Merge pull request #11854 from JiayiFeng/dev_data_balance
Data balance for the ParallelExecutoranalysis/code-clean
fengjiayi
7 years ago
committed by
GitHub
commit
58560622bc
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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("There is no next data.");
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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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@ -0,0 +1,187 @@
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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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import unittest
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import paddle.fluid as fluid
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import paddle.v2 as paddle
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import numpy as np
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class TestDataBalance(unittest.TestCase):
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def prepare_data(self):
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def fake_data_generator():
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for n in xrange(self.total_ins_num):
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yield np.ones((3, 4)) * n, n
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# Prepare data
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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reader = paddle.batch(
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fake_data_generator, batch_size=self.batch_size)
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feeder = fluid.DataFeeder(
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feed_list=[
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fluid.layers.data(
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name='image', shape=[3, 4], dtype='float32'),
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fluid.layers.data(
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name='label', shape=[1], dtype='int64'),
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],
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place=fluid.CPUPlace())
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self.num_batches = fluid.recordio_writer.convert_reader_to_recordio_file(
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self.data_file_name, reader, feeder)
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def prepare_lod_data(self):
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def fake_data_generator():
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for n in xrange(1, self.total_ins_num + 1):
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d1 = (np.ones((n, 3)) * n).astype('float32')
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d2 = (np.array(n).reshape((1, 1))).astype('int32')
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yield d1, d2
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# Prepare lod data
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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with fluid.recordio_writer.create_recordio_writer(
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filename=self.lod_data_file_name) as writer:
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eof = False
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generator = fake_data_generator()
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while (not eof):
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data_batch = [
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np.array([]).reshape((0, 3)), np.array([]).reshape(
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(0, 1))
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]
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lod = [0]
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for _ in xrange(self.batch_size):
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try:
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ins = generator.next()
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except StopIteration:
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eof = True
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break
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for i, d in enumerate(ins):
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data_batch[i] = np.concatenate(
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(data_batch[i], d), axis=0)
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lod.append(lod[-1] + ins[0].shape[0])
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if data_batch[0].shape[0] > 0:
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for i, d in enumerate(data_batch):
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t = fluid.LoDTensor()
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t.set(data_batch[i], fluid.CPUPlace())
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if i == 0:
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t.set_lod([lod])
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writer.append_tensor(t)
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writer.complete_append_tensor()
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def setUp(self):
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self.use_cuda = fluid.core.is_compiled_with_cuda()
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self.data_file_name = './data_balance_test.recordio'
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self.lod_data_file_name = './data_balance_with_lod_test.recordio'
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self.total_ins_num = 50
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self.batch_size = 10
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self.prepare_data()
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self.prepare_lod_data()
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def main(self):
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main_prog = fluid.Program()
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startup_prog = fluid.Program()
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with fluid.program_guard(main_prog, startup_prog):
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data_reader = fluid.layers.io.open_files(
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filenames=[self.data_file_name],
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shapes=[[-1, 3, 4], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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if self.use_cuda:
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data_reader = fluid.layers.double_buffer(data_reader)
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image, label = fluid.layers.read_file(data_reader)
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place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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parallel_exe = fluid.ParallelExecutor(
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use_cuda=self.use_cuda, main_program=main_prog)
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if (parallel_exe.device_count > self.batch_size):
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print("WARNING: Unittest TestDataBalance skipped. \
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For the result is not correct when device count \
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is larger than batch size.")
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exit(0)
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fetch_list = [image.name, label.name]
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data_appeared = [False] * self.total_ins_num
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while (True):
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try:
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image_val, label_val = parallel_exe.run(fetch_list,
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return_numpy=True)
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except fluid.core.EnforceNotMet as ex:
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self.assertIn("There is no next data.", ex.message)
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break
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ins_num = image_val.shape[0]
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broadcasted_label = np.ones(
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(ins_num, 3, 4)) * label_val.reshape((ins_num, 1, 1))
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self.assertEqual(image_val.all(), broadcasted_label.all())
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for l in label_val:
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self.assertFalse(data_appeared[l[0]])
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data_appeared[l[0]] = True
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for i in data_appeared:
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self.assertTrue(i)
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def main_lod(self):
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main_prog = fluid.Program()
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startup_prog = fluid.Program()
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with fluid.program_guard(main_prog, startup_prog):
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data_reader = fluid.layers.io.open_files(
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filenames=[self.lod_data_file_name],
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shapes=[[-1, 3], [-1, 1]],
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lod_levels=[1, 0],
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dtypes=['float32', 'int32'],
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thread_num=1)
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ins, label = fluid.layers.read_file(data_reader)
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place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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parallel_exe = fluid.ParallelExecutor(
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use_cuda=self.use_cuda, main_program=main_prog)
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if (parallel_exe.device_count > self.batch_size):
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print("WARNING: Unittest TestDataBalance skipped. \
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For the result is not correct when device count \
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is larger than batch size.")
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exit(0)
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fetch_list = [ins.name, label.name]
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data_appeared = [False] * self.total_ins_num
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while (True):
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try:
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ins_tensor, label_tensor = parallel_exe.run(
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fetch_list, return_numpy=False)
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except fluid.core.EnforceNotMet as ex:
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self.assertIn("There is no next data.", ex.message)
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break
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ins_val = np.array(ins_tensor)
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label_val = np.array(label_tensor)
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ins_lod = ins_tensor.lod()[0]
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self.assertEqual(ins_val.shape[1], 3)
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self.assertEqual(label_val.shape[1], 1)
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self.assertEqual(len(ins_lod) - 1, label_val.shape[0])
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for i in range(0, len(ins_lod) - 1):
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ins_elem = ins_val[ins_lod[i]:ins_lod[i + 1]][:]
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label_elem = label_val[i][0]
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self.assertEqual(ins_elem.all(), label_elem.all())
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self.assertFalse(data_appeared[int(label_elem - 1)])
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data_appeared[int(label_elem - 1)] = True
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for i in data_appeared:
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self.assertTrue(i)
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def test_all(self):
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self.main()
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self.main_lod()
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