v1.0

v1/worker/ubatch_utils.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import TypeAlias
+
+import numpy as np
+
+from vllm.config import ParallelConfig
+
+
+@dataclass
+class UBatchSlice:
+    request_slice: slice
+    token_slice: slice
+
+    def is_empty(self) -> bool:
+        return (
+            self.request_slice.start == self.request_slice.stop
+            or self.token_slice.start == self.token_slice.stop
+        )
+
+    @property
+    def num_tokens(self) -> int:
+        return self.token_slice.stop - self.token_slice.start
+
+
+UBatchSlices: TypeAlias = list[UBatchSlice]
+
+
+def is_second_ubatch_empty(orig_num_tokens: int, padded_num_tokens: int) -> bool:
+    return (padded_num_tokens // 2) >= orig_num_tokens
+
+
+def check_ubatch_thresholds(
+    config: ParallelConfig, num_tokens: int, uniform_decode: bool
+) -> bool:
+    if not config.enable_dbo:
+        return False
+    if uniform_decode:
+        return num_tokens >= config.dbo_decode_token_threshold
+    else:
+        return num_tokens >= config.dbo_prefill_token_threshold
+
+
+def create_ubatch_slices(
+    num_scheduled_tokens: np.ndarray, split_point: int
+) -> UBatchSlices:
+    # TODO(lucas): Refactor the gpu_model_runner.py so we can pass
+    # in cu_num_tokens directly (i.e. query_start_loc)
+    cu_num_tokens = np.zeros(len(num_scheduled_tokens) + 1, dtype=np.int32)
+    np.cumsum(num_scheduled_tokens, dtype=np.int32, out=cu_num_tokens[1:])
+
+    first_ubatch_token_slice = slice(0, split_point)
+    second_ubatch_token_slice = slice(split_point, cu_num_tokens[-1])
+
+    # Determine request slices using exclusive stop semantics
+    # First ubatch includes requests whose tokens overlap [0, split_point)
+    first_ubatch_req_stop = int(
+        np.searchsorted(cu_num_tokens, split_point, side="left")
+    )
+    first_ubatch_req_slice = slice(0, first_ubatch_req_stop)
+
+    # Second ubatch starts at the request that contains the split_point
+    # or the request starting exactly at split_point (if on boundary)
+    second_ubatch_req_start = int(
+        np.searchsorted(cu_num_tokens, split_point, side="right") - 1
+    )
+    second_ubatch_req_slice = slice(second_ubatch_req_start, len(cu_num_tokens) - 1)
+
+    return [
+        UBatchSlice(first_ubatch_req_slice, first_ubatch_token_slice),
+        UBatchSlice(second_ubatch_req_slice, second_ubatch_token_slice),
+    ]