diff --git a/examples/offline_profile.py b/examples/offline_profile.py
index 054c438036eb0..1c95b5bed451c 100644
--- a/examples/offline_profile.py
+++ b/examples/offline_profile.py
@@ -36,7 +36,10 @@ def run_profile(context: ProfileContext, csv_output: Optional[str],
         print(f"  {key} = {value}")
 
     # Create sampling params
-    sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=8)
+    sampling_params = SamplingParams(temperature=0.8,
+                                     top_p=0.95,
+                                     max_tokens=8,
+                                     ignore_eos=True)
 
     # Create LLM
     llm = LLM(
@@ -74,14 +77,14 @@ def run_profile(context: ProfileContext, csv_output: Optional[str],
         sys.exit(-1)
 
     for i in range(batch_size):
+        prompt_token_ids = torch.randint(
+            llm.llm_engine.model_config.get_vocab_size(),
+            size=(prompt_len, )).tolist()
+
         llm.llm_engine.add_request(
             request_id=f"seq{i}",
-            prompt=None,
-            prompt_token_ids=torch.randint(
-                128,  # 128 to skip over special tokens
-                llm.llm_engine.model_config.get_vocab_size() // 2,
-                size=(prompt_len, )).tolist(),
-            sampling_params=sampling_params)
+            inputs={'prompt_token_ids': prompt_token_ids},
+            params=sampling_params)
 
     with nm_profile() as prefill_prof:
         llm.llm_engine.step()  # First step is prefill
diff --git a/vllm/profiler/nm_profile.py b/vllm/profiler/nm_profile.py
index bc357978bec54..0cc98cd17f40e 100644
--- a/vllm/profiler/nm_profile.py
+++ b/vllm/profiler/nm_profile.py
@@ -191,7 +191,8 @@ def _get_kineto_gpu_event(self, node: _ModuleTreeNode):
         correlated_kineto_events = self._kineto_event_correlation_map.get(
             node.event.correlation_id, [])
         iterator = (x for x in correlated_kineto_events
-                    if x.device_type() == DeviceType.CUDA)
+                    if x.device_type() == DeviceType.CUDA
+                    and x.name() == node.event.name)
         return next(iterator, None)
 
     def _cumulative_cuda_time(self, node: _ModuleTreeNode):
diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py
index a321eafce1a2f..ff8df9b3ea56e 100644
--- a/vllm/worker/model_runner.py
+++ b/vllm/worker/model_runner.py
@@ -1,3 +1,5 @@
+# This file has been modified by Neural Magic
+
 import gc
 import time
 import warnings
@@ -986,9 +988,13 @@ def vocab_size(self) -> int:
         return self.model_config.get_vocab_size()
 
 
-class CUDAGraphRunner:
+# NOTE: this is nn.Module so the profiler can properly capture/group
+#  kernels calls made within the graph
+class CUDAGraphRunner(nn.Module):
 
     def __init__(self, model: nn.Module):
+        super().__init__()
+
         self.model = model
         self.input_buffers: Dict[str, torch.Tensor] = {}
         self.output_buffers: Dict[str, torch.Tensor] = {}
@@ -1084,9 +1090,6 @@ def forward(
         # Return the output tensor.
         return self.output_buffers["hidden_states"]
 
-    def __call__(self, *args, **kwargs):
-        return self.forward(*args, **kwargs)
-
 
 def _get_graph_batch_size(batch_size: int) -> int:
     """Returns the padded batch size given actual batch size.