[V1][Sampler] Avoid an operation during temperature application (#13587)

vllm/v1/sample/metadata.py

@@ -9,7 +9,7 @@ import torch
 @dataclass
 class SamplingMetadata:
 
-    temperature: torch.Tensor
+    temperature: Optional[torch.Tensor]
     all_greedy: bool
     all_random: bool
 

vllm/v1/sample/sampler.py

@@ -77,11 +77,8 @@ class Sampler(nn.Module):
         logits: torch.Tensor,
         temp: torch.Tensor,
     ) -> torch.Tensor:
-        # Avoid division by zero.
-        temp = torch.where(temp < _SAMPLING_EPS, 1.0, temp)
         # Use in-place division to avoid creating a new tensor.
-        logits.div_(temp.unsqueeze(dim=1))
-        return logits
+        return logits.div_(temp.unsqueeze(dim=1))
 
     def greedy_sample(self, logits: torch.Tensor) -> torch.Tensor:
         return logits.argmax(dim=-1).view(-1)
@@ -100,6 +97,8 @@ class Sampler(nn.Module):
             if sampling_metadata.all_greedy:
                 return greedy_sampled
 
+        assert sampling_metadata.temperature is not None
+
         # Apply temperature.
         logits = self.apply_temperature(logits, sampling_metadata.temperature)
 
@@ -122,6 +121,7 @@ class Sampler(nn.Module):
             sampling_metadata.temperature < _SAMPLING_EPS,
             greedy_sampled,
             random_sampled,
+            out=greedy_sampled,  # Reuse tensor
         )
         return sampled
 

vllm/v1/utils.py

@@ -191,11 +191,13 @@ def bind_kv_cache(
 
 
 def copy_slice(from_tensor: torch.Tensor, to_tensor: torch.Tensor,
-               length: int) -> None:
+               length: int) -> torch.Tensor:
     """
     Copy the first length elements of a tensor into another tensor in a
     non-blocking manner.
 
     Used to copy pinned CPU tensor data to pre-allocated GPU tensors.
+
+    Returns the sliced target tensor.
     """
-    to_tensor[:length].copy_(from_tensor[:length], non_blocking=True)
+    return to_tensor[:length].copy_(from_tensor[:length], non_blocking=True)

vllm/v1/worker/gpu_input_batch.py

@@ -242,10 +242,12 @@ class InputBatch:
         self.block_table.add_row(req_index, request.block_ids)
 
         sampling_params = request.sampling_params
-        self.temperature_cpu[req_index] = sampling_params.temperature
         if sampling_params.sampling_type == SamplingType.GREEDY:
+            # Avoid later division by zero.
+            self.temperature_cpu[req_index] = -1.0
             self.greedy_reqs.add(req_id)
         else:
+            self.temperature_cpu[req_index] = sampling_params.temperature
             self.random_reqs.add(req_id)
 
         self.top_p_cpu[req_index] = sampling_params.top_p
@@ -410,7 +412,11 @@ class InputBatch:
 
     def _make_sampling_metadata(self) -> SamplingMetadata:
         num_reqs = self.num_reqs
-        copy_slice(self.temperature_cpu_tensor, self.temperature, num_reqs)
+        if not self.all_greedy:
+            temperature = copy_slice(self.temperature_cpu_tensor,
+                                     self.temperature, num_reqs)
+        else:
+            temperature = None
         if not self.no_top_p:
             copy_slice(self.top_p_cpu_tensor, self.top_p, num_reqs)
         if not self.no_top_k:
@@ -437,7 +443,7 @@ class InputBatch:
             prompt_token_ids = None
 
         return SamplingMetadata(
-            temperature=self.temperature[:num_reqs],
+            temperature=temperature,
             all_greedy=self.all_greedy,
             all_random=self.all_random,
             top_p=None if self.no_top_p else self.top_p[:num_reqs],