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#!/usr/bin/env python3
import logging
import math
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import torch
import torch.nn as nn
from fairseq import checkpoint_utils, utils
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import Embedding, TransformerDecoder
from fairseq.modules import (
FairseqDropout,
LayerNorm,
PositionalEmbedding,
TransformerEncoderLayer,
)
from torch import Tensor
logger = logging.getLogger(__name__)
class Conv1dSubsampler(nn.Module):
"""Convolutional subsampler: a stack of 1D convolution (along temporal
dimension) followed by non-linear activation via gated linear units
(https://arxiv.org/abs/1911.08460)
Args:
in_channels (int): the number of input channels
mid_channels (int): the number of intermediate channels
out_channels (int): the number of output channels
kernel_sizes (List[int]): the kernel size for each convolutional layer
"""
def __init__(
self,
in_channels: int,
mid_channels: int,
out_channels: int,
kernel_sizes: List[int] = (3, 3),
):
super(Conv1dSubsampler, self).__init__()
self.n_layers = len(kernel_sizes)
self.conv_layers = nn.ModuleList(
nn.Conv1d(
in_channels if i == 0 else mid_channels // 2,
mid_channels if i < self.n_layers - 1 else out_channels * 2,
k,
stride=2,
padding=k // 2,
)
for i, k in enumerate(kernel_sizes)
)
def get_out_seq_lens_tensor(self, in_seq_lens_tensor):
out = in_seq_lens_tensor.clone()
for _ in range(self.n_layers):
out = ((out.float() - 1) / 2 + 1).floor().long()
return out
def forward(self, src_tokens, src_lengths):
bsz, in_seq_len, _ = src_tokens.size() # B x T x (C x D)
x = src_tokens.transpose(1, 2).contiguous() # -> B x (C x D) x T
for conv in self.conv_layers:
x = conv(x)
x = nn.functional.glu(x, dim=1)
_, _, out_seq_len = x.size()
x = x.transpose(1, 2).transpose(0, 1).contiguous() # -> T x B x (C x D)
return x, self.get_out_seq_lens_tensor(src_lengths)
@register_model("s2t_transformer")
class S2TTransformerModel(FairseqEncoderDecoderModel):
"""Adapted Transformer model (https://arxiv.org/abs/1706.03762) for
speech-to-text tasks. The Transformer encoder/decoder remains the same.
A trainable input subsampler is prepended to the Transformer encoder to
project inputs into the encoder dimension as well as downsample input
sequence for computational efficiency."""
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# input
parser.add_argument(
"--conv-kernel-sizes",
type=str,
metavar="N",
help="kernel sizes of Conv1d subsampling layers",
)
parser.add_argument(
"--conv-channels",
type=int,
metavar="N",
help="# of channels in Conv1d subsampling layers",
)
# Transformer
parser.add_argument(
"--activation-fn",
type=str,
default="relu",
choices=utils.get_available_activation_fns(),
help="activation function to use",
)
parser.add_argument(
"--dropout", type=float, metavar="D", help="dropout probability"
)
parser.add_argument(
"--attention-dropout",
type=float,
metavar="D",
help="dropout probability for attention weights",
)
parser.add_argument(
"--activation-dropout",
"--relu-dropout",
type=float,
metavar="D",
help="dropout probability after activation in FFN.",
)
parser.add_argument(
"--encoder-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension",
)
parser.add_argument(
"--encoder-ffn-embed-dim",
type=int,
metavar="N",
help="encoder embedding dimension for FFN",
)
parser.add_argument(
"--encoder-layers", type=int, metavar="N", help="num encoder layers"
)
parser.add_argument(
"--encoder-attention-heads",
type=int,
metavar="N",
help="num encoder attention heads",
)
parser.add_argument(
"--encoder-normalize-before",
action="store_true",
help="apply layernorm before each encoder block",
)
parser.add_argument(
"--decoder-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension",
)
parser.add_argument(
"--decoder-ffn-embed-dim",
type=int,
metavar="N",
help="decoder embedding dimension for FFN",
)
parser.add_argument(
"--decoder-layers", type=int, metavar="N", help="num decoder layers"
)
parser.add_argument(
"--decoder-attention-heads",
type=int,
metavar="N",
help="num decoder attention heads",
)
parser.add_argument(
"--decoder-normalize-before",
action="store_true",
help="apply layernorm before each decoder block",
)
parser.add_argument(
"--share-decoder-input-output-embed",
action="store_true",
help="share decoder input and output embeddings",
)
parser.add_argument(
"--layernorm-embedding",
action="store_true",
help="add layernorm to embedding",
)
parser.add_argument(
"--no-scale-embedding",
action="store_true",
help="if True, dont scale embeddings",
)
parser.add_argument(
"--load-pretrained-encoder-from",
type=str,
metavar="STR",
help="model to take encoder weights from (for initialization)",
)
parser.add_argument(
'--encoder-freezing-updates',
type=int,
metavar='N',
help='freeze encoder for first N updates'
)
@classmethod
def build_encoder(cls, args):
encoder = S2TTransformerEncoder(args)
pretraining_path = getattr(args, "load_pretrained_encoder_from", None)
if pretraining_path is not None:
if not Path(pretraining_path).exists():
logger.warning(
f"skipped pretraining because {pretraining_path} does not exist"
)
else:
encoder = checkpoint_utils.load_pretrained_component_from_model(
component=encoder, checkpoint=pretraining_path
)
logger.info(f"loaded pretrained encoder from: {pretraining_path}")
return encoder
@classmethod
def build_decoder(cls, args, task, embed_tokens):
return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
def build_embedding(dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
return Embedding(num_embeddings, embed_dim, padding_idx)
decoder_embed_tokens = build_embedding(
task.target_dictionary, args.decoder_embed_dim
)
encoder = cls.build_encoder(args)
decoder = cls.build_decoder(args, task, decoder_embed_tokens)
return cls(encoder, decoder)
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
# net_output['encoder_out'] is a (B, T, D) tensor
lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample)
lprobs.batch_first = True
return lprobs
def forward(self, src_tokens, src_lengths, prev_output_tokens):
"""
The forward method inherited from the base class has a **kwargs
argument in its input, which is not supported in torchscript. This
method overwrites the forward method definition without **kwargs.
"""
encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths)
decoder_out = self.decoder(
prev_output_tokens=prev_output_tokens, encoder_out=encoder_out
)
return decoder_out
class S2TTransformerEncoder(FairseqEncoder):
"""Speech-to-text Transformer encoder that consists of input subsampler and
Transformer encoder."""
def __init__(self, args):
super().__init__(None)
self.encoder_freezing_updates = args.encoder_freezing_updates
self.num_updates = 0
self.dropout_module = FairseqDropout(
p=args.dropout, module_name=self.__class__.__name__
)
self.embed_scale = math.sqrt(args.encoder_embed_dim)
if args.no_scale_embedding:
self.embed_scale = 1.0
self.padding_idx = 1
self.subsample = Conv1dSubsampler(
args.input_feat_per_channel * args.input_channels,
args.conv_channels,
args.encoder_embed_dim,
[int(k) for k in args.conv_kernel_sizes.split(",")],
)
self.embed_positions = PositionalEmbedding(
args.max_source_positions, args.encoder_embed_dim, self.padding_idx
)
self.transformer_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
def _forward(self, src_tokens, src_lengths):
x, input_lengths = self.subsample(src_tokens, src_lengths)
x = self.embed_scale * x
encoder_padding_mask = lengths_to_padding_mask(input_lengths)
positions = self.embed_positions(encoder_padding_mask).transpose(0, 1)
x += positions
x = self.dropout_module(x)
for layer in self.transformer_layers:
x = layer(x, encoder_padding_mask)
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.any() else [], # B x T
"encoder_embedding": [], # B x T x C
"encoder_states": [], # List[T x B x C]
"src_tokens": [],
"src_lengths": [],
}
def forward(self, src_tokens, src_lengths):
if self.num_updates < self.encoder_freezing_updates:
with torch.no_grad():
x = self._forward(src_tokens, src_lengths)
else:
x = self._forward(src_tokens, src_lengths)
return x
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
}
def set_num_updates(self, num_updates):
super().set_num_updates(num_updates)
self.num_updates = num_updates
class TransformerDecoderScriptable(TransformerDecoder):
def extract_features(
self,
prev_output_tokens,
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,
):
# call scriptable method from parent class
x, _ = self.extract_features_scriptable(
prev_output_tokens,
encoder_out,
incremental_state,
full_context_alignment,
alignment_layer,
alignment_heads,
)
return x, None
@register_model_architecture(model_name="s2t_transformer", arch_name="s2t_transformer")
def base_architecture(args):
args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0)
# Convolutional subsampler
args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5")
args.conv_channels = getattr(args, "conv_channels", 1024)
# Transformer
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.dropout = getattr(args, "dropout", 0.1)
args.attention_dropout = getattr(args, "attention_dropout", args.dropout)
args.activation_dropout = getattr(args, "activation_dropout", args.dropout)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
@register_model_architecture("s2t_transformer", "s2t_transformer_s")
def s2t_transformer_s(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
args.dropout = getattr(args, "dropout", 0.1)
base_architecture(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_xs")
def s2t_transformer_xs(args):
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.decoder_layers = getattr(args, "decoder_layers", 3)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4)
args.dropout = getattr(args, "dropout", 0.3)
s2t_transformer_s(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_sp")
def s2t_transformer_sp(args):
args.encoder_layers = getattr(args, "encoder_layers", 16)
s2t_transformer_s(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_m")
def s2t_transformer_m(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.dropout = getattr(args, "dropout", 0.15)
base_architecture(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_mp")
def s2t_transformer_mp(args):
args.encoder_layers = getattr(args, "encoder_layers", 16)
s2t_transformer_m(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_l")
def s2t_transformer_l(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.2)
base_architecture(args)
@register_model_architecture("s2t_transformer", "s2t_transformer_lp")
def s2t_transformer_lp(args):
args.encoder_layers = getattr(args, "encoder_layers", 16)
s2t_transformer_l(args)
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