wehub-resource-sync
448 lines
16 KiB
Python
448 lines
16 KiB
Python
#!/usr/bin/env python3
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import logging
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import math
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from typing import Dict, List, Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from fairseq.data.data_utils import lengths_to_padding_mask
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from fairseq import checkpoint_utils, utils
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from fairseq.models import (
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FairseqEncoder,
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FairseqEncoderDecoderModel,
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register_model,
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register_model_architecture,
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)
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from fairseq.models.transformer import Embedding, TransformerDecoder
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from fairseq.modules import LayerNorm, PositionalEmbedding, TransformerEncoderLayer
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from torch import Tensor
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logger = logging.getLogger(__name__)
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@register_model("convtransformer")
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class ConvTransformerModel(FairseqEncoderDecoderModel):
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"""
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Transformer-based Speech translation model from ESPNet-ST
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https://arxiv.org/abs/2004.10234
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"""
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def __init__(self, encoder, decoder):
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super().__init__(encoder, decoder)
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@staticmethod
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def add_args(parser):
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"""Add model-specific arguments to the parser."""
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parser.add_argument(
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"--input-feat-per-channel",
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type=int,
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metavar="N",
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help="encoder input dimension per input channel",
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)
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parser.add_argument(
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"--activation-fn",
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choices=utils.get_available_activation_fns(),
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help="activation function to use",
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)
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parser.add_argument(
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"--dropout", type=float, metavar="D", help="dropout probability"
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)
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parser.add_argument(
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"--attention-dropout",
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type=float,
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metavar="D",
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help="dropout probability for attention weights",
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)
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parser.add_argument(
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"--activation-dropout",
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"--relu-dropout",
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type=float,
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metavar="D",
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help="dropout probability after activation in FFN.",
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)
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parser.add_argument(
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"--encoder-embed-dim",
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type=int,
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metavar="N",
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help="encoder embedding dimension",
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)
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parser.add_argument(
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"--encoder-ffn-embed-dim",
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type=int,
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metavar="N",
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help="encoder embedding dimension for FFN",
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)
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parser.add_argument(
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"--encoder-layers", type=int, metavar="N", help="num encoder layers"
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)
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parser.add_argument(
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"--encoder-attention-heads",
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type=int,
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metavar="N",
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help="num encoder attention heads",
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)
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parser.add_argument(
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"--encoder-normalize-before",
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action="store_true",
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help="apply layernorm before each encoder block",
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)
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parser.add_argument(
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"--decoder-embed-dim",
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type=int,
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metavar="N",
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help="decoder embedding dimension",
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)
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parser.add_argument(
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"--decoder-ffn-embed-dim",
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type=int,
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metavar="N",
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help="decoder embedding dimension for FFN",
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)
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parser.add_argument(
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"--decoder-layers", type=int, metavar="N", help="num decoder layers"
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)
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parser.add_argument(
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"--decoder-attention-heads",
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type=int,
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metavar="N",
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help="num decoder attention heads",
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)
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parser.add_argument(
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"--decoder-normalize-before",
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action="store_true",
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help="apply layernorm before each decoder block",
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)
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parser.add_argument(
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"--decoder-output-dim",
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type=int,
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metavar="N",
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help="decoder output dimension (extra linear layer if different from decoder embed dim)",
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)
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parser.add_argument(
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"--share-decoder-input-output-embed",
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action="store_true",
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help="share decoder input and output embeddings",
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)
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parser.add_argument(
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"--layernorm-embedding",
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action="store_true",
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help="add layernorm to embedding",
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)
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parser.add_argument(
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"--no-scale-embedding",
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action="store_true",
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help="if True, dont scale embeddings",
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)
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parser.add_argument(
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"--load-pretrained-encoder-from",
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type=str,
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metavar="STR",
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help="model to take encoder weights from (for initialization)",
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)
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parser.add_argument(
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"--load-pretrained-decoder-from",
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type=str,
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metavar="STR",
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help="model to take decoder weights from (for initialization)",
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)
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parser.add_argument(
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"--conv-out-channels",
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type=int,
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metavar="INT",
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help="the number of output channels of conv layer",
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)
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@classmethod
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def build_encoder(cls, args):
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encoder = ConvTransformerEncoder(args)
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if getattr(args, "load_pretrained_encoder_from", None):
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encoder = checkpoint_utils.load_pretrained_component_from_model(
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component=encoder, checkpoint=args.load_pretrained_encoder_from
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)
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return encoder
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@classmethod
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def build_decoder(cls, args, task, embed_tokens):
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decoder = TransformerDecoderNoExtra(args, task.target_dictionary, embed_tokens)
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if getattr(args, "load_pretrained_decoder_from", None):
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decoder = checkpoint_utils.load_pretrained_component_from_model(
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component=decoder, checkpoint=args.load_pretrained_decoder_from
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)
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return decoder
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@classmethod
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def build_model(cls, args, task):
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"""Build a new model instance."""
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# make sure all arguments are present in older models
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base_architecture(args)
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def build_embedding(dictionary, embed_dim):
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num_embeddings = len(dictionary)
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padding_idx = dictionary.pad()
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return Embedding(num_embeddings, embed_dim, padding_idx)
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decoder_embed_tokens = build_embedding(
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task.target_dictionary, args.decoder_embed_dim
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)
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encoder = cls.build_encoder(args)
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decoder = cls.build_decoder(args, task, decoder_embed_tokens)
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return cls(encoder, decoder)
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@staticmethod
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@torch.jit.unused
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def set_batch_first(lprobs):
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lprobs.batch_first = True
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def get_normalized_probs(
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self,
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net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
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log_probs: bool,
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sample: Optional[Dict[str, Tensor]] = None,
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):
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# net_output['encoder_out'] is a (B, T, D) tensor
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lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample)
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if self.training:
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self.set_batch_first(lprobs)
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return lprobs
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def output_layout(self):
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return "BTD"
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"""
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The forward method inherited from the base class has a **kwargs argument in
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its input, which is not supported in torchscript. This method overrites the forward
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method definition without **kwargs.
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"""
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def forward(self, src_tokens, src_lengths, prev_output_tokens):
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encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths)
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decoder_out = self.decoder(
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prev_output_tokens=prev_output_tokens, encoder_out=encoder_out
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)
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return decoder_out
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class ConvTransformerEncoder(FairseqEncoder):
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"""Conv + Transformer encoder"""
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def __init__(self, args):
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"""Construct an Encoder object."""
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super().__init__(None)
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self.dropout = args.dropout
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self.embed_scale = (
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1.0 if args.no_scale_embedding else math.sqrt(args.encoder_embed_dim)
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)
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self.padding_idx = 1
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self.in_channels = 1
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self.input_dim = args.input_feat_per_channel
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, args.conv_out_channels, 3, stride=2, padding=3 // 2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(
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args.conv_out_channels,
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args.conv_out_channels,
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3,
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stride=2,
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padding=3 // 2,
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),
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torch.nn.ReLU(),
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)
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transformer_input_dim = self.infer_conv_output_dim(
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self.in_channels, self.input_dim, args.conv_out_channels
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)
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self.out = torch.nn.Linear(transformer_input_dim, args.encoder_embed_dim)
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self.embed_positions = PositionalEmbedding(
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args.max_source_positions,
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args.encoder_embed_dim,
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self.padding_idx,
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learned=False,
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)
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self.transformer_layers = nn.ModuleList([])
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self.transformer_layers.extend(
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[TransformerEncoderLayer(args) for i in range(args.encoder_layers)]
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)
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if args.encoder_normalize_before:
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self.layer_norm = LayerNorm(args.encoder_embed_dim)
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else:
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self.layer_norm = None
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def pooling_ratio(self):
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return 4
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def infer_conv_output_dim(self, in_channels, input_dim, out_channels):
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sample_seq_len = 200
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sample_bsz = 10
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x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim)
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x = torch.nn.Conv2d(1, out_channels, 3, stride=2, padding=3 // 2)(x)
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x = torch.nn.Conv2d(out_channels, out_channels, 3, stride=2, padding=3 // 2)(x)
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x = x.transpose(1, 2)
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mb, seq = x.size()[:2]
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return x.contiguous().view(mb, seq, -1).size(-1)
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def forward(self, src_tokens, src_lengths):
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"""Encode input sequence.
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:param torch.Tensor xs: input tensor
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:param torch.Tensor masks: input mask
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:return: position embedded tensor and mask
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:rtype Tuple[torch.Tensor, torch.Tensor]:
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"""
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bsz, max_seq_len, _ = src_tokens.size()
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x = (
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src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim)
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.transpose(1, 2)
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.contiguous()
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)
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x = self.conv(x)
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bsz, _, output_seq_len, _ = x.size()
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x = x.transpose(1, 2).transpose(0, 1).contiguous().view(output_seq_len, bsz, -1)
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x = self.out(x)
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x = self.embed_scale * x
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subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5)
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input_lengths = torch.min(
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(src_lengths.float() / subsampling_factor).ceil().long(),
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x.size(0) * src_lengths.new_ones([src_lengths.size(0)]).long()
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)
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encoder_padding_mask = lengths_to_padding_mask(input_lengths)
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positions = self.embed_positions(encoder_padding_mask).transpose(0, 1)
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x += positions
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x = F.dropout(x, p=self.dropout, training=self.training)
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for layer in self.transformer_layers:
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x = layer(x, encoder_padding_mask)
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if not encoder_padding_mask.any():
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maybe_encoder_padding_mask = None
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else:
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maybe_encoder_padding_mask = encoder_padding_mask
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return {
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"encoder_out": [x],
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"encoder_padding_mask": [maybe_encoder_padding_mask]
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if maybe_encoder_padding_mask is not None
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else [],
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"encoder_embedding": [],
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"encoder_states": [],
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"src_tokens": [],
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"src_lengths": [],
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}
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@torch.jit.export
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def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
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"""
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Reorder encoder output according to *new_order*.
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Args:
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encoder_out: output from the ``forward()`` method
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new_order (LongTensor): desired order
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Returns:
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*encoder_out* rearranged according to *new_order*
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"""
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new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
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if len(encoder_out["encoder_padding_mask"]) == 0:
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new_encoder_padding_mask = []
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else:
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new_encoder_padding_mask = [
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(encoder_out["encoder_padding_mask"][0]).index_select(0, new_order)
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]
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if len(encoder_out["encoder_embedding"]) == 0:
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new_encoder_embedding = []
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else:
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new_encoder_embedding = [
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(encoder_out["encoder_embedding"][0]).index_select(0, new_order)
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]
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encoder_states = encoder_out["encoder_states"]
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if len(encoder_states) > 0:
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for idx, state in enumerate(encoder_states):
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encoder_states[idx] = state.index_select(1, new_order)
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return {
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"encoder_out": new_encoder_out,
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"encoder_padding_mask": new_encoder_padding_mask,
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"encoder_embedding": new_encoder_embedding,
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"encoder_states": encoder_states,
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"src_tokens": [],
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"src_lengths": [],
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}
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class TransformerDecoderNoExtra(TransformerDecoder):
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def extract_features(
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self,
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prev_output_tokens,
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encoder_out: Optional[Dict[str, List[Tensor]]],
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incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
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full_context_alignment: bool = False,
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alignment_layer: Optional[int] = None,
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alignment_heads: Optional[int] = None,
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):
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# call scriptable method from parent class
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x, _ = self.extract_features_scriptable(
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prev_output_tokens,
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encoder_out,
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incremental_state,
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full_context_alignment,
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alignment_layer,
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alignment_heads,
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)
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return x, None
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@register_model_architecture(model_name="convtransformer", arch_name="convtransformer")
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def base_architecture(args):
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args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
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args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
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args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
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args.encoder_layers = getattr(args, "encoder_layers", 6)
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args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
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args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
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args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
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args.decoder_ffn_embed_dim = getattr(
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args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
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)
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args.decoder_layers = getattr(args, "decoder_layers", 6)
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args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
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args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
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args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
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args.attention_dropout = getattr(args, "attention_dropout", 0.0)
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args.activation_dropout = getattr(args, "activation_dropout", 0.0)
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args.activation_fn = getattr(args, "activation_fn", "relu")
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args.dropout = getattr(args, "dropout", 0.1)
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args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
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args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
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args.share_decoder_input_output_embed = getattr(
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args, "share_decoder_input_output_embed", False
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)
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args.no_token_positional_embeddings = getattr(
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args, "no_token_positional_embeddings", False
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)
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args.adaptive_input = getattr(args, "adaptive_input", False)
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args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
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args.decoder_output_dim = getattr(
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args, "decoder_output_dim", args.decoder_embed_dim
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)
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args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
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args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
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args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
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args.max_source_positions = getattr(args, "max_source_positions", 3000)
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args.max_target_positions = getattr(args, "max_target_positions", 1024)
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args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
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args.conv_out_channels = getattr(args, "conv_out_channels", args.encoder_embed_dim)
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@register_model_architecture("convtransformer", "convtransformer_espnet")
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def convtransformer_espnet(args):
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args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
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args.encoder_layers = getattr(args, "encoder_layers", 12)
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args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
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args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
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