espnet2/asr/decoder/rnn_decoder.py

import random

import numpy as np
import torch
import torch.nn.functional as F
from typeguard import check_argument_types

from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
from espnet.nets.pytorch_backend.nets_utils import to_device
from espnet.nets.pytorch_backend.rnn.attentions import initial_att
from espnet2.asr.decoder.abs_decoder import AbsDecoder
from espnet2.utils.get_default_kwargs import get_default_kwargs


def build_attention_list(
    eprojs: int,
    dunits: int,
    atype: str = "location",
    num_att: int = 1,
    num_encs: int = 1,
    aheads: int = 4,
    adim: int = 320,
    awin: int = 5,
    aconv_chans: int = 10,
    aconv_filts: int = 100,
    han_mode: bool = False,
    han_type=None,
    han_heads: int = 4,
    han_dim: int = 320,
    han_conv_chans: int = -1,
    han_conv_filts: int = 100,
    han_win: int = 5,
):

    att_list = torch.nn.ModuleList()
    if num_encs == 1:
        for i in range(num_att):
            att = initial_att(
                atype,
                eprojs,
                dunits,
                aheads,
                adim,
                awin,
                aconv_chans,
                aconv_filts,
            )
            att_list.append(att)
    elif num_encs > 1:  # no multi-speaker mode
        if han_mode:
            att = initial_att(
                han_type,
                eprojs,
                dunits,
                han_heads,
                han_dim,
                han_win,
                han_conv_chans,
                han_conv_filts,
                han_mode=True,
            )
            return att
        else:
            att_list = torch.nn.ModuleList()
            for idx in range(num_encs):
                att = initial_att(
                    atype[idx],
                    eprojs,
                    dunits,
                    aheads[idx],
                    adim[idx],
                    awin[idx],
                    aconv_chans[idx],
                    aconv_filts[idx],
                )
                att_list.append(att)
    else:
        raise ValueError(
            "Number of encoders needs to be more than one. {}".format(num_encs)
        )
    return att_list


class RNNDecoder(AbsDecoder):
    def __init__(
        self,
        vocab_size: int,
        encoder_output_size: int,
        rnn_type: str = "lstm",
        num_layers: int = 1,
        hidden_size: int = 320,
        sampling_probability: float = 0.0,
        dropout: float = 0.0,
        context_residual: bool = False,
        replace_sos: bool = False,
        num_encs: int = 1,
        att_conf: dict = get_default_kwargs(build_attention_list),
    ):
        # FIXME(kamo): The parts of num_spk should be refactored more more more
        assert check_argument_types()
        if rnn_type not in {"lstm", "gru"}:
            raise ValueError(f"Not supported: rnn_type={rnn_type}")

        super().__init__()
        eprojs = encoder_output_size
        self.dtype = rnn_type
        self.dunits = hidden_size
        self.dlayers = num_layers
        self.context_residual = context_residual
        self.sos = vocab_size - 1
        self.eos = vocab_size - 1
        self.odim = vocab_size
        self.sampling_probability = sampling_probability
        self.dropout = dropout
        self.num_encs = num_encs

        # for multilingual translation
        self.replace_sos = replace_sos

        self.embed = torch.nn.Embedding(vocab_size, hidden_size)
        self.dropout_emb = torch.nn.Dropout(p=dropout)

        self.decoder = torch.nn.ModuleList()
        self.dropout_dec = torch.nn.ModuleList()
        self.decoder += [
            torch.nn.LSTMCell(hidden_size + eprojs, hidden_size)
            if self.dtype == "lstm"
            else torch.nn.GRUCell(hidden_size + eprojs, hidden_size)
        ]
        self.dropout_dec += [torch.nn.Dropout(p=dropout)]
        for _ in range(1, self.dlayers):
            self.decoder += [
                torch.nn.LSTMCell(hidden_size, hidden_size)
                if self.dtype == "lstm"
                else torch.nn.GRUCell(hidden_size, hidden_size)
            ]
            self.dropout_dec += [torch.nn.Dropout(p=dropout)]
            # NOTE: dropout is applied only for the vertical connections
            # see https://arxiv.org/pdf/1409.2329.pdf

        if context_residual:
            self.output = torch.nn.Linear(hidden_size + eprojs, vocab_size)
        else:
            self.output = torch.nn.Linear(hidden_size, vocab_size)

        self.att_list = build_attention_list(
            eprojs=eprojs, dunits=hidden_size, **att_conf
        )

    def zero_state(self, hs_pad):
        return hs_pad.new_zeros(hs_pad.size(0), self.dunits)

    def rnn_forward(self, ey, z_list, c_list, z_prev, c_prev):
        if self.dtype == "lstm":
            z_list[0], c_list[0] = self.decoder[0](ey, (z_prev[0], c_prev[0]))
            for i in range(1, self.dlayers):
                z_list[i], c_list[i] = self.decoder[i](
                    self.dropout_dec[i - 1](z_list[i - 1]),
                    (z_prev[i], c_prev[i]),
                )
        else:
            z_list[0] = self.decoder[0](ey, z_prev[0])
            for i in range(1, self.dlayers):
                z_list[i] = self.decoder[i](
                    self.dropout_dec[i - 1](z_list[i - 1]), z_prev[i]
                )
        return z_list, c_list

    def forward(self, hs_pad, hlens, ys_in_pad, ys_in_lens, strm_idx=0):
        # to support mutiple encoder asr mode, in single encoder mode,
        # convert torch.Tensor to List of torch.Tensor
        if self.num_encs == 1:
            hs_pad = [hs_pad]
            hlens = [hlens]

        # attention index for the attention module
        # in SPA (speaker parallel attention),
        # att_idx is used to select attention module. In other cases, it is 0.
        att_idx = min(strm_idx, len(self.att_list) - 1)

        # hlens should be list of list of integer
        hlens = [list(map(int, hlens[idx])) for idx in range(self.num_encs)]

        # get dim, length info
        olength = ys_in_pad.size(1)

        # initialization
        c_list = [self.zero_state(hs_pad[0])]
        z_list = [self.zero_state(hs_pad[0])]
        for _ in range(1, self.dlayers):
            c_list.append(self.zero_state(hs_pad[0]))
            z_list.append(self.zero_state(hs_pad[0]))
        z_all = []
        if self.num_encs == 1:
            att_w = None
            self.att_list[att_idx].reset()  # reset pre-computation of h
        else:
            att_w_list = [None] * (self.num_encs + 1)  # atts + han
            att_c_list = [None] * self.num_encs  # atts
            for idx in range(self.num_encs + 1):
                # reset pre-computation of h in atts and han
                self.att_list[idx].reset()

        # pre-computation of embedding
        eys = self.dropout_emb(self.embed(ys_in_pad))  # utt x olen x zdim

        # loop for an output sequence
        for i in range(olength):
            if self.num_encs == 1:
                att_c, att_w = self.att_list[att_idx](
                    hs_pad[0], hlens[0], self.dropout_dec[0](z_list[0]), att_w
                )
            else:
                for idx in range(self.num_encs):
                    att_c_list[idx], att_w_list[idx] = self.att_list[idx](
                        hs_pad[idx],
                        hlens[idx],
                        self.dropout_dec[0](z_list[0]),
                        att_w_list[idx],
                    )
                hs_pad_han = torch.stack(att_c_list, dim=1)
                hlens_han = [self.num_encs] * len(ys_in_pad)
                att_c, att_w_list[self.num_encs] = self.att_list[self.num_encs](
                    hs_pad_han,
                    hlens_han,
                    self.dropout_dec[0](z_list[0]),
                    att_w_list[self.num_encs],
                )
            if i > 0 and random.random() < self.sampling_probability:
                z_out = self.output(z_all[-1])
                z_out = np.argmax(z_out.detach().cpu(), axis=1)
                z_out = self.dropout_emb(self.embed(to_device(self, z_out)))
                ey = torch.cat((z_out, att_c), dim=1)  # utt x (zdim + hdim)
            else:
                # utt x (zdim + hdim)
                ey = torch.cat((eys[:, i, :], att_c), dim=1)
            z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_list, c_list)
            if self.context_residual:
                z_all.append(
                    torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1)
                )  # utt x (zdim + hdim)
            else:
                z_all.append(self.dropout_dec[-1](z_list[-1]))  # utt x (zdim)

        z_all = torch.stack(z_all, dim=1)
        z_all = self.output(z_all)
        z_all.masked_fill_(
            make_pad_mask(ys_in_lens, z_all, 1),
            0,
        )
        return z_all, ys_in_lens

    def init_state(self, x):
        # to support mutiple encoder asr mode, in single encoder mode,
        # convert torch.Tensor to List of torch.Tensor
        if self.num_encs == 1:
            x = [x]

        c_list = [self.zero_state(x[0].unsqueeze(0))]
        z_list = [self.zero_state(x[0].unsqueeze(0))]
        for _ in range(1, self.dlayers):
            c_list.append(self.zero_state(x[0].unsqueeze(0)))
            z_list.append(self.zero_state(x[0].unsqueeze(0)))
        # TODO(karita): support strm_index for `asr_mix`
        strm_index = 0
        att_idx = min(strm_index, len(self.att_list) - 1)
        if self.num_encs == 1:
            a = None
            self.att_list[att_idx].reset()  # reset pre-computation of h
        else:
            a = [None] * (self.num_encs + 1)  # atts + han
            for idx in range(self.num_encs + 1):
                # reset pre-computation of h in atts and han
                self.att_list[idx].reset()
        return dict(
            c_prev=c_list[:],
            z_prev=z_list[:],
            a_prev=a,
            workspace=(att_idx, z_list, c_list),
        )

    def score(self, yseq, state, x):
        # to support mutiple encoder asr mode, in single encoder mode,
        # convert torch.Tensor to List of torch.Tensor
        if self.num_encs == 1:
            x = [x]

        att_idx, z_list, c_list = state["workspace"]
        vy = yseq[-1].unsqueeze(0)
        ey = self.dropout_emb(self.embed(vy))  # utt list (1) x zdim
        if self.num_encs == 1:
            att_c, att_w = self.att_list[att_idx](
                x[0].unsqueeze(0),
                [x[0].size(0)],
                self.dropout_dec[0](state["z_prev"][0]),
                state["a_prev"],
            )
        else:
            att_w = [None] * (self.num_encs + 1)  # atts + han
            att_c_list = [None] * self.num_encs  # atts
            for idx in range(self.num_encs):
                att_c_list[idx], att_w[idx] = self.att_list[idx](
                    x[idx].unsqueeze(0),
                    [x[idx].size(0)],
                    self.dropout_dec[0](state["z_prev"][0]),
                    state["a_prev"][idx],
                )
            h_han = torch.stack(att_c_list, dim=1)
            att_c, att_w[self.num_encs] = self.att_list[self.num_encs](
                h_han,
                [self.num_encs],
                self.dropout_dec[0](state["z_prev"][0]),
                state["a_prev"][self.num_encs],
            )
        ey = torch.cat((ey, att_c), dim=1)  # utt(1) x (zdim + hdim)
        z_list, c_list = self.rnn_forward(
            ey, z_list, c_list, state["z_prev"], state["c_prev"]
        )
        if self.context_residual:
            logits = self.output(
                torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1)
            )
        else:
            logits = self.output(self.dropout_dec[-1](z_list[-1]))
        logp = F.log_softmax(logits, dim=1).squeeze(0)
        return (
            logp,
            dict(
                c_prev=c_list[:],
                z_prev=z_list[:],
                a_prev=att_w,
                workspace=(att_idx, z_list, c_list),
            ),
        )