Introduce FutureMap (#10715)

python/sglang/srt/managers/overlap_utils.py

@@ -0,0 +1,53 @@
+import torch
+
+from sglang.srt.managers.schedule_batch import ModelWorkerBatch
+from sglang.srt.utils import get_compiler_backend
+
+
+@torch.compile(dynamic=True, backend=get_compiler_backend())
+def _resolve_future_token_ids(input_ids, future_token_ids_map):
+    input_ids[:] = torch.where(
+        input_ids < 0,
+        future_token_ids_map[torch.clamp(-input_ids, min=0)],
+        input_ids,
+    )
+
+
+class FutureMap:
+    def __init__(
+        self,
+        max_running_requests: int,
+        device: torch.device,
+    ):
+        self.future_ct = 0
+        # A factor of 3 is used to avoid collision in the circular buffer.
+        self.future_limit = max_running_requests * 3
+        # A factor of 5 is used to ensure the buffer is large enough.
+        self.future_buffer_len = max_running_requests * 5
+        self.device = device
+
+        self.token_ids_buf = torch.empty(
+            (self.future_buffer_len,), dtype=torch.int64, device=self.device
+        )
+
+    def update_ct(self, bs: int) -> int:
+        """Update the circular buffer pointer and return the current pointer."""
+        cur_future_ct = self.future_ct
+        self.future_ct = (cur_future_ct + bs) % self.future_limit
+        return cur_future_ct
+
+    def resolve_future(self, model_worker_batch: ModelWorkerBatch):
+        input_ids = model_worker_batch.input_ids
+        _resolve_future_token_ids(input_ids, self.token_ids_buf)
+
+    def update_next_future(self, future_ct: int, bs: int):
+        return torch.arange(
+            -(future_ct + 1),
+            -(future_ct + 1 + bs),
+            -1,
+            dtype=torch.int64,
+            device=self.device,
+        )
+
+    def store_to_map(self, future_ct: int, bs: int, next_token_ids: torch.Tensor):
+        self.token_ids_buf[future_ct + 1 : future_ct + bs + 1] = next_token_ids