提交 fbc16404 编写于 作者: T tensor-tang

Merge remote-tracking branch 'ups/develop' into refine/op/fc

......@@ -28,6 +28,38 @@ namespace paddle {
namespace framework {
namespace ir {
/*
* The graph is a Directed Acyclic Single Static Assignment Graph.
*
* In more detail, the following properties must hold:
*
* The graph shouldn't contain cycle. Each node is a black-box to the graph
* so the node itself could be a loop operator.
*
* Each Variable-type node has only one input (thus single static assignment).
*
* The output/input of operator is variable and the output/input of variable
* is operator.
*
* The following data harzards in Program are addressed in the Graph:
*
* Write-After-Read
* a = op1(x)
* x = op2(b)
* A control-dependency connection is created bettwen op1 and op2 such that
* op1->op2, so as to ensure correct order.
*
* Write-After-Write
* x = op1(a)
* x = op2(b)
* A control-dependency connection is created between op1 and op2 such that
* op1->op2, so as to ensure correct order.
*
* Other properties currently hold, but is not enforced yet:
*
* Variable-type node (not control dep) with the same variable name share
* the same underlying VarDesc.
*/
class Graph {
public:
explicit Graph(const ProgramDesc &program);
......
......@@ -36,7 +36,7 @@ class SumOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "");
AddOutput("Out", "").AsDuplicable();
AddComment("");
}
};
......@@ -59,11 +59,27 @@ class SumOpVarTypeInference : public VarTypeInference {
block->Var(out_var_name)->SetType(default_var_type);
}
};
class DummyOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "").AsDuplicable();
AddComment("");
}
};
class DummyOpVarTypeInference : public VarTypeInference {
public:
void operator()(const OpDesc &op_desc, BlockDesc *block) const override {}
};
} // namespace framework
} // namespace paddle
REGISTER_OPERATOR(sum, paddle::framework::NOP, paddle::framework::SumOpMaker,
paddle::framework::SumOpVarTypeInference);
REGISTER_OPERATOR(dummy, paddle::framework::NOP, paddle::framework::SumOpMaker,
paddle::framework::SumOpVarTypeInference);
REGISTER_OPERATOR(sum_without_infer_var_type, paddle::framework::NOP,
paddle::framework::SumOpMaker);
......@@ -110,5 +126,83 @@ TEST(GraphTest, Basic) {
}
ASSERT_EQ(nodes.size(), 5);
}
TEST(GraphTest, WriteAfterRead) {
// void Test() {
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"a"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
op = prog.MutableBlock(0)->AppendOp();
op->SetType("dummy");
op->SetInput("X", {"c"});
op->SetOutput("Out", {"a"});
op->SetAttr("op_role", 1);
prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c")->SetType(proto::VarType::LOD_TENSOR);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
ir::Node *control_dep1 = nullptr;
ir::Node *control_dep2 = nullptr;
for (ir::Node *n : g->Nodes()) {
if (n->Name() == "sum") {
ASSERT_EQ(n->outputs[0]->Name(), "b");
ASSERT_TRUE(ir::IsControlDepVar(*n->outputs[1]));
control_dep1 = n->outputs[1];
ASSERT_EQ(n->outputs.size(), 2);
}
if (n->Name() == "dummy") {
ASSERT_EQ(n->inputs[0]->Name(), "c");
ASSERT_TRUE(ir::IsControlDepVar(*n->inputs[1]));
control_dep2 = n->inputs[1];
ASSERT_EQ(n->inputs.size(), 2);
}
}
ASSERT_EQ(control_dep1, control_dep2);
}
TEST(GraphTest, WriteAfterWrite) {
// void Test() {
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"a"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
op = prog.MutableBlock(0)->AppendOp();
op->SetType("dummy");
op->SetInput("X", {"c"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c")->SetType(proto::VarType::LOD_TENSOR);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
ir::Node *control_dep1 = nullptr;
ir::Node *control_dep2 = nullptr;
for (ir::Node *n : g->Nodes()) {
if (n->Name() == "sum") {
ASSERT_EQ(n->outputs[0]->Name(), "b");
ASSERT_TRUE(ir::IsControlDepVar(*n->outputs[1]));
ASSERT_EQ(n->outputs.size(), 2);
control_dep1 = n->outputs[1];
}
if (n->Name() == "dummy") {
ASSERT_EQ(n->inputs[0]->Name(), "c");
ASSERT_TRUE(ir::IsControlDepVar(*n->inputs[1]));
control_dep2 = n->inputs[1];
ASSERT_EQ(n->inputs.size(), 2);
ASSERT_EQ(control_dep1, control_dep2);
}
}
}
} // namespace framework
} // namespace paddle
......@@ -270,12 +270,13 @@ struct EventItem {
double min_time;
double max_time;
double ave_time;
float ratio;
};
// Print results
void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
const std::string& sorted_domain, const size_t name_width,
const size_t data_width) {
const size_t data_width, double total) {
// Output header information
std::cout << "\n------------------------->"
<< " Profiling Report "
......@@ -300,7 +301,8 @@ void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
std::cout << std::setw(name_width) << "Event" << std::setw(data_width)
<< "Calls" << std::setw(data_width) << "Total"
<< std::setw(data_width) << "Min." << std::setw(data_width)
<< "Max." << std::setw(data_width) << "Ave." << std::endl;
<< "Max." << std::setw(data_width) << "Ave."
<< std::setw(data_width) << "Ratio." << std::endl;
for (size_t i = 0; i < events_table.size(); ++i) {
for (size_t j = 0; j < events_table[i].size(); ++j) {
const EventItem& event_item = events_table[i][j];
......@@ -309,7 +311,9 @@ void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
<< std::setw(data_width) << event_item.total_time
<< std::setw(data_width) << event_item.min_time
<< std::setw(data_width) << event_item.max_time
<< std::setw(data_width) << event_item.ave_time << std::endl;
<< std::setw(data_width) << event_item.ave_time
<< std::setw(data_width) << event_item.total_time / total
<< std::endl;
}
}
std::cout << std::endl;
......@@ -359,6 +363,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
std::vector<std::vector<EventItem>> events_table;
size_t max_name_width = 0;
double total = 0.; // the total time
for (size_t i = 0; i < events.size(); i++) {
std::list<Event> pushed_events;
std::vector<EventItem> event_items;
......@@ -379,6 +384,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
g_state == ProfilerState::kAll)
? rit->CudaElapsedMs(events[i][j])
: rit->CpuElapsedMs(events[i][j]);
total += event_time;
std::string event_name =
"thread" + std::to_string(rit->thread_id()) + "::" + rit->name();
......@@ -387,7 +393,8 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
if (event_idx.find(event_name) == event_idx.end()) {
event_idx[event_name] = event_items.size();
EventItem event_item = {event_name, 1, event_time,
event_time, event_time, event_time};
event_time, event_time, event_time,
0.};
event_items.push_back(event_item);
} else {
int index = event_idx[event_name];
......@@ -431,7 +438,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
}
// Print report
PrintProfiler(events_table, sorted_domain, max_name_width + 4, 12);
PrintProfiler(events_table, sorted_domain, max_name_width + 4, 12, total);
}
void DisableProfiler(EventSortingKey sorted_key,
......
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