ReadFromArray/WriteToArray op (#5407)
* Use stable_sort in lod_rank_table It is easy to debug and test when use `stable_sort`and the time complexity is not changed. * Add LoDTensorArray * Stash * Better debug message for IsInitialized * Stash * Better debug message for IsInitialized * Complete array read/write op unittestsmobile_baidu
Yu Yang
7 years ago
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GitHub
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/* 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/framework/lod_tensor_array.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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class ArrayOpBase : public framework::OperatorBase {
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public:
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ArrayOpBase(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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void Run(const framework::Scope &scope,
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const platform::DeviceContext &dev_ctx) const override {}
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protected:
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size_t GetOffset(const framework::Scope &scope,
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const platform::DeviceContext &dev_ctx) const {
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auto *i = scope.FindVar(Input("I"));
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PADDLE_ENFORCE(i != nullptr, "I must be set");
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auto &i_tensor = i->Get<framework::LoDTensor>();
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PADDLE_ENFORCE_EQ(i_tensor.numel(), 1);
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size_t offset;
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if (platform::is_gpu_place(i_tensor.place())) {
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// FIXME: Avoid copy from GPU to CPU
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framework::Tensor t;
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t.CopyFrom(i_tensor, platform::CPUPlace(), dev_ctx);
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dev_ctx.Wait();
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offset = static_cast<size_t>(*t.data<int64_t>());
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} else {
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offset = static_cast<size_t>(*i_tensor.data<int64_t>());
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}
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return offset;
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}
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};
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class WriteToArrayOp : public ArrayOpBase {
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public:
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WriteToArrayOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: ArrayOpBase(type, inputs, outputs, attrs) {}
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void Run(const framework::Scope &scope,
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const platform::DeviceContext &dev_ctx) const override {
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auto *x = scope.FindVar(Input("X"));
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PADDLE_ENFORCE(x != nullptr, "X must be set");
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auto &x_tensor = x->Get<framework::LoDTensor>();
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size_t offset = GetOffset(scope, dev_ctx);
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auto *out =
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scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensorArray>();
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if (offset >= out->size()) {
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out->resize(offset + 1);
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}
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auto *out_tensor = &out->at(offset);
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out_tensor->CopyFrom(x_tensor, dev_ctx.GetPlace(), dev_ctx);
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out_tensor->set_lod(x_tensor.lod());
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}
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};
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class WriteToArrayOpProtoMaker : public framework::OpProtoAndCheckerMaker {
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public:
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WriteToArrayOpProtoMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "(LoDTensor) the tensor will be written to tensor array");
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AddInput(
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"I",
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"(Tensor) the subscript index in tensor array. The number of element "
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"should be 1");
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AddOutput("Out", "(TensorArray) the tensor array will be written");
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AddComment(R"DOC(Write a LoDTensor to a LoDTensor array.
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Assume T is LoDTensor, i is the subscript of the array, and A is the array. The
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equation is
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A[i] = T
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)DOC");
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}
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};
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class WriteToArrayInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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PADDLE_ENFORCE(context->HasInput("I"), "Must set the subscript index");
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PADDLE_ENFORCE_EQ(framework::product(context->GetInputDim("I")), 1,
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"The number of element of subscript index must be 1");
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PADDLE_ENFORCE(context->HasInput("X"), NotHasXError());
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PADDLE_ENFORCE(context->HasOutput("Out"), NotHasOutError());
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context->SetOutputDim("Out", context->GetInputDim("X"));
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}
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protected:
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virtual const char *NotHasXError() const { return "Must set the lod tensor"; }
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virtual const char *NotHasOutError() const {
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return "Must set the lod tensor array";
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}
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};
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class WriteToArrayInferVarType : public framework::VarTypeInference {
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public:
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void operator()(const framework::OpDescBind &op_desc,
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framework::BlockDescBind *block) const override {
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VLOG(10) << "I am here?";
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for (auto &out_var : op_desc.OutputArgumentNames()) {
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VLOG(10) << "Set Variable " << out_var << " as LOD_TENSOR_ARRAY";
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block->Var(out_var)->SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
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}
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}
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};
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class ReadFromArrayOp : public ArrayOpBase {
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public:
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ReadFromArrayOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: ArrayOpBase(type, inputs, outputs, attrs) {}
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void Run(const framework::Scope &scope,
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const platform::DeviceContext &dev_ctx) const override {
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auto *x = scope.FindVar(Input("X"));
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PADDLE_ENFORCE(x != nullptr, "X must be set");
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auto &x_array = x->Get<framework::LoDTensorArray>();
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auto *out = scope.FindVar(Output("Out"));
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PADDLE_ENFORCE(out != nullptr, "Out must be set");
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auto *out_tesnor = out->GetMutable<framework::LoDTensor>();
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size_t offset = GetOffset(scope, dev_ctx);
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PADDLE_ENFORCE_LT(offset, x_array.size());
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out_tesnor->CopyFrom(x_array[offset], dev_ctx.GetPlace(), dev_ctx);
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out_tesnor->set_lod(x_array[offset].lod());
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}
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};
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class ReadFromArrayProtoMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ReadFromArrayProtoMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "(TensorArray) the array will be read from.");
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AddInput("I",
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"(Tensor) the subscript index in tensor array. The number of "
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"element should be 1");
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AddOutput("Out", "(LoDTensor) the tensor will be read from.");
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AddComment(R"DOC(Read a LoDTensor from a LoDTensor Array
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Assume T is LoDTensor, i is th e subscript of the array, and A is the array. The
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equation is
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T = A[i]
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)DOC");
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}
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};
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class ReadFromArrayInferShape : public WriteToArrayInferShape {
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protected:
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const char *NotHasXError() const override {
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return "The input array X must be set";
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}
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const char *NotHasOutError() const override {
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return "The output tensor out must be set";
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}
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};
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class WriteToArrayGradMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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protected:
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std::unique_ptr<framework::OpDescBind> Apply() const override {
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auto *grad_op = new framework::OpDescBind();
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grad_op->SetType("read_from_array");
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grad_op->SetInput("I", Input("I"));
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grad_op->SetInput("X", OutputGrad("Out"));
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grad_op->SetOutput("Out", InputGrad("X"));
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grad_op->SetAttrMap(Attrs());
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return std::unique_ptr<framework::OpDescBind>(grad_op);
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}
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};
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class ReadFromArrayGradMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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protected:
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std::unique_ptr<framework::OpDescBind> Apply() const override {
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auto *grad_op = new framework::OpDescBind();
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grad_op->SetType("write_to_array");
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grad_op->SetInput("I", Input("I"));
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grad_op->SetInput("X", OutputGrad("Out"));
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grad_op->SetOutput("Out", InputGrad("X"));
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grad_op->SetAttrMap(Attrs());
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return std::unique_ptr<framework::OpDescBind>(grad_op);
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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(write_to_array, ops::WriteToArrayOp,
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ops::WriteToArrayInferShape, ops::WriteToArrayOpProtoMaker,
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ops::WriteToArrayGradMaker, ops::WriteToArrayInferVarType);
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REGISTER_OPERATOR(read_from_array, ops::ReadFromArrayOp,
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ops::ReadFromArrayInferShape, ops::ReadFromArrayProtoMaker,
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ops::ReadFromArrayGradMaker);
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import unittest
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import numpy
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import paddle.v2.framework.core as core
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import paddle.v2.framework.layers as layers
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from paddle.v2.framework.executor import Executor
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class TestArrayReadWrite(unittest.TestCase):
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def test_read_write(self):
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x = [
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layers.data(
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name='x0', shape=[100]), layers.data(
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name='x1', shape=[100]), layers.data(
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name='x2', shape=[100])
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]
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for each_x in x:
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each_x.stop_gradient = False
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i = layers.zeros(shape=[1], dtype='int64')
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arr = layers.array_write(x=x[0], i=i)
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layers.increment(x=i)
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arr = layers.array_write(x=x[1], i=i, array=arr)
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layers.increment(x=i)
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arr = layers.array_write(x=x[2], i=i, array=arr)
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i = layers.zeros(shape=[1], dtype='int64')
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a0 = layers.array_read(array=arr, i=i)
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layers.increment(x=i)
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a1 = layers.array_read(array=arr, i=i)
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layers.increment(x=i)
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a2 = layers.array_read(array=arr, i=i)
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mean_a0 = layers.mean(x=a0)
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mean_a1 = layers.mean(x=a1)
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mean_a2 = layers.mean(x=a2)
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a_sum = layers.sums(input=[mean_a0, mean_a1, mean_a2])
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mean_x0 = layers.mean(x=x[0])
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mean_x1 = layers.mean(x=x[1])
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mean_x2 = layers.mean(x=x[2])
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x_sum = layers.sums(input=[mean_x0, mean_x1, mean_x2])
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scope = core.Scope()
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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tensor = core.LoDTensor()
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tensor.set(numpy.random.random(size=(100, 100)).astype('float32'), cpu)
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outs = map(numpy.array,
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exe.run(feed={'x0': tensor,
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'x1': tensor,
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'x2': tensor},
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fetch_list=[a_sum, x_sum],
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scope=scope))
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self.assertEqual(outs[0], outs[1])
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,13 @@
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import unittest
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from paddle.v2.framework.framework import Program
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class TestDebugStringFramework(unittest.TestCase):
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def test_debug_str(self):
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p = Program()
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p.current_block().create_var(name='t', shape=[0, 1])
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self.assertRaises(ValueError, callableObj=p.__str__)
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if __name__ == '__main__':
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unittest.main()
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