optimize allpool forward

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/pooler/tokwise/methods.py

@@ -52,8 +52,8 @@ class AllPool(TokenPoolingMethod):
             # DispatchPooler passes the full hidden_states tensor.
             # slice out the subgroup once, then split it by
             # per-request token counts
-            group_start = int(pooling_cursor.first_token_indices_gpu[0].item())
+            group_start = int(pooling_cursor.first_token_indices_cpu[0])
-            group_end = int(pooling_cursor.last_token_indices_gpu[-1].item()) + 1
+            group_end = group_start + sum(split_sizes)
             hidden_states_group = hidden_states[group_start:group_end]
             hidden_states_lst = list(hidden_states_group.split(split_sizes))
         else:

vllm/v1/pool/metadata.py

@@ -16,6 +16,7 @@ pin_memory = is_pin_memory_available()
 class PoolingCursor:
     first_token_indices_gpu: torch.Tensor
     last_token_indices_gpu: torch.Tensor
+    first_token_indices_cpu: torch.Tensor
     prompt_lens_cpu: torch.Tensor
     seq_lens_cpu: torch.Tensor
     num_scheduled_tokens_cpu: torch.Tensor
@@ -24,6 +25,7 @@ class PoolingCursor:
         return PoolingCursor(
             first_token_indices_gpu=self.first_token_indices_gpu[indices],
             last_token_indices_gpu=self.last_token_indices_gpu[indices],
+            first_token_indices_cpu=self.first_token_indices_cpu[indices],
             prompt_lens_cpu=self.prompt_lens_cpu[indices],
             seq_lens_cpu=self.seq_lens_cpu[indices],
             num_scheduled_tokens_cpu=self.num_scheduled_tokens_cpu[indices],
@@ -117,12 +119,13 @@ class PoolingMetadata:
         assert len(prompt_lens) == n_seq
 
         num_scheduled_tokens_cpu = torch.from_numpy(num_scheduled_tokens_np)
+        cumsum_cpu = torch.zeros(
+            n_seq + 1, dtype=torch.int64, pin_memory=pin_memory, device="cpu"
+        )
+        torch.cumsum(num_scheduled_tokens_cpu, dim=0, out=cumsum_cpu[1:])
+
         if query_start_loc_gpu is None:
-            cumsum = torch.zeros(
+            cumsum_gpu = cumsum_cpu.to(device, non_blocking=True)
-                n_seq + 1, dtype=torch.int64, pin_memory=pin_memory, device="cpu"
-            )
-            torch.cumsum(num_scheduled_tokens_cpu, dim=0, out=cumsum[1:])
-            cumsum = cumsum.to(device, non_blocking=True)
         else:
             if query_start_loc_gpu.shape[0] != n_seq + 1:
                 raise ValueError(
@@ -135,10 +138,11 @@ class PoolingMetadata:
                     "query_start_loc_gpu must be on the same device as the "
                     f"hidden states: {query_start_loc_gpu.device} != {device}."
                 )
-            cumsum = query_start_loc_gpu
+            cumsum_gpu = query_start_loc_gpu
         self.pooling_cursor = PoolingCursor(
-            first_token_indices_gpu=cumsum[:n_seq],
+            first_token_indices_gpu=cumsum_gpu[:n_seq],
-            last_token_indices_gpu=cumsum[1:] - 1,
+            last_token_indices_gpu=cumsum_gpu[1:] - 1,
+            first_token_indices_cpu=cumsum_cpu[:n_seq],
             prompt_lens_cpu=prompt_lens,
             seq_lens_cpu=seq_lens_cpu,
             num_scheduled_tokens_cpu=num_scheduled_tokens_cpu,