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[Model] Use autoweightloader for dbrx (#18251)

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Signed-off-by: learner0810 <zhongjun.li@daocloud.io>
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learner0810 2025-05-16 22:54:13 +08:00 committed by GitHub
parent a5f8c111c2
commit 87d871470d
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1 changed files with 53 additions and 47 deletions
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100
vllm/model_executor/models/dbrx.py
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@ -26,7 +26,7 @@ from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs.dbrx import DbrxConfig
from .interfaces import SupportsPP
from .utils import (is_pp_missing_parameter,
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
@ -319,6 +319,7 @@ class DbrxModel(nn.Module):
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.quant_config = quant_config
self.wte = VocabParallelEmbedding(
config.vocab_size,
config.d_model,
@ -364,6 +365,55 @@ class DbrxModel(nn.Module):
hidden_states = self.norm_f(hidden_states)
return hidden_states
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
expert_params_mapping = [(
"w13" if weight_name in ["w1", "v1"] else "w2",
f"mlp.{weight_name}",
) for weight_name in ["w1", "v1", "w2"]]
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if (self.quant_config is not None and
(scale_name := self.quant_config.get_cache_scale(name))):
# Loading kv cache quantization scales
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
if name.endswith(("w1", "w2", "v1")):
name = name + "_weight"
for param_name, weight_name in expert_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, weight_name, name)
break
else:
if is_pp_missing_parameter(name, self):
continue
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
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 DbrxForCausalLM(nn.Module, SupportsPP):
@ -417,49 +467,5 @@ class DbrxForCausalLM(nn.Module, SupportsPP):
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
expert_params_mapping = [(
"w13" if weight_name in ["w1", "v1"] else "w2",
f"mlp.{weight_name}",
) for weight_name in ["w1", "v1", "w2"]]
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if (self.quant_config is not None and
(scale_name := self.quant_config.get_cache_scale(name))):
# Loading kv cache quantization scales
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
if name.endswith(("w1", "w2", "v1")):
name = name + "_weight"
for param_name, weight_name in expert_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, weight_name, name)
break
else:
if is_pp_missing_parameter(name, self):
continue
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
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)
return loader.load_weights(weights)