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[Misc][Quark] Upstream Quark format to VLLM (#10765)

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Signed-off-by: kewang-xlnx <kewang@xilinx.com>
Signed-off-by: kewang2 <kewang2@amd.com>
Co-authored-by: kewang2 <kewang2@amd.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>
This commit is contained in:
kewang-xlnx
2025-01-16 00:05:15 +08:00
committed by GitHub
parent 5ecf3e0aaf
commit de0526f668
32 changed files with 1264 additions and 70 deletions
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13
vllm/model_executor/models/qwen2.py
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@@ -279,6 +279,7 @@ class Qwen2Model(nn.Module):
))
self.config = config
self.quant_config = quant_config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
@@ -364,6 +365,18 @@ class Qwen2Model(nn.Module):
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
if (self.quant_config is not None and
(scale_name := self.quant_config.get_cache_scale(name))):
# Loading kv cache scales for quark and
# compressed-tensors quantization
param = params_dict[scale_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
loaded_weight[0])
weight_loader(param, loaded_weight)
loaded_params.add(scale_name)
continue
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue