Rewrite feed/fetch op (#4815)
* Feed/Fetch op just plain operator, not a OpWithKernel * Do not register OpInfoMaker since Feed/Fetch will never be configured by users * Feed/Fetch op has empty gradient * Feed/Fetch op do not hard code `feed_variable`, `fetch_variable` as its input and output, make it as a plain Operator input/outputrevert-4814-Add_sequence_project_op
Yu Yang
7 years ago
committed by
GitHub
parent
440ad999b7
commit
4df6cf4d16
@ -0,0 +1,24 @@
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/* 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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#pragma once
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#include <vector>
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#include "paddle/framework/lod_tensor.h"
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namespace paddle {
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namespace framework {
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using FeedFetchType = LoDTensor;
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using FeedFetchList = std::vector<FeedFetchType>;
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} // namespace framework
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} // namespace paddle
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@ -1,59 +1,57 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/* 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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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 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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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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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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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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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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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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See the License for the specific language governing permissions and
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limitations under the License. */
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limitations under the License. */
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#include "paddle/operators/feed_op.h"
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#include "paddle/framework/feed_fetch_type.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/framework/operator.h"
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namespace paddle {
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namespace paddle {
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namespace operators {
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namespace operators {
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class FeedOp : public framework::OperatorBase {
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class FeedOp : 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->HasOutput("Out"), "Output should be not null.");
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auto& shape = ctx->Attrs().Get<std::vector<int>>("dims");
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std::vector<int64_t> shape_int64(shape.size(), 0);
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std::transform(shape.begin(), shape.end(), shape_int64.begin(),
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[](int a) { return static_cast<int64_t>(a); });
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ctx->SetOutputDim("Out", framework::make_ddim(shape_int64));
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// TODO(qijun): need to handle LodTensor later
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}
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framework::DataType IndicateDataType(
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const framework::ExecutionContext& ctx) const override {
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return static_cast<framework::DataType>(Attr<int>("dataType"));
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}
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};
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class FeedOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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public:
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FeedOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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FeedOp(const std::string &type, const framework::VariableNameMap &inputs,
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: OpProtoAndCheckerMaker(proto, op_checker) {
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const framework::VariableNameMap &outputs,
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AddAttr<int>("dataType", "output data type")
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const framework::AttributeMap &attrs)
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.SetDefault(framework::DataType::FP32);
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: OperatorBase(type, inputs, outputs, attrs) {}
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AddAttr<int>("col", "The col in global feed variable").SetDefault(0);
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void Run(const framework::Scope &scope,
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AddAttr<std::vector<int>>("dims", "The dimension of feed tensor.");
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const platform::DeviceContext &dev_ctx) const override {
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AddOutput("Out", "The output of feed op.");
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auto feed_var_name = Input("Input");
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AddComment(R"DOC(Feed data from global feed variable)DOC");
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auto *feed_var = scope.FindVar(feed_var_name);
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PADDLE_ENFORCE(feed_var != nullptr,
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"Cannot find feed_var in scope, feed_var_name is %s",
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feed_var_name);
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auto out_name = this->Output("Out");
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auto *out_var = scope.FindVar(out_name);
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PADDLE_ENFORCE(out_var != nullptr,
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"Cannot find out_var in scope, out_var_name is %s",
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out_name);
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auto col = Attr<int>("col");
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auto &feed_list = feed_var->Get<framework::FeedFetchList>();
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auto &feed_item = feed_list.at(static_cast<size_t>(col));
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auto *out_item = out_var->GetMutable<framework::FeedFetchType>();
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out_item->CopyFromTensor(feed_item, dev_ctx.GetPlace(), dev_ctx);
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out_item->set_lod(feed_item.lod());
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}
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}
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};
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};
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} // namespace operators
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} // namespace operators
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} // namespace paddle
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} // namespace paddle
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namespace ops = paddle::operators;
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// We do not need to register OpInfoMaker,
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REGISTER_OP_WITHOUT_GRADIENT(feed, ops::FeedOp, ops::FeedOpMaker);
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// since feed operator will not be used by end users directly
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REGISTER_OP_CPU_KERNEL(feed, ops::FeedKernel<float>);
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REGISTER_OPERATOR(feed, paddle::operators::FeedOp,
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paddle::framework::EmptyGradOpMaker);
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@ -1,18 +0,0 @@
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/* 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/feed_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(feed, ops::FeedKernel<float>);
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@ -1,42 +0,0 @@
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/* 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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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class FeedKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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framework::Tensor* out = ctx.Output<framework::Tensor>("Out");
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out->mutable_data<T>(ctx.GetPlace());
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framework::Variable* g_feed_variable =
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framework::GetGlobalScope().FindVar("feed_value");
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const auto& tensors =
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g_feed_variable->Get<std::vector<framework::Tensor>>();
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int col = ctx.template Attr<int>("col");
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PADDLE_ENFORCE_GT(tensors.size(), static_cast<size_t>(col));
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// TODO(qijun):
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// check tensors[col].dims() with attribute,
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// except the first dimenson.
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out->CopyFrom<T>(tensors[col], ctx.GetPlace(), ctx.device_context());
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -1,52 +1,64 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/* 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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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 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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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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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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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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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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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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See the License for the specific language governing permissions and
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limitations under the License. */
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limitations under the License. */
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#include "paddle/operators/fetch_op.h"
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#include "paddle/framework/feed_fetch_type.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace paddle {
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namespace operators {
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namespace operators {
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class FetchOp : public framework::OperatorWithKernel {
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class FetchOp : public framework::OperatorBase {
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public:
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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FetchOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorBase(type, inputs, outputs, attrs) {}
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protected:
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void Run(const framework::Scope &scope,
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void InferShape(framework::InferShapeContext* ctx) const override {
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const platform::DeviceContext &dev_ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Input"), "Input should be not null.");
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auto fetch_var_name = Input("Input");
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}
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auto *fetch_var = scope.FindVar(fetch_var_name);
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PADDLE_ENFORCE(fetch_var != nullptr,
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"Cannot find fetch variable in scope, fetch_var_name is %s",
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fetch_var_name);
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framework::DataType IndicateDataType(
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auto out_name = this->Output("Out");
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const framework::ExecutionContext& ctx) const override {
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auto *out_var = scope.FindVar(out_name);
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return static_cast<framework::DataType>(Attr<int>("dataType"));
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PADDLE_ENFORCE(out_var != nullptr,
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}
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"Cannot find out_var in scope, out_var_name is %s",
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};
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out_name);
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class FetchOpMaker : public framework::OpProtoAndCheckerMaker {
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auto col = static_cast<size_t>(Attr<int>("col"));
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public:
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FetchOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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auto *fetch_list = out_var->GetMutable<framework::FeedFetchList>();
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: OpProtoAndCheckerMaker(proto, op_checker) {
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auto &src_item = fetch_var->Get<framework::FeedFetchType>();
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AddAttr<int>("dataType", "output data type")
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.SetDefault(framework::DataType::FP32);
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if (col >= fetch_list->size()) {
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AddAttr<int>("col", "The col in global fetch variable").SetDefault(0);
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fetch_list->resize(col + 1);
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AddInput("Input", "The output of fetch op.");
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}
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AddComment(R"DOC(Fetch data to global fetch variable)DOC");
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auto &dst_item = fetch_list->at(col);
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// FIXME(yuyang18): Should we assume the fetch operator always generate
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// CPU outputs?
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dst_item.CopyFromTensor(src_item, platform::CPUPlace(), dev_ctx);
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}
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}
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};
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};
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} // namespace operators
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} // namespace operators
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} // namespace paddle
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} // namespace paddle
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namespace ops = paddle::operators;
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// We do not need to register OpInfoMaker,
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REGISTER_OP_WITHOUT_GRADIENT(fetch, ops::FetchOp, ops::FetchOpMaker);
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// since fetch operator will not be used by end users directly
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REGISTER_OP_CPU_KERNEL(fetch, ops::FetchKernel<float>);
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REGISTER_OPERATOR(fetch, paddle::operators::FetchOp,
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paddle::framework::EmptyGradOpMaker);
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@ -1,18 +0,0 @@
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/* 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/fetch_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(fetch, ops::FetchKernel<float>);
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@ -1,45 +0,0 @@
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/* 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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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class FetchKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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const framework::Tensor* input = ctx.Input<framework::Tensor>("Input");
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framework::Variable* g_fetch_variable =
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framework::GetGlobalScope().FindVar("fetch_value");
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auto* tensors =
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g_fetch_variable->GetMutable<std::vector<framework::Tensor>>();
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int col = ctx.template Attr<int>("col");
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if (tensors->size() < static_cast<size_t>(col + 1)) {
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tensors->resize(col + 1);
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}
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PADDLE_ENFORCE_GT(tensors->size(), static_cast<size_t>(col));
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(*tensors)[col].Resize(input->dims());
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(*tensors)[col].mutable_data<T>(platform::CPUPlace());
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(*tensors)[col].CopyFrom<T>(*input, platform::CPUPlace(),
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ctx.device_context());
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// TODO(qijun): need to handle LodTensor later
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}
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};
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} // namespace operators
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} // namespace paddle
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