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README.md
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@@ -132,7 +132,7 @@ Notes:
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```python
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import
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from pathlib import Path
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import torch
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import torchaudio
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@@ -148,6 +148,28 @@ pretrained_model_name_or_path = "OpenMOSS-Team/MOSS-VoiceGenerator"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.bfloat16 if device == "cuda" else torch.float32
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processor = AutoProcessor.from_pretrained(
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pretrained_model_name_or_path,
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trust_remote_code=True,
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@@ -179,14 +201,13 @@ conversations = [
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model = AutoModel.from_pretrained(
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pretrained_model_name_or_path,
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trust_remote_code=True,
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attn_implementation=
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torch_dtype=dtype,
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).to(device)
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model.eval()
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batch_size = 1
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messages = []
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save_dir = Path("inference_root")
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save_dir.mkdir(exist_ok=True, parents=True)
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sample_idx = 0
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```python
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import importlib.util
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from pathlib import Path
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import torch
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import torchaudio
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.bfloat16 if device == "cuda" else torch.float32
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def resolve_attn_implementation() -> str:
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# Prefer FlashAttention 2 when package + device conditions are met.
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if (
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device == "cuda"
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and importlib.util.find_spec("flash_attn") is not None
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and dtype in {torch.float16, torch.bfloat16}
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):
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major, _ = torch.cuda.get_device_capability()
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if major >= 8:
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return "flash_attention_2"
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# CUDA fallback: use PyTorch SDPA kernels.
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if device == "cuda":
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return "sdpa"
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# CPU fallback.
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return "eager"
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attn_implementation = resolve_attn_implementation()
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print(f"[INFO] Using attn_implementation={attn_implementation}")
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processor = AutoProcessor.from_pretrained(
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pretrained_model_name_or_path,
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trust_remote_code=True,
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model = AutoModel.from_pretrained(
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pretrained_model_name_or_path,
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trust_remote_code=True,
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attn_implementation=attn_implementation,
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torch_dtype=dtype,
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).to(device)
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model.eval()
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batch_size = 1
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save_dir = Path("inference_root")
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save_dir.mkdir(exist_ok=True, parents=True)
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sample_idx = 0
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