e7644daa7f
Signed-off-by: univa-HARRY <harry@univa.co.kr> Signed-off-by: Harry <harry@univa.co.kr> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>
98 lines
4.3 KiB
Python
98 lines
4.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""OmniVoice configuration for vLLM-Omni two-stage pipeline."""
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import os
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from transformers import AutoConfig
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from transformers.configuration_utils import PretrainedConfig
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class OmniVoiceConfig(PretrainedConfig):
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"""Configuration for OmniVoice model in vLLM-Omni.
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This mirrors the HuggingFace OmniVoiceConfig but adds fields needed
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for the two-stage serving pipeline.
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"""
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model_type = "omnivoice"
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def get_text_config(self, **kwargs):
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"""Return self so vLLM uses our top-level config (which has
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num_attention_heads etc.) instead of trying to extract a sub-config."""
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return self
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def __init__(self, **kwargs):
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# HF repos (e.g. k2-fsa/OmniVoice) may nest generation hyperparameters.
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gen_cfg = kwargs.pop("generation_config", None)
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if isinstance(gen_cfg, dict):
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for k, v in gen_cfg.items():
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kwargs.setdefault(k, v)
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super().__init__(**kwargs)
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# Audio codec params (prefer values set by PretrainedConfig from config.json)
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self.audio_vocab_size = getattr(self, "audio_vocab_size", 1025)
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self.audio_mask_id = getattr(self, "audio_mask_id", 1024)
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self.num_audio_codebook = getattr(self, "num_audio_codebook", 8)
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self.audio_codebook_weights = getattr(
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self,
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"audio_codebook_weights",
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[8, 8, 6, 6, 4, 4, 2, 2],
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)
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# LLM backbone params (Qwen3-0.6B defaults from HF config)
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llm_config = getattr(self, "llm_config", None) or {}
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if isinstance(llm_config, PretrainedConfig):
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llm_config = llm_config.to_dict()
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elif not isinstance(llm_config, dict):
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llm_config = {}
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self.llm_hidden_size = llm_config.get("hidden_size", 1024)
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self.llm_num_hidden_layers = llm_config.get("num_hidden_layers", 28)
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self.llm_num_attention_heads = llm_config.get("num_attention_heads", 16)
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self.llm_num_key_value_heads = llm_config.get("num_key_value_heads", 8)
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self.llm_intermediate_size = llm_config.get("intermediate_size", 3072)
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self.llm_vocab_size = llm_config.get("vocab_size", 151676)
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self.llm_max_position_embeddings = llm_config.get("max_position_embeddings", 40960)
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self.llm_rope_theta = llm_config.get("rope_theta", 1000000.0)
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self.llm_rms_norm_eps = llm_config.get("rms_norm_eps", 1e-6)
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self.llm_head_dim = llm_config.get("head_dim", self.llm_hidden_size // self.llm_num_attention_heads)
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# Expose LLM params at top level for vLLM ModelConfig compatibility
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# (vLLM expects num_attention_heads, hidden_size, etc. on the config)
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self.num_attention_heads = self.llm_num_attention_heads
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self.num_key_value_heads = self.llm_num_key_value_heads
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self.num_hidden_layers = self.llm_num_hidden_layers
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self.hidden_size = self.llm_hidden_size
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self.head_dim = self.llm_head_dim
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if not hasattr(self, "vocab_size"):
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self.vocab_size = self.llm_vocab_size
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# Generation params (defaults from OmniVoiceGenerationConfig)
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self.num_step = getattr(self, "num_step", 32)
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self.guidance_scale = getattr(self, "guidance_scale", 2.0)
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self.t_shift = getattr(self, "t_shift", 0.1)
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self.layer_penalty_factor = getattr(self, "layer_penalty_factor", 5.0)
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self.position_temperature = getattr(self, "position_temperature", 5.0)
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self.class_temperature = getattr(self, "class_temperature", 0.0)
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# Audio output
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self.sample_rate = getattr(self, "sample_rate", 24000)
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self.frame_rate = getattr(self, "frame_rate", 25)
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# Serving
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self.speculative_config = None
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# Precedence: config.json > OMNIVOICE_CUDA_GRAPH env var > default (enabled).
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# Read once at init; changing OMNIVOICE_CUDA_GRAPH at runtime has no effect.
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self.enable_cuda_graph = getattr(self, "enable_cuda_graph", os.environ.get("OMNIVOICE_CUDA_GRAPH", "1") != "0")
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self.cuda_graph_capture_sizes = getattr(
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self,
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"cuda_graph_capture_sizes",
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[128, 192, 256, 320, 384, 448, 512, 640, 768, 1024],
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)
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AutoConfig.register("omnivoice", OmniVoiceConfig)
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__all__ = ["OmniVoiceConfig"]
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