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Fix RoPE related failures in Transformers nightly tests (#29333)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Harry Mellor 2025-11-25 16:23:45 +00:00 committed by GitHub
parent a1f2676879
commit 0353d2e162
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GPG Key ID: B5690EEEBB952194
5 changed files with 37 additions and 33 deletions
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2
vllm/model_executor/models/baichuan.py
View File

@ -233,7 +233,7 @@ class BaiChuanDecoderLayer(nn.Module):
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
position_embedding=position_embedding,
rope_parameters=config.rope_parameters,
rope_parameters=getattr(config, "rope_parameters", None),
max_position_embeddings=max_position_embeddings,
cache_config=cache_config,
quant_config=quant_config,

2
vllm/model_executor/models/gpt_j.py
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@ -100,7 +100,7 @@ class GPTJAttention(nn.Module):
self.head_size,
rotary_dim=config.rotary_dim,
max_position=max_position_embeddings,
rope_parameters=config.rope_parameters,
rope_parameters=getattr(config, "rope_parameters", None),
is_neox_style=False,
)
self.attn = Attention(

2
vllm/model_executor/models/grok1.py
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@ -239,7 +239,7 @@ class Grok1DecoderLayer(nn.Module):
num_heads=config.num_attention_heads,
max_position=config.max_position_embeddings,
num_kv_heads=config.num_key_value_heads,
rope_parameters=config.rope_parameters,
rope_parameters=getattr(config, "rope_parameters", None),
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",

2
vllm/model_executor/models/llama.py
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@ -262,7 +262,7 @@ class LlamaAttention(nn.Module):
self.head_dim,
rotary_dim=self.head_dim,
max_position=self.max_position_embeddings,
rope_parameters=config.rope_parameters,
rope_parameters=getattr(config, "rope_parameters", None),
is_neox_style=is_neox_style,
partial_rotary_factor=self.partial_rotary_factor,
)

62
vllm/transformers_utils/config.py
View File

@ -456,51 +456,55 @@ def set_default_rope_theta(config: PretrainedConfig, default_theta: float) -> No
def patch_rope_parameters(config: PretrainedConfig) -> None:
"""Provide backwards compatibility for RoPE."""
# Retrieve rope_parameters differently based on Transformers version
# Patch rope_parameters differently based on Transformers version
if Version(version("transformers")) >= Version("5.0.0.dev0"):
from transformers.modeling_rope_utils import RopeParameters
rope_parameters: RopeParameters | dict[str, RopeParameters] | None = getattr(
config, "rope_parameters", None
from transformers.modeling_rope_utils import (
rope_config_validation,
standardize_rope_params,
)
elif hasattr(config, "rope_parameters"):
# We are in Transformers v4 and rope_parameters
# has already been patched for this config
return
# When Transformers v5 is installed, legacy rope_theta may be present
# when using custom code models written for Transformers v4
if (rope_theta := getattr(config, "rope_theta", None)) is not None:
standardize_rope_params(config, rope_theta=rope_theta)
rope_config_validation(config)
# Delete rope_theta to avoid confusion in downstream code
del config.rope_theta
else:
# Convert Transformers v4 rope_theta and rope_scaling into rope_parameters
rope_theta: float | None = getattr(config, "rope_theta", None)
rope_scaling: dict | None = getattr(config, "rope_scaling", None)
rope_parameters = rope_scaling
# Move rope_theta into rope_parameters
if rope_theta is not None:
rope_parameters = rope_parameters or {"rope_type": "default"}
rope_parameters["rope_theta"] = rope_theta
# Add original_max_position_embeddings if present
if rope_parameters and (
ompe := getattr(config, "original_max_position_embeddings", None)
):
rope_parameters["original_max_position_embeddings"] = ompe
# Write back to config
config.rope_parameters = rope_parameters
# When Transformers v4 is installed, legacy rope_scaling may be present
if (rope_scaling := getattr(config, "rope_scaling", None)) is not None:
config.rope_parameters = rope_scaling
# When Transformers v4 is installed, legacy rope_theta may be present
if (rope_theta := getattr(config, "rope_theta", None)) is not None:
if not hasattr(config, "rope_parameters"):
config.rope_parameters = {"rope_type": "default"}
config.rope_parameters["rope_theta"] = rope_theta
# No RoPE parameters to patch
if rope_parameters is None:
if not hasattr(config, "rope_parameters"):
return
# Add original_max_position_embeddings if present
if ompe := getattr(config, "original_max_position_embeddings", None):
config.rope_parameters["original_max_position_embeddings"] = ompe
# Handle nested rope_parameters in interleaved sliding attention models
if set(rope_parameters.keys()).issubset(ALLOWED_LAYER_TYPES):
for rope_parameters_layer_type in rope_parameters.values():
if set(config.rope_parameters.keys()).issubset(ALLOWED_LAYER_TYPES):
for rope_parameters_layer_type in config.rope_parameters.values():
patch_rope_parameters_dict(rope_parameters_layer_type)
else:
patch_rope_parameters_dict(rope_parameters)
patch_rope_parameters_dict(config.rope_parameters)
def patch_rope_parameters_dict(rope_parameters: dict[str, Any]) -> None:
if "rope_type" in rope_parameters and "type" in rope_parameters:
rope_type = rope_parameters["rope_type"]
rope_type_legacy = rope_parameters["type"]
if rope_type != rope_type_legacy:
if (rope_type_legacy == "su" and rope_type == "longrope") or (
rope_type_legacy == "mrope" and rope_type == "default"
):
pass # No action needed
elif rope_type != rope_type_legacy:
raise ValueError(
f"Found conflicts between 'rope_type={rope_type}' (modern "
f"field) and 'type={rope_type_legacy}' (legacy field). "