Yang Yang
7 years ago
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1a0d8fab97
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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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/*
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* DyBatchSeqPosition stores indices of the basic element in tensor. It is used
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* after lod-tensor's re-assembling, its info can be used to recover the order
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* in original lod-tensor.
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*/
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struct DySeqMeta {
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size_t begin;
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size_t end; // not included
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size_t ori_idx;
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};
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/*
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* TensorArray is a C-array-like array of tensors, it is meant to be used with
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* dynamic iteration primitives such as while_loop. It is used to segment inputs
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* and store states in all time steps.
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*
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* By providing some methods similar to a C++ array, the difinition of some
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* state-based dynamic models such as RNN cound be more natural and highly
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* flexible.
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*/
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class TensorArray {
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public:
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using value_type = float;
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// max number of values allowed to store.
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const size_t MAX_SIZE{100000};
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/*
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* Read the value at location `index` in the `TensorArray`.
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*/
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const LoDTensor &Read(size_t index) const;
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/*
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* Write value into the index of the TensorArray.
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*/
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void Write(size_t index, const LoDTensor &value);
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/*
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* Write value into the index of the TensorArray, with memory shared.
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*/
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void WriteShared(size_t index, const LoDTensor &value);
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/*
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* Recover the original LoD-arranged LoDTensor with the `values`, `level` and
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* `indice_map`.
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*/
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LoDTensor Pack(size_t level, const std::vector<DySeqMeta> &meta,
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const LoD &lod) const;
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/*
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* Split LoDTensor in some `level` and write the generated batches to
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* `values`, if set `desend`, will sort by length in descending order else in
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* ascending order.
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*/
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std::vector<DySeqMeta> Unpack(const LoDTensor &source, int level,
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bool length_desend);
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/*
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* Pack the values into a tensor with rank one higher than each tensor in
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* values.
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*/
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LoDTensor Stack() const;
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/*
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* Unpacks the given division of a rank-`R` tensor into rank-`(R-1)` tensors.
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*/
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void Unstack(const LoDTensor &source) const;
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/*
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* Unpacks the given division of a rank-`R` tensor into rank-`(R-1)` tensors,
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* with memory of tensors shared.
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*/
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void UnstackShared(const LoDTensor &source) const;
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/*
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* Return the number of values.
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*/
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size_t size() const;
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protected:
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void Unstack(const LoDTensor &source, bool data_shared) const;
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private:
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mutable std::vector<LoDTensor> values_;
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}; // class TensorArray
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,130 @@
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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/framework/tensor_array.h"
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#include <gtest/gtest.h>
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namespace paddle {
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namespace framework {
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class TensorArrayTester : public ::testing::Test {
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protected:
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void SetUp() override {
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LoDTensor source;
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source.Resize(make_ddim({batch_size, dim}));
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int* data = source.mutable_data<int>(platform::CPUPlace());
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for (int i = 0; i < 16 * 32; i++) {
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data[i] = i;
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}
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ta.Unstack(source);
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}
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TensorArray ta;
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const int batch_size = 16;
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const int dim = 32;
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};
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TEST_F(TensorArrayTester, Read) {
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for (int i = 0; i < batch_size; i++) {
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const auto& tensor = ta.Read(i);
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ASSERT_EQ(tensor.dims()[0], 1);
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ASSERT_EQ(tensor.dims()[1], dim);
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}
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}
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TEST_F(TensorArrayTester, Write) {
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LoDTensor source;
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source.Resize(make_ddim({1, dim}));
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for (int i = 0; i < dim; i++) {
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*(source.mutable_data<int>(platform::CPUPlace()) + i) = i;
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}
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ta.Write(2, source);
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const auto& tensor = ta.Read(2);
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for (int i = 0; i < dim; i++) {
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EXPECT_EQ(*(tensor.data<int>() + i), *(source.data<int>() + i));
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}
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}
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TEST_F(TensorArrayTester, WriteShared) {
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LoDTensor source;
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source.Resize(make_ddim({1, dim}));
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for (int i = 0; i < dim; i++) {
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*(source.mutable_data<int>(platform::CPUPlace()) + i) = i;
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}
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ta.WriteShared(2, source);
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const auto& tensor = ta.Read(2);
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for (int i = 0; i < dim; i++) {
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EXPECT_EQ(*(tensor.data<int>() + i), *(source.data<int>() + i));
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}
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EXPECT_EQ(source.data<int>(), tensor.data<int>());
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}
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class TensorArrayPackTester : public ::testing::Test {
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protected:
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virtual void SetUp() override {
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lod.push_back(std::vector<size_t>{0, 2, 9, 13});
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source.set_lod(lod);
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source.Resize(make_ddim({13, 128}));
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source.mutable_data<int>(platform::CPUPlace());
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// content of each setence: 0 1 2 3 4
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const auto& level = lod.front();
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for (size_t i = 0; i < level.size() - 1; i++) {
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size_t begin = level[i];
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size_t end = level[i + 1];
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for (size_t j = begin; j < end; j++) {
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auto record = source.Slice<int>(j, j + 1);
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for (int dim = 0; dim < 128; dim++) {
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record.mutable_data<int>(platform::CPUPlace())[dim] = j - begin;
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}
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}
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}
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// unpack
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meta = ta.Unpack(source, 0, true);
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}
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LoD lod;
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TensorArray ta;
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LoDTensor source;
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std::vector<DySeqMeta> meta;
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};
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TEST_F(TensorArrayPackTester, Unpack) {
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ASSERT_EQ(ta.size(), 7UL);
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const auto& t0 = ta.Read(0);
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const auto& t1 = ta.Read(1);
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ASSERT_EQ(t0.data<int>()[0], int(0));
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ASSERT_EQ(t1.data<int>()[0], int(1));
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}
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TEST_F(TensorArrayPackTester, Pack) {
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LoDTensor packed = ta.Pack(0, meta, lod);
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}
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TEST_F(TensorArrayTester, size) {
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ASSERT_EQ(ta.size(), static_cast<size_t>(batch_size));
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}
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,38 @@
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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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|
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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 <functional>
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#include <map>
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#include "paddle/platform/variant.h"
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namespace paddle {
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namespace framework {
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class OperatorBase;
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using VariableNameMap = std::map<std::string, std::vector<std::string>>;
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// The order should be as same as framework.proto
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using Attribute =
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boost::variant<boost::blank, int, float, std::string, std::vector<int>,
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std::vector<float>, std::vector<std::string>, bool,
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std::vector<bool>, BlockDesc*>;
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using AttributeMap = std::unordered_map<std::string, Attribute>;
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using OpCreator = std::function<OperatorBase*(
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const std::string& /*type*/, const VariableNameMap& /*inputs*/,
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const VariableNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
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} // namespace framework
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} // namespace paddle
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