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import logging
from hashlib import md5
from pathlib import Path
from datasets import (
load_from_disk,
load_dataset,
IterableDataset,
Dataset,
concatenate_datasets,
DatasetDict,
)
from huggingface_hub import hf_hub_download
from transformers import PreTrainedTokenizerBase
from axolotl.datasets import TokenizedPromptDataset, ConstantLengthDataset
from axolotl.prompt_strategies import load
from axolotl.prompt_tokenizers import (
AlpacaPromptTokenizingStrategy,
GPTeacherPromptTokenizingStrategy,
OpenAssistantPromptTokenizingStrategy,
AlpacaReflectionPTStrategy,
ShareGPTPromptTokenizingStrategy,
JeopardyPromptTokenizingStrategy,
CompletionPromptTokenizingStrategy,
AlpacaMultipleChoicePromptTokenizingStrategy,
SummarizeTLDRPromptTokenizingStrategy,
)
from axolotl.prompters import (
AlpacaPrompter,
GPTeacherPrompter,
ReflectAlpacaPrompter,
ShareGPTPrompter,
JeopardyPrompter,
CompletionPrompter,
MultipleChoiceExplainPrompter,
SummarizeTLDRPrompter,
MultipleChoiceConcisePrompter,
)
def load_tokenized_prepared_datasets(
tokenizer, cfg, default_dataset_prepared_path
) -> DatasetDict:
tokenizer_name = tokenizer.__class__.__name__
ds_hash = str(
md5(
(
str(cfg.sequence_len)
+ "@"
+ "|".join(sorted([f"{d.path}:{d.type}" for d in cfg.datasets]))
+ "|"
+ tokenizer_name
).encode("utf-8")
).hexdigest()
)
prepared_ds_path = (
Path(cfg.dataset_prepared_path) / ds_hash
if cfg.dataset_prepared_path
else Path(default_dataset_prepared_path) / ds_hash
)
dataset = None
try:
if cfg.push_dataset_to_hub:
dataset = load_dataset(
f"{cfg.push_dataset_to_hub}/{ds_hash}", use_auth_token=True
)
dataset = dataset["train"]
except:
pass
if dataset:
...
elif any(prepared_ds_path.glob("*")):
logging.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
dataset = load_from_disk(str(prepared_ds_path))
logging.info("Prepared dataset loaded from disk...")
else:
logging.info(f"Unable to find prepared dataset in {prepared_ds_path}")
logging.info("Loading raw datasets...")
datasets = []
for d in cfg.datasets:
ds = None
ds_from_hub = False
try:
load_dataset(d.path, streaming=True, use_auth_token=True)
ds_from_hub = True
except FileNotFoundError:
pass
# prefer local dataset, even if hub exists
if Path(d.path).exists():
ds: IterableDataset = load_dataset(
"json", data_files=d.path, streaming=False, split=None
)
elif ds_from_hub:
if d.data_files:
ds = load_dataset(
d.path,
streaming=False,
data_files=d.data_files,
use_auth_token=True,
)
else:
ds = load_dataset(d.path, streaming=False, use_auth_token=True)
else:
fp = hf_hub_download(
repo_id=d.path, repo_type="dataset", filename=d.data_files
)
ds = load_dataset("json", data_files=fp, streaming=False, split=None)
if not ds:
raise Exception("unhandled dataset load")
# support for using a subset of the data
if d.shards:
ds = ds.shuffle(seed=42)["train"].shard(num_shards=cfg.shards, index=0)
d_type = d.type
d_type_split = d_type.split(":")
d_base_type = d_type_split[0]
d_prompt_style = d_type_split[1] if len(d_type_split) > 1 else None
if ds_strategy := load(d.type, tokenizer, cfg):
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "alpaca":
ds_strategy = AlpacaPromptTokenizingStrategy(
AlpacaPrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "explainchoice":
ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
MultipleChoiceExplainPrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "concisechoice":
ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
MultipleChoiceConcisePrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "summarizetldr":
ds_strategy = SummarizeTLDRPromptTokenizingStrategy(
SummarizeTLDRPrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "jeopardy":
ds_strategy = JeopardyPromptTokenizingStrategy(
JeopardyPrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "oasst":
ds_strategy = OpenAssistantPromptTokenizingStrategy(
AlpacaPrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "gpteacher":
ds_strategy = GPTeacherPromptTokenizingStrategy(
GPTeacherPrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "reflection":
ds_strategy = AlpacaReflectionPTStrategy(
ReflectAlpacaPrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "sharegpt":
ds_strategy = ShareGPTPromptTokenizingStrategy(
ShareGPTPrompter(d_prompt_style),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d_base_type == "completion":
ds_strategy = CompletionPromptTokenizingStrategy(
CompletionPrompter(),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
else:
logging.error(f"unhandled prompt tokenization strategy: {d.type}")
logging.info("tokenizing, merging, and shuffling master dataset")
samples = []
for d in datasets:
samples = samples + [i for i in d]
dataset = Dataset.from_list(samples).shuffle(seed=42)
if cfg.local_rank == 0:
logging.info(
f"Saving merged prepared dataset to disk... {prepared_ds_path}"
)
dataset.save_to_disk(prepared_ds_path)
if cfg.push_dataset_to_hub:
logging.info(
f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset.push_to_hub(
f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
)
return dataset
def load_prepare_datasets(
tokenizer: PreTrainedTokenizerBase, cfg, default_dataset_prepared_path
) -> (Dataset, Dataset):
max_packed_sequence_len = (
cfg.max_packed_sequence_len if cfg.max_packed_sequence_len else cfg.sequence_len
)
max_packed_sequence_len = min(
max_packed_sequence_len, cfg.sequence_len
) # make sure we don't accidentally set it larger than sequence_len
tokenizer_name = tokenizer.__class__.__name__
if cfg.max_packed_sequence_len is not None:
# see if we can go ahead and load the stacked dataset
seed = f"@{str(cfg.seed)}" if cfg.seed else ""
ds_hash = str(
md5(
(
str(cfg.sequence_len)
+ "@"
+ str(max_packed_sequence_len)
+ seed
+ "|".join(sorted([f"{d.path}:{d.type}" for d in cfg.datasets]))
+ "|"
+ tokenizer_name
).encode("utf-8")
).hexdigest()
)
prepared_ds_path = (
Path(cfg.dataset_prepared_path) / ds_hash
if cfg.dataset_prepared_path
else Path(default_dataset_prepared_path) / ds_hash
)
dataset = None
try:
if cfg.push_dataset_to_hub:
logging.info(
f"Checking for packed prepared dataset from hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset = load_dataset(
f"{cfg.push_dataset_to_hub}/{ds_hash}", use_auth_token=True
)
dataset = dataset["train"]
except:
pass
if dataset:
...
elif any(prepared_ds_path.glob("*")):
logging.info(
f"Loading prepared packed dataset from disk at {prepared_ds_path}..."
)
dataset = load_from_disk(str(prepared_ds_path))
logging.info("Prepared packed dataset loaded from disk...")
if cfg.push_dataset_to_hub:
logging.info(
f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset.push_to_hub(
f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
)
else:
dataset = load_tokenized_prepared_datasets(
tokenizer, cfg, default_dataset_prepared_path
)
if cfg.seed:
dataset = dataset.shuffle(seed=cfg.seed)
constant_len_dataset = ConstantLengthDataset(
tokenizer,
[dataset],
seq_length=max_packed_sequence_len,
)
logging.info(
f"packing master dataset to len: {cfg.max_packed_sequence_len}"
)
dataset = Dataset.from_list([_ for _ in constant_len_dataset])
# filter out bad data
dataset = Dataset.from_list(
[
d
for d in dataset
if len(d["input_ids"]) < cfg.sequence_len
and len(d["input_ids"]) > 0
and len(d["input_ids"]) == len(d["attention_mask"])
and len(d["input_ids"]) == len(d["labels"])
]
)
if cfg.local_rank == 0:
logging.info(
f"Saving packed prepared dataset to disk... {prepared_ds_path}"
)
dataset.save_to_disk(prepared_ds_path)
if cfg.push_dataset_to_hub:
logging.info(
f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
)
dataset.push_to_hub(
f"{cfg.push_dataset_to_hub}/{ds_hash}", private=True
)
else:
dataset = load_tokenized_prepared_datasets(
tokenizer, cfg, default_dataset_prepared_path
)
if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
logging.info(
f"Using index #{cfg.dataset_shard_idx} of {cfg.dataset_shard_num} shards"
)
dataset = dataset.shard(
num_shards=cfg.dataset_shard_num, index=cfg.dataset_shard_idx
)
dataset = dataset.train_test_split(test_size=cfg.val_set_size, shuffle=False)
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
return train_dataset, eval_dataset
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