DL-Art-School/codes/models/audio/tts/unet_diffusion_tts8.py

import functools
import random

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import autocast

from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import TimestepEmbedSequential, \
    Downsample, Upsample
from models.audio.tts.mini_encoder import AudioMiniEncoder
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
from trainer.networks import register_model
from x_transformers import Encoder, ContinuousTransformerWrapper


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 torch.utils.checkpoint.checkpoint(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, **xtransformer_kwargs):
        super().__init__()
        self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)

        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):
        x = x.permute(0,2,1)
        h = self.transformer(x, **kwargs)
        return h.permute(0,2,1)


class DiffusionTts(nn.Module):
    def __init__(
            self,
            model_channels,
            in_channels=1,
            num_tokens=32,
            out_channels=2,  # mean and variance
            dropout=0,
            # res           1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
            channel_mult=  (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
            # spec_cond:    1, 0, 0, 1, 0, 0, 1, 0,   0,  1,  0,  0)
            # attn:         0, 0, 0, 0, 0, 0, 0, 0,   0,  1,  1,  1
            token_conditioning_resolutions=(1,16,),
            dims=1,
            use_fp16=False,
            time_embed_dim_multiplier=4,
            cond_transformer_depth=8,
            mid_transformer_depth=8,
            nil_guidance_fwd_proportion=.3,
            # Parameters for super-sampling.
            super_sampling=False,
            super_sampling_max_noising_factor=.1,
            # Parameters for unaligned inputs.
            enabled_unaligned_inputs=False,
            num_unaligned_tokens=164,
            unaligned_encoder_depth=8,
    ):
        super().__init__()

        if super_sampling:
            in_channels *= 2  # In super-sampling mode, the LR input is concatenated directly onto the input.
        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.dtype = torch.float16 if use_fp16 else torch.float32
        self.dims = dims
        self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
        self.mask_token_id = num_tokens
        self.super_sampling_enabled = super_sampling
        self.super_sampling_max_noising_factor = super_sampling_max_noising_factor

        time_embed_dim = model_channels * time_embed_dim_multiplier
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        embedding_dim = model_channels * 8
        self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
        self.contextual_embedder = AudioMiniEncoder(1, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
                         attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
        self.conditioning_conv = nn.Conv1d(embedding_dim*3, embedding_dim, 1)

        self.enable_unaligned_inputs = enabled_unaligned_inputs
        if enabled_unaligned_inputs:
            self.unaligned_embedder = nn.Embedding(num_unaligned_tokens, embedding_dim)
            self.unaligned_encoder = CheckpointedXTransformerEncoder(
                max_seq_len=-1,
                use_pos_emb=False,
                attn_layers=Encoder(
                    dim=embedding_dim,
                    depth=unaligned_encoder_depth,
                    heads=embedding_dim//128,
                    ff_dropout=dropout,
                    attn_dropout=dropout,
                    use_rmsnorm=True,
                    ff_glu=True,
                    rotary_emb_dim=True,
                )
            )

        self.conditioning_encoder = CheckpointedXTransformerEncoder(
                max_seq_len=-1,  # Should be unused
                use_pos_emb=False,
                attn_layers=Encoder(
                    dim=embedding_dim,
                    depth=cond_transformer_depth,
                    heads=embedding_dim//128,
                    ff_dropout=dropout,
                    attn_dropout=dropout,
                    use_rmsnorm=True,
                    ff_glu=True,
                    rotary_pos_emb=True,
                    cross_attend=self.enable_unaligned_inputs,
                )
        )

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        token_conditioning_blocks = []
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1

        for level, mult in enumerate(channel_mult):
            if ds in token_conditioning_resolutions:
                token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1)
                token_conditioning_block.weight.data *= .02
                self.input_blocks.append(token_conditioning_block)
                token_conditioning_blocks.append(token_conditioning_block)

            out_ch = int(mult * model_channels)
            if level != len(channel_mult) - 1:
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        Downsample(
                            ch, use_conv=True, dims=dims, out_channels=out_ch, factor=2, ksize=3, pad=1
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2

        self.middle_block = CheckpointedXTransformerEncoder(
                max_seq_len=-1,  # Should be unused
                use_pos_emb=False,
                attn_layers=Encoder(
                    dim=ch,
                    depth=mid_transformer_depth,
                    heads=ch//128,
                    ff_dropout=dropout,
                    attn_dropout=dropout,
                    use_rmsnorm=True,
                    ff_glu=True,
                    rotary_pos_emb=True,
                )
            )

        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]:
            ich = ch + input_block_chans.pop()
            out_ch = int(model_channels * mult)
            if level != 0:
                self.output_blocks.append(
                    TimestepEmbedSequential(Upsample(ich, use_conv=True, dims=dims, out_channels=out_ch, factor=2))
                )
            else:
                self.output_blocks.append(
                    TimestepEmbedSequential(conv_nd(dims, ich, out_ch, 3, padding=1))
                )
            ch = out_ch
            ds //= 2

        self.out = nn.Sequential(
            normalization(ch),
            nn.SiLU(),
            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
        )


    def forward(self, x, timesteps, tokens=None, conditioning_input=None, lr_input=None, unaligned_input=None):
        """
        Apply the model to an input batch.

        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param tokens: an aligned text input.
        :param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
        :param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
        :param unaligned_input: A structural input that is not properly aligned with the output of the diffusion model.
                                Can be combined with a conditioning input to produce more robust conditioning.
        :return: an [N x C x ...] Tensor of outputs.
        """
        assert conditioning_input is not None
        if self.super_sampling_enabled:
            assert lr_input is not None
            if self.training and self.super_sampling_max_noising_factor > 0:
                noising_factor = random.uniform(0,self.super_sampling_max_noising_factor)
                lr_input = torch.randn_like(lr_input) * noising_factor + lr_input
            lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest')
            x = torch.cat([x, lr_input], dim=1)

        if self.enable_unaligned_inputs:
            assert unaligned_input is not None
            unaligned_h = self.unaligned_embedder(unaligned_input).permute(0,2,1)
            unaligned_h = self.unaligned_encoder(unaligned_h).permute(0,2,1)

        with autocast(x.device.type):
            orig_x_shape = x.shape[-1]
            cm = ceil_multiple(x.shape[-1], 2048)
            if cm != 0:
                pc = (cm-x.shape[-1])/x.shape[-1]
                x = F.pad(x, (0,cm-x.shape[-1]))
                if tokens is not None:
                    tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))

            hs = []
            time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))

            cond_emb = self.contextual_embedder(conditioning_input)
            if tokens is not None:
                # Mask out guidance tokens for un-guided diffusion.
                if self.training and self.nil_guidance_fwd_proportion > 0:
                    token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion
                    tokens = torch.where(token_mask, self.mask_token_id, tokens)
                code_emb = self.code_embedding(tokens).permute(0,2,1)
                cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
                cond_time_emb = timestep_embedding(torch.zeros_like(timesteps), code_emb.shape[1])  # This was something I was doing (adding timesteps into this computation), but removed on second thought. TODO: completely remove.
                cond_time_emb = cond_time_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
                code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb, cond_time_emb], dim=1))
            else:
                code_emb = cond_emb.unsqueeze(-1)
            if self.enable_unaligned_inputs:
                code_emb = self.conditioning_encoder(code_emb, context=unaligned_h)
            else:
                code_emb = self.conditioning_encoder(code_emb)

            first = True
            time_emb = time_emb.float()
            h = x
            for k, module in enumerate(self.input_blocks):
                if isinstance(module, nn.Conv1d):
                    h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
                    h = h + h_tok
                else:
                    with autocast(x.device.type, enabled=not first):
                        # First block has autocast disabled to allow a high precision signal to be properly vectorized.
                        h = module(h, time_emb)
                    hs.append(h)
                first = False
            h = self.middle_block(h)
            for module in self.output_blocks:
                h = torch.cat([h, hs.pop()], dim=1)
                h = module(h, time_emb)

        # Last block also has autocast disabled for high-precision outputs.
        h = h.float()
        out = self.out(h)
        return out[:, :, :orig_x_shape]


@register_model
def register_diffusion_tts8(opt_net, opt):
    return DiffusionTts(**opt_net['kwargs'])


# Test for ~4 second audio clip at 22050Hz
if __name__ == '__main__':
    clip = torch.randn(2, 1, 32768)
    tok = torch.randint(0,30, (2,388))
    cond = torch.randn(2, 1, 44000)
    ts = torch.LongTensor([600, 600])
    lr = torch.randn(2,1,10000)
    un = torch.randint(0,120, (2,100))
    model = DiffusionTts(128,
                         channel_mult=[1,1.5,2, 3, 4, 6, 8],
                         token_conditioning_resolutions=[1,4,16,64],
                         time_embed_dim_multiplier=4, super_sampling=False,
                         enabled_unaligned_inputs=True)
    model(clip, ts, tok, cond, lr, un)