TTS/tts/layers/tortoise/arch_utils.py

import functools
import math

import fsspec
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import LogitsWarper

from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
from TTS.utils.generic_utils import is_pytorch_at_least_2_4


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


class GroupNorm32(nn.GroupNorm):
    def forward(self, x):
        return super().forward(x.float()).type(x.dtype)


def normalization(channels):
    """
    Make a standard normalization layer.

    :param channels: number of input channels.
    :return: an nn.Module for normalization.
    """
    groups = 32
    if channels <= 16:
        groups = 8
    elif channels <= 64:
        groups = 16
    while channels % groups != 0:
        groups = int(groups / 2)
    assert groups > 2
    return GroupNorm32(groups, channels)


class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/output heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv, mask=None, rel_pos=None):
        """
        Apply QKV attention.

        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
        if rel_pos is not None:
            weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(
                bs * self.n_heads, weight.shape[-2], weight.shape[-1]
            )
        if mask is not None:
            mask = mask.repeat(self.n_heads, 1, 1)
            weight[mask.logical_not()] = -torch.inf
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = torch.einsum("bts,bcs->bct", weight, v)

        return a.reshape(bs, -1, length)


class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other.

    Originally ported from here, but adapted to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        *,
        relative_pos_embeddings=False,
        tortoise_norm=False,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.norm = normalization(channels)
        self.qkv = nn.Conv1d(channels, channels * 3, 1)
        # split heads before split qkv
        self.attention = QKVAttentionLegacy(self.num_heads)
        self.tortoise_norm = tortoise_norm

        self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
        if relative_pos_embeddings:
            self.relative_pos_embeddings = RelativePositionBias(
                scale=(channels // self.num_heads) ** 0.5,
                causal=False,
                heads=num_heads,
                num_buckets=32,
                max_distance=64,
            )
        else:
            self.relative_pos_embeddings = None

    def forward(self, x, mask=None):
        b, c, *spatial = x.shape
        x = x.reshape(b, c, -1)
        x_norm = self.norm(x)
        qkv = self.qkv(x_norm)
        h = self.attention(qkv, mask, self.relative_pos_embeddings)
        h = self.proj_out(h)
        if self.tortoise_norm:
            return (x + h).reshape(b, c, *spatial)
        return (x_norm + h).reshape(b, c, *spatial)


class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.

    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    """

    def __init__(self, channels, use_conv, out_channels=None, factor=4):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.factor = factor
        if use_conv:
            ksize = 5
            pad = 2
            self.conv = nn.Conv1d(self.channels, self.out_channels, ksize, padding=pad)

    def forward(self, x):
        assert x.shape[1] == self.channels
        x = F.interpolate(x, scale_factor=self.factor, mode="nearest")
        if self.use_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.

    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    """

    def __init__(self, channels, use_conv, out_channels=None, factor=4, ksize=5, pad=2):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv

        stride = factor
        if use_conv:
            self.op = nn.Conv1d(self.channels, self.out_channels, ksize, stride=stride, padding=pad)
        else:
            assert self.channels == self.out_channels
            self.op = nn.AvgPool1d(kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


DEFAULT_MEL_NORM_FILE = "https://github.com/coqui-ai/TTS/releases/download/v0.14.1_models/mel_norms.pth"


class TorchMelSpectrogram(nn.Module):
    def __init__(
        self,
        filter_length=1024,
        hop_length=256,
        win_length=1024,
        n_mel_channels=80,
        mel_fmin=0,
        mel_fmax=8000,
        sampling_rate=22050,
        normalize=False,
        mel_norm_file=DEFAULT_MEL_NORM_FILE,
    ):
        super().__init__()
        # These are the default tacotron values for the MEL spectrogram.
        self.filter_length = filter_length
        self.hop_length = hop_length
        self.win_length = win_length
        self.n_mel_channels = n_mel_channels
        self.mel_fmin = mel_fmin
        self.mel_fmax = mel_fmax
        self.sampling_rate = sampling_rate
        self.mel_stft = torchaudio.transforms.MelSpectrogram(
            n_fft=self.filter_length,
            hop_length=self.hop_length,
            win_length=self.win_length,
            power=2,
            normalized=normalize,
            sample_rate=self.sampling_rate,
            f_min=self.mel_fmin,
            f_max=self.mel_fmax,
            n_mels=self.n_mel_channels,
            norm="slaney",
        )
        self.mel_norm_file = mel_norm_file
        if self.mel_norm_file is not None:
            with fsspec.open(self.mel_norm_file) as f:
                self.mel_norms = torch.load(f, weights_only=is_pytorch_at_least_2_4())
        else:
            self.mel_norms = None

    def forward(self, inp):
        if (
            len(inp.shape) == 3
        ):  # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
            inp = inp.squeeze(1)
        assert len(inp.shape) == 2
        self.mel_stft = self.mel_stft.to(inp.device)
        mel = self.mel_stft(inp)
        # Perform dynamic range compression
        mel = torch.log(torch.clamp(mel, min=1e-5))
        if self.mel_norms is not None:
            self.mel_norms = self.mel_norms.to(mel.device)
            mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
        return mel


class CheckpointedLayer(nn.Module):
    """
    Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
    checkpoint for all other args.
    """

    def __init__(self, wrap):
        super().__init__()
        self.wrap = wrap

    def forward(self, x, *args, **kwargs):
        for k, v in kwargs.items():
            assert not (isinstance(v, torch.Tensor) and v.requires_grad)  # This would screw up checkpointing.
        partial = functools.partial(self.wrap, **kwargs)
        return partial(x, *args)


class CheckpointedXTransformerEncoder(nn.Module):
    """
    Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
    to channels-last that XTransformer expects.
    """

    def __init__(self, needs_permute=True, exit_permute=True, checkpoint=True, **xtransformer_kwargs):
        super().__init__()
        self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
        self.needs_permute = needs_permute
        self.exit_permute = exit_permute

        if not checkpoint:
            return
        for i in range(len(self.transformer.attn_layers.layers)):
            n, b, r = self.transformer.attn_layers.layers[i]
            self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])

    def forward(self, x, **kwargs):
        if self.needs_permute:
            x = x.permute(0, 2, 1)
        h = self.transformer(x, **kwargs)
        if self.exit_permute:
            h = h.permute(0, 2, 1)
        return h


class TypicalLogitsWarper(LogitsWarper):
    def __init__(
        self,
        mass: float = 0.9,
        filter_value: float = -float("Inf"),
        min_tokens_to_keep: int = 1,
    ):
        self.filter_value = filter_value
        self.mass = mass
        self.min_tokens_to_keep = min_tokens_to_keep

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
        # calculate entropy
        normalized = torch.nn.functional.log_softmax(scores, dim=-1)
        p = torch.exp(normalized)
        ent = -(normalized * p).nansum(-1, keepdim=True)

        # shift and sort
        shifted_scores = torch.abs((-normalized) - ent)
        sorted_scores, sorted_indices = torch.sort(shifted_scores, descending=False)
        sorted_logits = scores.gather(-1, sorted_indices)
        cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)

        # Remove tokens with cumulative mass above the threshold
        last_ind = (cumulative_probs < self.mass).sum(dim=1)
        last_ind[last_ind < 0] = 0
        sorted_indices_to_remove = sorted_scores > sorted_scores.gather(1, last_ind.view(-1, 1))
        if self.min_tokens_to_keep > 1:
            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
            sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)

        scores = scores.masked_fill(indices_to_remove, self.filter_value)
        return scores