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support streaming data, fix #284 #274 #268

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Former-commit-id: 819cc1353599e5fa45658bc56dd0dbe4b258b197
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hiyouga
2023-07-31 23:33:00 +08:00
parent 124f61b404
commit dd3f3e9749
28 changed files with 478 additions and 344 deletions
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132
src/llmtuner/dsets/preprocess.py
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@@ -1,65 +1,63 @@
from typing import Literal
from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal
from itertools import chain
from transformers import Seq2SeqTrainingArguments
from transformers.tokenization_utils import PreTrainedTokenizer
from datasets import Dataset
from llmtuner.extras.constants import IGNORE_INDEX
from llmtuner.extras.template import get_template
from llmtuner.hparams import DataArguments
if TYPE_CHECKING:
from datasets import Dataset
from transformers import Seq2SeqTrainingArguments
from transformers.tokenization_utils import PreTrainedTokenizer
from llmtuner.hparams import DataArguments
def preprocess_dataset(
dataset: Dataset,
tokenizer: PreTrainedTokenizer,
data_args: DataArguments,
training_args: Seq2SeqTrainingArguments,
dataset: "Dataset",
tokenizer: "PreTrainedTokenizer",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo"]
) -> Dataset:
) -> "Dataset":
column_names = list(dataset.column_names or [])
template = get_template(data_args.template)
column_names = list(dataset.column_names)
prompt_template = get_template(data_args.prompt_template)
# support question with a single answer or multiple answers
def get_dialog(examples):
def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]:
for i in range(len(examples["prompt"])):
if examples["prompt"][i] and examples["response"][i]:
query, answer = examples["prompt"][i], examples["response"][i]
query = query + "\n" + examples["query"][i] if examples["query"][i] else query
prefix = examples["prefix"][i] if examples["prefix"][i] else ""
dialog = prompt_template.get_dialog(query, answer, examples["history"][i], prefix)
yield dialog
query, response = examples["prompt"][i], examples["response"][i]
query = query + "\n" + examples["query"][i] if "query" in examples and examples["query"][i] else query
history = history if "history" in examples and examples["history"][i] else []
prefix = prefix if "prefix" in examples and examples["prefix"][i] else ""
yield query, response, history, prefix
def preprocess_pretrain_dataset(examples):
def preprocess_pretrain_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
# build grouped texts with format `<bos> X1 X2 X3 ...` (without <eos>)
text_ids = tokenizer(examples["prompt"], add_special_tokens=False)["input_ids"]
concatenated_ids = list(chain(*text_ids))
total_length = len(concatenated_ids)
block_size = data_args.max_source_length - 1
tokenized_examples = tokenizer(examples["prompt"], add_special_tokens=False)
concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
block_size = data_args.max_source_length
# we drop the small remainder, and if the total_length < block_size, we exclude this batch
total_length = (total_length // block_size) * block_size
# split by chunks of max_source_length
result = [[tokenizer.bos_token_id] + concatenated_ids[i: i + block_size]
for i in range(0, total_length, block_size)]
return {
"input_ids": result,
"labels": result.copy()
result = {
k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
def preprocess_supervised_dataset(examples):
def preprocess_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
# for input with history, we build multiple input-label pairs just like:
# https://github.com/lm-sys/FastChat/blob/f17c092f64840fa6354ed52789dccb2daa793d0b/fastchat/train/train.py#L112
model_inputs = {"input_ids": [], "labels": []}
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
max_length = data_args.max_source_length + data_args.max_target_length
for dialog in get_dialog(examples):
for query, response, history, prefix in construct_example(examples):
input_ids, labels = [], []
for i in range(len(dialog) // 2):
source_ids = tokenizer.encode(text=dialog[2*i], add_special_tokens=(i == 0))
target_ids = tokenizer.encode(text=dialog[2*i+1], add_special_tokens=False)
for i, (query_i, resp_i) in enumerate(template.get_dialog(query, response, history, prefix)):
source_ids = tokenizer.encode(text=query_i, add_special_tokens=(i == 0))
target_ids = tokenizer.encode(text=resp_i, add_special_tokens=False)
if len(source_ids) > data_args.max_source_length:
source_ids = source_ids[:data_args.max_source_length]
@@ -73,19 +71,20 @@ def preprocess_dataset(
labels += [IGNORE_INDEX] * len(source_ids) + target_ids + [tokenizer.eos_token_id]
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
return model_inputs
def preprocess_unsupervised_dataset(examples):
def preprocess_unsupervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
# build inputs with format `<bos> X` and labels with format `<bos> Y`
model_inputs = {"input_ids": [], "labels": []}
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
for dialog in get_dialog(examples):
prompt, answer = "".join(dialog[:-1]), dialog[-1]
for query, response, history, prefix in construct_example(examples):
prompt = template.get_prompt(query, history, prefix, tokenizer.eos_token)
source_ids = tokenizer.encode(text=prompt, add_special_tokens=True)
target_ids = tokenizer.encode(text=answer, add_special_tokens=True)
target_ids = tokenizer.encode(text=response, add_special_tokens=True)
if len(source_ids) > data_args.max_source_length:
source_ids = source_ids[:data_args.max_source_length]
@@ -93,6 +92,7 @@ def preprocess_dataset(
target_ids = target_ids[:data_args.max_target_length]
model_inputs["input_ids"].append(source_ids)
model_inputs["attention_mask"].append([1] * len(source_ids))
model_inputs["labels"].append(target_ids)
return model_inputs
@@ -100,12 +100,12 @@ def preprocess_dataset(
def preprocess_pairwise_dataset(examples):
# build input pairs with format `<bos> X Y1 <eos>` and `<bos> X Y2 <eos>`
model_inputs = {"accept_ids": [], "reject_ids": []}
for dialog in get_dialog(examples):
prompt, answer = "".join(dialog[:-1]), dialog[-1]
for query, response, history, prefix in construct_example(examples):
prompt = template.get_prompt(query, history, prefix, tokenizer.eos_token)
source_ids = tokenizer.encode(text=prompt, add_special_tokens=True)
accept_ids = tokenizer.encode(text=answer[0], add_special_tokens=False)
reject_ids = tokenizer.encode(text=answer[1], add_special_tokens=False)
accept_ids = tokenizer.encode(text=response[0], add_special_tokens=False)
reject_ids = tokenizer.encode(text=response[1], add_special_tokens=False)
if len(source_ids) > data_args.max_source_length:
source_ids = source_ids[:data_args.max_source_length]
@@ -141,34 +141,44 @@ def preprocess_dataset(
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
if stage == "pt":
dataset = dataset.filter(lambda example: example["prompt"])
preprocess_function = preprocess_pretrain_dataset
elif stage == "sft":
if not training_args.predict_with_generate:
preprocess_function = preprocess_supervised_dataset
else:
preprocess_function = preprocess_unsupervised_dataset
elif stage == "sft" and not training_args.predict_with_generate:
dataset = dataset.filter(lambda example: example["prompt"] and example["response"])
preprocess_function = preprocess_supervised_dataset
elif stage == "rm":
dataset = dataset.filter(lambda example: example["prompt"] and len(example["response"]) > 1)
preprocess_function = preprocess_pairwise_dataset
elif stage == "ppo":
else:
dataset = dataset.filter(lambda example: example["prompt"])
preprocess_function = preprocess_unsupervised_dataset
with training_args.main_process_first(desc="dataset map pre-processing"):
kwargs = {}
if not data_args.streaming:
kwargs = dict(
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset"
)
dataset = dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
batched=True,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset"
**kwargs
)
if data_args.streaming:
dataset = dataset.shuffle(buffer_size=data_args.buffer_size)
if stage == "pt":
print_unsupervised_dataset_example(dataset[0])
print_unsupervised_dataset_example(next(iter(dataset)))
elif stage == "sft":
print_supervised_dataset_example(dataset[0])
print_supervised_dataset_example(next(iter(dataset)))
elif stage == "rm":
print_pairwise_dataset_example(dataset[0])
print_pairwise_dataset_example(next(iter(dataset)))
elif stage == "ppo":
print_unsupervised_dataset_example(dataset[0])
print_unsupervised_dataset_example(next(iter(dataset)))
return dataset