[Sampler] Use FlashInfer sampling from logits

Author: Ubuntu

vllm/v1/sample/ops/topk_topp_sampler.py

@@ -89,18 +89,18 @@ def forward_cuda(
         p: Optional[torch.Tensor],
     ) -> torch.Tensor:
         """More optimized implementation for top-k and top-p sampling."""
-        probs = logits.softmax(dim=-1, dtype=torch.float32)
         if k is None and p is None:
             # We prefer `random_sample` over `flashinfer_sample` when sorting is
             # not needed. This is because `random_sample` does not require
             # CPU-GPU synchronization while `flashinfer_sample` does.
+            probs = logits.softmax(dim=-1, dtype=torch.float32)
             return random_sample(probs, generators)
         if generators:
             logger.warning("FlashInfer 0.2.3+ does not support "
                            "per-request generators. Falling back to "
                            "PyTorch-native implementation.")
             return self.forward_native(logits, generators, k, p)
-        return flashinfer_sample(probs, k, p, generators)
+        return flashinfer_sample(logits, k, p, generators)
 
     def forward_tpu(
         self,
@@ -254,7 +254,7 @@ def random_sample(
 
 
 def flashinfer_sample(
-    probs: torch.Tensor,
+    logits: torch.Tensor,
     k: Optional[torch.Tensor],
     p: Optional[torch.Tensor],
     generators: dict[int, torch.Generator],
@@ -264,7 +264,7 @@ def flashinfer_sample(
     Statistically, this function is equivalent to the `random_sample` function.
     However, this function is faster because it avoids sorting the logits tensor
     via rejection sampling.
-    
+
     NOTE: The outputs of this function do not necessarily match the outputs of
     the `random_sample` function. It only guarantees that the outputs are
     statistically equivalent.
@@ -274,18 +274,19 @@ def flashinfer_sample(
     the synchronization overhead.
     """
     assert not (k is None and p is None)
-
     if k is None:
         # Top-p only.
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
         next_token_ids = flashinfer.sampling.top_p_sampling_from_probs(
             probs, p, deterministic=True)
     elif p is None:
         # Top-k only.
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
         next_token_ids = flashinfer.sampling.top_k_sampling_from_probs(
             probs, k, deterministic=True)
     else:
         # Both top-k and top-p.
-        next_token_ids = (flashinfer.sampling.top_k_top_p_sampling_from_probs(
-            probs, k, p, deterministic=True))
+        next_token_ids = flashinfer.sampling.top_k_top_p_sampling_from_logits(
+            logits, k, p, deterministic=True)
 
     return next_token_ids.view(-1)