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Add the support for the qwen3 next model (a hybrid attention model). (#24526)

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Signed-off-by: Tao He <linzhu.ht@alibaba-inc.com>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>
This commit is contained in:
Tao He
2025-09-11 15:32:09 +08:00
committed by GitHub
parent 2048c4e379
commit e93f4cc9e3
29 changed files with 2476 additions and 61 deletions
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20
vllm/v1/worker/block_table.py
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@@ -156,9 +156,14 @@ class BlockTable:
class MultiGroupBlockTable:
"""The BlockTables for each KV cache group."""
def __init__(self, max_num_reqs: int, max_model_len: int,
max_num_batched_tokens: int, pin_memory: bool,
device: torch.device, block_sizes: list[int]) -> None:
def __init__(self,
max_num_reqs: int,
max_model_len: int,
max_num_batched_tokens: int,
pin_memory: bool,
device: torch.device,
block_sizes: list[int],
num_speculative_tokens: int = 0) -> None:
# Note(hc): each dcp rank only store
# (max_model_len//dcp_world_size) tokens in kvcache,
# so the block_size which used for calc max_num_blocks_per_req
@@ -170,10 +175,11 @@ class MultiGroupBlockTable:
dcp_world_size = 1
self.block_tables = [
BlockTable(block_size, max_num_reqs,
cdiv(max_model_len, block_size * dcp_world_size),
max_num_batched_tokens, pin_memory, device)
for block_size in block_sizes
BlockTable(
block_size, max_num_reqs,
max(cdiv(max_model_len, block_size * dcp_world_size),
1 + num_speculative_tokens), max_num_batched_tokens,
pin_memory, device) for block_size in block_sizes
]
def append_row(self, block_ids: tuple[list[int], ...],