biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Misc][Model][Refactor] Pass the prefix into Linear layers (#28259)

Browse Source 
Signed-off-by: MengqingCao <cmq0113@163.com>
This commit is contained in:
Mengqing Cao
2025-11-07 19:38:38 +08:00
committed by GitHub
parent 7bdb42b2f2
commit 1958bda9b4
26 changed files with 190 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
vllm/model_executor/models/chameleon.py
View File

@@ -227,6 +227,7 @@ class ChameleonMLP(nn.Module):
hidden_act: str,
quant_config: QuantizationConfig | None = None,
bias: bool = False,
prefix: str = "",
) -> None:
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
@@ -234,12 +235,14 @@ class ChameleonMLP(nn.Module):
output_sizes=[intermediate_size] * 2,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.gate_up_proj",
)
self.down_proj = RowParallelLinear(
input_size=intermediate_size,
output_size=hidden_size,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.down_proj",
)
if hidden_act != "silu":
raise ValueError(
@@ -299,12 +302,14 @@ class ChameleonAttention(nn.Module):
total_num_kv_heads=self.total_num_kv_heads,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.o_proj = RowParallelLinear(
input_size=self.total_num_heads * self.head_dim,
output_size=hidden_size,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.o_proj",
)
self.q_norm = ChameleonLayerNorm((self.num_heads, self.head_dim))
self.k_norm = ChameleonLayerNorm((self.num_kv_heads, self.head_dim))
@@ -393,6 +398,7 @@ class ChameleonDecoderLayer(nn.Module):
hidden_act=config.hidden_act,
quant_config=quant_config,
bias=getattr(config, "mlp_bias", False),
prefix=f"{prefix}.mlp",
)
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(
@@ -462,6 +468,7 @@ class ChameleonSwinDecoderLayer(nn.Module):
hidden_act=config.hidden_act,
quant_config=quant_config,
bias=getattr(config, "mlp_bias", False),
prefix=f"{prefix}.mlp",
)
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(