feat: Enhance device compatibility by dynamically selecting CUDA, MPS, or CPU and updating autocast usage.

demo1.py

@@ -1,13 +1,19 @@
+import torch
 from funasr import AutoModel
 
 
 def main():
     model_dir = "FunAudioLLM/Fun-ASR-Nano-2512"
+    device = (
+        "cuda:0"
+        if torch.cuda.is_available()
+        else "mps" if torch.backends.mps.is_available() else "cpu"
+    )
     model = AutoModel(
         model=model_dir,
         trust_remote_code=True,
         remote_code="./model.py",
-        device="cuda:0",
+        device=device,
     )
 
     wav_path = f"{model.model_path}/example/zh.mp3"
@@ -28,7 +34,7 @@ def main():
         vad_model="fsmn-vad",
         vad_kwargs={"max_single_segment_time": 30000},
         remote_code="./model.py",
-        device="cuda:0",
+        device=device,
     )
     res = model.generate(input=[wav_path], cache={}, batch_size=1)
     text = res[0]["text"]

demo2.py

@@ -1,9 +1,15 @@
+import torch
 from model import FunASRNano
 
 
 def main():
     model_dir = "FunAudioLLM/Fun-ASR-Nano-2512"
-    m, kwargs = FunASRNano.from_pretrained(model=model_dir, device="cuda:0")
+    device = (
+        "cuda:0"
+        if torch.cuda.is_available()
+        else "mps" if torch.backends.mps.is_available() else "cpu"
+    )
+    m, kwargs = FunASRNano.from_pretrained(model=model_dir, device=device)
     m.eval()
 
     wav_path = f"{kwargs['model_path']}/example/zh.mp3"

model.py

@@ -204,7 +204,9 @@ class FunASRNano(nn.Module):
                 stats["batch_size_x_frames"] - stats["batch_size_real_frames"]
             )
 
-        with torch.cuda.amp.autocast(
+        device_type = next(self.parameters()).device.type
+        with torch.autocast(
+            device_type=device_type if device_type in ["cuda", "mps"] else "cpu",
             enabled=True if self.llm_dtype != "fp32" else False,
             dtype=dtype_map[self.llm_dtype],
         ):
@@ -624,8 +626,11 @@ class FunASRNano(nn.Module):
             llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype
             llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype
 
-        with torch.cuda.amp.autocast(
-            enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype]
+        device_type = torch.device(kwargs.get("device", "cuda")).type
+        with torch.autocast(
+            device_type=device_type if device_type in ["cuda", "mps"] else "cpu",
+            enabled=True if llm_dtype != "fp32" else False,
+            dtype=dtype_map[llm_dtype]
         ):
             label = contents["assistant"][-1]
             self.llm = self.llm.to(dtype_map[llm_dtype])