mirror of
https://github.com/modelscope/FunASR
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404 lines
14 KiB
Python
404 lines
14 KiB
Python
"""Positional Encoding Module."""
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import math
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import torch
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import torch.nn as nn
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from funasr.modules.embedding import (
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LegacyRelPositionalEncoding, PositionalEncoding, RelPositionalEncoding,
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ScaledPositionalEncoding, StreamPositionalEncoding)
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from funasr.modules.subsampling import (
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Conv2dSubsampling, Conv2dSubsampling2, Conv2dSubsampling6,
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Conv2dSubsampling8)
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from funasr.modules.subsampling_without_posenc import \
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Conv2dSubsamplingWOPosEnc
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from funasr.export.models.language_models.subsampling import (
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OnnxConv2dSubsampling, OnnxConv2dSubsampling2, OnnxConv2dSubsampling6,
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OnnxConv2dSubsampling8)
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def get_pos_emb(pos_emb, max_seq_len=512, use_cache=True):
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if isinstance(pos_emb, LegacyRelPositionalEncoding):
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return OnnxLegacyRelPositionalEncoding(pos_emb, max_seq_len, use_cache)
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elif isinstance(pos_emb, ScaledPositionalEncoding):
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return OnnxScaledPositionalEncoding(pos_emb, max_seq_len, use_cache)
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elif isinstance(pos_emb, RelPositionalEncoding):
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return OnnxRelPositionalEncoding(pos_emb, max_seq_len, use_cache)
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elif isinstance(pos_emb, PositionalEncoding):
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return OnnxPositionalEncoding(pos_emb, max_seq_len, use_cache)
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elif isinstance(pos_emb, StreamPositionalEncoding):
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return OnnxStreamPositionalEncoding(pos_emb, max_seq_len, use_cache)
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elif (isinstance(pos_emb, nn.Sequential) and len(pos_emb) == 0) or (
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isinstance(pos_emb, Conv2dSubsamplingWOPosEnc)
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):
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return pos_emb
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else:
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raise ValueError("Embedding model is not supported.")
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class Embedding(nn.Module):
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def __init__(self, model, max_seq_len=512, use_cache=True):
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super().__init__()
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self.model = model
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if not isinstance(model, nn.Embedding):
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if isinstance(model, Conv2dSubsampling):
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self.model = OnnxConv2dSubsampling(model)
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self.model.out[-1] = get_pos_emb(model.out[-1], max_seq_len)
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elif isinstance(model, Conv2dSubsampling2):
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self.model = OnnxConv2dSubsampling2(model)
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self.model.out[-1] = get_pos_emb(model.out[-1], max_seq_len)
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elif isinstance(model, Conv2dSubsampling6):
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self.model = OnnxConv2dSubsampling6(model)
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self.model.out[-1] = get_pos_emb(model.out[-1], max_seq_len)
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elif isinstance(model, Conv2dSubsampling8):
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self.model = OnnxConv2dSubsampling8(model)
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self.model.out[-1] = get_pos_emb(model.out[-1], max_seq_len)
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else:
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self.model[-1] = get_pos_emb(model[-1], max_seq_len)
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def forward(self, x, mask=None):
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if mask is None:
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return self.model(x)
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else:
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return self.model(x, mask)
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def _pre_hook(
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state_dict,
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prefix,
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local_metadata,
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strict,
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missing_keys,
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unexpected_keys,
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error_msgs,
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):
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"""Perform pre-hook in load_state_dict for backward compatibility.
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Note:
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We saved self.pe until v.0.5.2 but we have omitted it later.
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Therefore, we remove the item "pe" from `state_dict` for backward compatibility.
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"""
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k = prefix + "pe"
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if k in state_dict:
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state_dict.pop(k)
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class OnnxPositionalEncoding(torch.nn.Module):
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"""Positional encoding.
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Args:
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d_model (int): Embedding dimension.
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dropout_rate (float): Dropout rate.
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max_seq_len (int): Maximum input length.
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reverse (bool): Whether to reverse the input position. Only for
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the class LegacyRelPositionalEncoding. We remove it in the current
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class RelPositionalEncoding.
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"""
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def __init__(self, model, max_seq_len=512, reverse=False, use_cache=True):
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"""Construct an PositionalEncoding object."""
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super(OnnxPositionalEncoding, self).__init__()
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self.d_model = model.d_model
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self.reverse = reverse
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self.max_seq_len = max_seq_len
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self.xscale = math.sqrt(self.d_model)
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self._register_load_state_dict_pre_hook(_pre_hook)
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self.pe = model.pe
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self.use_cache = use_cache
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self.model = model
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if self.use_cache:
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self.extend_pe()
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else:
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self.div_term = torch.exp(
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torch.arange(0, self.d_model, 2, dtype=torch.float32)
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* -(math.log(10000.0) / self.d_model)
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)
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def extend_pe(self):
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"""Reset the positional encodings."""
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pe_length = len(self.pe[0])
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if self.max_seq_len < pe_length:
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self.pe = self.pe[:, : self.max_seq_len]
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else:
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self.model.extend_pe(torch.tensor(0.0).expand(1, self.max_seq_len))
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self.pe = self.model.pe
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def _add_pe(self, x):
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"""Computes positional encoding"""
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if self.reverse:
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position = torch.arange(
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x.size(1) - 1, -1, -1.0, dtype=torch.float32
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).unsqueeze(1)
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else:
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position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
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x = x * self.xscale
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x[:, :, 0::2] += torch.sin(position * self.div_term)
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x[:, :, 1::2] += torch.cos(position * self.div_term)
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return x
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def forward(self, x: torch.Tensor):
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"""Add positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, time, `*`).
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Returns:
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torch.Tensor: Encoded tensor (batch, time, `*`).
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"""
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if self.use_cache:
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x = x * self.xscale + self.pe[:, : x.size(1)]
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else:
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x = self._add_pe(x)
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return x
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class OnnxScaledPositionalEncoding(OnnxPositionalEncoding):
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"""Scaled positional encoding module.
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See Sec. 3.2 https://arxiv.org/abs/1809.08895
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Args:
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d_model (int): Embedding dimension.
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dropout_rate (float): Dropout rate.
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max_seq_len (int): Maximum input length.
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"""
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def __init__(self, model, max_seq_len=512, use_cache=True):
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"""Initialize class."""
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super().__init__(model, max_seq_len, use_cache=use_cache)
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self.alpha = torch.nn.Parameter(torch.tensor(1.0))
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def reset_parameters(self):
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"""Reset parameters."""
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self.alpha.data = torch.tensor(1.0)
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def _add_pe(self, x):
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"""Computes positional encoding"""
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if self.reverse:
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position = torch.arange(
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x.size(1) - 1, -1, -1.0, dtype=torch.float32
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).unsqueeze(1)
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else:
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position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
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x = x * self.alpha
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x[:, :, 0::2] += torch.sin(position * self.div_term)
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x[:, :, 1::2] += torch.cos(position * self.div_term)
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return x
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def forward(self, x):
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"""Add positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, time, `*`).
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Returns:
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torch.Tensor: Encoded tensor (batch, time, `*`).
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"""
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if self.use_cache:
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x = x + self.alpha * self.pe[:, : x.size(1)]
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else:
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x = self._add_pe(x)
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return x
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class OnnxLegacyRelPositionalEncoding(OnnxPositionalEncoding):
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"""Relative positional encoding module (old version).
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Details can be found in https://github.com/espnet/espnet/pull/2816.
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See : Appendix B in https://arxiv.org/abs/1901.02860
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Args:
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d_model (int): Embedding dimension.
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dropout_rate (float): Dropout rate.
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max_seq_len (int): Maximum input length.
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"""
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def __init__(self, model, max_seq_len=512, use_cache=True):
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"""Initialize class."""
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super().__init__(model, max_seq_len, reverse=True, use_cache=use_cache)
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def _get_pe(self, x):
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"""Computes positional encoding"""
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if self.reverse:
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position = torch.arange(
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x.size(1) - 1, -1, -1.0, dtype=torch.float32
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).unsqueeze(1)
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else:
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position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
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pe = torch.zeros(x.shape)
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pe[:, :, 0::2] += torch.sin(position * self.div_term)
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pe[:, :, 1::2] += torch.cos(position * self.div_term)
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return pe
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def forward(self, x):
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"""Compute positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, time, `*`).
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Returns:
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torch.Tensor: Encoded tensor (batch, time, `*`).
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torch.Tensor: Positional embedding tensor (1, time, `*`).
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"""
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x = x * self.xscale
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if self.use_cache:
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pos_emb = self.pe[:, : x.size(1)]
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else:
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pos_emb = self._get_pe(x)
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return x, pos_emb
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class OnnxRelPositionalEncoding(torch.nn.Module):
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"""Relative positional encoding module (new implementation).
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Details can be found in https://github.com/espnet/espnet/pull/2816.
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See : Appendix B in https://arxiv.org/abs/1901.02860
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Args:
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d_model (int): Embedding dimension.
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dropout_rate (float): Dropout rate.
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max_seq_len (int): Maximum input length.
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"""
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def __init__(self, model, max_seq_len=512, use_cache=True):
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"""Construct an PositionalEncoding object."""
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super(OnnxRelPositionalEncoding, self).__init__()
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self.d_model = model.d_model
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self.xscale = math.sqrt(self.d_model)
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self.pe = None
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self.use_cache = use_cache
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if self.use_cache:
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self.extend_pe(torch.tensor(0.0).expand(1, max_seq_len))
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else:
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self.div_term = torch.exp(
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torch.arange(0, self.d_model, 2, dtype=torch.float32)
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* -(math.log(10000.0) / self.d_model)
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)
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def extend_pe(self, x):
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"""Reset the positional encodings."""
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if self.pe is not None and self.pe.size(1) >= x.size(1) * 2 - 1:
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# self.pe contains both positive and negative parts
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# the length of self.pe is 2 * input_len - 1
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if self.pe.dtype != x.dtype or self.pe.device != x.device:
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self.pe = self.pe.to(dtype=x.dtype, device=x.device)
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return
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# Suppose `i` means to the position of query vecotr and `j` means the
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# position of key vector. We use position relative positions when keys
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# are to the left (i>j) and negative relative positions otherwise (i<j).
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pe_positive = torch.zeros(x.size(1), self.d_model)
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pe_negative = torch.zeros(x.size(1), self.d_model)
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position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
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div_term = torch.exp(
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torch.arange(0, self.d_model, 2, dtype=torch.float32)
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* -(math.log(10000.0) / self.d_model)
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)
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pe_positive[:, 0::2] = torch.sin(position * div_term)
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pe_positive[:, 1::2] = torch.cos(position * div_term)
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pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
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pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
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# Reserve the order of positive indices and concat both positive and
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# negative indices. This is used to support the shifting trick
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# as in https://arxiv.org/abs/1901.02860
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pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
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pe_negative = pe_negative[1:].unsqueeze(0)
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pe = torch.cat([pe_positive, pe_negative], dim=1)
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self.pe = pe.to(device=x.device, dtype=x.dtype)
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def _get_pe(self, x):
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pe_positive = torch.zeros(x.size(1), self.d_model)
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pe_negative = torch.zeros(x.size(1), self.d_model)
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theta = (
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torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) * self.div_term
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)
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pe_positive[:, 0::2] = torch.sin(theta)
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pe_positive[:, 1::2] = torch.cos(theta)
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pe_negative[:, 0::2] = -1 * torch.sin(theta)
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pe_negative[:, 1::2] = torch.cos(theta)
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# Reserve the order of positive indices and concat both positive and
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# negative indices. This is used to support the shifting trick
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# as in https://arxiv.org/abs/1901.02860
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pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
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pe_negative = pe_negative[1:].unsqueeze(0)
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return torch.cat([pe_positive, pe_negative], dim=1)
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def forward(self, x: torch.Tensor, use_cache=True):
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"""Add positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, time, `*`).
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Returns:
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torch.Tensor: Encoded tensor (batch, time, `*`).
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"""
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x = x * self.xscale
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if self.use_cache:
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pos_emb = self.pe[
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:,
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self.pe.size(1) // 2 - x.size(1) + 1 : self.pe.size(1) // 2 + x.size(1),
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]
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else:
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pos_emb = self._get_pe(x)
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return x, pos_emb
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class OnnxStreamPositionalEncoding(torch.nn.Module):
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"""Streaming Positional encoding."""
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def __init__(self, model, max_seq_len=5000, use_cache=True):
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"""Construct an PositionalEncoding object."""
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super(StreamPositionalEncoding, self).__init__()
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self.use_cache = use_cache
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self.d_model = model.d_model
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self.xscale = model.xscale
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self.pe = model.pe
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self.use_cache = use_cache
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self.max_seq_len = max_seq_len
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if self.use_cache:
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self.extend_pe()
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else:
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self.div_term = torch.exp(
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torch.arange(0, self.d_model, 2, dtype=torch.float32)
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* -(math.log(10000.0) / self.d_model)
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)
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self._register_load_state_dict_pre_hook(_pre_hook)
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def extend_pe(self):
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"""Reset the positional encodings."""
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pe_length = len(self.pe[0])
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if self.max_seq_len < pe_length:
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self.pe = self.pe[:, : self.max_seq_len]
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else:
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self.model.extend_pe(self.max_seq_len)
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self.pe = self.model.pe
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def _add_pe(self, x, start_idx):
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position = torch.arange(start_idx, x.size(1), dtype=torch.float32).unsqueeze(1)
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x = x * self.xscale
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x[:, :, 0::2] += torch.sin(position * self.div_term)
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x[:, :, 1::2] += torch.cos(position * self.div_term)
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return x
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def forward(self, x: torch.Tensor, start_idx: int = 0):
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"""Add positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, time, `*`).
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Returns:
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torch.Tensor: Encoded tensor (batch, time, `*`).
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"""
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if self.use_cache:
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return x * self.xscale + self.pe[:, start_idx : start_idx + x.size(1)]
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else:
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return self._add_pe(x, start_idx)
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