Fix for MPS support on Apple Silicon

.gitignore

@@ -19,3 +19,6 @@ img_me*
 !models/place-your-models-here.txt
 !softprompts/place-your-softprompts-here.txt
 !torch-dumps/place-your-pt-models-here.txt
+
+venv/
+.venv/

modules/models.py

@@ -46,6 +46,13 @@ def load_model(model_name):
     if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
         if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
             model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
+        if torch.has_mps:
+            model = AutoModelForCausalLM.from_pretrained(
+                Path(f"models/{shared.model_name}"),low_cpu_mem_usage=True,
+                torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16
+            )
+            device = torch.device('mps')
+            model = model.to(device)
         else:
             model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16).cuda()
 
@@ -97,7 +104,7 @@ def load_model(model_name):
     # Custom
     else:
         params = {"low_cpu_mem_usage": True}
-        if not shared.args.cpu and not torch.cuda.is_available():
+        if not shared.args.cpu and not torch.cuda.is_available() and not torch.has_mps:
             print("Warning: torch.cuda.is_available() returned False.\nThis means that no GPU has been detected.\nFalling back to CPU mode.\n")
             shared.args.cpu = True
 

modules/text_generation.py

@@ -33,9 +33,13 @@ def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
             return input_ids.numpy()
         elif shared.args.deepspeed:
             return input_ids.to(device=local_rank)
+        elif torch.has_mps:
+            device = torch.device('mps')
+            return input_ids.to(device)
         else:
             return input_ids.cuda()
 
+
 def decode(output_ids):
     # Open Assistant relies on special tokens like <|endoftext|>
     if re.match('(oasst|galactica)-*', shared.model_name.lower()):