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Use 'torch.backends.mps.is_available' to check if mps is supported (#3164)
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@ -132,7 +132,7 @@ def add_lora_transformers(lora_names):
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if not shared.args.load_in_8bit and not shared.args.cpu:
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shared.model.half()
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if not hasattr(shared.model, "hf_device_map"):
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if torch.has_mps:
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if torch.backends.mps.is_available():
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device = torch.device('mps')
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shared.model = shared.model.to(device)
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else:
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@ -147,7 +147,7 @@ def huggingface_loader(model_name):
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# Load the model in simple 16-bit mode by default
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None]):
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model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=shared.args.trust_remote_code)
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if torch.has_mps:
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if torch.backends.mps.is_available():
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device = torch.device('mps')
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model = model.to(device)
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else:
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@ -167,7 +167,7 @@ def huggingface_loader(model_name):
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"trust_remote_code": shared.args.trust_remote_code
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}
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if not any((shared.args.cpu, torch.cuda.is_available(), torch.has_mps)):
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if not any((shared.args.cpu, torch.cuda.is_available(), torch.backends.mps.is_available())):
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logger.warning("torch.cuda.is_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.")
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shared.args.cpu = True
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@ -57,7 +57,7 @@ def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_lengt
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return input_ids.numpy()
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elif shared.args.deepspeed:
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return input_ids.to(device=local_rank)
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elif torch.has_mps:
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elif torch.backends.mps.is_available():
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device = torch.device('mps')
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return input_ids.to(device)
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else:
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