Merge pull request #6 from reyoung/feature/refactorize_framework_proto
Feature/refactorize framework protorevert-3824-remove_grad_op_type
Yu Yang
8 years ago
committed by
GitHub
commit
c7e8c1aa0c
@ -0,0 +1,106 @@
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package client_test
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import (
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"io/ioutil"
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"net/url"
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"os"
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"strings"
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"sync"
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"testing"
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"github.com/PaddlePaddle/Paddle/go/pserver/client"
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"github.com/coreos/etcd/embed"
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)
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func TestSelector(t *testing.T) {
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etcdDir, err := ioutil.TempDir("", "")
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if err != nil {
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t.Fatal(err)
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}
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cfg := embed.NewConfig()
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lpurl, _ := url.Parse("http://localhost:0")
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lcurl, _ := url.Parse("http://localhost:0")
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cfg.LPUrls = []url.URL{*lpurl}
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cfg.LCUrls = []url.URL{*lcurl}
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cfg.Dir = etcdDir
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e, err := embed.StartEtcd(cfg)
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if err != nil {
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t.Fatal(err)
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}
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defer func() {
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e.Close()
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if err := os.RemoveAll(etcdDir); err != nil {
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t.Fatal(err)
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}
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}()
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<-e.Server.ReadyNotify()
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port := strings.Split(e.Clients[0].Addr().String(), ":")[1]
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endpoint := "127.0.0.1:" + port
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var mu sync.Mutex
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selectedCount := 0
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var wg sync.WaitGroup
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selectAndDone := func(c *client.Etcd) {
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defer wg.Done()
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selected, err := c.Select()
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if err != nil {
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panic(err)
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}
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if selected {
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mu.Lock()
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selectedCount++
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mu.Unlock()
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err = c.Done()
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if err != nil {
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t.Fatal(err)
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}
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}
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}
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c0 := client.NewEtcd(endpoint)
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c1 := client.NewEtcd(endpoint)
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c2 := client.NewEtcd(endpoint)
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c3 := client.NewEtcd(endpoint)
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wg.Add(3)
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go selectAndDone(c0)
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go selectAndDone(c1)
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go selectAndDone(c2)
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wg.Wait()
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// simulate trainer crashed and restarted after the
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// initialization process.
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wg.Add(1)
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go selectAndDone(c3)
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wg.Wait()
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mu.Lock()
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if selectedCount != 1 {
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t.Fatal("selected count wrong:", selectedCount)
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}
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mu.Unlock()
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err = c0.Close()
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if err != nil {
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t.Fatal(err)
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}
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err = c1.Close()
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if err != nil {
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t.Fatal(err)
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}
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err = c2.Close()
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if err != nil {
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t.Fatal(err)
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}
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err = c3.Close()
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if err != nil {
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t.Fatal(err)
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}
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}
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@ -0,0 +1,62 @@
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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/lod_tensor.h"
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#include <memory>
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namespace paddle {
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namespace framework {
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namespace details {
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using LOD = LODTensor::LOD;
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std::shared_ptr<LOD> SliceLOD(const LOD &lod, size_t level_begin,
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size_t level_end) {
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auto new_lod = std::make_shared<LOD>();
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new_lod->reserve(level_end - level_begin);
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for (size_t i = level_begin; i < level_end; i++) {
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new_lod->emplace_back(lod[i]);
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}
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return new_lod;
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}
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std::shared_ptr<LOD> SliceLOD(const LOD &lod, size_t level, size_t elem_begin,
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size_t elem_end, bool tensor_shared) {
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// slice the lod.
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auto new_lod = std::make_shared<LOD>();
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new_lod->reserve(lod.size() - level);
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auto start = lod.at(level)[elem_begin];
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auto end = lod.at(level)[elem_end];
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for (auto it = lod.begin() + level; it != lod.end(); it++) {
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auto it_begin = std::find(it->begin(), it->end(), start);
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auto it_end = std::find(it_begin, it->end(), end);
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PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
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PADDLE_ENFORCE(it_end != it->end(), "error in parsing lod info");
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new_lod->emplace_back(it_begin, it_end + 1);
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if (!tensor_shared) {
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// reset offset if tensor is copyed and sliced.
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std::transform(new_lod->back().begin(), new_lod->back().end(),
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new_lod->back().begin(),
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[start](int v) { return v - start; });
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PADDLE_ENFORCE(new_lod->back().front() == 0, "error in slice LOD");
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}
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}
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return new_lod;
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}
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,46 @@
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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 <memory>
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namespace paddle {
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namespace framework {
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namespace details {
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/*
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* Slice levels from LOD.
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*
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* @lod: LOD to slice.
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* @level_begin: level to begin slice.
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* @level_end: level to end slice.
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*/
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std::shared_ptr<LODTensor::LOD> SliceLOD(const LODTensor::LOD &lod,
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size_t level_begin, size_t level_end);
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/*
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* Slice elements from a level of LOD.
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*
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* @lod: LOD to slice.
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* @level: which level to slice.
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* @elem_begin: element's index to begin slice.
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* @elem_end: element's index to end slice.
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*/
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std::shared_ptr<LODTensor::LOD> SliceLOD(const LODTensor::LOD &lod,
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size_t level, size_t elem_begin,
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size_t elem_end, bool tensor_shared);
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,51 @@
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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/lod_tensor.h"
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#include <glog/logging.h>
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namespace paddle {
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namespace framework {
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LODTensor LODTensor::SliceShared(size_t level_begin, size_t level_end) const {
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PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
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auto new_lod = details::SliceLOD(*lod_start_pos_, level_begin, level_end);
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// slice levels just need to update LOD info, each level will contains the
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// whole tensor_, so no need to modify tensor_.
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return LODTensor(tensor_, new_lod);
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}
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LODTensor LODTensor::SliceShared(size_t level, size_t elem_begin,
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size_t elem_end) const {
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PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
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PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
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NumLevels());
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PADDLE_ENFORCE(elem_begin < NumElements(level),
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"element begin [%d] out of range [%d]", elem_begin,
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NumElements(level));
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PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
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"element end [%d] out of range [%d]", elem_end,
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NumElements(level));
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auto new_lod = details::SliceLOD(*lod_start_pos_, level, elem_begin, elem_end,
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true /*tensor_shared*/);
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// slice elements just need to update LOD info, because offsets are not
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// changed, so the original tensor_ can be reused.
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return LODTensor(tensor_, new_lod);
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}
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,145 @@
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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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|
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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 <memory>
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#if (!PADDLE_ONLY_CPU)
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#include <thrust/device_vector.h>
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#include <thrust/host_vector.h>
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#endif
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#include "paddle/framework/ddim.h"
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#include "paddle/framework/tensor.h"
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#include "paddle/platform/enforce.h"
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namespace paddle {
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namespace framework {
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/*
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* LODTensor (Level of details Tensor)
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* see https://en.wikipedia.org/wiki/Level_of_details for reference.
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*/
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class LODTensor {
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public:
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// Level save offsets of each unit.
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#ifdef PADDLE_ONLY_CPU
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using Level = std::vector<size_t>;
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#else
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using Level = thrust::device_vector<size_t>;
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#endif
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// LOD stores offsets of each level of units, the largest units level first,
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// then the smaller units level. Each Level stores the offsets of units in
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// Tesor.
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typedef std::vector<Level> LOD;
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LODTensor() {}
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LODTensor(const std::shared_ptr<Tensor> &tensor,
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const std::shared_ptr<LOD> &lod) {
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Reset(tensor, lod);
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}
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void Reset(const std::shared_ptr<Tensor> &tensor,
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const std::shared_ptr<LOD> &lod) {
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tensor_ = tensor;
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lod_start_pos_ = lod;
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}
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/*
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* Get a element from LOD.
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*/
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size_t lod_element(size_t level, size_t elem) const {
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PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
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NumLevels());
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PADDLE_ENFORCE(elem < NumElements(level),
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"element begin [%d] out of range [%d]", elem,
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NumElements(level));
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return (*lod_start_pos_)[level][elem];
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}
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/*
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* Number of LODTensor's levels, each level has units of data, for example,
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* in the sentence's view, article, paragraph, sentence are 3 levels.
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*/
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size_t NumLevels() const {
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return lod_start_pos_ ? lod_start_pos_->size() : 0UL;
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}
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/*
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* Number of elements in a level.
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*/
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size_t NumElements(size_t level = 0) const {
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PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
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NumLevels());
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// the last offset is the end of last element
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return lod_start_pos_->at(level).size() - 1;
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}
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/*
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* Slice of levels[level_begin:level_end], with tensor copied.
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*/
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template <typename T>
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LODTensor SliceCopied(size_t level_begin, size_t level_end,
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const platform::Place &dst_place) const;
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/*
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* Slice of levels[level_begin:level_end], with tensor shared.
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*/
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LODTensor SliceShared(size_t level_begin, size_t level_end) const;
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/*
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* Slice of elements of a level, [elem_begin: elem_end], with tensor copied.
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* @note: low performance in slice lod_start_pos_.
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*/
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template <typename T>
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LODTensor SliceCopied(size_t level, size_t elem_begin, size_t elem_end,
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const platform::Place &dst_place) const;
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/*
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* Slice of elements of a level, [elem_begin: elem_end], with tensor shared.
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* @note: low performance in slice lod_start_pos_.
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*/
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LODTensor SliceShared(size_t level, size_t elem_begin, size_t elem_end) const;
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/*
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* Copy other's lod_start_pos_, to share LOD info.
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* @note: the LOD info should not be changed.
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*/
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void ShareLOD(const LODTensor &other) {
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lod_start_pos_ = other.lod_start_pos_;
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}
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/*
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* Copy other's lod_start_pos_'s content, free to mutate.
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*/
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void CopyLOD(const LODTensor &other) {
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lod_start_pos_ = std::make_shared<LOD>(*other.lod_start_pos_);
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}
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/*
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* Determine whether LODTensor has a valid LOD info.
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*/
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bool HasLOD() const { return bool(lod_start_pos_); }
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LOD *lod() const { return lod_start_pos_.get(); }
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std::shared_ptr<Tensor> &tensor() { return tensor_; }
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Tensor *raw_tensor() { return tensor_.get(); }
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private:
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std::shared_ptr<LOD> lod_start_pos_;
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std::shared_ptr<Tensor> tensor_;
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};
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} // namespace framework
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} // namespace paddle
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#include "paddle/framework/lod_tensor_impl.h"
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@ -0,0 +1,60 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
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|
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#include "paddle/framework/details/lod_tensor.h"
|
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|
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namespace paddle {
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namespace framework {
|
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|
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template <typename T>
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LODTensor LODTensor::SliceCopied(size_t level_begin, size_t level_end,
|
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const platform::Place &dst_place) const {
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PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
|
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auto new_lod = details::SliceLOD(*lod_start_pos_, level_begin, level_end);
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auto new_tensor = std::make_shared<Tensor>();
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new_tensor->CopyFrom<T>(*tensor_, dst_place);
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|
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return LODTensor(new_tensor, new_lod);
|
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}
|
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|
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template <typename T>
|
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LODTensor LODTensor::SliceCopied(size_t level, size_t elem_begin,
|
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size_t elem_end,
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const platform::Place &dst_place) const {
|
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PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
|
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PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
|
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NumLevels());
|
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PADDLE_ENFORCE(elem_begin < NumElements(level),
|
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"element begin [%d] out of range [%d]", elem_begin,
|
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NumElements(level));
|
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PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
|
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"element end [%d] out of range [%d]", elem_end,
|
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NumElements(level));
|
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|
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auto new_lod = details::SliceLOD(*lod_start_pos_, level, elem_begin, elem_end,
|
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false /*tensor_shared*/);
|
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|
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auto start_idx = new_lod->front().front();
|
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auto end_idx = new_lod->front().back() - 1 /*the next element's start*/;
|
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auto sliced_tensor = tensor_->Slice<T>(start_idx, end_idx);
|
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auto new_tensor = std::make_shared<Tensor>();
|
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new_tensor->CopyFrom<T>(sliced_tensor, dst_place);
|
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|
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return LODTensor(new_tensor, new_lod);
|
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}
|
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|
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} // namespace framework
|
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} // namespace paddle
|
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@ -0,0 +1,165 @@
|
||||
/*
|
||||
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
*/
|
||||
|
||||
#include "paddle/framework/lod_tensor.h"
|
||||
|
||||
#include <glog/logging.h>
|
||||
#include <gtest/gtest.h>
|
||||
#include <memory>
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
|
||||
class LODTensorTester : public ::testing::Test {
|
||||
public:
|
||||
virtual void SetUp() override {
|
||||
lod_tensor.reset(new LODTensor);
|
||||
// tensor's batch_size: 30
|
||||
// 3 levels
|
||||
// 0 10 20
|
||||
// 0 5 10 15 20
|
||||
// 0 2 5 7 10 12 15 20
|
||||
auto lod = std::make_shared<LODTensor::LOD>();
|
||||
lod->push_back(std::vector<size_t>{0, 10, 20});
|
||||
lod->push_back(std::vector<size_t>{0, 5, 10, 15, 20});
|
||||
lod->push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
|
||||
|
||||
auto tensor = std::make_shared<Tensor>();
|
||||
tensor->Resize({20 /*batch size*/, 128 /*dim*/});
|
||||
// malloc memory
|
||||
tensor->mutable_data<float>(place);
|
||||
|
||||
lod_tensor->Reset(tensor, lod);
|
||||
}
|
||||
|
||||
protected:
|
||||
std::unique_ptr<LODTensor> lod_tensor;
|
||||
platform::CPUPlace place;
|
||||
};
|
||||
|
||||
TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor->NumLevels(), 3UL); }
|
||||
|
||||
TEST_F(LODTensorTester, NumElements) {
|
||||
ASSERT_EQ(lod_tensor->NumElements(0), 2UL);
|
||||
ASSERT_EQ(lod_tensor->NumElements(1), 4UL);
|
||||
ASSERT_EQ(lod_tensor->NumElements(2), 8UL);
|
||||
}
|
||||
|
||||
TEST_F(LODTensorTester, SliceShared_Level) {
|
||||
// slice 1 level
|
||||
for (size_t level = 0; level < 3UL; ++level) {
|
||||
auto new_lod_tensor = lod_tensor->SliceShared(level, level + 1);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
|
||||
ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
|
||||
}
|
||||
// slice 2 level
|
||||
for (size_t level = 0; level < 2UL; ++level) {
|
||||
auto new_lod_tensor = lod_tensor->SliceShared(level, level + 2);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(1),
|
||||
lod_tensor->NumElements(level + 1));
|
||||
ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
|
||||
}
|
||||
}
|
||||
|
||||
TEST_F(LODTensorTester, SliceCopied_Level) {
|
||||
// slice 1 level
|
||||
for (size_t level = 0; level < 3UL; ++level) {
|
||||
auto new_lod_tensor =
|
||||
lod_tensor->SliceCopied<float>(level, level + 1, place);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
|
||||
// ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
|
||||
// TODO(superjom) add tensor comparation here.
|
||||
}
|
||||
// slice 2 level
|
||||
for (size_t level = 0; level < 2UL; ++level) {
|
||||
auto new_lod_tensor =
|
||||
lod_tensor->SliceCopied<float>(level, level + 2, place);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(1),
|
||||
lod_tensor->NumElements(level + 1));
|
||||
// ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
|
||||
// TODO(superjom) add tensor comparation here.
|
||||
}
|
||||
}
|
||||
|
||||
TEST_F(LODTensorTester, SliceShared_Element) {
|
||||
size_t level = 0;
|
||||
auto new_lod_tensor = lod_tensor->SliceShared(level, 0, 2);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(2), 8UL);
|
||||
ASSERT_EQ(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
|
||||
|
||||
level = 1;
|
||||
new_lod_tensor = lod_tensor->SliceShared(level, 0, 2);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
|
||||
ASSERT_EQ(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
|
||||
}
|
||||
|
||||
TEST_F(LODTensorTester, SliceCopied_Element) {
|
||||
size_t level = 0;
|
||||
auto new_lod_tensor = lod_tensor->SliceCopied<float>(level, 0, 2, place);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(2), 8UL);
|
||||
ASSERT_NE(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
|
||||
|
||||
level = 1;
|
||||
new_lod_tensor = lod_tensor->SliceCopied<float>(level, 0, 2, place);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
|
||||
ASSERT_NE(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
|
||||
|
||||
level = 1;
|
||||
// LOD is
|
||||
// 0 5 10
|
||||
// 0 2 5 7 10
|
||||
new_lod_tensor = lod_tensor->SliceCopied<float>(level, 1, 3, place);
|
||||
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
|
||||
|
||||
ASSERT_EQ(new_lod_tensor.lod_element(0, 0), 0UL);
|
||||
ASSERT_EQ(new_lod_tensor.lod_element(0, 1), 5UL);
|
||||
ASSERT_EQ(new_lod_tensor.lod_element(1, 0), 0UL);
|
||||
ASSERT_EQ(new_lod_tensor.lod_element(1, 1), 2UL);
|
||||
ASSERT_EQ(new_lod_tensor.lod_element(1, 2), 5UL);
|
||||
ASSERT_EQ(new_lod_tensor.lod_element(1, 3), 7UL);
|
||||
|
||||
// TODO(superjom) compare the content of these tensors
|
||||
}
|
||||
|
||||
TEST_F(LODTensorTester, ShareLOD) {
|
||||
LODTensor new_lod_tensor;
|
||||
new_lod_tensor.ShareLOD(*lod_tensor);
|
||||
ASSERT_EQ(new_lod_tensor.lod(), lod_tensor->lod());
|
||||
}
|
||||
|
||||
TEST_F(LODTensorTester, CopyLOD) {
|
||||
LODTensor new_lod_tensor;
|
||||
new_lod_tensor.CopyLOD(*lod_tensor);
|
||||
ASSERT_NE(new_lod_tensor.lod(), lod_tensor->lod());
|
||||
}
|
||||
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
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Reference in new issue