framework::OpKernelType::kDefaultCustomizedTypeValue;
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auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
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#ifdef PADDLE_WITH_MKLDNN
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if (library == framework::LibraryType::kPlain &&
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platform::CanMKLDNNBeUsed(ctx)) {
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library = framework::LibraryType::kMKLDNN;
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layout = framework::DataLayout::kMKLDNN;
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if (input_data_type == framework::DataTypeTrait<int8_t>::DataType() ||
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input_data_type == framework::DataTypeTrait<uint8_t>::DataType()) {
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customized_type_value = kMULMKLDNNINT8;
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}
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}
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#endif
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return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
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library, customized_type_value);
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}
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};
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class AddMMOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Input", "(Tensor), tensor to be added to the final result.");
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AddInput("X", "(Tensor), The first input tensor for mul.");
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AddInput("Y", "(Tensor), The second input tensor for mul.");
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AddOutput("Out", "(Tensor), The output tensor of addmm op.");
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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<float>("Alpha", "coefficient of x*y.").SetDefault(1.0f);
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AddAttr<float>("Beta", "coefficient of input.").SetDefault(1.0f);
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AddComment(R"DOC(
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AddMM Operator.
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This operator is used to perform matrix multiplication for input $x$ and $y$ with coefficient $alpha$.
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$input$ with coefficient $beta$ is added to the final result.
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The equation is:
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$$Out = alpha * x * y + beta * input$$
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$x$ and $y$ must be two-dimensional, and $input$ can be broadcastable.
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)DOC");
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}
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};
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class AddMMGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("Input"), true,
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platform::errors::NotFound("Input(Input) should not be null"));
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("X"), true,
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platform::errors::NotFound("Input(X) should not be null"));
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PADDLE_ENFORCE_EQ(
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ctx->HasInput("Y"), true,
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platform::errors::NotFound("Input(Y) should not be null"));
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PADDLE_ENFORCE_EQ(
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ctx->HasInput(framework::GradVarName("Out")), true,
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platform::errors::NotFound("Input(Out@GRAD) should not be null"));
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const auto& input_dims = ctx->GetInputDim("Input");
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const auto& x_dims = ctx->GetInputDim("X");
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const auto& y_dims = ctx->GetInputDim("Y");
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auto input_grad_name = framework::GradVarName("Input");
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auto x_grad_name = framework::GradVarName("X");
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auto y_grad_name = framework::GradVarName("Y");
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if (ctx->HasOutput(input_grad_name)) {
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ctx->SetOutputDim(input_grad_name, input_dims);
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}
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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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if (ctx->HasOutput(y_grad_name)) {
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ctx->SetOutputDim(y_grad_name, y_dims);
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}
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}
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};
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template <typename T>
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class AddMMOpGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> retv) const override {
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retv->SetType("addmm_grad");
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retv->SetInput("Input", this->Input("Input"));
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retv->SetInput("X", this->Input("X"));
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retv->SetInput("Y", this->Input("Y"));
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retv->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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retv->SetOutput(framework::GradVarName("Input"), this->InputGrad("Input"));
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retv->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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retv->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
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retv->SetAttrMap(this->Attrs());
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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_OPERATOR(addmm, ops::AddMMOp, ops::AddMMOpMaker,
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ops::AddMMOpGradMaker<paddle::framework::OpDesc>,
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ops::AddMMOpGradMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(addmm_grad, ops::AddMMGradOp);
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REGISTER_OP_CPU_KERNEL(
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addmm, ops::AddMMKernel<paddle::platform::CPUDeviceContext, float>,
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ops::AddMMKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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addmm_grad, ops::AddMMGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::AddMMGradKernel<paddle::platform::CPUDeviceContext, double>);
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