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[LoRA][1/N]Remove LoRA extra vocab (#28382)

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Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
This commit is contained in:
Jee Jee Li
2025-11-12 03:06:21 +08:00
committed by GitHub
parent 8c32c6e4b4
commit 9d1c474704
65 changed files with 197 additions and 754 deletions
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28
vllm/model_executor/models/mamba2.py
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@@ -20,7 +20,6 @@ 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,
)
@@ -107,18 +106,12 @@ class Mamba2Model(nn.Module):
assert not is_lora_enabled
self.config = config
lora_vocab = (
(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
if lora_config
else 0
)
self.vocab_size = config.vocab_size + lora_vocab
self.org_vocab_size = config.vocab_size
self.vocab_size = config.vocab_size
self.embeddings = VocabParallelEmbedding(
self.vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
)
self.start_layer, self.end_layer, self.layers = make_layers(
@@ -238,7 +231,7 @@ class Mamba2ForCausalLM(
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
lora_config = vllm_config.lora_config
scheduler_config = vllm_config.scheduler_config
super().__init__()
@@ -249,27 +242,16 @@ class Mamba2ForCausalLM(
self.backbone = Mamba2Model(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "backbone")
)
self.unpadded_vocab_size = config.vocab_size
if lora_config:
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
self.lm_head = ParallelLMHead(
self.unpadded_vocab_size,
config.vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
padding_size=DEFAULT_VOCAB_PADDING_SIZE
# We need bigger padding if using lora for kernel
# compatibility
if not lora_config
else lora_config.lora_vocab_padding_size,
prefix=maybe_prefix(prefix, "lm_head"),
)
if config.tie_word_embeddings:
self.lm_head = self.lm_head.tie_weights(self.backbone.embeddings)
self.logits_processor = LogitsProcessor(
self.unpadded_vocab_size, config.vocab_size
)
self.logits_processor = LogitsProcessor(config.vocab_size)
self.make_empty_intermediate_tensors = (
self.backbone.make_empty_intermediate_tensors