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Clean up the transformers loader

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oobabooga 2023-09-24 20:23:05 -07:00
parent 36c38d7561
commit 63de9eb24f
1 changed files with 32 additions and 35 deletions
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67
modules/models.py
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@ -2,6 +2,7 @@ import gc
import os import os
import re import re
import time import time
import traceback
from pathlib import Path from pathlib import Path
import torch import torch
@ -117,12 +118,17 @@ def load_tokenizer(model_name, model):
def huggingface_loader(model_name): def huggingface_loader(model_name):
path_to_model = Path(f'{shared.args.model_dir}/{model_name}') path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code) params = {
'low_cpu_mem_usage': True,
'trust_remote_code': shared.args.trust_remote_code,
'torch_dtype': torch.bfloat16 if shared.args.bf16 else torch.float16
}
config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=params['trust_remote_code'])
if 'chatglm' in model_name.lower(): if 'chatglm' in model_name.lower():
LoaderClass = AutoModel LoaderClass = AutoModel
else: else:
if config.to_dict().get("is_encoder_decoder", False): if config.to_dict().get('is_encoder_decoder', False):
LoaderClass = AutoModelForSeq2SeqLM LoaderClass = AutoModelForSeq2SeqLM
shared.is_seq2seq = True shared.is_seq2seq = True
else: else:
@ -130,7 +136,7 @@ def huggingface_loader(model_name):
# Load the model in simple 16-bit mode by default # Load the model in simple 16-bit mode by default
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, shared.args.compress_pos_emb > 1, shared.args.alpha_value > 1, shared.args.disable_exllama]): 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, shared.args.compress_pos_emb > 1, shared.args.alpha_value > 1, shared.args.disable_exllama]):
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) model = LoaderClass.from_pretrained(path_to_model, **params)
if torch.backends.mps.is_available(): if torch.backends.mps.is_available():
device = torch.device('mps') device = torch.device('mps')
model = model.to(device) model = model.to(device)
@ -139,28 +145,23 @@ def huggingface_loader(model_name):
# DeepSpeed ZeRO-3 # DeepSpeed ZeRO-3
elif shared.args.deepspeed: elif shared.args.deepspeed:
model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16) model = LoaderClass.from_pretrained(path_to_model, torch_dtype=params['torch_dtype'])
model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0] model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
model.module.eval() # Inference model.module.eval() # Inference
logger.info(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}") logger.info(f'DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}')
# Custom # Load with quantization and/or offloading
else: else:
params = {
"low_cpu_mem_usage": True,
"trust_remote_code": shared.args.trust_remote_code
}
if not any((shared.args.cpu, torch.cuda.is_available(), torch.backends.mps.is_available())): if not any((shared.args.cpu, torch.cuda.is_available(), torch.backends.mps.is_available())):
logger.warning("torch.cuda.is_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.") logger.warning('torch.cuda.is_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.')
shared.args.cpu = True shared.args.cpu = True
if shared.args.cpu: if shared.args.cpu:
params["torch_dtype"] = torch.float32 params['torch_dtype'] = torch.float32
else: else:
params["device_map"] = 'auto' params['device_map'] = 'auto'
params['max_memory'] = get_max_memory_dict()
if shared.args.load_in_4bit: if shared.args.load_in_4bit:
# See https://github.com/huggingface/transformers/pull/23479/files # See https://github.com/huggingface/transformers/pull/23479/files
# and https://huggingface.co/blog/4bit-transformers-bitsandbytes # and https://huggingface.co/blog/4bit-transformers-bitsandbytes
quantization_config_params = { quantization_config_params = {
@ -170,7 +171,7 @@ def huggingface_loader(model_name):
'bnb_4bit_use_double_quant': shared.args.use_double_quant, 'bnb_4bit_use_double_quant': shared.args.use_double_quant,
} }
logger.warning("Using the following 4-bit params: " + str(quantization_config_params)) logger.info('Using the following 4-bit params: ' + str(quantization_config_params))
params['quantization_config'] = BitsAndBytesConfig(**quantization_config_params) params['quantization_config'] = BitsAndBytesConfig(**quantization_config_params)
elif shared.args.load_in_8bit: elif shared.args.load_in_8bit:
@ -178,27 +179,10 @@ def huggingface_loader(model_name):
params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True) params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True)
else: else:
params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True) params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True)
elif shared.args.bf16:
params["torch_dtype"] = torch.bfloat16
else:
params["torch_dtype"] = torch.float16
params['max_memory'] = get_max_memory_dict() if params['max_memory'] is not None:
if shared.args.disk:
params["offload_folder"] = shared.args.disk_cache_dir
if shared.args.disable_exllama:
try:
gptq_config = GPTQConfig(bits=config.quantization_config.get('bits', 4), disable_exllama=True)
params['quantization_config'] = gptq_config
logger.info('Loading with ExLlama kernel disabled.')
except:
logger.error('Failed to disable exllama. Does the config.json for this model contain the necessary quantization info?')
if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto':
config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
with init_empty_weights(): with init_empty_weights():
model = LoaderClass.from_config(config, trust_remote_code=shared.args.trust_remote_code) model = LoaderClass.from_config(config, trust_remote_code=params['trust_remote_code'])
model.tie_weights() model.tie_weights()
params['device_map'] = infer_auto_device_map( params['device_map'] = infer_auto_device_map(
@ -208,6 +192,19 @@ def huggingface_loader(model_name):
no_split_module_classes=model._no_split_modules no_split_module_classes=model._no_split_modules
) )
if shared.args.disk:
params['offload_folder'] = shared.args.disk_cache_dir
if shared.args.disable_exllama:
try:
gptq_config = GPTQConfig(bits=config.quantization_config.get('bits', 4), disable_exllama=True)
params['quantization_config'] = gptq_config
logger.info('Loading with ExLlama kernel disabled.')
except:
exc = traceback.format_exc()
logger.error('Failed to disable exllama. Does the config.json for this model contain the necessary quantization info?')
print(exc)
if shared.args.compress_pos_emb > 1: if shared.args.compress_pos_emb > 1:
params['rope_scaling'] = {'type': 'linear', 'factor': shared.args.compress_pos_emb} params['rope_scaling'] = {'type': 'linear', 'factor': shared.args.compress_pos_emb}
elif shared.args.alpha_value > 1: elif shared.args.alpha_value > 1: