[Speed]Refine ParallelExecutor (#16190)
* refine parallelExecutor test=develop * Polish op_handle test=develop * Remove unnecessary op_handle test=develop * Fix Travis CI test=develop * Fix fetch bug test=develop * Remove WaitInputVarGenerated * Fix OpHandleBase::Run test=develop * debug test=develop * use origin fetch_op_handle test=develop * Revert op_handle_base.cc test=develop * Polish code test=develop * Fix OpHandleBase::Run test=develop * code refine * test CI and CE test=develop * fix OpHandle::Run test=develop * refine AllReduceOpHandle test=develop * Polish code test=developrevert-16190-refine_parallel_executor
chengduo
6 years ago
committed by
GitHub
parent
33965527fd
commit
a6a3b2fbbc
@ -1,154 +0,0 @@
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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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#include "paddle/fluid/framework/details/data_balance_op_handle.h"
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#include <algorithm>
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#include "paddle/fluid/framework/details/container_cast.h"
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namespace paddle {
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namespace framework {
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namespace details {
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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DataBalanceOpHandle::DataBalanceOpHandle(
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ir::Node *node, const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places,
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const platform::NCCLContextMap *ctxs)
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: OpHandleBase(node), local_scopes_(local_scopes), places_(places) {
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if (ctxs) {
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for (auto &p : places_) {
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this->SetDeviceContext(p, ctxs->DevCtx(p));
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}
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}
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}
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#else
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DataBalanceOpHandle::DataBalanceOpHandle(
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ir::Node *node, const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places)
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: OpHandleBase(node), local_scopes_(local_scopes), places_(places) {}
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#endif
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std::string DataBalanceOpHandle::Name() const { return "data balance"; }
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std::vector<std::array<int, 3>> DataBalanceOpHandle::GetBalancePlan(
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const std::vector<int> &device_sizes) {
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int device_num = device_sizes.size();
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int total_size = 0;
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int empty_num = 0;
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std::vector<std::array<int, 2>> size_device_vec;
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size_device_vec.reserve(device_num);
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for (int i = 0; i < device_num; ++i) {
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if (device_sizes[i] == 0) {
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++empty_num;
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}
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total_size += device_sizes[i];
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size_device_vec.push_back({{device_sizes[i], i}});
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}
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std::vector<std::array<int, 3>> res;
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if (empty_num == 0) {
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// No need to do data balance.
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return res;
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}
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if (total_size < device_num) {
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// No enough data.
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PADDLE_THROW_EOF();
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}
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std::sort(size_device_vec.begin(), size_device_vec.end(),
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[](const std::array<int, 2> &a, const std::array<int, 2> &b) {
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return a[0] > b[0];
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});
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int expected_device_size = total_size / device_num;
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int src_idx = 0;
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for (int dst_idx = device_num - empty_num; dst_idx < device_num; ++dst_idx) {
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if (size_device_vec[src_idx][0] <= expected_device_size) {
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++src_idx;
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PADDLE_ENFORCE_LT(
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src_idx, device_num - empty_num,
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"In current srategy an empty tensor should not be copy source.");
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}
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size_device_vec[src_idx][0] -= expected_device_size;
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size_device_vec[dst_idx][0] += expected_device_size;
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res.push_back({{size_device_vec[src_idx][1], size_device_vec[dst_idx][1],
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expected_device_size}});
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}
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return res;
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}
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void DataBalanceOpHandle::RunImpl() {
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PADDLE_ENFORCE_GT(places_.size(), 1UL,
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"Data balance can only be enabled when the number of "
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"places to run larger than 1.");
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auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
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auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
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PADDLE_ENFORCE(in_var_handles.size() % places_.size() == 0);
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PADDLE_ENFORCE_EQ(
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in_var_handles.size(), out_var_handles.size(),
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"The NoDummyInputSize and NoDummyOutputSize should be equal.");
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int data_num = in_var_handles.size() / places_.size();
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WaitInputVarGenerated();
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std::vector<std::vector<LoDTensor *>> lod_tensors(data_num);
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std::vector<int> device_sizes;
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for (int i = 0; i < static_cast<int>(in_var_handles.size()); ++i) {
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PADDLE_ENFORCE_EQ(in_var_handles[i]->name(), out_var_handles[i]->name(),
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"The name of input and output should be equal.");
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int place_idx = i / data_num;
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int data_idx = i % data_num;
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auto *local_scope =
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local_scopes_[place_idx]->FindVar(kLocalExecScopeName)->Get<Scope *>();
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auto *tensor_var = local_scope->FindVar(in_var_handles[i]->name());
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PADDLE_ENFORCE(tensor_var->IsType<LoDTensor>());
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auto *tensor = tensor_var->GetMutable<LoDTensor>();
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lod_tensors[data_idx].push_back(tensor);
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int ins_size =
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tensor->lod().empty() ? tensor->dims()[0] : tensor->NumElements();
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if (data_idx == 0) {
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device_sizes.emplace_back(ins_size);
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} else {
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PADDLE_ENFORCE_EQ(
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ins_size, device_sizes.at(place_idx),
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"All data on the same device shall have the same batch size.");
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}
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}
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const auto &balance_plan = GetBalancePlan(device_sizes);
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for (const auto &trans : balance_plan) {
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for (int data_idx = 0; data_idx < data_num; ++data_idx) {
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LoDTensor *src_tensor = lod_tensors[data_idx][trans[0]];
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LoDTensor *dst_tensor = lod_tensors[data_idx][trans[1]];
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int trans_ins_size = trans[2];
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LoD src_lod = src_tensor->lod();
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int src_ins_size =
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src_lod.empty() ? src_tensor->dims()[0] : src_tensor->NumElements();
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int cut_point = src_ins_size - trans_ins_size;
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if (!src_lod.empty()) {
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for (auto &level : src_lod) {
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cut_point = level[cut_point];
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}
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}
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TensorCopySync(src_tensor->Slice(cut_point, src_tensor->dims()[0]),
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dst_tensor->place(), dst_tensor);
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src_tensor->ShareDataWith(src_tensor->Slice(0, cut_point));
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if (!src_lod.empty()) {
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dst_tensor->set_lod(SliceInLevel(
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src_lod, 0, src_ins_size - trans_ins_size, src_ins_size));
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src_tensor->set_lod(
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SliceInLevel(src_lod, 0, 0, src_ins_size - trans_ins_size));
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}
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}
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}
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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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@ -1,59 +0,0 @@
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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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#pragma once
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/details/op_handle_base.h"
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/framework/scope.h"
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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#include "paddle/fluid/platform/nccl_helper.h"
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#endif
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namespace paddle {
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namespace framework {
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namespace details {
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struct DataBalanceOpHandle : public OpHandleBase {
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public:
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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DataBalanceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places,
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const platform::NCCLContextMap *ctxs);
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#else
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DataBalanceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places);
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#endif
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std::string Name() const override;
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bool IsMultiDeviceTransfer() override { return false; };
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protected:
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void RunImpl() override;
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private:
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// std::vector<(src_dev_id, dst_dev_id, trans_size)>
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std::vector<std::array<int, 3>> GetBalancePlan(
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const std::vector<int> &batch_size_per_device);
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const std::vector<Scope *> local_scopes_;
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const std::vector<platform::Place> places_;
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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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@ -1,51 +0,0 @@
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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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#include "paddle/fluid/framework/details/fuse_vars_op_handle.h"
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namespace paddle {
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namespace framework {
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namespace details {
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void FuseVarsOpHandle::RunImpl() {
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WaitInputVarGenerated(place_);
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auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
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auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
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PADDLE_ENFORCE_EQ(in_var_handles.size(), 0UL);
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PADDLE_ENFORCE_EQ(out_var_handles.size() - 1, inputs_numel_.size(), "");
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auto scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
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auto out_var_handle = out_var_handles[0];
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auto out_var = scope->Var(out_var_handle->name());
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auto out_tensor = out_var->GetMutable<LoDTensor>();
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out_tensor->Resize({total_numel_}).mutable_data(this->place_, type_);
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int64_t s = 0;
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for (size_t i = 1; i < out_var_handles.size(); ++i) {
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auto out_name = out_var_handles[i]->name();
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auto out_t = scope->Var(out_name)->GetMutable<LoDTensor>();
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auto numel = this->inputs_numel_.at(out_name);
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out_t->ShareDataWith(out_tensor->Slice(s, s + numel));
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s += numel;
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}
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this->RunAndRecordEvent([] {});
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}
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std::string FuseVarsOpHandle::Name() const { return "fuse vars"; }
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} // namespace details
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} // namespace framework
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} // namespace paddle
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@ -1,65 +0,0 @@
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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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#pragma once
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#include <map>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/details/container_cast.h"
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#include "paddle/fluid/framework/details/op_handle_base.h"
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/framework/scope.h"
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#include "paddle/fluid/platform/device_context.h"
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namespace paddle {
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namespace framework {
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namespace details {
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struct FuseVarsOpHandle : public OpHandleBase {
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public:
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FuseVarsOpHandle(ir::Node *node, Scope *local_scope,
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const platform::Place &place,
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const std::unordered_map<std::string, int64_t> &inputs_numel,
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const proto::VarType::Type var_type)
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: OpHandleBase(node),
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local_scope_(local_scope),
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place_(place),
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inputs_numel_(inputs_numel),
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type_(var_type) {
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total_numel_ = 0;
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for (auto in_numel : inputs_numel) {
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PADDLE_ENFORCE_GT(in_numel.second, 0);
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total_numel_ += in_numel.second;
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}
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}
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std::string Name() const override;
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bool IsMultiDeviceTransfer() override { return false; };
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protected:
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void RunImpl() override;
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private:
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Scope *local_scope_;
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const platform::Place place_;
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const std::unordered_map<std::string, int64_t> inputs_numel_;
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const proto::VarType::Type type_;
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int64_t total_numel_;
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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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