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# --------------------------------------------------------
# ArTST: Arabic Text and Speech Transformer (https://arxiv.org/abs/2310.16621)
# Github source: https://github.com/mbzuai-nlp/ArTST
# Based on speecht5, fairseq and espnet code bases
# https://github.com/microsoft/SpeechT5/tree/main/SpeechT5; https://github.com/pytorch/fairseq; https://github.com/espnet/espnet
# --------------------------------------------------------
from typing import Any, Dict, List, Optional
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
FairseqIncrementalDecoder,
)
from fairseq.modules import (
FairseqDropout,
LayerDropModuleList,
LayerNorm,
)
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
from torch import Tensor
from .encoder import RelativePositionalEncoding
from .transformer_layer import TransformerDecoderLayer
DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8)
class TransformerDecoder(FairseqIncrementalDecoder):
"""
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,
no_encoder_attn=False,
):
self.args = args
super().__init__(None)
self.register_buffer("version", torch.Tensor([3]))
self._future_mask = torch.empty(0)
self.dropout_module = FairseqDropout(
args.dropout, module_name=self.__class__.__name__
)
self.decoder_layerdrop = args.decoder_layerdrop
# self.max_s_positions = args.max_target_positions
export = getattr(args, "export", False)
self.cross_self_attention = getattr(args, "cross_self_attention", False)
if self.decoder_layerdrop > 0.0:
self.layers = LayerDropModuleList(p=self.decoder_layerdrop)
else:
self.layers = nn.ModuleList([])
self.layers.extend(
[
self.build_decoder_layer(args, no_encoder_attn)
for _ in range(args.decoder_layers)
]
)
self.num_layers = len(self.layers)
if args.decoder_normalize_before and not getattr(
args, "no_decoder_final_norm", False
):
self.layer_norm = LayerNorm(args.decoder_embed_dim, eps=args.layer_norm_eps, export=export)
else:
self.layer_norm = None
if args.relative_position_embedding:
self.pos_emb = RelativePositionalEncoding(args.encoder_embed_dim//args.encoder_attention_heads, args.decoder_max_relative_position)
def build_decoder_layer(self, args, no_encoder_attn=False):
layer = TransformerDecoderLayer(args, no_encoder_attn=no_encoder_attn, has_relative_attention_bias=args.relative_position_embedding)
checkpoint = getattr(args, "checkpoint_activations", False)
if checkpoint:
offload_to_cpu = getattr(args, "offload_activations", False)
layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
# if we are checkpointing, enforce that FSDP always wraps the
# checkpointed layer, regardless of layer size
min_params_to_wrap = (
getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
if not checkpoint
else 0
)
layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
return layer
def forward(
self,
prev_output_tokens,
tgt_mask,
encoder_out: Optional[Dict[str, List[Tensor]]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
src_lengths: Optional[Any] = None,
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).
full_context_alignment (bool, optional): don't apply
auto-regressive mask to self-attention (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,
tgt_mask,
encoder_out=encoder_out,
incremental_state=incremental_state,
full_context_alignment=full_context_alignment,
alignment_layer=alignment_layer,
alignment_heads=alignment_heads,
)
return x, extra
def extract_features(
self,
prev_output_tokens,
tgt_mask,
encoder_out: Optional[Dict[str, List[Tensor]]],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
):
return self.extract_features_scriptable(
prev_output_tokens,
tgt_mask,
encoder_out,
incremental_state,
full_context_alignment,
alignment_layer,
alignment_heads,
)
"""
A scriptable subclass of this class has an extract_features method and calls
super().extract_features, but super() is not supported in torchscript. A copy of
this function is made to be used in the subclass instead.
"""
def extract_features_scriptable(
self,
prev_output_tokens,
tgt_mask,
encoder_out: Optional[Dict[str, List[Tensor]]],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = 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).
Args:
full_context_alignment (bool, optional): don't apply
auto-regressive mask to self-attention (default: False).
alignment_layer (int, optional): return mean alignment over
heads at this layer (default: last layer).
alignment_heads (int, optional): only average alignment over
this many heads (default: all heads).
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
bs = prev_output_tokens.size(0)
if alignment_layer is None:
alignment_layer = self.num_layers - 1
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]
# B x T x C -> T x B x C
x = prev_output_tokens.transpose(0, 1)
self_attn_padding_mask: Optional[Tensor] = None
if self.cross_self_attention or tgt_mask is not None:
self_attn_padding_mask = tgt_mask
## relative position embedding
if self.args.relative_position_embedding:
x_len = x.shape[0]
pos_seq = torch.arange(0, x_len).long().to(x.device)
pos_seq = pos_seq[:, None] - pos_seq[None, :]
pos_k, pos_v = self.pos_emb(pos_seq)
else:
pos_k = None
# decoder layers
attn_list = []
attn: Optional[Tensor] = None
inner_states: List[Optional[Tensor]] = [x]
for idx, layer in enumerate(self.layers):
if incremental_state is None and not full_context_alignment:
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=bool((idx == alignment_layer or alignment_layer == -1)),
need_head_weights=bool((idx == alignment_layer or alignment_layer == -1)),
pos_bias=pos_k,
)
inner_states.append(x)
if layer_attn is not None and (idx == alignment_layer or alignment_layer == -1):
attn = layer_attn.float().to(x)
attn_list.append(attn.transpose(0, 1))
if attn is not None and len(attn_list) == 1:
if alignment_heads is not None:
attn = attn[:alignment_heads]
# average probabilities over heads
attn = attn.mean(dim=0)
if self.layer_norm is not None:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
return x, {"attn": [attn if len(attn_list) <= 1 else attn_list], "inner_states": inner_states}
# def max_positions(self):
# """Maximum output length supported by the decoder."""
# return self.max_target_positions
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], device=tensor.device)), 1,
)
else:
self._future_mask = self._future_mask.to(tensor)
return self._future_mask[:dim, :dim]
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
for i in range(self.num_layers):
# update layer norms
layer_norm_map = {
"0": "self_attn_layer_norm",
"1": "encoder_attn_layer_norm",
"2": "final_layer_norm",
}
for old, new in layer_norm_map.items():
for m in ("weight", "bias"):
k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m)
if k in state_dict:
state_dict[
"{}.layers.{}.{}.{}".format(name, i, new, m)
] = state_dict[k]
del state_dict[k]
version_key = "{}.version".format(name)
if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2:
# earlier checkpoints did not normalize after the stack of layers
self.layer_norm = None
self.normalize = False
state_dict[version_key] = torch.Tensor([1])
return state_dict
def set_num_updates(self, num_updates):
"""State from trainer to pass along to model at every update."""
def _apply(m):
if hasattr(m, "set_num_updates") and m != self:
m.set_num_updates(num_updates)
self.apply(_apply)
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