mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-04 12:00:10 +00:00
110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
import glob
|
|
import torch
|
|
from datasets import load_dataset, concatenate_datasets
|
|
import os
|
|
from torch.utils.data import DataLoader
|
|
from transformers import DefaultDataCollator
|
|
|
|
|
|
|
|
def tokenize_inputs(config, tokenizer, examples):
|
|
max_length = config["max_length"]
|
|
# ignore bos
|
|
newline_tokens = tokenizer("\n", return_tensors="pt")["input_ids"][0]
|
|
if newline_tokens[0] == tokenizer.bos_token_id:
|
|
newline_tokens = newline_tokens[1:]
|
|
|
|
# hacky backward compatible
|
|
different_eos = tokenizer.eos_token != "</s>"
|
|
out = {"labels": [], "input_ids": []}
|
|
for prompt, response in zip(examples["prompt"], examples["response"]):
|
|
if different_eos:
|
|
if response.count("</s>") > 0:
|
|
response = response.replace("</s>", tokenizer.eos_token)
|
|
|
|
prompt_len = len(tokenizer(prompt, truncation=True, return_tensors="pt")["input_ids"][0])
|
|
|
|
# hack if our prompt is super long
|
|
# we need to include some labels
|
|
if prompt_len >= max_length - 1:
|
|
prompt = prompt[:len(prompt) // 2]
|
|
prompt_len = len(tokenizer(prompt, truncation=True, return_tensors="pt")["input_ids"][0])
|
|
|
|
input_tokens = tokenizer(prompt + "\n" + response + tokenizer.eos_token,
|
|
truncation=True, max_length=max_length, return_tensors="pt")["input_ids"].squeeze()
|
|
|
|
|
|
labels = input_tokens.clone()
|
|
labels[:prompt_len + len(newline_tokens)] = -100
|
|
if len(labels) < max_length:
|
|
# pad to max_length with -100
|
|
labels = torch.cat([labels, torch.full((max_length - len(labels),), -100)])
|
|
|
|
input_tokens = tokenizer.pad({"input_ids": input_tokens}, padding="max_length", max_length=max_length)["input_ids"]
|
|
out["labels"].append(labels)
|
|
out["input_ids"].append(input_tokens)
|
|
|
|
out = {k: torch.stack(v) if isinstance(v, list) else v for k, v in out.items()}
|
|
|
|
return out
|
|
|
|
|
|
def load_data(config, tokenizer):
|
|
dataset_path = config["dataset_path"]
|
|
|
|
if os.path.exists(dataset_path):
|
|
# check if path is a directory
|
|
if os.path.isdir(dataset_path):
|
|
files = glob.glob(os.path.join(dataset_path, "*_clean.jsonl"))
|
|
else:
|
|
files = [dataset_path]
|
|
|
|
print(f"Reading files {files}")
|
|
|
|
dataset = load_dataset("json", data_files=files, split="train")
|
|
|
|
else:
|
|
dataset = load_dataset(dataset_path)
|
|
|
|
dataset = dataset.train_test_split(test_size=.05, seed=config["seed"])
|
|
|
|
train_dataset, val_dataset = dataset["train"], dataset["test"]
|
|
|
|
if config["streaming"] is False:
|
|
kwargs = {"num_proc": config["num_proc"]}
|
|
else:
|
|
kwargs = {}
|
|
|
|
# tokenize inputs and return labels and attention mask
|
|
train_dataset = train_dataset.map(
|
|
lambda ele: tokenize_inputs(config, tokenizer, ele),
|
|
batched=True,
|
|
remove_columns=["source", "prompt"],
|
|
**kwargs
|
|
)
|
|
val_dataset = val_dataset.map(
|
|
lambda ele: tokenize_inputs(config, tokenizer, ele),
|
|
batched=True,
|
|
remove_columns=["source", "prompt"],
|
|
**kwargs
|
|
)
|
|
|
|
train_dataset = train_dataset.with_format("torch")
|
|
val_dataset = val_dataset.with_format("torch")
|
|
|
|
# create dataloader with default data collator since we already have labels
|
|
|
|
train_dataloader = DataLoader(
|
|
train_dataset,
|
|
collate_fn=DefaultDataCollator(),
|
|
batch_size=config["batch_size"],
|
|
)
|
|
|
|
val_dataloader = DataLoader(
|
|
val_dataset,
|
|
collate_fn=DefaultDataCollator(),
|
|
batch_size=config["batch_size"],
|
|
)
|
|
|
|
return train_dataloader, val_dataloader
|