[TPU] Use mark_dynamic only for dummy run (#7634)

vllm/worker/tpu_model_runner.py

@@ -144,7 +144,11 @@ class TPUModelRunner(ModelRunnerBase[ModelInputForTPU]):
             )
         model = model.eval()
         xm.wait_device_ops()
-        self.model = CompiledModelWrapper(model)
+        model = ModelWrapper(model)
+        self.model = torch.compile(model,
+                                   backend="openxla",
+                                   fullgraph=True,
+                                   dynamic=False)
 
     def _dummy_run(
         self,
@@ -206,9 +210,31 @@ class TPUModelRunner(ModelRunnerBase[ModelInputForTPU]):
             )
         t = torch.ones((batch_size, ), dtype=torch.float32, device=self.device)
         p = torch.ones((batch_size, ), dtype=torch.float32, device=self.device)
-
-        # Dummy run.
         num_samples = _MAX_NUM_SAMPLES if is_prompt else 1
+
+        # NOTE(woosuk): There are two stages of compilation: torch.compile and
+        # XLA compilation. Using `mark_dynamic` can reduce the torch.compile
+        # overhead by reusing the FX graph for different shapes.
+        # However, the XLA graph will still require static shapes and needs to
+        # be re-compiled for every different shapes. This overhead is inevitable
+        # in the first run, but can be skipped afterwards as we cache the XLA
+        # graphs in the disk (VLLM_XLA_CACHE_PATH).
+        if is_prompt:
+            # Prefll
+            torch._dynamo.mark_dynamic(token_ids, 1)
+            torch._dynamo.mark_dynamic(position_ids, 1)
+            torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 1)
+        else:
+            # Decode
+            torch._dynamo.mark_dynamic(token_ids, 0)
+            torch._dynamo.mark_dynamic(position_ids, 0)
+            torch._dynamo.mark_dynamic(input_lens, 0)
+            torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
+            torch._dynamo.mark_dynamic(attn_metadata.context_lens, 0)
+            torch._dynamo.mark_dynamic(attn_metadata.block_tables, 0)
+            torch._dynamo.mark_dynamic(t, 0)
+            torch._dynamo.mark_dynamic(p, 0)
+        # Dummy run.
         self.model(token_ids, position_ids, attn_metadata, input_lens, t, p,
                    num_samples, kv_caches)
 
@@ -682,52 +708,6 @@ class ModelWrapper(nn.Module):
         return next_token_ids
 
 
-class CompiledModelWrapper:
-
-    def __init__(self, model: nn.Module):
-        model = ModelWrapper(model)
-        self.model = torch.compile(model,
-                                   backend="openxla",
-                                   fullgraph=True,
-                                   dynamic=False)
-
-    def __call__(
-        self,
-        token_ids: torch.Tensor,
-        position_ids: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-        input_lens: torch.Tensor,
-        t: torch.Tensor,
-        p: torch.Tensor,
-        num_samples: int,
-        kv_caches: List[Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]],
-    ) -> torch.Tensor:
-        # NOTE(woosuk): There are two stages of compilation: torch.compile and
-        # XLA compilation. Using `mark_dynamic` can reduce the torch.compile
-        # overhead by reusing the FX graph for different shapes.
-        # However, the XLA graph will still require static shapes and needs to
-        # be re-compiled for every different shapes. This overhead is inevitable
-        # in the first run, but can be skipped afterwards as we cache the XLA
-        # graphs in the disk (VLLM_XLA_CACHE_PATH).
-        if attn_metadata.num_prefills > 0:
-            # Prefll
-            torch._dynamo.mark_dynamic(token_ids, 1)
-            torch._dynamo.mark_dynamic(position_ids, 1)
-            torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 1)
-        else:
-            # Decode
-            torch._dynamo.mark_dynamic(token_ids, 0)
-            torch._dynamo.mark_dynamic(position_ids, 0)
-            torch._dynamo.mark_dynamic(input_lens, 0)
-            torch._dynamo.mark_dynamic(attn_metadata.slot_mapping, 0)
-            torch._dynamo.mark_dynamic(attn_metadata.context_lens, 0)
-            torch._dynamo.mark_dynamic(attn_metadata.block_tables, 0)
-            torch._dynamo.mark_dynamic(t, 0)
-            torch._dynamo.mark_dynamic(p, 0)
-        return self.model(token_ids, position_ids, attn_metadata, input_lens,
-                          t, p, num_samples, kv_caches)
-
-
 def _get_padded_prefill_len(x: int) -> int:
     # NOTE(woosuk): The pallas FlashAttention kernel requires the sequence
     # length to be a multiple of 16. We pad the prompt length to the nearest