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未验证 提交 278debab 编写于 作者: L liuwei1031 提交者： GitHub
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fix comments of 16410, test=develop (#16499) 

* fix comments of 16410, test=develop

* modify inplace_op_inference_test according to pass interface change, test=develop
上级 4c1ec41d
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Showing with 184 addition and 154 deletion
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 278debab
......@@ -195,8 +195,7 @@ 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 memory_optimize_helper)
cc_test(inplace_op_inference_test SRCS inplace_op_inference_test.cc DEPS inplace_op_pass op_registry proto_desc op_info memory_optimize_helper pass_builder)
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/inplace_op_pass.cc
浏览文件 @ 278debab
......@@ -156,7 +156,6 @@ void InplacePass::ApplyImpl(ir::Graph* graph) const {
continue;
TryInplaceOpInputOutput(op, graph);
}
// graph->ResolveHazard(var_nodes_);
}
void InplacePass::InplaceModifyDesc(const std::string& var,
......@@ -168,7 +167,7 @@ void InplacePass::InplaceModifyDesc(const std::string& var,
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();
}
}
......@@ -265,8 +264,6 @@ void InplacePass::WithdrawModify(const NodeSwapQueue& nodes,
void InplacePass::TryInplaceOpInputOutput(ir::Node* op,
ir::Graph* graph) const {
VLOG(4) << "Try to inplace op " << op->Name();
// PADDLE_ENFORCE(op->Op() != nullptr && op->Op()->Block() != nullptr,
// "op_desc is nullptr");
// some pre-requirments need to meet if the op want to inplaced.
PADDLE_ENFORCE(op->Op() != nullptr, "op_desc is nullptr");
......@@ -446,6 +443,7 @@ bool GraphView::CheckDeps(ir::Node* var, ir::Node* current_op) const {
// check if op2 depends on op1's output
bool GraphView::CheckOpDeps(ir::Node* op1, ir::Node* op2) const {
if (VLOG_IS_ON(4)) {
auto print_op = [&](ir::Node* op, const char* name) {
std::ostringstream os;
os << " " << name << " : " << op->Name() << " ";
......@@ -458,7 +456,7 @@ bool GraphView::CheckOpDeps(ir::Node* op1, ir::Node* op2) const {
};
print_op(op1, "OP1");
print_op(op2, "OP2");
}
if (op1 == op2) return true;
if (op_level_.at(op1) >= op_level_.at(op2)) return false;
......
paddle/fluid/framework/details/memory_optimize_helper_test.cc
浏览文件 @ 278debab
......@@ -142,16 +142,15 @@ TEST(OrderedSet, FindBestFitNode) {
for (auto& node : nodes) {
pool.Insert(node.get());
}
// FIXME(liuwei1031) this API has changed,
// disable these tests temporarily
// FindNextBestFitNode
// auto* n = nodes[0].get();
// auto* cache = pool.FindBestFitNode(n);
// PADDLE_ENFORCE(cache->Name() == "a");
// cache = pool.FindNextBestFitNode(n, cache);
// PADDLE_ENFORCE(cache->Name() == "c");
// cache = pool.FindNextBestFitNode(n, cache);
// PADDLE_ENFORCE(cache->Name() == "b");
auto* n = nodes[0].get();
auto* cache = pool.FindBestFitNode(n);
ASSERT_TRUE(cache->Name() == "a" || cache->Name() == "c");
auto* cache_b = pool.FindNextBestFitNode(n, cache);
ASSERT_TRUE(cache_b->Name() != cache->Name());
ASSERT_TRUE(cache_b->Name() == "a" || cache_b->Name() == "c");
cache = pool.FindNextBestFitNode(n, cache_b);
ASSERT_TRUE(cache == nullptr);
}
} // namespace details
......
paddle/fluid/framework/inplace_op_inference_test.cc
浏览文件 @ 278debab
......@@ -12,9 +12,14 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <iostream>
#include <iterator>
#include <memory>
#include <string>
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/details/inplace_op_pass.h"
#include "paddle/fluid/framework/ir/pass_builder.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
......@@ -165,118 +170,147 @@ REGISTER_OPERATOR(multi_out_grad, f::NOP, f::MultiOutGradInplaceInToOut,
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, 128, 128});
// 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, 128, 128});
//
// auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
// auto in_to_outs = infer_inplace(*op);
// 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, 1024, 1024});
// 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, 1024, 1024});
//
// auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
// auto in_to_outs = infer_inplace(*op);
// 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, 1024, 1024});
// prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
//
// auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
// auto in_to_outs = infer_inplace(*op);
// 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, 1024, 1024});
// prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
//
// auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
// auto in_to_outs = infer_inplace(*op);
//
// 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);
// }
void FakeSuccData(ProgramDesc* prog) { // NOLINT
prog->MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
prog->MutableBlock(0)->Var("test2_a")->SetShape({32, 64, 128, 128});
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({64, 32, 128, 128});
}
void FakeNoInplaceData(ProgramDesc* prog) { // NOLINT
prog->MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
prog->MutableBlock(0)->Var("test2_a")->SetShape({32, 64, 128, 128});
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({64, 31, 128, 128});
}
ir::Node* GetNodeFromGraph(ir::Graph* g, std::string name) {
ir::Node* op_node = nullptr;
for (auto& item : g->Nodes()) {
if (item->Name() == name) {
op_node = item;
break;
}
}
return op_node;
}
std::unique_ptr<ir::Graph> test_SingleOpInplaceInToOut(
std::unique_ptr<ir::Graph> g) {
std::unique_ptr<details::InplacePass> pass(new details::InplacePass());
ir::Node* op_node = GetNodeFromGraph(g.get(), "single_op");
EXPECT_NE(op_node, nullptr);
pass->Apply(g.get());
return g;
}
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"});
FakeSuccData(&prog);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
g = test_SingleOpInplaceInToOut(std::move(g));
auto op_node = GetNodeFromGraph(g.get(), "single_op");
EXPECT_EQ(op_node->outputs[0]->Name(), "test2_a");
}
TEST(InferInplace, SingleOpInplaceInToOutNoInplace) {
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"});
FakeNoInplaceData(&prog);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
g = test_SingleOpInplaceInToOut(std::move(g));
auto op_node = GetNodeFromGraph(g.get(), "single_op");
EXPECT_EQ(op_node->outputs[0]->Name(), "test2_out");
}
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, 1024, 1024});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
std::unique_ptr<details::InplacePass> pass(new details::InplacePass());
pass->Apply(g.get());
auto op_node = GetNodeFromGraph(g.get(), "multi_out_op");
ASSERT_TRUE(op_node != nullptr);
EXPECT_EQ(op_node->outputs[0]->Name(), "a0");
EXPECT_EQ(op_node->outputs[1]->Name(), "b0");
EXPECT_EQ(op_node->outputs[2]->Name(), "c0");
}
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, 1024, 1024});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 15, 1024, 1024});
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
std::unique_ptr<details::InplacePass> pass(new details::InplacePass());
pass->Apply(g.get());
auto op_node = GetNodeFromGraph(g.get(), "multi_out_grad");
ASSERT_TRUE(op_node != nullptr);
EXPECT_EQ(op_node->outputs[0]->Name(), "o0");
EXPECT_EQ(op_node->outputs[2]->Name(), "y0");
EXPECT_EQ(op_node->outputs[3]->Name(), "c0");
std::unordered_map<std::string, std::string> expects = {
{"o0", "a0"}, {"y0", "b0"}, {"z0", "c0"},
};
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @ 278debab
......@@ -56,7 +56,7 @@ proto::VarType::Type GetDataTypeOfVar(const Variable* var) {
}
}
static DDim GetDims(const Scope& scope, const std::string& name,
static DDim GetDimsDebug(const Scope& scope, const std::string& name,
bool get_actual_dim = false) {
Variable* var = scope.FindVar(name);
if (var == nullptr) {
......@@ -65,9 +65,9 @@ static DDim GetDims(const Scope& scope, const std::string& name,
if (var->IsType<LoDTensor>()) {
const LoDTensor& tensor = var->Get<LoDTensor>();
// if (UNLIKELY(!tensor.IsInitialized())) {
// return DDim({-1});
// }
if (UNLIKELY(!tensor.IsInitialized())) {
return DDim({-1});
}
return tensor.dims();
} else if (var->IsType<SelectedRows>()) {
if (get_actual_dim) {
......@@ -123,7 +123,7 @@ static int GetRowSize(const Scope& scope, const std::string& name) {
return -1;
}
static LoD GetLoD(const Scope& scope, const std::string& name) {
static LoD GetLoDDebug(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
auto default_lod = LoD({{}});
......@@ -133,9 +133,9 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
if (var->IsType<LoDTensor>()) {
const LoDTensor& tensor = var->Get<LoDTensor>();
// if (UNLIKELY(!tensor.IsInitialized())) {
// return default_lod;
// }
if (UNLIKELY(!tensor.IsInitialized())) {
return default_lod;
}
return tensor.lod();
} else {
return default_lod;
......@@ -274,8 +274,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
}
std::string dtype = GetDtype(*scope, var_name);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
ss << "[" << GetDimsDebug(*scope, var_name, true) << "]";
ss << "(" << GetLoDDebug(*scope, var_name) << ")";
}
}
if (i != input.second.size() - 1) {
......@@ -305,8 +305,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
}
std::string dtype = GetDtype(*scope, output.second[i]);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
ss << "[" << GetDimsDebug(*scope, var_name, true) << "]";
ss << "(" << GetLoDDebug(*scope, var_name) << ")";
}
}
if (i != output.second.size() - 1) {
......
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