Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into complete_py_reader_python
sneaxiy
7 years ago
commit
9b28260029
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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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#ifdef PADDLE_WITH_CUDA
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DataBalanceOpHandle::DataBalanceOpHandle(
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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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: 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->dev_ctxes_[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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const std::vector<Scope *> &local_scopes,
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const std::vector<platform::Place> &places)
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: 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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if (places_.size() == 1) {
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return;
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}
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auto in_var_handles = DynamicCast<VarHandle>(inputs_);
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auto out_var_handles = DynamicCast<VarHandle>(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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@ -0,0 +1,59 @@
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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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#ifdef PADDLE_WITH_CUDA
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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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#ifdef PADDLE_WITH_CUDA
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DataBalanceOpHandle(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(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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