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fix: macOS MPS support improvements (dtype, xpu safety, memory) #382

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fix: macOS MPS support improvements (dtype, xpu safety, memory) #382

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8 changes: 4 additions & 4 deletions demo/vibevoice_asr_inference_from_file.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,12 +54,12 @@ def __init__(
print(f"Using attention implementation: {attn_implementation}")
self.model = VibeVoiceASRForConditionalGeneration.from_pretrained(
model_path,
dtype=dtype,
torch_dtype=dtype,
device_map=device if device == "auto" else None,
attn_implementation=attn_implementation,
trust_remote_code=True
)

if device != "auto":
self.model = self.model.to(device)

Expand Down Expand Up @@ -443,7 +443,7 @@ def main():
parser.add_argument(
"--device",
type=str,
default="cuda" if torch.cuda.is_available() else ("xpu" if torch.backends.xpu.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu") ),
default="cuda" if torch.cuda.is_available() else ("xpu" if hasattr(torch.backends, 'xpu') and torch.backends.xpu.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu") ),
choices=["cuda", "cpu", "mps","xpu", "auto"],
help="Device to run inference on"
)
Expand Down Expand Up @@ -533,7 +533,7 @@ def main():
# Initialize model
# Handle MPS device and dtype
if args.device == "mps":
model_dtype = torch.float32 # MPS works better with float32
model_dtype = torch.float16 # float16 to fit within MPS memory (~14GB vs ~28GB for float32)
elif args.device == "xpu":
model_dtype = torch.float32
elif args.device == "cpu":
Expand Down
16 changes: 7 additions & 9 deletions vibevoice/modular/configuration_vibevoice.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,17 +14,15 @@
def _convert_dtype_to_string(config_dict: dict) -> dict:
"""
Convert torch.dtype objects to their string representation for JSON serialization.

This fixes the "Object of type dtype is not JSON serializable" error that occurs
when transformers tries to log/serialize the config with torch_dtype as a torch.dtype object.

Recursively handles nested dicts.

See: https://github.com/microsoft/VibeVoice/issues/199
"""
if "torch_dtype" in config_dict and config_dict["torch_dtype"] is not None:
dtype = config_dict["torch_dtype"]
if isinstance(dtype, torch.dtype):
# Convert torch.dtype to string (e.g., torch.bfloat16 -> "bfloat16")
config_dict["torch_dtype"] = str(dtype).replace("torch.", "")
for key, value in config_dict.items():
if isinstance(value, torch.dtype):
config_dict[key] = str(value).replace("torch.", "")
elif isinstance(value, dict):
config_dict[key] = _convert_dtype_to_string(value)
return config_dict


Expand Down