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[Model] use AutoWeightsLoader for BigCode, GPT-J (#16823)

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Signed-off-by: Jonghyun Choe <andy.choe729@gmail.com>
This commit is contained in:
Jonghyun Choe
2025-04-18 19:42:41 +09:00
committed by GitHub
parent 26507f8973
commit 87e067de41
2 changed files with 91 additions and 79 deletions
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54
vllm/model_executor/models/gpt_bigcode.py
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@@ -43,7 +43,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (is_pp_missing_parameter,
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
@@ -244,6 +244,30 @@ class GPTBigCodeModel(nn.Module):
hidden_states = self.ln_f(hidden_states)
return hidden_states
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: Set[str] = set()
for name, loaded_weight in weights:
if ".attn.bias" in name:
# Skip attention mask.
# NOTE: "c_attn.bias" should not be skipped.
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
# TODO (@robertgshaw2-neuralmagic): move to fp8 linear method
if "c_attn.input_scale" in name or "c_attn.weight_scale" in name:
weight_loader(param, loaded_weight, 'q')
weight_loader(param, loaded_weight, 'k')
weight_loader(param, loaded_weight, 'v')
else:
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
packed_modules_mapping = {"c_attn": ["c_attn"]}
@@ -315,26 +339,8 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: Set[str] = set()
for name, loaded_weight in weights:
if "lm_head.weight" in name:
continue
if ".attn.bias" in name:
# Skip attention mask.
# NOTE: "c_attn.bias" should not be skipped.
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
# TODO (@robertgshaw2-neuralmagic): move to fp8 linear method
if "c_attn.input_scale" in name or "c_attn.weight_scale" in name:
weight_loader(param, loaded_weight, 'q')
weight_loader(param, loaded_weight, 'k')
weight_loader(param, loaded_weight, 'v')
else:
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
loader = AutoWeightsLoader(
self,
skip_prefixes=(["lm_head."]),
)
return loader.load_weights(weights)