Fix runtime error when run dynamo with a profiler scope (#6913)

test/dynamo/test_dynamo.py

@@ -7,6 +7,7 @@
 import torch_xla.utils.utils as xu
 import torch_xla.debug.metrics as met
 from torch_xla import runtime as xr
+import torch_xla.debug.profiler as xp
 import torch.optim as optim
 import torch.nn as nn
 import torch._dynamo as dynamo
@@ -91,6 +92,20 @@ def test_mark_step_after_dynamo(self):
       self.assertEqual(current_execute_time, met.metric_data('ExecuteTime')[0])
 
 
+class DynamoProfilerTest(unittest.TestCase):
+
+  def dummy_fn(self, a):
+    return torch.sin(a) + a
+
+  def test_dynamo_with_trace(self):
+    dynamo_dummy = torch.compile(
+        self.dummy_fn, backend="openxla", fullgraph=True)
+    t = torch.randn(2, 3, 4, device=xm.xla_device())
+    for i in range(10):
+      with xp.Trace('build_graph'):
+        t = dynamo_dummy(t)
+
+
 class DynamoInferenceBasicTest(unittest.TestCase):
 
   @classmethod

torch_xla/core/dynamo_bridge.py

@@ -429,7 +429,8 @@ def extract_internal(xla_model: torch.fx.GraphModule):
     for xla_arg in xla_model.xla_args:
       if isinstance(xla_arg, torch.Tensor):
         print(torch_xla._XLAC._get_xla_tensor_debug_info(xla_arg))
-  xm.mark_step()
+  # Don't reset the scope as we might be under some profiler trace scope.
+  xm.mark_step(reset_scope=False)
   (xla_args_sharding_spec, args_and_out, graph_hash,
    arg_index_to_need_update_index, none_remover, graph_input_matcher,
    dumb_return_handler, xla_args_need_update) = extract_graph_helper(xla_model)
@@ -614,8 +615,9 @@ def extract_compiled_graph(xla_model: torch.fx.GraphModule, xla_args):
     if isinstance(a, torch.Tensor) and torch._is_functional_tensor(a):
       torch._functionalize_sync(a)
 
-  # This call is critical to make sure xla_args' tensor id show up in graph_input_tensor_ids
-  xm.mark_step()
+  # This call is critical to make sure xla_args' tensor id show up in graph_input_tensor_ids.
+  # Don't reset the scope as we might be under some profiler trace scope.
+  xm.mark_step(reset_scope=False)
 
   # Find tensor constructor nodes that create CPU tensors, and make
   # them create XLA tensors, where possible, instead. i.e. replace the

torch_xla/core/xla_model.py

@@ -1045,7 +1045,7 @@ def _run_step_closures():
   return devctx
 
 
-def mark_step(wait=False):
+def mark_step(wait=False, reset_scope=True):
   if xu.getenv_as('XLA_EMIT_STEPLOG', bool, False):
     print(
         'torch_xla.core.xla_model::mark_step\n',
@@ -1054,7 +1054,8 @@ def mark_step(wait=False):
         flush=True)
   torch_xla._XLAC._xla_step_marker(
       torch_xla._XLAC._xla_get_default_device(), [],
-      wait=xu.getenv_as('XLA_SYNC_WAIT', bool, wait))
+      wait=xu.getenv_as('XLA_SYNC_WAIT', bool, wait),
+      reset_scope=reset_scope)
   # Only emit metrics from the first local device index, to avoid emitting the
   # same values from different threads.
   if is_master_ordinal():

torch_xla/csrc/init_python_bindings.cpp

@@ -458,12 +458,13 @@ void SyncLiveTensors(const std::string& device_str,
 }
 
 void StepMarker(const std::string& device_str,
-                const std::vector<std::string>& devices, bool wait) {
+                const std::vector<std::string>& devices, bool wait,
+                bool reset_scope) {
   tsl::profiler::TraceMe activity("StepMarker",
                                   tsl::profiler::TraceMeLevel::kInfo);
   torch::lazy::BackendDevice device = GetDeviceOrCurrent(device_str);
   XLAGraphExecutor::Get()->SyncLiveTensorsGraph(&device, devices, wait);
-  XLAGraphExecutor::Get()->MarkStep(device);
+  XLAGraphExecutor::Get()->MarkStep(device, reset_scope);
   bool debug_mode = runtime::sys_util::GetEnvBool("PT_XLA_DEBUG", false);
   if (TF_PREDICT_FALSE(debug_mode)) {
     std::string report = runtime::metrics::CreatePerformanceReport(
@@ -1649,11 +1650,12 @@ void InitXlaModuleBindings(py::module m) {
   m.def(
       "_xla_step_marker",
       [](const std::string& device, const std::vector<std::string>& devices,
-         bool wait) {
+         bool wait, bool reset_scope) {
         NoGilSection nogil;
-        StepMarker(device, devices, wait);
+        StepMarker(device, devices, wait, reset_scope);
       },
-      py::arg("device") = "", py::arg("devices"), py::arg("wait") = true);
+      py::arg("device") = "", py::arg("devices"), py::arg("wait") = true,
+      py::arg("reset_scope") = true);
   m.def("_get_stablehlo",
         [](const std::vector<at::Tensor>& tensors, const std::string& device,
            const std::vector<std::string>& devices,

torch_xla/csrc/xla_graph_executor.cpp

@@ -404,14 +404,17 @@ void XLAGraphExecutor::SyncLiveTensorsGraph(
   SyncTensorsGraph(&tensors, devices, wait, /*sync_ltc_data=*/true);
 }
 
-void XLAGraphExecutor::MarkStep(const torch::lazy::BackendDevice& device) {
+void XLAGraphExecutor::MarkStep(const torch::lazy::BackendDevice& device,
+                                bool reset_scope) {
   // TODO(jwtan): Replace this with TORCH_LAZY_COUNTER. We need MarkStep to
   // remain as XLA_COUNTER to support
   // runtime::metrics::CreatePerformanceReport(). For more information, see
   // NOTE: [TORCH_LAZY_COUNTER v.s. XLA_COUNTER].
   XLA_COUNTER("MarkStep", 1);
   DeviceContextArena::Get()->MarkStep(device);
-  torch::lazy::ScopePusher::ResetScopes();
+  if (reset_scope) {
+    torch::lazy::ScopePusher::ResetScopes();
+  }
   ResetTrimCounter();
 }
 

torch_xla/csrc/xla_graph_executor.h

@@ -134,7 +134,7 @@ class XLAGraphExecutor : public torch::lazy::LazyGraphExecutor {
   // Marks an execution step, which allows the tensor framework to understand
   // the computation boundaries.
   // Override to use our own DeviceContextArena.
-  void MarkStep(const torch::lazy::BackendDevice& device) final;
+  void MarkStep(const torch::lazy::BackendDevice& device, bool reset_scope);
 
   // Waits for all the outstanding operations on all the supplied devices.
   // If devices is empty, the wait will happen for all local devices.