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LLM-fastAPI / models /transformer /module /subsequent_mask.py

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f3b11f9 almost 2 years ago

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import numpy as np
import torch


def subsequent_mask(size):
    "Mask out subsequent positions."
    attn_shape = (1, size, size)
    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
    return torch.from_numpy(subsequent_mask) == 0

	import numpy as np
	import torch


	def subsequent_mask(size):
	"Mask out subsequent positions."
	attn_shape = (1, size, size)
	subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
	return torch.from_numpy(subsequent_mask) == 0