[Misc] Use config definitions from Transformers library

vllm/model_executor/models/aimv2.py

@@ -23,6 +23,9 @@
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 
+# NOTE: The Aimv2Config used here is defined by Ovis
+# (https://huggingface.co/AIDC-AI/Ovis2-1B/tree/main)
+# It is different from the one inside Transformers library
 class AIMv2SwiGLUFFN(nn.Module):
 
     def __init__(self, config: PretrainedConfig,

vllm/model_executor/models/dbrx.py

@@ -6,7 +6,7 @@
 
 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 = "",

vllm/model_executor/models/deepseek_v2.py

@@ -29,7 +29,7 @@
 
 import torch
 from torch import nn
-from transformers import PretrainedConfig
+from transformers import 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: 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: 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: 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: DeepseekV3Config,
         prefix: str,
         model_config: ModelConfig,
         cache_config: Optional[CacheConfig] = None,
@@ -971,7 +971,9 @@ 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: DeepseekV3Config,
                                         weight_name: str) -> Optional[int]:
     if (hasattr(config, "num_nextn_predict_layers")
             and config.num_nextn_predict_layers > 0):

vllm/model_executor/models/dots1.py

@@ -29,7 +29,7 @@
 
 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 @@ def __init__(
         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,

vllm/model_executor/models/minimax_text_01.py

@@ -12,7 +12,7 @@
 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.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig, VllmConfig
@@ -585,7 +585,7 @@ 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,
@@ -788,7 +788,7 @@ class MiniMaxText01Model(nn.Module):
 
     def __init__(
         self,
-        config: PretrainedConfig,
+        config: MiniMaxConfig,
         quant_config: Optional[QuantizationConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         scheduler_config=None,

vllm/model_executor/models/mpt.py

@@ -8,7 +8,7 @@
 
 import torch
 import torch.nn as nn
-from transformers import PretrainedConfig
+from transformers import MptConfig
 
 from vllm.attention import Attention
 from vllm.compilation.decorators import support_torch_compile
@@ -50,7 +50,7 @@ class MPTAttention(nn.Module):
 
     def __init__(
         self,
-        config: PretrainedConfig,
+        config: MptConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -144,7 +144,7 @@ class MPTMLP(nn.Module):
 
     def __init__(
         self,
-        config: PretrainedConfig,
+        config: MptConfig,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -176,7 +176,7 @@ class MPTBlock(nn.Module):
 
     def __init__(
         self,
-        config: PretrainedConfig,
+        config: MptConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",

vllm/model_executor/models/olmoe.py

@@ -19,7 +19,7 @@
 
 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 = "",

vllm/model_executor/models/qwen2_moe.py

@@ -30,7 +30,7 @@
 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 = "",

vllm/model_executor/models/qwen3_moe.py

@@ -27,7 +27,7 @@
 
 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
@@ -98,7 +98,7 @@ class Qwen3MoeSparseMoeBlock(nn.Module):
 
     def __init__(
         self,
-        config: PretrainedConfig,
+        config: Qwen3MoeConfig,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
     ):
@@ -242,7 +242,7 @@ class Qwen3MoeDecoderLayer(nn.Module):
 
     def __init__(
         self,
-        config: PretrainedConfig,
+        config: Qwen3MoeConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",