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[Bugfix] fix modelopt exclude_modules name mapping (#24178)

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Signed-off-by: Tomer Asida <57313761+tomeras91@users.noreply.github.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
This commit is contained in:
tomeras91
2025-09-10 20:20:46 +03:00
committed by GitHub
parent 2bef2d1405
commit 08abfa78ec
3 changed files with 58 additions and 37 deletions
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68
vllm/model_executor/models/nemotron_h.py
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@@ -44,15 +44,16 @@ from vllm.model_executor.layers.mamba.mamba_utils import (
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.models.interfaces import (HasInnerState, IsHybrid,
SupportsLoRA, SupportsPP,
SupportsQuant)
from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
MambaCacheParams)
from vllm.model_executor.models.utils import (
AutoWeightsLoader, make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
AutoWeightsLoader, WeightsMapper, make_empty_intermediate_tensors_factory,
make_layers, maybe_prefix)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import NemotronHConfig
@@ -426,38 +427,36 @@ class NemotronHModel(nn.Module):
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
attb_params_mapping = {
"q_proj": "q",
"k_proj": "k",
"v_proj": "v",
}
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
("qkv_proj", "k_proj", "k"),
("qkv_proj", "v_proj", "v"),
]
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "embeddings" in name:
name = name.replace("embeddings", "embed_tokens")
if "scale" in name:
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue
if "A_log" in name:
name = name.replace("A_log", "A")
loaded_weight = loaded_weight.to(torch.float32)
# load stacked params
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 "D" in name:
loaded_weight = loaded_weight.to(torch.float32)
if "dt_bias" in name:
loaded_weight = loaded_weight.to(torch.float32)
# load attn params
if any(proj in name for proj in ["q_proj", "k_proj", "v_proj"]):
weight_name = next(proj
for proj in ["q_proj", "k_proj", "v_proj"]
if proj in name)
name = name.replace(weight_name, "qkv_proj")
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight,
attb_params_mapping[weight_name])
weight_loader(param, loaded_weight, shard_id)
break
# load other params
else:
param = params_dict[name]
@@ -471,6 +470,14 @@ class NemotronHModel(nn.Module):
class NemotronHForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
IsHybrid, SupportsQuant):
hf_to_vllm_mapper = WeightsMapper(
orig_to_new_prefix={"backbone": "model"},
orig_to_new_substr={
"A_log": "A",
"embeddings": "embed_tokens"
},
)
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
@@ -622,10 +629,5 @@ class NemotronHForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
# update name in weights before passing to loader
updated_weights = []
for name, loaded_weight in weights:
name = name.replace("backbone", "model")
updated_weights.append((name, loaded_weight))
loader = AutoWeightsLoader(self)
return loader.load_weights(updated_weights)
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)