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.
100 lines
4.0 KiB
Python
100 lines
4.0 KiB
Python
# Adapted from https://github.com/fixie-ai/ultravox/blob/ecd58c4041030bae2ad15aa6bcf04ab43199ea02/ultravox/model/ultravox_config.py
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from typing import Any, Dict, Optional
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import transformers
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class UltravoxConfig(transformers.PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a
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[`UltravoxForConditionalGeneration`]. It is used to instantiate an
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Ultravox model according to the specified arguments, defining the model
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architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to
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control the model outputs. Read the documentation from [`PretrainedConfig`]
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for more information.
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Args:
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audio_config (`Union[AutoConfig, dict]`, *optional*):
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Custom audio config or dict
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text_config (`Union[AutoConfig, dict]`, *optional*):
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The config object of the text backbone. Can be any of `LlamaConfig`
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or `MistralConfig`.
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ignore_index (`int`, *optional*, defaults to -100):
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The ignore index for the loss function.
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audio_token_index (`int`, *optional*, defaults to 32000):
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The audio token index to encode the audio prompt.
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stack_factor (`int`, *optional*, defaults to 8):
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Audio downsampling factor for the multimodal projector.
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norm_init (`float`, *optional*, defaults to 0.4):
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The initialization value for the layer normalization.
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projector_act (`str`, *optional*, defaults to `"swiglu"`):
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The activation function used by the multimodal projector.
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text_model_lora_config (`LoraConfigSimplified`, *optional*):
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The LoRA configuration for finetuning the text model.
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audio_model_lora_config (`LoraConfigSimplified`, *optional*):
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The LoRA configuration for finetuning the audio model.
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"""
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model_type = "ultravox"
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is_composition = False
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def __init__(
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self,
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audio_config: Optional[Dict[str, Any]] = None,
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text_config: Optional[Dict[str, Any]] = None,
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audio_model_id: Optional[str] = None,
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text_model_id: Optional[str] = None,
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ignore_index: int = -100,
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audio_token_index: int = 32000,
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hidden_size: int = 4096,
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stack_factor: int = 8,
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norm_init: float = 0.4,
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projector_act: str = "swiglu",
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text_model_lora_config: Optional[Dict[str, Any]] = None,
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audio_model_lora_config: Optional[Dict[str, Any]] = None,
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**kwargs,
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):
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self.ignore_index = ignore_index
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self.audio_model_id = audio_model_id
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self.text_model_id = text_model_id
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self.audio_token_index = audio_token_index
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self.hidden_size = hidden_size
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self.stack_factor = stack_factor
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self.norm_init = norm_init
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self.projector_act = projector_act
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if text_model_id is not None:
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# Avoid circular import
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from vllm.transformers_utils.config import get_config
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self.text_config = get_config(text_model_id,
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trust_remote_code=False)
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else:
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text_config = text_config or {}
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self.text_config = transformers.CONFIG_MAPPING[text_config.get(
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"model_type", "llama")](**text_config)
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if audio_model_id is not None:
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# Avoid circular import
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from vllm.transformers_utils.config import get_config
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self.audio_config = get_config(audio_model_id,
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trust_remote_code=False)
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else:
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audio_config = audio_config or {}
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self.audio_config = transformers.CONFIG_MAPPING[audio_config.get(
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"model_type", "whisper")](**audio_config)
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self.text_model_lora_config = text_model_lora_config or {}
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self.audio_model_lora_config = audio_model_lora_config or {}
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self.vocab_size = self.text_config.vocab_size
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self.initializer_range = self.text_config.initializer_range
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super().__init__(**kwargs)
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