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LLaMA-Factory-Mirror
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fix #296

This commit is contained in:
hiyouga 2023-08-01 18:43:53 +08:00
parent e6a3894b99
commit e3f80774c4
2 changed files with 12 additions and 12 deletions
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8
src/llmtuner/dsets/loader.py
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@ -2,7 +2,7 @@ import os
import hashlib import hashlib
from typing import TYPE_CHECKING, List, Optional from typing import TYPE_CHECKING, List, Optional
from datasets import concatenate_datasets, interleave_datasets, load_dataset from datasets import Value, concatenate_datasets, interleave_datasets, load_dataset
from llmtuner.extras.logging import get_logger from llmtuner.extras.logging import get_logger
@ -93,7 +93,11 @@ def get_dataset(
dataset = dataset.rename_column(getattr(dataset_attr, column_name), column_name) dataset = dataset.rename_column(getattr(dataset_attr, column_name), column_name)
if dataset_attr.source_prefix: # add prefix if dataset_attr.source_prefix: # add prefix
dataset = dataset.map(lambda _: {"prefix": dataset_attr.source_prefix}) features = None
if data_args.streaming:
features = dataset.features
features["prefix"] = Value(dtype="string", id=None)
dataset = dataset.map(lambda _: {"prefix": dataset_attr.source_prefix}, features=features)
all_datasets.append(dataset) all_datasets.append(dataset)

16
src/llmtuner/dsets/preprocess.py
View File

@ -18,7 +18,7 @@ def preprocess_dataset(
training_args: "Seq2SeqTrainingArguments", training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo"] stage: Literal["pt", "sft", "rm", "ppo"]
) -> "Dataset": ) -> "Dataset":
column_names = list(dataset.column_names or []) column_names = list(dataset.column_names)
template = get_template(data_args.template) template = get_template(data_args.template)
def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]: def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]:
@ -143,15 +143,19 @@ def preprocess_dataset(
if stage == "pt": if stage == "pt":
dataset = dataset.filter(lambda example: example["prompt"]) dataset = dataset.filter(lambda example: example["prompt"])
preprocess_function = preprocess_pretrain_dataset preprocess_function = preprocess_pretrain_dataset
print_function = print_unsupervised_dataset_example
elif stage == "sft" and not training_args.predict_with_generate: elif stage == "sft" and not training_args.predict_with_generate:
dataset = dataset.filter(lambda example: example["prompt"] and example["response"]) dataset = dataset.filter(lambda example: example["prompt"] and example["response"])
preprocess_function = preprocess_supervised_dataset preprocess_function = preprocess_supervised_dataset
print_function = print_supervised_dataset_example
elif stage == "rm": elif stage == "rm":
dataset = dataset.filter(lambda example: example["prompt"] and len(example["response"]) > 1) dataset = dataset.filter(lambda example: example["prompt"] and len(example["response"]) > 1)
preprocess_function = preprocess_pairwise_dataset preprocess_function = preprocess_pairwise_dataset
print_function = print_pairwise_dataset_example
else: else:
dataset = dataset.filter(lambda example: example["prompt"]) dataset = dataset.filter(lambda example: example["prompt"])
preprocess_function = preprocess_unsupervised_dataset preprocess_function = preprocess_unsupervised_dataset
print_function = print_unsupervised_dataset_example
with training_args.main_process_first(desc="dataset map pre-processing"): with training_args.main_process_first(desc="dataset map pre-processing"):
kwargs = {} kwargs = {}
@ -172,13 +176,5 @@ def preprocess_dataset(
if data_args.streaming: if data_args.streaming:
dataset = dataset.shuffle(buffer_size=data_args.buffer_size) dataset = dataset.shuffle(buffer_size=data_args.buffer_size)
if stage == "pt": print_function(next(iter(dataset)))
print_unsupervised_dataset_example(next(iter(dataset)))
elif stage == "sft":
print_supervised_dataset_example(next(iter(dataset)))
elif stage == "rm":
print_pairwise_dataset_example(next(iter(dataset)))
elif stage == "ppo":
print_unsupervised_dataset_example(next(iter(dataset)))
return dataset return dataset