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Logging message to make transformers loader a bit more transparent

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oobabooga 2024-04-05 18:40:02 -07:00
parent 4e739dc211
commit 6b7f7555fc
1 changed files with 15 additions and 5 deletions
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20
modules/models.py
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@ -1,6 +1,7 @@
import gc import gc
import logging import logging
import os import os
import pprint
import re import re
import time import time
import traceback import traceback
@ -126,15 +127,19 @@ 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}')
params = { params = {
'low_cpu_mem_usage': True, 'low_cpu_mem_usage': True,
'trust_remote_code': shared.args.trust_remote_code,
'torch_dtype': torch.bfloat16 if shared.args.bf16 else torch.float16, 'torch_dtype': torch.bfloat16 if shared.args.bf16 else torch.float16,
'use_safetensors': True if shared.args.force_safetensors else None
} }
if shared.args.trust_remote_code:
params['trust_remote_code'] = True
if shared.args.use_flash_attention_2: if shared.args.use_flash_attention_2:
params['use_flash_attention_2'] = True params['use_flash_attention_2'] = True
config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=params['trust_remote_code']) if shared.args.force_safetensors:
params['max_memory'] = True
config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
if 'chatglm' in model_name.lower(): if 'chatglm' in model_name.lower():
LoaderClass = AutoModel LoaderClass = AutoModel
@ -147,6 +152,8 @@ def huggingface_loader(model_name):
# Load the model without any special settings # Load the model without any special settings
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, shared.args.disable_exllamav2]): 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, shared.args.disable_exllamav2]):
logger.info("TRANSFORMERS_PARAMS=")
pprint.PrettyPrinter(indent=4, sort_dicts=False).pprint(params)
model = LoaderClass.from_pretrained(path_to_model, **params) model = LoaderClass.from_pretrained(path_to_model, **params)
if not (hasattr(model, 'is_loaded_in_4bit') and model.is_loaded_in_4bit): if not (hasattr(model, 'is_loaded_in_4bit') and model.is_loaded_in_4bit):
if torch.backends.mps.is_available(): if torch.backends.mps.is_available():
@ -175,7 +182,9 @@ def huggingface_loader(model_name):
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 x := get_max_memory_dict():
params['max_memory'] = x
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
@ -186,7 +195,6 @@ 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.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:
@ -230,6 +238,8 @@ def huggingface_loader(model_name):
elif shared.args.alpha_value > 1: elif shared.args.alpha_value > 1:
params['rope_scaling'] = {'type': 'dynamic', 'factor': RoPE.get_alpha_value(shared.args.alpha_value, shared.args.rope_freq_base)} params['rope_scaling'] = {'type': 'dynamic', 'factor': RoPE.get_alpha_value(shared.args.alpha_value, shared.args.rope_freq_base)}
logger.info("TRANSFORMERS_PARAMS=")
pprint.PrettyPrinter(indent=4, sort_dicts=False).pprint(params)
model = LoaderClass.from_pretrained(path_to_model, **params) model = LoaderClass.from_pretrained(path_to_model, **params)
return model return model