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PaddleDetection
提交 8f3b2523 编写于 作者: D dzhwinter
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paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 8f3b2523
......@@ -200,6 +200,7 @@ cc_library(prune SRCS prune.cc DEPS framework_proto)
cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry
proto_desc)
cc_test(inplace_op_inference_test SRCS inplace_op_inference_test.cc DEPS op_registry proto_desc op_info)
cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 8f3b2523
......@@ -50,7 +50,8 @@ cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_
cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
cc_library(memory_optimize_pass SRCS analysis_var_pass.cc memory_reuse_types.cc DEPS graph graph_helper pass)
cc_library(memory_optimize_pass SRCS memory_optimize_pass.cc memory_optimize_helper.cc DEPS graph graph_helper pass)
cc_library(inplace_op_pass SRCS inplace_op_pass DEPS memory_optimize_pass op_info)
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(memory_early_delete_pass SRCS memory_early_delete_pass.cc DEPS memory_optimize_pass computation_op_handle scale_loss_grad_op_handle rpc_op_handle
all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass)
......@@ -65,12 +66,12 @@ cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_he
cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass memory_early_delete_pass)
set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass memory_early_delete_pass inplace_op_pass)
if (WITH_GPU)
list(APPEND SSA_GRAPH_EXECUTOR_DEPS reference_count_pass)
endif()
cc_test(memory_reuse_types_test SRCS memory_reuse_types_test.cc memory_reuse_types.cc DEPS framework_proto graph)
cc_test(analysis_var_pass_test SRCS analysis_var_pass_test.cc analysis_var_pass.cc memory_reuse_types.cc DEPS framework_proto graph graph_helper op_registry pass)
cc_test(memory_optimize_helper_test SRCS memory_optimize_helper_test.cc memory_optimize_helper.cc DEPS framework_proto graph)
cc_test(memory_optimize_pass_test SRCS memory_optimize_pass_test.cc memory_optimize_pass.cc memory_optimize_helper.cc DEPS framework_proto graph graph_helper op_registry pass)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUTOR_DEPS})
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 8f3b2523
......@@ -17,7 +17,7 @@ limitations under the License. */
#include <glog/logging.h>
#include <memory>
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
......@@ -42,6 +42,9 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
public:
explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
: ir::PassBuilder(), strategy_(strategy) {
if (strategy_.enable_inplace_) {
AppendPass("inplace_pass");
}
if (strategy_.enable_sequential_execution_) {
AppendPass("sequential_execution_pass");
}
......@@ -87,7 +90,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// A side-effect of that, memory optimize cannot forsee the fetched vars
// , so fetchlist should be set persistable before call the Run interface.
if (strategy.memory_optimize_) {
auto analysis_var_pass = AppendPass("analysis_var_pass");
auto memory_optimize_pass = AppendPass("memory_optimize_pass");
}
AppendMultiDevPass(strategy);
......@@ -185,8 +188,7 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
} else if (pass->Type() == "analysis_var_pass") {
} else if (pass->Type() == "memory_optimize_pass") {
const std::vector<OpDesc *> *all_op_descs =
new std::vector<OpDesc *>(main_program.Block(0).AllOps());
graph->Set<const std::vector<OpDesc *>>(kAllOpDescs,
......@@ -213,6 +215,13 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
pass->Set<const std::vector<OpDesc *>>(
kAllOpDescs,
new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
} else if (pass->Type() == "inplace_pass") {
if (graph->Has(kAllOpDescs)) {
graph->Erase(kAllOpDescs);
}
graph->Set<const std::vector<OpDesc *>>(
kAllOpDescs,
new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
} else if (pass->Type() == "fuse_relu_depthwise_conv_pass") {
if (!use_cuda) {
LOG(WARNING) << "fuse_relu_depthwise_conv_pass is only supported on "
......@@ -238,8 +247,9 @@ USE_PASS(allreduce_mode_multi_devices_pass);
USE_PASS(dist_multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
USE_PASS(analysis_var_pass);
USE_PASS(memory_optimize_pass);
USE_PASS(sequential_execution_pass);
USE_PASS(all_reduce_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
USE_PASS(inplace_pass);
USE_PASS(lock_free_optimize_pass);
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 8f3b2523
......@@ -80,6 +80,8 @@ struct BuildStrategy {
bool memory_early_delete_{false};
bool enable_inplace_{false};
bool enable_sequential_execution_{false};
bool fuse_broadcast_op_{false};
......
paddle/fluid/framework/details/inplace_op_pass.cc 0 → 100644
浏览文件 @ 8f3b2523
// 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/inplace_op_pass.h"
#include <algorithm>
#include <deque>
#include <iterator>
#include <stack>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/details/memory_optimize_pass.h"
#include "paddle/fluid/framework/op_info.h"
// NOTE(dzhwinter): inplace means one op output variable reuse the input space.
// By our design, one operator only can read its input(const Variable),
// write its output(non-const Variable). If one operator is inplaced, means
// user have chance to write the space before reading happens.
// Especially when some optimize code writing style is applied.
//
//
// /* wrong case in operator */
// /*In this case, a larger allocation is allocated, input content is lost*/
// const Tensor* in = ctx.Input<Tensor>("In")
// Tensor* out = ctx.Output<Tensor>("Out");
// auto* out_ptr = out->mutable_data<T>(ctx.GetPlace());
// out_ptr[0] = 0; // input contect is overwrited.
// For backward compacity. if enable_inplace_whitelist is turn on.
// only the ops in whitelist will be use inplace strategy.
// if not, all the op will be inplaced if it registered with InplaceClass
DEFINE_bool(
enable_inplace_whitelist, true,
"If this option turns on, only these op in whitelist can be inplaced."
"If it turns off, all of the running op can be candidate of inplaced op."
"Such as scale, elementwise_add"
"By default, it's turned on");
// clang-format off
const std::string kInplacedOpWhiteList[] = { // NOLINT
"sigmoid",
"exp",
"relu",
"tanh",
"sqrt",
"ceil",
"floor",
"reciprocal",
"relu6",
"soft_relu",
"hard_sigmoid",
"batch_norm",
"batch_norm_grad",
"sum",
"sum_grad",
"scale",
"reshape",
"elementwise_add",
"elementwise_add_grad",
};
// clang-format on
namespace paddle {
namespace framework {
namespace details {
static inline ir::Node* GetNextInplacedOpOutput(ir::Node* var) {
// if next op is inplaced, then return the output var
// otherwise return nullptr
PADDLE_ENFORCE(var && var->IsVar() && !var->IsCtrlVar());
ir::Node* inplaced_var = nullptr;
// only has one output op can be inplaced
if (var->outputs.size() == 1 && var->outputs[0]->IsOp()) {
auto* op = var->outputs[0];
for (auto* out_var : op->outputs) {
if (!out_var->IsVar() || out_var->IsCtrlVar() ||
out_var->Var() == nullptr)
continue;
if (out_var->Name() == var->Name()) {
inplaced_var = out_var;
break;
}
}
}
return inplaced_var;
}
static inline ir::Node* GetPrevInplacedOpInput(ir::Node* var) {
PADDLE_ENFORCE(var && var->IsVar() && !var->IsCtrlVar());
ir::Node* inplaced_var = nullptr;
if (var->inputs.size() == 1 && var->inputs[0]->IsOp()) {
auto* op = var->inputs[0];
for (auto* in_var : op->inputs) {
if (!in_var->IsVar() || in_var->IsCtrlVar() || in_var->Var() == nullptr)
continue;
if (in_var->Name() == var->Name()) {
inplaced_var = in_var;
break;
}
}
}
return inplaced_var;
}
template <typename Container>
static inline bool ConnectByCtrlVar(const Container& group1,
const Container& group2) {
bool connected = false;
std::unordered_set<ir::Node*> outputs;
for (auto* op : group1) {
for (auto* var : op->outputs) {
if (var->IsCtrlVar()) outputs.emplace(var);
}
}
for (auto* op : group2) {
for (auto* var : op->inputs) {
if (outputs.count(var)) connected = true;
}
}
return connected;
}
InplacePass::InplacePass() : Pass() {
if (FLAGS_enable_inplace_whitelist) {
for (auto& s : kInplacedOpWhiteList) {
whitelist_.emplace(s);
}
}
}
void InplacePass::InitSSAGraphNodes() const {
std::unordered_map<std::string, std::unordered_set<ir::Node*>> all_vars;
for (auto* op : view_.AllOps()) {
for (auto* node : op->inputs) {
if (!node->IsVar() || node->IsCtrlVar()) continue;
if (all_vars[node->Name()].count(node) == 0) {
all_vars[node->Name()].emplace(node);
var_nodes_[node->Name()].emplace_back(node);
}
}
for (auto* node : op->outputs) {
if (!node->IsVar() || node->IsCtrlVar()) continue;
if (all_vars[node->Name()].count(node) == 0) {
all_vars[node->Name()].emplace(node);
var_nodes_[node->Name()].emplace_back(node);
}
}
}
}
std::unique_ptr<ir::Graph> InplacePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
var_nodes_.clear();
view_.Build(graph.get());
InitSSAGraphNodes();
for (auto* op : view_.AllOps()) {
if (FLAGS_enable_inplace_whitelist && !whitelist_.count(op->Name()))
continue;
TryInplaceOpInputOutput(op, graph.get());
}
graph->ResolveHazard(var_nodes_);
return graph;
}
void InplacePass::InplaceModifyDesc(const std::string& var,
const std::string& cache_var,
const size_t& idx) const {
for (size_t i = idx; i < view_.AllOps().size(); ++i) {
auto* op = view_.AllOps()[i];
PADDLE_ENFORCE(op->IsOp() && op->Op());
auto* op_desc = op->Op();
op_desc->RenameInput(var, cache_var);
op_desc->RenameOutput(var, cache_var);
if (op_desc->Block()->HasVar(var)) op_desc->Block()->RemoveVar(var);
op_desc->Flush();
}
}
void InplacePass::InplaceModifyVar(const std::string& var,
const std::string& cache_var,
const size_t& idx, ir::Graph* graph) const {
PADDLE_ENFORCE(var_nodes_[var].size() >= 1 &&
var_nodes_[var].at(0)->Var() != nullptr);
std::unique_ptr<VarDesc> var_desc(new VarDesc(*var_nodes_[var].at(0)->Var()));
var_desc->SetName(cache_var);
for (size_t i = idx; i < view_.AllOps().size(); ++i) {
auto* op = view_.AllOps()[i];
// redirect the input to the latest version of cache_var
for (auto* node : op->inputs) {
if (node->Name() == var) {
ir::Node* cache_node = var_nodes_[cache_var].back();
// swap node to cache_node
cache_node->outputs.insert(cache_node->outputs.end(),
node->outputs.begin(), node->outputs.end());
for (auto* next_op : node->outputs) {
std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
cache_node);
}
}
}
// if we need to rename the output,
// always create a newer version of cache_var
for (auto* node : op->outputs) {
if (node->Name() == var) {
ir::Node* cache_node = graph->CreateVarNode(var_desc.get());
var_nodes_[cache_var].emplace_back(cache_node);
// swap node to cache node
cache_node->outputs.insert(cache_node->outputs.end(),
node->outputs.begin(), node->outputs.end());
cache_node->inputs.emplace_back(op);
std::replace(op->outputs.begin(), op->outputs.end(), node, cache_node);
for (auto* next_op : node->outputs) {
std::replace(next_op->inputs.begin(), next_op->inputs.end(), node,
cache_node);
}
}
}
}
// release node of unused var in graph
for (auto* node : var_nodes_[var]) {
graph->RemoveNode(node);
}
var_nodes_.at(var).clear();
}
void InplacePass::TryInplaceOpInputOutput(ir::Node* op,
ir::Graph* graph) const {
PADDLE_ENFORCE(op->Op() != nullptr && op->Op()->Block() != nullptr,
"op_desc is nullptr");
// 3 pre-requirments need to meet if the op want to inplaced.
// 1. infer_inplace_ is registered.
auto* op_desc = op->Op();
auto& infer_inplace =
OpInfoMap::Instance().Get(op_desc->Type()).infer_inplace_;
if (!static_cast<bool>(infer_inplace)) return;
PADDLE_ENFORCE(static_cast<bool>(infer_inplace),
"%s's infer_inplace has not been registered", op_desc->Type());
auto* block = op_desc->Block();
auto in_to_outs = infer_inplace(*op_desc, block);
auto& all_ops = view_.AllOps();
auto cursor = std::find(all_ops.begin(), all_ops.end(), op);
size_t idx = std::distance(all_ops.begin(), cursor);
for (auto& pair : in_to_outs) {
auto& in_var_name = pair.first;
auto& out_var_name = pair.second;
auto* in_node = view_.GetNodeByName(in_var_name, op->inputs);
auto* out_node = view_.GetNodeByName(out_var_name, op->outputs);
// 2. there is no external pending op on the input node
if (view_.PendingOpsOnVar(in_node).size() > 1) {
VLOG(3) << string::Sprintf(
"!!! %s input has external dependency, can not inplaced, %s => %s "
"skiped",
op->Name(), out_var_name, in_var_name);
continue;
}
// 3. if output reuse input inplaced, the dependency group is not changed.
// For detail, check
// the function description in "OutConnectInputByCtrlVar"
if (view_.OutConnectInputByCtrlVar(in_node, out_node)) {
VLOG(3) << string::Sprintf(
"!!! %s input output connect by ctrl var, cannot inplaced, %s => %s "
"skiped",
op->Name(), out_var_name, in_var_name);
continue;
}
VLOG(3) << string::Sprintf("!!! %s, %s => %s inplaced", op->Name(),
out_var_name, in_var_name);
InplaceModifyDesc(out_var_name, in_var_name, idx);
InplaceModifyVar(out_var_name, in_var_name, idx, graph);
}
}
ir::Node* GraphView::GetNodeByName(const std::string& name,
const std::vector<ir::Node*>& nodes) const {
// nodes should be op->inputs/outputs
// node in same node do have different name.
std::unordered_set<std::string> nodes_in_op;
bool has_dup_node =
std::all_of(nodes.begin(), nodes.end(), [&nodes_in_op](ir::Node* node) {
if (!node->IsVar() || node->IsCtrlVar() || node->Var() == nullptr) {
if (nodes_in_op.count(node->Name())) return true;
nodes_in_op.emplace(node->Name());
}
return false;
});
PADDLE_ENFORCE(has_dup_node == false, "nodes has same name!");
ir::Node* node = nullptr;
for (auto* it : nodes) {
if (!it->IsVar() || it->IsCtrlVar() || it->Var() == nullptr) continue;
if (it->Name() == name) {
node = it;
break;
}
}
PADDLE_ENFORCE(node != nullptr,
string::Sprintf("Not found var %s in nodes!", name));
return node;
}
std::vector<ir::Node*> GraphView::PendingOpsOnVar(ir::Node* node) {
return node->outputs;
}
void GraphView::Build(ir::Graph* g) { ops_ = SortOpLikeDescOrder(*g); }
const std::vector<ir::Node*> GraphView::AllOps() { return ops_; }
bool GraphView::OutConnectInputByCtrlVar(ir::Node* in_var, ir::Node* out_var) {
// assume v_a0, v_a1 is variable. v_a0 -> v_a0 means already inplaced.
// v_a1 -> v_a1 means already inplaced.
// Currently we make decision to check if the v_a0 -> v_a1 can be inplace.
//
// v_a0
// +
// |
// v
// v_a0
// +
// |
// v
// v_a1
// +
// |
// v
// v_a1
// start from the first inplaced input v_a0(on the top one).
// Do a DFSSearch, get all its paths. If there is one path connect
// the in_var and out_var which contains control dep var.
// Means there a control path. out_var can not be inplaced use in_var.
std::unordered_set<ir::Node *> out_var_set, in_var_set;
ir::Node* out = out_var;
// get the ops with same output name
while (out != nullptr) {
out_var_set.emplace(out);
out = GetNextInplacedOpOutput(out);
}
// get ops with same input name
ir::Node* in = in_var;
while (in != nullptr) {
in_var_set.emplace(in);
in = GetPrevInplacedOpInput(in);
}
// find if there is path with control dep var connect the in_var_set and
// out_var_set
return ConnectByCtrlVar(in_var_set, out_var_set);
}
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(inplace_pass, paddle::framework::details::InplacePass);
paddle/fluid/framework/details/inplace_op_pass.h 0 → 100644
浏览文件 @ 8f3b2523
// 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 <unordered_set>
#include <vector>
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace details {
class GraphView {
public:
GraphView() = default;
void Build(ir::Graph* g);
const std::vector<ir::Node*> AllOps();
ir::Node* GetNodeByName(const std::string& name,
const std::vector<ir::Node*>& nodes) const;
std::vector<ir::Node*> PendingOpsOnVar(ir::Node* var);
bool OutConnectInputByCtrlVar(ir::Node* in_var, ir::Node* out_var);
private:
std::vector<ir::Node*> ops_;
};
class InplacePass : public ir::Pass {
public:
InplacePass();
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
void InitSSAGraphNodes() const;
private:
void InplaceModifyVar(const std::string& in_var, const std::string& out_var,
const size_t& idx, ir::Graph* graph) const;
void InplaceModifyDesc(const std::string& in_var, const std::string& out_var,
const size_t& idx) const;
void TryInplaceOpInputOutput(ir::Node* op, ir::Graph* graph) const;
mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
mutable std::unordered_set<std::string> whitelist_;
mutable GraphView view_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/memory_early_delete_pass.cc
浏览文件 @ 8f3b2523
......@@ -16,7 +16,7 @@
#include <queue>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
......
paddle/fluid/framework/details/memory_reuse_types.cc → paddle/fluid/framework/details/memory_optimize_helper.cc
浏览文件 @ 8f3b2523
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include <iostream>
#include <sstream>
#include <string>
......@@ -83,7 +83,7 @@ struct NodeComparator {
}
};
void OrderedNodePairPool::Insert(ir::Node* var, ir::Node* op) {
void OrderedNodeList::Insert(ir::Node* var, ir::Node* op) {
PADDLE_ENFORCE(var->IsVar() && !var->IsCtrlVar());
PADDLE_ENFORCE(op->IsOp());
if (mark_table_.count(var->Name()) != 0) {
......@@ -119,11 +119,11 @@ void OrderedNodePairPool::Insert(ir::Node* var, ir::Node* op) {
mark_table_[var->Name()] = it;
}
int OrderedNodePairPool::GetIndex(ir::Node* var) {
int OrderedNodeList::GetIndex(ir::Node* var) {
return std::distance(nodes_.begin(), mark_table_[var->Name()]);
}
ir::Node* OrderedNodePairPool::NodeMatch(ir::Node* var) const {
ir::Node* OrderedNodeList::NodeMatch(ir::Node* var) const {
ir::Node* found_node = nullptr;
NodeComparator compare_node;
......@@ -136,13 +136,15 @@ ir::Node* OrderedNodePairPool::NodeMatch(ir::Node* var) const {
return found_node;
}
void OrderedNodePairPool::Erase(ir::Node* var) {
PADDLE_ENFORCE(mark_table_.count(var->Name()));
nodes_.erase(mark_table_[var->Name()]);
mark_table_.erase(var->Name());
void OrderedNodeList::Erase(ir::Node* var) { Erase(var->Name()); }
void OrderedNodeList::Erase(const std::string& var) {
PADDLE_ENFORCE(mark_table_.count(var));
nodes_.erase(mark_table_[var]);
mark_table_.erase(var);
}
std::string OrderedNodePairPool::ToString() const {
std::string OrderedNodeList::ToString() const {
std::stringstream ss;
for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
ss << DebugString(it->first) << " ";
......@@ -150,6 +152,38 @@ std::string OrderedNodePairPool::ToString() const {
return ss.str();
}
bool NodeCanReused(ir::Node* node) {
if (node == nullptr || !node->IsVar() || node->IsCtrlVar()) return false;
auto* desc = node->Var();
auto type = desc->GetType();
if (desc->Persistable() || type != proto::VarType::LOD_TENSOR ||
desc->GetShape().empty()) {
return false;
}
// vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
std::string name = node->Name();
if (!name.empty() && name[0] == '@' && name[name.size() - 1] == '@')
return false;
for (auto* op : node->inputs) {
if (op->Op()->HasAttr("force_cpu")) {
// op output force generated in cpu, can not be reused.
return framework::AttrReader(op->Op()->GetAttrMap())
.Get<bool>("force_cpu") == 0;
}
}
return true;
}
bool OpHasSubBlock(OpDesc* desc) {
const AttributeMap& attrs = desc->GetAttrMap();
for (auto& attr : attrs) {
if (attr.second.type() == typeid(BlockDesc*) || // NOLINT
attr.second.type() == typeid(std::vector<BlockDesc*>)) // NOLINT
return true;
}
return false;
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/memory_reuse_types.h → paddle/fluid/framework/details/memory_optimize_helper.h
浏览文件 @ 8f3b2523
......@@ -43,7 +43,7 @@ using GraphNodePool = std::vector<
// For example,
// node0[-1, 1] node1[-1, 1, 1], node2[1,1], node3[1,1024], ..
// O(1) insert, delete
class OrderedNodePairPool {
class OrderedNodeList {
public:
using NodePair = std::pair<ir::Node*, std::unordered_set<ir::Node*>>;
using Iter = typename std::list<NodePair>::iterator;
......@@ -53,8 +53,12 @@ class OrderedNodePairPool {
void Erase(ir::Node* var);
void Erase(const std::string& var);
bool Has(ir::Node* var) { return mark_table_.count(var->Name()); }
bool Has(const std::string& var) { return mark_table_.count(var); }
ir::Node* NodeMatch(ir::Node* var) const;
// map store non-const iterator, can not promise const
int GetIndex(ir::Node* var);
......@@ -67,6 +71,11 @@ class OrderedNodePairPool {
ConstIter end() const { return nodes_.end(); }
size_t size() const { return nodes_.size(); }
void Clear() {
mark_table_.clear();
nodes_.clear();
}
private:
// for searching.
std::unordered_map<std::string, Iter> mark_table_;
......@@ -74,14 +83,47 @@ class OrderedNodePairPool {
std::list<NodePair> nodes_;
};
// valid a tensor can be reuse or not
bool NodeCanReused(ir::Node* node);
// check op has subblock or not
bool OpHasSubBlock(OpDesc* desc);
// node memory size in bytes
size_t NodeSizeInBytes(ir::Node* n);
std::string DebugString(ir::Node* var);
// std::string DebugString(VarDesc* var);
VarDesc* FindVarDescInBlock(ir::Node* n);
template <typename Container, typename Callback>
class FilterVariableImpl {
public:
void operator()(const Container& nodes, Callback callback) {
for (auto* node : nodes) {
callback(node);
}
}
};
// filter var node for op->inputs/outputs
template <typename Callback>
class FilterVariableImpl<std::vector<ir::Node*>, Callback> {
public:
void operator()(const std::vector<ir::Node*>& nodes, Callback callback) {
for (auto* var : nodes) {
if (var->IsVar() && !var->IsCtrlVar()) {
callback(var);
}
}
}
};
template <typename Container, typename Callback>
void FilterVariables(const Container& nodes, Callback callback) {
FilterVariableImpl<Container, Callback>()(nodes, callback);
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/memory_reuse_types_test.cc → paddle/fluid/framework/details/memory_optimize_helper_test.cc
浏览文件 @ 8f3b2523
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include <algorithm>
#include <iostream>
#include <memory>
......@@ -27,8 +27,8 @@ namespace paddle {
namespace framework {
namespace details {
TEST(OrderedNodePairPool, Normal) {
OrderedNodePairPool pool;
TEST(OrderedNodeList, Normal) {
OrderedNodeList pool;
std::vector<std::unique_ptr<ir::Node>> nodes;
// clang-format off
......
paddle/fluid/framework/details/analysis_var_pass.cc → paddle/fluid/framework/details/memory_optimize_pass.cc
浏览文件 @ 8f3b2523
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/analysis_var_pass.h"
#include "paddle/fluid/framework/details/memory_optimize_pass.h"
#include <algorithm>
#include <atomic>
#include <deque>
......@@ -48,35 +48,7 @@ static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
op1->Outputs() == op2->Outputs();
}
template <typename Container, typename Callback>
class FilterVariableImpl {
public:
void operator()(const Container& nodes, Callback callback) {
for (auto* node : nodes) {
callback(node);
}
}
};
// filter var node for op->inputs/outputs
template <typename Callback>
class FilterVariableImpl<std::vector<ir::Node*>, Callback> {
public:
void operator()(const std::vector<ir::Node*>& nodes, Callback callback) {
for (auto* var : nodes) {
if (var->IsVar() && !var->IsCtrlVar()) {
callback(var);
}
}
}
};
template <typename Container, typename Callback>
void FilterVariables(const Container& nodes, Callback callback) {
FilterVariableImpl<Container, Callback>()(nodes, callback);
}
std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
std::unique_ptr<ir::Graph> MemoryOptimizePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
auto nodes = graph->Nodes();
auto subblock_vars = GetSubBlockVars(nodes);
......@@ -103,8 +75,11 @@ std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
}
for (auto& var : op->outputs) {
if (NodeCanReused(var) && cfg_->Use(op).count(var->Name()) == 0) {
if (!NodeCanReused(var) || cfg_->Use(op).count(var->Name()) == 0 ||
skip_set_.count(var->Name()))
continue;
ir::Node* cache = pool_.NodeMatch(var);
if (var->Name() == FLAGS_memory_optimize_debug) {
VLOG(3) << "start match var " << DebugString(var) << " of op "
<< op->Name();
......@@ -112,14 +87,14 @@ std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
VLOG(3) << "matched in pool : "
<< ((cache == nullptr) ? "False" : "True");
}
if (cache != nullptr) {
if (cache == nullptr) continue;
if (var->Name() == cache->Name()) {
VLOG(3) << "The same cache variable is cascade reused."
<< var->Name() << " is re-filled to the pool after"
VLOG(3) << "The same cache variable is cascade reused." << var->Name()
<< " is re-filled to the pool after"
<< "the reused op is finished. Current op can not "
<< "replace it again. Skip this candidate.";
continue;
}
int node_idx_in_pool = pool_.GetIndex(cache);
VLOG(3) << string::Sprintf(
......@@ -138,13 +113,15 @@ std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
pool_.Erase(cache);
}
}
}
// fill the pool
std::unordered_set<std::string> unlived_vars;
for (auto var : cfg_->LiveIn(op)) {
if (cfg_->LiveOut(op).count(var) == 0) {
unlived_vars.emplace(var);
}
}
for (auto var : unlived_vars) {
ir::Node* var_node = cfg_->GetNodeFromVarName(var, op);
if (var_node == nullptr) continue;
if (NodeCanReused(var_node) && !pool_.Has(var_node)) {
pool_.Insert(var_node, op);
}
......@@ -177,7 +154,7 @@ std::unique_ptr<ir::Graph> AnalysisVarPass::ApplyImpl(
return graph;
}
void AnalysisVarPass::SubGraphOptimize(OpDesc* op_desc) const {
void MemoryOptimizePass::SubGraphOptimize(OpDesc* op_desc) const {
// conditional block, while op and their grad op
auto* sub_block_desc =
AttrReader(op_desc->GetAttrMap()).Get<BlockDesc*>("sub_block");
......@@ -247,7 +224,7 @@ void AnalysisVarPass::SubGraphOptimize(OpDesc* op_desc) const {
}
}
std::unordered_set<std::string> AnalysisVarPass::GetSubBlockVars(
std::unordered_set<std::string> MemoryOptimizePass::GetSubBlockVars(
const std::unordered_set<ir::Node*>& nodes) const {
std::unordered_set<std::string> vars;
for (auto& op : nodes) {
......@@ -263,7 +240,7 @@ std::unordered_set<std::string> AnalysisVarPass::GetSubBlockVars(
return vars;
}
void AnalysisVarPass::RenameVarInGraphDesc(const std::string& var,
void MemoryOptimizePass::RenameVarInGraphDesc(const std::string& var,
const std::string& cache_var,
size_t idx) const {
for (size_t i = idx; i < cfg_->Ops().size(); ++i) {
......@@ -277,7 +254,7 @@ void AnalysisVarPass::RenameVarInGraphDesc(const std::string& var,
}
}
void AnalysisVarPass::InitSSAGraphNodes() const {
void MemoryOptimizePass::InitSSAGraphNodes() const {
std::unordered_map<std::string, std::unordered_set<ir::Node*>> all_vars;
if (var_nodes_.empty()) {
for (auto* op : cfg_->Ops()) {
......@@ -297,9 +274,10 @@ void AnalysisVarPass::InitSSAGraphNodes() const {
}
}
void AnalysisVarPass::RenameVarInGraphNode(const std::string& var,
void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
const std::string& cache_var,
size_t idx, ir::Graph* graph) const {
size_t idx,
ir::Graph* graph) const {
// if replace happens, we need to create a newer version cache_var
// but use the same dims/data_type with var.
PADDLE_ENFORCE(var_nodes_[var].size() >= 1 &&
......@@ -358,39 +336,6 @@ void AnalysisVarPass::RenameVarInGraphNode(const std::string& var,
var_nodes_.at(var).clear();
}
bool AnalysisVarPass::NodeCanReused(ir::Node* node) const {
if (!node->IsVar() || node->IsCtrlVar()) return false;
auto* desc = node->Var();
auto type = desc->GetType();
if (desc->Persistable() || type != proto::VarType::LOD_TENSOR ||
desc->GetShape().empty()) {
return false;
}
// vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
std::string name = node->Name();
if (!name.empty() && name[0] == '@' && name[name.size() - 1] == '@')
return false;
if (skip_set_.count(name)) return false;
for (auto* op : node->inputs) {
if (op->Op()->HasAttr("force_cpu")) {
// op output force generated in cpu, can not be reused.
return framework::AttrReader(op->Op()->GetAttrMap())
.Get<bool>("force_cpu") == 0;
}
}
return true;
}
bool AnalysisVarPass::OpHasSubBlock(OpDesc* desc) const {
const AttributeMap& attrs = desc->GetAttrMap();
for (auto& attr : attrs) {
if (attr.second.type() == typeid(BlockDesc*) || // NOLINT
attr.second.type() == typeid(std::vector<BlockDesc*>)) // NOLINT
return true;
}
return false;
}
std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph) {
PADDLE_ENFORCE(graph.Has(kAllOpDescs),
"Graph has no attribute of kAllOpDescs.");
......@@ -651,6 +596,7 @@ ir::Node* ControlFlowGraph::GetNodeFromVarName(const std::string& name,
} // namespace framework
} // namespace paddle
REGISTER_PASS(analysis_var_pass, paddle::framework::details::AnalysisVarPass)
REGISTER_PASS(memory_optimize_pass,
paddle::framework::details::MemoryOptimizePass)
.RequireGraphAttr(paddle::framework::details::kGraphNodePool)
.RequireGraphAttr(paddle::framework::details::kAllOpDescs);
paddle/fluid/framework/details/analysis_var_pass.h → paddle/fluid/framework/details/memory_optimize_pass.h
浏览文件 @ 8f3b2523
......@@ -25,7 +25,7 @@
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
......@@ -35,12 +35,10 @@ namespace details {
constexpr char kAllOpDescs[] = "all_op_descs";
std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph);
// sort op in bfs order
std::vector<ir::Node*> BFSSortGraphOps(const ir::Graph& graph);
class ControlFlowGraph;
class AnalysisVarPass : public ir::Pass {
class MemoryOptimizePass : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
......@@ -57,17 +55,13 @@ class AnalysisVarPass : public ir::Pass {
ir::Graph* graph) const;
void SubGraphOptimize(OpDesc* op_desc) const;
// valid a tensor can be reuse or not
bool NodeCanReused(ir::Node* node) const;
// scan subblock and collect the output/input variables.
std::unordered_set<std::string> GetSubBlockVars(
const std::unordered_set<ir::Node*>&) const;
// check op has subblock or not
bool OpHasSubBlock(OpDesc* desc) const;
private:
// Reuse Node Pool, Owned.
mutable OrderedNodePairPool pool_;
mutable OrderedNodeList pool_;
// controlflow Graph
mutable std::unique_ptr<ControlFlowGraph> cfg_;
// skip set
......
paddle/fluid/framework/details/analysis_var_pass_test.cc → paddle/fluid/framework/details/memory_optimize_pass_test.cc
浏览文件 @ 8f3b2523
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/analysis_var_pass.h"
#include "paddle/fluid/framework/details/memory_optimize_pass.h"
#include <algorithm>
#include <iostream>
#include <iterator>
......
paddle/fluid/framework/details/op_registry.h
浏览文件 @ 8f3b2523
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <tuple>
#include <vector>
#include "paddle/fluid/framework/grad_op_desc_maker.h"
#include "paddle/fluid/framework/inplace_op_inference.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
......@@ -32,7 +33,8 @@ enum OpInfoFillType {
kOpProtoAndCheckerMaker = 1,
kGradOpDescMaker = 2,
kVarTypeInference = 3,
kShapeInference = 4
kShapeInference = 4,
kInplaceOpInference = 5
};
template <typename T>
......@@ -48,8 +50,11 @@ struct OpInfoFillTypeID {
? kVarTypeInference
: (std::is_base_of<InferShapeBase, T>::value
? kShapeInference
: (std::is_base_of<
InplaceOpInference, T>::value
? kInplaceOpInference
: static_cast<OpInfoFillType>(
-1)))));
-1))))));
}
};
......@@ -139,6 +144,16 @@ struct OpInfoFiller<T, kShapeInference> {
}
};
template <typename T>
struct OpInfoFiller<T, kInplaceOpInference> {
void operator()(const char* op_type, OpInfo* info) const {
info->infer_inplace_ = [](const OpDesc& op_desc, BlockDesc* block) {
T infer;
return infer(op_desc, block);
};
}
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/inplace_op_inference.h 0 → 100644
浏览文件 @ 8f3b2523
// 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 <functional>
#include <numeric>
#include <string>
#include <unordered_map>
#include "glog/logging.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/type_defs.h"
namespace paddle {
namespace framework {
/*
Inplace Inference for create In->Out pairs for inplaced operator.
If we specify a pair of corresponding names. For example, X->Out.
then Out will inplaced use X's memory. The base class will do
legality validation for both variables.
*/
class InplaceOpInference {
public:
virtual ~InplaceOpInference() {}
virtual std::unordered_map<std::string, std::string> operator()(
const OpDesc& op_desc, BlockDesc* block) const = 0;
};
class InplaceInToOut : public InplaceOpInference {
public:
std::unordered_map<std::string, std::string> operator()(
const OpDesc& op_desc, BlockDesc* block) const {
std::unordered_map<std::string, std::string> ret;
auto in_out_var_names_pair = this->Apply(op_desc, block);
for (auto& pair : in_out_var_names_pair) {
PADDLE_ENFORCE(!op_desc.Input(pair.first).empty(),
string::Sprintf("op %s do not have input of %s!",
op_desc.Type(), pair.first));
PADDLE_ENFORCE(!op_desc.Output(pair.second).empty(),
string::Sprintf("op %s do not have output of %s!",
op_desc.Type(), pair.second));
auto& in_name = op_desc.Input(pair.first).at(0);
auto& out_name = op_desc.Output(pair.second).at(0);
auto in = block->FindRecursiveOrCreateVar(in_name);
auto out = block->FindRecursiveOrCreateVar(out_name);
if (TryInplaceInputOutput(in, out)) ret.insert({in_name, out_name});
}
return ret;
}
protected:
virtual std::unordered_map<std::string, std::string> Apply(
const OpDesc& op_desc, BlockDesc* block) const = 0;
bool TryInplaceInputOutput(const VarDesc& in, const VarDesc& out) const {
auto var_can_reused = [&](const VarDesc& node) -> bool {
auto type = node.GetType();
if (node.Persistable() || type != proto::VarType::LOD_TENSOR ||
node.GetShape().empty()) {
return false;
}
// vars can be @EMPTY@, @LR_DECAY_REUSE_ID@. For example, while_grad
std::string name = node.Name();
if (!name.empty() && name[0] == '@' && name[name.size() - 1] == '@')
return false;
return true;
};
auto var_size_in_bytes = [&](const VarDesc& node) -> size_t {
auto shape = node.GetShape();
int size = std::accumulate(shape.begin(), shape.end(), 1,
std::multiplies<int>());
size_t type_size = SizeOfType(node.GetDataType());
return type_size * std::abs(size);
};
return in.Name() != out.Name() && var_can_reused(in) &&
var_can_reused(out) &&
var_size_in_bytes(out) <= var_size_in_bytes(in);
}
};
/*
Inplace In and Out for operator only have an Input and an Output.
For example, activation op.
*/
class SingleOpInplaceInToOut : public InplaceInToOut {
protected:
std::unordered_map<std::string, std::string> Apply(
const OpDesc& op_desc, BlockDesc* block) const override {
PADDLE_ENFORCE(!op_desc.InputNames().empty(),
"Op inputs must not be empty");
PADDLE_ENFORCE(!op_desc.OutputNames().empty(),
"Op outputs must not be empty");
auto x_name = op_desc.InputNames().at(0);
auto out_name = op_desc.OutputNames().at(0);
return std::unordered_map<std::string, std::string>{{x_name, out_name}};
}
};
/*
Gradient op. Inplace output use it's Input.
For example, Input@Grad->Input reuse strategy.
*/
class GradOpInplaceInToOut : public InplaceInToOut {
protected:
std::unordered_map<std::string, std::string> Apply(
const OpDesc& op_desc, BlockDesc* block) const override {
std::unordered_map<std::string, std::string> ret;
std::unordered_set<std::string> output_names(op_desc.OutputNames().begin(),
op_desc.OutputNames().end());
for (auto& input_name : op_desc.InputNames()) {
if (output_names.count(GradVarName(input_name))) {
ret.insert({input_name, GradVarName(input_name)});
}
}
return ret;
}
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/inplace_op_inference_test.cc 0 → 100644
浏览文件 @ 8f3b2523
/* 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 <iterator>
#include <string>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/var_type_inference.h"
namespace paddle {
namespace framework {
class NOP : public OperatorBase {
public:
NOP(const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {}
};
class SingleOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "");
AddComment("");
}
};
class SingleGradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType("single_op_grad");
op->SetInput("Out", OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
return std::unique_ptr<OpDesc>(op);
}
};
class SingleOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {
ctx->HasInput("X");
ctx->HasOutput("Out");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
}
};
class SingleGradOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {
ctx->HasInput(framework::GradVarName("Out"));
ctx->HasOutput(framework::GradVarName("X"));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
}
};
class MultiOutOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddInput("Y", "").AsDuplicable();
AddInput("Z", "").AsDuplicable();
AddOutput("Out", "");
AddOutput("YOut", "");
AddOutput("ZOut", "");
AddOutput("NotReuseOut", "");
AddComment("");
}
};
class MultiOutShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {
ctx->ShareDim("X", "Out");
ctx->ShareDim("Y", "YOut");
ctx->ShareDim("Z", "ZOut");
}
};
class MultiGradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType("multi_out_grad");
op->SetInput("X", Input("X"));
op->SetOutput(framework::GradVarName("Y"), OutputGrad("YOut"));
op->SetOutput(framework::GradVarName("X"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("Z"), OutputGrad("ZOut"));
return std::unique_ptr<framework::OpDesc>(op);
}
};
class MultiOutGradShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("Y"),
ctx->GetInputDim(framework::GradVarName("YOut")));
ctx->SetOutputDim(framework::GradVarName("X"),
ctx->GetInputDim(framework::GradVarName("Out")));
ctx->SetOutputDim(framework::GradVarName("Z"),
ctx->GetInputDim(framework::GradVarName("ZOut")));
}
};
class MultiOutInplaceInToOut : public framework::InplaceInToOut {
public:
using framework::InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const OpDesc& op_desc, BlockDesc* block) const override {
return std::unordered_map<std::string, std::string>{
{"X", "Out"}, {"Y", "YOut"}, {"Z", "ZOut"},
};
}
};
class MultiOutGradInplaceInToOut : public framework::InplaceInToOut {
public:
using framework::InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const OpDesc& op_desc, BlockDesc* block) const override {
return std::unordered_map<std::string, std::string>{
{framework::GradVarName("YOut"), framework::GradVarName("Y")},
{framework::GradVarName("Out"), framework::GradVarName("X")},
{framework::GradVarName("ZOut"), framework::GradVarName("Z")},
};
}
};
} // namespace framework
} // namespace paddle
namespace f = paddle::framework;
REGISTER_OPERATOR(single_op, f::NOP, f::SingleOpMaker, f::SingleGradOpMaker,
f::SingleOpInplaceInToOut, f::SingleOpShapeInference);
REGISTER_OPERATOR(single_op_grad, f::NOP, f::SingleOpInplaceInToOut,
f::SingleGradOpShapeInference);
REGISTER_OPERATOR(multi_out_op, f::NOP, f::MultiOutOpMaker, f::MultiGradOpMaker,
f::MultiOutInplaceInToOut, f::MultiOutShapeInference);
REGISTER_OPERATOR(multi_out_grad, f::NOP, f::MultiOutGradInplaceInToOut,
f::MultiOutGradShapeInference);
namespace paddle {
namespace framework {
TEST(InferInplace, SingleOpInplaceInToOut) {
ProgramDesc prog;
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("single_op");
op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
op->SetOutput("Out", {"test2_out"});
prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 64});
prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_out");
prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
auto in_to_outs = infer_inplace(*op, op->Block());
EXPECT_EQ(in_to_outs.size(), 1ul);
auto it = in_to_outs.begin();
EXPECT_EQ(it->first, "test2_a");
EXPECT_EQ(it->second, "test2_out");
}
TEST(InferInplace, SingleGradOpInplaceInToOut) {
ProgramDesc prog;
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("single_op_grad");
op->SetInput(GradVarName("Out"), {"test2_out"});
op->SetOutput(GradVarName("X"), {"test2_a", "test2_b", "test2_c"});
prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 16});
prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("test2_out");
prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16});
auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
auto in_to_outs = infer_inplace(*op, op->Block());
EXPECT_EQ(in_to_outs.size(), 1ul);
auto it = in_to_outs.begin();
EXPECT_EQ(it->first, "test2_out");
EXPECT_EQ(it->second, "test2_a");
}
TEST(InferInplace, MultiOutInplaceInToOut) {
ProgramDesc prog;
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("multi_out_op");
op->SetInput("X", {"a0", "a1"});
op->SetInput("Y", {"b0"});
op->SetInput("Z", {"c0", "c1"});
op->SetOutput("Out", {"o0"});
op->SetOutput("YOut", {"y0"});
op->SetOutput("ZOut", {"z0"});
prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("o0");
prog.MutableBlock(0)->Var("y0");
prog.MutableBlock(0)->Var("z0");
prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
auto in_to_outs = infer_inplace(*op, op->Block());
EXPECT_EQ(in_to_outs.size(), 3ul);
std::unordered_map<std::string, std::string> expects = {
{"a0", "o0"}, {"b0", "y0"}, {"c0", "z0"},
};
EXPECT_TRUE(expects == in_to_outs);
}
TEST(InferInplace, MultiGradInplaceInToOut) {
ProgramDesc prog;
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("multi_out_grad");
op->SetInput(GradVarName("Out"), {"o0"});
op->SetInput(GradVarName("YOut"), {"y0"});
op->SetInput(GradVarName("ZOut"), {"z0"});
op->SetOutput(GradVarName("X"), {"a0", "a1"});
op->SetOutput(GradVarName("Y"), {"b0"});
op->SetOutput(GradVarName("Z"), {"c0", "c1"});
prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("o0");
prog.MutableBlock(0)->Var("y0");
prog.MutableBlock(0)->Var("z0");
prog.MutableBlock(0)->Var("a0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 16});
auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
auto in_to_outs = infer_inplace(*op, op->Block());
EXPECT_EQ(in_to_outs.size(), 3ul);
std::unordered_map<std::string, std::string> expects = {
{"o0", "a0"}, {"y0", "b0"}, {"z0", "c0"},
};
EXPECT_TRUE(expects == in_to_outs);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @ 8f3b2523
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <functional>
#include <string>
#include <typeindex>
#include <typeinfo>
......
paddle/fluid/framework/op_info.h
浏览文件 @ 8f3b2523
......@@ -38,6 +38,7 @@ struct OpInfo {
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
InferInplaceOpFN infer_inplace_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
......
paddle/fluid/framework/type_defs.h
浏览文件 @ 8f3b2523
......@@ -57,5 +57,8 @@ using InferVarTypeFN =
using InferShapeFN = std::function<void(InferShapeContext*)>;
using InplacePair = std::unordered_map<std::string, std::string>;
using InferInplaceOpFN = std::function<InplacePair(const OpDesc&, BlockDesc*)>;
} // namespace framework
} // namespace paddle
paddle/fluid/operators/activation_op.cc
浏览文件 @ 8f3b2523
......@@ -551,8 +551,10 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
::paddle::operators::OP_NAME##OpMaker, \
::paddle::operators::ActivationOpInferVarType, \
::paddle::operators::OP_NAME##GradMaker); \
REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad)
::paddle::operators::OP_NAME##GradMaker, \
::paddle::framework::SingleOpInplaceInToOut); \
REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad, \
::paddle::framework::SingleOpInplaceInToOut)
#define REGISTER_ACTIVATION_OP(OP_NAME, KERNEL_TYPE) \
REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 8f3b2523
......@@ -602,13 +602,48 @@ class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
}
};
class BatchNormInplaceInToOut : public framework::InplaceInToOut {
public:
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
std::unordered_map<std::string, std::string> inplace_in_to_out = {
{"Mean", "MeanOut"}, {"Variance", "VarianceOut"}, {"X", "Y"},
};
return inplace_in_to_out;
}
};
class BatchNormGradInplaceInToOut : public framework::InplaceInToOut {
public:
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
std::unordered_map<std::string, std::string> inplace_in_to_out = {
// Scale, Bias, SavedMean, SavedVariance shape is [batch_size, C]
{framework::GradVarName("Y"), framework::GradVarName("X")},
{"SavedMean", framework::GradVarName("Scale")},
{"SavedVariance", framework::GradVarName("Bias")},
};
return inplace_in_to_out;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
ops::BatchNormOpInferVarType, ops::BatchNormGradMaker);
REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp);
ops::BatchNormOpInferVarType, ops::BatchNormGradMaker,
ops::BatchNormInplaceInToOut);
REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp,
ops::BatchNormGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL(
batch_norm, ops::BatchNormKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/elementwise/elementwise_add_op.cc
浏览文件 @ 8f3b2523
......@@ -18,6 +18,7 @@ namespace ops = paddle::operators;
REGISTER_ELEMWISE_GRAD_MAKER(elementwise_add, Add);
REGISTER_ELEMWISE_EXPLICIT_OP(elementwise_add, "Add", "Out = X + Y", "Out",
"X");
REGISTER_OP_CPU_KERNEL(
elementwise_add,
ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/elementwise/elementwise_op.h
浏览文件 @ 8f3b2523
......@@ -250,6 +250,20 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
}
};
class ElementwiseOpInplace : public framework::InplaceInToOut {
public:
using framework::InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
return std::unordered_map<std::string, std::string>{
{"X", "Out"},
};
}
};
} // namespace operators
} // namespace paddle
......@@ -299,6 +313,7 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp, \
__ElemwiseOp##op_type##Maker__, \
::paddle::operators::ElementwiseOpInferVarType, \
op_type##GradMaker); \
op_type##GradMaker, \
::paddle::operators::ElementwiseOpInplace); \
REGISTER_OPERATOR(op_type##_grad, \
::paddle::operators::ElementwiseOpExplicitGrad)
paddle/fluid/operators/flatten_op.cc
浏览文件 @ 8f3b2523
......@@ -267,6 +267,35 @@ class Flatten2GradOp : public framework::OperatorBase {
}
};
class FlattenOpInplaceInToOut : public framework::InplaceInToOut {
public:
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
std::unordered_map<std::string, std::string> inplace_in_to_out = {
{"X", "Out"},
};
return inplace_in_to_out;
}
};
class FlattenGradInplaceinToOut : public framework::InplaceInToOut {
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
std::unordered_map<std::string, std::string> inplace_in_to_out = {
{framework::GradVarName("Out"), framework::GradVarName("X")},
};
return inplace_in_to_out;
}
};
} // namespace operators
} // namespace paddle
......@@ -275,10 +304,13 @@ USE_OP(reshape);
namespace ops = paddle::operators;
REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
ops::FlattenOpInferShape,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape);
paddle::framework::DefaultGradOpDescMaker<true>,
ops::FlattenOpInplaceInToOut);
REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape,
ops::FlattenGradInplaceinToOut);
REGISTER_OPERATOR(flatten2, ops::Flatten2Op, ops::Flatten2OpMaker,
ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker);
ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker,
ops::FlattenOpInplaceInToOut);
REGISTER_OPERATOR(flatten2_grad, ops::Flatten2GradOp,
ops::Flatten2GradInferShape);
ops::Flatten2GradInferShape, ops::FlattenGradInplaceinToOut);
paddle/fluid/operators/reshape_op.cc
浏览文件 @ 8f3b2523
......@@ -327,13 +327,44 @@ class Reshape2GradOp : public framework::OperatorWithKernel {
}
};
class ReshapeOpInplaceInToOut : public framework::InplaceInToOut {
public:
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
std::unordered_map<std::string, std::string> inplace_in_to_out = {
{"X", "Out"},
};
return inplace_in_to_out;
}
};
class ReshapeGradInplaceInToOut : public framework::InplaceInToOut {
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
std::unordered_map<std::string, std::string> inplace_in_to_out = {
{framework::GradVarName("Out"), framework::GradVarName("X")},
};
return inplace_in_to_out;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(reshape, ops::ReshapeOp, ops::ReshapeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp);
paddle::framework::DefaultGradOpDescMaker<true>,
ops::ReshapeOpInplaceInToOut);
REGISTER_OPERATOR(reshape_grad, ops::ReshapeGradOp,
ops::ReshapeGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
......@@ -343,8 +374,9 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
ops::ReshapeGradKernel);
REGISTER_OPERATOR(reshape2, ops::Reshape2Op, ops::Reshape2OpMaker,
ops::Reshape2GradMaker);
REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp);
ops::Reshape2GradMaker, ops::ReshapeOpInplaceInToOut);
REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp,
ops::ReshapeGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
......
paddle/fluid/operators/scale_op.cc
浏览文件 @ 8f3b2523
......@@ -100,13 +100,14 @@ class ScaleGradMaker : public framework::SingleGradOpDescMaker {
}
};
using ScaleOpInplace = framework::SingleOpInplaceInToOut;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker, ops::ScaleGradMaker,
ops::ScaleOpVarTypeInference);
ops::ScaleOpVarTypeInference, ops::ScaleOpInplace);
REGISTER_OP_CPU_KERNEL(
scale, ops::ScaleKernel<paddle::platform::CPUDeviceContext, float>,
ops::ScaleKernel<paddle::platform::CPUDeviceContext, double>,
......
paddle/fluid/operators/softmax_op.cc
浏览文件 @ 8f3b2523
......@@ -198,6 +198,21 @@ class SoftmaxOpGradMaker : public framework::SingleGradOpDescMaker {
return std::unique_ptr<framework::OpDesc>(op);
}
};
class SoftmaxInplaceInToOut : public framework::InplaceInToOut {
public:
using framework::InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
return std::unordered_map<std::string, std::string>{
{"X", "Out"},
};
}
};
} // namespace operators
} // namespace paddle
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 8f3b2523
......@@ -1049,6 +1049,10 @@ All parameter, weight, gradient are variables in Paddle.
"memory_early_delete",
[](const BuildStrategy &self) { return self.memory_early_delete_; },
[](BuildStrategy &self, bool b) { self.memory_early_delete_ = b; })
.def_property(
"enable_inplace",
[](const BuildStrategy &self) { return self.enable_inplace_; },
[](BuildStrategy &self, bool b) { self.enable_inplace_ = b; })
.def("_finalize_strategy_and_create_passes",
[](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
return self.CreatePassesFromStrategy(true);
......
python/paddle/fluid/__init__.py
浏览文件 @ 8f3b2523
......@@ -158,7 +158,8 @@ def __bootstrap__():
'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
'cudnn_exhaustive_search', 'memory_optimize_debug', 'selected_gpus',
'sync_nccl_allreduce', 'limit_of_tmp_allocation',
'times_excess_than_required_tmp_allocation'
'times_excess_than_required_tmp_allocation',
'enable_inplace_whitelist'
]
core.init_gflags([sys.argv[0]] +
......
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @ 8f3b2523
......@@ -41,6 +41,7 @@ class TestParallelExecutorBase(unittest.TestCase):
use_parallel_executor=True,
use_reduce=False,
use_ir_memory_optimize=False,
enable_inplace=True,
fuse_elewise_add_act_ops=False,
fuse_relu_depthwise_conv=False,
optimizer=fluid.optimizer.Adam,
......@@ -80,6 +81,7 @@ class TestParallelExecutorBase(unittest.TestCase):
build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
build_strategy.fuse_relu_depthwise_conv = fuse_relu_depthwise_conv
build_strategy.memory_optimize = use_ir_memory_optimize
build_strategy.enable_inplace = enable_inplace
build_strategy.enable_sequential_execution = enable_sequential_execution
if use_cuda and core.is_compiled_with_cuda():
build_strategy.remove_unnecessary_lock = True
......
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