29e102720b
GLM-4 uses multiple EOS tokens [151329, 151336, 151338] where 151336 (<|user|>) should also stop generation. Previously only the first EOS from tokenizer was used, causing generation to always hit max_tokens. Changes: - config.py: Change eos type to int | list[int] - llm_engine.py: Read eos_token_id from hf_config (contains full list) - scheduler.py: Use set for efficient multi-EOS lookup Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
80 lines
3.5 KiB
Python
80 lines
3.5 KiB
Python
import os
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from dataclasses import dataclass
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from enum import Enum, auto
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from transformers import AutoConfig
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import torch
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class SparsePolicyType(Enum):
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"""Sparse attention policy types."""
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FULL = auto() # No sparse attention (load all blocks)
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QUEST = auto() # Query-aware Top-K block selection (decode only)
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XATTN_BSA = auto() # XAttention Block Sparse Attention (prefill only, chunked)
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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 | list[int] = -1 # Single EOS token or list of EOS tokens (e.g., GLM-4)
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kvcache_block_size: int = 1024
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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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# FULL: no sparse attention (load all blocks)
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# QUEST: decode-only sparse attention with Top-K block selection
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# XATTN_BSA: prefill-only block sparse attention with chunk-level selection
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sparse_policy: SparsePolicyType = SparsePolicyType.FULL
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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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# XAttention BSA specific parameters
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sparse_block_size: int = 128 # Block size for BSA (tokens per block)
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sparse_samples_per_chunk: int = 128 # Samples per chunk for estimation
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sparse_threshold: float = 0.95 # Cumulative attention threshold (tau in XAttention)
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sparse_use_triton: bool = True # Use Triton kernels for estimation
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sparse_stride: int = 8 # Stride for Q/K downsampling
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sparse_chunk_size: int = 16384 # Triton kernel chunk size for estimation
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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, trust_remote_code=True)
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# Get max position embeddings (GLM-4 uses seq_length instead of max_position_embeddings)
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max_pos = getattr(self.hf_config, 'max_position_embeddings',
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getattr(self.hf_config, 'seq_length', 4096))
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self.max_model_len = min(self.max_model_len, max_pos)
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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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