mirror of
https://github.com/FunAudioLLM/CosyVoice.git
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903 lines
39 KiB
Python
903 lines
39 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Tuple, Optional, Dict, Any
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import pack, rearrange, repeat
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from diffusers.models.attention_processor import Attention, AttnProcessor2_0, inspect, logger, deprecate
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from cosyvoice.utils.common import mask_to_bias
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from cosyvoice.utils.mask import add_optional_chunk_mask
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from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D
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from matcha.models.components.transformer import BasicTransformerBlock, maybe_allow_in_graph
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class Transpose(torch.nn.Module):
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def __init__(self, dim0: int, dim1: int):
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super().__init__()
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self.dim0 = dim0
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self.dim1 = dim1
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
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x = torch.transpose(x, self.dim0, self.dim1)
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return x
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class CausalConv1d(torch.nn.Conv1d):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: int,
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stride: int = 1,
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dilation: int = 1,
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groups: int = 1,
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bias: bool = True,
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padding_mode: str = 'zeros',
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device=None,
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dtype=None
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) -> None:
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super(CausalConv1d, self).__init__(in_channels, out_channels,
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kernel_size, stride,
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padding=0, dilation=dilation,
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groups=groups, bias=bias,
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padding_mode=padding_mode,
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device=device, dtype=dtype)
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assert stride == 1
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self.causal_padding = kernel_size - 1
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def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
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if cache.size(2) == 0:
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x = F.pad(x, (self.causal_padding, 0), value=0.0)
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else:
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assert cache.size(2) == self.causal_padding
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x = torch.concat([cache, x], dim=2)
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cache = x[:, :, -self.causal_padding:]
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x = super(CausalConv1d, self).forward(x)
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return x, cache
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class CausalBlock1D(Block1D):
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def __init__(self, dim: int, dim_out: int):
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super(CausalBlock1D, self).__init__(dim, dim_out)
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self.block = torch.nn.Sequential(
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CausalConv1d(dim, dim_out, 3),
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Transpose(1, 2),
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nn.LayerNorm(dim_out),
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Transpose(1, 2),
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nn.Mish(),
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)
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def forward(self, x: torch.Tensor, mask: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
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output, cache = self.block[0](x * mask, cache)
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for i in range(1, len(self.block)):
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output = self.block[i](output)
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return output * mask, cache
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class CausalResnetBlock1D(ResnetBlock1D):
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def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
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super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
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self.block1 = CausalBlock1D(dim, dim_out)
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self.block2 = CausalBlock1D(dim_out, dim_out)
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def forward(self, x: torch.Tensor, mask: torch.Tensor, time_emb: torch.Tensor,
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block1_cache: torch.Tensor = torch.zeros(0, 0, 0), block2_cache: torch.Tensor = torch.zeros(0, 0, 0)
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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h, block1_cache = self.block1(x, mask, block1_cache)
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h += self.mlp(time_emb).unsqueeze(-1)
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h, block2_cache = self.block2(h, mask, block2_cache)
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output = h + self.res_conv(x * mask)
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return output, block1_cache, block2_cache
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class CausalAttnProcessor2_0(AttnProcessor2_0):
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r"""
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Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
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"""
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def __init__(self):
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super(CausalAttnProcessor2_0, self).__init__()
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.FloatTensor,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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temb: Optional[torch.FloatTensor] = None,
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cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
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*args,
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**kwargs,
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) -> Tuple[torch.FloatTensor, torch.Tensor]:
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if len(args) > 0 or kwargs.get("scale", None) is not None:
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deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. \
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`scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
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deprecate("scale", "1.0.0", deprecation_message)
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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# NOTE do not use attn.prepare_attention_mask as we have already provided the correct attention_mask
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.unsqueeze(dim=1).repeat(1, attn.heads, 1, 1)
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
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query = attn.to_q(hidden_states)
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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elif attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
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key_cache = attn.to_k(encoder_hidden_states)
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value_cache = attn.to_v(encoder_hidden_states)
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# NOTE here we judge cache.size(0) instead of cache.size(1), because init_cache has size (2, 0, 512, 2)
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if cache.size(0) != 0:
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key = torch.concat([cache[:, :, :, 0], key_cache], dim=1)
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value = torch.concat([cache[:, :, :, 1], value_cache], dim=1)
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else:
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key, value = key_cache, value_cache
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cache = torch.stack([key_cache, value_cache], dim=3)
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inner_dim = key.shape[-1]
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head_dim = inner_dim // attn.heads
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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hidden_states = F.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
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hidden_states = hidden_states.to(query.dtype)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states, cache
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@maybe_allow_in_graph
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class CausalAttention(Attention):
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def __init__(
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self,
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query_dim: int,
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cross_attention_dim: Optional[int] = None,
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heads: int = 8,
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dim_head: int = 64,
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dropout: float = 0.0,
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bias: bool = False,
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upcast_attention: bool = False,
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upcast_softmax: bool = False,
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cross_attention_norm: Optional[str] = None,
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cross_attention_norm_num_groups: int = 32,
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qk_norm: Optional[str] = None,
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added_kv_proj_dim: Optional[int] = None,
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norm_num_groups: Optional[int] = None,
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spatial_norm_dim: Optional[int] = None,
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out_bias: bool = True,
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scale_qk: bool = True,
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only_cross_attention: bool = False,
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eps: float = 1e-5,
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rescale_output_factor: float = 1.0,
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residual_connection: bool = False,
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_from_deprecated_attn_block: bool = False,
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processor: Optional["AttnProcessor2_0"] = None,
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out_dim: int = None,
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):
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super(CausalAttention, self).__init__(query_dim, cross_attention_dim, heads, dim_head, dropout, bias, upcast_attention, upcast_softmax,
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cross_attention_norm, cross_attention_norm_num_groups, qk_norm, added_kv_proj_dim, norm_num_groups,
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spatial_norm_dim, out_bias, scale_qk, only_cross_attention, eps, rescale_output_factor, residual_connection,
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_from_deprecated_attn_block, processor, out_dim)
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processor = CausalAttnProcessor2_0()
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self.set_processor(processor)
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
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**cross_attention_kwargs,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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r"""
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The forward method of the `Attention` class.
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Args:
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hidden_states (`torch.Tensor`):
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The hidden states of the query.
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encoder_hidden_states (`torch.Tensor`, *optional*):
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The hidden states of the encoder.
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attention_mask (`torch.Tensor`, *optional*):
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The attention mask to use. If `None`, no mask is applied.
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**cross_attention_kwargs:
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Additional keyword arguments to pass along to the cross attention.
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Returns:
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`torch.Tensor`: The output of the attention layer.
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"""
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# The `Attention` class can call different attention processors / attention functions
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# here we simply pass along all tensors to the selected processor class
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# For standard processors that are defined here, `**cross_attention_kwargs` is empty
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attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
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unused_kwargs = [k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters]
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if len(unused_kwargs) > 0:
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logger.warning(
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f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
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)
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cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters}
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return self.processor(
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self,
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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attention_mask=attention_mask,
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cache=cache,
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**cross_attention_kwargs,
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)
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@maybe_allow_in_graph
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class CausalBasicTransformerBlock(BasicTransformerBlock):
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def __init__(
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self,
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dim: int,
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num_attention_heads: int,
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attention_head_dim: int,
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dropout=0.0,
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cross_attention_dim: Optional[int] = None,
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activation_fn: str = "geglu",
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num_embeds_ada_norm: Optional[int] = None,
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attention_bias: bool = False,
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only_cross_attention: bool = False,
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double_self_attention: bool = False,
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upcast_attention: bool = False,
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norm_elementwise_affine: bool = True,
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norm_type: str = "layer_norm",
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final_dropout: bool = False,
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):
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super(CausalBasicTransformerBlock, self).__init__(dim, num_attention_heads, attention_head_dim, dropout,
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cross_attention_dim, activation_fn, num_embeds_ada_norm,
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attention_bias, only_cross_attention, double_self_attention,
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upcast_attention, norm_elementwise_affine, norm_type, final_dropout)
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self.attn1 = CausalAttention(
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query_dim=dim,
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heads=num_attention_heads,
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dim_head=attention_head_dim,
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dropout=dropout,
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bias=attention_bias,
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cross_attention_dim=cross_attention_dim if only_cross_attention else None,
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upcast_attention=upcast_attention,
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)
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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timestep: Optional[torch.LongTensor] = None,
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cross_attention_kwargs: Dict[str, Any] = None,
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class_labels: Optional[torch.LongTensor] = None,
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cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Notice that normalization is always applied before the real computation in the following blocks.
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# 1. Self-Attention
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if self.use_ada_layer_norm:
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norm_hidden_states = self.norm1(hidden_states, timestep)
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elif self.use_ada_layer_norm_zero:
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norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
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hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
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)
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else:
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norm_hidden_states = self.norm1(hidden_states)
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cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
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attn_output, cache = self.attn1(
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norm_hidden_states,
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encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
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attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask,
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cache=cache,
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**cross_attention_kwargs,
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)
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if self.use_ada_layer_norm_zero:
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attn_output = gate_msa.unsqueeze(1) * attn_output
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hidden_states = attn_output + hidden_states
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# 2. Cross-Attention
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if self.attn2 is not None:
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norm_hidden_states = (
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self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
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)
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attn_output = self.attn2(
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norm_hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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attention_mask=encoder_attention_mask,
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**cross_attention_kwargs,
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)
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hidden_states = attn_output + hidden_states
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# 3. Feed-forward
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norm_hidden_states = self.norm3(hidden_states)
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if self.use_ada_layer_norm_zero:
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norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
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if self._chunk_size is not None:
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# "feed_forward_chunk_size" can be used to save memory
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if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
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raise ValueError(f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: \
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{self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.")
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num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
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ff_output = torch.cat(
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[self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
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dim=self._chunk_dim,
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)
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else:
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ff_output = self.ff(norm_hidden_states)
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if self.use_ada_layer_norm_zero:
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ff_output = gate_mlp.unsqueeze(1) * ff_output
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hidden_states = ff_output + hidden_states
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return hidden_states, cache
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class ConditionalDecoder(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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channels=(256, 256),
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dropout=0.05,
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attention_head_dim=64,
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n_blocks=1,
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num_mid_blocks=2,
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num_heads=4,
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act_fn="snake",
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):
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"""
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This decoder requires an input with the same shape of the target. So, if your text content
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is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
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"""
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super().__init__()
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channels = tuple(channels)
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.time_embeddings = SinusoidalPosEmb(in_channels)
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time_embed_dim = channels[0] * 4
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self.time_mlp = TimestepEmbedding(
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in_channels=in_channels,
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time_embed_dim=time_embed_dim,
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act_fn="silu",
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)
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self.down_blocks = nn.ModuleList([])
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self.mid_blocks = nn.ModuleList([])
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self.up_blocks = nn.ModuleList([])
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output_channel = in_channels
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for i in range(len(channels)): # pylint: disable=consider-using-enumerate
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input_channel = output_channel
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output_channel = channels[i]
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is_last = i == len(channels) - 1
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resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
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transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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dim=output_channel,
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num_attention_heads=num_heads,
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attention_head_dim=attention_head_dim,
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dropout=dropout,
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activation_fn=act_fn,
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)
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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
|
|
|
|
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()
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|
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,
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|
attention_mask=attn_mask,
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|
timestep=t,
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|
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()
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|
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
|