[Bugfix] Update profile example to new add request interface + fix profiler not picking up kernels within cudagraphs

examples/offline_profile.py

@@ -74,14 +74,15 @@ def run_profile(context: ProfileContext, csv_output: Optional[str],
         sys.exit(-1)
 
     for i in range(batch_size):
-        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)
+        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()
+        prompt_token_ids = {'prompt_token_ids': prompt_token_ids}
+
+        llm.llm_engine.add_request(request_id=f"seq{i}",
+                                   inputs=prompt_token_ids,
+                                   params=sampling_params)
 
     with nm_profile() as prefill_prof:
         llm.llm_engine.step()  # First step is prefill

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):

vllm/worker/model_runner.py

@@ -1,3 +1,5 @@
+# This file has been modified by Neural Magic
+
 import gc
 import time
 import warnings
@@ -969,9 +971,11 @@ def vocab_size(self) -> int:
         return self.model_config.get_vocab_size()
 
 
-class CUDAGraphRunner:
+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] = {}
@@ -1067,9 +1071,6 @@ 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.