305 lines
10 KiB
Python
Executable File
305 lines
10 KiB
Python
Executable File
# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
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# ## Citations
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# ```bibtex
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# @inproceedings{yao2021wenet,
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# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
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# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
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# booktitle={Proc. Interspeech},
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# year={2021},
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# address={Brno, Czech Republic },
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# organization={IEEE}
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# }
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# @article{zhang2022wenet,
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# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
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# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
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# journal={arXiv preprint arXiv:2203.15455},
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# year={2022}
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# }
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#
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import torch
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'''
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def subsequent_mask(
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size: int,
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device: torch.device = torch.device("cpu"),
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) -> torch.Tensor:
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"""Create mask for subsequent steps (size, size).
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This mask is used only in decoder which works in an auto-regressive mode.
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This means the current step could only do attention with its left steps.
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In encoder, fully attention is used when streaming is not necessary and
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the sequence is not long. In this case, no attention mask is needed.
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When streaming is need, chunk-based attention is used in encoder. See
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subsequent_chunk_mask for the chunk-based attention mask.
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Args:
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size (int): size of mask
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str device (str): "cpu" or "cuda" or torch.Tensor.device
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dtype (torch.device): result dtype
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Returns:
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torch.Tensor: mask
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Examples:
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>>> subsequent_mask(3)
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[[1, 0, 0],
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[1, 1, 0],
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[1, 1, 1]]
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"""
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ret = torch.ones(size, size, device=device, dtype=torch.bool)
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return torch.tril(ret)
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'''
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def subsequent_mask(
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size: int,
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device: torch.device = torch.device("cpu"),
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) -> torch.Tensor:
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"""Create mask for subsequent steps (size, size).
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This mask is used only in decoder which works in an auto-regressive mode.
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This means the current step could only do attention with its left steps.
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In encoder, fully attention is used when streaming is not necessary and
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the sequence is not long. In this case, no attention mask is needed.
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When streaming is need, chunk-based attention is used in encoder. See
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subsequent_chunk_mask for the chunk-based attention mask.
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Args:
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size (int): size of mask
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str device (str): "cpu" or "cuda" or torch.Tensor.device
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dtype (torch.device): result dtype
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Returns:
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torch.Tensor: mask
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Examples:
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>>> subsequent_mask(3)
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[[1, 0, 0],
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[1, 1, 0],
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[1, 1, 1]]
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"""
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arange = torch.arange(size, device=device)
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mask = arange.expand(size, size)
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arange = arange.unsqueeze(-1)
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mask = mask <= arange
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return mask
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def subsequent_chunk_mask(
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size: int,
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chunk_size: int,
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num_left_chunks: int = -1,
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device: torch.device = torch.device("cpu"),
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) -> torch.Tensor:
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"""Create mask for subsequent steps (size, size) with chunk size,
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this is for streaming encoder
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Args:
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size (int): size of mask
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chunk_size (int): size of chunk
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num_left_chunks (int): number of left chunks
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<0: use full chunk
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>=0: use num_left_chunks
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device (torch.device): "cpu" or "cuda" or torch.Tensor.device
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Returns:
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torch.Tensor: mask
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Examples:
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>>> subsequent_chunk_mask(4, 2)
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[[1, 1, 0, 0],
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[1, 1, 0, 0],
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[1, 1, 1, 1],
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[1, 1, 1, 1]]
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"""
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ret = torch.zeros(size, size, device=device, dtype=torch.bool)
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for i in range(size):
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if num_left_chunks < 0:
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start = 0
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else:
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start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)
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ending = min((i // chunk_size + 1) * chunk_size, size)
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ret[i, start:ending] = True
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return ret
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def add_optional_chunk_mask(
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xs: torch.Tensor,
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masks: torch.Tensor,
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use_dynamic_chunk: bool,
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use_dynamic_left_chunk: bool,
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decoding_chunk_size: int,
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static_chunk_size: int,
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num_decoding_left_chunks: int,
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):
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"""Apply optional mask for encoder.
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Args:
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xs (torch.Tensor): padded input, (B, L, D), L for max length
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mask (torch.Tensor): mask for xs, (B, 1, L)
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use_dynamic_chunk (bool): whether to use dynamic chunk or not
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use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
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training.
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decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
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0: default for training, use random dynamic chunk.
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<0: for decoding, use full chunk.
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>0: for decoding, use fixed chunk size as set.
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static_chunk_size (int): chunk size for static chunk training/decoding
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if it's greater than 0, if use_dynamic_chunk is true,
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this parameter will be ignored
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num_decoding_left_chunks: number of left chunks, this is for decoding,
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the chunk size is decoding_chunk_size.
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>=0: use num_decoding_left_chunks
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<0: use all left chunks
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Returns:
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torch.Tensor: chunk mask of the input xs.
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"""
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# Whether to use chunk mask or not
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if use_dynamic_chunk:
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max_len = xs.size(1)
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if decoding_chunk_size < 0:
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chunk_size = max_len
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num_left_chunks = -1
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elif decoding_chunk_size > 0:
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chunk_size = decoding_chunk_size
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num_left_chunks = num_decoding_left_chunks
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else:
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# chunk size is either [1, 25] or full context(max_len).
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# Since we use 4 times subsampling and allow up to 1s(100 frames)
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# delay, the maximum frame is 100 / 4 = 25.
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chunk_size = torch.randint(1, max_len, (1,)).item()
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num_left_chunks = -1
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if chunk_size > max_len // 2:
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chunk_size = max_len
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else:
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chunk_size = chunk_size % 25 + 1
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if use_dynamic_left_chunk:
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max_left_chunks = (max_len - 1) // chunk_size
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num_left_chunks = torch.randint(0, max_left_chunks, (1,)).item()
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chunk_masks = subsequent_chunk_mask(
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xs.size(1), chunk_size, num_left_chunks, xs.device
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) # (L, L)
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chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
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chunk_masks = masks & chunk_masks # (B, L, L)
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elif static_chunk_size > 0:
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num_left_chunks = num_decoding_left_chunks
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chunk_masks = subsequent_chunk_mask(
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xs.size(1), static_chunk_size, num_left_chunks, xs.device
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) # (L, L)
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chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
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chunk_masks = masks & chunk_masks # (B, L, L)
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else:
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chunk_masks = masks
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return chunk_masks
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def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
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"""Make mask tensor containing indices of padded part.
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See description of make_non_pad_mask.
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Args:
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lengths (torch.Tensor): Batch of lengths (B,).
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Returns:
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torch.Tensor: Mask tensor containing indices of padded part.
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Examples:
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>>> lengths = [5, 3, 2]
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>>> make_pad_mask(lengths)
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masks = [[0, 0, 0, 0 ,0],
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[0, 0, 0, 1, 1],
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[0, 0, 1, 1, 1]]
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"""
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batch_size = lengths.size(0)
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max_len = max_len if max_len > 0 else lengths.max().item()
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seq_range = torch.arange(0, max_len, dtype=torch.int64, device=lengths.device)
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seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
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seq_length_expand = lengths.unsqueeze(-1)
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mask = seq_range_expand >= seq_length_expand
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return mask
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def make_non_pad_mask(lengths: torch.Tensor) -> torch.Tensor:
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"""Make mask tensor containing indices of non-padded part.
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The sequences in a batch may have different lengths. To enable
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batch computing, padding is need to make all sequence in same
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size. To avoid the padding part pass value to context dependent
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block such as attention or convolution , this padding part is
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masked.
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This pad_mask is used in both encoder and decoder.
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1 for non-padded part and 0 for padded part.
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Args:
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lengths (torch.Tensor): Batch of lengths (B,).
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Returns:
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torch.Tensor: mask tensor containing indices of padded part.
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Examples:
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>>> lengths = [5, 3, 2]
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>>> make_non_pad_mask(lengths)
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masks = [[1, 1, 1, 1 ,1],
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[1, 1, 1, 0, 0],
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[1, 1, 0, 0, 0]]
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"""
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return ~make_pad_mask(lengths)
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def mask_finished_scores(score: torch.Tensor, flag: torch.Tensor) -> torch.Tensor:
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"""
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If a sequence is finished, we only allow one alive branch. This function
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aims to give one branch a zero score and the rest -inf score.
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Args:
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score (torch.Tensor): A real value array with shape
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(batch_size * beam_size, beam_size).
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flag (torch.Tensor): A bool array with shape
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(batch_size * beam_size, 1).
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Returns:
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torch.Tensor: (batch_size * beam_size, beam_size).
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"""
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beam_size = score.size(-1)
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zero_mask = torch.zeros_like(flag, dtype=torch.bool)
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if beam_size > 1:
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unfinished = torch.cat((zero_mask, flag.repeat([1, beam_size - 1])), dim=1)
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finished = torch.cat((flag, zero_mask.repeat([1, beam_size - 1])), dim=1)
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else:
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unfinished = zero_mask
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finished = flag
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score.masked_fill_(unfinished, -float("inf"))
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score.masked_fill_(finished, 0)
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return score
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def mask_finished_preds(
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pred: torch.Tensor, flag: torch.Tensor, eos: int
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) -> torch.Tensor:
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"""
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If a sequence is finished, all of its branch should be <eos>
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Args:
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pred (torch.Tensor): A int array with shape
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(batch_size * beam_size, beam_size).
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flag (torch.Tensor): A bool array with shape
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(batch_size * beam_size, 1).
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Returns:
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torch.Tensor: (batch_size * beam_size).
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"""
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beam_size = pred.size(-1)
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finished = flag.repeat([1, beam_size])
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return pred.masked_fill_(finished, eos)
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