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[CI Failure] Fix NomicBert max_model_len validation #31662

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Merged
noooop merged 7 commits into from
Jan 5, 2026
+33 −26
Merged

[CI Failure] Fix NomicBert max_model_len validation #31662

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noooop Jan 4, 2026
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Merge branch 'main' into fix_nomic
noooop Jan 4, 2026
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Merge branch 'main' into fix_nomic
noooop Jan 5, 2026
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6 changes: 0 additions & 6 deletions vllm/config/vllm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1264,12 +1264,6 @@ def _set_compile_ranges(self):
computed_compile_ranges_split_points
)

def recalculate_max_model_len(self, max_model_len: int):
# Can only be called in try_verify_and_update_config
model_config = self.model_config
max_model_len = model_config.get_and_verify_max_len(max_model_len)
self.model_config.max_model_len = max_model_len

def try_verify_and_update_config(self):
if self.model_config is None:
return
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53 changes: 33 additions & 20 deletions vllm/model_executor/models/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,8 +113,8 @@ def verify_and_update_model_config(model_config: "ModelConfig") -> None:

class NomicBertModelConfig(VerifyAndUpdateConfig):
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
config = vllm_config.model_config.hf_config
def verify_and_update_model_config(model_config: "ModelConfig") -> None:
config = model_config.hf_config

assert config.__class__.__name__ == "NomicBertConfig"
assert config.activation_function in ["swiglu", "gelu"]
Expand All @@ -137,6 +137,10 @@ def verify_and_update_config(vllm_config: "VllmConfig") -> None:
config.intermediate_size = config.n_inner
config.hidden_size = config.n_embd
config.num_hidden_layers = config.n_layer
model_config.model_arch_config.hidden_size = config.hidden_size
model_config.model_arch_config.total_num_hidden_layers = (
config.num_hidden_layers
)

head_dim = config.hidden_size // config.num_attention_heads
max_trained_positions = getattr(config, "max_trained_positions", 2048)
Expand All @@ -153,56 +157,65 @@ def verify_and_update_config(vllm_config: "VllmConfig") -> None:
# The context extension uses vllm style rope_theta and rope_parameters.
# See #17785 #18755
if (
not vllm_config.model_config.hf_overrides
and vllm_config.model_config.original_max_model_len is None
not model_config.hf_overrides
and model_config.original_max_model_len is None
):
# Default
# Reset max_model_len to max_trained_positions.
# nomic-embed-text-v2-moe the length is set to 512
# by sentence_bert_config.json.
max_model_len_before = vllm_config.model_config.max_model_len
max_model_len = min(
vllm_config.model_config.max_model_len, max_trained_positions
)
max_model_len_before = model_config.max_model_len
max_model_len = min(model_config.max_model_len, max_trained_positions)

vllm_config.recalculate_max_model_len(max_model_len)
logger.warning(
"Nomic context extension is disabled. "
"Changing max_model_len from %s to %s. "
"To enable context extension, see: "
"https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.html",
max_model_len_before,
vllm_config.model_config.max_model_len,
model_config.max_model_len = model_config.get_and_verify_max_len(
max_model_len
)

if model_config.max_model_len != max_model_len_before:
logger.warning(
"Nomic context extension is disabled. "
"Changing max_model_len from %s to %s. "
"To enable context extension, see: "
"https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.html",
max_model_len_before,
model_config.max_model_len,
)
else:
# We need to re-verify max_model_len to avoid lengths
# greater than position_embedding.
model_config = vllm_config.model_config
hf_text_config = model_config.hf_text_config

if isinstance(model_config.hf_overrides, dict):
# hf_overrides_kw
max_model_len = model_config.hf_overrides.get(
"max_model_len", vllm_config.model_config.max_model_len
"max_model_len", model_config.max_model_len
)
else:
# hf_overrides_fn
# This might be overridden by sentence_bert_config.json.
max_model_len = vllm_config.model_config.max_model_len
max_model_len = model_config.max_model_len

# reset hf_text_config for recalculate_max_model_len.
if hasattr(hf_text_config, "max_model_len"):
delattr(hf_text_config, "max_model_len")
hf_text_config.max_position_embeddings = max_trained_positions
hf_text_config.rope_parameters = config.rotary_kwargs["rope_parameters"]

# Update the cached derived_max_model_len to enforce the limit
model_config.model_arch_config.derived_max_model_len_and_key = (
float(max_trained_positions),
"max_position_embeddings",
)

# The priority of sentence_bert_config.json is higher
# than max_position_embeddings
encoder_config = deepcopy(model_config.encoder_config)
encoder_config.pop("max_seq_length", None)
model_config.encoder_config = encoder_config

vllm_config.recalculate_max_model_len(max_model_len)
model_config.max_model_len = model_config.get_and_verify_max_len(
max_model_len
)


class Qwen2ForProcessRewardModelConfig(VerifyAndUpdateConfig):
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