|
|
|
@ -1,6 +1,6 @@
|
|
|
|
|
import glob
|
|
|
|
|
import torch
|
|
|
|
|
from datasets import load_dataset
|
|
|
|
|
from datasets import load_dataset, concatenate_datasets
|
|
|
|
|
import os
|
|
|
|
|
from torch.utils.data import DataLoader
|
|
|
|
|
from transformers import DefaultDataCollator
|
|
|
|
@ -20,7 +20,7 @@ def tokenize_inputs(config, tokenizer, examples):
|
|
|
|
|
|
|
|
|
|
# plus one since we remove bos from response
|
|
|
|
|
# but we subtract one since we want to add eos token
|
|
|
|
|
remaining_tokens = max_length - input_len - len(newline_tokens)
|
|
|
|
|
remaining_tokens = max_length - input_len - len(newline_tokens) + 1
|
|
|
|
|
# remove bos
|
|
|
|
|
target_tokens = tokenizer(response, truncation=True, max_length=remaining_tokens, return_tensors="pt")["input_ids"].squeeze()[1:]
|
|
|
|
|
|
|
|
|
@ -31,8 +31,10 @@ def tokenize_inputs(config, tokenizer, examples):
|
|
|
|
|
|
|
|
|
|
# add target tokens, remove bos
|
|
|
|
|
input_ids[i, newline_plus_inputs: newline_plus_inputs + len(target_tokens)] = target_tokens
|
|
|
|
|
# add eos token, enforce stopping
|
|
|
|
|
input_ids[i, newline_plus_inputs + len(target_tokens)] = tokenizer.eos_token_id
|
|
|
|
|
# add eos token, enforce stopping if we don't truncate
|
|
|
|
|
# we don't want long code to stop generating if truncated during training
|
|
|
|
|
if newline_plus_inputs + len(target_tokens) < max_length:
|
|
|
|
|
input_ids[i, newline_plus_inputs + len(target_tokens)] = tokenizer.eos_token_id
|
|
|
|
|
|
|
|
|
|
labels = input_ids[i].clone()
|
|
|
|
|
labels[: newline_plus_inputs] = -100
|
|
|
|
@ -51,7 +53,6 @@ def tokenize_inputs(config, tokenizer, examples):
|
|
|
|
|
return out
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def load_data(config, tokenizer):
|
|
|
|
|
dataset_path = config["dataset_path"]
|
|
|
|
|
|
|
|
|
@ -62,16 +63,22 @@ def load_data(config, tokenizer):
|
|
|
|
|
else:
|
|
|
|
|
files = [dataset_path]
|
|
|
|
|
|
|
|
|
|
print(f"Reading files {files}")
|
|
|
|
|
|
|
|
|
|
dataset = load_dataset("json", data_files=files, split="train")
|
|
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
dataset = load_dataset(dataset_path)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
uuids = dataset.filter(lambda x: x["source"] == "nomic")
|
|
|
|
|
dataset = dataset.filter(lambda x: x["source"] != "nomic")
|
|
|
|
|
dataset = dataset.train_test_split(test_size=.05, seed=config["seed"])
|
|
|
|
|
|
|
|
|
|
train_dataset, val_dataset = dataset["train"], dataset["test"]
|
|
|
|
|
|
|
|
|
|
train_dataset = concatenate_datasets([train_dataset, uuids])
|
|
|
|
|
train_dataset = train_dataset.shuffle(seed=config["seed"])
|
|
|
|
|
|
|
|
|
|
if config["streaming"] is False:
|
|
|
|
|
kwargs = {"num_proc": config["num_proc"]}
|
|
|
|
|
else:
|
|
|
|
|