Align with huggingface Top K sampling (#753)

vllm/model_executor/layers/sampler.py

@@ -71,19 +71,19 @@ class Sampler(nn.Module):
             # Use in-place division to avoid creating a new tensor.
             logits.div_(t.unsqueeze(dim=1))
 
-        # We use float32 for probabilities and log probabilities.
-        # Compute the probabilities.
-        probs = torch.softmax(logits, dim=-1, dtype=torch.float)
-        # Compute the log probabilities (before applying top-p and top-k).
-        logprobs = torch.log(probs)
-
         # Apply top-p and top-k truncation.
         top_ps, top_ks = _get_top_p_top_k(input_metadata, self.vocab_size)
-        assert len(top_ps) == len(top_ks) == probs.shape[0]
+        assert len(top_ps) == len(top_ks) == logits.shape[0]
         do_top_p = any(p < 1.0 - _SAMPLING_EPS for p in top_ps)
         do_top_k = any(k != self.vocab_size for k in top_ks)
         if do_top_p or do_top_k:
-            probs = _apply_top_p_top_k(probs, top_ps, top_ks)
+            logits = _apply_top_p_top_k(logits, top_ps, top_ks)
+
+        # We use float32 for probabilities and log probabilities.
+        # Compute the probabilities.
+        probs = torch.softmax(logits, dim=-1, dtype=torch.float)
+        # Compute the log probabilities (before applying top-p and top-k).
+        logprobs = torch.log(probs)
 
         # Sample the next tokens.
         return _sample(probs, logprobs, input_metadata)
@@ -235,31 +235,32 @@ def _get_top_p_top_k(
 
 
 def _apply_top_p_top_k(
-    probs: torch.Tensor,
+    logits: torch.Tensor,
     top_ps: List[float],
     top_ks: List[int],
 ) -> torch.Tensor:
-    p = torch.tensor(top_ps, dtype=probs.dtype, device=probs.device)
-    k = torch.tensor(top_ks, dtype=torch.int, device=probs.device)
-    probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
+    p = torch.tensor(top_ps, dtype=logits.dtype, device=logits.device)
+    k = torch.tensor(top_ks, dtype=torch.int, device=logits.device)
+    logits_sort, logits_idx = logits.sort(dim=-1, descending=True)
 
     # Apply top-p.
-    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    probs_sort = logits_sort.softmax(dim=-1)
+    probs_sum = probs_sort.cumsum(dim=-1)
     top_p_mask = (probs_sum - probs_sort) > p.unsqueeze(dim=1)
-    probs_sort[top_p_mask] = 0.0
+    logits_sort[top_p_mask] = -float("inf")
 
     # Apply top-k.
     # Create a mask for the top-k elements.
-    top_k_mask = torch.arange(probs_idx.shape[-1], device=probs_idx.device)
-    top_k_mask = top_k_mask.expand(probs_idx.shape[0], -1)
+    top_k_mask = torch.arange(logits_idx.shape[-1], device=logits_idx.device)
+    top_k_mask = top_k_mask.expand(logits_idx.shape[0], -1)
     top_k_mask = top_k_mask >= k.unsqueeze(dim=1)
-    probs_sort[top_k_mask] = 0.0
+    logits_sort[top_k_mask] = -float("inf")
 
     # Re-sort the probabilities.
-    probs = torch.gather(probs_sort,
+    logits = torch.gather(logits_sort,
                           dim=-1,
-                         index=torch.argsort(probs_idx, dim=-1))
-    return probs
+                          index=torch.argsort(logits_idx, dim=-1))
+    return logits
 
 
 def _get_topk_logprobs(
@@ -301,9 +302,7 @@ def _sample_from_prompt(
         # Random sampling.
         # Sample `best_of` tokens for the prompt.
         num_seqs = sampling_params.best_of
-        next_token_ids = torch.multinomial(prob,
-                                           num_samples=num_seqs,
-                                           replacement=True)
+        next_token_ids = torch.multinomial(prob, num_samples=num_seqs)
         next_token_ids = next_token_ids.tolist()
     return next_token_ids