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import torch
from peft import get_peft_model, LoraConfig, PromptTuningConfig, TaskType, PrefixTuningConfig
from torch import nn, autocast
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.deepspeed import HfDeepSpeedConfig
from utils.format_inputs import TASK_TYPE
from utils.format_inputs import format_causal_personachat_input, format_personachat_input, \
format_generate_persona_input
from utils.model_helpers import print_trainable_parameters
# TODO: we need to extract LORA Weight and Bias from the model
# TODO: we need to do adaptive applied LORA
class LLMChat(nn.Module):
def __init__(self, config, batch_size, ds_config=None):
if ds_config is not None:
_hfdsc = HfDeepSpeedConfig(ds_config)
super(LLMChat, self).__init__()
self.model_name = config.model.model_name
self.load_bit = config.model.load_bit
self.left_tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=False)
original_vocab_size = len(self.left_tokenizer)
if config.training.mode != 'causal':
self.left_tokenizer.add_special_tokens({'pad_token': '[PAD]',
'bos_token': '[BOS]',
'eos_token': '[EOS]',
'unk_token': '[UNK]',
'sep_token': '[SEP]',
'cls_token': '[CLS]',
'mask_token': '[MASK]'})
self.left_tokenizer.padding_side = 'left'
self.left_tokenizer.truncation_side = 'left'
self.right_tokenizer = AutoTokenizer.from_pretrained(self.model_name, use_fast=False)
if config.training.mode != 'causal':
self.right_tokenizer.add_special_tokens({'pad_token': '[PAD]',
'bos_token': '[BOS]',
'eos_token': '[EOS]',
'unk_token': '[UNK]',
'sep_token': '[SEP]',
'cls_token': '[CLS]',
'mask_token': '[MASK]'})
self.right_tokenizer.padding_side = 'right'
self.right_tokenizer.truncation_side = 'right'
if self.left_tokenizer.pad_token is None and config.model.pad_token == 'bos':
self.left_tokenizer.pad_token = self.left_tokenizer.bos_token
self.right_tokenizer.pad_token = self.right_tokenizer.bos_token
elif self.left_tokenizer.pad_token_id is None:
self.left_tokenizer.pad_token = self.left_tokenizer.eos_token
self.right_tokenizer.pad_token = self.right_tokenizer.eos_token
self.batch_size = batch_size
load_bit_map = {4: {'load_in_4bit': True,
'bnb_4bit_compute_dtype': torch.bfloat16},
8: {'load_in_8bit': True},
16: {'torch_dtype': torch.float16},
32: {'torch_dtype': torch.float32}}
assert config.model.load_bit in [16, 32], 'deepspeed is not friendly with bnb!'
model = AutoModelForCausalLM.from_pretrained(
config.model.model_name,
**load_bit_map[config.model.load_bit],
)
if config.training.mode != 'causal':
model.resize_token_embeddings(len(self.left_tokenizer))
# for m in model.children():
# if hasattr(m, 'gradient_checkpointing_enable'):
# m.gradient_checkpointing_enable()
model.gradient_checkpointing_enable()
if config.model.peft_config is not None:
for param in model.parameters():
param.requires_grad = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
param.data = param.data.to(torch.float32)
model.enable_input_require_grads()
# # enable special token embedding params, since we resized the vocabulary
# for name, param in model.named_parameters():
# if 'embed_tokens' in name:
# param[original_vocab_size:].requires_grad = True
class CastOutputToFloat(nn.Sequential):
def forward(self, x): return super().forward(x).to(torch.float32)
if config.model.peft_type == 'prompt_tuning':
peft_config = PromptTuningConfig(
**config.model.peft_config,
task_type=TaskType.CAUSAL_LM,
)
elif config.model.peft_type == 'prefix_tuning':
peft_config = PrefixTuningConfig(
**config.model.peft_config,
task_type=TaskType.CAUSAL_LM,
)
else:
peft_config = LoraConfig(**config.model.peft_config)
model.lm_head = CastOutputToFloat(model.lm_head)
model = get_peft_model(model, peft_config)
self.using_nn_modulelist = False
if config.model.using_nn_modulelist.__class__ is bool and config.model.using_nn_modulelist:
self.using_nn_modulelist = config.model.using_nn_modulelist
self.model = nn.ModuleList([model])
else:
self.model = model
if config.model.add_extra_layers.__class__ is bool and config.model.add_extra_layers:
self.prompt_normalizer = nn.Linear(
self.model[0].prompt_encoder.default.embedding.weight.shape[1],
self.model[0].word_embeddings.weight.shape[1])
self.score_activation = nn.Softplus(threshold=1, beta=10)
self.learning_rate = config.training.learning_rate
self.warmup_steps = config.training.warmup_steps
self.config = config
self.find_batch = False
print_trainable_parameters(self)
def print_llm_trainable_parameters(self):
print_trainable_parameters(self.model)
@autocast('cuda')
def forward(self, x):
if self.config._non_exists == 1:
self.prompt_normalizer(x)
self.score_activation(x)
for k in x.keys():
x[k] = x[k].cuda()
if self.find_batch:
x['attention_mask'] = x['attention_mask'].new_ones(x['attention_mask'].shape)
if self.using_nn_modulelist:
if self.config.model.using_output_stack.__class__ is bool and self.config.model.using_output_stack:
_outputs = [_model(**x) for _model in self.model]
_logits = torch.stack([_output['logits'] for _output in _outputs])
return {'logits': _logits}
return self.model[0](**x)
return self.model(**x)
def on_train_start(self) -> None:
self.print_llm_trainable_parameters()
@staticmethod
def training_step(model, batch, left_tokenizer, right_tokenizer, config, find_batch=False, mode='normal',
task_type=TASK_TYPE.GENERATE_RESPONSE, **_kwargs):
assert mode in ['normal', 'causal']
if task_type == TASK_TYPE.GENERATE_PERSONA and mode == 'normal':
lm_input, lm_target = format_generate_persona_input(batch, left_tokenizer, right_tokenizer,
config)
elif task_type == TASK_TYPE.GENERATE_RESPONSE and mode == 'causal':
lm_input, lm_target = format_causal_personachat_input(batch, left_tokenizer, right_tokenizer,
config)
elif task_type == TASK_TYPE.GENERATE_RESPONSE and mode == 'normal':
lm_input, lm_target = format_personachat_input(batch, left_tokenizer, right_tokenizer, config)
else:
raise NotImplementedError('mode and task_type not implemented')
output = model(lm_input)
if find_batch:
loss = nn.CrossEntropyLoss()(output['logits'].view(-1, output['logits'].shape[-1]),
lm_target.cuda().view(-1))
else:
if config.model.peft_type == 'prompt_tuning':
virtual_tokens = config.model.peft_config.num_virtual_tokens
batch_size = lm_target.size()[0]
_lm_target = torch.cat(
(lm_target.new_ones((batch_size, virtual_tokens)) * left_tokenizer.pad_token_id, lm_target), dim=1)
else:
_lm_target = lm_target
loss = nn.CrossEntropyLoss(ignore_index=left_tokenizer.pad_token_id)(
output['logits'].view(-1, output['logits'].shape[-1]),
_lm_target.cuda().view(-1))
# self.log('train_loss', loss, on_step=True, on_epoch=False, prog_bar=True, logger=True)
if config.training.normalize_loss.__class__ == bool and config.training.normalize_loss.__class__:
model.module.normalize()
return loss
def normalize(self):
raise NotImplementedError('normalize trainable weights needs implementation')
return None
@staticmethod
def validation_step(model, batch, left_tokenizer, right_tokenizer, config, task_type, mode='normal'):
loss = LLMChat.training_step(model, batch, left_tokenizer, right_tokenizer, config, task_type=task_type,
find_batch=False, mode=mode)
return loss
def on_test_start(self) -> None:
from peft import get_peft_model_state_dict, set_peft_model_state_dict
peft_weight = get_peft_model_state_dict(self.model).copy()
peft_config = self.model.peft_config
del self.model
model = AutoModelForCausalLM.from_pretrained(
self.config.model.model_name,
torch_dtype=torch.bfloat16, low_cpu_mem_usage=True,
)
self.model = get_peft_model(model, peft_config['default'])
set_peft_model_state_dict(self.model, peft_weight, adapter_name='default')
self.model.merge_and_unload()
self.model.eval()
@staticmethod
@autocast('cuda')
def test_step(model, batch, left_tokenizer, right_tokenizer, config, max_new_tokens=16, tqdm_instance=None, **kwargs):
model.eval()
task_type = TASK_TYPE(config.training.task_type)
with torch.no_grad():
if config.training.mode == 'causal':
lm_input, lm_target, inference_tokenized = format_causal_personachat_input(batch,
left_tokenizer,
right_tokenizer,
config,
for_test=True)
else:
lm_input, lm_target, inference_tokenized = format_personachat_input(batch, left_tokenizer,
right_tokenizer, config,
for_test=True)
inference_tokenized.to('cuda')
model_for_generation = None
if 'deepspeed' in str(model.__class__):
model_for_generation = model.module.model
else:
model_for_generation = model.model
if model_for_generation.__class__ is nn.ModuleList:
model_for_generation = model_for_generation[0]
# adding do_sample=False to avoid inf error!
raw_output = model_for_generation.generate(**inference_tokenized, max_new_tokens=max_new_tokens,
do_sample=False)
trunc_output = raw_output[:, inference_tokenized['input_ids'].shape[1]:]
if trunc_output[trunc_output >= len(left_tokenizer)].size()[0] > 0:
trunc_output[trunc_output >= len(left_tokenizer)] = left_tokenizer.pad_token_id
text_output = right_tokenizer.batch_decode(trunc_output, skip_special_tokens=True)
return trunc_output, text_output, []
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