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import math
import os
import torch
import torch.nn as nn
from accelerate import Accelerator
from tqdm import tqdm
from transformers import AutoTokenizer, LlamaForCausalLM, AutoModelForCausalLM
from src.model.super_tokenizer import SuperTokenizer
DTYPE_DICT = {
"bf16": torch.bfloat16,
"fp16": torch.float16,
"fp32": torch.float32,
}
class LM(nn.Module):
def __init__(
self,
model_name_or_path: str,
super_tokenizer_name_or_path: str,
cache_dir: str = None,
super_tokenizer_num_hidden_layers: int = 6,
use_flash_attention_2: bool = True,
is_model_frozen: bool = True,
dtype: str = "bf16",
device_map=None,
accelerator: Accelerator = None,
):
super().__init__()
# * set dtype
if dtype not in DTYPE_DICT:
raise ValueError(f"dtype must be one of {DTYPE_DICT.keys()}")
dtype = DTYPE_DICT[dtype]
# * load model and super_tokenizer
self.model = LlamaForCausalLM.from_pretrained(
model_name_or_path,
cache_dir=cache_dir,
local_files_only=True,
torch_dtype=dtype,
use_flash_attention_2=use_flash_attention_2,
device_map=device_map,
)
# self.model = AutoModelForCausalLM.from_pretrained(
# model_name_or_path,
# cache_dir=cache_dir,
# local_files_only=True,
# torch_dtype=dtype,
# trust_remote_code=True,
# device_map=device_map,
# )
self.super_tokenizer = None
if super_tokenizer_name_or_path != "no":
self.super_tokenizer = SuperTokenizer.from_pretrained(
super_tokenizer_name_or_path,
cache_dir=cache_dir,
local_files_only=True,
torch_dtype=dtype,
device_map=device_map,
num_hidden_layers=super_tokenizer_num_hidden_layers,
)
# * load tokenzier
self.tokenizer = AutoTokenizer.from_pretrained(
"meta-llama/Llama-2-7b-chat-hf",
cache_dir=cache_dir,
local_files_only=True,
use_fast=False,
)
self.tokenizer.pad_token = self.tokenizer.eos_token
# * freeze model or not
self.is_model_frozen = is_model_frozen
if self.is_model_frozen:
self.freeze_model()
# * set accelerator
self.accelerator = accelerator
if device_map is None:
if self.accelerator is not None:
device = self.accelerator.device
else:
device = torch.device("cpu")
self.model.to(device)
if self.super_tokenizer is not None:
self.super_tokenizer.to(device)
def forward(
self,
input_ids=None,
attention_mask=None,
super_input_ids=None,
super_attention_mask=None,
placeholder_indices=None,
super_token_indices=None,
labels=None,
):
inputs_embeds = self.prepare_model_inputs_embeds(
input_ids,
super_input_ids,
super_attention_mask,
placeholder_indices,
super_token_indices,
)
output = self.model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
labels=labels,
)
return output
def prepare_model_inputs_embeds(
self,
input_ids=None,
super_input_ids=None,
super_attention_mask=None,
placeholder_indices=None,
super_token_indices=None,
):
inputs_embeds = self.model.get_input_embeddings()(input_ids)
if self.super_tokenizer is not None and len(super_token_indices) != 0:
super_inputs_embeds = self.super_tokenizer(
super_input_ids,
super_attention_mask,
super_token_indices,
)
inputs_embeds = inputs_embeds.type_as(super_inputs_embeds)
cur_idx = 0
for i, idx_lst in enumerate(placeholder_indices):
if len(idx_lst) == 0:
continue
inputs_embeds[i][idx_lst] = super_inputs_embeds[cur_idx:cur_idx + len(idx_lst)]
cur_idx += len(idx_lst)
return inputs_embeds
@torch.no_grad()
def generate(self, dataloader, return_new_tokens_only=True, decode=True, **gen_kwargs):
self.eval()
all_generations = []
for _, inputs in enumerate(tqdm(dataloader, desc='Generate')):
inputs = self._move_to_device(inputs) # * move to gpu
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
super_input_ids = inputs["super_input_ids"]
super_attention_mask = inputs["super_attention_mask"]
placeholder_indices = inputs["placeholder_indices"]
super_token_indices = inputs["super_token_indices"]
inputs_embeds = self.prepare_model_inputs_embeds(
input_ids=input_ids,
super_input_ids=super_input_ids,
super_attention_mask=super_attention_mask,
placeholder_indices=placeholder_indices,
super_token_indices=super_token_indices,
)
# * generate
outputs = self.model.generate(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
use_cache=True,
**gen_kwargs,
)
if return_new_tokens_only:
start_idx = input_ids.shape[1]
outputs = outputs[:, start_idx:]
if self.accelerator is not None:
outputs = outputs.contiguous() # must be contiguous
# FIXME: dim cannot be -1
outputs = self.accelerator.pad_across_processes(outputs, pad_index=self.tokenizer.pad_token_id, dim=1)
outputs = self.accelerator.gather_for_metrics(outputs)
outputs = outputs.tolist()
if decode:
outputs = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
all_generations.extend(outputs)
return all_generations
@torch.no_grad()
def compute_perplexity(self, dataloader):
self.eval()
all_nlls = []
for inputs in tqdm(dataloader):
inputs = self._move_to_device(inputs)
outputs = self.forward(**inputs)
nll = outputs.loss
if self.accelerator is not None:
nll = self.accelerator.gather_for_metrics(nll).mean()
all_nlls.append(nll.tolist())
perplexity = math.exp(sum(all_nlls) / len(all_nlls))
return perplexity
def freeze_model(self):
self.is_model_frozen = True
for _, param in self.model.named_parameters():
param.requires_grad = False
def _move_to_device(self, inputs):
for k, v in inputs.items():
if isinstance(v, torch.Tensor):
inputs[k] = v.to(self.device)
return inputs
@property
def device(self):
if self.accelerator is not None:
return self.accelerator.device
else:
return torch.device("cpu")
def gradient_checkpointing_enable(self):
self.model.gradient_checkpointing_enable()
self.super_tokenizer.gradient_checkpointing_enable()
def save(self, output_dir, deepspeed=False):
if self.super_tokenizer is not None:
self.super_tokenizer.save_pretrained(os.path.join(output_dir, "super_tokenizer"))
self.tokenizer.save_pretrained(os.path.join(output_dir, "super_tokenizer"))
if not self.is_model_frozen:
self.model.save_pretrained(
os.path.join(output_dir, "model")
)
self.tokenizer.save_pretrained(
os.path.join(output_dir, "model")
) |