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@@ -97,10 +97,16 @@ def load_model(model_name):
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settings.append("device_map='auto'")
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settings.append("load_in_8bit=True" if args.load_in_8bit else "torch_dtype=torch.float16")
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- if args.gpu_memory and args.cpu_memory:
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- settings.append(f"max_memory={{0: '{args.gpu_memory}GiB', 'cpu': '{args.cpu_memory}GiB'}}")
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- elif args.gpu_memory:
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- settings.append(f"max_memory={{0: '{args.gpu_memory}GiB', 'cpu': '99GiB'}}")
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+ if args.gpu_memory:
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+ settings.append(f"max_memory={{0: '{args.gpu_memory or '99'}GiB', 'cpu': '{args.cpu_memory or '99'}GiB'}}")
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+ elif not args.load_in_8bit:
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+ total_mem = (torch.cuda.get_device_properties(0).total_memory/(1024*1024))
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+ suggestion = round((total_mem-1000)/1000)*1000
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+ if total_mem-suggestion < 800:
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+ suggestion -= 1000
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+ suggestion = int(round(suggestion/1000))
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+ print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m")
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+ settings.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{args.cpu_memory or '99'}GiB'}}")
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if args.disk:
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settings.append(f"offload_folder='{args.disk_cache_dir or 'cache'}'")
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