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Merge pull request #358 from mayaeary/8bit-offload

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Add support for memory maps with --load-in-8bit
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oobabooga 2023-03-16 17:27:03 -03:00 committed by GitHub
commit 0a2aa79c4e
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1 changed files with 42 additions and 21 deletions
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63
modules/models.py
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@ -7,7 +7,9 @@ from pathlib import Path
import numpy as np import numpy as np
import torch import torch
import transformers import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer from accelerate import infer_auto_device_map, init_empty_weights
from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig)
import modules.shared as shared import modules.shared as shared
@ -94,39 +96,58 @@ def load_model(model_name):
# Custom # Custom
else: else:
command = "AutoModelForCausalLM.from_pretrained" params = {"low_cpu_mem_usage": True}
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():
print("Warning: torch.cuda.is_available() returned False.\nThis means that no GPU has been detected.\nFalling back to CPU mode.\n") 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 shared.args.cpu = True
if shared.args.cpu: if shared.args.cpu:
params.append("low_cpu_mem_usage=True") params["torch_dtype"] = torch.float32
params.append("torch_dtype=torch.float32")
else: else:
params.append("device_map='auto'") params["device_map"] = 'auto'
params.append("load_in_8bit=True" if shared.args.load_in_8bit else "torch_dtype=torch.bfloat16" if shared.args.bf16 else "torch_dtype=torch.float16") if shared.args.load_in_8bit:
params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True)
elif shared.args.bf16:
params["torch_dtype"] = torch.bfloat16
else:
params["torch_dtype"] = torch.float16
if shared.args.gpu_memory: if shared.args.gpu_memory:
memory_map = shared.args.gpu_memory memory_map = shared.args.gpu_memory
max_memory = f"max_memory={{0: '{memory_map[0]}GiB'" max_memory = {}
for i in range(1, len(memory_map)): for i in range(len(memory_map)):
max_memory += (f", {i}: '{memory_map[i]}GiB'") max_memory[i] = f'{memory_map[i]}GiB'
max_memory += (f", 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}") max_memory['cpu'] = f'{shared.args.cpu_memory or 99}GiB'
params.append(max_memory) params['max_memory'] = max_memory
elif not shared.args.load_in_8bit: else:
total_mem = (torch.cuda.get_device_properties(0).total_memory/(1024*1024)) total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024*1024))
suggestion = round((total_mem-1000)/1000)*1000 suggestion = round((total_mem-1000) / 1000) * 1000
if total_mem-suggestion < 800: if total_mem - suggestion < 800:
suggestion -= 1000 suggestion -= 1000
suggestion = int(round(suggestion/1000)) suggestion = int(round(suggestion/1000))
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") 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")
params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
if shared.args.disk:
params.append(f"offload_folder='{shared.args.disk_cache_dir}'")
command = f"{command}(Path(f'models/{shared.model_name}'), {', '.join(set(params))})" max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'}
model = eval(command) params['max_memory'] = max_memory
if shared.args.disk:
params["offload_folder"] = shared.args.disk_cache_dir
checkpoint = Path(f'models/{shared.model_name}')
if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto':
config = AutoConfig.from_pretrained(checkpoint)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config)
model.tie_weights()
params['device_map'] = infer_auto_device_map(
model,
dtype=torch.int8,
max_memory=params['max_memory'],
no_split_module_classes = model._no_split_modules
)
model = AutoModelForCausalLM.from_pretrained(checkpoint, **params)
# Loading the tokenizer # Loading the tokenizer
if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path("models/gpt-j-6B/").exists(): if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path("models/gpt-j-6B/").exists():