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[BugFix] fix qwen2.5vl quant bug (#3426)

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### What this PR does / why we need it?
This PR fixes issues:
1. Resolve the issue of qwen2.5-VL quantization service startup failure:
AttributeError, 'Parameter' object has no attribute 'weight_loader'.

### Does this PR introduce _any_ user-facing change?
None.

### How was this patch tested?
- ci & e2e
- vLLM version: v0.11.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: elilzhu <2435754260@qq.com>
This commit is contained in:
elilzhu
2025-10-14 17:31:26 +08:00
committed by GitHub
parent ee25a517d1
commit 5c45c227dc
1 changed files with 4 additions and 1 deletions
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5
vllm_ascend/quantization/quant_config.py
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@ -33,6 +33,7 @@ from vllm.model_executor.layers.quantization.base_config import (
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.vocab_parallel_embedding import ( from vllm.model_executor.layers.vocab_parallel_embedding import (
UnquantizedEmbeddingMethod, VocabParallelEmbedding) UnquantizedEmbeddingMethod, VocabParallelEmbedding)
from vllm.model_executor.parameter import PerTensorScaleParameter
from vllm.model_executor.utils import set_weight_attrs from vllm.model_executor.utils import set_weight_attrs
from vllm_ascend.distributed.parallel_state import (get_mlp_tp_group, from vllm_ascend.distributed.parallel_state import (get_mlp_tp_group,
@ -250,6 +251,7 @@ class AscendLinearMethod(LinearMethodBase):
**extra_weight_attrs, **extra_weight_attrs,
) -> None: ) -> None:
output_size_per_partition = sum(output_partition_sizes) output_size_per_partition = sum(output_partition_sizes)
weight_loader = extra_weight_attrs.get("weight_loader")
weight_dict = self.quant_method.get_weight(input_size_per_partition, weight_dict = self.quant_method.get_weight(input_size_per_partition,
output_size_per_partition, output_size_per_partition,
@ -262,7 +264,8 @@ class AscendLinearMethod(LinearMethodBase):
pertensor_dict = self.quant_method.get_pertensor_param(params_dtype) pertensor_dict = self.quant_method.get_pertensor_param(params_dtype)
for pertensor_name, pertensor_param in pertensor_dict.items(): for pertensor_name, pertensor_param in pertensor_dict.items():
param = torch.nn.Parameter(pertensor_param, requires_grad=False) param = PerTensorScaleParameter(data=pertensor_param,
weight_loader=weight_loader)
# disable warning # disable warning
param.ignore_warning = True param.ignore_warning = True
layer.register_parameter(pertensor_name, param) layer.register_parameter(pertensor_name, param)