Refine train.
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wit/train.py
22
wit/train.py
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@ -1,8 +1,3 @@
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import argparse
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from functools import partial
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from itertools import chain
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from typing import Dict, Tuple
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import pytorch_lightning as pl
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import torch
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@ -24,23 +19,24 @@ max_epochs = 1000
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strategy = "auto"
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resume_from_ckpt_path = None
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seed = 42
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dataloader_works = 2
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vocab_size = 256
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level_ratio = 6
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level = 4
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level_ratio = 5
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level = 5
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dataset_level = 1.5
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min_subitem = 2
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hidden_size = 1024 # 128 1024 2048 32
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hidden_size = 128 # 128 1024 2048 32
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num_attention_heads = 16 # 8 8 16
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num_hidden_layers = 6 # 6 12 24 3
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mask_level = [0]
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mask_idx = [-1]
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mask_level = [0, 1]
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mask_idx = [0, -1]
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# name = "vocab_ratio_level_data_hidden_head_layer"
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# name = "mask_level_idx"
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name = "small"
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name = "hard"
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ver = f"{vocab_size}" + "_" + f"{level_ratio}" + "_" + f"{level}" + "_" + f"{min_subitem}" + "_" + f"{dataset_level}"
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ver = ver + "_" + f"{hidden_size}" + "_" + f"{num_attention_heads}" + "_" + f"{num_hidden_layers}"
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@ -63,8 +59,8 @@ if __name__ == "__main__":
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raw_dataset = MeaningDataset(start, start + size, size, vocab_size, level_ratio, min_subitem)
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raw_dataset.set_mask(mask_level, mask_idx)
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train_dataset, val_dataset = raw_dataset.split(0.9)
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train_dataloader = BatchGroupMeaningDataloader(train_dataset, train_batch_size)
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val_dataloader = BatchGroupMeaningDataloader(val_dataset, val_batch_size)
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train_dataloader = BatchGroupMeaningDataloader(train_dataset, train_batch_size).dataloader(dataloader_works)
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val_dataloader = BatchGroupMeaningDataloader(val_dataset, val_batch_size).dataloader(dataloader_works)
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# for i in range(len(train_dataloader)):
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# print(train_dataloader.print_mapping(i))
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