Support nvprof for auto parallel (#55347)

* Support nvprof for auto parallel

* Fix CI errors

* Fix CI errors

paddle/fluid/framework/new_executor/standalone_executor.cc

@@ -109,6 +109,11 @@ paddle::framework::FetchList StandaloneExecutor::Run(
     const auto& job = jobs[job_idx];
     const std::string& job_type = job->Type();
 
+    platform::RecordEvent record_event(
+        job_type + "-" + std::to_string(job->MicroBatchId()),
+        platform::TracerEventType::UserDefined,
+        1);
+
     VLOG(6) << "Run job (" << job_idx << "), type = " << job_type
             << ", micro_batch_id =" << job->MicroBatchId();
 

python/paddle/distributed/auto_parallel/static/engine.py

@@ -868,6 +868,7 @@ class Engine:
         collate_fn=None,
         callbacks=None,
         verbose=2,
+        nvprof_range=[-1, -1],
     ):
         """
         Trains the model for a fixed number of epochs. If `valid_data` is set,
@@ -905,6 +906,7 @@ class Engine:
                 0. Default None.
             callbacks (Callback|None, optional): A list of `Callback` instances to apply
                 during training. Default: None. (Unused for now)
+            nvprof_range(list, optional): A list of integers indicating nvprof ranges in form of [start_step, end_step]. Note that if start_step >= end_step, the nvprof will not apply.
 
         Returns:
             None
@@ -975,7 +977,11 @@ class Engine:
         for epoch in range(epochs):
             logs = {}
             cbks.on_epoch_begin(epoch)
+
             for step, _ in enumerate(train_dataloader):
+                with paddle.profiler.utils._nvprof_range(
+                    iter_id=step, start=nvprof_range[0], end=nvprof_range[1]
+                ):
                     cbks.on_batch_begin('train', step, logs)
                     try:
                         outs = self._executor.run(

python/paddle/profiler/utils.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 
 import functools
-from contextlib import ContextDecorator
+import sys
+from contextlib import ContextDecorator, contextmanager
 from typing import Any
 from warnings import warn
 
@@ -192,3 +193,34 @@ def wrap_optimizers():
             if getattr(classobject, 'step', None) is not None:
                 classobject.step = optimizer_warpper(classobject.step)
     _has_optimizer_wrapped = True
+
+
+@contextmanager
+def _nvprof_range(iter_id, start, end, exit_after_prof=True):
+    '''
+    A range profiler interface (not public yet).
+    Examples:
+        .. code-block:: python
+            model = Model()
+            for i in range(max_iter):
+                paddle.fluid.profiler._nvprof_range(i, 10, 20):
+                    out = model(in)
+    '''
+    if start >= end:
+        yield
+        return
+
+    try:
+        if iter_id == start:
+            core.nvprof_start()
+            core.nvprof_enable_record_event()
+        if iter_id >= start:
+            core.nvprof_nvtx_push(str(iter_id))
+        yield
+    finally:
+        if iter_id < end:
+            core.nvprof_nvtx_pop()
+        if iter_id == end - 1:
+            core.nvprof_stop()
+            if exit_after_prof:
+                sys.exit()