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"""
Alternative way to load trained models for evaluation
"""
import copy
import sys
from os.path import join
from omegaconf import OmegaConf
import torch
from src.utils.logging import print_header, print_config, _format_arg
from .pretrained import get_pretrained_loader
from .peft import create_peft_config
from .load_model import load_and_convert_attns
from .convert_model import remove_base_attention, toggle_attention
# Helpers
def get_args_from_checkpoint(fname: str):
"""
Get arguments from checkpoint filename
"""
id_to_name = {
'lk': 'learned_kernel',
'tqk': 'tie_qk_kernels',
'tq': 'train_qk',
'lzi': 'lk_zero_init',
'lsc': 'lk_skip_connection',
'pmnop': 'pretrained_model_name_or_path',
}
id_to_type = {
'lk': str,
'tqk': bool,
'tq': bool,
'lzi': bool,
'lsc': bool,
'pmnop': str,
}
args = {v: None for k, v in id_to_name.items()}
args['run_name'] = ''
for id_val in fname.split('-'):
try:
_id, val = id_val.split('=')
if val[-len('_distill.pt'):] == '_distill.pt': # hardcode hack
val = val[:-len('_distill.pt')]
if _id in id_to_type:
_type = id_to_type[_id]
args[id_to_name[_id]] = _type(val)
except Exception:
pass
return OmegaConf.create(args)
def update_model_config_from_args(model_config, args):
"""Override default configs"""
# Overall attention distillation
for arg in ['learned_kernel', 'tie_qk_kernels', 'train_qk']:
argval = getattr(args, arg)
if argval is not None:
setattr(model_config['attention'], arg, argval)
args.run_name += f'-{_format_arg(arg)}={argval}'
# Learned kernel
for arg in ['lk_skip_connection', 'lk_zero_init']:
argval = getattr(args, arg)
if argval is not None:
setattr(model_config['attention']['learned_kernel_kwargs'],
arg[len('lk_'):], argval)
args.run_name += f'-{_format_arg(arg)}={argval}'
# Pretrained model
if args.pretrained_model_name_or_path is not None: # if specified
pmnop = args.pretrained_model_name_or_path
model_config.model.pretrained_model_name_or_path = pmnop
args.run_name += f'-pmnop={pmnop.split("/")[-1]}'
return model_config
def get_lm_eval_model(model_kwargs: dict, # model_loader.loading_kwargs
path_to_lm_eval_harness: str, # ../../lm-evaluation-harness
hedgehog_model: bool = False,
long_model: bool = False,
):
"""
Load model for evaluation using LM Evaluation Harness
"""
lm_kwargs = copy.deepcopy(model_kwargs)
lm_kwargs['pretrained'] = lm_kwargs['pretrained_model_name_or_path']
lm_kwargs['dtype'] = str(lm_kwargs['torch_dtype']).split('.')[-1]
del lm_kwargs['torch_dtype']
# lm_kwargs['use_cache'] = False
lm_kwargs['output_attentions'] = False
lm_kwargs['output_hidden_states'] = False
print('-> Loading as lm-evaluation-harness model')
if hedgehog_model:
if 'mistral' in lm_kwargs['pretrained']:
from lm_eval_harness.models import LolcatsMistralForCausalLM as ModelClass
else:
from lm_eval_harness.models import LolcatsLlamaForCausalLM as ModelClass
lm = ModelClass.create_from_arg_string('', lm_kwargs)
else:
sys.path.append(path_to_lm_eval_harness)
from lm_eval.models import get_model
lm = get_model('hf-causal-experimental').create_from_arg_string('', lm_kwargs)
return lm
def load_model_from_config(model_config_name: str,
config_dir: str = './configs',
lm_eval_model: bool = False,
path_to_lm_eval_harness: str = '/juice2/scr2/mzhang/projects/lm-evaluation-harness',
):
"""
Load model from a config file
"""
# Load model configs
model_config_path = join(config_dir, 'model', f'{model_config_name}.yaml')
model_config = OmegaConf.load(model_config_path)
model_loader = get_pretrained_loader(**model_config.model)
tokenizer = model_loader.load_tokenizer()
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer.padding_side = 'left'
if lm_eval_model: # Instantiate as lm_eval.base.LM object
lm = get_lm_eval_model(model_loader.loading_kwargs, path_to_lm_eval_harness)
model = lm.model
else:
model = model_loader.load()
model.eval()
if lm_eval_model:
lm.model = model
model = lm
return model, model_config, tokenizer
def load_model_from_checkpoint(attn_mlp_checkpoint_path: str = None,
finetune_checkpoint_path: str = None,
config_dir: str = './configs',
print_model: bool = False,
debug: bool = False,
lm_eval_model: bool = False,
path_to_lm_eval_harness: str = '/juice2/scr2/mzhang/projects/lm-evaluation-harness',
profile_model: bool = False,
):
"""
Load model architecture from a checkpoint path
-> attn_mlp_checkpoint_path should direct to checkpoint with learned MLPs
-> finetune_checkpoint_path should direct to checkpoint with all other parameters
-> Assumes checkpoint_path stings have names for model_config and finetune_configs
"""
# Load model configs
if attn_mlp_checkpoint_path is not None:
if len(attn_mlp_checkpoint_path.split('/')) == 4:
model_config = attn_mlp_checkpoint_path.split('/')[2]
else:
model_config = attn_mlp_checkpoint_path.split('/')[-1].split('-m=')[-1].split('-')[0]
model_config_path = join(config_dir, 'model', f'{model_config}.yaml')
model_config = OmegaConf.load(model_config_path)
args = get_args_from_checkpoint(attn_mlp_checkpoint_path.split('/')[-1])
model_config = update_model_config_from_args(model_config, args)
else:
if len(finetune_checkpoint_path.split('/')) == 4:
model_config = finetune_checkpoint_path.split('/')[2]
else:
model_config = finetune_checkpoint_path.split('/')[-1].split('-m=')[-1].split('-')[0]
model_config_path = join(config_dir, 'model', f'{model_config}.yaml')
model_config = OmegaConf.load(model_config_path)
if profile_model:
model_config['attention']['attention_type'] += '_profile'
if finetune_checkpoint_path is not None:
finetune_config = finetune_checkpoint_path.split('-f=')[-1].split('-')[0]
finetune_config_path = join(config_dir, 'experiment', f'{finetune_config}.yaml')
finetune_config = OmegaConf.load(finetune_config_path)
if debug:
print_header('-- Model Config --')
print_config(model_config)
try:
print_header('-- Finetune Config --')
print_config(finetune_config)
except NameError:
pass
# Get base model
model_loader = get_pretrained_loader(**model_config.model)
tokenizer = model_loader.load_tokenizer()
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer.padding_side = 'left'
if lm_eval_model and attn_mlp_checkpoint_path is not None:
lm = get_lm_eval_model(model_loader.loading_kwargs, path_to_lm_eval_harness,
hedgehog_model=True)
model = lm.model # Do this way because we call the larger object
elif lm_eval_model: # Instantiate as lm_eval.base.LM object
lm = get_lm_eval_model(model_loader.loading_kwargs, path_to_lm_eval_harness)
model = lm.model
elif attn_mlp_checkpoint_path is None:
model = model_loader.load()
else:
model = model_loader.load(model_type=model_config['attention']['attention_type'])
try:
model.state_chunk_len = model_config['attention']['state_chunk_len']
except KeyError:
pass
if attn_mlp_checkpoint_path is not None:
# Update and load attentions
model = load_and_convert_attns(model, model_config,
checkpoint_path=attn_mlp_checkpoint_path)[0]
if 'peft' in model_config['attention']: # Merge back q and k proj
model = model.merge_and_unload()
# Already removed in load_and_convert_attns
# model = remove_base_attention(model) # , model_config.attention)
model = toggle_attention(model, False)
if debug:
print_header('*** Model after attention converion ***')
print(model)
if finetune_checkpoint_path is not None:
# Update architecture with LoRAs
if finetune_config.finetune.method == 'lora':
model, _ = create_peft_config(model, finetune_config.finetune)
else:
for p in model.parameters():
p.requires_grad = True
# Load weights
state_dict = torch.load(finetune_checkpoint_path)['model_state_dict']
_keys = model.load_state_dict(state_dict, strict=False)
try:
assert len(_keys.unexpected_keys) == 0
print_header('*** All expected keys matched successfully ***')
except AssertionError:
print_header('*** Error: unexpected keys in checkpoint ***')
print('Unexpected keys:')
for k in _keys.unexpected_keys:
print(k)
if debug:
print_header('Missing keys:')
for k in _keys.missing_keys:
print(k)
print_header('Unexpected keys:')
for k in _keys.unexpected_keys:
print(k)
try:
# model = model.merge_and_unload()
print('-> Training attention:', model.model.layers[0].self_attn.train_attention)
except AttributeError as e:
print('Error at:', e)
_train_attn = model.model.model.layers[0].self_attn.train_attention
print(f"But it's ok, {type(model.model.model)} has attribute 'layers'")
print('-> Training attention:', _train_attn)
if print_model or debug: # Look at model
print_header('*** Model ***')
print(model)
print_header('*** Trainable Parameters ***')
for n, p in model.named_parameters():
if p.requires_grad:
print(f'├── {n}.requires_grad: {p.requires_grad}')
model.eval()
if lm_eval_model:
lm.model = model
model = lm
return model, model_config, tokenizer
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