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import torch
import torch.nn.functional as F
from torch import nn
from .multihead_attention import MultiheadAttention
from .position_embedding import SinusoidalPositionalEmbedding
class TransformerEncoder(nn.Module):
"""
Transformer encoder consisting of *args.encoder_layers* layers. Each layer
is a :class:`TransformerEncoderLayer`.
Args:
embed_tokens (torch.nn.Embedding): input embedding
num_heads (int): number of heads
layers (int): number of layers
attn_dropout (float): dropout applied on the attention weights
relu_dropout (float): dropout applied on the first layer of the residual block
res_dropout (float): dropout applied on the residual block
attn_mask (bool): whether to apply mask on the attention weights
"""
def __init__(self, embed_dim, num_heads, layers, attn_dropout=0.0, relu_dropout=0.0, res_dropout=0.0,
embed_dropout=0.0, attn_mask=False):
super().__init__()
self.dropout = embed_dropout # Embedding dropout
self.attn_dropout = attn_dropout
self.embed_dim = embed_dim
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = SinusoidalPositionalEmbedding(embed_dim)
self.attn_mask = attn_mask
self.layers = nn.ModuleList([]) #define multiple transformer layers
for layer in range(layers):
new_layer = TransformerEncoderLayer(embed_dim,
num_heads=num_heads,
attn_dropout=attn_dropout,
relu_dropout=relu_dropout,
res_dropout=res_dropout,
attn_mask=attn_mask)
self.layers.append(new_layer)
self.register_buffer('version', torch.Tensor([2]))
self.normalize = True
if self.normalize:
self.layer_norm = LayerNorm(embed_dim)
def forward(self, x_in, x_in_k = None, x_in_v = None):
"""
Args:
x_in (FloatTensor): embedded input of shape `(src_len, batch, embed_dim)`
x_in_k (FloatTensor): embedded input of shape `(src_len, batch, embed_dim)`
x_in_v (FloatTensor): embedded input of shape `(src_len, batch, embed_dim)`
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
"""
# embed tokens and positions
x = self.embed_scale * x_in
#breakpoint()
if self.embed_positions is not None:
x += self.embed_positions(x_in.transpose(0, 1)[:, :, 0]).transpose(0, 1) # Add positional embedding
x = F.dropout(x, p=self.dropout, training=self.training)
if x_in_k is not None and x_in_v is not None:
# embed tokens and positions
x_k = self.embed_scale * x_in_k
x_v = self.embed_scale * x_in_v
if self.embed_positions is not None:
x_k += self.embed_positions(x_in_k.transpose(0, 1)[:, :, 0]).transpose(0, 1) # Add positional embedding
x_v += self.embed_positions(x_in_v.transpose(0, 1)[:, :, 0]).transpose(0, 1) # Add positional embedding
x_k = F.dropout(x_k, p=self.dropout, training=self.training)
x_v = F.dropout(x_v, p=self.dropout, training=self.training)
# encoder layers
intermediates = [x]
for layer in self.layers:
if x_in_k is not None and x_in_v is not None:
x = layer(x, x_k, x_v)
else:
x = layer(x)
intermediates.append(x)
if self.normalize:
x = self.layer_norm(x)
return x
def max_positions(self):
"""Maximum input length supported by the encoder."""
if self.embed_positions is None:
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions())
class TransformerEncoderLayer(nn.Module):
"""Encoder layer block.
In the original paper each operation (multi-head attention or FFN) is
postprocessed with: `dropout -> add residual -> layernorm`. In the
tensor2tensor code they suggest that learning is more robust when
preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.encoder_normalize_before* to ``True``.
Args:
embed_dim: Embedding dimension
"""
def __init__(self, embed_dim, num_heads=4, attn_dropout=0.1, relu_dropout=0.1, res_dropout=0.1,
attn_mask=False):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.self_attn = MultiheadAttention(
embed_dim=self.embed_dim,
num_heads=self.num_heads,
attn_dropout=attn_dropout
)
self.attn_mask = attn_mask
self.relu_dropout = relu_dropout
self.res_dropout = res_dropout
self.normalize_before = True #true means using tensor2tensor approach
self.fc1 = Linear(self.embed_dim, 4*self.embed_dim) # The "Add & Norm" part in the paper
self.fc2 = Linear(4*self.embed_dim, self.embed_dim)
self.layer_norms = nn.ModuleList([LayerNorm(self.embed_dim) for _ in range(2)]) #Define two layer_norms layers
def forward(self, x, x_k=None, x_v=None): #Two Transformer layers
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
x_k (Tensor): same as x
x_v (Tensor): same as x
Returns:
encoded output of shape `(batch, src_len, embed_dim)`
"""
residual = x
x = self.maybe_layer_norm(0, x, before=True)
mask = buffered_future_mask(x, x_k) if self.attn_mask else None
if x_k is None and x_v is None:
x, _ = self.self_attn(query=x, key=x, value=x, attn_mask=mask)
else:
x_k = self.maybe_layer_norm(0, x_k, before=True)
x_v = self.maybe_layer_norm(0, x_v, before=True)
x, _ = self.self_attn(query=x, key=x_k, value=x_v, attn_mask=mask)
x = F.dropout(x, p=self.res_dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(0, x, after=True) #First Transformer layer
residual = x
x = self.maybe_layer_norm(1, x, before=True)
x = F.relu(self.fc1(x))
x = F.dropout(x, p=self.relu_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.res_dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(1, x, after=True)
return x #The second one
def maybe_layer_norm(self, i, x, before=False, after=False):
assert before ^ after #before XOR after, allow only one is true
if after ^ self.normalize_before:
return self.layer_norms[i](x)
else:
return x
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float('-inf')).type_as(t)
def buffered_future_mask(tensor, tensor2=None):
dim1 = dim2 = tensor.size(0)
if tensor2 is not None:
dim2 = tensor2.size(0)
future_mask = torch.triu(fill_with_neg_inf(torch.ones(dim1, dim2)), 1+abs(dim2-dim1))
if tensor.is_cuda:
future_mask = future_mask.to(tensor.device)
return future_mask[:dim1, :dim2]
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.)
return m
def LayerNorm(embedding_dim):
m = nn.LayerNorm(embedding_dim)
return m
if __name__ == '__main__':
encoder = TransformerEncoder(300, 4, 2) #embed_dim, num_heads, layers
x = torch.tensor(torch.rand(20, 2, 300))
print(encoder(x).shape)
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