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"""
Alpaca Clean dataset with Llama3-Instruct prompt formatting
"""
from functools import partial
from os.path import join
import numpy as np
from tqdm import tqdm
import torch
from torch.utils.data import Dataset, DataLoader
from datasets import load_metric, load_dataset
from transformers import AutoTokenizer
from transformers import DataCollatorForSeq2Seq, DefaultDataCollator, DataCollatorWithPadding
from .utils import (
get_lm_loader, get_seq2seq_loader,
convert_to_hf_dataset,
get_tokenizer_from_config,
download_scrolls_metric as download_metric
)
from .utils.packing import ConcatDataset
SYSTEM_PROMPT = "You are a helpful AI assistant who always responds to appropriately complete a user's request."
def encode_response(response: str, tokenizer) -> list[int]:
tokens = tokenizer.encode(response.strip(), add_special_tokens=False)
# For Llama 3 Instruct: tokens.append(tokenizer.get_added_vocab()["<|eot_id|>"])
tokens.append(tokenizer.eos_token_id)
try: # Llama 3 Instruct
tokens.append(tokenizer.get_added_vocab()["<|end_of_text|>"])
except KeyError:
pass
return tokens
def load_data(name: str, dataset_config: dict, pretrained_model_config: dict,
preprocess_config: dict, **loader_kwargs: any):
# Misc. setup
cache_dir = dataset_config['cache_dir']
input_len = dataset_config['chunk_size']
concat_data = dataset_config['concat_data']
load_from_cache_file = False # False if want to retokenize dataset
# Hard-code system prompt handling
if 'istral' in pretrained_model_config['pretrained_model_name_or_path']:
system_prompt = ''
else:
system_prompt = SYSTEM_PROMPT
tokenizer_name = pretrained_model_config['pretrained_model_name_or_path']
tokenizer_name = tokenizer_name.split('/')[-1]
save_path = join(cache_dir, f'{name}_{tokenizer_name}')
# Setup tokenizer
tokenizer = get_tokenizer_from_config(pretrained_model_config)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print(f'Setting tokenizer.pad_token to {tokenizer.pad_token}')
tokenizer.padding_side = 'left' # for decoder-only generation
# Get initial data
ignore_kwargs = ['concat_data', 'chunk_size', 'pose_kwargs', 'system_prompt', 'name']
train_set = load_dataset(
**{k: v for k, v in dataset_config.items() if k not in ignore_kwargs},
split='train[100:-100]',
)
val_set = load_dataset( # we just use this dataset as a validation set
**{k: v for k, v in dataset_config.items() if k not in ignore_kwargs},
split='train[:100]+train[-100:]',
)
test_set = load_dataset(
**{k: v for k, v in dataset_config.items() if k not in ignore_kwargs},
split='train[:100]+train[-100:]',
)
# Convert to dicts of {input_ids, attention_mask, labels}
train_set = train_set.map(partial(template_and_tokenize, tokenizer=tokenizer,
include_label=True, system_prompt=system_prompt),
remove_columns=list(train_set.features),
load_from_cache_file=load_from_cache_file)
val_set = val_set.map(partial(template_and_tokenize, tokenizer=tokenizer,
include_label=True, system_prompt=system_prompt),
remove_columns=list(val_set.features),
load_from_cache_file=load_from_cache_file)
test_set = test_set.map(partial(template_and_tokenize, tokenizer=tokenizer,
include_label=False, system_prompt=system_prompt),
remove_columns=list(test_set.features),
load_from_cache_file=load_from_cache_file)
# Chunk together train and val sets
if concat_data:
train_set = ConcatDataset(train_set, chunk_size=input_len)
val_set = ConcatDataset(val_set, chunk_size=input_len)
# Get dataloaders
dataloaders = {
'train': get_lm_loader(train_set, tokenizer, 'train', input_len, **loader_kwargs),
'validation': get_lm_loader(val_set, tokenizer, 'validation', input_len, **loader_kwargs),
'test': get_seq2seq_loader(test_set, tokenizer, 'test', **loader_kwargs),
}
# Evaluation metric
metric = load_metric(download_metric(), 'gov_report') # hack but we want rouge
# Finishing touches
for k, v in dataloaders.items(): # Make tokenizer accessible
dataloaders[k].dataset.tokenizer = tokenizer
dataloaders[k].dataset.metric = metric
return dataloaders
def template_and_tokenize(sample, tokenizer, include_label: bool = True,
system_prompt: str = None):
if system_prompt is None:
system_prompt = SYSTEM_PROMPT
prompt = sample['instruction']
if sample['input'] != '':
prompt += f"\n\n{sample['input']}"
messages = [
{"role": "system", "content": system_prompt},
] if system_prompt != '' else []
messages.append({"role": "user", "content": prompt})
prompt_ids = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True,
)
if include_label:
answer = encode_response(sample['output'], tokenizer)
else:
answer = []
target = encode_response(sample['output'], tokenizer)
input_ids = prompt_ids + answer
attn_mask = [1] * len(input_ids)
sample = {
"input_ids": input_ids,
"attention_mask" : attn_mask,
"labels": [-100] * len(prompt_ids) + answer if include_label else target,
}
return sample
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