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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Contents of this file were adapted from the open source fairseq repository.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Any, Dict, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from esm.modules import SinusoidalPositionalEmbedding
from .transformer_layer import TransformerDecoderLayer
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float("-inf")).type_as(t)
class TransformerDecoder(nn.Module):
"""
Transformer decoder consisting of *args.decoder.layers* layers. Each layer
is a :class:`TransformerDecoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): decoding dictionary
embed_tokens (torch.nn.Embedding): output embedding
no_encoder_attn (bool, optional): whether to attend to encoder outputs
(default: False).
"""
def __init__(
self,
args,
dictionary,
embed_tokens,
):
super().__init__()
self.args = args
self.dictionary = dictionary
self._future_mask = torch.empty(0)
self.dropout_module = nn.Dropout(args.dropout)
input_embed_dim = embed_tokens.embedding_dim
embed_dim = args.decoder_embed_dim
self.embed_dim = embed_dim
self.padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.project_in_dim = (
nn.Linear(input_embed_dim, embed_dim, bias=False)
if embed_dim != input_embed_dim
else None
)
self.embed_positions = SinusoidalPositionalEmbedding(
embed_dim,
self.padding_idx,
)
self.layers = nn.ModuleList([])
self.layers.extend(
[
self.build_decoder_layer(args)
for _ in range(args.decoder_layers)
]
)
self.num_layers = len(self.layers)
self.layer_norm = nn.LayerNorm(embed_dim)
self.build_output_projection(args, dictionary)
def build_output_projection(self, args, dictionary):
self.output_projection = nn.Linear(
args.decoder_embed_dim, len(dictionary), bias=False
)
nn.init.normal_(
self.output_projection.weight, mean=0, std=args.decoder_embed_dim ** -0.5
)
def build_decoder_layer(self, args):
return TransformerDecoderLayer(args)
def forward(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
features_only: bool = False,
return_all_hiddens: bool = False,
):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (optional): output from the encoder, used for
encoder-side attention, should be of size T x B x C
incremental_state (dict): dictionary used for storing state during
:ref:`Incremental decoding`
features_only (bool, optional): only return features without
applying output layer (default: False).
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
x, extra = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
incremental_state=incremental_state,
)
if not features_only:
x = self.output_layer(x)
x = x.transpose(1, 2) # B x T x C -> B x C x T
return x, extra
def extract_features(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
):
"""
Similar to *forward* but only return features.
Includes several features from "Jointly Learning to Align and
Translate with Transformer Models" (Garg et al., EMNLP 2019).
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
bs, slen = prev_output_tokens.size()
enc: Optional[Tensor] = None
padding_mask: Optional[Tensor] = None
if encoder_out is not None and len(encoder_out["encoder_out"]) > 0:
enc = encoder_out["encoder_out"][0]
assert (
enc.size()[1] == bs
), f"Expected enc.shape == (t, {bs}, c) got {enc.shape}"
if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0:
padding_mask = encoder_out["encoder_padding_mask"][0]
# embed positions
positions = self.embed_positions(
prev_output_tokens
)
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
positions = positions[:, -1:]
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
x += positions
x = self.dropout_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
self_attn_padding_mask: Optional[Tensor] = None
if prev_output_tokens.eq(self.padding_idx).any():
self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
# decoder layers
attn: Optional[Tensor] = None
inner_states: List[Optional[Tensor]] = [x]
for idx, layer in enumerate(self.layers):
if incremental_state is None:
self_attn_mask = self.buffered_future_mask(x)
else:
self_attn_mask = None
x, layer_attn, _ = layer(
x,
enc,
padding_mask,
incremental_state,
self_attn_mask=self_attn_mask,
self_attn_padding_mask=self_attn_padding_mask,
need_attn=False,
need_head_weights=False,
)
inner_states.append(x)
if self.layer_norm is not None:
x = self.layer_norm(x)
# T x B x C -> B x C x T
x = x.transpose(0, 1)
return x, {"inner_states": inner_states}
def output_layer(self, features):
"""Project features to the vocabulary size."""
return self.output_projection(features)
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(
fill_with_neg_inf(torch.zeros([dim, dim])), 1
)
self._future_mask = self._future_mask.to(tensor)
return self._future_mask[:dim, :dim]
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