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[Misc] Add BNB support to GLM4-V model (#12184)

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Signed-off-by: Isotr0py <2037008807@qq.com>
This commit is contained in:
Isotr0py
2025-01-19 19:49:22 +08:00
committed by GitHub
parent 936db119ed
commit edaae198e7
3 changed files with 60 additions and 53 deletions
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95
vllm/model_executor/models/chatglm.py
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@@ -41,7 +41,7 @@ from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
from vllm.transformers_utils.configs import ChatGLMConfig
from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
from .utils import (is_pp_missing_parameter,
from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
@@ -605,9 +605,50 @@ class ChatGLMModel(nn.Module):
return IntermediateTensors({"hidden_states": hidden_states})
return hidden_states
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("linear_proj.merged_proj", "linear_proj.gate_proj", 0),
("linear_proj.merged_proj", "linear_proj.dense_h_to_4h", 1),
]
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
for name, loaded_weight in weights:
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:
if "rotary_pos_emb.inv_freq" in name:
continue
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 ChatGLMBaseModel(nn.Module, SupportsLoRA, SupportsPP):
hf_to_vllm_mapper = WeightsMapper(
orig_to_new_substr={".word_embeddings": ""}, )
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
@@ -660,52 +701,9 @@ class ChatGLMBaseModel(nn.Module, SupportsLoRA, SupportsPP):
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
# Merge two ColumnParallelLinear into one MergedColumnParallelLinear
merged_weights_dict: Dict[str, Dict[str, Optional[torch.Tensor]]] = {
"transformer.vision.linear_proj.merged_proj.weight": {
"transformer.vision.linear_proj.gate_proj.weight": None,
"transformer.vision.linear_proj.dense_h_to_4h.weight": None,
}
}
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: Set[str] = set()
for name, loaded_weight in weights:
is_weight_to_be_merge = False
for _, merged_weight_dict in merged_weights_dict.items():
if name in merged_weight_dict:
assert merged_weight_dict[name] is None
merged_weight_dict[name] = loaded_weight
is_weight_to_be_merge = True
if is_weight_to_be_merge:
continue
if "rotary_pos_emb.inv_freq" in name:
continue
if "word_embeddings" in name:
name = name.replace(".word_embeddings", "")
# 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)
for combined_name, merged_weight_dict in merged_weights_dict.items():
if combined_name in params_dict:
param = params_dict[combined_name]
combined_weight = torch.cat(list(merged_weight_dict.values()),
dim=0)
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, combined_weight)
loaded_params.add(combined_name)
return loaded_params
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
loader = AutoWeightsLoader(self)
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
class ChatGLM(ChatGLMBaseModel):
@@ -726,6 +724,7 @@ class ChatGLM(ChatGLMBaseModel):
class ChatGLMV(ChatGLMBaseModel, SupportsMultiModal):
packed_modules_mapping = {
"query_key_value": ["query_key_value"],
"dense_h_to_4h": ["dense_h_to_4h"],
@@ -777,7 +776,7 @@ class ChatGLMForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
) -> None:
config = vllm_config.model_config.hf_config
# Initialize VL
if hasattr(config, "visual"):
if hasattr(config, "vision_config"):
return ChatGLMV(vllm_config=vllm_config, prefix=prefix)
# Initialize LLM
else: