241 lines
7.7 KiB
Python
241 lines
7.7 KiB
Python
"""
|
|
TODO(now)
|
|
"""
|
|
|
|
from typing import Optional
|
|
|
|
import torch
|
|
import torch.nn.functional as F # noqa: N812
|
|
from torch import Tensor, nn
|
|
|
|
|
|
class Transformer(nn.Module):
|
|
def __init__(
|
|
self,
|
|
d_model=512,
|
|
nhead=8,
|
|
num_encoder_layers=6,
|
|
num_decoder_layers=6,
|
|
dim_feedforward=2048,
|
|
dropout=0.1,
|
|
activation="relu",
|
|
normalize_before=False,
|
|
):
|
|
super().__init__()
|
|
self.encoder = TransformerEncoder(
|
|
num_encoder_layers, d_model, nhead, dim_feedforward, dropout, activation, normalize_before
|
|
)
|
|
self.decoder = TransformerDecoder(
|
|
num_decoder_layers, d_model, nhead, dim_feedforward, dropout, activation, normalize_before
|
|
)
|
|
self.d_model = d_model
|
|
self.nhead = nhead
|
|
self._init_params() # TODO(now): move to somewhere common
|
|
|
|
def _init_params(self):
|
|
for p in self.parameters():
|
|
if p.dim() > 1:
|
|
nn.init.xavier_uniform_(p)
|
|
|
|
def forward(self, x, encoder_pos, decoder_pos):
|
|
"""
|
|
Args:
|
|
x: ((E)ncoder (S)equence, (B)atch, (C)hannels)
|
|
decoder_pos: (Decoder Sequence, C) tensor for the decoder's positional embedding.
|
|
encoder_pos: (ES, C) tenso
|
|
"""
|
|
# TODO flatten only when input has H and W
|
|
bs = x.shape[1]
|
|
|
|
encoder_out = self.encoder(x, pos=encoder_pos)
|
|
decoder_in = torch.zeros(
|
|
(decoder_pos.shape[0], bs, decoder_pos.shape[2]),
|
|
dtype=decoder_pos.dtype,
|
|
device=decoder_pos.device,
|
|
)
|
|
decoder_out = self.decoder(decoder_in, encoder_out, encoder_pos=encoder_pos, decoder_pos=decoder_pos)
|
|
return decoder_out
|
|
|
|
|
|
class TransformerEncoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
num_layers,
|
|
d_model,
|
|
nhead,
|
|
dim_feedforward=2048,
|
|
dropout=0.1,
|
|
activation="relu",
|
|
normalize_before=False,
|
|
):
|
|
super().__init__()
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
TransformerEncoderLayer(
|
|
d_model, nhead, dim_feedforward, dropout, activation, normalize_before
|
|
)
|
|
for _ in range(num_layers)
|
|
]
|
|
)
|
|
self.norm = nn.LayerNorm(d_model) if normalize_before else nn.Identity()
|
|
|
|
def forward(self, x, pos: Optional[Tensor] = None):
|
|
for layer in self.layers:
|
|
x = layer(x, pos=pos)
|
|
x = self.norm(x)
|
|
return x
|
|
|
|
|
|
class TransformerEncoderLayer(nn.Module):
|
|
def __init__(
|
|
self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False
|
|
):
|
|
super().__init__()
|
|
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
|
# Implementation of Feedforward model
|
|
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
|
self.dropout = nn.Dropout(dropout)
|
|
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
|
|
|
self.norm1 = nn.LayerNorm(d_model)
|
|
self.norm2 = nn.LayerNorm(d_model)
|
|
self.dropout1 = nn.Dropout(dropout)
|
|
self.dropout2 = nn.Dropout(dropout)
|
|
|
|
self.activation = _get_activation_fn(activation)
|
|
self.normalize_before = normalize_before
|
|
|
|
def forward(self, x, pos: Optional[Tensor] = None):
|
|
skip = x
|
|
if self.normalize_before:
|
|
x = self.norm1(x)
|
|
q = k = x if pos is None else x + pos
|
|
x = self.self_attn(q, k, value=x)[0]
|
|
x = skip + self.dropout1(x)
|
|
if self.normalize_before:
|
|
skip = x
|
|
x = self.norm2(x)
|
|
else:
|
|
x = self.norm1(x)
|
|
skip = x
|
|
x = self.linear2(self.dropout(self.activation(self.linear1(x))))
|
|
x = skip + self.dropout2(x)
|
|
if not self.normalize_before:
|
|
x = self.norm2(x)
|
|
return x
|
|
|
|
|
|
class TransformerDecoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
num_layers,
|
|
d_model,
|
|
nhead,
|
|
dim_feedforward=2048,
|
|
dropout=0.1,
|
|
activation="relu",
|
|
normalize_before=False,
|
|
):
|
|
super().__init__()
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
TransformerDecoderLayer(
|
|
d_model, nhead, dim_feedforward, dropout, activation, normalize_before
|
|
)
|
|
for _ in range(num_layers)
|
|
]
|
|
)
|
|
self.num_layers = num_layers
|
|
self.norm = nn.LayerNorm(d_model)
|
|
|
|
def forward(self, x, encoder_out, decoder_pos: Tensor | None = None, encoder_pos: Tensor | None = None):
|
|
for layer in self.layers:
|
|
x = layer(x, encoder_out, decoder_pos=decoder_pos, encoder_pos=encoder_pos)
|
|
if self.norm is not None:
|
|
x = self.norm(x)
|
|
return x
|
|
|
|
|
|
class TransformerDecoderLayer(nn.Module):
|
|
def __init__(
|
|
self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False
|
|
):
|
|
super().__init__()
|
|
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
|
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
|
# Implementation of Feedforward model
|
|
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
|
self.dropout = nn.Dropout(dropout)
|
|
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
|
|
|
self.norm1 = nn.LayerNorm(d_model)
|
|
self.norm2 = nn.LayerNorm(d_model)
|
|
self.norm3 = nn.LayerNorm(d_model)
|
|
self.dropout1 = nn.Dropout(dropout)
|
|
self.dropout2 = nn.Dropout(dropout)
|
|
self.dropout3 = nn.Dropout(dropout)
|
|
|
|
self.activation = _get_activation_fn(activation)
|
|
self.normalize_before = normalize_before
|
|
|
|
def maybe_add_pos_embed(self, tensor: Tensor, pos: Tensor | None) -> Tensor:
|
|
return tensor if pos is None else tensor + pos
|
|
|
|
def forward(
|
|
self,
|
|
x: Tensor,
|
|
encoder_out: Tensor,
|
|
decoder_pos: Tensor | None = None,
|
|
encoder_pos: Tensor | None = None,
|
|
) -> Tensor:
|
|
"""
|
|
Args:
|
|
x: (Decoder Sequence, Batch, Channel) tensor of input tokens.
|
|
encoder_out: (Encoder Sequence, B, C) output features from the last layer of the encoder we are
|
|
cross-attending with.
|
|
decoder_pos: (ES, 1, C) positional embedding for keys (from the encoder).
|
|
encoder_pos: (DS, 1, C) Positional_embedding for the queries (from the decoder).
|
|
Returns:
|
|
(DS, B, C) tensor of decoder output features.
|
|
"""
|
|
skip = x
|
|
if self.normalize_before:
|
|
x = self.norm1(x)
|
|
q = k = self.maybe_add_pos_embed(x, decoder_pos)
|
|
x = self.self_attn(q, k, value=x)[0]
|
|
x = skip + self.dropout1(x)
|
|
if self.normalize_before:
|
|
skip = x
|
|
x = self.norm2(x)
|
|
else:
|
|
x = self.norm1(x)
|
|
skip = x
|
|
x = self.multihead_attn(
|
|
query=self.maybe_add_pos_embed(x, decoder_pos),
|
|
key=self.maybe_add_pos_embed(encoder_out, encoder_pos),
|
|
value=encoder_out,
|
|
)[0]
|
|
x = skip + self.dropout2(x)
|
|
if self.normalize_before:
|
|
skip = x
|
|
x = self.norm3(x)
|
|
else:
|
|
x = self.norm2(x)
|
|
skip = x
|
|
x = self.linear2(self.dropout(self.activation(self.linear1(x))))
|
|
x = skip + self.dropout3(x)
|
|
if not self.normalize_before:
|
|
x = self.norm3(x)
|
|
return x
|
|
|
|
|
|
def _get_activation_fn(activation):
|
|
"""Return an activation function given a string"""
|
|
if activation == "relu":
|
|
return F.relu
|
|
if activation == "gelu":
|
|
return F.gelu
|
|
if activation == "glu":
|
|
return F.glu
|
|
raise RuntimeError(f"activation should be relu/gelu/glu, not {activation}.")
|