File size: 3,836 Bytes
901e06a | 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 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq import utils
import torch.nn as nn
from fairseq.models import FairseqEncoder
from fairseq.modules import LayerNorm
from uni_unity.modules.transformer_layer import TransformerEncoderLayer
class UniTransformerEncoderNoEmb(FairseqEncoder):
"""Transformer encoder without token embeddings."""
def __init__(self, args):
super().__init__(None)
self.layers = nn.ModuleList(
[TransformerEncoderLayer(args) for _ in range(args.encoder_layers)]
)
if args.encoder_normalize_before:
self.layer_norm = LayerNorm(args.encoder_embed_dim)
else:
self.layer_norm = None
self._future_mask = torch.empty(0)
self.unidirectional = getattr(args, "uni_encoder", False)
def forward(self, x, encoder_padding_mask, return_all_hiddens=False):
encoder_states = []
extra = {
"encoder_mask": (
self.buffered_future_mask(x) if self.unidirectional else None
)
}
for layer in self.layers:
x = layer(x, encoder_padding_mask, extra=extra)
if return_all_hiddens:
encoder_states.append(x)
if self.layer_norm is not None:
x = self.layer_norm(x)
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": (
[encoder_padding_mask]
if encoder_padding_mask is not None and encoder_padding_mask.any()
else []
), # B x T
"encoder_embedding": [], # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [],
"src_lengths": [],
}
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
# self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround.
if (
self._future_mask.size(0) == 0
or (not self._future_mask.device == tensor.device)
or self._future_mask.size(0) < dim
):
self._future_mask = torch.triu(
utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1
)
self._future_mask = self._future_mask.to(tensor)
return self._future_mask[:dim, :dim]
def reorder_encoder_out(self, encoder_out, new_order):
new_encoder_out = (
[]
if len(encoder_out["encoder_out"]) == 0
else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
)
new_encoder_padding_mask = (
[]
if len(encoder_out["encoder_padding_mask"]) == 0
else [
x.index_select(0, new_order)
for x in encoder_out["encoder_padding_mask"]
]
)
new_encoder_embedding = (
[]
if len(encoder_out["encoder_embedding"]) == 0
else [
x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]
]
)
encoder_states = encoder_out["encoder_states"]
if len(encoder_states) > 0:
for idx, state in enumerate(encoder_states):
encoder_states[idx] = state.index_select(1, new_order)
return {
"encoder_out": new_encoder_out, # T x B x C
"encoder_padding_mask": new_encoder_padding_mask, # B x T
"encoder_embedding": new_encoder_embedding, # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [], # B x T
"src_lengths": [], # B x 1
}
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