38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
import os
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from dataclasses import dataclass
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from transformers import AutoConfig
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@dataclass
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class Config:
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model: str
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max_num_batched_tokens: int = 16384
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max_num_seqs: int = 512
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max_model_len: int = 4096
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gpu_memory_utilization: float = 0.9
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tensor_parallel_size: int = 1
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enforce_eager: bool = False
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hf_config: AutoConfig | None = None
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eos: int = -1
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kvcache_block_size: int = 256
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num_kvcache_blocks: int = -1
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# CPU Offload configuration
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enable_cpu_offload: bool = False
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offload_policy: str = "lru" # "lru", "fifo", or full class path
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num_transfer_streams: int = 4 # Number of CUDA streams for async transfers
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num_gpu_blocks: int = -1 # User-specified GPU blocks count, -1 = auto (use max available)
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num_prefetch_blocks: int = 2 # Number of prefetch blocks for three-region GPU buffer design
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# Computed fields for offload (set in __post_init__ or by ModelRunner)
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num_gpu_kvcache_blocks: int = -1
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num_cpu_kvcache_blocks: int = -1
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def __post_init__(self):
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assert os.path.isdir(self.model)
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assert self.kvcache_block_size % 256 == 0
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assert 1 <= self.tensor_parallel_size <= 8
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self.hf_config = AutoConfig.from_pretrained(self.model)
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self.max_model_len = min(self.max_model_len, self.hf_config.max_position_embeddings)
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assert self.max_num_batched_tokens >= self.max_model_len
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