Open gc by default (#18836)
* open gc by default, test=develop * fix test_train_recognize_digits and disable gc when ngraph is enabled, test=develop * fix conditional_block op eager deletion bug, test=develop * add some comments to reviewers, test=developpadding_in_crf
Zeng Jinle
6 years ago
committed by
GitHub
parent
3816d221ff
commit
7ac748adb4
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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.
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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/computation_op_handle.h"
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#include "paddle/fluid/framework/ir/graph_helper.h"
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#include "paddle/fluid/framework/ir/pass.h"
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#include "paddle/fluid/operators/controlflow/conditional_block_op_helper.h"
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#include "paddle/fluid/operators/controlflow/op_variant.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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class ConditionalOpEagerDeletionPass : public Pass {
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protected:
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void ApplyImpl(Graph *graph) const override {
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auto all_ops = ir::FilterByNodeWrapper<details::OpHandleBase>(*graph);
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// Find all conditional_op and conditional_grad_op
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std::unordered_map<size_t, std::pair<std::vector<OperatorBase *>,
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std::vector<OperatorBase *>>>
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target_ops;
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for (auto *op : all_ops) {
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auto compute_op = dynamic_cast<details::ComputationOpHandle *>(op);
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if (compute_op == nullptr) continue;
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if (compute_op->Name() == "conditional_block") {
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target_ops[compute_op->GetScopeIdx()].first.emplace_back(
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compute_op->GetOp());
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} else if (compute_op->Name() == "conditional_block_grad") {
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target_ops[compute_op->GetScopeIdx()].second.emplace_back(
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compute_op->GetOp());
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}
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}
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for (auto &ops_pair : target_ops) {
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auto &ifelse_ops = ops_pair.second.first;
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auto &ifelse_grad_ops = ops_pair.second.second;
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operators::PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOp(
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ifelse_ops, ifelse_grad_ops);
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}
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}
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};
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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REGISTER_PASS(conditional_block_op_eager_deletion_pass,
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paddle::framework::ir::ConditionalOpEagerDeletionPass);
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@ -1,7 +1,10 @@
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include(operators)
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register_operators(DEPS naive_executor)
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cc_library(op_variant SRCS op_variant.cc DEPS operator proto_desc)
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cc_library(conditional_block_op_helper SRCS conditional_block_op_helper.cc DEPS operator op_variant conditional_block_op)
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cc_library(recurrent_op_helper SRCS recurrent_op_helper.cc DEPS operator op_variant recurrent_op)
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cc_library(while_op_helper SRCS while_op_helper.cc DEPS operator op_variant)
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target_link_libraries(conditional_block_infer_op conditional_block_op)
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file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")
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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.
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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/operators/controlflow/conditional_block_op_helper.h"
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#include <string>
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#include <unordered_set>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/operators/controlflow/op_variant.h"
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namespace paddle {
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namespace operators {
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static bool IsMatchedConditionalBlockOpAndConditionalBlockGradOp(
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const OpVariant &fwd_op, const OpVariant &bwd_op) {
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return fwd_op.Outputs().at(ConditionalOp::kScope) ==
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bwd_op.Inputs().at(ConditionalOp::kScope);
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}
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static void FindAllConditionalBlockAndConditionalBlockGradOp(
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std::vector<OpVariant> *fwd_ops, std::vector<OpVariant> *bwd_ops) {
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PADDLE_ENFORCE_GE(fwd_ops->size(), bwd_ops->size());
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if (fwd_ops->empty()) return;
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const auto *program =
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fwd_ops->front().Attr<framework::BlockDesc *>("sub_block")->Program();
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for (size_t i = 1; i < program->Size(); ++i) {
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auto &block = program->Block(i);
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for (size_t j = 0; j < block.OpSize(); ++j) {
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auto *op = block.Op(j);
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if (op->Type() == "conditional_block") {
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fwd_ops->emplace_back(op);
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} else if (op->Type() == "conditional_block_grad") {
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bwd_ops->emplace_back(op);
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}
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}
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}
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PADDLE_ENFORCE_GE(
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fwd_ops->size(), bwd_ops->size(),
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"There are extra conditional_block_grad ops in the graph or program");
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}
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static void SetSkipVarsForConditionalBlockOp(OpVariant *fwd_op,
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OpVariant *bwd_op) {
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auto *grad_block = bwd_op->Attr<framework::BlockDesc *>("sub_block");
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auto is_skippable_in_fwd = [grad_block](const std::string &var_name) {
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return var_name != framework::kEmptyVarName &&
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!grad_block->HasVar(var_name);
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};
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std::unordered_set<std::string> forward_skip_vars;
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for (auto *op_desc : grad_block->AllOps()) {
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for (auto &in_arg_name : op_desc->InputArgumentNames()) {
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if (is_skippable_in_fwd(in_arg_name)) {
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forward_skip_vars.insert(in_arg_name);
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}
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}
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for (auto &out_arg_name : op_desc->OutputArgumentNames()) {
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if (is_skippable_in_fwd(out_arg_name)) {
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forward_skip_vars.insert(out_arg_name);
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}
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}
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}
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auto &fwd_attrs = const_cast<framework::AttributeMap &>(fwd_op->Attrs());
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std::vector<std::string> skip_vars_vec(forward_skip_vars.begin(),
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forward_skip_vars.end());
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VLOG(2) << "Prepare to skip " << skip_vars_vec.size()
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<< " var(s): " << string::join_strings(skip_vars_vec, ' ');
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fwd_attrs[ConditionalOp::kSkipEagerDeletionVars] = std::move(skip_vars_vec);
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}
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static void PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOpImpl(
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std::vector<OpVariant> *ifelse_ops,
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std::vector<OpVariant> *ifelse_grad_ops) {
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FindAllConditionalBlockAndConditionalBlockGradOp(ifelse_ops, ifelse_grad_ops);
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VLOG(2) << "Found conditional_block op num: " << ifelse_ops->size()
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<< ", conditional_block_grad op num: " << ifelse_grad_ops->size();
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if (ifelse_grad_ops->empty()) {
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return;
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}
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std::unordered_set<OpVariant, OpVariant::Hasher> ifelse_op_set(
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ifelse_ops->begin(), ifelse_ops->end());
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for (auto &bwd_op : *ifelse_grad_ops) {
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const OpVariant *matched_fwd_op = nullptr;
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for (auto &fwd_op : ifelse_op_set) {
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if (IsMatchedConditionalBlockOpAndConditionalBlockGradOp(fwd_op,
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bwd_op)) {
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PADDLE_ENFORCE(matched_fwd_op == nullptr,
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"Found multiple matched conditional_block ops");
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matched_fwd_op = &fwd_op;
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}
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}
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PADDLE_ENFORCE_NOT_NULL(matched_fwd_op,
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"Cannot find matched forward conditional_block op");
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SetSkipVarsForConditionalBlockOp(const_cast<OpVariant *>(matched_fwd_op),
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&bwd_op);
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ifelse_op_set.erase(*matched_fwd_op);
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}
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}
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void PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOp(
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int block_id,
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const std::vector<std::unique_ptr<framework::OperatorBase>> &all_ops) {
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// If block_id is not 0, returns
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// This is because all conditional_block_ops and conditional_block_grad_ops
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// in the whole program would be processed when block_id is 0 (i.e.
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// when Executor::Run() or ParallelExecutor constructs).
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// What's more, all conditional_block_ops and conditional_block_grad_ops
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// must be processed when block_id is zero. If not, conditional_block_op
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// may run first and erase variables used in conditional_block_grad_op,
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// and in this moment, conditional_block_grad_ops may be not constructed yet.
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if (block_id != 0) return;
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std::vector<OpVariant> fwd_ops, bwd_ops;
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for (auto &op : all_ops) {
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if (op->Type() == "conditional_block") {
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fwd_ops.emplace_back(op.get());
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} else if (op->Type() == "conditional_block_grad") {
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bwd_ops.emplace_back(op.get());
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}
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}
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PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOpImpl(&fwd_ops,
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&bwd_ops);
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}
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void PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOp(
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const std::vector<framework::OperatorBase *> &ifelse_ops,
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const std::vector<framework::OperatorBase *> &ifelse_grad_ops) {
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std::vector<OpVariant> fwd_ops, bwd_ops;
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fwd_ops.reserve(ifelse_ops.size());
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for (auto *op : ifelse_ops) {
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fwd_ops.emplace_back(op);
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}
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bwd_ops.reserve(ifelse_grad_ops.size());
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for (auto *op : ifelse_grad_ops) {
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bwd_ops.emplace_back(op);
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}
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PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOpImpl(&fwd_ops,
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&bwd_ops);
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}
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,34 @@
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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.
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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 <memory>
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#include <vector>
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/operators/controlflow/conditional_block_op.h"
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namespace paddle {
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namespace operators {
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void PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOp(
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int block_id,
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const std::vector<std::unique_ptr<framework::OperatorBase>> &all_ops);
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void PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOp(
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const std::vector<framework::OperatorBase *> &ifelse_ops,
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const std::vector<framework::OperatorBase *> &ifelse_grad_ops);
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} // namespace operators
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} // namespace paddle
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