easyanimate/vae/ldm/modules/vaemodules/attention.py

import inspect

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange

from .attention_processors import AttnProcessor, AttnProcessor2_0
from .common import SpatialNorm3D


class Attention(nn.Module):
    r"""
    A cross attention layer.

    Parameters:
        query_dim (`int`):
            The number of channels in the query.
        cross_attention_dim (`int`, *optional*):
            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
        nheads (`int`,  *optional*, defaults to 8):
            The number of heads to use for multi-head attention.
        head_dim (`int`,  *optional*, defaults to 64):
            The number of channels in each head.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability to use.
        bias (`bool`, *optional*, defaults to False):
            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
        upcast_attention (`bool`, *optional*, defaults to False):
            Set to `True` to upcast the attention computation to `float32`.
        upcast_softmax (`bool`, *optional*, defaults to False):
            Set to `True` to upcast the softmax computation to `float32`.
        cross_attention_norm (`str`, *optional*, defaults to `None`):
            The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
        cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
            The number of groups to use for the group norm in the cross attention.
        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the added key and value projections. If `None`, no projection is used.
        norm_num_groups (`int`, *optional*, defaults to `None`):
            The number of groups to use for the group norm in the attention.
        spatial_norm_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the spatial normalization.
        out_bias (`bool`, *optional*, defaults to `True`):
            Set to `True` to use a bias in the output linear layer.
        scale_qk (`bool`, *optional*, defaults to `True`):
            Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
        only_cross_attention (`bool`, *optional*, defaults to `False`):
            Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
            `added_kv_proj_dim` is not `None`.
        eps (`float`, *optional*, defaults to 1e-5):
            An additional value added to the denominator in group normalization that is used for numerical stability.
        rescale_output_factor (`float`, *optional*, defaults to 1.0):
            A factor to rescale the output by dividing it with this value.
        residual_connection (`bool`, *optional*, defaults to `False`):
            Set to `True` to add the residual connection to the output.
        _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
            Set to `True` if the attention block is loaded from a deprecated state dict.
        processor (`AttnProcessor`, *optional*, defaults to `None`):
            The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
            `AttnProcessor` otherwise.
    """

    def __init__(
        self,
        query_dim: int,
        cross_attention_dim: int = None,
        nheads: int = 8,
        head_dim: int = 64,
        dropout: float = 0.0,
        bias: bool = False,
        upcast_attention: bool = False,
        upcast_softmax: bool = False,
        cross_attention_norm = None,
        cross_attention_norm_num_groups: int = 32,
        added_kv_proj_dim = None,
        norm_num_groups = None,
        spatial_norm_dim = 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,
        processor = None,
        out_dim: int = None,
    ):
        super().__init__()

        self.query_dim = query_dim
        self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
        self.inner_dim = out_dim if out_dim is not None else head_dim * nheads
        self.nheads = out_dim // head_dim if out_dim is not None else nheads
        self.out_dim = out_dim if out_dim is not None else query_dim

        self.upcast_attention = upcast_attention
        self.upcast_softmax = upcast_softmax
        self.added_kv_proj_dim = added_kv_proj_dim
        self.only_cross_attention = only_cross_attention

        if self.added_kv_proj_dim is None and self.only_cross_attention:
            raise ValueError(
                "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
            )

        self.scale_qk = scale_qk
        self.scale = head_dim ** -0.5 if scale_qk else 1.0

        self.rescale_output_factor = rescale_output_factor
        self.residual_connection = residual_connection

        if norm_num_groups is not None:
            self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)
        else:
            self.group_norm = None

        if spatial_norm_dim is not None:
            self.spatial_norm = SpatialNorm3D(f_channels=query_dim, zq_channels=spatial_norm_dim)
        else:
            self.spatial_norm = None

        if cross_attention_norm is None:
            self.norm_cross = None
        elif cross_attention_norm == "layer_norm":
            self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
        elif cross_attention_norm == "group_norm":
            if self.added_kv_proj_dim is not None:
                # The given `encoder_hidden_states` are initially of shape
                # (batch_size, seq_len, added_kv_proj_dim) before being projected
                # to (batch_size, seq_len, cross_attention_dim). The norm is applied
                # before the projection, so we need to use `added_kv_proj_dim` as
                # the number of channels for the group norm.
                norm_cross_num_channels = added_kv_proj_dim
            else:
                norm_cross_num_channels = self.cross_attention_dim

            self.norm_cross = nn.GroupNorm(
                num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
            )
        else:
            raise ValueError(
                f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
            )

        self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)

        if not self.only_cross_attention:
            self.to_k = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias)
            self.to_v = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias)
        else:
            self.to_k = None
            self.to_v = None

        if self.added_kv_proj_dim is not None:
            self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_dim)
            self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_dim)

        self.to_out = nn.Linear(self.inner_dim, self.out_dim, bias=out_bias)
        self.dropout = nn.Dropout(dropout)

        if processor is None:
            processor = (
                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor()
            )
        self.set_processor(processor)

    def set_processor(self, processor: AttnProcessor) -> None:
        r"""
        Set the attention processor to use.

        Args:
            processor (`AttnProcessor`):
                The attention processor to use.
        """
        # if current processor is in `self._modules` and if passed `processor` is not, we need to
        # pop `processor` from `self._modules`
        if (
            hasattr(self, "processor")
            and isinstance(self.processor, torch.nn.Module)
            and not isinstance(processor, torch.nn.Module)
        ):
            self._modules.pop("processor")

        self.processor = processor
        self._attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())

    def prepare_attention_mask(
        self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3
    ) -> torch.Tensor:
        r"""
        Prepare the attention mask for the attention computation.

        Args:
            attention_mask (`torch.Tensor`):
                The attention mask to prepare.
            target_length (`int`):
                The target length of the attention mask. This is the length of the attention mask after padding.
            batch_size (`int`):
                The batch size, which is used to repeat the attention mask.
            out_dim (`int`, *optional*, defaults to `3`):
                The output dimension of the attention mask. Can be either `3` or `4`.

        Returns:
            `torch.Tensor`: The prepared attention mask.
        """
        head_size = self.nheads
        if attention_mask is None:
            return attention_mask

        current_length: int = attention_mask.shape[-1]
        if current_length != target_length:
            if attention_mask.device.type == "mps":
                # HACK: MPS: Does not support padding by greater than dimension of input tensor.
                # Instead, we can manually construct the padding tensor.
                padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)
                padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)
                attention_mask = torch.cat([attention_mask, padding], dim=2)
            else:
                # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
                #       we want to instead pad by (0, remaining_length), where remaining_length is:
                #       remaining_length: int = target_length - current_length
                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)

        if out_dim == 3:
            if attention_mask.shape[0] < batch_size * head_size:
                attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
        elif out_dim == 4:
            attention_mask = attention_mask.unsqueeze(1)
            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)

        return attention_mask

    def get_attention_scores(
        self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None
    ) -> torch.Tensor:
        r"""
        Compute the attention scores.

        Args:
            query (`torch.Tensor`): The query tensor.
            key (`torch.Tensor`): The key tensor.
            attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.

        Returns:
            `torch.Tensor`: The attention probabilities/scores.
        """
        dtype = query.dtype
        if self.upcast_attention:
            query = query.float()
            key = key.float()

        if attention_mask is None:
            baddbmm_input = torch.empty(
                query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
            )
            beta = 0
        else:
            baddbmm_input = attention_mask
            beta = 1

        attention_scores = torch.baddbmm(
            baddbmm_input,
            query,
            key.transpose(-1, -2),
            beta=beta,
            alpha=self.scale,
        )
        del baddbmm_input

        if self.upcast_softmax:
            attention_scores = attention_scores.float()

        attention_probs = attention_scores.softmax(dim=-1)
        del attention_scores

        attention_probs = attention_probs.to(dtype)

        return attention_probs

    def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
        r"""
        Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
        `Attention` class.

        Args:
            encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.

        Returns:
            `torch.Tensor`: The normalized encoder hidden states.
        """
        assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"

        if isinstance(self.norm_cross, nn.LayerNorm):
            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
        elif isinstance(self.norm_cross, nn.GroupNorm):
            # Group norm norms along the channels dimension and expects
            # input to be in the shape of (N, C, *). In this case, we want
            # to norm along the hidden dimension, so we need to move
            # (batch_size, sequence_length, hidden_size) ->
            # (batch_size, hidden_size, sequence_length)
            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
        else:
            assert False

        return encoder_hidden_states

    def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
        r"""
        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // nheads, seq_len, dim * nheads]`. `nheads`
        is the number of heads initialized while constructing the `Attention` class.

        Args:
            tensor (`torch.Tensor`): The tensor to reshape.

        Returns:
            `torch.Tensor`: The reshaped tensor.
        """
        head_size = self.nheads
        batch_size, seq_len, dim = tensor.shape
        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
        return tensor

    def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
        r"""
        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, nheads, dim // nheads]` `nheads` is
        the number of heads initialized while constructing the `Attention` class.

        Args:
            tensor (`torch.Tensor`): The tensor to reshape.
            out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
                reshaped to `[batch_size * nheads, seq_len, dim // nheads]`.

        Returns:
            `torch.Tensor`: The reshaped tensor.
        """
        head_size = self.nheads
        batch_size, seq_len, dim = tensor.shape
        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
        tensor = tensor.permute(0, 2, 1, 3)

        if out_dim == 3:
            tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)

        return tensor

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor = None,
        attention_mask: torch.FloatTensor = None,
        **cross_attention_kwargs,
    ) -> 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

        unused_kwargs = [k for k, _ in cross_attention_kwargs.items() if k not in self._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 self._attn_parameters}

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


class SpatialAttention(Attention):
    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor = None,
        attention_mask: torch.FloatTensor = None,
        **cross_attention_kwargs,
    ) -> torch.Tensor:
        is_image = hidden_states.ndim == 4
        if is_image:
            hidden_states = rearrange(hidden_states, "b c h w -> b c 1 h w")

        bsz, h = hidden_states.shape[0], hidden_states.shape[3]
        hidden_states = rearrange(hidden_states, "b c t h w -> (b t) (h w) c")
        if encoder_hidden_states is not None:
            encoder_hidden_states = rearrange(encoder_hidden_states, "b c t h w -> (b t) (h w) c")

        if attention_mask is not None:
            attention_mask = rearrange(attention_mask, "b t h w -> (b t) (h w)")

        hidden_states = super().forward(
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )

        hidden_states = rearrange(hidden_states, "(b t) (h w) c -> b c t h w", b=bsz, h=h)

        if is_image:
            hidden_states = rearrange(hidden_states, "b c 1 h w -> b c h w")

        return hidden_states


class TemporalAttention(Attention):
    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor = None,
        attention_mask: torch.FloatTensor = None,
        **cross_attention_kwargs,
    ) -> torch.Tensor:
        bsz, h = hidden_states.shape[0], hidden_states.shape[3]
        hidden_states = rearrange(hidden_states, "b c t h w -> (b h w) t c")
        if encoder_hidden_states is not None:
            encoder_hidden_states = rearrange(encoder_hidden_states, "b c t h w -> (b h w) t c")

        if attention_mask is not None:
            attention_mask = rearrange(attention_mask, "b t h w -> (b h w) t")

        hidden_states = super().forward(
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )

        hidden_states = rearrange(hidden_states, "(b h w) t c -> b c t h w", b=bsz, h=h)

        return hidden_states


class Attention3D(Attention):
    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor = None,
        attention_mask: torch.FloatTensor = None,
        **cross_attention_kwargs,
    ) -> torch.Tensor:
        t, h = hidden_states.shape[2], hidden_states.shape[3]
        hidden_states = rearrange(hidden_states, "b c t h w -> b (t h w) c")
        if encoder_hidden_states is not None:
            encoder_hidden_states = rearrange(encoder_hidden_states, "b c t h w -> b (t h w) c")

        if attention_mask is not None:
            attention_mask = rearrange(attention_mask, "b t h w -> b (t h w)")

        hidden_states = super().forward(
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )

        hidden_states = rearrange(hidden_states, "b (t h w) c -> b c t h w", t=t, h=h)

        return hidden_states