109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
# Copyright (c) 2023 Amphion.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.nn as nn
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import math
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class SinePositionalEmbedding(nn.Module):
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def __init__(
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self,
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dim_model: int,
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dropout: float = 0.0,
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scale: bool = False,
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alpha: bool = False,
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):
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super().__init__()
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self.dim_model = dim_model
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self.x_scale = math.sqrt(dim_model) if scale else 1.0
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self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
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self.dropout = torch.nn.Dropout(p=dropout)
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self.reverse = False
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self.pe = None
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self.extend_pe(torch.tensor(0.0).expand(1, 4000))
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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:
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if self.pe.size(1) >= x.size(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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pe = torch.zeros(x.size(1), self.dim_model)
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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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div_term = torch.exp(
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torch.arange(0, self.dim_model, 2, dtype=torch.float32)
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* -(math.log(10000.0) / self.dim_model)
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)
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0)
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self.pe = pe.to(device=x.device, dtype=x.dtype).detach()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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self.extend_pe(x)
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output = x.unsqueeze(-1) if x.ndim == 2 else x
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output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
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return self.dropout(output)
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# import torch
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# import torch.nn as nn
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# import math
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# class SinePositionalEmbedding(nn.Module):
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# def __init__(
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# self,
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# dim_model: int,
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# dropout: float = 0.0,
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# scale: bool = False,
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# alpha: bool = False,
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# ):
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# super().__init__()
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# self.dim_model = dim_model
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# self.x_scale = math.sqrt(dim_model) if scale else 1.0
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# self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
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# self.dropout = torch.nn.Dropout(p=dropout)
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# self.reverse = False
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# self.pe = None
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# self.extend_pe(torch.zeros(1, 4000))
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# def extend_pe(self, x):
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# """Reset the positional encodings."""
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# if self._pe_needs_extension(x):
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# self.pe = self._generate_positional_encodings(x)
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# def _pe_needs_extension(self, x):
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# return self.pe is None or self.pe.size(1) < x.size(1) or self.pe.dtype != x.dtype or self.pe.device != x.device
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# def _generate_positional_encodings(self, x):
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# pe = torch.zeros(x.size(1), self.dim_model)
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# position = self._get_position_tensor(x)
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# div_term = self._get_div_term()
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# pe[:, 0::2] = torch.sin(position * div_term)
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# pe[:, 1::2] = torch.cos(position * div_term)
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# return pe.unsqueeze(0).to(device=x.device, dtype=x.dtype).detach()
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# def _get_position_tensor(self, x):
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# position = torch.arange(x.size(1), dtype=torch.float32).unsqueeze(1)
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# return position.flip(0) if self.reverse else position
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# def _get_div_term(self):
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# return torch.exp(torch.arange(0, self.dim_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.dim_model))
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# def forward(self, x: torch.Tensor) -> torch.Tensor:
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# self.extend_pe(x)
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# output = x.unsqueeze(-1) if x.ndim == 2 else x
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# output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
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# return self.dropout(output)
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