From 597dc65e76669e381cf4c52dfeeb671d5eecc4e6 Mon Sep 17 00:00:00 2001 From: sneaxiy Date: Tue, 5 Mar 2019 10:11:10 +0000 Subject: [PATCH] enhance gc test=develop --- paddle/fluid/framework/CMakeLists.txt | 2 +- paddle/fluid/framework/details/CMakeLists.txt | 3 +- .../framework/details/computation_op_handle.h | 2 + .../details/eager_deletion_op_handle.cc | 5 +- .../framework/details/eager_deletion_pass.cc | 165 ++++++---- .../framework/details/eager_deletion_pass.h | 32 -- .../details/while_op_eager_deletion_pass.cc | 62 ++++ paddle/fluid/framework/executor.cc | 8 +- .../operators/controlflow/CMakeLists.txt | 1 + .../fluid/operators/controlflow/while_op.cc | 9 +- .../operators/controlflow/while_op_helper.cc | 292 ++++++++++++++++++ .../operators/controlflow/while_op_helper.h | 43 +++ .../unittests/test_eager_deletion_while_op.py | 153 +++++++++ ...test_partial_eager_deletion_transformer.py | 26 ++ 14 files changed, 692 insertions(+), 111 deletions(-) delete mode 100644 paddle/fluid/framework/details/eager_deletion_pass.h create mode 100644 paddle/fluid/framework/details/while_op_eager_deletion_pass.cc create mode 100644 paddle/fluid/operators/controlflow/while_op_helper.cc create mode 100644 paddle/fluid/operators/controlflow/while_op_helper.h create mode 100644 python/paddle/fluid/tests/unittests/test_eager_deletion_while_op.py create mode 100644 python/paddle/fluid/tests/unittests/test_partial_eager_deletion_transformer.py diff --git a/paddle/fluid/framework/CMakeLists.txt b/paddle/fluid/framework/CMakeLists.txt index b9491c953f..ad19d729eb 100644 --- a/paddle/fluid/framework/CMakeLists.txt +++ b/paddle/fluid/framework/CMakeLists.txt @@ -174,7 +174,7 @@ else() cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op) endif() -target_link_libraries(executor garbage_collector) +target_link_libraries(executor garbage_collector while_op_helper) cc_library(parallel_executor SRCS parallel_executor.cc DEPS threaded_ssa_graph_executor scope_buffered_ssa_graph_executor parallel_ssa_graph_executor diff --git a/paddle/fluid/framework/details/CMakeLists.txt b/paddle/fluid/framework/details/CMakeLists.txt index dc308fd259..9f06455ea5 100644 --- a/paddle/fluid/framework/details/CMakeLists.txt +++ b/paddle/fluid/framework/details/CMakeLists.txt @@ -61,7 +61,8 @@ cc_library(inplace_op_pass SRCS inplace_op_pass.cc DEPS memory_optimize_pass op_ cc_library(modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper) cc_library(reference_count_pass_helper SRCS reference_count_pass_helper.cc DEPS garbage_collector computation_op_handle) cc_library(eager_deletion_op_handle SRCS eager_deletion_op_handle.cc DEPS lod_tensor selected_rows reference_count_pass_helper) -cc_library(eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass) +cc_library(while_op_eager_deletion_pass SRCS while_op_eager_deletion_pass.cc DEPS while_op_helper graph_helper pass computation_op_handle) +cc_library(eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass while_op_eager_deletion_pass) cc_library(reference_count_pass SRCS reference_count_pass.cc DEPS computation_op_handle graph graph_helper pass op_graph_view reference_count_pass_helper) cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS graph graph_helper pass) diff --git a/paddle/fluid/framework/details/computation_op_handle.h b/paddle/fluid/framework/details/computation_op_handle.h index 1e3dbb1e44..67f7cb738f 100644 --- a/paddle/fluid/framework/details/computation_op_handle.h +++ b/paddle/fluid/framework/details/computation_op_handle.h @@ -31,6 +31,8 @@ class ComputationOpHandle : public OpHandleBase { ComputationOpHandle(ir::Node *node, Scope *scope, platform::Place place, size_t scope_idx); + OperatorBase *GetOp() { return op_.get(); } + std::string Name() const override; const Scope *GetScope() const { return scope_; } diff --git a/paddle/fluid/framework/details/eager_deletion_op_handle.cc b/paddle/fluid/framework/details/eager_deletion_op_handle.cc index 9faef8a186..e58e501e6d 100644 --- a/paddle/fluid/framework/details/eager_deletion_op_handle.cc +++ b/paddle/fluid/framework/details/eager_deletion_op_handle.cc @@ -25,8 +25,6 @@ namespace paddle { namespace framework { namespace details { -static const std::string kEagerDeletionOpName{"eager_deletion"}; // NOLINT - EagerDeletionOpHandle::EagerDeletionOpHandle( ir::Node *node, const Scope *scope, const platform::Place &place, const std::unordered_set &var_names, GarbageCollector *gc, @@ -61,10 +59,9 @@ EagerDeletionOpHandle::~EagerDeletionOpHandle() { #endif } -std::string EagerDeletionOpHandle::Name() const { return kEagerDeletionOpName; } +std::string EagerDeletionOpHandle::Name() const { return "eager_deletion"; } void EagerDeletionOpHandle::RunImpl() { - platform::RecordEvent event(kEagerDeletionOpName, nullptr); Scope *exec_scope = nullptr; std::deque> garbages; for (auto &name : var_names_) { diff --git a/paddle/fluid/framework/details/eager_deletion_pass.cc b/paddle/fluid/framework/details/eager_deletion_pass.cc index 6c8cb66b10..566bc15c17 100644 --- a/paddle/fluid/framework/details/eager_deletion_pass.cc +++ b/paddle/fluid/framework/details/eager_deletion_pass.cc @@ -21,35 +21,42 @@ #include "paddle/fluid/framework/details/computation_op_handle.h" #include "paddle/fluid/framework/details/eager_deletion_op_handle.h" -#include "paddle/fluid/framework/details/eager_deletion_pass.h" #include "paddle/fluid/framework/details/multi_devices_helper.h" #include "paddle/fluid/framework/ir/graph_helper.h" -DEFINE_double(fraction_of_eager_deletion, 1.0, "Fraction of eager deletion"); -DEFINE_bool(eager_delete_tensor_only, false, ""); +DEFINE_double(memory_fraction_of_eager_deletion, 1.0, + "Fraction of eager deletion"); namespace paddle { namespace framework { namespace details { -namespace { // NOLINT +// op -> variables which can be deleted after op runs using OpToVarNameSetMap = std::unordered_map>; -} // NOLINT +// Check whether the variable is LoDTensor based on static VarDesc info static bool IsLoDTensor(VarDesc *var) { return var->Proto()->type().type() == proto::VarType::LOD_TENSOR; } -static int64_t GetNumel(const GraphVars &vars, const std::string &var_name, - size_t scope_idx) { - auto *var_desc = TryGetLatestVarDesc(vars[scope_idx].at(var_name)); +// Get memory size of LoDTensor +static int64_t GetMemorySize( + const std::unordered_map> &vars, + const std::string &var_name) { + auto *var_desc = TryGetLatestVarDesc(vars.at(var_name)); + PADDLE_ENFORCE_NOT_NULL(var_desc); PADDLE_ENFORCE(IsLoDTensor(var_desc)); auto dims = var_desc->GetShape(); - return std::accumulate(dims.begin(), dims.end(), static_cast(1), + return SizeOfType(var_desc->GetDataType()) * + std::accumulate(dims.begin(), dims.end(), static_cast(1), std::multiplies()); } +// Split all variables in the graph into LoDTensor and Non-LoDTensor (e.g. +// SelectedRows, LoDTensorArray) +// Since partial GC is based on static analysis of memory size of each variable +// So we should skip SelectedRows and LoDTensorArray here static void SplitIntoLoDTensorAndNonLoDTensorVars( const OpToVarNameSetMap &m, const GraphVars &vars, OpToVarNameSetMap *lod_tensors, OpToVarNameSetMap *other_vars) { @@ -69,76 +76,106 @@ static void SplitIntoLoDTensorAndNonLoDTensorVars( } } -static OpToVarNameSetMap ShrinkGCVars(const OpToVarNameSetMap &m, - const GraphVars &vars, - double fraction_of_memory_size, - bool delete_lod_tensor_only = false) { - // Do not perform gc +struct GCVarInfo { + GCVarInfo(const std::string &name, int64_t memory_size, + ComputationOpHandle *op, size_t scope_idx) + : name_(name), + memory_size_(memory_size), + op_(op), + scope_idx_(scope_idx) {} + + std::string name_; // variable name + int64_t memory_size_; // memory size + ComputationOpHandle *op_; // op after which the variable could be deleted + size_t scope_idx_; // scope index where the variable locates + + int64_t AbsMemorySize() const { return std::abs(memory_size_); } +}; + +// Delete delete_lod_tensor_only is not used currently +static OpToVarNameSetMap ShrinkGCVars( + const OpToVarNameSetMap &m, const GraphVars &vars, + const std::vector &places, double fraction_of_memory_size, + bool delete_lod_tensor_only = false) { + // Do not perform gc when fraction_of_memory_size = 0 if (fraction_of_memory_size <= 0.0) return {}; - // Perform complete gc + /** + * Step 1: Split all variables into LoDTensor and Non-LoDTensor. + * We can only calculate memory size of LoDTensors + */ + OpToVarNameSetMap lod_tensors, other_vars; + SplitIntoLoDTensorAndNonLoDTensorVars(m, vars, &lod_tensors, &other_vars); + + // Perform complete gc when fraction_of_memory_size >= 1 if (fraction_of_memory_size >= 1.0) { - if (delete_lod_tensor_only) { - OpToVarNameSetMap lod_tensors, other_vars; - SplitIntoLoDTensorAndNonLoDTensorVars(m, vars, &lod_tensors, &other_vars); - return lod_tensors; - } else { - return m; - } + return delete_lod_tensor_only ? lod_tensors : m; } - // Perform partial gc - OpToVarNameSetMap lod_tensors, other_vars; - SplitIntoLoDTensorAndNonLoDTensorVars(m, vars, &lod_tensors, &other_vars); + /** + * Step 2: build GCVarInfos, and calculate total memory sizes of each device + */ - using TupleType = std::tuple; + // place -> variable info (name, memory size, place, scope_idx) + std::map> place_to_vars; - std::unordered_map> place_to_vars; - std::unordered_map total_memory_size; + // place -> total memory sizes + std::map place_to_size; for (auto &op_vars_pair : lod_tensors) { - auto scope_idx = op_vars_pair.first->GetScopeIdx(); - int64_t size = 0; - for (auto &var_name : op_vars_pair.second) { - auto var_size = GetNumel(vars, var_name, scope_idx); - size += std::abs(var_size); - place_to_vars[scope_idx].emplace_back(var_name, op_vars_pair.first, - var_size); + auto *op = op_vars_pair.first; + auto &var_names = op_vars_pair.second; + auto scope_idx = op->GetScopeIdx(); + auto &place = places[scope_idx]; + + for (auto &var_name : var_names) { + auto var_size = GetMemorySize(vars[scope_idx], var_name); + GCVarInfo var_info(var_name, var_size, op, scope_idx); + place_to_size[place] += var_info.AbsMemorySize(); + place_to_vars[place].emplace_back(std::move(var_info)); } - total_memory_size.emplace(scope_idx, size); } - for (auto &pair : place_to_vars) { - std::sort(pair.second.begin(), pair.second.end(), - [](const TupleType &t1, const TupleType &t2) { - return std::abs(std::get<2>(t1)) > std::abs(std::get<2>(t2)); + /** + * Step 3: sort GCVarInfos, and only delete the largest variables. + */ + OpToVarNameSetMap partial_vars; + for (auto &place_to_var_pair : place_to_vars) { + auto &place = place_to_var_pair.first; + auto &gc_vars = place_to_var_pair.second; + std::sort(gc_vars.begin(), gc_vars.end(), + [](const GCVarInfo &var1, const GCVarInfo &var2) { + return var1.AbsMemorySize() > var2.AbsMemorySize(); }); - } - OpToVarNameSetMap ret; - for (auto &pair : place_to_vars) { - auto desired_delete_size = static_cast( - fraction_of_memory_size * total_memory_size.at(pair.first)); - int64_t cur_size = 0; - for (size_t i = 0; i < pair.second.size() && cur_size < desired_delete_size; + int64_t accumulated_size = 0; + int64_t size_threshold = + static_cast(fraction_of_memory_size * place_to_size[place]); + for (size_t i = 0; i < gc_vars.size() && accumulated_size < size_threshold; ++i) { - auto &var_name = std::get<0>(pair.second[i]); - auto *op = std::get<1>(pair.second[i]); - cur_size += std::get<2>(pair.second[i]); - ret[op].insert(var_name); + partial_vars[gc_vars[i].op_].insert(gc_vars[i].name_); + accumulated_size += gc_vars[i].AbsMemorySize(); } } + /** + * Step 4: Combine other vars (SelectedRows, LoDTensorArray) + */ if (!delete_lod_tensor_only) { for (auto &op_vars_pair : other_vars) { - for (auto &var_name : op_vars_pair.second) { - ret[op_vars_pair.first].insert(var_name); - } + partial_vars[op_vars_pair.first].insert(op_vars_pair.second.begin(), + op_vars_pair.second.end()); } } - return ret; + return partial_vars; } +class EagerDeletionPass : public ir::Pass { + protected: + std::unique_ptr ApplyImpl( + std::unique_ptr graph) const override; +}; + std::unique_ptr EagerDeletionPass::ApplyImpl( std::unique_ptr graph) const { auto &ref_cnts = @@ -166,9 +203,8 @@ std::unique_ptr EagerDeletionPass::ApplyImpl( } } - op_vars_map = - ShrinkGCVars(op_vars_map, vars, FLAGS_fraction_of_eager_deletion, - FLAGS_eager_delete_tensor_only); + op_vars_map = ShrinkGCVars(op_vars_map, vars, places, + FLAGS_memory_fraction_of_eager_deletion); for (auto &pair : op_vars_map) { auto *op = pair.first; @@ -200,12 +236,13 @@ std::unique_ptr EagerDeletionPass::ApplyImpl( eager_deletion_op->AddOutput(dummy_leaf); } - VLOG(10) << "FLAGS_fraction_of_eager_deletion = " - << FLAGS_fraction_of_eager_deletion; - VLOG(10) << "FLAGS_eager_delete_tensor_only = " - << FLAGS_eager_delete_tensor_only; + VLOG(10) << "FLAGS_memory_fraction_of_eager_deletion = " + << FLAGS_memory_fraction_of_eager_deletion; VLOG(10) << "Create " << op_vars_map.size() << " EagerDeletionOpHandle(s)"; - return graph; + + auto while_op_eager_deletion_pass = + ir::PassRegistry::Instance().Get("while_op_eager_deletion_pass"); + return while_op_eager_deletion_pass->Apply(std::move(graph)); } } // namespace details @@ -218,3 +255,5 @@ REGISTER_PASS(eager_deletion_pass, .RequirePassAttr(paddle::framework::details::kLastLiveOpsOfVars) .RequirePassAttr(paddle::framework::details::kAllPlaces) .RequirePassAttr(paddle::framework::details::kGarbageCollector); + +USE_PASS(while_op_eager_deletion_pass); diff --git a/paddle/fluid/framework/details/eager_deletion_pass.h b/paddle/fluid/framework/details/eager_deletion_pass.h deleted file mode 100644 index d7a7a9709d..0000000000 --- a/paddle/fluid/framework/details/eager_deletion_pass.h +++ /dev/null @@ -1,32 +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 "paddle/fluid/framework/ir/graph.h" -#include "paddle/fluid/framework/ir/pass.h" - -namespace paddle { -namespace framework { -namespace details { - -class EagerDeletionPass : public ir::Pass { - protected: - std::unique_ptr ApplyImpl( - std::unique_ptr graph) const override; -}; - -} // namespace details -} // namespace framework -} // namespace paddle diff --git a/paddle/fluid/framework/details/while_op_eager_deletion_pass.cc b/paddle/fluid/framework/details/while_op_eager_deletion_pass.cc new file mode 100644 index 0000000000..fd6b6dd227 --- /dev/null +++ b/paddle/fluid/framework/details/while_op_eager_deletion_pass.cc @@ -0,0 +1,62 @@ +// 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/computation_op_handle.h" +#include "paddle/fluid/framework/details/multi_devices_helper.h" +#include "paddle/fluid/framework/ir/graph_helper.h" +#include "paddle/fluid/operators/controlflow/while_op_helper.h" + +namespace paddle { +namespace framework { +namespace details { + +class WhileOpEagerDeletionPass : public ir::Pass { + protected: + std::unique_ptr ApplyImpl( + std::unique_ptr graph) const override { + auto all_ops = ir::FilterByNodeWrapper(*graph); + + // Find all while_op and while_grad_op + std::unordered_map, + std::vector>> + target_ops; + for (auto *op : all_ops) { + auto compute_op = dynamic_cast(op); + if (compute_op == nullptr) continue; + + if (compute_op->Name() == "while") { + target_ops[compute_op->GetScopeIdx()].first.emplace_back( + compute_op->GetOp()); + } else if (compute_op->Name() == "while_grad") { + target_ops[compute_op->GetScopeIdx()].second.emplace_back( + compute_op->GetOp()); + } + } + + for (auto &ops_pair : target_ops) { + auto &while_ops = ops_pair.second.first; + auto &while_grad_ops = ops_pair.second.second; + operators::PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp( + while_ops, while_grad_ops); + } + return graph; + } +}; + +} // namespace details +} // namespace framework +} // namespace paddle + +REGISTER_PASS(while_op_eager_deletion_pass, + paddle::framework::details::WhileOpEagerDeletionPass); diff --git a/paddle/fluid/framework/executor.cc b/paddle/fluid/framework/executor.cc index c31d0beec3..5555679412 100644 --- a/paddle/fluid/framework/executor.cc +++ b/paddle/fluid/framework/executor.cc @@ -23,6 +23,7 @@ limitations under the License. */ #include "paddle/fluid/framework/threadpool.h" #include "paddle/fluid/framework/transfer_scope_cache.h" #include "paddle/fluid/framework/variable_helper.h" +#include "paddle/fluid/operators/controlflow/while_op_helper.h" #include "paddle/fluid/operators/distributed/distributed.h" #include "paddle/fluid/platform/place.h" #include "paddle/fluid/platform/profiler.h" @@ -409,8 +410,7 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, int64_t max_memory_size = GetEagerDeletionThreshold(); std::unique_ptr gc; - // skip while_op and while_grad_op temporarily - if (max_memory_size >= 0 && !keep_kids) { + if (max_memory_size >= 0) { ctx->ResetReferenceCount(); #ifdef PADDLE_WITH_CUDA if (platform::is_gpu_place(place_)) { @@ -428,6 +428,10 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, #ifdef PADDLE_WITH_CUDA } #endif + if (gc) { + operators::PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp(ctx->block_id_, + ctx->ops_); + } } for (auto& op : ctx->ops_) { diff --git a/paddle/fluid/operators/controlflow/CMakeLists.txt b/paddle/fluid/operators/controlflow/CMakeLists.txt index b614e9b035..7aa1c44eaa 100644 --- a/paddle/fluid/operators/controlflow/CMakeLists.txt +++ b/paddle/fluid/operators/controlflow/CMakeLists.txt @@ -1,4 +1,5 @@ include(operators) register_operators(DEPS naive_executor) +cc_library(while_op_helper SRCS while_op_helper.cc DEPS operator) file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n") diff --git a/paddle/fluid/operators/controlflow/while_op.cc b/paddle/fluid/operators/controlflow/while_op.cc index 0360cf5273..8352ba4f2b 100644 --- a/paddle/fluid/operators/controlflow/while_op.cc +++ b/paddle/fluid/operators/controlflow/while_op.cc @@ -18,6 +18,7 @@ #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/framework/var_type.h" +#include "paddle/fluid/operators/controlflow/while_op_helper.h" #include "paddle/fluid/operators/detail/safe_ref.h" namespace paddle { @@ -26,14 +27,6 @@ namespace operators { using StepScopeVar = std::vector; using LoDTensor = framework::LoDTensor; -static constexpr char kStepBlock[] = "sub_block"; -static constexpr char kCondition[] = "Condition"; -static constexpr char kStepScopes[] = "StepScopes"; -static constexpr char kX[] = "X"; -static constexpr char kXGRAD[] = "X@GRAD"; -static constexpr char kOutputs[] = "Out"; -static constexpr char kSkipEagerDeletionVars[] = "skip_eager_deletion_vars"; - namespace { // NOLINT static std::string GetSkipEagerDeletionVarsDebugString( const std::vector &vars) { diff --git a/paddle/fluid/operators/controlflow/while_op_helper.cc b/paddle/fluid/operators/controlflow/while_op_helper.cc new file mode 100644 index 0000000000..0324a1586a --- /dev/null +++ b/paddle/fluid/operators/controlflow/while_op_helper.cc @@ -0,0 +1,292 @@ +// 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/operators/controlflow/while_op_helper.h" +#include +#include "paddle/fluid/framework/program_desc.h" + +namespace paddle { +namespace operators { + +// OpVariant is a wrapper class of OpDesc and OperatorBase +// So that API would be the same. +class OpVariant { + struct InputsVisitor + : public boost::static_visitor { + template + const framework::VariableNameMap *operator()(const OpType *op) const { + return &(op->Inputs()); + } + }; + + struct OutputsVisitor + : public boost::static_visitor { + template + const framework::VariableNameMap *operator()(const OpType *op) const { + return &(op->Outputs()); + } + }; + + struct AttributeMapVisitor + : public boost::static_visitor { + const framework::AttributeMap *operator()( + const framework::OpDesc *op) const { + return &(op->GetAttrMap()); + } + + const framework::AttributeMap *operator()( + const framework::OperatorBase *op) const { + return &(op->Attrs()); + } + }; + + struct RawPointerVisitor : public boost::static_visitor { + template + const void *operator()(const OpType *op) const { + return op; + } + }; + + public: + OpVariant(const framework::OperatorBase *op) : op_(op) {} // NOLINT + + OpVariant(const framework::OpDesc *op) : op_(op) {} // NOLINT + + const framework::VariableNameMap &Inputs() const { + return *boost::apply_visitor(InputsVisitor(), op_); + } + + const framework::VariableNameMap &Outputs() const { + return *boost::apply_visitor(OutputsVisitor(), op_); + } + + const framework::AttributeMap &Attrs() const { + return *boost::apply_visitor(AttributeMapVisitor(), op_); + } + + template + const AttrType &Attr(const std::string &name) const { + auto &attrs = Attrs(); + auto it = attrs.find(name); + PADDLE_ENFORCE(it != attrs.end(), "Cannot find attribute %s", name); + return boost::get(it->second); + } + + bool operator==(const OpVariant &other) const { + return RawPointer() == other.RawPointer(); + } + + const void *RawPointer() const { + return boost::apply_visitor(RawPointerVisitor(), op_); + } + + int which() const { return static_cast(op_.which()); } + + struct Hasher { + size_t operator()(const OpVariant &op) const { + return reinterpret_cast(op.RawPointer()); + } + }; + + private: + const boost::variant + op_; +}; + +static std::string GetDebugString(const std::vector &names) { + if (names.empty()) return ""; + std::string ret = names[0]; + for (size_t i = 1; i < names.size(); ++i) { + ret += (" " + names[i]); + } + return ret; +} + +// Set skip variables of while_op and while_grad_op +// These variables should be skipped when eager deletion enables. +// It is because: +// 1. while_grad_op needs some variables defined in while_op. +// 2. while_grad_op needs variables from the previous time step. +static void SetSkipVars(const OpVariant &op, std::vector attr) { + auto &attrs = const_cast(op.Attrs()); + VLOG(2) << "Prepare to skip " << attr.size() + << " var(s): " << GetDebugString(attr); + attrs[kSkipEagerDeletionVars] = std::move(attr); +} + +// Check whether the forward while_op and while_grad_op match +// The program may have many while_ops. +static bool IsMatchedWhileOpAndWhileGradOp(const OpVariant &fwd_op, + const OpVariant &grad_op) { + return fwd_op.Inputs().at(kX) == grad_op.Inputs().at(kX) && + fwd_op.Outputs().at(kOutputs) == grad_op.Inputs().at(kOutputs); +} + +// Test whether the variable is skippable in forward while_op +// The variable is skippable in while_op when the variable used in while_grad +// is not from grad_block. +static bool IsSkippableVar(const std::string &name, + framework::BlockDesc *grad_block) { + return name != framework::kEmptyVarName && !grad_block->HasVar(name); +} + +static void ModifyWhileOpAndWhileGradOpAttr(const OpVariant &fwd_op, + const OpVariant &bwd_op) { + auto *grad_block = bwd_op.Attr(kStepBlock); + + // Find all skippable variables in forward while_op + std::unordered_set forward_skip_vars; + for (auto *op_desc : grad_block->AllOps()) { + for (auto &in_arg_name : op_desc->InputArgumentNames()) { + if (IsSkippableVar(in_arg_name, grad_block)) { + forward_skip_vars.insert(in_arg_name); + } + } + + for (auto &out_arg_name : op_desc->OutputArgumentNames()) { + if (IsSkippableVar(out_arg_name, grad_block)) { + forward_skip_vars.insert(out_arg_name); + } + } + } + + SetSkipVars(fwd_op, std::vector(forward_skip_vars.begin(), + forward_skip_vars.end())); + + // Find all skippable variables in while_grad_op + // The skipped variables are those which would be used across time steps. + auto &fwd_input = fwd_op.Inputs().at(kX); + auto &in_grads = bwd_op.Outputs().at(framework::GradVarName(kX)); + PADDLE_ENFORCE_EQ( + fwd_input.size(), in_grads.size(), + "Backward input gradient number does not match forward input number."); + + std::unordered_set backward_skip_vars; + for (size_t i = 0; i < in_grads.size(); ++i) { + if (in_grads[i] == framework::kEmptyVarName) { + continue; + } + backward_skip_vars.insert(in_grads[i]); + backward_skip_vars.insert(framework::GradVarName(fwd_input[i])); + } + + SetSkipVars(bwd_op, std::vector(backward_skip_vars.begin(), + backward_skip_vars.end())); +} + +// Find all while_ops and while_grad_ops in the graph or program +// The while_grad_op and while_op may located in different blocks +// So we should traverse all blocks in the program and find them out. +static void FindAllWhileAndWhileGradOp(std::vector *while_ops, + std::vector *while_grad_ops) { + PADDLE_ENFORCE_GE(while_ops->size(), while_grad_ops->size()); + + if (while_ops->empty()) return; + + const auto *program = + while_ops->front().Attr(kStepBlock)->Program(); + for (size_t i = 1; i < program->Size(); ++i) { + auto &block = program->Block(i); + for (size_t j = 0; j < block.OpSize(); ++j) { + auto *op = block.Op(j); + if (op->Type() == "while") { + while_ops->emplace_back(op); + } else if (op->Type() == "while_grad") { + while_grad_ops->emplace_back(op); + } + } + } + + PADDLE_ENFORCE_GE(while_ops->size(), while_grad_ops->size(), + "There are extra while_grad ops in the graph or program"); +} + +static void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl( + std::vector *while_ops, std::vector *while_grad_ops) { + FindAllWhileAndWhileGradOp(while_ops, while_grad_ops); + + VLOG(2) << "Found while op num: " << while_ops->size() + << ", while grad op num: " << while_grad_ops->size(); + + if (while_grad_ops->empty()) { + return; + } + + std::unordered_set while_op_set( + while_ops->begin(), while_ops->end()); + + for (auto &bwd_op : *while_grad_ops) { + const OpVariant *matched_fwd_op = nullptr; + for (auto &fwd_op : while_op_set) { + if (IsMatchedWhileOpAndWhileGradOp(fwd_op, bwd_op)) { + PADDLE_ENFORCE(matched_fwd_op == nullptr, + "Found multiple matched while ops"); + matched_fwd_op = &fwd_op; + } + } + PADDLE_ENFORCE_NOT_NULL(matched_fwd_op, + "Cannot find matched forward while op."); + ModifyWhileOpAndWhileGradOpAttr(*matched_fwd_op, bwd_op); + while_op_set.erase(*matched_fwd_op); + } + + PADDLE_ENFORCE(while_op_set.empty(), + "There are not matched while_grad op in graph."); +} + +void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp( + int block_id, + const std::vector> &all_ops) { + // If block_id is not 0, returns + // This is because all while_ops and while_grad_ops in the whole program + // would be processed when block_id is 0 (i.e. when Executor::Run() or + // ParallelExecutor constructs). + + // What's more, all while_ops and while_grad_ops must be processed when + // block_id is zero. If not, while_op may run first and erase variables + // used in while_grad_op, and in this moment, while_grad_ops may be not + // constructed yet. + if (block_id != 0) return; + + std::vector fwd_ops, bwd_ops; + for (auto &op : all_ops) { + if (op->Type() == "while") { + fwd_ops.emplace_back(op.get()); + } else if (op->Type() == "while_grad") { + bwd_ops.emplace_back(op.get()); + } + } + PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl(&fwd_ops, &bwd_ops); +} + +void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp( + const std::vector &while_ops, + const std::vector &while_grad_ops) { + std::vector fwd_ops, bwd_ops; + fwd_ops.reserve(while_ops.size()); + for (auto *op : while_ops) { + fwd_ops.emplace_back(op); + } + + bwd_ops.reserve(while_grad_ops.size()); + for (auto *op : while_grad_ops) { + bwd_ops.emplace_back(op); + } + + PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl(&fwd_ops, &bwd_ops); +} + +} // namespace operators +} // namespace paddle diff --git a/paddle/fluid/operators/controlflow/while_op_helper.h b/paddle/fluid/operators/controlflow/while_op_helper.h new file mode 100644 index 0000000000..456ba8642b --- /dev/null +++ b/paddle/fluid/operators/controlflow/while_op_helper.h @@ -0,0 +1,43 @@ +// 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. + +#pragma once + +#include +#include +#include +#include "paddle/fluid/framework/operator.h" +#include "paddle/fluid/platform/variant.h" + +namespace paddle { +namespace operators { + +static constexpr char kStepBlock[] = "sub_block"; +static constexpr char kCondition[] = "Condition"; +static constexpr char kStepScopes[] = "StepScopes"; +static constexpr char kX[] = "X"; +static constexpr char kXGRAD[] = "X@GRAD"; +static constexpr char kOutputs[] = "Out"; +static constexpr char kSkipEagerDeletionVars[] = "skip_eager_deletion_vars"; + +void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp( + int block_id, + const std::vector> &all_ops); + +void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp( + const std::vector &while_ops, + const std::vector &while_grad_ops); + +} // namespace operators +} // namespace paddle diff --git a/python/paddle/fluid/tests/unittests/test_eager_deletion_while_op.py b/python/paddle/fluid/tests/unittests/test_eager_deletion_while_op.py new file mode 100644 index 0000000000..7fa1636579 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_eager_deletion_while_op.py @@ -0,0 +1,153 @@ +# 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. + +from __future__ import print_function + +import os +os.environ['CPU_NUM'] = '2' +os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0' +os.environ['FLAGS_fast_eager_deletion_mode'] = '1' + +import unittest +import paddle.fluid as fluid +import paddle.fluid.layers as layers +from paddle.fluid.executor import Executor +import paddle.fluid.core as core +from paddle.fluid.backward import append_backward +import paddle.fluid.compiler as compiler +import numpy +import multiprocessing + + +class TestEagerDeletionWhileOpBase(unittest.TestCase): + def test_main(self): + places = [core.CPUPlace(), ] + if core.is_compiled_with_cuda(): + places.append(core.CUDAPlace(0)) + + for p in places: + for with_data_parallel in [False, True]: + with fluid.program_guard(fluid.Program(), fluid.Program()): + with fluid.scope_guard(fluid.Scope()): + self.run_main(p, with_data_parallel) + + def run_main(self, place, with_data_parallel): + self.place = place + self.with_data_parallel = with_data_parallel + + if not core.is_compiled_with_cuda() and isinstance(self.place, + core.CUDPlace): + return + + if isinstance(self.place, core.CUDAPlace): + device_cnt = core.get_cuda_device_count( + ) if self.with_data_parallel else 1 + else: + device_cnt = int( + os.environ['CPU_NUM'], + multiprocessing.cpu_count()) if self.with_data_parallel else 1 + + d0 = layers.data( + "d0", shape=[10], append_batch_size=False, dtype='float32') + d1 = layers.data( + "d1", shape=[10], append_batch_size=False, dtype='float32') + d2 = layers.data( + "d2", shape=[10], append_batch_size=False, dtype='float32') + + i = layers.zeros(shape=[1], dtype='int64') + i.stop_gradient = True + + init = layers.zeros(shape=[10], dtype='float32') + mem_array = layers.array_write(x=init, i=i) + data_array = layers.array_write(x=d0, i=i) + + i = layers.increment(i) + layers.array_write(d1, i, array=data_array) + + i = layers.increment(i) + layers.array_write(d2, i, array=data_array) + + i = layers.zeros(shape=[1], dtype='int64') + i.stop_gradient = True + + array_len = layers.fill_constant(shape=[1], dtype='int64', value=1) + array_len.stop_gradient = True + cond = layers.less_than(x=i, y=array_len) + + j = layers.fill_constant(shape=[1], dtype='int64', value=1) + j.stop_gradient = True + + array_len2 = layers.fill_constant(shape=[1], dtype='int64', value=3) + array_len2.stop_gradient = True + cond2 = layers.less_than(x=j, y=array_len2) + + while_op = layers.While(cond=cond) + while_op2 = layers.While(cond=cond2) + with while_op.block(): + d = layers.array_read(array=data_array, i=i) + prev = layers.array_read(array=mem_array, i=i) + d = layers.reshape(d, shape=[10]) + prev = layers.reshape(prev, shape=[10]) + result = layers.sums(input=[d, prev]) + + i = layers.increment(x=i, in_place=True) + layers.array_write(result, i=i, array=mem_array) + layers.less_than(x=i, y=array_len, cond=cond) + with while_op2.block(): + d2 = layers.array_read(array=data_array, i=j) + prev2 = layers.array_read(array=mem_array, i=j) + d2 = layers.reshape(d2, shape=[10]) + prev2 = layers.reshape(prev2, shape=[10]) + result2 = layers.sums(input=[d2, prev2]) + + j = layers.increment(x=j, in_place=True) + layers.array_write(result2, i=j, array=mem_array) + layers.less_than(x=j, y=array_len2, cond=cond2) + + sum_result = layers.array_read(array=mem_array, i=j) + sum_result.persistable = True + tmp = layers.unsqueeze(sum_result, axes=[0]) + tmp = layers.expand(tmp, expand_times=[10, 1]) + fc = layers.fc(tmp, size=256) + loss = layers.mean(sum_result) + + optim = fluid.optimizer.Adam(learning_rate=1e-3) + optim.minimize(loss) + + exe = Executor(self.place) + exe.run(fluid.default_startup_program()) + + prog = compiler.CompiledProgram(fluid.default_main_program()) + if self.with_data_parallel: + prog = prog.with_data_parallel() + + for _ in range(5): + d = [] + for i in range(3): + tmp = numpy.random.random(size=[10]).astype('float32') + if not self.with_data_parallel: + d.append(tmp) + else: + d.append(numpy.array([tmp] * device_cnt)) + + outs = exe.run(program=prog, + feed={'d0': d[0], + 'd1': d[1], + 'd2': d[2]}, + fetch_list=[sum_result]) + self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_partial_eager_deletion_transformer.py b/python/paddle/fluid/tests/unittests/test_partial_eager_deletion_transformer.py new file mode 100644 index 0000000000..d44e4627d8 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_partial_eager_deletion_transformer.py @@ -0,0 +1,26 @@ +# 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. + +import os +import unittest +os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0" +os.environ['FLAGS_memory_fraction_of_eager_deletion'] = "0.55" + +os.environ[ + 'RECORDIO_FILENAME'] = '/tmp/eager_deletion_transformer.wmt16.recordio' + +from test_parallel_executor_transformer import TestTransformer + +if __name__ == '__main__': + unittest.main() -- GitLab