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Qwen FP8 ModelOPT support #21978

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Qwen FP8 ModelOPT support #21978

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5 changes: 3 additions & 2 deletions vllm/config/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1066,8 +1066,9 @@ def _parse_quant_hf_config(self):
# Set quant_method for ModelOpt models.
producer_name = quant_cfg.get("producer", {}).get("name")
if producer_name == "modelopt":
quant_algo = quant_cfg.get("quantization",
{}).get("quant_algo")
quant_algo = (quant_cfg.get("quantization",
{}).get("quant_algo")
or quant_cfg.get("quant_algo"))
if quant_algo == "FP8":
quant_cfg["quant_method"] = "modelopt"
elif quant_algo == "NVFP4":
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11 changes: 10 additions & 1 deletion vllm/model_executor/models/qwen3_moe.py
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Expand Up @@ -455,6 +455,12 @@ def load_weights(self, weights: Iterable[tuple[str,
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
continue
if name.endswith("scale"):
remapped_name = maybe_remap_kv_scale_name(
name, params_dict)
if remapped_name is None:
continue
name = remapped_name
# We have mlp.experts[0].gate_proj in the checkpoint.
# Since we handle the experts below in expert_params_mapping,
# we need to skip here BEFORE we update the name, otherwise
Expand All @@ -475,8 +481,11 @@ def load_weights(self, weights: Iterable[tuple[str,
if name.endswith("scale"):
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
remapped_name = maybe_remap_kv_scale_name(
name, params_dict)
if remapped_name is None:
continue
name = remapped_name
if name not in params_dict:
continue

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