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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from modules.general.utils import Conv1d, normalization, zero_module | |
| from .basic import UNetBlock | |
| class AttentionBlock(UNetBlock): | |
| r"""A spatial transformer encoder block that allows spatial positions to attend | |
| to each other. Reference from `latent diffusion repo | |
| <https://github.com/Stability-AI/generative-models/blob/main/sgm/modules/attention.py#L531>`_. | |
| Args: | |
| channels: Number of channels in the input. | |
| num_head_channels: Number of channels per attention head. | |
| num_heads: Number of attention heads. Overrides ``num_head_channels`` if set. | |
| encoder_channels: Number of channels in the encoder output for cross-attention. | |
| If ``None``, then self-attention is performed. | |
| use_self_attention: Whether to use self-attention before cross-attention, only applicable if encoder_channels is set. | |
| dims: Number of spatial dimensions, i.e. 1 for temporal signals, 2 for images. | |
| h_dim: The dimension of the height, would be applied if ``dims`` is 2. | |
| encoder_hdim: The dimension of the height of the encoder output, would be applied if ``dims`` is 2. | |
| p_dropout: Dropout probability. | |
| """ | |
| def __init__( | |
| self, | |
| channels: int, | |
| num_head_channels: int = 32, | |
| num_heads: int = -1, | |
| encoder_channels: int = None, | |
| use_self_attention: bool = False, | |
| dims: int = 1, | |
| h_dim: int = 100, | |
| encoder_hdim: int = 384, | |
| p_dropout: float = 0.0, | |
| ): | |
| super().__init__() | |
| self.channels = channels | |
| self.p_dropout = p_dropout | |
| self.dims = dims | |
| if dims == 1: | |
| self.channels = channels | |
| elif dims == 2: | |
| # We consider the channel as product of channel and height, i.e. C x H | |
| # This is because we want to apply attention on the audio signal, which is 1D | |
| self.channels = channels * h_dim | |
| else: | |
| raise ValueError(f"invalid number of dimensions: {dims}") | |
| if num_head_channels == -1: | |
| assert ( | |
| self.channels % num_heads == 0 | |
| ), f"q,k,v channels {self.channels} is not divisible by num_heads {num_heads}" | |
| self.num_heads = num_heads | |
| self.num_head_channels = self.channels // num_heads | |
| else: | |
| assert ( | |
| self.channels % num_head_channels == 0 | |
| ), f"q,k,v channels {self.channels} is not divisible by num_head_channels {num_head_channels}" | |
| self.num_heads = self.channels // num_head_channels | |
| self.num_head_channels = num_head_channels | |
| if encoder_channels is not None: | |
| self.use_self_attention = use_self_attention | |
| if dims == 1: | |
| self.encoder_channels = encoder_channels | |
| elif dims == 2: | |
| self.encoder_channels = encoder_channels * encoder_hdim | |
| else: | |
| raise ValueError(f"invalid number of dimensions: {dims}") | |
| if use_self_attention: | |
| self.self_attention = BasicAttentionBlock( | |
| self.channels, | |
| self.num_head_channels, | |
| self.num_heads, | |
| p_dropout=self.p_dropout, | |
| ) | |
| self.cross_attention = BasicAttentionBlock( | |
| self.channels, | |
| self.num_head_channels, | |
| self.num_heads, | |
| self.encoder_channels, | |
| p_dropout=self.p_dropout, | |
| ) | |
| else: | |
| self.encoder_channels = None | |
| self.self_attention = BasicAttentionBlock( | |
| self.channels, | |
| self.num_head_channels, | |
| self.num_heads, | |
| p_dropout=self.p_dropout, | |
| ) | |
| def forward(self, x: torch.Tensor, encoder_output: torch.Tensor = None): | |
| r""" | |
| Args: | |
| x: input tensor with shape [B x ``channels`` x ...] | |
| encoder_output: feature tensor with shape [B x ``encoder_channels`` x ...], if ``None``, then self-attention is performed. | |
| Returns: | |
| output tensor with shape [B x ``channels`` x ...] | |
| """ | |
| shape = x.size() | |
| x = x.reshape(shape[0], self.channels, -1).contiguous() | |
| if self.encoder_channels is None: | |
| assert ( | |
| encoder_output is None | |
| ), "encoder_output must be None for self-attention." | |
| h = self.self_attention(x) | |
| else: | |
| assert ( | |
| encoder_output is not None | |
| ), "encoder_output must be given for cross-attention." | |
| encoder_output = encoder_output.reshape( | |
| shape[0], self.encoder_channels, -1 | |
| ).contiguous() | |
| if self.use_self_attention: | |
| x = self.self_attention(x) | |
| h = self.cross_attention(x, encoder_output) | |
| return h.reshape(*shape).contiguous() | |
| class BasicAttentionBlock(nn.Module): | |
| def __init__( | |
| self, | |
| channels: int, | |
| num_head_channels: int = 32, | |
| num_heads: int = -1, | |
| context_channels: int = None, | |
| p_dropout: float = 0.0, | |
| ): | |
| super().__init__() | |
| self.channels = channels | |
| self.p_dropout = p_dropout | |
| self.context_channels = context_channels | |
| if num_head_channels == -1: | |
| assert ( | |
| self.channels % num_heads == 0 | |
| ), f"q,k,v channels {self.channels} is not divisible by num_heads {num_heads}" | |
| self.num_heads = num_heads | |
| self.num_head_channels = self.channels // num_heads | |
| else: | |
| assert ( | |
| self.channels % num_head_channels == 0 | |
| ), f"q,k,v channels {self.channels} is not divisible by num_head_channels {num_head_channels}" | |
| self.num_heads = self.channels // num_head_channels | |
| self.num_head_channels = num_head_channels | |
| if context_channels is not None: | |
| self.to_q = nn.Sequential( | |
| normalization(self.channels), | |
| Conv1d(self.channels, self.channels, 1), | |
| ) | |
| self.to_kv = Conv1d(context_channels, 2 * self.channels, 1) | |
| else: | |
| self.to_qkv = nn.Sequential( | |
| normalization(self.channels), | |
| Conv1d(self.channels, 3 * self.channels, 1), | |
| ) | |
| self.linear = Conv1d(self.channels, self.channels) | |
| self.proj_out = nn.Sequential( | |
| normalization(self.channels), | |
| Conv1d(self.channels, self.channels, 1), | |
| nn.GELU(), | |
| nn.Dropout(p=self.p_dropout), | |
| zero_module(Conv1d(self.channels, self.channels, 1)), | |
| ) | |
| def forward(self, q: torch.Tensor, kv: torch.Tensor = None): | |
| r""" | |
| Args: | |
| q: input tensor with shape [B, ``channels``, L] | |
| kv: feature tensor with shape [B, ``context_channels``, T], if ``None``, then self-attention is performed. | |
| Returns: | |
| output tensor with shape [B, ``channels``, L] | |
| """ | |
| N, C, L = q.size() | |
| if self.context_channels is not None: | |
| assert kv is not None, "kv must be given for cross-attention." | |
| q = ( | |
| self.to_q(q) | |
| .reshape(self.num_heads, self.num_head_channels, -1) | |
| .transpose(-1, -2) | |
| .contiguous() | |
| ) | |
| kv = ( | |
| self.to_kv(kv) | |
| .reshape(2, self.num_heads, self.num_head_channels, -1) | |
| .transpose(-1, -2) | |
| .chunk(2) | |
| ) | |
| k, v = ( | |
| kv[0].squeeze(0).contiguous(), | |
| kv[1].squeeze(0).contiguous(), | |
| ) | |
| else: | |
| qkv = ( | |
| self.to_qkv(q) | |
| .reshape(3, self.num_heads, self.num_head_channels, -1) | |
| .transpose(-1, -2) | |
| .chunk(3) | |
| ) | |
| q, k, v = ( | |
| qkv[0].squeeze(0).contiguous(), | |
| qkv[1].squeeze(0).contiguous(), | |
| qkv[2].squeeze(0).contiguous(), | |
| ) | |
| h = F.scaled_dot_product_attention(q, k, v, dropout_p=self.p_dropout).transpose( | |
| -1, -2 | |
| ) | |
| h = h.reshape(N, -1, L).contiguous() | |
| h = self.linear(h) | |
| x = q + h | |
| h = self.proj_out(x) | |
| return x + h | |