attention2d.py

# unet_grad_tts.py
class LinearAttention(torch.nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super(LinearAttention, self).__init__()
        self.heads = heads
        self.dim_head = dim_head
        hidden_dim = dim_head * heads
        self.to_qkv = torch.nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
        self.to_out = torch.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)
        q, k, v = (
            qkv.reshape(b, 3, self.heads, self.dim_head, h, w)
            .permute(1, 0, 2, 3, 4, 5)
            .reshape(3, b, self.heads, self.dim_head, -1)
        )
        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)
        out = out.reshape(b, self.heads, self.dim_head, h, w).reshape(b, self.heads * self.dim_head, h, w)
        return self.to_out(out)

# unet.py
class AttnBlock(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 = q.reshape(b, c, h * w)
        q = q.permute(0, 2, 1)  # b,hw,c
        k = k.reshape(b, c, h * w)  # b,c,hw
        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b, c, h * w)
        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
        h_ = h_.reshape(b, c, h, w)

        h_ = self.proj_out(h_)

        return x + h_

# unet_glide.py
class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other.

    Originally ported from here, but adapted to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_checkpoint=False,
        encoder_channels=None,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.use_checkpoint = use_checkpoint
        self.norm = normalization(channels, swish=0.0)
        self.qkv = conv_nd(1, channels, channels * 3, 1)
        self.attention = QKVAttention(self.num_heads)

        if encoder_channels is not None:
            self.encoder_kv = conv_nd(1, encoder_channels, channels * 2, 1)
        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))

    def forward(self, x, encoder_out=None):
        b, c, *spatial = x.shape
        qkv = self.qkv(self.norm(x).view(b, c, -1))
        if encoder_out is not None:
            encoder_out = self.encoder_kv(encoder_out)
            h = self.attention(qkv, encoder_out)
        else:
            h = self.attention(qkv)
        h = self.proj_out(h)
        return x + h.reshape(b, c, *spatial)

class QKVAttention(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv, encoder_kv=None):
        """
        Apply QKV attention.

        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after
        attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        if encoder_kv is not None:
            assert encoder_kv.shape[1] == self.n_heads * ch * 2
            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
            k = torch.cat([ek, k], dim=-1)
            v = torch.cat([ev, v], dim=-1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = torch.einsum("bts,bcs->bct", weight, v)
        return a.reshape(bs, -1, length)


# unet_ldm.py
class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted
    to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_checkpoint=False,
        use_new_attention_order=False,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.use_checkpoint = use_checkpoint
        self.norm = normalization(channels)
        self.qkv = conv_nd(1, channels, channels * 3, 1)
        # split heads before split qkv
        self.attention = QKVAttentionLegacy(self.num_heads)

        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))

    def forward(self, x):
        b, c, *spatial = x.shape
        x = x.reshape(b, c, -1)
        qkv = self.qkv(self.norm(x))
        h = self.attention(qkv)
        h = self.proj_out(h)
        return (x + h).reshape(b, c, *spatial)

class QKVAttention(nn.Module):
    """
    A module which performs QKV attention and splits in a different order.
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x
        T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.chunk(3, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = torch.einsum(
            "bct,bcs->bts",
            (q * scale).view(bs * self.n_heads, ch, length),
            (k * scale).view(bs * self.n_heads, ch, length),
        )  # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)

# unet_score_estimation.py
class AttnBlockpp(nn.Module):
    """Channel-wise self-attention block. Modified from DDPM."""

    def __init__(self, channels, skip_rescale=False, init_scale=0.0):
        super().__init__()
        self.GroupNorm_0 = nn.GroupNorm(num_groups=min(channels // 4, 32), num_channels=channels, eps=1e-6)
        self.NIN_0 = NIN(channels, channels)
        self.NIN_1 = NIN(channels, channels)
        self.NIN_2 = NIN(channels, channels)
        self.NIN_3 = NIN(channels, channels, init_scale=init_scale)
        self.skip_rescale = skip_rescale

    def forward(self, x):
        B, C, H, W = x.shape
        h = self.GroupNorm_0(x)
        q = self.NIN_0(h)
        k = self.NIN_1(h)
        v = self.NIN_2(h)

        w = torch.einsum("bchw,bcij->bhwij", q, k) * (int(C) ** (-0.5))
        w = torch.reshape(w, (B, H, W, H * W))
        w = F.softmax(w, dim=-1)
        w = torch.reshape(w, (B, H, W, H, W))
        h = torch.einsum("bhwij,bcij->bchw", w, v)
        h = self.NIN_3(h)
        if not self.skip_rescale:
            return x + h
        else:
            return (x + h) / np.sqrt(2.0)