mirror of
https://github.com/wassname/Open-Assistant.git
synced 2026-06-27 16:10:30 +08:00
theblackcat102
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
from argparse import Namespace
|
|
|
|
from custom_datasets import QA_DATASETS, SUMMARIZATION_DATASETS, get_one_dataset
|
|
from custom_datasets.dialogue_collator import DialogueDataCollator
|
|
|
|
|
|
def test_all_datasets():
|
|
qa_base = QA_DATASETS
|
|
summarize_base = SUMMARIZATION_DATASETS
|
|
others = ["prompt_dialogue", "webgpt", "soda", "joke", "instruct_tuning", "explain_prosocial", "prosocial_dialogue"]
|
|
translation = ["dive_mt", "wmt2019_zh-en", "wmt2019_ru-en", "ted_trans_de-ja", "ted_trans_nl-en"]
|
|
|
|
config = Namespace(cache_dir=".cache")
|
|
for dataset_name in translation + others + summarize_base + qa_base:
|
|
print(dataset_name)
|
|
train, eval = get_one_dataset(config, dataset_name)
|
|
# sanity check
|
|
for idx in range(min(len(train), 1000)):
|
|
train[idx]
|
|
for idx in range(min(len(eval), 1000)):
|
|
eval[idx]
|
|
|
|
|
|
def test_collate_fn():
|
|
from torch.utils.data import ConcatDataset, DataLoader
|
|
from utils import get_tokenizer
|
|
|
|
config = Namespace(cache_dir=".cache", model_name="Salesforce/codegen-2B-multi")
|
|
tokenizer = get_tokenizer(config)
|
|
collate_fn = DialogueDataCollator(tokenizer, max_length=620)
|
|
qa_base = QA_DATASETS
|
|
summarize_base = SUMMARIZATION_DATASETS
|
|
others = ["prompt_dialogue", "webgpt", "soda", "joke", "gsm8k"]
|
|
trains, evals = [], []
|
|
for dataset_name in others + qa_base + summarize_base:
|
|
print(dataset_name)
|
|
train, eval = get_one_dataset(config, dataset_name)
|
|
trains.append(train)
|
|
evals.append(eval)
|
|
|
|
dataloader = DataLoader(ConcatDataset(trains), collate_fn=collate_fn, batch_size=128)
|
|
for batch in dataloader:
|
|
print(batch["targets"].shape[0])
|
|
print(tokenizer.decode(batch["input_ids"][0]))
|
|
print("-----")
|
|
print(tokenizer.decode(batch["targets"][0][batch["label_masks"][0]]))
|
|
assert batch["targets"].shape[1] <= 620
|
|
dataloader = DataLoader(ConcatDataset(evals), collate_fn=collate_fn, batch_size=128)
|
|
for batch in dataloader:
|
|
assert batch["targets"].shape[1] <= 620
|
|
|
|
|
|
test_collate_fn()
|