60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
import os
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from dataclasses import dataclass
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from transformers import AutoConfig
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import torch
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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 = 4096
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num_kvcache_blocks: int = -1
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dtype: str | None = None # "float16", "bfloat16", or None (use model default)
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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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# 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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# Sparse attention configuration
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sparse_policy: str | None = None # "vertical_slash", "quest", "streaming_llm", or None
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sparse_num_sink_blocks: int = 1 # Number of sink blocks for sparse patterns
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sparse_local_window_blocks: int = 2 # Local window size for VerticalSlash
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sparse_topk_blocks: int = 8 # Top-K blocks for Quest
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sparse_threshold_blocks: int = 4 # Apply sparse only when blocks > threshold
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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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# Override torch_dtype if user specified
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if self.dtype is not None:
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dtype_map = {
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"float16": torch.float16,
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"fp16": torch.float16,
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"bfloat16": torch.bfloat16,
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"bf16": torch.bfloat16,
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"float32": torch.float32,
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"fp32": torch.float32,
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}
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if self.dtype not in dtype_map:
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raise ValueError(f"Invalid dtype: {self.dtype}. Choose from: {list(dtype_map.keys())}")
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self.hf_config.torch_dtype = dtype_map[self.dtype]
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