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[Misc] Use config definitions from Transformers library (#21913)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung 2025-08-09 14:10:51 +08:00 committed by GitHub
parent 7ad7adb67f
commit 65552b476b
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GPG Key ID: B5690EEEBB952194
11 changed files with 54 additions and 51 deletions
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22
vllm/model_executor/models/aimv2.py
View File

@ -8,7 +8,6 @@ from typing import Optional
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.attention.layer import MultiHeadAttention
from vllm.distributed import get_tensor_model_parallel_world_size
@ -21,12 +20,13 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.transformers_utils.configs.ovis import AIMv2Config
class AIMv2SwiGLUFFN(nn.Module):
def __init__(self, config: PretrainedConfig,
quant_config: QuantizationConfig, prefix: str):
def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
prefix: str):
super().__init__()
hidden_features = config.intermediate_size
in_features = config.hidden_size
@ -57,7 +57,7 @@ class AIMv2SwiGLUFFN(nn.Module):
class AIMv2PatchEmbed(nn.Module):
def __init__(self, config: PretrainedConfig):
def __init__(self, config: AIMv2Config):
super().__init__()
self.proj = nn.Conv2d(
config.num_channels,
@ -75,7 +75,7 @@ class AIMv2PatchEmbed(nn.Module):
class AIMv2ViTPreprocessor(nn.Module):
def __init__(self, config: PretrainedConfig):
def __init__(self, config: AIMv2Config):
super().__init__()
num_patches = (config.image_size // config.patch_size)**2
@ -93,8 +93,8 @@ class AIMv2ViTPreprocessor(nn.Module):
class AIMv2Attention(nn.Module):
def __init__(self, config: PretrainedConfig,
quant_config: QuantizationConfig, prefix: str):
def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
prefix: str):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
@ -141,8 +141,8 @@ class AIMv2Attention(nn.Module):
class AIMv2Block(nn.Module):
def __init__(self, config: PretrainedConfig,
quant_config: QuantizationConfig, prefix: str):
def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
prefix: str):
super().__init__()
self.attn = AIMv2Attention(config,
quant_config=quant_config,
@ -163,7 +163,7 @@ class AIMv2Transformer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: AIMv2Config,
quant_config: QuantizationConfig,
*,
require_post_norm: Optional[bool] = None,
@ -193,7 +193,7 @@ class AIMv2Transformer(nn.Module):
class AIMv2Model(torch.nn.Module):
def __init__(self,
config: PretrainedConfig,
config: AIMv2Config,
quant_config: QuantizationConfig,
*,
require_post_norm: Optional[bool] = None,

8
vllm/model_executor/models/commandr.py
View File

@ -27,7 +27,7 @@ from typing import Optional, Union
import torch
from torch import nn
from transformers import CohereConfig
from transformers import Cohere2Config, CohereConfig
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
@ -89,7 +89,7 @@ class CohereMLP(nn.Module):
def __init__(
self,
config: CohereConfig,
config: Union[CohereConfig, Cohere2Config],
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
@ -124,7 +124,7 @@ class CohereAttention(nn.Module):
def __init__(
self,
config: CohereConfig,
config: Union[CohereConfig, Cohere2Config],
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
@ -242,7 +242,7 @@ class CohereAttention(nn.Module):
class CohereDecoderLayer(nn.Module):
def __init__(self,
config: CohereConfig,
config: Union[CohereConfig, Cohere2Config],
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = ""):

14
vllm/model_executor/models/dbrx.py
View File

@ -6,7 +6,7 @@ from typing import Optional, Union
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from transformers import DbrxConfig
from vllm.attention import Attention
from vllm.config import CacheConfig, VllmConfig
@ -39,7 +39,7 @@ class DbrxRouter(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: DbrxConfig,
params_dtype: Optional[torch.dtype] = None,
):
super().__init__()
@ -63,7 +63,7 @@ class DbrxExperts(FusedMoE):
def __init__(
self,
config: PretrainedConfig,
config: DbrxConfig,
quant_config: Optional[QuantizationConfig] = None,
params_dtype: Optional[torch.dtype] = None,
prefix: str = "",
@ -138,7 +138,7 @@ class DbrxMoE(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: DbrxConfig,
quant_config: Optional[QuantizationConfig] = None,
params_dtype: Optional[torch.dtype] = None,
prefix: str = "",
@ -169,7 +169,7 @@ class DbrxAttention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: DbrxConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
@ -249,7 +249,7 @@ class DbrxFusedNormAttention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: DbrxConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
@ -284,7 +284,7 @@ class DbrxBlock(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: DbrxConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",

15
vllm/model_executor/models/deepseek_v2.py
View File

@ -29,7 +29,7 @@ from typing import Any, Optional, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from transformers import DeepseekV2Config, DeepseekV3Config
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
@ -100,7 +100,7 @@ class DeepseekV2MoE(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
enable_eplb: bool = False,
@ -221,7 +221,7 @@ class DeepseekV2Attention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
hidden_size: int,
num_heads: int,
qk_nope_head_dim: int,
@ -373,7 +373,7 @@ class DeepseekV2MLAAttention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
hidden_size: int,
num_heads: int,
qk_nope_head_dim: int,
@ -538,7 +538,7 @@ class DeepseekV2DecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Union[DeepseekV2Config, DeepseekV3Config],
prefix: str,
model_config: ModelConfig,
cache_config: Optional[CacheConfig] = None,
@ -973,7 +973,10 @@ class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
pass
def get_spec_layer_idx_from_weight_name(config: PretrainedConfig,
# Compatibility with
# https://huggingface.co/deepseek-ai/DeepSeek-V3-Base/blob/main/configuration_deepseek.py
def get_spec_layer_idx_from_weight_name(config: Union[DeepseekV2Config,
DeepseekV3Config],
weight_name: str) -> Optional[int]:
if (hasattr(config, "num_nextn_predict_layers")
and config.num_nextn_predict_layers > 0):

8
vllm/model_executor/models/dots1.py
View File

@ -29,7 +29,7 @@ from typing import Any, Optional, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from transformers import Dots1Config
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
@ -99,7 +99,7 @@ class Dots1MoE(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Dots1Config,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
@ -174,7 +174,7 @@ class Dots1Attention(nn.Module):
hidden_size: int,
num_heads: int,
num_kv_heads: int,
config: PretrainedConfig,
config: Dots1Config,
rope_theta: float = 10000,
rope_scaling: Optional[dict[str, Any]] = None,
max_position_embeddings: int = 8192,
@ -260,7 +260,7 @@ class Dots1DecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Dots1Config,
prefix: str,
model_config: ModelConfig,
cache_config: Optional[CacheConfig] = None,

6
vllm/model_executor/models/exaone4.py
View File

@ -26,7 +26,7 @@ from typing import Any, Optional, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from transformers import Exaone4Config
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
@ -96,7 +96,7 @@ class Exaone4Attention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Exaone4Config,
hidden_size: int,
num_heads: int,
num_kv_heads: int,
@ -224,7 +224,7 @@ class Exaone4DecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Exaone4Config,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",

10
vllm/model_executor/models/glm4_moe.py
View File

@ -28,7 +28,7 @@ from typing import Any, Optional, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from transformers.models.glm4_moe import Glm4MoeConfig
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
@ -100,7 +100,7 @@ class Glm4MoE(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Glm4MoeConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
enable_eplb: bool = False,
@ -198,7 +198,7 @@ class Glm4MoeAttention(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Glm4MoeConfig,
hidden_size: int,
num_heads: int,
num_kv_heads: int,
@ -297,7 +297,7 @@ class Glm4MoeDecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Glm4MoeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
@ -681,7 +681,7 @@ class Glm4MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
return self.model.get_expert_mapping()
def get_spec_layer_idx_from_weight_name(config: PretrainedConfig,
def get_spec_layer_idx_from_weight_name(config: Glm4MoeConfig,
weight_name: str) -> Optional[int]:
if hasattr(config,
"num_nextn_predict_layers") and (config.num_nextn_predict_layers

6
vllm/model_executor/models/minimax_text_01.py
View File

@ -12,7 +12,7 @@ import torch.distributed
import torch.nn.functional as F
from einops import rearrange
from torch import nn
from transformers.configuration_utils import PretrainedConfig
from transformers import MiniMaxConfig
from vllm import envs
from vllm.attention import Attention, AttentionMetadata
@ -656,7 +656,7 @@ class MiniMaxText01DecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: MiniMaxConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
expert_num: int = 1,
@ -860,7 +860,7 @@ class MiniMaxText01Model(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: MiniMaxConfig,
quant_config: Optional[QuantizationConfig] = None,
cache_config: Optional[CacheConfig] = None,
scheduler_config=None,

4
vllm/model_executor/models/olmoe.py
View File

@ -19,7 +19,7 @@ from typing import Any, Optional, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from transformers import OlmoeConfig
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
@ -205,7 +205,7 @@ class OlmoeDecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: OlmoeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",

6
vllm/model_executor/models/qwen2_moe.py
View File

@ -30,7 +30,7 @@ from typing import Any, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from transformers import PretrainedConfig
from transformers import Qwen2MoeConfig
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
@ -98,7 +98,7 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Qwen2MoeConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
@ -256,7 +256,7 @@ class Qwen2MoeDecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Qwen2MoeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",

6
vllm/model_executor/models/qwen3_moe.py
View File

@ -28,7 +28,7 @@ from typing import Any, Optional, Union
import torch
from torch import nn
from transformers import PretrainedConfig
from transformers import Qwen3MoeConfig
from vllm.attention import Attention
from vllm.compilation.decorators import support_torch_compile
@ -101,7 +101,7 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Qwen3MoeConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
enable_eplb: bool = False,
@ -278,7 +278,7 @@ class Qwen3MoeDecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
config: Qwen3MoeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",