diff --git a/clip_interrogator/clip_interrogator.py b/clip_interrogator/clip_interrogator.py
index 02a9b896..77a902cb 100644
--- a/clip_interrogator/clip_interrogator.py
+++ b/clip_interrogator/clip_interrogator.py
@@ -197,7 +197,7 @@ def generate_caption(self, pil_image: Image) -> str:
     def image_to_features(self, image: Image) -> torch.Tensor:
         self._prepare_clip()
         images = self.clip_preprocess(image).unsqueeze(0).to(self.device)
-        with torch.no_grad(), torch.cuda.amp.autocast():
+        with torch.no_grad(), torch.amp.autocast(device_type='cuda' if self.device == 'cuda' else 'cpu'):
             image_features = self.clip_model.encode_image(images)
             image_features /= image_features.norm(dim=-1, keepdim=True)
         return image_features
@@ -257,7 +257,7 @@ def interrogate(self, image: Image, min_flavors: int=8, max_flavors: int=32, cap
     def rank_top(self, image_features: torch.Tensor, text_array: List[str], reverse: bool=False) -> str:
         self._prepare_clip()
         text_tokens = self.tokenize([text for text in text_array]).to(self.device)
-        with torch.no_grad(), torch.cuda.amp.autocast():
+        with torch.no_grad(), torch.amp.autocast(device_type='cuda' if self.device == 'cuda' else 'cpu'):
             text_features = self.clip_model.encode_text(text_tokens)
             text_features /= text_features.norm(dim=-1, keepdim=True)
             similarity = text_features @ image_features.T
@@ -268,7 +268,7 @@ def rank_top(self, image_features: torch.Tensor, text_array: List[str], reverse:
     def similarity(self, image_features: torch.Tensor, text: str) -> float:
         self._prepare_clip()
         text_tokens = self.tokenize([text]).to(self.device)
-        with torch.no_grad(), torch.cuda.amp.autocast():
+        with torch.no_grad(), torch.amp.autocast(device_type='cuda' if self.device == 'cuda' else 'cpu'):
             text_features = self.clip_model.encode_text(text_tokens)
             text_features /= text_features.norm(dim=-1, keepdim=True)
             similarity = text_features @ image_features.T
@@ -277,7 +277,7 @@ def similarity(self, image_features: torch.Tensor, text: str) -> float:
     def similarities(self, image_features: torch.Tensor, text_array: List[str]) -> List[float]:
         self._prepare_clip()
         text_tokens = self.tokenize([text for text in text_array]).to(self.device)
-        with torch.no_grad(), torch.cuda.amp.autocast():
+        with torch.no_grad(), torch.amp.autocast(device_type='cuda' if self.device == 'cuda' else 'cpu'):
             text_features = self.clip_model.encode_text(text_tokens)
             text_features /= text_features.norm(dim=-1, keepdim=True)
             similarity = text_features @ image_features.T
@@ -319,7 +319,7 @@ def __init__(self, labels:List[str], desc:str, ci: Interrogator):
             chunks = np.array_split(self.labels, max(1, len(self.labels)/config.chunk_size))
             for chunk in tqdm(chunks, desc=f"Preprocessing {desc}" if desc else None, disable=self.config.quiet):
                 text_tokens = self.tokenize(chunk).to(self.device)
-                with torch.no_grad(), torch.cuda.amp.autocast():
+                with torch.no_grad(), torch.amp.autocast(device_type='cuda' if self.device == 'cuda' else 'cpu'):
                     text_features = clip_model.encode_text(text_tokens)
                     text_features /= text_features.norm(dim=-1, keepdim=True)
                     text_features = text_features.half().cpu().numpy()
@@ -373,7 +373,7 @@ def _load_cached(self, desc:str, hash:str, sanitized_name:str) -> bool:
     def _rank(self, image_features: torch.Tensor, text_embeds: torch.Tensor, top_count: int=1, reverse: bool=False) -> str:
         top_count = min(top_count, len(text_embeds))
         text_embeds = torch.stack([torch.from_numpy(t) for t in text_embeds]).to(self.device)
-        with torch.cuda.amp.autocast():
+        with torch.amp.autocast(device_type='cuda' if self.device == 'cuda' else 'cpu'):
             similarity = image_features @ text_embeds.T
             if reverse:
                 similarity = -similarity