Fix regressing unit tests

test/dynamo/test_dynamo.py

@@ -362,7 +362,6 @@ def test_resnet18(self, initialize_on_cuda):
     met.clear_all()
     dynamo_resnet18 = torch.compile(device_resnet18, backend='openxla')
     for data, _ in loader:
-      # dynamo_resnet18 = torch.compile(device_resnet18, backend='openxla')
       output = dynamo_resnet18(data)
       output_cpu = resnet18(data.cpu())
       self.assertTrue(
@@ -408,7 +407,6 @@ def test_dynamic_shape_resnet18(self, initialize_on_cuda):
         'DynamoExtractCompiledGraph')
 
     loader_new_shape = self.get_loader(device, sample_count, batch_size=2)
-    diffs = []
     for data, _ in loader_new_shape:
       output_new_shape = dynamo_resnet18(data)
       output_cpu_new_shape = resnet18(data.cpu())

torch_xla/core/dynamo_bridge.py

@@ -435,8 +435,9 @@ def extract_internal(xla_model: torch.fx.GraphModule):
   # We maintain a mapping of input shapes to outputs of extract_graph_helper.
   # When dynamic=True in torch.compile call, TorchDynamo will not trigger a
   # new recompile. Then TorchXLA needs to figure out if these input shapes have
-  # been seen before. The keys are tuple of input shapes, and values are a tuple
-  # of (xla_args_sharding_spec, args_and_out, graph_hash,
+  # been seen before.
+  # Keys: tuple of input shapes
+  # Values: tuple of (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).
   input_shape_mappings: dict[tuple[int, ...], tuple[object, ...]] = {}
@@ -557,7 +558,6 @@ def optimized_mod(*args: tuple):
     print(
         '=================== OpenXLA Dynamo Compile Debug End =====================\n'
     )
-
   return optimized_mod
 
 
@@ -568,10 +568,9 @@ def __init__(self, module):
     self._unsupported_nodes = []
 
   def run_node(self, n: torch.fx.Node):
-    original_metrics = {
-        counter_name: metrics.counter_value(counter_name)
-        for counter_name in metrics.counter_names()
-    }
+    # We need to restore this metric count later, so save it in a separate variable
+    dynamo_extract_graph_helper_metric_count = metrics.counter_value(
+        'DynamoExtractCompiledGraph')
 
     metrics.clear_counters()
     result = super().run_node(n)
@@ -607,8 +606,9 @@ def all_tensors_on_xla_device(value):
       if not (result_is_supported and args_are_supported):
         self._unsupported_nodes.append(n)
 
-    for name, value in original_metrics.items():
-      torch_xla._XLAC._xla_increment_counter(name, value)
+    # Restore this metric counter
+    torch_xla._XLAC._xla_increment_counter(
+        'DynamoExtractCompiledGraph', dynamo_extract_graph_helper_metric_count)
 
     return result