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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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6
vllm/model_executor/models/gemma2.py
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@@ -31,8 +31,6 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
get_compressed_tensors_cache_scale)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -254,6 +252,7 @@ class Gemma2Model(nn.Module):
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.embed_tokens = VocabParallelEmbedding(
config.vocab_size,
@@ -329,7 +328,8 @@ class Gemma2Model(nn.Module):
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
for name, loaded_weight in weights:
if scale_name := get_compressed_tensors_cache_scale(name):
if (self.quant_config is not None and
(scale_name := self.quant_config.get_cache_scale(name))):
# Loading kv cache scales for compressed-tensors quantization
param = params_dict[scale_name]
weight_loader = getattr(param, "weight_loader",