ef6abf3dc7
Adds ALL files needed for Dockerfile build:
- qwen3_6_scripts/ (baseline patches + our optimizations)
- vllm/ (full vllm package)
- paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
- Dockerfile + computility-run.yaml
Our optimizations vs baseline:
1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
Triton try/fallback, V2 heuristic, threshold 32K→64K
2. paged_attention_v2_pytorch.py: fills NotImplementedError,
single-bmm Phase 1 (195 launches → 3)
3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
max-num-batched-tokens 8192→16384
This repo can now be submitted to dev.modelhub.org.cn as-is.
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
import os
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from typing import Optional, Union
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from transformers import PretrainedConfig
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class MedusaConfig(PretrainedConfig):
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model_type = "medusa"
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def __init__(self,
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hidden_size: int = 4096,
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vocab_size: int = 32001,
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num_heads: int = 5,
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num_hidden_layers: int = 1,
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max_paths: int = 64,
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topk: int = 10,
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truncated_vocab_size: Optional[int] = None,
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**kwargs):
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.num_heads = num_heads
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self.num_hidden_layers = num_hidden_layers
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self.max_paths = max_paths
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self.topk = topk
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self.max_seq_len = int(2**20)
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self.truncated_vocab_size = vocab_size if truncated_vocab_size is None\
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else truncated_vocab_size
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if "architectures" not in kwargs:
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kwargs["architectures"] = ["MedusaModel"]
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super().__init__(**kwargs)
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@classmethod
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def from_pretrained(
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cls,
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pretrained_model_name_or_path: Union[str, os.PathLike],
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**kwargs,
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) -> "MedusaConfig":
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config_dict, kwargs = cls.get_config_dict(
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pretrained_model_name_or_path, **kwargs)
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for k in list(config_dict.keys()):
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if 'num' in k:
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if 'heads' in k:
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config_dict["num_heads"] = config_dict.pop(k)
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elif 'layers' in k:
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config_dict["num_hidden_layers"] = config_dict.pop(k)
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return cls.from_dict(config_dict, **kwargs)
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@property
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def num_attention_heads(self):
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return 0
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@property
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def num_lookahead_tokens(self):
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return self.num_heads
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@num_lookahead_tokens.setter
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def num_lookahead_tokens(self, num_lookahead_tokens: int):
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self.num_heads = num_lookahead_tokens
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