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hiyouga
2024-01-20 20:15:56 +08:00
parent f6d6e00337
commit 638234ceee
73 changed files with 1492 additions and 2325 deletions
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41
src/llmtuner/data/preprocess.py
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@@ -5,6 +5,7 @@ from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Tuple
from ..extras.constants import IGNORE_INDEX
from ..extras.logging import get_logger
if TYPE_CHECKING:
from transformers import Seq2SeqTrainingArguments
from transformers.tokenization_utils import PreTrainedTokenizer
@@ -17,9 +18,7 @@ logger = get_logger(__name__)
def preprocess_pretrain_dataset(
examples: Dict[str, List[Any]],
tokenizer: "PreTrainedTokenizer",
data_args: "DataArguments"
examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
) -> Dict[str, List[List[int]]]:
# build grouped texts with format `X1 X2 X3 ...`
text_examples = [examples["prompt"][i][0]["content"] for i in range(len(examples["prompt"]))]
@@ -35,7 +34,7 @@ def preprocess_pretrain_dataset(
total_length = (total_length // block_size) * block_size
# split by chunks of cutoff_len
result = {
k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
return result
@@ -57,9 +56,11 @@ def preprocess_supervised_dataset(
messages = examples["prompt"][i] + examples["response"][i]
input_ids, labels = [], []
for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
tokenizer, messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
)):
for turn_idx, (source_ids, target_ids) in enumerate(
template.encode_multiturn(
tokenizer, messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
)
):
if data_args.train_on_prompt:
source_mask = source_ids
elif turn_idx != 0 and template.efficient_eos:
@@ -96,9 +97,9 @@ def preprocess_packed_supervised_dataset(
continue
messages = examples["prompt"][i] + examples["response"][i]
for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
tokenizer, messages, examples["system"][i], examples["tools"][i]
)):
for turn_idx, (source_ids, target_ids) in enumerate(
template.encode_multiturn(tokenizer, messages, examples["system"][i], examples["tools"][i])
):
if data_args.train_on_prompt:
source_mask = source_ids
elif turn_idx != 0 and template.efficient_eos:
@@ -119,9 +120,9 @@ def preprocess_packed_supervised_dataset(
total_length = (total_length // block_size) * block_size
# split by chunks of cutoff_len
for i in range(0, total_length, block_size):
model_inputs["input_ids"].append(input_ids[i: i + block_size])
model_inputs["input_ids"].append(input_ids[i : i + block_size])
model_inputs["attention_mask"].append([1] * block_size)
model_inputs["labels"].append(labels[i: i + block_size])
model_inputs["labels"].append(labels[i : i + block_size])
return model_inputs
@@ -191,9 +192,11 @@ def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
print("label_ids:\n{}".format(example["labels"]))
print("labels:\n{}".format(
tokenizer.decode(list(filter(lambda x: x != IGNORE_INDEX, example["labels"])), skip_special_tokens=False)
))
print(
"labels:\n{}".format(
tokenizer.decode(list(filter(lambda x: x != IGNORE_INDEX, example["labels"])), skip_special_tokens=False)
)
)
def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
@@ -232,10 +235,14 @@ def get_preprocess_and_print_func(
print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
elif stage == "rm":
preprocess_func = partial(preprocess_pairwise_dataset, tokenizer=tokenizer, template=template, data_args=data_args)
preprocess_func = partial(
preprocess_pairwise_dataset, tokenizer=tokenizer, template=template, data_args=data_args
)
print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
else:
preprocess_func = partial(preprocess_unsupervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args)
preprocess_func = partial(
preprocess_unsupervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
)
print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
return preprocess_func, print_function