• perf(v1): 增加可选的快速 token-id 拷贝路径

  - 新增环境变量 `VLLM_V1_FAST_TOKEN_ID_COPY`（默认关闭）
  - 在 `CachedRequestState` 中缓存 int32 的 prompt token ids（numpy 数组）
  - 开启后在 `InputBatch` 中使用 `np.copyto` 拷贝 prompt/output token ids

vllm/envs.py

@@ -293,6 +293,7 @@ if TYPE_CHECKING:
     VLLM_W8A8_BACKEND: int = 3
     VLLM_REJECT_SAMPLE_OPT: bool = False
     VLLM_USE_MOE_W16A16_TRITON: bool = False
+    VLLM_V1_FAST_TOKEN_ID_COPY: bool = False
 
 
 def get_default_cache_root():
@@ -1847,6 +1848,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_MOE_W16A16_TRITON":
         lambda: (os.environ.get("VLLM_USE_MOE_W16A16_TRITON", "0").lower() in
                  ("true", "1")),
+    #If set to 1/True, enable the V1 fast token-id copy path in InputBatch.
+    "VLLM_V1_FAST_TOKEN_ID_COPY":
+        lambda: (os.environ.get("VLLM_V1_FAST_TOKEN_ID_COPY", "False").lower() in
+                    ("true", "1")),
 }
 
 # --8<-- [end:env-vars-definition]

vllm/v1/worker/gpu_input_batch.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Datastructures defining a GPU input batch
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Optional, cast
 
 import numpy as np
@@ -47,6 +47,12 @@ class CachedRequestState:
 
     lora_request: LoRARequest | None = None
     prompt_embeds: torch.Tensor | None = None
+    _prompt_token_ids_np: np.ndarray | None = field(
+        default=None,
+        init=False,
+        repr=False,
+        compare=False,
+    )
 
     # Used when both async_scheduling and spec_decode are enabled.
     prev_num_draft_len: int = 0
@@ -332,15 +338,41 @@ class InputBatch:
         )
         self.num_prompt_tokens[req_index] = num_prompt_tokens
         start_idx = num_prompt_tokens
-        end_idx = start_idx + len(request.output_token_ids)
         if request.prompt_token_ids is not None:
+            if not envs.VLLM_V1_FAST_TOKEN_ID_COPY:
                 self.token_ids_cpu[req_index, :num_prompt_tokens] = request.prompt_token_ids
+            else:
+                prompt_token_ids_np = request._prompt_token_ids_np
+                rebuild_prompt_cache = True
+                if prompt_token_ids_np is not None:
+                    rebuild_prompt_cache = (
+                        prompt_token_ids_np.dtype != np.int32
+                        or prompt_token_ids_np.size != num_prompt_tokens
+                    )
+                if rebuild_prompt_cache:
+                    prompt_token_ids_np = np.asarray(request.prompt_token_ids, dtype=np.int32)
+                    request._prompt_token_ids_np = prompt_token_ids_np
+                np.copyto(
+                    self.token_ids_cpu[req_index, :num_prompt_tokens],
+                    prompt_token_ids_np,
+                    casting="no",
+                )
             self.is_token_ids[req_index, :num_prompt_tokens] = True
         else:
             self.is_token_ids[req_index, :num_prompt_tokens] = False
         if request.prompt_embeds is not None:
             self.req_prompt_embeds[req_index] = request.prompt_embeds
+        if not envs.VLLM_V1_FAST_TOKEN_ID_COPY:
+            end_idx = start_idx + len(request.output_token_ids)
             self.token_ids_cpu[req_index, start_idx:end_idx] = request.output_token_ids
+        else:
+            output_token_ids_np = np.asarray(request.output_token_ids, dtype=np.int32)
+            end_idx = start_idx + output_token_ids_np.size
+            np.copyto(
+                self.token_ids_cpu[req_index, start_idx:end_idx],
+                output_token_ids_np,
+                casting="no",
+            )
         self.is_token_ids[req_index, start_idx:end_idx] = True
         # Number of tokens without spec decode tokens.
         self.num_tokens_no_spec[req_index] = request.num_tokens