Add lod tensor array to tensor op (#13990)
* add lod tensor array concat * add lod tensor array concat * test=develop * add lod tensor array concat test=develop * Fix API.spec test=develop * add lod tensor array concat test=develop * revise some bug of lod tensor array concat test=develop * add unittest for tensor array concat test=develop * change to tensor array to tensor test=develop * revise bug test=develop * revise a bug test=develop * revise a bug test=develop * revise a bug of python3 test=developrevert-14324-fix_vlog
li099
6 years ago
committed by
whs
parent
6c6e638550
commit
688ed60116
@ -0,0 +1,246 @@
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/* Copyright (c) 2018 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 <string>
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#include <vector>
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#include "paddle/fluid/framework/lod_tensor_array.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/variable.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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void LodTensorArray2LodTensorVector(const framework::Scope &scope,
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const std::string &base_name,
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const std::string &lod_tensor_array_name,
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std::vector<std::string> *res_names) {
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auto &inx =
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scope.FindVar(lod_tensor_array_name)->Get<framework::LoDTensorArray>();
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for (size_t i = 0; i < inx.size(); i++) {
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std::string var_name = base_name + std::to_string(i);
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framework::Variable *g_feed_value =
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const_cast<framework::Scope &>(scope).Var(var_name);
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auto &feed_input =
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*(g_feed_value->GetMutable<paddle::framework::LoDTensor>());
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feed_input.ShareDataWith(inx[i]);
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res_names->push_back(var_name);
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}
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}
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void LodTensorVectorResizeFromLodTensorArray(
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const framework::Scope &scope, const std::string &base_name,
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const std::string &lod_tensor_array_name,
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std::vector<std::string> *res_names) {
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auto &inx =
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scope.FindVar(lod_tensor_array_name)->Get<framework::LoDTensorArray>();
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for (size_t i = 0; i < inx.size(); i++) {
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std::string var_name = base_name + std::to_string(i);
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framework::Variable *g_feed_value =
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const_cast<framework::Scope &>(scope).Var(var_name);
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auto &feed_input =
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*(g_feed_value->GetMutable<paddle::framework::LoDTensor>());
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auto dims = inx[i].dims();
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feed_input.Resize(dims);
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res_names->push_back(var_name);
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}
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}
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void LodTensorArrayCreateFromLodTensorArray(
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const framework::Scope &scope,
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const std::string &input_lod_tensor_array_name,
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const std::string &output_lod_tensor_array_name) {
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auto &inx = scope.FindVar(input_lod_tensor_array_name)
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->Get<framework::LoDTensorArray>();
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auto &grad_inx = *scope.FindVar(output_lod_tensor_array_name)
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->GetMutable<framework::LoDTensorArray>();
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for (size_t i = 0; i < inx.size(); i++) {
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std::string var_name = output_lod_tensor_array_name + std::to_string(i);
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framework::Variable *g_feed_value =
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const_cast<framework::Scope &>(scope).Var(var_name);
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auto &feed_input =
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*(g_feed_value->GetMutable<paddle::framework::LoDTensor>());
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grad_inx.push_back(feed_input);
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}
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}
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class LoDTensorArray2TensorOp : public framework::OperatorBase {
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public:
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using OperatorBase::OperatorBase;
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &place) const override {
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auto axis = Attr<int>("axis");
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framework::AttributeMap attrs;
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attrs["axis"] = axis;
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auto &inx = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
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auto &out =
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*scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
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auto &out_inx =
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*scope.FindVar(Output("OutIndex"))->GetMutable<framework::LoDTensor>();
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const size_t n = inx.size();
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PADDLE_ENFORCE_GT(n, 0, "Input tensorarray size should > 0.");
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std::string base_name = Inputs("X")[0];
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std::vector<std::string> names;
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// get the input tensorarray items' dim in out_inx
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auto out_inx_dim = out_inx.dims();
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out_inx_dim[0] = inx.size();
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out_inx.Resize(out_inx_dim);
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std::string var_name = "out_index";
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framework::Variable *tmp_index_var =
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const_cast<framework::Scope &>(scope).Var(var_name);
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auto &tmp_index_tensor =
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*(tmp_index_var->GetMutable<paddle::framework::LoDTensor>());
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tmp_index_tensor.Resize(out_inx_dim);
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int *tmp_index_data =
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tmp_index_tensor.mutable_data<int>(platform::CPUPlace());
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auto out_dims = inx[0].dims();
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size_t out_dim_sum = 0;
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for (size_t index = 0; index < inx.size(); index++) {
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auto inx_dims = inx[index].dims();
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out_dim_sum += inx_dims[axis];
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tmp_index_data[index] = inx_dims[axis];
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}
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out_inx.ShareDataWith(tmp_index_tensor);
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// get input array items' dims
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out_dims[axis] = out_dim_sum;
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out.Resize(out_dims);
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LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
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// Invoke Reshape Op
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auto concat_op = framework::OpRegistry::CreateOp(
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"concat", {{"X", names}}, {{"Out", {Output("Out")}}}, attrs);
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concat_op->Run(scope, place);
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}
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};
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class LoDTensorArray2TensorOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "Input LoDTensorArray of tensor_array_to_tensor operator.");
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AddOutput("Out", "Output tensor of tensor_array_to_tensor operator.");
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AddOutput("OutIndex",
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"Output input LoDTensorArray items' dims of "
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"tensor_array_to_tensor operator.");
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AddAttr<int>("axis",
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"The axis along which the input tensors will be concatenated.")
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.SetDefault(0);
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AddComment(R"DOC(
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tensor_array_to_tensor Operator.
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Concatenate the input LoDTensorArray along dimension axis to the output Tensor.
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Examples:
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Input = {[1,2], [3,4], [5,6]}
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axis = 0
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Output = [[1,2],
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[3,4],
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[5,6]]
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OutputIndex = [1,1,1]
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)DOC");
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}
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};
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class LoDTensorArray2TensorOpInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *ctx) const override {}
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};
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class LoDTensorArray2TensorGradInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {}
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};
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class LoDTensorArray2TensorGradInferVarType
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: public framework::VarTypeInference {
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public:
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void operator()(const framework::OpDesc &op_desc,
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framework::BlockDesc *block) const override {
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for (auto &out_var : op_desc.Output(framework::GradVarName("X"))) {
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block->Var(out_var)->SetType(framework::proto::VarType::LOD_TENSOR_ARRAY);
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}
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}
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};
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class LoDTensorArray2TensorGradOp : public framework::OperatorBase {
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public:
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using OperatorBase::OperatorBase;
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &place) const override {
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auto axis = Attr<int>("axis");
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framework::AttributeMap attrs;
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attrs["axis"] = axis;
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auto &inx = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
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const size_t n = inx.size();
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PADDLE_ENFORCE_GT(n, 0, "Input tensorarray size should > 0.");
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std::string base_name = Inputs("X")[0];
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std::vector<std::string> names;
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LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
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// grad
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auto dx_name = Output(framework::GradVarName("X"));
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auto dout_name = Input(framework::GradVarName("Out"));
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std::vector<std::string> grad_names;
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LodTensorVectorResizeFromLodTensorArray(scope, "grad_name", Input("X"),
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&grad_names);
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auto concat_grad_op = framework::OpRegistry::CreateOp(
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"concat_grad", {{"X", names}, {"Out@GRAD", {dout_name}}},
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{{"X@GRAD", grad_names}}, attrs);
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concat_grad_op->Run(scope, place);
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LodTensorArrayCreateFromLodTensorArray(scope, Input("X"), dx_name);
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auto &grad_inx =
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*scope.FindVar(dx_name)->GetMutable<framework::LoDTensorArray>();
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for (size_t i = 0; i < grad_names.size(); i++) {
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std::string var_name = grad_names[i];
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auto &feed_input = scope.FindVar(var_name)->Get<framework::LoDTensor>();
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grad_inx[i].ShareDataWith(feed_input);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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USE_OP(concat);
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(tensor_array_to_tensor, ops::LoDTensorArray2TensorOp,
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ops::LoDTensorArray2TensorOpMaker,
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ops::LoDTensorArray2TensorOpInferShape,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(tensor_array_to_tensor_grad, ops::LoDTensorArray2TensorGradOp,
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ops::LoDTensorArray2TensorGradInferShape,
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ops::LoDTensorArray2TensorGradInferVarType);
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@ -0,0 +1,142 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from __future__ import print_function
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import unittest
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import numpy
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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from paddle.fluid.executor import Executor
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class TestLoDTensorArrayConcat(unittest.TestCase):
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def setUp(self):
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self.op_type = "tensor_array_to_tensor"
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self.attrs = {"axis": 0}
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self.outputs = ["Out"]
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def test_get_set(self):
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scope = core.Scope()
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program = fluid.Program()
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block = program.global_block()
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input_arr = block.create_var(
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name="tmp_lod_tensor_array",
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type=core.VarDesc.VarType.LOD_TENSOR_ARRAY)
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input_arr.persistable = True
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input_arr_var = scope.var('tmp_lod_tensor_array')
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input_tensor_array = input_arr_var.get_lod_tensor_array()
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self.assertEqual(0, len(input_tensor_array))
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cpu = core.CPUPlace()
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for i in range(10):
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t = core.LoDTensor()
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if i == 0:
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t.set(numpy.array([[i], [i]], dtype='float32'), cpu)
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else:
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t.set(numpy.array([[i]], dtype='float32'), cpu)
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input_tensor_array.append(t)
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self.assertEqual(10, len(input_tensor_array))
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random_grad = numpy.random.random_sample([11]).astype(numpy.float32)
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y_out = block.create_var(name="Out")
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y_out.persistable = True
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y_out_index = block.create_var(name="OutIndex")
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y_out_index.persistable = True
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y_grad_arr = block.create_var(
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name='Out@GRAD', dtype='float32', shape=[11])
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y_grad_arr.persistable = True
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y_grad = scope.var('Out@GRAD')
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y_grad_tensor = y_grad.get_tensor()
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y_grad_tensor.set(random_grad, cpu)
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op = block.append_op(
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type=self.op_type,
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inputs={"X": input_arr},
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outputs={"Out": y_out,
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"OutIndex": y_out_index},
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attrs=self.attrs)
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out_grad = block.create_var(
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name="tmp_lod_tensor_array@GRAD",
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type=core.VarDesc.VarType.LOD_TENSOR_ARRAY)
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out_grad.persistable = True
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grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(op.desc,
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set(), [])
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grad_op_desc = grad_op_desc_list[0]
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new_op_desc = block.desc.append_op()
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new_op_desc.copy_from(grad_op_desc)
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for var_name in grad_op_desc.output_arg_names():
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block.desc.var(var_name.encode("ascii"))
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grad_op_desc.infer_var_type(block.desc)
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grad_op_desc.infer_shape(block.desc)
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for arg in grad_op_desc.output_arg_names():
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grad_var = block.desc.find_var(arg.encode("ascii"))
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grad_var.set_dtype(core.VarDesc.VarType.FP32)
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fetch_list = []
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fetch_list.append(block.var('Out'))
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fetch_list.append(block.var('OutIndex'))
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exe = fluid.Executor(fluid.CPUPlace())
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out = exe.run(program, fetch_list=fetch_list, scope=scope)
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#print ("index: ", numpy.array(out[1]))
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# test forward
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tensor_res = numpy.array(out[0])
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tensor_res_out_idx = numpy.array(out[1])
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tensor_gt = numpy.array(
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[0] + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='float32')
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self.assertEqual(len(tensor_res), len(tensor_gt))
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self.assertEqual(len(tensor_res_out_idx), 10)
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for i in range(len(tensor_res)):
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self.assertEqual(tensor_res[i], tensor_gt[i])
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for i in range(len(tensor_res_out_idx)):
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if i == 0:
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self.assertEqual(tensor_res_out_idx[i], 2)
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else:
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self.assertEqual(tensor_res_out_idx[i], 1)
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# test backward
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grad_tensor = scope.var('tmp_lod_tensor_array@GRAD')
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grad_tensor_array = grad_tensor.get_lod_tensor_array()
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self.assertEqual(10, len(grad_tensor_array))
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for i in range(len(grad_tensor_array)):
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if i == 0:
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self.assertEqual(
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numpy.array(grad_tensor_array[i])[0],
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numpy.array(random_grad[i]))
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self.assertEqual(
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numpy.array(grad_tensor_array[i])[1],
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numpy.array(random_grad[i + 1]))
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if i == 1:
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self.assertEqual(
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numpy.array(grad_tensor_array[i]),
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numpy.array(random_grad[i + 1]))
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if __name__ == '__main__':
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unittest.main()
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