Skip to content

[Misc] Add qwen2.5-vl BNB support #12944

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Feb 8, 2025
Merged

[Misc] Add qwen2.5-vl BNB support #12944

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
59 changes: 29 additions & 30 deletions vllm/model_executor/models/qwen2_5_vl.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@

from vllm.attention import AttentionMetadata
from vllm.config import VllmConfig
from vllm.distributed import parallel_state
from vllm.distributed import parallel_state, tensor_model_parallel_all_gather
from vllm.distributed import utils as dist_utils
from vllm.logger import init_logger
from vllm.model_executor import SamplingMetadata
Expand Down Expand Up @@ -207,11 +207,12 @@ def __init__(
) -> None:
super().__init__()
# Per attention head and per partition values.
world_size = parallel_state.get_tensor_model_parallel_world_size()
self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
self.hidden_size_per_attention_head = dist_utils.divide(
projection_size, num_heads)
self.num_attention_heads_per_partition = dist_utils.divide(
num_heads, world_size)
num_heads, self.tp_size)

self.qkv = ColumnParallelLinear(input_size=embed_dim,
output_size=3 * projection_size,
Expand All @@ -231,6 +232,29 @@ def __init__(
f"Qwen2.5-VL does not support {self.attn_backend} backend now."
)

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 = tensor_model_parallel_all_gather(qkv)

# [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)
q, k, v = (x.view(*new_shape) for x in (q, k, v))
return q, k, v

def forward(
self,
x: torch.Tensor,
Expand All @@ -240,15 +264,8 @@ def forward(
# [s, b, c] --> [s, b, head * 3 * head_dim]
x, _ = self.qkv(x)

# [s, b, head * 3 * head_dim] --> [s, b, head, 3 * head_dim]
new_x_shape = x.size()[:-1] + (
self.num_attention_heads_per_partition,
3 * self.hidden_size_per_attention_head,
)
x = x.view(*new_x_shape)

# [s, b, head, 3 * head_dim] --> 3 [s, b, head, head_dim]
q, k, v = dist_utils.split_tensor_along_last_dim(x, 3)
# [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
q, k, v = self.split_qkv(x)
batch_size = q.shape[1]

q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
Expand Down Expand Up @@ -665,24 +682,6 @@ def load_weights(self, weights: Iterable[Tuple[str,
weight_loader(param, loaded_weight, shard_id)
break
else:
if name.endswith("qkv.weight"):
visual_num_heads = self.num_heads
visual_embed_dim = self.hidden_size
head_size = visual_embed_dim // visual_num_heads
loaded_weight = loaded_weight.view(3, visual_num_heads,
head_size,
visual_embed_dim)
loaded_weight = loaded_weight.transpose(0, 1)
loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
elif name.endswith("qkv.bias"):
visual_num_heads = self.num_heads
visual_embed_dim = self.hidden_size
head_size = visual_embed_dim // visual_num_heads
loaded_weight = loaded_weight.view(3, visual_num_heads,
head_size)
loaded_weight = loaded_weight.transpose(0, 1)
loaded_weight = loaded_weight.reshape(-1)

param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
Expand Down