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Delete useless allgather in qwen2_5_vl vit attention #21493

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Delete useless allgather in qwen2_5_vl vit attention #21493

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30 changes: 0 additions & 30 deletions vllm/model_executor/models/qwen2_5_vl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,25 +196,6 @@ def forward(self, x: torch.Tensor):
return x_down


def all_gather_interleave(local_tensor, hidden_size: int, tp_size: int):
"""All-gather the input tensor interleavely across model parallel group."""
import torch.distributed as dist
gathered_tensors = [torch.zeros_like(local_tensor) for _ in range(tp_size)]
dist.all_gather(gathered_tensors,
local_tensor,
group=parallel_state.get_tp_group().device_group)

gathered_tensors_split = [
torch.split(tensor, hidden_size // tp_size, -1)
for tensor in gathered_tensors
]
ordered_tensors = [
tensor for pair in zip(*gathered_tensors_split) for tensor in pair
]
result_tensor = torch.cat(ordered_tensors, dim=-1)
return result_tensor


class Qwen2_5_VisionAttention(nn.Module):

def __init__(
Expand Down Expand Up @@ -259,21 +240,10 @@ def __init__(
def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
# [s, b, 3 * head * head_dim]
seq_len, bs, _ = qkv.shape
if self.tp_size > 1:
qkv = all_gather_interleave(qkv, self.qkv.hidden_size,
self.tp_size)

# [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
q, k, v = qkv.chunk(3, dim=2)

# 3 * [s, b, head * head_dim]
if self.tp_size > 1:
splitter = partial(dist_utils.split_tensor_along_last_dim,
num_partitions=self.tp_size)
q = splitter(q)[self.tp_rank]
k = splitter(k)[self.tp_rank]
v = splitter(v)[self.tp_rank]

# 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
new_shape = (seq_len, bs, self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head)
Expand Down