[Minor][Spec Decode] Remove compiled_softmax (#15416)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

vllm/v1/sample/ops/utils.py

-# SPDX-License-Identifier: Apache-2.0
-from typing import Union
-
-import torch
-
-
-def compiled_softmax(
-    logits: torch.Tensor,
-    temperature: Union[float, torch.Tensor] = 1.0,
-) -> torch.Tensor:
-    """Faster softmax kernel generated by torch.compile.
-
-    Args:
-        logits: [n, vocab_size]
-        temperature: [n] or float
-    """
-    # NOTE(woosuk): Avoid recompilation by marking the first dim as dynamic.
-    torch._dynamo.mark_dynamic(logits, index=0)
-    if isinstance(temperature, torch.Tensor):
-        torch._dynamo.mark_dynamic(temperature, index=0)
-    return _softmax(logits, temperature)
-
-
-@torch.compile
-def _softmax(
-    logits: torch.Tensor,
-    temperature: Union[float, torch.Tensor],
-) -> torch.Tensor:
-    logits = logits / temperature
-    return torch.softmax(logits, dim=-1, dtype=torch.float32)

vllm/v1/sample/rejection_sampler.py

@@ -9,7 +9,6 @@ import triton.language as tl
 from vllm.logger import init_logger
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
-from vllm.v1.sample.ops.utils import compiled_softmax
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 
 logger = init_logger(__name__)
@@ -275,8 +274,7 @@ def compute_probs(
     # NOTE(woosuk): `apply_top_k_top_p` uses sorting to calculate the mask,
     # which is slow for large vocab sizes. This may cause performance issues.
     logits = apply_top_k_top_p(logits, top_k, top_p)
-
-    output_prob = compiled_softmax(logits)
+    output_prob = logits.softmax(dim=-1, dtype=torch.float32)
     return output_prob