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@@ -7,8 +7,9 @@ from pathlib import Path
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import numpy as np
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import torch
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import transformers
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-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoConfig
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-from accelerate import infer_auto_device_map, init_empty_weights, load_checkpoint_and_dispatch
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+from accelerate import infer_auto_device_map, init_empty_weights
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+from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
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+ BitsAndBytesConfig)
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import modules.shared as shared
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@@ -113,23 +114,20 @@ def load_model(model_name):
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if shared.args.gpu_memory:
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memory_map = shared.args.gpu_memory
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- max_memory = { 0: f'{memory_map[0]}GiB' }
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- for i in range(1, len(memory_map)):
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+ max_memory = {}
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+ for i in range(len(memory_map)):
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max_memory[i] = f'{memory_map[i]}GiB'
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max_memory['cpu'] = f'{shared.args.cpu_memory or 99}GiB'
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params['max_memory'] = max_memory
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else:
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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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+ 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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- max_memory = {
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- 0: f'{suggestion}GiB',
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- 'cpu': f'{shared.args.cpu_memory or 99}GiB'
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- }
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+ max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'}
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params['max_memory'] = max_memory
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if shared.args.disk:
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oobabooga