audioldm2/latent_diffusion/modules/attention.py

from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat

from audioldm2.latent_diffusion.modules.diffusionmodules.util import checkpoint


def exists(val):
    return val is not None


def uniq(arr):
    return {el: True for el in arr}.keys()


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def max_neg_value(t):
    return -torch.finfo(t.dtype).max


def init_(tensor):
    dim = tensor.shape[-1]
    std = 1 / math.sqrt(dim)
    tensor.uniform_(-std, std)
    return tensor


# feedforward
class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = (
            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
            if not glu
            else GEGLU(dim, inner_dim)
        )

        self.net = nn.Sequential(
            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
        )

    def forward(self, x):
        return self.net(x)


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def Normalize(in_channels):
    return torch.nn.GroupNorm(
        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
    )


class LinearAttention(nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super().__init__()
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x)
        q, k, v = rearrange(
            qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
        )
        k = k.softmax(dim=-1)
        context = torch.einsum("bhdn,bhen->bhde", k, v)
        out = torch.einsum("bhde,bhdn->bhen", context, q)
        out = rearrange(
            out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
        )
        return self.to_out(out)


class SpatialSelfAttention(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = rearrange(q, "b c h w -> b (h w) c")
        k = rearrange(k, "b c h w -> b c (h w)")
        w_ = torch.einsum("bij,bjk->bik", q, k)

        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = rearrange(v, "b c h w -> b c (h w)")
        w_ = rearrange(w_, "b i j -> b j i")
        h_ = torch.einsum("bij,bjk->bik", v, w_)
        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
        h_ = self.proj_out(h_)

        return x + h_


# class CrossAttention(nn.Module):
#     """
#     ### Cross Attention Layer
#     This falls-back to self-attention when conditional embeddings are not specified.
#     """

#     use_flash_attention: bool = True

#     # use_flash_attention: bool = False
#     def __init__(
#         self,
#         query_dim,
#         context_dim=None,
#         heads=8,
#         dim_head=64,
#         dropout=0.0,
#         is_inplace: bool = True,
#     ):
#         # def __init__(self, d_model: int, d_cond: int, n_heads: int, d_head: int, is_inplace: bool = True):
#         """
#         :param d_model: is the input embedding size
#         :param n_heads: is the number of attention heads
#         :param d_head: is the size of a attention head
#         :param d_cond: is the size of the conditional embeddings
#         :param is_inplace: specifies whether to perform the attention softmax computation inplace to
#             save memory
#         """
#         super().__init__()

#         self.is_inplace = is_inplace
#         self.n_heads = heads
#         self.d_head = dim_head

#         # Attention scaling factor
#         self.scale = dim_head**-0.5

#         # The normal self-attention layer
#         if context_dim is None:
#             context_dim = query_dim

#         # Query, key and value mappings
#         d_attn = dim_head * heads
#         self.to_q = nn.Linear(query_dim, d_attn, bias=False)
#         self.to_k = nn.Linear(context_dim, d_attn, bias=False)
#         self.to_v = nn.Linear(context_dim, d_attn, bias=False)

#         # Final linear layer
#         self.to_out = nn.Sequential(nn.Linear(d_attn, query_dim), nn.Dropout(dropout))

#         # Setup [flash attention](https://github.com/HazyResearch/flash-attention).
#         # Flash attention is only used if it's installed
#         # and `CrossAttention.use_flash_attention` is set to `True`.
#         try:
#             # You can install flash attention by cloning their Github repo,
#             # [https://github.com/HazyResearch/flash-attention](https://github.com/HazyResearch/flash-attention)
#             # and then running `python setup.py install`
#             from flash_attn.flash_attention import FlashAttention

#             self.flash = FlashAttention()
#             # Set the scale for scaled dot-product attention.
#             self.flash.softmax_scale = self.scale
#         # Set to `None` if it's not installed
#         except ImportError:
#             self.flash = None

#     def forward(self, x, context=None, mask=None):
#         """
#         :param x: are the input embeddings of shape `[batch_size, height * width, d_model]`
#         :param cond: is the conditional embeddings of shape `[batch_size, n_cond, d_cond]`
#         """

#         # If `cond` is `None` we perform self attention
#         has_cond = context is not None
#         if not has_cond:
#             context = x

#         # Get query, key and value vectors
#         q = self.to_q(x)
#         k = self.to_k(context)
#         v = self.to_v(context)

#         # Use flash attention if it's available and the head size is less than or equal to `128`
#         if (
#             CrossAttention.use_flash_attention
#             and self.flash is not None
#             and not has_cond
#             and self.d_head <= 128
#         ):
#             return self.flash_attention(q, k, v)
#         # Otherwise, fallback to normal attention
#         else:
#             return self.normal_attention(q, k, v)

#     def flash_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
#         """
#         #### Flash Attention
#         :param q: are the query vectors before splitting heads, of shape `[batch_size, seq, d_attn]`
#         :param k: are the query vectors before splitting heads, of shape `[batch_size, seq, d_attn]`
#         :param v: are the query vectors before splitting heads, of shape `[batch_size, seq, d_attn]`
#         """

#         # Get batch size and number of elements along sequence axis (`width * height`)
#         batch_size, seq_len, _ = q.shape

#         # Stack `q`, `k`, `v` vectors for flash attention, to get a single tensor of
#         # shape `[batch_size, seq_len, 3, n_heads * d_head]`
#         qkv = torch.stack((q, k, v), dim=2)
#         # Split the heads
#         qkv = qkv.view(batch_size, seq_len, 3, self.n_heads, self.d_head)

#         # Flash attention works for head sizes `32`, `64` and `128`, so we have to pad the heads to
#         # fit this size.
#         if self.d_head <= 32:
#             pad = 32 - self.d_head
#         elif self.d_head <= 64:
#             pad = 64 - self.d_head
#         elif self.d_head <= 128:
#             pad = 128 - self.d_head
#         else:
#             raise ValueError(f"Head size ${self.d_head} too large for Flash Attention")

#         # Pad the heads
#         if pad:
#             qkv = torch.cat(
#                 (qkv, qkv.new_zeros(batch_size, seq_len, 3, self.n_heads, pad)), dim=-1
#             )

#         # Compute attention
#         # $$\underset{seq}{softmax}\Bigg(\frac{Q K^\top}{\sqrt{d_{key}}}\Bigg)V$$
#         # This gives a tensor of shape `[batch_size, seq_len, n_heads, d_padded]`
#         # TODO here I add the dtype changing
#         out, _ = self.flash(qkv.type(torch.float16))
#         # Truncate the extra head size
#         out = out[:, :, :, : self.d_head].float()
#         # Reshape to `[batch_size, seq_len, n_heads * d_head]`
#         out = out.reshape(batch_size, seq_len, self.n_heads * self.d_head)

#         # Map to `[batch_size, height * width, d_model]` with a linear layer
#         return self.to_out(out)

#     def normal_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
#         """
#         #### Normal Attention

#         :param q: are the query vectors before splitting heads, of shape `[batch_size, seq, d_attn]`
#         :param k: are the query vectors before splitting heads, of shape `[batch_size, seq, d_attn]`
#         :param v: are the query vectors before splitting heads, of shape `[batch_size, seq, d_attn]`
#         """

#         # Split them to heads of shape `[batch_size, seq_len, n_heads, d_head]`
#         q = q.view(*q.shape[:2], self.n_heads, -1)  # [bs, 64, 20, 32]
#         k = k.view(*k.shape[:2], self.n_heads, -1)  # [bs, 1, 20, 32]
#         v = v.view(*v.shape[:2], self.n_heads, -1)

#         # Calculate attention $\frac{Q K^\top}{\sqrt{d_{key}}}$
#         attn = torch.einsum("bihd,bjhd->bhij", q, k) * self.scale

#         # Compute softmax
#         # $$\underset{seq}{softmax}\Bigg(\frac{Q K^\top}{\sqrt{d_{key}}}\Bigg)$$
#         if self.is_inplace:
#             half = attn.shape[0] // 2
#             attn[half:] = attn[half:].softmax(dim=-1)
#             attn[:half] = attn[:half].softmax(dim=-1)
#         else:
#             attn = attn.softmax(dim=-1)

#         # Compute attention output
#         # $$\underset{seq}{softmax}\Bigg(\frac{Q K^\top}{\sqrt{d_{key}}}\Bigg)V$$
#         # attn: [bs, 20, 64, 1]
#         # v: [bs, 1, 20, 32]
#         out = torch.einsum("bhij,bjhd->bihd", attn, v)
#         # Reshape to `[batch_size, height * width, n_heads * d_head]`
#         out = out.reshape(*out.shape[:2], -1)
#         # Map to `[batch_size, height * width, d_model]` with a linear layer
#         return self.to_out(out)


class CrossAttention(nn.Module):
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head**-0.5
        self.heads = heads

        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
        )

    def forward(self, x, context=None, mask=None):
        h = self.heads

        q = self.to_q(x)
        context = default(context, x)

        k = self.to_k(context)
        v = self.to_v(context)

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))

        sim = einsum("b i d, b j d -> b i j", q, k) * self.scale

        if exists(mask):
            mask = rearrange(mask, "b ... -> b (...)")
            max_neg_value = -torch.finfo(sim.dtype).max
            mask = repeat(mask, "b j -> (b h) () j", h=h)
            sim.masked_fill_(~(mask == 1), max_neg_value)

        # attention, what we cannot get enough of
        attn = sim.softmax(dim=-1)

        out = einsum("b i j, b j d -> b i d", attn, v)
        out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
        return self.to_out(out)


class BasicTransformerBlock(nn.Module):
    def __init__(
        self,
        dim,
        n_heads,
        d_head,
        dropout=0.0,
        context_dim=None,
        gated_ff=True,
        checkpoint=True,
    ):
        super().__init__()
        self.attn1 = CrossAttention(
            query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
        )  # is a self-attention
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = CrossAttention(
            query_dim=dim,
            context_dim=context_dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
        )  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward(self, x, context=None, mask=None):
        if context is None:
            return checkpoint(self._forward, (x,), self.parameters(), self.checkpoint)
        else:
            return checkpoint(
                self._forward, (x, context, mask), self.parameters(), self.checkpoint
            )

    def _forward(self, x, context=None, mask=None):
        x = self.attn1(self.norm1(x)) + x
        x = self.attn2(self.norm2(x), context=context, mask=mask) + x
        x = self.ff(self.norm3(x)) + x
        return x


class SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """

    def __init__(
        self,
        in_channels,
        n_heads,
        d_head,
        depth=1,
        dropout=0.0,
        context_dim=None,
    ):
        super().__init__()

        context_dim = context_dim

        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = Normalize(in_channels)

        self.proj_in = nn.Conv2d(
            in_channels, inner_dim, kernel_size=1, stride=1, padding=0
        )

        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim
                )
                for d in range(depth)
            ]
        )

        self.proj_out = zero_module(
            nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
        )

    def forward(self, x, context=None, mask=None):
        # note: if no context is given, cross-attention defaults to self-attention
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        x = self.proj_in(x)
        x = rearrange(x, "b c h w -> b (h w) c")
        for block in self.transformer_blocks:
            x = block(x, context=context, mask=mask)
        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
        x = self.proj_out(x)
        return x + x_in