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[Misc] Remove _maybe_ignore_quant_config from GLM4.1v (#20432)

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Signed-off-by: zRzRzRzRzRzRzR <2448370773@qq.com>
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Yuxuan Zhang 2025-07-04 05:41:13 +08:00 committed by GitHub
parent 1dba2c4ebe
commit 2f2fcb31b8
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1 changed files with 9 additions and 18 deletions
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27
vllm/model_executor/models/glm4_1v.py
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@ -55,9 +55,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.gptq import GPTQConfig
from vllm.model_executor.layers.quantization.gptq_marlin import (
GPTQMarlinConfig)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
@ -179,6 +176,7 @@ class Glm4vVisionMLP(nn.Module):
hidden_features: int,
bias: bool = False,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
@ -186,13 +184,12 @@ class Glm4vVisionMLP(nn.Module):
output_sizes=[hidden_features] * 2,
bias=bias,
quant_config=quant_config,
)
self.down_proj = RowParallelLinear(
hidden_features,
in_features,
bias=bias,
quant_config=quant_config,
)
prefix=f"{prefix}.gate_up_proj")
self.down_proj = RowParallelLinear(hidden_features,
in_features,
bias=bias,
quant_config=quant_config,
prefix=f"{prefix}.down_proj")
self.act_fn = SiluAndMul()
def forward(self, x: torch.Tensor):
@ -407,6 +404,7 @@ class Glm4vVisionBlock(nn.Module):
mlp_hidden_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.mlp",
)
def forward(
@ -1278,7 +1276,7 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
self.visual = Glm4vVisionTransformer(
config.vision_config,
norm_eps=getattr(config, "rms_norm_eps", 1e-5),
quant_config=self._maybe_ignore_quant_config(quant_config),
quant_config=quant_config,
prefix=maybe_prefix(prefix, "visual"),
)
@ -1291,13 +1289,6 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors)
def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
# GPTQ configs do not have a list of ignored modules, however AutoGPTQ
# seems to avoid vision encoder sections for some models.
if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
return None
return quant_config
def _validate_and_reshape_mm_tensor(self, mm_input: object,
name: str) -> torch.Tensor:
if not isinstance(mm_input, (torch.Tensor, list)):