Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,090 Bytes
be6f6cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/embedding.py
import math
import torch
from torch import nn
class TokenEmbedding(nn.Module):
def __init__(
self,
embedding_dim: int,
vocab_size: int,
dropout: float = 0.0,
):
super().__init__()
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.dropout = torch.nn.Dropout(p=dropout)
self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim)
@property
def weight(self) -> torch.Tensor:
return self.word_embeddings.weight
def embedding(self, index: int) -> torch.Tensor:
return self.word_embeddings.weight[index : index + 1]
def forward(self, x: torch.Tensor):
x = self.word_embeddings(x)
x = self.dropout(x)
return x
class SinePositionalEmbedding(nn.Module):
def __init__(
self,
embedding_dim: int,
dropout: float = 0.0,
scale: bool = False,
alpha: bool = False,
):
super().__init__()
self.embedding_dim = embedding_dim
self.x_scale = math.sqrt(embedding_dim) if scale else 1.0
self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
self.dropout = torch.nn.Dropout(p=dropout)
self.reverse = False
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, 4000))
def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
if self.pe.size(1) >= x.size(1):
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = torch.zeros(x.size(1), self.embedding_dim)
if self.reverse:
position = torch.arange(x.size(1) - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
else:
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) * -(math.log(10000.0) / self.embedding_dim)
)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe.to(device=x.device, dtype=x.dtype).detach()
def forward(self, x: torch.Tensor) -> torch.Tensor:
self.extend_pe(x)
output = x.unsqueeze(-1) if x.ndim == 2 else x
output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
return self.dropout(output)
class SinePositionalEmbeddingNested(nn.Module):
def __init__(
self,
embedding_dim: int,
dropout: float = 0.0,
scale: bool = False,
alpha: bool = False,
max_batch_size: int = 20,
max_seq_len: int = 2500,
):
super().__init__()
self.embedding_dim = embedding_dim
self.x_scale = math.sqrt(embedding_dim) if scale else 1.0
self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
self.dropout = torch.nn.Dropout(p=dropout)
self.max_batch_size = max_batch_size
self.max_seq_len = max_seq_len
self.reverse = False
self.register_buffer("pe", torch.zeros(max_batch_size, max_seq_len, embedding_dim), persistent=False)
self.pe: torch.Tensor
self.compute_pe()
def compute_pe(self):
"""Reset the positional encodings."""
if self.reverse:
position = torch.arange(self.max_seq_len - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
else:
position = torch.arange(self.max_seq_len, dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) * -(math.log(10000.0) / self.embedding_dim)
)
pe = self.pe
pe[:, :, 0::2] = torch.sin(position * div_term)
pe[:, :, 1::2] = torch.cos(position * div_term)
def forward(self, input_pos: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
"""
Args:
input_pos (Tensor): [batch_size, ]
x (Tensor): [batch_size, 1, embed_dim]
Returns:
embedded_x (Tensor): [batch_size, 1, embed_dim]
"""
batch_size = x.shape[0]
pe_values = self.pe[torch.arange(batch_size), input_pos - 1] # (batch_size, embed_dim)
return x * self.x_scale + self.alpha * pe_values.unsqueeze(1) # (batch_size, 1, embed_dim)
def prefill(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x (Tensor): Nested Seqlen [batch_size, seq_len, embed_dim]
Returns:
embedded_x (Tensor): Nested Seqlen [batch_size, seq_len, embed_dim]
"""
input_pos: torch.Tensor = torch.tensor([i.shape[0] for i in x.unbind()])
pe_values = torch.nested.nested_tensor([self.pe[i, : input_pos[i], :] for i in range(input_pos.size(0))])
return x * self.x_scale + self.alpha.item() * pe_values
|