Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into imperative_lr_scheduler
minqiyang
6 years ago
commit
b5bbb13ac1
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@ -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.
|
||||
// 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
|
||||
// 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
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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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@ -0,0 +1,195 @@
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// Copyright (c) 2019 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.
|
||||
// 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.
|
||||
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
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#include "paddle/fluid/framework/details/container_cast.h"
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#include "paddle/fluid/framework/details/fused_all_reduce_op_handle.h"
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#include "paddle/fluid/framework/details/multi_devices_helper.h"
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#include "paddle/fluid/framework/ir/graph_helper.h"
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namespace paddle {
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namespace framework {
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namespace details {
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class FuseAllReduceOpPass : public ir::Pass {
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protected:
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std::unique_ptr<ir::Graph> ApplyImpl(
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std::unique_ptr<ir::Graph> graph) const override {
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ir::Graph &result = *graph;
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auto &places = Get<const std::vector<platform::Place>>(kPlaces);
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auto &local_scopes = Get<const std::vector<Scope *>>(kLocalScopes);
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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auto *nccl_ctxs = &Get<platform::NCCLContextMap>(kNCCLCtxs);
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#endif
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std::unordered_set<std::string> grads;
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auto ¶ms_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
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size_t num_of_all_reduce = params_grads.size();
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grads.reserve(num_of_all_reduce);
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for (auto p_g : params_grads) {
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grads.insert(p_g.second);
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}
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size_t num_place = places.size();
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std::unordered_map<std::string, ir::Node *> all_reduce_ops;
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all_reduce_ops.reserve(grads.size());
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for (auto &node : result.Nodes()) {
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if (node->IsOp()) {
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PADDLE_ENFORCE(node->IsWrappedBy<OpHandleBase>());
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auto *all_reduce_op_handle =
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dynamic_cast<AllReduceOpHandle *>(&node->Wrapper<OpHandleBase>());
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if (all_reduce_op_handle) {
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auto inputs = DynamicCast<VarHandle>(all_reduce_op_handle->Inputs());
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PADDLE_ENFORCE_EQ(inputs.size(), num_place);
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// The inputs' name should be the same.
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auto &grad_name = inputs[0]->name();
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for (size_t i = 1; i < inputs.size(); ++i) {
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PADDLE_ENFORCE_EQ(inputs[i]->name(), grad_name,
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"The input name should be the same.");
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}
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PADDLE_ENFORCE_NE(grads.count(grad_name), static_cast<size_t>(0));
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all_reduce_ops.emplace(grad_name, node);
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}
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}
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}
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VLOG(10) << "Find all_reduce_ops: " << all_reduce_ops.size();
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if (all_reduce_ops.size() == 0) {
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return std::move(graph);
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}
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PADDLE_ENFORCE_EQ(all_reduce_ops.size(), grads.size(),
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"The number of all_reduce OpHandle is not equal to the "
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"number of grads. Maybe some gradients are sparse type, "
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"it is not supported currently.");
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VLOG(10) << "Insert fused_all_reduce";
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auto &group_grads_params =
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graph->Get<GroupGradsAndParams>(kGroupGradsAndParams);
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for (auto &group_g_p : group_grads_params) {
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size_t group_size = group_g_p.size();
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PADDLE_ENFORCE_GT(group_size, static_cast<size_t>(0));
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std::vector<ir::Node *> group_all_reduce_ops;
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group_all_reduce_ops.reserve(group_size);
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for (auto &g_p : group_g_p) {
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group_all_reduce_ops.emplace_back(all_reduce_ops.at(g_p.first));
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}
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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InsertFusedAllReduce(places, local_scopes, group_size,
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group_all_reduce_ops, nccl_ctxs, &result);
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#else
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InsertFusedAllReduce(places, local_scopes, group_size,
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group_all_reduce_ops, &result);
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#endif
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}
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return std::move(graph);
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}
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void InsertFusedAllReduce(const std::vector<platform::Place> &places,
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const std::vector<Scope *> &local_scopes,
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const size_t num_of_all_reduce,
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const std::vector<ir::Node *> &all_reduce_ops,
|
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
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const platform::NCCLContextMap *nccl_ctxs,
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#endif
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ir::Graph *result) const {
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std::vector<VarHandleBase *> inputs;
|
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std::vector<VarHandleBase *> outputs;
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for (auto &op : all_reduce_ops) {
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auto &op_handle = op->Wrapper<OpHandleBase>();
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inputs.insert(inputs.end(), op_handle.Inputs().begin(),
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op_handle.Inputs().end());
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// Remove output
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for_each(op_handle.Inputs().begin(), op_handle.Inputs().end(),
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[&op_handle](VarHandleBase *var_handle) {
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var_handle->RemoveOutput(&op_handle, op_handle.Node());
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});
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outputs.insert(outputs.end(), op_handle.Outputs().begin(),
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op_handle.Outputs().end());
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// Remove Input
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for_each(
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op_handle.Outputs().begin(), op_handle.Outputs().end(),
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[](VarHandleBase *var_handle) { var_handle->ClearGeneratedOp(); });
|
||||
|
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result->RemoveNode(op_handle.Node());
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}
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||||
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#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
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CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
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local_scopes, nccl_ctxs, result);
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#else
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CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
|
||||
local_scopes, result);
|
||||
#endif
|
||||
}
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||||
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||||
private:
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void CreateFusedAllReduceOp(const std::vector<VarHandleBase *> &inputs,
|
||||
const std::vector<VarHandleBase *> &outputs,
|
||||
const size_t num_of_all_reduce,
|
||||
const std::vector<platform::Place> &places,
|
||||
const std::vector<Scope *> &local_scopes,
|
||||
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
||||
const platform::NCCLContextMap *nccl_ctxs,
|
||||
#endif
|
||||
ir::Graph *result) const {
|
||||
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
||||
auto *op_handle = new FusedAllReduceOpHandle(
|
||||
result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
|
||||
local_scopes, places, num_of_all_reduce, nccl_ctxs);
|
||||
#else
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||||
auto *op_handle = new FusedAllReduceOpHandle(
|
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result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
|
||||
local_scopes, places, num_of_all_reduce);
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||||
#endif
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||||
|
||||
for (auto in : inputs) {
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||||
op_handle->AddInput(in);
|
||||
}
|
||||
|
||||
for (auto out : outputs) {
|
||||
op_handle->AddOutput(out);
|
||||
}
|
||||
|
||||
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
||||
if (!nccl_ctxs) {
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||||
SetCommunicationContext(places, op_handle);
|
||||
}
|
||||
#else
|
||||
SetCommunicationContext(places, op_handle);
|
||||
#endif
|
||||
}
|
||||
|
||||
void SetCommunicationContext(const std::vector<platform::Place> &places,
|
||||
FusedAllReduceOpHandle *op_handle) const {
|
||||
for (size_t i = 0; i < places.size(); ++i) {
|
||||
op_handle->SetDeviceContext(
|
||||
places[i], platform::DeviceContextPool::Instance().Get(places[i]));
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace details
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
|
||||
REGISTER_PASS(fuse_all_reduce_op_pass,
|
||||
paddle::framework::details::FuseAllReduceOpPass);
|
||||
@ -1,51 +0,0 @@
|
||||
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// 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/fluid/framework/details/fuse_vars_op_handle.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace details {
|
||||
|
||||
void FuseVarsOpHandle::RunImpl() {
|
||||
WaitInputVarGenerated(place_);
|
||||
|
||||
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
|
||||
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
|
||||
PADDLE_ENFORCE_EQ(in_var_handles.size(), 0UL);
|
||||
PADDLE_ENFORCE_EQ(out_var_handles.size() - 1, inputs_numel_.size(), "");
|
||||
|
||||
auto scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
|
||||
|
||||
auto out_var_handle = out_var_handles[0];
|
||||
auto out_var = scope->Var(out_var_handle->name());
|
||||
|
||||
auto out_tensor = out_var->GetMutable<LoDTensor>();
|
||||
out_tensor->Resize({total_numel_}).mutable_data(this->place_, type_);
|
||||
|
||||
int64_t s = 0;
|
||||
for (size_t i = 1; i < out_var_handles.size(); ++i) {
|
||||
auto out_name = out_var_handles[i]->name();
|
||||
auto out_t = scope->Var(out_name)->GetMutable<LoDTensor>();
|
||||
auto numel = this->inputs_numel_.at(out_name);
|
||||
out_t->ShareDataWith(out_tensor->Slice(s, s + numel));
|
||||
s += numel;
|
||||
}
|
||||
this->RunAndRecordEvent([] {});
|
||||
}
|
||||
|
||||
std::string FuseVarsOpHandle::Name() const { return "fuse vars"; }
|
||||
} // namespace details
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
@ -1,65 +0,0 @@
|
||||
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// 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
|
||||
|
||||
#include <map>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "paddle/fluid/framework/details/container_cast.h"
|
||||
#include "paddle/fluid/framework/details/op_handle_base.h"
|
||||
#include "paddle/fluid/framework/lod_tensor.h"
|
||||
#include "paddle/fluid/framework/scope.h"
|
||||
#include "paddle/fluid/platform/device_context.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace details {
|
||||
|
||||
struct FuseVarsOpHandle : public OpHandleBase {
|
||||
public:
|
||||
FuseVarsOpHandle(ir::Node *node, Scope *local_scope,
|
||||
const platform::Place &place,
|
||||
const std::unordered_map<std::string, int64_t> &inputs_numel,
|
||||
const proto::VarType::Type var_type)
|
||||
: OpHandleBase(node),
|
||||
local_scope_(local_scope),
|
||||
place_(place),
|
||||
inputs_numel_(inputs_numel),
|
||||
type_(var_type) {
|
||||
total_numel_ = 0;
|
||||
for (auto in_numel : inputs_numel) {
|
||||
PADDLE_ENFORCE_GT(in_numel.second, 0);
|
||||
total_numel_ += in_numel.second;
|
||||
}
|
||||
}
|
||||
|
||||
std::string Name() const override;
|
||||
|
||||
bool IsMultiDeviceTransfer() override { return false; };
|
||||
|
||||
protected:
|
||||
void RunImpl() override;
|
||||
|
||||
private:
|
||||
Scope *local_scope_;
|
||||
const platform::Place place_;
|
||||
const std::unordered_map<std::string, int64_t> inputs_numel_;
|
||||
const proto::VarType::Type type_;
|
||||
int64_t total_numel_;
|
||||
};
|
||||
} // namespace details
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,249 @@
|
||||
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// 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/fluid/framework/details/fused_all_reduce_op_handle.h"
|
||||
#include <algorithm>
|
||||
#include <utility>
|
||||
#include "paddle/fluid/framework/details/container_cast.h"
|
||||
#include "paddle/fluid/framework/details/reduce_and_gather.h"
|
||||
#include "paddle/fluid/framework/details/variable_visitor.h"
|
||||
#include "paddle/fluid/platform/profiler.h"
|
||||
|
||||
DEFINE_bool(skip_fused_all_reduce_check, false, "");
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace details {
|
||||
|
||||
typedef std::vector<std::vector<std::pair<std::string, const LoDTensor *>>>
|
||||
GradientAndLoDTensor;
|
||||
|
||||
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
||||
FusedAllReduceOpHandle::FusedAllReduceOpHandle(
|
||||
ir::Node *node, const std::vector<Scope *> &local_scopes,
|
||||
const std::vector<platform::Place> &places, const size_t num_of_all_reduce,
|
||||
const platform::NCCLContextMap *ctxs)
|
||||
: OpHandleBase(node),
|
||||
local_scopes_(local_scopes),
|
||||
places_(places),
|
||||
num_of_all_reduce_(num_of_all_reduce),
|
||||
nccl_ctxs_(ctxs) {
|
||||
if (nccl_ctxs_) {
|
||||
for (auto &p : places_) {
|
||||
this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
|
||||
}
|
||||
}
|
||||
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
|
||||
}
|
||||
#else
|
||||
|
||||
FusedAllReduceOpHandle::FusedAllReduceOpHandle(
|
||||
ir::Node *node, const std::vector<Scope *> &local_scopes,
|
||||
const std::vector<platform::Place> &places, const size_t num_of_all_reduce)
|
||||
: OpHandleBase(node),
|
||||
local_scopes_(local_scopes),
|
||||
places_(places),
|
||||
num_of_all_reduce_(num_of_all_reduce) {
|
||||
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
void FusedAllReduceOpHandle::RunImpl() {
|
||||
platform::RecordEvent record_event(Name());
|
||||
|
||||
VLOG(4) << this->DebugString();
|
||||
|
||||
WaitInputVarGenerated();
|
||||
// The input: grad0(dev0), grad0(dev1), grad1(dev0), grad1(dev1)...
|
||||
// The output: grad0(dev0), grad0(dev1), grad1(dev0), grad1(dev1)...
|
||||
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
|
||||
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
|
||||
|
||||
size_t place_num = places_.size();
|
||||
PADDLE_ENFORCE_EQ(
|
||||
in_var_handles.size(), place_num * num_of_all_reduce_,
|
||||
"The NoDummyInputSize should be equal to the number of places.");
|
||||
PADDLE_ENFORCE_EQ(
|
||||
in_var_handles.size(), out_var_handles.size(),
|
||||
"The NoDummyInputSize and NoDummyOutputSize should be equal.");
|
||||
|
||||
GradientAndLoDTensor grads_tensor;
|
||||
grads_tensor.resize(place_num);
|
||||
|
||||
int64_t numel = -1;
|
||||
auto dtype = static_cast<framework::proto::VarType::Type>(0);
|
||||
for (size_t scope_idx = 0; scope_idx < local_scopes_.size(); ++scope_idx) {
|
||||
auto &g_tensor = grads_tensor.at(scope_idx);
|
||||
g_tensor.reserve(num_of_all_reduce_);
|
||||
|
||||
GetGradLoDTensor(scope_idx, in_var_handles, out_var_handles, &g_tensor);
|
||||
|
||||
int64_t element_num = 0;
|
||||
framework::proto::VarType::Type ele_dtype =
|
||||
static_cast<framework::proto::VarType::Type>(0);
|
||||
GetDTypeAndNumel(g_tensor, &ele_dtype, &element_num);
|
||||
|
||||
if (numel == -1) {
|
||||
numel = element_num;
|
||||
}
|
||||
if (dtype == static_cast<framework::proto::VarType::Type>(0)) {
|
||||
dtype = ele_dtype;
|
||||
PADDLE_ENFORCE_NE(ele_dtype,
|
||||
static_cast<framework::proto::VarType::Type>(0));
|
||||
}
|
||||
PADDLE_ENFORCE_EQ(ele_dtype, dtype);
|
||||
|
||||
// Check whether the address space is contiguous.
|
||||
std::sort(
|
||||
g_tensor.begin(), g_tensor.end(),
|
||||
[](const std::pair<std::string, const LoDTensor *> &grad1,
|
||||
const std::pair<std::string, const LoDTensor *> &grad2) -> bool {
|
||||
return grad1.second->data<void>() < grad2.second->data<void>();
|
||||
});
|
||||
|
||||
for (size_t k = 1; k < g_tensor.size(); ++k) {
|
||||
const void *cur_address = g_tensor.at(k - 1).second->data<void>();
|
||||
int64_t len = g_tensor.at(k - 1).second->numel();
|
||||
auto offset = len * framework::SizeOfType(dtype);
|
||||
void *infer_next_address = reinterpret_cast<void *>(
|
||||
reinterpret_cast<uintptr_t>(cur_address) + offset);
|
||||
const void *next_address = g_tensor.at(k).second->data<void>();
|
||||
|
||||
VLOG(10) << string::Sprintf(
|
||||
"Input[%d](%s) address: 0X%02x, Input[%d](%s) address: 0X%02x, Infer "
|
||||
"input[%d] address: 0X%02x. The offset: %d",
|
||||
k - 1, g_tensor.at(k - 1).first, cur_address, g_tensor.at(k).first, k,
|
||||
next_address, k, infer_next_address, offset);
|
||||
PADDLE_ENFORCE_EQ(infer_next_address, next_address,
|
||||
"The address is not consistent.");
|
||||
}
|
||||
}
|
||||
|
||||
if (!FLAGS_skip_fused_all_reduce_check) {
|
||||
for (size_t scope_idx = 0; scope_idx < place_num; ++scope_idx) {
|
||||
for (size_t j = 1; j < num_of_all_reduce_; ++j) {
|
||||
PADDLE_ENFORCE_EQ(grads_tensor.at(0).at(j).first,
|
||||
grads_tensor.at(scope_idx).at(j).first);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<const void *> lod_tensor_data;
|
||||
for (size_t scope_idx = 0; scope_idx < place_num; ++scope_idx) {
|
||||
auto data = grads_tensor.at(scope_idx).at(0).second->data<void>();
|
||||
lod_tensor_data.emplace_back(data);
|
||||
}
|
||||
|
||||
if (platform::is_gpu_place(places_[0])) {
|
||||
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
|
||||
PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
|
||||
int nccl_dtype = platform::ToNCCLDataType(dtype);
|
||||
std::vector<std::function<void()>> all_reduce_calls;
|
||||
for (size_t i = 0; i < local_scopes_.size(); ++i) {
|
||||
auto &p = places_[i];
|
||||
void *buffer = const_cast<void *>(lod_tensor_data.at(i));
|
||||
|
||||
int dev_id = boost::get<platform::CUDAPlace>(p).device;
|
||||
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
|
||||
auto stream = nccl_ctx.stream();
|
||||
auto comm = nccl_ctx.comm_;
|
||||
all_reduce_calls.emplace_back([=] {
|
||||
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
|
||||
buffer, buffer, numel, static_cast<ncclDataType_t>(nccl_dtype),
|
||||
ncclSum, comm, stream));
|
||||
});
|
||||
}
|
||||
|
||||
this->RunAndRecordEvent([&] {
|
||||
if (all_reduce_calls.size() == 1UL) {
|
||||
// Do not use NCCLGroup when manage NCCL by per thread per device
|
||||
all_reduce_calls[0]();
|
||||
} else {
|
||||
platform::NCCLGroupGuard guard;
|
||||
for (auto &call : all_reduce_calls) {
|
||||
call();
|
||||
}
|
||||
}
|
||||
});
|
||||
#else
|
||||
PADDLE_THROW("Not compiled with CUDA");
|
||||
#endif
|
||||
} else {
|
||||
// Special handle CPU only Operator's gradient. Like CRF
|
||||
auto grad_name = grads_tensor.at(0).at(0).first;
|
||||
auto &trg = *this->local_scopes_[0]
|
||||
->FindVar(kLocalExecScopeName)
|
||||
->Get<Scope *>()
|
||||
->FindVar(grad_name)
|
||||
->GetMutable<framework::LoDTensor>();
|
||||
|
||||
// Reduce All data to trg in CPU
|
||||
ReduceBufferData func(lod_tensor_data, trg.data<void>(), numel);
|
||||
VisitDataType(trg.type(), func);
|
||||
|
||||
for (size_t i = 1; i < local_scopes_.size(); ++i) {
|
||||
auto &scope =
|
||||
*local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
|
||||
auto &p = places_[i];
|
||||
auto *var = scope.FindVar(grad_name);
|
||||
auto *dev_ctx = dev_ctxes_.at(p);
|
||||
size_t size = numel * SizeOfType(trg.type());
|
||||
RunAndRecordEvent(p, [&trg, var, dev_ctx, p, size] {
|
||||
auto dst_ptr = var->GetMutable<framework::LoDTensor>()->data<void>();
|
||||
platform::CPUPlace cpu_place;
|
||||
memory::Copy(cpu_place, dst_ptr, cpu_place, trg.data<void>(), size);
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void FusedAllReduceOpHandle::GetGradLoDTensor(
|
||||
const size_t &scope_idx, const std::vector<VarHandle *> &in_var_handles,
|
||||
const std::vector<VarHandle *> &out_var_handles,
|
||||
std::vector<std::pair<std::string, const LoDTensor *>> *grad_tensor) const {
|
||||
auto *local_scope =
|
||||
local_scopes_.at(scope_idx)->FindVar(kLocalExecScopeName)->Get<Scope *>();
|
||||
size_t place_num = places_.size();
|
||||
|
||||
for (size_t j = 0; j < in_var_handles.size(); j += place_num) {
|
||||
auto var_name = in_var_handles[j]->name();
|
||||
PADDLE_ENFORCE_EQ(var_name, out_var_handles[j]->name());
|
||||
auto &lod_tensor = local_scope->FindVar(var_name)->Get<LoDTensor>();
|
||||
PADDLE_ENFORCE_EQ(lod_tensor.place(), places_.at(scope_idx));
|
||||
grad_tensor->emplace_back(std::make_pair(var_name, &lod_tensor));
|
||||
}
|
||||
}
|
||||
|
||||
void FusedAllReduceOpHandle::GetDTypeAndNumel(
|
||||
const std::vector<std::pair<std::string, const LoDTensor *>> &grad_tensor,
|
||||
proto::VarType::Type *dtype, int64_t *numel) const {
|
||||
*numel = 0;
|
||||
for (size_t i = 0; i < grad_tensor.size(); ++i) {
|
||||
// Get element number
|
||||
int64_t len = grad_tensor.at(i).second->numel();
|
||||
PADDLE_ENFORCE_GT(len, 0);
|
||||
*numel += len;
|
||||
|
||||
// Get dtype
|
||||
auto ele_type = grad_tensor.at(i).second->type();
|
||||
if (i == 0) {
|
||||
*dtype = ele_type;
|
||||
}
|
||||
PADDLE_ENFORCE_EQ(ele_type, *dtype);
|
||||
}
|
||||
}
|
||||
|
||||
std::string FusedAllReduceOpHandle::Name() const { return "fused_all_reduce"; }
|
||||
} // namespace details
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
Some files were not shown because too many files have changed in this diff Show More
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Reference in new issue