Merge pull request #12468 from panyx0718/improve_profiler2

Improve profiler

Author: panyx0718

doc/fluid/howto/optimization/timeline_cn.md

@@ -1,21 +1,27 @@
 # 如何使用timeline工具做性能分析
 
-1. 在训练的主循环外加上`with profiler.profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个profile的记录文件。
+1. 在训练的主循环外加上`profiler.start_profiler(...)`和`profiler.stop_profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个profile的记录文件。
 
 	**提示：**
 	请不要在timeline记录信息时运行太多次迭代，因为timeline中的记录数量和迭代次数是成正比的。
 
 	```python
-	with profiler.profiler('All', 'total', '/tmp/profile') as prof:
-	    for pass_id in range(pass_num):
-	        for batch_id, data in enumerate(train_reader()):
-	            exe.run(fluid.default_main_program(),
-	                    feed=feeder.feed(data),
-	                    fetch_list=[])
+    for pass_id in range(pass_num):
+        for batch_id, data in enumerate(train_reader()):
+            if pass_id == 0 and batch_id == 5:
+                profiler.start_profiler("All")
+            elif pass_id == 0 and batch_id == 10:
+                profiler.stop_profiler("total", "/tmp/profile")
+            exe.run(fluid.default_main_program(),
+                    feed=feeder.feed(data),
+                    fetch_list=[])
 	            ...
 	```
 
 1. 运行`python paddle/tools/timeline.py`来处理`/tmp/profile`，这个程序默认会生成一个`/tmp/timeline`文件，你也可以用命令行参数来修改这个路径，请参考[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py)。
+```python
+python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline
+```
 
 1. 打开chrome浏览器，访问<chrome://tracing/>，用`load`按钮来加载生成的`timeline`文件。
 

doc/fluid/howto/optimization/timeline_en.md

@@ -1,22 +1,28 @@
 # how to use timeline tool to do profile
 
-1. Add `with profiler.profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number.
+1. Add `profiler.start_profiler(...)`和`profiler.stop_profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number.
 
 	```python
-	with profiler.profiler('All', 'total', '/tmp/profile') as prof:
-	    for pass_id in range(pass_num):
-	        for batch_id, data in enumerate(train_reader()):
-	            exe.run(fluid.default_main_program(),
-	                    feed=feeder.feed(data),
-	                    fetch_list=[],
-	                    use_program_cache=True)
+    for pass_id in range(pass_num):
+        for batch_id, data in enumerate(train_reader()):
+            if pass_id == 0 and batch_id == 5:
+                profiler.start_profiler("All")
+            elif pass_id == 0 and batch_id == 10:
+                profiler.stop_profiler("total", "/tmp/profile")
+            exe.run(fluid.default_main_program(),
+                    feed=feeder.feed(data),
+                    fetch_list=[])
 	            ...
 	```
 
 1. Run `python paddle/tools/timeline.py` to process `/tmp/profile`, it will generate another
 file `/tmp/timeline` by default. You can change the path by cmd parameter, please take a look at
 [timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py) for details.
 
+```python
+python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline
+```
+
 1. Open chrome and visit <chrome://tracing/>, use `load` button to load the generated `timeline` file.
 
 	![chrome tracing](./tracing.jpeg)

paddle/fluid/framework/details/all_reduce_op_handle.cc

@@ -17,6 +17,7 @@
 #include "paddle/fluid/framework/details/container_cast.h"
 #include "paddle/fluid/framework/details/reduce_and_gather.h"
 #include "paddle/fluid/framework/details/variable_visitor.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace framework {
@@ -45,6 +46,7 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
 #endif
 
 void AllReduceOpHandle::RunImpl() {
+  platform::RecordEvent r("all_reduce", nullptr);
   if (NoDummyInputSize() == 1) {
     return;  // No need to all reduce when GPU count = 1;
   } else {

paddle/fluid/framework/details/reduce_op_handle.cc

@@ -16,12 +16,14 @@
 #include "paddle/fluid/framework/details/container_cast.h"
 #include "paddle/fluid/framework/details/reduce_and_gather.h"
 #include "paddle/fluid/framework/details/variable_visitor.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace framework {
 namespace details {
 
 void ReduceOpHandle::RunImpl() {
+  platform::RecordEvent r("reduce", nullptr);
   if (places_.size() == 1) return;
   // the input and output may have dummy var.
   auto in_var_handles = DynamicCast<VarHandle>(inputs_);

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc

@@ -17,6 +17,7 @@
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace framework {
@@ -62,6 +63,7 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
     eptr = std::current_exception();
   }
 
+  platform::RecordEvent e("ScopeBufferedSSAGraphExecutorAfterRun", nullptr);
   drop_scope_counter_ += 1;
   if (!fetch_tensors.empty() ||
       drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 
 #include "paddle/fluid/framework/details/ssa_graph_builder.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace framework {
@@ -34,6 +35,8 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
 
 FeedFetchList ThreadedSSAGraphExecutor::Run(
     const std::vector<std::string> &fetch_tensors) {
+  std::unique_ptr<platform::RecordEvent> event(
+      new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare", nullptr));
   std::unordered_map<OpHandleBase *, size_t> pending_ops;
   std::unordered_set<VarHandleBase *> pending_vars;
   BlockingQueue<VarHandleBase *> ready_vars;
@@ -84,6 +87,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   // Clean run context
   run_op_futures_.clear();
   exception_holder_.Clear();
+  event.reset(nullptr);
 
   // Step 3. Execution
   while (!pending_vars.empty()) {

paddle/fluid/operators/parallel_do_op.cc

@@ -18,7 +18,6 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/threadpool.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
-#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
@@ -166,8 +165,6 @@ class ParallelDoOp : public framework::OperatorBase {
 
       workers.emplace_back(
           framework::Async([program, cur_scope, place, block, place_idx] {
-            // Give the thread an id to distinguish parallel block with same id.
-            platform::RecordThread rt(static_cast<int>(place_idx) + 1);
             framework::Executor executor(place);
             executor.Run(*program, cur_scope, block->ID(),
                          false /*create_local_scope*/);
@@ -244,8 +241,6 @@ class ParallelDoGradOp : public framework::OperatorBase {
       // execute
       workers.emplace_back(
           framework::Async([program, cur_scope, place, block, i] {
-            // Give the thread an id to distinguish parallel block with same id.
-            platform::RecordThread rt(static_cast<int>(i) + 1);
             framework::Executor executor(place);
             executor.Run(*program, cur_scope, block->ID(),
                          false /*create_local_scope*/);

paddle/fluid/platform/device_tracer.cc

@@ -30,9 +30,6 @@ limitations under the License. */
 namespace paddle {
 namespace platform {
 namespace {
-// Current thread's id. Note, we don't distinguish nested threads
-// for now.
-thread_local int cur_thread_id = 0;
 // Tracking the nested block stacks of each thread.
 thread_local std::deque<int> block_id_stack;
 // Tracking the nested event stacks.
@@ -413,12 +410,5 @@ void SetCurBlock(int block_id) { block_id_stack.push_back(block_id); }
 void ClearCurBlock() { block_id_stack.pop_back(); }
 
 int BlockDepth() { return block_id_stack.size(); }
-
-void SetCurThread(int thread_id) { cur_thread_id = thread_id; }
-
-void ClearCurThread() { cur_thread_id = 0; }
-
-int CurThread() { return cur_thread_id; }
-
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/device_tracer.h

@@ -99,9 +99,5 @@ std::string CurAnnotation();
 void SetCurBlock(int block_id);
 void ClearCurBlock();
 int BlockDepth();
-
-void SetCurThread(int thread_id);
-void ClearCurThread();
-int CurThread();
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/profiler.cc

@@ -190,7 +190,7 @@ RecordEvent::~RecordEvent() {
   DeviceTracer* tracer = GetDeviceTracer();
   if (tracer) {
     tracer->AddCPURecords(CurAnnotation(), start_ns_, PosixInNsec(),
-                          BlockDepth(), CurThread());
+                          BlockDepth(), g_thread_id);
   }
   ClearCurAnnotation();
   PopEvent(name_, dev_ctx_);
@@ -211,21 +211,11 @@ RecordBlock::~RecordBlock() {
     // We try to put all blocks at the same nested depth in the
     // same timeline lane. and distinguish the using thread_id.
     tracer->AddCPURecords(name_, start_ns_, PosixInNsec(), BlockDepth(),
-                          CurThread());
+                          g_thread_id);
   }
   ClearCurBlock();
 }
 
-RecordThread::RecordThread(int thread_id) {
-  if (g_state == ProfilerState::kDisabled) return;
-  SetCurThread(thread_id);
-}
-
-RecordThread::~RecordThread() {
-  if (g_state == ProfilerState::kDisabled) return;
-  ClearCurThread();
-}
-
 void EnableProfiler(ProfilerState state) {
   PADDLE_ENFORCE(state != ProfilerState::kDisabled,
                  "Can't enbale profling, since the input state is ",