CosyVoice/cosyvoice/flow/decoder.py

# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple, Optional, Dict, Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import pack, rearrange, repeat
from diffusers.models.attention_processor import Attention, AttnProcessor2_0, inspect, logger, deprecate
from cosyvoice.utils.common import mask_to_bias
from cosyvoice.utils.mask import add_optional_chunk_mask
from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D
from matcha.models.components.transformer import BasicTransformerBlock, maybe_allow_in_graph


class Transpose(torch.nn.Module):
    def __init__(self, dim0: int, dim1: int):
        super().__init__()
        self.dim0 = dim0
        self.dim1 = dim1

    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
        x = torch.transpose(x, self.dim0, self.dim1)
        return x


class CausalConv1d(torch.nn.Conv1d):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        dilation: int = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = 'zeros',
        device=None,
        dtype=None
    ) -> None:
        super(CausalConv1d, self).__init__(in_channels, out_channels,
                                           kernel_size, stride,
                                           padding=0, dilation=dilation,
                                           groups=groups, bias=bias,
                                           padding_mode=padding_mode,
                                           device=device, dtype=dtype)
        assert stride == 1
        self.causal_padding = kernel_size - 1

    def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
        if cache.size(2) == 0:
            x = F.pad(x, (self.causal_padding, 0), value=0.0)
        else:
            assert cache.size(2) == self.causal_padding
            x = torch.concat([cache, x], dim=2)
        cache = x[:, :, -self.causal_padding:]
        x = super(CausalConv1d, self).forward(x)
        return x, cache


class CausalBlock1D(Block1D):
    def __init__(self, dim: int, dim_out: int):
        super(CausalBlock1D, self).__init__(dim, dim_out)
        self.block = torch.nn.Sequential(
            CausalConv1d(dim, dim_out, 3),
            Transpose(1, 2),
            nn.LayerNorm(dim_out),
            Transpose(1, 2),
            nn.Mish(),
        )

    def forward(self, x: torch.Tensor, mask: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
        output, cache = self.block[0](x * mask, cache)
        for i in range(1, len(self.block)):
            output = self.block[i](output)
        return output * mask, cache


class CausalResnetBlock1D(ResnetBlock1D):
    def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
        super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
        self.block1 = CausalBlock1D(dim, dim_out)
        self.block2 = CausalBlock1D(dim_out, dim_out)

    def forward(self, x: torch.Tensor, mask: torch.Tensor, time_emb: torch.Tensor,
                block1_cache: torch.Tensor = torch.zeros(0, 0, 0), block2_cache: torch.Tensor = torch.zeros(0, 0, 0)
                ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        h, block1_cache = self.block1(x, mask, block1_cache)
        h += self.mlp(time_emb).unsqueeze(-1)
        h, block2_cache = self.block2(h, mask, block2_cache)
        output = h + self.res_conv(x * mask)
        return output, block1_cache, block2_cache


class CausalAttnProcessor2_0(AttnProcessor2_0):
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(self):
        super(CausalAttnProcessor2_0, self).__init__()

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
        *args,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, torch.Tensor]:
        if len(args) > 0 or kwargs.get("scale", None) is not None:
            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. \
                `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
            deprecate("scale", "1.0.0", deprecation_message)

        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            # NOTE do not use attn.prepare_attention_mask as we have already provided the correct attention_mask
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.unsqueeze(dim=1).repeat(1, attn.heads, 1, 1)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key_cache = attn.to_k(encoder_hidden_states)
        value_cache = attn.to_v(encoder_hidden_states)
        # NOTE always concat cache for interface compatibility
        key = torch.concat([cache[:, :, :, 0], key_cache], dim=1)
        value = torch.concat([cache[:, :, :, 1], value_cache], dim=1)
        cache = torch.stack([key_cache, value_cache], dim=3)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states, cache


@maybe_allow_in_graph
class CausalAttention(Attention):
    def __init__(
        self,
        query_dim: int,
        cross_attention_dim: Optional[int] = None,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        bias: bool = False,
        upcast_attention: bool = False,
        upcast_softmax: bool = False,
        cross_attention_norm: Optional[str] = None,
        cross_attention_norm_num_groups: int = 32,
        qk_norm: Optional[str] = None,
        added_kv_proj_dim: Optional[int] = None,
        norm_num_groups: Optional[int] = None,
        spatial_norm_dim: Optional[int] = None,
        out_bias: bool = True,
        scale_qk: bool = True,
        only_cross_attention: bool = False,
        eps: float = 1e-5,
        rescale_output_factor: float = 1.0,
        residual_connection: bool = False,
        _from_deprecated_attn_block: bool = False,
        processor: Optional["AttnProcessor2_0"] = None,
        out_dim: int = None,
    ):
        super(CausalAttention, self).__init__(query_dim, cross_attention_dim, heads, dim_head, dropout, bias, upcast_attention, upcast_softmax,
                                              cross_attention_norm, cross_attention_norm_num_groups, qk_norm, added_kv_proj_dim, norm_num_groups,
                                              spatial_norm_dim, out_bias, scale_qk, only_cross_attention, eps, rescale_output_factor, residual_connection,
                                              _from_deprecated_attn_block, processor, out_dim)
        processor = CausalAttnProcessor2_0()
        self.set_processor(processor)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
        **cross_attention_kwargs,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        r"""
        The forward method of the `Attention` class.

        Args:
            hidden_states (`torch.Tensor`):
                The hidden states of the query.
            encoder_hidden_states (`torch.Tensor`, *optional*):
                The hidden states of the encoder.
            attention_mask (`torch.Tensor`, *optional*):
                The attention mask to use. If `None`, no mask is applied.
            **cross_attention_kwargs:
                Additional keyword arguments to pass along to the cross attention.

        Returns:
            `torch.Tensor`: The output of the attention layer.
        """
        # The `Attention` class can call different attention processors / attention functions
        # here we simply pass along all tensors to the selected processor class
        # For standard processors that are defined here, `**cross_attention_kwargs` is empty

        attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
        unused_kwargs = [k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters]
        if len(unused_kwargs) > 0:
            logger.warning(
                f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
            )
        cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters}

        return self.processor(
            self,
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=attention_mask,
            cache=cache,
            **cross_attention_kwargs,
        )


@maybe_allow_in_graph
class CausalBasicTransformerBlock(BasicTransformerBlock):
    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        dropout=0.0,
        cross_attention_dim: Optional[int] = None,
        activation_fn: str = "geglu",
        num_embeds_ada_norm: Optional[int] = None,
        attention_bias: bool = False,
        only_cross_attention: bool = False,
        double_self_attention: bool = False,
        upcast_attention: bool = False,
        norm_elementwise_affine: bool = True,
        norm_type: str = "layer_norm",
        final_dropout: bool = False,
    ):
        super(CausalBasicTransformerBlock, self).__init__(dim, num_attention_heads, attention_head_dim, dropout,
                                                          cross_attention_dim, activation_fn, num_embeds_ada_norm,
                                                          attention_bias, only_cross_attention, double_self_attention,
                                                          upcast_attention, norm_elementwise_affine, norm_type, final_dropout)
        self.attn1 = CausalAttention(
            query_dim=dim,
            heads=num_attention_heads,
            dim_head=attention_head_dim,
            dropout=dropout,
            bias=attention_bias,
            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
            upcast_attention=upcast_attention,
        )

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        class_labels: Optional[torch.LongTensor] = None,
        cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # Notice that normalization is always applied before the real computation in the following blocks.
        # 1. Self-Attention
        if self.use_ada_layer_norm:
            norm_hidden_states = self.norm1(hidden_states, timestep)
        elif self.use_ada_layer_norm_zero:
            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
            )
        else:
            norm_hidden_states = self.norm1(hidden_states)

        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}

        attn_output, cache = self.attn1(
            norm_hidden_states,
            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
            attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask,
            cache=cache,
            **cross_attention_kwargs,
        )
        if self.use_ada_layer_norm_zero:
            attn_output = gate_msa.unsqueeze(1) * attn_output
        hidden_states = attn_output + hidden_states

        # 2. Cross-Attention
        if self.attn2 is not None:
            norm_hidden_states = (
                self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
            )

            attn_output = self.attn2(
                norm_hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                **cross_attention_kwargs,
            )
            hidden_states = attn_output + hidden_states

        # 3. Feed-forward
        norm_hidden_states = self.norm3(hidden_states)

        if self.use_ada_layer_norm_zero:
            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                raise ValueError(f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: \
                                 {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.")

            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
            ff_output = torch.cat(
                [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
                dim=self._chunk_dim,
            )
        else:
            ff_output = self.ff(norm_hidden_states)

        if self.use_ada_layer_norm_zero:
            ff_output = gate_mlp.unsqueeze(1) * ff_output

        hidden_states = ff_output + hidden_states

        return hidden_states, cache


class ConditionalDecoder(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        channels=(256, 256),
        dropout=0.05,
        attention_head_dim=64,
        n_blocks=1,
        num_mid_blocks=2,
        num_heads=4,
        act_fn="snake",
    ):
        """
        This decoder requires an input with the same shape of the target. So, if your text content
        is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
        """
        super().__init__()
        channels = tuple(channels)
        self.in_channels = in_channels
        self.out_channels = out_channels

        self.time_embeddings = SinusoidalPosEmb(in_channels)
        time_embed_dim = channels[0] * 4
        self.time_mlp = TimestepEmbedding(
            in_channels=in_channels,
            time_embed_dim=time_embed_dim,
            act_fn="silu",
        )
        self.down_blocks = nn.ModuleList([])
        self.mid_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

        output_channel = in_channels
        for i in range(len(channels)):  # pylint: disable=consider-using-enumerate
            input_channel = output_channel
            output_channel = channels[i]
            is_last = i == len(channels) - 1
            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
            transformer_blocks = nn.ModuleList(
                [
                    BasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )
            downsample = (
                Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
            )
            self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))

        for _ in range(num_mid_blocks):
            input_channel = channels[-1]
            out_channels = channels[-1]
            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)

            transformer_blocks = nn.ModuleList(
                [
                    BasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )

            self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))

        channels = channels[::-1] + (channels[0],)
        for i in range(len(channels) - 1):
            input_channel = channels[i] * 2
            output_channel = channels[i + 1]
            is_last = i == len(channels) - 2
            resnet = ResnetBlock1D(
                dim=input_channel,
                dim_out=output_channel,
                time_emb_dim=time_embed_dim,
            )
            transformer_blocks = nn.ModuleList(
                [
                    BasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )
            upsample = (
                Upsample1D(output_channel, use_conv_transpose=True)
                if not is_last
                else nn.Conv1d(output_channel, output_channel, 3, padding=1)
            )
            self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
        self.final_block = Block1D(channels[-1], channels[-1])
        self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
        self.initialize_weights()

    def initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.GroupNorm):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
        """Forward pass of the UNet1DConditional model.

        Args:
            x (torch.Tensor): shape (batch_size, in_channels, time)
            mask (_type_): shape (batch_size, 1, time)
            t (_type_): shape (batch_size)
            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
            cond (_type_, optional): placeholder for future use. Defaults to None.

        Raises:
            ValueError: _description_
            ValueError: _description_

        Returns:
            _type_: _description_
        """

        t = self.time_embeddings(t).to(t.dtype)
        t = self.time_mlp(t)

        x = pack([x, mu], "b * t")[0]

        if spks is not None:
            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
            x = pack([x, spks], "b * t")[0]
        if cond is not None:
            x = pack([x, cond], "b * t")[0]

        hiddens = []
        masks = [mask]
        for resnet, transformer_blocks, downsample in self.down_blocks:
            mask_down = masks[-1]
            x = resnet(x, mask_down, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for transformer_block in transformer_blocks:
                x = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()
            hiddens.append(x)  # Save hidden states for skip connections
            x = downsample(x * mask_down)
            masks.append(mask_down[:, :, ::2])
        masks = masks[:-1]
        mask_mid = masks[-1]

        for resnet, transformer_blocks in self.mid_blocks:
            x = resnet(x, mask_mid, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for transformer_block in transformer_blocks:
                x = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()

        for resnet, transformer_blocks, upsample in self.up_blocks:
            mask_up = masks.pop()
            skip = hiddens.pop()
            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
            x = resnet(x, mask_up, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for transformer_block in transformer_blocks:
                x = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()
            x = upsample(x * mask_up)
        x = self.final_block(x, mask_up)
        output = self.final_proj(x * mask_up)
        return output * mask


class CausalConditionalDecoder(ConditionalDecoder):
    def __init__(
        self,
        in_channels,
        out_channels,
        channels=(256, 256),
        dropout=0.05,
        attention_head_dim=64,
        n_blocks=1,
        num_mid_blocks=2,
        num_heads=4,
        act_fn="snake",
        static_chunk_size=50,
        num_decoding_left_chunks=2,
    ):
        """
        This decoder requires an input with the same shape of the target. So, if your text content
        is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
        """
        torch.nn.Module.__init__(self)
        channels = tuple(channels)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.time_embeddings = SinusoidalPosEmb(in_channels)
        time_embed_dim = channels[0] * 4
        self.time_mlp = TimestepEmbedding(
            in_channels=in_channels,
            time_embed_dim=time_embed_dim,
            act_fn="silu",
        )
        self.static_chunk_size = static_chunk_size
        self.num_decoding_left_chunks = num_decoding_left_chunks
        self.down_blocks = nn.ModuleList([])
        self.mid_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

        output_channel = in_channels
        for i in range(len(channels)):  # pylint: disable=consider-using-enumerate
            input_channel = output_channel
            output_channel = channels[i]
            is_last = i == len(channels) - 1
            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
            transformer_blocks = nn.ModuleList(
                [
                    CausalBasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )
            downsample = (
                Downsample1D(output_channel) if not is_last else CausalConv1d(output_channel, output_channel, 3)
            )
            self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))

        for _ in range(num_mid_blocks):
            input_channel = channels[-1]
            out_channels = channels[-1]
            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)

            transformer_blocks = nn.ModuleList(
                [
                    CausalBasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )

            self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))

        channels = channels[::-1] + (channels[0],)
        for i in range(len(channels) - 1):
            input_channel = channels[i] * 2
            output_channel = channels[i + 1]
            is_last = i == len(channels) - 2
            resnet = CausalResnetBlock1D(
                dim=input_channel,
                dim_out=output_channel,
                time_emb_dim=time_embed_dim,
            )
            transformer_blocks = nn.ModuleList(
                [
                    CausalBasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )
            upsample = (
                Upsample1D(output_channel, use_conv_transpose=True)
                if not is_last
                else CausalConv1d(output_channel, output_channel, 3)
            )
            self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
        self.final_block = CausalBlock1D(channels[-1], channels[-1])
        self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
        self.initialize_weights()

    def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
        """Forward pass of the UNet1DConditional model.

        Args:
            x (torch.Tensor): shape (batch_size, in_channels, time)
            mask (_type_): shape (batch_size, 1, time)
            t (_type_): shape (batch_size)
            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
            cond (_type_, optional): placeholder for future use. Defaults to None.

        Raises:
            ValueError: _description_
            ValueError: _description_

        Returns:
            _type_: _description_
        """

        t = self.time_embeddings(t).to(t.dtype)
        t = self.time_mlp(t)

        x = pack([x, mu], "b * t")[0]

        if spks is not None:
            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
            x = pack([x, spks], "b * t")[0]
        if cond is not None:
            x = pack([x, cond], "b * t")[0]

        hiddens = []
        masks = [mask]
        for resnet, transformer_blocks, downsample in self.down_blocks:
            mask_down = masks[-1]
            x, _, _ = resnet(x, mask_down, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            if streaming is True:
                attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, self.num_decoding_left_chunks)
            else:
                attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for transformer_block in transformer_blocks:
                x, _ = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()
            hiddens.append(x)  # Save hidden states for skip connections
            x, _ = downsample(x * mask_down)
            masks.append(mask_down[:, :, ::2])
        masks = masks[:-1]
        mask_mid = masks[-1]

        for resnet, transformer_blocks in self.mid_blocks:
            x, _, _ = resnet(x, mask_mid, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            if streaming is True:
                attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, self.num_decoding_left_chunks)
            else:
                attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for transformer_block in transformer_blocks:
                x, _ = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()

        for resnet, transformer_blocks, upsample in self.up_blocks:
            mask_up = masks.pop()
            skip = hiddens.pop()
            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
            x, _, _ = resnet(x, mask_up, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            if streaming is True:
                attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, self.num_decoding_left_chunks)
            else:
                attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, 0, -1).repeat(1, x.size(1), 1)
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for transformer_block in transformer_blocks:
                x, _ = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()
            x, _ = upsample(x * mask_up)
        x, _ = self.final_block(x, mask_up)
        output = self.final_proj(x * mask_up)
        return output * mask

    @torch.inference_mode()
    def forward_chunk(self, x, mask, mu, t, spks=None, cond=None,
                      down_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
                      down_blocks_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0, 0),
                      mid_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
                      mid_blocks_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0, 0),
                      up_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
                      up_blocks_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0, 0),
                      final_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0)
                      ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """Forward pass of the UNet1DConditional model.

        Args:
            x (torch.Tensor): shape (batch_size, in_channels, time)
            mask (_type_): shape (batch_size, 1, time)
            t (_type_): shape (batch_size)
            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
            cond (_type_, optional): placeholder for future use. Defaults to None.

        Raises:
            ValueError: _description_
            ValueError: _description_

        Returns:
            _type_: _description_
        """

        t = self.time_embeddings(t).to(t.dtype)
        t = self.time_mlp(t)

        x = pack([x, mu], "b * t")[0]

        if spks is not None:
            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
            x = pack([x, spks], "b * t")[0]
        if cond is not None:
            x = pack([x, cond], "b * t")[0]

        hiddens = []
        masks = [mask]

        down_blocks_kv_cache_new = torch.zeros(1, 4, 2, x.size(2), 512, 2).to(x.device)
        mid_blocks_kv_cache_new = torch.zeros(12, 4, 2, x.size(2), 512, 2).to(x.device)
        up_blocks_kv_cache_new = torch.zeros(1, 4, 2, x.size(2), 512, 2).to(x.device)
        for index, (resnet, transformer_blocks, downsample) in enumerate(self.down_blocks):
            mask_down = masks[-1]
            x, down_blocks_conv_cache[index][:, :320], down_blocks_conv_cache[index][:, 320: 576] = \
                resnet(x, mask_down, t, down_blocks_conv_cache[index][:, :320], down_blocks_conv_cache[index][:, 320: 576])
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = torch.ones(x.size(0), x.size(1), x.size(1) + down_blocks_kv_cache.size(3), device=x.device).bool()
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for i, transformer_block in enumerate(transformer_blocks):
                x, down_blocks_kv_cache_new[index, i] = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                    cache=down_blocks_kv_cache[index, i],
                )
            x = rearrange(x, "b t c -> b c t").contiguous()
            hiddens.append(x)  # Save hidden states for skip connections
            x, down_blocks_conv_cache[index][:, 576:] = downsample(x * mask_down, down_blocks_conv_cache[index][:, 576:])
            masks.append(mask_down[:, :, ::2])
        masks = masks[:-1]
        mask_mid = masks[-1]

        for index, (resnet, transformer_blocks) in enumerate(self.mid_blocks):
            x, mid_blocks_conv_cache[index][:, :256], mid_blocks_conv_cache[index][:, 256:] = \
                resnet(x, mask_mid, t, mid_blocks_conv_cache[index][:, :256], mid_blocks_conv_cache[index][:, 256:])
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = torch.ones(x.size(0), x.size(1), x.size(1) + mid_blocks_kv_cache.size(3), device=x.device).bool()
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for i, transformer_block in enumerate(transformer_blocks):
                x, mid_blocks_kv_cache_new[index, i] = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                    cache=mid_blocks_kv_cache[index, i]
                )
            x = rearrange(x, "b t c -> b c t").contiguous()

        for index, (resnet, transformer_blocks, upsample) in enumerate(self.up_blocks):
            mask_up = masks.pop()
            skip = hiddens.pop()
            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
            x, up_blocks_conv_cache[index][:, :512], up_blocks_conv_cache[index][:, 512: 768] = \
                resnet(x, mask_up, t, up_blocks_conv_cache[index][:, :512], up_blocks_conv_cache[index][:, 512: 768])
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = torch.ones(x.size(0), x.size(1), x.size(1) + up_blocks_kv_cache.size(3), device=x.device).bool()
            attn_mask = mask_to_bias(attn_mask, x.dtype)
            for i, transformer_block in enumerate(transformer_blocks):
                x, up_blocks_kv_cache_new[index, i] = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                    cache=up_blocks_kv_cache[index, i]
                )
            x = rearrange(x, "b t c -> b c t").contiguous()
            x, up_blocks_conv_cache[index][:, 768:] = upsample(x * mask_up, up_blocks_conv_cache[index][:, 768:])
        x, final_blocks_conv_cache = self.final_block(x, mask_up, final_blocks_conv_cache)
        output = self.final_proj(x * mask_up)
        return output * mask, down_blocks_conv_cache, down_blocks_kv_cache_new, mid_blocks_conv_cache, mid_blocks_kv_cache_new, \
            up_blocks_conv_cache, up_blocks_kv_cache_new, final_blocks_conv_cache