[5/N][torch.compile] torch.jit.script --> torch.compile

vllm/model_executor/layers/rejection_sampler.py

@@ -368,7 +368,7 @@ def _smallest_positive_value(self) -> float:
 # Note that we always sample with replacement.
 # probs will be modified in place, but this is fine, as we pass
 # in a copy already.
-@torch.jit.script
+@torch.compile(dynamic=True)
 def _multinomial(
     probs: torch.Tensor,
     num_samples: int,

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -133,13 +133,13 @@ def __post_init__(self):
         assert self.num_added_elements <= self.num_added_elements_padded
 
 
-@torch.jit.script
+@torch.compile(dynamic=True)
 def get_masked_input_and_mask(
         input_: torch.Tensor, org_vocab_start_index: int,
         org_vocab_end_index: int, num_org_vocab_padding: int,
         added_vocab_start_index: int,
         added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
-    # torch.jit.script will fuse all of the pointwise ops below
+    # torch.compile will fuse all of the pointwise ops below
     # into a single kernel, making it very fast
     org_vocab_mask = (input_ >= org_vocab_start_index) & (input_ <
                                                           org_vocab_end_index)

vllm/model_executor/models/phi3_small.py

@@ -54,12 +54,12 @@ def weight_loader(self, param: torch.nn.Parameter,
         return load_column_parallel_weight(param, loaded_weight)
 
 
-@torch.jit.script
+@torch.compile(dynamic=True)
 def quick_gelu(x):
     return x * torch.sigmoid(1.702 * x)
 
 
-@torch.jit.script
+@torch.compile(dynamic=True)
 def gegelu(input, limit: Optional[float] = None):
     a_gelu, a_linear = input[..., ::2], input[..., 1::2]
     if limit is not None:

vllm/worker/model_runner.py

@@ -1770,7 +1770,7 @@ def capture(
         # Run the model a few times without capturing the graph.
         # This is to make sure that the captured graph does not include the
         # kernel launches for initial benchmarking (e.g., Triton autotune).
-        # Note one iteration is not enough for torch.jit.script
+        # Note one iteration is not enough for torch.compile
         for _ in range(_NUM_WARMUP_ITERS):
             self.model(
                 input_ids=input_ids,