Refine train,
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wit/train.py
22
wit/train.py
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@ -17,7 +17,7 @@ pretrain_model_name = None # "qwen/Qwen-1_8B-Chat"
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learning_rate = 0.0001
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use_tril_attention_mask = None
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precision = "32-true" # "precision:bf16-mixed,16-mixed,32-true"
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train_batch_size = 2
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train_batch_size = 1
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val_batch_size = 1
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num_proc = 8
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max_epochs = 1000
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@ -28,23 +28,21 @@ seed = 42
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vocab_size = 256
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level_ratio = 6
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level = 4
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dataset_level = 1
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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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num_attention_heads = 16 # 8 8 16
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num_hidden_layers = 3 # 6 12 24 3
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num_hidden_layers = 6 # 6 12 24 3
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mask_level = [0, 1]
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mask_idx = [0, 0]
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# mask_level = [0, 1]
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# mask_idx = [0, -1]
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mask_level = [0]
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mask_idx = [-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 = "single_token"
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name = "small"
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ver = f"{vocab_size}" + "_" + f"{level_ratio}" + "_" + f"{level}" + "_" + f"{dataset_level}"
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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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ver = ver + "_" + f"{mask_level}" + "_" + f"{mask_idx}"
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@ -61,8 +59,8 @@ if __name__ == "__main__":
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tokenizer = QWenTokenizer("./wit_b64.tiktoken", "./wit_char.tiktoken")
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start = vocab_size * (level_ratio**level)
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size = vocab_size * (level_ratio**dataset_level)
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raw_dataset = MeaningDataset(start, start + size, size, vocab_size, level_ratio)
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size = vocab_size * int((level_ratio**dataset_level))
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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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