[1/N] Remove CacheConfig import in all model files (#1658)

python/sglang/srt/models/baichuan.py

@@ -24,7 +24,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -330,7 +329,7 @@ class BaiChuanBaseForCausalLM(nn.Module):
         self,
         config: PretrainedConfig,
         position_embedding: str,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -404,7 +403,7 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
     def __init__(
         self,
         config,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         if config.hidden_size == 4096:  # baichuan2 7b

python/sglang/srt/models/chatglm.py

@@ -22,7 +22,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from torch.nn import LayerNorm
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -52,7 +51,7 @@ class GLMAttention(nn.Module):
         self,
         config,
         layer_id: int = 0,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -188,7 +187,7 @@ class GLMBlock(nn.Module):
         self,
         config,
         layer_id: int,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -260,7 +259,7 @@ class GLMTransformer(nn.Module):
     def __init__(
         self,
         config,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -308,7 +307,7 @@ class ChatGLMModel(nn.Module):
     def __init__(
         self,
         config,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -359,7 +358,7 @@ class ChatGLMForCausalLM(nn.Module):
     def __init__(
         self,
         config: ChatGLMConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoraConfig] = None,
     ):

python/sglang/srt/models/commandr.py

@@ -45,7 +45,6 @@ import torch.utils.checkpoint
 from torch import nn
 from torch.nn.parameter import Parameter
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -320,7 +319,7 @@ class CohereForCausalLM(nn.Module):
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/dbrx.py

@@ -20,7 +20,6 @@ from typing import Iterable, Optional, Tuple
 
 import torch
 import torch.nn as nn
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -368,7 +367,7 @@ class DbrxForCausalLM(nn.Module):
         self,
         config: DbrxConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ):
         super().__init__()
         self.config = config

python/sglang/srt/models/deepseek.py

@@ -21,7 +21,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -185,7 +184,7 @@ class DeepseekAttention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -262,7 +261,7 @@ class DeepseekDecoderLayer(nn.Module):
         self,
         config: PretrainedConfig,
         layer_id: int,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -331,7 +330,7 @@ class DeepseekModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -374,7 +373,7 @@ class DeepseekForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()

python/sglang/srt/models/deepseek_v2.py

@@ -21,7 +21,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_world_size,
     tensor_model_parallel_all_reduce,
@@ -188,7 +187,7 @@ class DeepseekV2Attention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         layer_id=None,
     ) -> None:
@@ -336,7 +335,7 @@ class DeepseekV2AttentionMLA(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         layer_id=None,
     ) -> None:
@@ -498,7 +497,7 @@ class DeepseekV2DecoderLayer(nn.Module):
         self,
         config: PretrainedConfig,
         layer_id: int,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -594,7 +593,7 @@ class DeepseekV2Model(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -640,7 +639,7 @@ class DeepseekV2ForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()

python/sglang/srt/models/exaone.py

@@ -21,7 +21,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
 from torch import nn
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -295,7 +294,7 @@ class ExaoneForCausalLM(nn.Module):
         self,
         config,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/gemma.py

@@ -21,7 +21,7 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig, LoRAConfig
+from vllm.config import LoRAConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
@@ -279,7 +279,7 @@ class GemmaForCausalLM(nn.Module):
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         del lora_config  # Unused.
         super().__init__()

python/sglang/srt/models/gemma2.py

@@ -20,7 +20,7 @@ from typing import Iterable, Optional, Set, Tuple, Union
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig, LoRAConfig
+from vllm.config import LoRAConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 
 # from vllm.model_executor.layers.rotary_embedding import GemmaRotaryEmbedding
@@ -105,7 +105,7 @@ class Gemma2Attention(nn.Module):
         head_dim: int,
         max_position_embeddings: int,
         rope_theta: float,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -190,7 +190,7 @@ class Gemma2DecoderLayer(nn.Module):
         self,
         layer_idx: int,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -257,7 +257,7 @@ class Gemma2Model(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -336,7 +336,7 @@ class Gemma2ForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ) -> None:

python/sglang/srt/models/gpt_bigcode.py

@@ -21,7 +21,7 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import GPTBigCodeConfig
-from vllm.config import CacheConfig, LoRAConfig
+from vllm.config import LoRAConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -44,7 +44,7 @@ class GPTBigCodeAttention(nn.Module):
         self,
         layer_id: int,
         config: GPTBigCodeConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -145,7 +145,7 @@ class GPTBigCodeBlock(nn.Module):
         self,
         layer_id: int,
         config: GPTBigCodeConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__()
@@ -183,7 +183,7 @@ class GPTBigCodeModel(nn.Module):
     def __init__(
         self,
         config: GPTBigCodeConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):
@@ -243,7 +243,7 @@ class GPTBigCodeForCausalLM(nn.Module):
     def __init__(
         self,
         config: GPTBigCodeConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
     ):

python/sglang/srt/models/grok.py

@@ -23,7 +23,6 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -289,7 +288,7 @@ class Grok1ForCausalLM(nn.Module):
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/internlm2.py

@@ -21,7 +21,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -254,7 +253,7 @@ class InternLM2ForCausalLM(nn.Module):
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/llama.py

@@ -22,7 +22,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import LlamaConfig
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -295,7 +294,7 @@ class LlamaForCausalLM(nn.Module):
         self,
         config: LlamaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/llama_classification.py

@@ -18,7 +18,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import LlamaConfig
-from vllm.config import CacheConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
@@ -32,7 +31,7 @@ class LlamaForClassification(nn.Module):
         self,
         config: LlamaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/llama_reward.py

@@ -18,7 +18,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import LlamaConfig
-from vllm.config import CacheConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
@@ -33,7 +32,7 @@ class LlamaForSequenceClassification(nn.Module):
         self,
         config: LlamaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config
@@ -92,7 +91,7 @@ class LlamaForSequenceClassificationWithNormal_Weights(LlamaForSequenceClassific
         self,
         config: LlamaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__(config, quant_config, cache_config)
         self.weights = self.Weights(config.hidden_size, self.num_labels)

python/sglang/srt/models/llava.py

@@ -31,7 +31,6 @@ from transformers import (
     SiglipVisionModel,
 )
 from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
-from vllm.config import CacheConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
@@ -450,7 +449,7 @@ class LlavaLlamaForCausalLM(LlavaBaseForCausalLM):
         self,
         config: LlavaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
 
@@ -472,7 +471,7 @@ class LlavaQwenForCausalLM(LlavaBaseForCausalLM):
         self,
         config: LlavaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
 
@@ -505,7 +504,7 @@ class LlavaMistralForCausalLM(LlavaBaseForCausalLM):
         self,
         config: LlavaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
 

python/sglang/srt/models/llavavid.py

@@ -22,7 +22,6 @@ import torch
 from torch import nn
 from transformers import CLIPVisionModel, LlavaConfig
 from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
-from vllm.config import CacheConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
@@ -36,7 +35,7 @@ class LlavaVidForCausalLM(nn.Module):
         self,
         config: LlavaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/minicpm.py

@@ -20,7 +20,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
 from torch import nn
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -278,7 +277,7 @@ class MiniCPMForCausalLM(nn.Module):
         self,
         config,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/minicpm3.py

@@ -21,7 +21,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -108,7 +107,7 @@ class MiniCPM3Attention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         layer_id=None,
     ) -> None:
@@ -252,7 +251,7 @@ class MiniCPM3AttentionMLA(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         layer_id=None,
     ) -> None:
@@ -409,7 +408,7 @@ class MiniCPM3DecoderLayer(nn.Module):
         self,
         config: PretrainedConfig,
         layer_id: int,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -501,7 +500,7 @@ class MiniCPM3Model(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -552,7 +551,7 @@ class MiniCPM3ForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()

python/sglang/srt/models/mixtral.py

@@ -21,7 +21,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import MixtralConfig
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.rotary_embedding import get_rope
@@ -293,7 +292,7 @@ class MixtralForCausalLM(nn.Module):
         self,
         config: MixtralConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/mixtral_quant.py

@@ -23,7 +23,6 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 from transformers import MixtralConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -325,7 +324,7 @@ class QuantMixtralForCausalLM(nn.Module):
         self,
         config: MixtralConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/olmoe.py

@@ -23,7 +23,6 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_world_size,
     tensor_model_parallel_all_reduce,
@@ -298,7 +297,7 @@ class OlmoeForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()

python/sglang/srt/models/qwen.py

@@ -20,7 +20,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -243,7 +242,7 @@ class QWenLMHeadModel(nn.Module):
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ):
         super().__init__()
         self.config = config

python/sglang/srt/models/qwen2.py

@@ -20,7 +20,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 
 import torch
 from torch import nn
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -268,7 +267,7 @@ class Qwen2ForCausalLM(nn.Module):
         self,
         config: Qwen2Config,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/qwen2_moe.py

@@ -23,7 +23,6 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_world_size,
     tensor_model_parallel_all_reduce,
@@ -160,7 +159,7 @@ class Qwen2MoeAttention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -236,7 +235,7 @@ class Qwen2MoeDecoderLayer(nn.Module):
         self,
         config: PretrainedConfig,
         layer_id: int,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -306,7 +305,7 @@ class Qwen2MoeModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -355,7 +354,7 @@ class Qwen2MoeForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()

python/sglang/srt/models/stablelm.py

@@ -22,7 +22,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -241,7 +240,7 @@ class StableLmForCausalLM(nn.Module):
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/torch_native_llama.py

@@ -24,7 +24,6 @@ import torch
 from torch import nn
 from torch.nn.parameter import Parameter
 from transformers import LlamaConfig
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -380,7 +379,7 @@ class TorchNativeLlamaForCausalLM(nn.Module):
         self,
         config: LlamaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/xverse.py

@@ -22,7 +22,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import LlamaConfig
-from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -297,7 +296,7 @@ class XverseForCausalLM(nn.Module):
         self,
         config: LlamaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         efficient_weight_load=False,
     ) -> None:
         super().__init__()

python/sglang/srt/models/xverse_moe.py

@@ -19,7 +19,6 @@ from typing import Any, Dict, Iterable, Optional, Tuple
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.config import CacheConfig
 from vllm.distributed import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
@@ -183,7 +182,7 @@ class XverseAttention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -260,7 +259,7 @@ class XverseDecoderLayer(nn.Module):
         self,
         config: PretrainedConfig,
         layer_id: int,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -328,7 +327,7 @@ class XverseModel(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
@@ -371,7 +370,7 @@ class XverseMoeForCausalLM(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()

python/sglang/srt/models/yivl.py

@@ -20,7 +20,6 @@ from typing import Iterable, Optional, Tuple
 import torch
 import torch.nn as nn
 from transformers import CLIPVisionModel, LlavaConfig
-from vllm.config import CacheConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
@@ -32,7 +31,7 @@ class YiVLForCausalLM(LlavaLlamaForCausalLM):
         self,
         config: LlavaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
+        cache_config=None,
     ) -> None:
         super().__init__(config, quant_config, cache_config)