PoliBa-DeepLearning/NanoSocrates
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added method fot batched attention_mask

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GassiGiuseppe 2025-10-06 12:00:11 +02:00
parent 7e40a36701
commit 87409fecd5
2 changed files with 9 additions and 4 deletions
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4
Project_Model/Libs/Transformer/Utils/__init__.py
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@ -1,4 +1,4 @@
from .attention_mask import get_attention_mask from .attention_mask import get_causal_attention_mask,get_causal_attention_mask_batched
from .task_type import TaskType from .task_type import TaskType
__all__ = ["get_attention_mask", "TaskType"] __all__ = ["get_causal_attention_mask", "TaskType", "get_causal_attention_mask_batched"]

9
Project_Model/Libs/Transformer/Utils/attention_mask.py
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import torch import torch
def get_attention_mask(embedding_dimension: int) -> torch.Tensor: def get_causal_attention_mask(embedding_dimension: int) -> torch.Tensor:
return torch.triu(torch.ones(embedding_dimension, embedding_dimension, dtype=torch.bool), diagonal=1) return torch.triu(torch.ones(embedding_dimension, embedding_dimension, dtype=torch.bool), diagonal=1)
def get_causal_attention_mask_batched(embedding_dimension: int, batch_size: int ) -> torch.Tensor:
base_mask = get_causal_attention_mask(embedding_dimension)
return base_mask.unsqueeze(0).expand(batch_size, -1, -1) # add another dimension at the beginning, big as batch_size
# the result is that z,x,y where x,y are repeated along z