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Refine train dataset.

This commit is contained in:
Colin 2024-04-03 17:09:30 +08:00
parent 3c774983d4
commit 2bc9e3b57e
1 changed files with 9 additions and 11 deletions
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wit/train.py
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@ -27,12 +27,12 @@ seed = 42
vocab_size = 1024 vocab_size = 1024
level_ratio = 4 level_ratio = 4
level = 4 level = 6
dataset_level = 1 dataset_level = 1
hidden_size = 256 # 128 1024 2048 32 hidden_size = 2048 # 128 1024 2048 32
num_attention_heads = 8 # 8 8 16 num_attention_heads = 16 # 8 8 16
num_hidden_layers = 2 # 6 12 24 3 num_hidden_layers = 12 # 6 12 24 3
name = "vocab_ratio_level_data_hidden_head_layer" name = "vocab_ratio_level_data_hidden_head_layer"
ver = f"{vocab_size}" + "_" + f"{level_ratio}" + "_" + f"{level}" + "_" + f"{dataset_level}" ver = f"{vocab_size}" + "_" + f"{level_ratio}" + "_" + f"{level}" + "_" + f"{dataset_level}"
@ -51,16 +51,14 @@ if __name__ == "__main__":
tokenizer = QWenTokenizer("./wit_b64.tiktoken", "./wit_char.tiktoken") tokenizer = QWenTokenizer("./wit_b64.tiktoken", "./wit_char.tiktoken")
start = vocab_size * (level_ratio**level) start = vocab_size * (level_ratio**level)
end = start * level_ratio size = vocab_size * (level_ratio**dataset_level)
size = int(vocab_size * (level_ratio**dataset_level)) raw_dataset = MeaningDataset(start, start + size, size, vocab_size, level_ratio)
raw_dataset = MeaningDataset(start, end, size, vocab_size, level_ratio)
train_dataset, val_dataset = raw_dataset.split(0.9) train_dataset, val_dataset = raw_dataset.split(0.9)
train_dataloader = BatchGroupMeaningDataloader(train_dataset, train_batch_size) train_dataloader = BatchGroupMeaningDataloader(train_dataset, train_batch_size)
val_dataloader = BatchGroupMeaningDataloader(val_dataset, val_batch_size) val_dataloader = BatchGroupMeaningDataloader(val_dataset, val_batch_size)
# it = iter(train_dataloader)
# print("data samples:") # for i in range(len(train_dataloader)):
# for i in range(10): # print(train_dataloader.print_mapping(i))
# print(next(it)["input_ids"].numpy().tolist())
torch.set_float32_matmul_precision("medium") torch.set_float32_matmul_precision("medium")
lit_trainer = pl.Trainer( lit_trainer = pl.Trainer(