feature/while_op (#5502)
* first commit * Python API for while op * Python Unittest for simple while_op forward * fix out to be list * Fix UT * VarType * Fix several bugs * Fix bug * Fix bug * Fix Bug * Fix bug * Fix unittest * Remove debug log * Add comments * add PADDLE_ENFORCE * while_grad_op first commit * Add `BlockDescBind::FindRecursiveOrCreateVar()` and fix bugs * refine code * fix unittest bugmobile_baidu
Yang Yang(Tony)
8 years ago
committed by
fengjiayi
parent
3d6ca6ade5
commit
40367d18d4
@ -0,0 +1,197 @@
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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 <vector>
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#include "paddle/framework/executor.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 operators {
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using StepScopeVar = std::vector<framework::Scope *>;
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using LoDTensor = framework::LoDTensor;
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constexpr char kStepBlock[] = "step_block";
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constexpr char kCondition[] = "Condition";
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constexpr char kStepScopes[] = "StepScopes";
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constexpr char kParamGrads[] = "X@Grad";
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constexpr char kParameters[] = "X";
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class WhileOp : public framework::OperatorBase {
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public:
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WhileOp(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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: framework::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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PADDLE_ENFORCE_NOT_NULL(scope.FindVar(Input(kCondition)));
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auto &cond = scope.FindVar(Input(kCondition))->Get<LoDTensor>();
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PADDLE_ENFORCE_EQ(cond.dims(), paddle::framework::make_ddim({1}));
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framework::Executor executor(dev_ctx);
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auto *block = Attr<framework::BlockDescBind *>(kStepBlock);
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auto *program = block->Program();
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auto step_scopes =
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scope.FindVar(Output(kStepScopes))->GetMutable<StepScopeVar>();
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while (cond.data<bool>()[0]) {
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auto ¤t_scope = scope.NewScope();
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step_scopes->push_back(¤t_scope);
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executor.Run(*program, ¤t_scope, block->ID(),
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false /*create_local_scope*/);
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}
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}
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};
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class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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WhileOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(kParameters,
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"A set of variables, which are required by operators inside the "
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"block of While Op.")
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.AsDuplicable();
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AddInput(
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kCondition,
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"(Bool) An scalar. When it's False, the While Op will be terminated.")
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.AsDuplicable();
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AddOutput("Out",
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"A set of variables, which will be assigned with values "
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"generated by perators inside the block of While Op.")
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.AsDuplicable();
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AddOutput(kStepScopes,
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"(StepScopeVar) A vector of local scope, which size equals the "
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"step number of While Op. The i'th scope storages temporary "
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"variables generated in the i'th step.");
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AddAttr<framework::BlockDescBind *>(kStepBlock,
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"The step block inside WhileOp");
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AddComment(R"DOC(
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)DOC");
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}
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};
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class WhileGradOp : public framework::OperatorBase {
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public:
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WhileGradOp(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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: framework::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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// PADDLE_ENFORCE(...)
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framework::Executor executor(dev_ctx);
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auto *block = Attr<framework::BlockDescBind *>(kStepBlock);
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auto *program = block->Program();
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auto *step_scopes =
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scope.FindVar(Input(kStepScopes))->GetMutable<StepScopeVar>();
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for (auto cur_scope_iter = step_scopes->rbegin();
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cur_scope_iter != step_scopes->rend(); ++cur_scope_iter) {
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executor.Run(*program, *cur_scope_iter, block->ID(), false);
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auto &pg_names = Outputs(kParamGrads);
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auto &p_names = Inputs(kParameters);
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PADDLE_ENFORCE_EQ(pg_names.size(), p_names.size());
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for (size_t prog_id = 0; prog_id < pg_names.size(); ++prog_id) {
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auto inside_grad_name = framework::GradVarName(p_names[prog_id]);
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// // TODO(tonyyang-savil: Not sure we need the following
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// // If does not compute gradient of that variable inside rnn,
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// just
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// // continue
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// if (local_var_names.find(inside_grad_name) ==
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// local_var_names.end()) {
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// continue;
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// }
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// zero gradient variable in step 0
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if (cur_scope_iter == step_scopes->rbegin()) {
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auto *var = (*cur_scope_iter)->FindVar(inside_grad_name);
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PADDLE_ENFORCE_NOT_NULL(var);
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if (var->IsType<LoDTensor>()) {
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auto &inside_tensor = var->Get<framework::LoDTensor>();
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framework::AttributeMap attrs;
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attrs["data_type"] = framework::ToDataType(inside_tensor.type());
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attrs["shape"] = framework::vectorize2int(inside_tensor.dims());
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attrs["value"] = 0.0f;
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auto zero_op = framework::OpRegistry::CreateOp(
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"fill_constant", {}, {{"Out", {pg_names[prog_id]}}}, attrs);
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zero_op->Run(scope, dev_ctx);
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}
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}
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// sum gradient
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auto *outside_var = scope.FindVar(pg_names[prog_id]);
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PADDLE_ENFORCE_NOT_NULL(outside_var);
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auto &outside_tensor = *outside_var->GetMutable<framework::LoDTensor>();
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std::string result_var_name;
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auto *local_result_var = (*cur_scope_iter)->Var(&result_var_name);
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auto &local_result_tensor =
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*local_result_var->GetMutable<framework::LoDTensor>();
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local_result_tensor.ShareDataWith(outside_tensor);
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auto sum_op = framework::OpRegistry::CreateOp(
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"sum", {{"X", {result_var_name, inside_grad_name}}},
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{{"Out", {result_var_name}}}, {});
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sum_op->Run(**cur_scope_iter, dev_ctx);
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}
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}
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}
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};
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class WhileGradOpDescMaker : 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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virtual std::unique_ptr<framework::OpDescBind> Apply() const {
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auto *grad = new framework::OpDescBind();
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grad->SetType("while_grad");
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for (auto &input_param : this->InputNames()) {
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grad->SetInput(input_param, this->Input(input_param));
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grad->SetOutput(framework::GradVarName(input_param),
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this->InputGrad(input_param));
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}
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for (auto &output_param : this->OutputNames()) {
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grad->SetInput(output_param, this->Output(output_param));
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if (output_param != kStepScopes) {
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grad->SetInput(framework::GradVarName(output_param),
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this->OutputGrad(output_param));
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}
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}
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grad->SetAttrMap(this->Attrs());
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grad->SetBlockAttr(kStepBlock, *grad_block_[0]);
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return std::unique_ptr<framework::OpDescBind>(grad);
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OPERATOR(while, paddle::operators::WhileOp,
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paddle::operators::WhileOpMaker,
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paddle::operators::WhileGradOpDescMaker);
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@ -0,0 +1,68 @@
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import unittest
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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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import paddle.v2.framework.core as core
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import numpy
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class TestWhileOp(unittest.TestCase):
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def test_simple_forward(self):
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d0 = layers.data(
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"d0", shape=[10], append_batch_size=False, data_type='float32')
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d1 = layers.data(
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"d1", shape=[10], append_batch_size=False, data_type='float32')
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d2 = layers.data(
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"d2", shape=[10], append_batch_size=False, data_type='float32')
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i = layers.zeros(shape=[1], dtype='int64')
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i.stop_gradient = True
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init = layers.zeros(shape=[10], dtype='float32')
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mem_array = layers.array_write(init, i=i)
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data_array = layers.array_write(x=d0, i=i)
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i = layers.increment(i)
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layers.array_write(d1, i, array=data_array)
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i = layers.increment(i)
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layers.array_write(d2, i, array=data_array)
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i = layers.zeros(shape=[1], dtype='int64')
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i.stop_gradient = True
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array_len = layers.fill_constant(shape=[1], dtype='int64', value=3)
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cond = layers.less_than(x=i, y=array_len)
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while_op = layers.While(cond=cond)
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with while_op.block():
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d = layers.array_read(array=data_array, i=i)
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prev = layers.array_read(array=mem_array, i=i)
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i = layers.increment(x=i, in_place=True)
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result = layers.sums(input=[d, prev])
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layers.array_write(result, i=i, array=mem_array)
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layers.less_than(x=i, y=array_len, cond=cond)
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sum_result = layers.array_read(mem_array, i=array_len)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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d = []
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for i in xrange(3):
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d.append(numpy.random.random(size=[10]).astype('float32'))
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d_tensor = []
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for item in d:
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t = core.LoDTensor()
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t.set(item, cpu)
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d_tensor.append(t)
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outs = map(numpy.array,
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exe.run(feed={
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'd0': d_tensor[0],
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'd1': d_tensor[1],
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'd2': d_tensor[2]
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},
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fetch_list=[sum_result]))
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self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
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if __name__ == '__main__':
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unittest.main()
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