audioldm/latent_diffusion/attention.py

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

from audioldm.latent_diffusion.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.):
#     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, 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):
        if context is None:
            return checkpoint(self._forward, (x,), self.parameters(), self.checkpoint)
        else:
            return checkpoint(
                self._forward, (x, context), self.parameters(), self.checkpoint
            )

    def _forward(self, x, context=None):
        x = self.attn1(self.norm1(x)) + x
        x = self.attn2(self.norm2(x), context=context) + 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,
        no_context=False,
    ):
        super().__init__()

        if no_context:
            context_dim = None

        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):
        # 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)
        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
        x = self.proj_out(x)
        return x + x_in