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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 9, 23:00 UTC

[Bugfix] Fix Off-by-one error in _num_tokens_to_min_blocks calculation (#32603)

Signed-off-by: linhaifeng <1371675203@qq.com>

linhaifeng committed Jan 21, 2026 at 00:13 UTC 7901109ea5c9794a5e7e01481343235e3a042971
1 file changed +2 -2
tests/kernels/utils.py
+2 -2
@@ -609,7 +609,7 @@ def _num_tokens_to_min_blocks(num_tokens: int, block_size: int) -> int:
609 Compute the minimum number of blocks required to hold num_tokens tokens,
610 given block_size
611 """
612 - return (num_tokens + block_size) // block_size
612 + return (num_tokens + block_size - 1) // block_size
613
614
615 def make_empty_slot_mapping_tensor(device: torch.device | str):
@@ -694,7 +694,7 @@ def make_block_tables_slot_mapping(
694 For a sequence with num_tokens tokens the minimum number
695 of required KV cache blocks is
696
697 - num_blocks = (num_tokens + block_size) // block_size
697 + num_blocks = (num_tokens + block_size - 1) // block_size
698
699 Then the minimum KV cache size in blocks is
700