diff --git a/optimum_benchmark/import_utils.py b/optimum_benchmark/import_utils.py
index 09a2a08d..7fdff6ef 100644
--- a/optimum_benchmark/import_utils.py
+++ b/optimum_benchmark/import_utils.py
@@ -246,16 +246,16 @@ def get_hf_libs_info():
     return {
         "optimum_benchmark_version": optimum_benchmark_version(),
         "optimum_benchmark_commit": get_git_revision_hash("optimum_benchmark"),
-        "transformers_version": transformers_version(),
+        "transformers_version": transformers_version() if is_transformers_available() else None,
         "transformers_commit": get_git_revision_hash("transformers"),
-        "accelerate_version": accelerate_version(),
+        "accelerate_version": accelerate_version() if is_accelerate_available else None,
         "accelerate_commit": get_git_revision_hash("accelerate"),
-        "diffusers_version": diffusers_version(),
+        "diffusers_version": diffusers_version() if is_diffusers_available() else None,
         "diffusers_commit": get_git_revision_hash("diffusers"),
-        "optimum_version": optimum_version(),
+        "optimum_version": optimum_version() if is_optimum_available() else None,
         "optimum_commit": get_git_revision_hash("optimum"),
-        "timm_version": timm_version(),
+        "timm_version": timm_version() if is_timm_available() else None,
         "timm_commit": get_git_revision_hash("timm"),
-        "peft_version": peft_version(),
+        "peft_version": peft_version() if is_peft_available() else None,
         "peft_commit": get_git_revision_hash("peft"),
     }
diff --git a/optimum_benchmark/trackers/latency.py b/optimum_benchmark/trackers/latency.py
index 1f74a377..6b8d614f 100644
--- a/optimum_benchmark/trackers/latency.py
+++ b/optimum_benchmark/trackers/latency.py
@@ -264,7 +264,7 @@ def __init__(self, device: str, backend: str):
         self.start_time: Optional[float] = None
         self.prefilled: Optional[bool] = None
 
-        self.per_token_events: List[Union[float, torch.cuda.Event]] = []
+        self.per_token_events: List[List[Union[float, torch.cuda.Event]]] = []
         self.prefill_start_events: List[Union[float, torch.cuda.Event]] = []
         self.prefill_end_events: List[Union[float, torch.cuda.Event]] = []
         self.decode_start_events: List[Union[float, torch.cuda.Event]] = []
@@ -282,6 +282,9 @@ def reset(self):
 
     @contextmanager
     def track(self):
+        self.prefilled = False
+        self.per_token_events.append([])
+
         if self.is_distributed:
             torch.distributed.barrier()
 
@@ -291,14 +294,10 @@ def track(self):
         else:
             self.prefill_start_events.append(time.perf_counter())
 
-        self.prefilled = False
-
         # this is where generate is called,
         # and for each decoded token, we record an event
         yield
 
-        self.prefilled = None
-
         if self.is_asynchronous:
             self.decode_end_events.append(torch.cuda.Event(enable_timing=True))
             self.decode_end_events[-1].record()
@@ -308,6 +307,8 @@ def track(self):
         if self.is_distributed:
             torch.distributed.barrier()
 
+        self.prefilled = False
+
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
         assert (
             self.prefilled is not None
@@ -319,13 +320,13 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
         else:
             event = time.perf_counter()
 
-        self.per_token_events.append(event)
-
         if not self.prefilled:
             self.prefill_end_events.append(event)
             self.decode_start_events.append(event)
             self.prefilled = True
 
+        self.per_token_events[-1].append(event)
+
         return scores
 
     def get_prefill_latency(self) -> Latency:
@@ -368,13 +369,15 @@ def get_per_token_latency(self) -> Latency:
             torch.cuda.synchronize()
 
             latencies_list = [
-                self.per_token_events[i].elapsed_time(self.per_token_events[i + 1]) / 1e3
-                for i in range(0, len(self.per_token_events) - 1)
+                self.per_token_events[i][j].elapsed_time(self.per_token_events[i][j + 1]) / 1e3
+                for i in range(len(self.per_token_events))
+                for j in range(0, len(self.per_token_events[i]) - 1)
             ]
         else:
             latencies_list = [
-                (self.per_token_events[i + 1] - self.per_token_events[i])
-                for i in range(0, len(self.per_token_events) - 1)
+                (self.per_token_events[i][j + 1] - self.per_token_events[i][j])
+                for i in range(len(self.per_token_events))
+                for j in range(0, len(self.per_token_events[i]) - 1)
             ]
 
         assert not any(latency < 0 for latency in latencies_list), "Negative latency detected"