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[Model] Use AutoWeightsLoader for InternLM2 (#38278)

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Signed-off-by: Jesus De Jesus <dejesus.9297@gmail.com>
Signed-off-by: javierdejesusda <javier.dejesusj9@gmail.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
This commit is contained in:
Javier De Jesus
2026-05-26 03:39:26 -07:00
committed by GitHub
co-authored by Cyrus Leung
parent 739af5c7e1
commit ebd0692f80
1 changed files with 42 additions and 34 deletions
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vllm/model_executor/models/internlm2.py
+42 -34
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@@ -41,6 +41,7 @@ from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .interfaces_base import default_pooling_type
from .utils import (
AutoWeightsLoader,
StageMissingLayer,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
@@ -308,6 +309,42 @@ class InternLM2Model(nn.Module):
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("gate_up_proj", "w1", 0),
("gate_up_proj", "w3", 1),
]
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
packed_modules_mapping = {
@@ -368,40 +405,11 @@ class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
return logits
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("gate_up_proj", "w1", 0),
("gate_up_proj", "w3", 1),
]
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
loader = AutoWeightsLoader(
self,
skip_prefixes=(["output."] if self.config.tie_word_embeddings else None),
)
return loader.load_weights(weights)
@default_pooling_type(tok_pooling_type="ALL")