fix

huggingface · Dec 9, 2024 · b846e9e · b846e9e
1 parent 2723460
commit b846e9e
Showing 1 changed file with 0 additions and 10 deletions.
diff --git a/optimum_benchmark/trackers/latency.py b/optimum_benchmark/trackers/latency.py
@@ -246,8 +246,6 @@ def elapsed(self):
 
     @contextmanager
     def track(self):
-        assert self.start_time is not None, "This method can only be called inside of a '.session()' context"
-
         if self.is_pytorch_cuda:
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
@@ -338,8 +336,6 @@ def elapsed(self):
 
     @contextmanager
     def track(self):
-        assert self.start_time is not None, "This method can only be called inside of a '.session()' context"
-
         if self.is_pytorch_cuda:
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
@@ -359,8 +355,6 @@ def track(self):
         self.per_token_end_events.extend(self.per_token_events[1:])
 
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
-        assert self.start_time is not None, "This method can only be called inside of a '.session()' context"
-
         if self.is_pytorch_cuda:
             event = torch.cuda.Event(enable_timing=True)
             event.record()
@@ -486,8 +480,6 @@ def elapsed(self):
 
     @contextmanager
     def track(self):
-        assert self.start_time is not None, "This method can only be called inside of a '.session()' context"
-
         if self.is_pytorch_cuda:
             start_event = torch.cuda.Event(enable_timing=True)
             end_event = torch.cuda.Event(enable_timing=True)
@@ -507,8 +499,6 @@ def track(self):
         self.per_step_end_events.extend(self.per_step_events[1:])
 
     def __call__(self, pipeline, step_index, timestep, callback_kwargs):
-        assert self.start_time is not None, "This method can only be called inside of a '.session()' context"
-
         if self.is_pytorch_cuda:
             event = torch.cuda.Event(enable_timing=True)
             event.record()