Skip to content

[Bugfix] Revert QKVCrossParallelLinear usage in Mllama to keep BNB quantization work #14498

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 3 commits into from
Mar 9, 2025
Merged

[Bugfix] Revert QKVCrossParallelLinear usage in Mllama to keep BNB quantization work #14498

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
61d61f7
init
Isotr0py Mar 8, 2025
893c187
init
Isotr0py Mar 8, 2025
3c13626
revert
Isotr0py Mar 8, 2025
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
39 changes: 29 additions & 10 deletions vllm/model_executor/models/mllama.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,6 @@
from vllm.logger import init_logger
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVCrossParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
Expand Down Expand Up @@ -814,11 +813,20 @@ def __init__(
self.q_local_size = self.num_local_heads * self.head_dim
self.kv_local_size = self.num_local_key_value_heads * self.head_dim

self.qkv_proj = QKVCrossParallelLinear(
# TODO(Isotr0py): Use QKVCrossParallelLinear when it supports
# quantization
self.q_proj = ColumnParallelLinear(
input_size=self.hidden_size,
output_size=self.num_heads * self.head_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.q_proj",
)
self.kv_proj = QKVParallelLinear(
self.hidden_size,
self.head_dim,
self.num_heads,
self.num_key_value_heads,
total_num_heads=0,
total_num_kv_heads=self.num_key_value_heads,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
Expand Down Expand Up @@ -854,11 +862,15 @@ def forward(
kv_range_for_decode: Optional[List[Tuple[int, int]]],
cross_attention_states: Optional[torch.Tensor],
) -> torch.Tensor:
q, k, v = self.qkv_proj(hidden_states, cross_attention_states)
q, _ = self.q_proj(hidden_states)
if cross_attention_states is not None:
kv, _ = self.kv_proj(cross_attention_states)
k, v = kv.split([self.kv_local_size, self.kv_local_size], dim=-1)
k = k.view(-1, self.num_local_key_value_heads, self.head_dim)
v = v.view(-1, self.num_local_key_value_heads, self.head_dim)
k = self.k_norm(k)
else:
k = v = None

q = q.view(-1, self.num_local_heads, self.head_dim)
q = self.q_norm(q)
Expand Down Expand Up @@ -1149,8 +1161,13 @@ def forward(
class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal,
SupportsV0Only):
packed_modules_mapping = {
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
"gate_up_proj": ["gate_proj", "up_proj"]
"self_attn.qkv_proj": [
"self_attn.q_proj",
"self_attn.k_proj",
"self_attn.v_proj",
],
"cross_attn.kv_proj": ["cross_attn.k_proj", "cross_attn.v_proj"],
"gate_up_proj": ["gate_proj", "up_proj"],
}

def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
Expand Down Expand Up @@ -1420,9 +1437,11 @@ def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
(".qkv_proj", ".q_proj", "q"),
(".qkv_proj", ".k_proj", "k"),
(".qkv_proj", ".v_proj", "v"),
(".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
(".self_attn.qkv_proj", ".self_attn.k_proj", "k"),
(".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
(".cross_attn.kv_proj", ".cross_attn.k_proj", "k"),
(".cross_attn.kv_proj", ".cross_attn.v_proj", "v"),
(".gate_up_proj", ".gate_proj", 0),
(".gate_up_proj", ".up_proj", 1),
]
Expand Down