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

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This commit is contained in:
Byron Hsu
2024-10-14 09:06:34 -07:00
committed by GitHub
parent 02bc95796d
commit 56503d9bc9
30 changed files with 64 additions and 91 deletions
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python/sglang/srt/models/baichuan.py
+2 -3
View File
@@ -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
+5 -6
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+4 -5
View File
@@ -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
+5 -6
View File
@@ -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
+1 -2
View File
@@ -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
+2 -2
View File
@@ -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
+5 -5
View File
@@ -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
+5 -5
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+2 -3
View File
@@ -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
+3 -4
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+5 -6
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+4 -5
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+1 -2
View File
@@ -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
+4 -5
View File
@@ -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
+1 -2
View File
@@ -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)