tools/uvr5/bs_roformer/bs_roformer.py

from functools import partial

import torch
from torch import nn, einsum, Tensor
from torch.nn import Module, ModuleList
import torch.nn.functional as F

from bs_roformer.attend import Attend

from typing import Tuple, Optional, List, Callable
# from beartype.typing import Tuple, Optional, List, Callable
# from beartype import beartype

from rotary_embedding_torch import RotaryEmbedding

from einops import rearrange, pack, unpack
from einops.layers.torch import Rearrange

# helper functions

def exists(val):
    return val is not None


def default(v, d):
    return v if exists(v) else d


def pack_one(t, pattern):
    return pack([t], pattern)


def unpack_one(t, ps, pattern):
    return unpack(t, ps, pattern)[0]


# norm

def l2norm(t):
    return F.normalize(t, dim = -1, p = 2)


class RMSNorm(Module):
    def __init__(self, dim):
        super().__init__()
        self.scale = dim ** 0.5
        self.gamma = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        return F.normalize(x, dim=-1) * self.scale * self.gamma


# attention

class FeedForward(Module):
    def __init__(

            self,

            dim,

            mult=4,

            dropout=0.

    ):
        super().__init__()
        dim_inner = int(dim * mult)
        self.net = nn.Sequential(
            RMSNorm(dim),
            nn.Linear(dim, dim_inner),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(dim_inner, dim),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        return self.net(x)


class Attention(Module):
    def __init__(

            self,

            dim,

            heads=8,

            dim_head=64,

            dropout=0.,

            rotary_embed=None,

            flash=True

    ):
        super().__init__()
        self.heads = heads
        self.scale = dim_head ** -0.5
        dim_inner = heads * dim_head

        self.rotary_embed = rotary_embed

        self.attend = Attend(flash=flash, dropout=dropout)

        self.norm = RMSNorm(dim)
        self.to_qkv = nn.Linear(dim, dim_inner * 3, bias=False)

        self.to_gates = nn.Linear(dim, heads)

        self.to_out = nn.Sequential(
            nn.Linear(dim_inner, dim, bias=False),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        x = self.norm(x)

        q, k, v = rearrange(self.to_qkv(x), 'b n (qkv h d) -> qkv b h n d', qkv=3, h=self.heads)

        if exists(self.rotary_embed):
            q = self.rotary_embed.rotate_queries_or_keys(q)
            k = self.rotary_embed.rotate_queries_or_keys(k)

        out = self.attend(q, k, v)

        gates = self.to_gates(x)
        out = out * rearrange(gates, 'b n h -> b h n 1').sigmoid()

        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)


class LinearAttention(Module):
    """

    this flavor of linear attention proposed in https://arxiv.org/abs/2106.09681 by El-Nouby et al.

    """

    # @beartype
    def __init__(

            self,

            *,

            dim,

            dim_head=32,

            heads=8,

            scale=8,

            flash=False,

            dropout=0.

    ):
        super().__init__()
        dim_inner = dim_head * heads
        self.norm = RMSNorm(dim)

        self.to_qkv = nn.Sequential(
            nn.Linear(dim, dim_inner * 3, bias=False),
            Rearrange('b n (qkv h d) -> qkv b h d n', qkv=3, h=heads)
        )

        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))

        self.attend = Attend(
            scale=scale,
            dropout=dropout,
            flash=flash
        )

        self.to_out = nn.Sequential(
            Rearrange('b h d n -> b n (h d)'),
            nn.Linear(dim_inner, dim, bias=False)
        )

    def forward(

            self,

            x

    ):
        x = self.norm(x)

        q, k, v = self.to_qkv(x)

        q, k = map(l2norm, (q, k))
        q = q * self.temperature.exp()

        out = self.attend(q, k, v)

        return self.to_out(out)


class Transformer(Module):
    def __init__(

            self,

            *,

            dim,

            depth,

            dim_head=64,

            heads=8,

            attn_dropout=0.,

            ff_dropout=0.,

            ff_mult=4,

            norm_output=True,

            rotary_embed=None,

            flash_attn=True,

            linear_attn=False

    ):
        super().__init__()
        self.layers = ModuleList([])

        for _ in range(depth):
            if linear_attn:
                attn = LinearAttention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, flash=flash_attn)
            else:
                attn = Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout,
                                 rotary_embed=rotary_embed, flash=flash_attn)

            self.layers.append(ModuleList([
                attn,
                FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)
            ]))

        self.norm = RMSNorm(dim) if norm_output else nn.Identity()

    def forward(self, x):

        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x

        return self.norm(x)


# bandsplit module

class BandSplit(Module):
    # @beartype
    def __init__(

            self,

            dim,

            dim_inputs: Tuple[int, ...]

    ):
        super().__init__()
        self.dim_inputs = dim_inputs
        self.to_features = ModuleList([])

        for dim_in in dim_inputs:
            net = nn.Sequential(
                RMSNorm(dim_in),
                nn.Linear(dim_in, dim)
            )

            self.to_features.append(net)

    def forward(self, x):
        x = x.split(self.dim_inputs, dim=-1)

        outs = []
        for split_input, to_feature in zip(x, self.to_features):
            split_output = to_feature(split_input)
            outs.append(split_output)

        return torch.stack(outs, dim=-2)


def MLP(

        dim_in,

        dim_out,

        dim_hidden=None,

        depth=1,

        activation=nn.Tanh

):
    dim_hidden = default(dim_hidden, dim_in)

    net = []
    dims = (dim_in, *((dim_hidden,) * (depth - 1)), dim_out)

    for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):
        is_last = ind == (len(dims) - 2)

        net.append(nn.Linear(layer_dim_in, layer_dim_out))

        if is_last:
            continue

        net.append(activation())

    return nn.Sequential(*net)


class MaskEstimator(Module):
    # @beartype
    def __init__(

            self,

            dim,

            dim_inputs: Tuple[int, ...],

            depth,

            mlp_expansion_factor=4

    ):
        super().__init__()
        self.dim_inputs = dim_inputs
        self.to_freqs = ModuleList([])
        dim_hidden = dim * mlp_expansion_factor

        for dim_in in dim_inputs:
            net = []

            mlp = nn.Sequential(
                MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth),
                nn.GLU(dim=-1)
            )

            self.to_freqs.append(mlp)

    def forward(self, x):
        x = x.unbind(dim=-2)

        outs = []

        for band_features, mlp in zip(x, self.to_freqs):
            freq_out = mlp(band_features)
            outs.append(freq_out)

        return torch.cat(outs, dim=-1)


# main class

DEFAULT_FREQS_PER_BANDS = (
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
    2, 2, 2, 2,
    4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
    12, 12, 12, 12, 12, 12, 12, 12,
    24, 24, 24, 24, 24, 24, 24, 24,
    48, 48, 48, 48, 48, 48, 48, 48,
    128, 129,
)


class BSRoformer(Module):

    # @beartype
    def __init__(

            self,

            dim,

            *,

            depth,

            stereo=False,

            num_stems=1,

            time_transformer_depth=2,

            freq_transformer_depth=2,

            linear_transformer_depth=0,

            freqs_per_bands: Tuple[int, ...] = DEFAULT_FREQS_PER_BANDS,

            # in the paper, they divide into ~60 bands, test with 1 for starters

            dim_head=64,

            heads=8,

            attn_dropout=0.,

            ff_dropout=0.,

            flash_attn=True,

            dim_freqs_in=1025,

            stft_n_fft=2048,

            stft_hop_length=512,

            # 10ms at 44100Hz, from sections 4.1, 4.4 in the paper - @faroit recommends // 2 or // 4 for better reconstruction

            stft_win_length=2048,

            stft_normalized=False,

            stft_window_fn: Optional[Callable] = None,

            mask_estimator_depth=2,

            multi_stft_resolution_loss_weight=1.,

            multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),

            multi_stft_hop_size=147,

            multi_stft_normalized=False,

            multi_stft_window_fn: Callable = torch.hann_window

    ):
        super().__init__()

        self.stereo = stereo
        self.audio_channels = 2 if stereo else 1
        self.num_stems = num_stems

        self.layers = ModuleList([])

        transformer_kwargs = dict(
            dim=dim,
            heads=heads,
            dim_head=dim_head,
            attn_dropout=attn_dropout,
            ff_dropout=ff_dropout,
            flash_attn=flash_attn,
            norm_output=False
        )

        time_rotary_embed = RotaryEmbedding(dim=dim_head)
        freq_rotary_embed = RotaryEmbedding(dim=dim_head)

        for _ in range(depth):
            tran_modules = []
            if linear_transformer_depth > 0:
                tran_modules.append(Transformer(depth=linear_transformer_depth, linear_attn=True, **transformer_kwargs))
            tran_modules.append(
                Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs)
            )
            tran_modules.append(
                Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs)
            )
            self.layers.append(nn.ModuleList(tran_modules))

        self.final_norm = RMSNorm(dim)

        self.stft_kwargs = dict(
            n_fft=stft_n_fft,
            hop_length=stft_hop_length,
            win_length=stft_win_length,
            normalized=stft_normalized
        )

        self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)

        freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]

        assert len(freqs_per_bands) > 1
        assert sum(
            freqs_per_bands) == freqs, f'the number of freqs in the bands must equal {freqs} based on the STFT settings, but got {sum(freqs_per_bands)}'

        freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in freqs_per_bands)

        self.band_split = BandSplit(
            dim=dim,
            dim_inputs=freqs_per_bands_with_complex
        )

        self.mask_estimators = nn.ModuleList([])

        for _ in range(num_stems):
            mask_estimator = MaskEstimator(
                dim=dim,
                dim_inputs=freqs_per_bands_with_complex,
                depth=mask_estimator_depth
            )

            self.mask_estimators.append(mask_estimator)

        # for the multi-resolution stft loss

        self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight
        self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes
        self.multi_stft_n_fft = stft_n_fft
        self.multi_stft_window_fn = multi_stft_window_fn

        self.multi_stft_kwargs = dict(
            hop_length=multi_stft_hop_size,
            normalized=multi_stft_normalized
        )

    def forward(

            self,

            raw_audio,

            target=None,

            return_loss_breakdown=False

    ):
        """

        einops



        b - batch

        f - freq

        t - time

        s - audio channel (1 for mono, 2 for stereo)

        n - number of 'stems'

        c - complex (2)

        d - feature dimension

        """

        device = raw_audio.device

        if raw_audio.ndim == 2:
            raw_audio = rearrange(raw_audio, 'b t -> b 1 t')

        channels = raw_audio.shape[1]
        assert (not self.stereo and channels == 1) or (
                    self.stereo and channels == 2), 'stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)'

        # to stft

        raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, '* t')

        stft_window = self.stft_window_fn(device=device)

        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)
        stft_repr = torch.view_as_real(stft_repr)

        stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, '* f t c')
        stft_repr = rearrange(stft_repr,
                              'b s f t c -> b (f s) t c')  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting

        x = rearrange(stft_repr, 'b f t c -> b t (f c)')
        # print("460:", x.dtype)#fp32
        x = self.band_split(x)

        # axial / hierarchical attention

        # print("487:",x.dtype)#fp16
        for transformer_block in self.layers:

            if len(transformer_block) == 3:
                linear_transformer, time_transformer, freq_transformer = transformer_block

                x, ft_ps = pack([x], 'b * d')
                # print("494:", x.dtype)#fp16
                x = linear_transformer(x)
                # print("496:", x.dtype)#fp16
                x, = unpack(x, ft_ps, 'b * d')
            else:
                time_transformer, freq_transformer = transformer_block

            # print("501:", x.dtype)#fp16
            x = rearrange(x, 'b t f d -> b f t d')
            x, ps = pack([x], '* t d')

            x = time_transformer(x)
            # print("505:", x.dtype)#fp16
            x, = unpack(x, ps, '* t d')
            x = rearrange(x, 'b f t d -> b t f d')
            x, ps = pack([x], '* f d')

            x = freq_transformer(x)

            x, = unpack(x, ps, '* f d')

        # print("515:", x.dtype)######fp16
        x = self.final_norm(x)

        num_stems = len(self.mask_estimators)
        # print("519:", x.dtype)#fp32
        mask = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)
        mask = rearrange(mask, 'b n t (f c) -> b n f t c', c=2)

        # modulate frequency representation

        stft_repr = rearrange(stft_repr, 'b f t c -> b 1 f t c')

        # complex number multiplication

        stft_repr = torch.view_as_complex(stft_repr)
        mask = torch.view_as_complex(mask)

        stft_repr = stft_repr * mask

        # istft

        stft_repr = rearrange(stft_repr, 'b n (f s) t -> (b n s) f t', s=self.audio_channels)

        recon_audio = torch.istft(stft_repr, **self.stft_kwargs, window=stft_window, return_complex=False)

        recon_audio = rearrange(recon_audio, '(b n s) t -> b n s t', s=self.audio_channels, n=num_stems)

        if num_stems == 1:
            recon_audio = rearrange(recon_audio, 'b 1 s t -> b s t')

        # if a target is passed in, calculate loss for learning

        if not exists(target):
            return recon_audio

        if self.num_stems > 1:
            assert target.ndim == 4 and target.shape[1] == self.num_stems

        if target.ndim == 2:
            target = rearrange(target, '... t -> ... 1 t')

        target = target[..., :recon_audio.shape[-1]]  # protect against lost length on istft

        loss = F.l1_loss(recon_audio, target)

        multi_stft_resolution_loss = 0.

        for window_size in self.multi_stft_resolutions_window_sizes:
            res_stft_kwargs = dict(
                n_fft=max(window_size, self.multi_stft_n_fft),  # not sure what n_fft is across multi resolution stft
                win_length=window_size,
                return_complex=True,
                window=self.multi_stft_window_fn(window_size, device=device),
                **self.multi_stft_kwargs,
            )

            recon_Y = torch.stft(rearrange(recon_audio, '... s t -> (... s) t'), **res_stft_kwargs)
            target_Y = torch.stft(rearrange(target, '... s t -> (... s) t'), **res_stft_kwargs)

            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)

        weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight

        total_loss = loss + weighted_multi_resolution_loss

        if not return_loss_breakdown:
            return total_loss

        return total_loss, (loss, multi_stft_resolution_loss)