unet_ldm.py

import math
from inspect import isfunction

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D


# from .resnet import ResBlock


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)


def convert_module_to_f16(l):
    """
    Convert primitive modules to float16.
    """
    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
        l.weight.data = l.weight.data.half()
        if l.bias is not None:
            l.bias.data = l.bias.data.half()


def convert_module_to_f32(l):
    """
    Convert primitive modules to float32, undoing convert_module_to_f16().
    """
    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
        l.weight.data = l.weight.data.float()
        if l.bias is not None:
            l.bias.data = l.bias.data.float()


def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def linear(*args, **kwargs):
    """
    Create a linear module.
    """
    return nn.Linear(*args, **kwargs)


class GroupNorm32(nn.GroupNorm):
    def __init__(self, num_groups, num_channels, swish, eps=1e-5):
        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
        self.swish = swish

    def forward(self, x):
        y = super().forward(x.float()).to(x.dtype)
        if self.swish == 1.0:
            y = F.silu(y)
        elif self.swish:
            y = y * F.sigmoid(y * float(self.swish))
        return y


def normalization(channels, swish=0.0):
    """
    Make a standard normalization layer, with an optional swish activation.

    :param channels: number of input channels. :return: an nn.Module for normalization.
    """
    return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)


class TimestepEmbedSequential(nn.Sequential):
    """
    A sequential module that passes timestep embeddings to the children that support it as an extra input.
    """

    def forward(self, x, emb, context=None):
        for layer in self:
            if isinstance(layer, ResnetBlock2D) or isinstance(layer, TimestepEmbedSequential):
                x = layer(x, emb)
            elif isinstance(layer, SpatialTransformer):
                x = layer(x, context)
            else:
                x = layer(x)
        return x


def count_flops_attn(model, _x, y):
    """
    A counter for the `thop` package to count the operations in an attention operation. Meant to be used like:
        macs, params = thop.profile(
            model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops},
        )
    """
    b, c, *spatial = y[0].shape
    num_spatial = int(np.prod(spatial))
    # We perform two matmuls with the same number of ops.
    # The first computes the weight matrix, the second computes
    # the combination of the value vectors.
    matmul_ops = 2 * b * (num_spatial**2) * c
    model.total_ops += torch.DoubleTensor([matmul_ops])


class UNetLDMModel(ModelMixin, ConfigMixin):
    """
    The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param
    model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param
    num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample
    rates at which
        attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x
        downsampling, attention will be used.
    :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param
    conv_resample: if True, use learned convolutions for upsampling and
        downsampling.
    :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this
    model will be
        class-conditional with `num_classes` classes.
    :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention
    heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use
                               a fixed channel width per attention head.
    :param num_heads_upsample: works with num_heads to set a different number
                               of heads for upsampling. Deprecated.
    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks
    for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially
                                    increased efficiency.
    """

    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        num_classes=None,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=-1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        use_spatial_transformer=False,  # custom transformer support
        transformer_depth=1,  # custom transformer support
        context_dim=None,  # custom transformer support
        n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
        legacy=True,
    ):
        super().__init__()

        # register all __init__ params with self.register
        self.register_to_config(
            image_size=image_size,
            in_channels=in_channels,
            model_channels=model_channels,
            out_channels=out_channels,
            num_res_blocks=num_res_blocks,
            attention_resolutions=attention_resolutions,
            dropout=dropout,
            channel_mult=channel_mult,
            conv_resample=conv_resample,
            dims=dims,
            num_classes=num_classes,
            use_fp16=use_fp16,
            num_heads=num_heads,
            num_head_channels=num_head_channels,
            num_heads_upsample=num_heads_upsample,
            use_scale_shift_norm=use_scale_shift_norm,
            resblock_updown=resblock_updown,
            use_spatial_transformer=use_spatial_transformer,
            transformer_depth=transformer_depth,
            context_dim=context_dim,
            n_embed=n_embed,
            legacy=legacy,
        )

        if use_spatial_transformer:
            assert (
                context_dim is not None
            ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..."

        if context_dim is not None:
            assert (
                use_spatial_transformer
            ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..."

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        if num_heads == -1:
            assert num_head_channels != -1, "Either num_heads or num_head_channels has to be set"

        if num_head_channels == -1:
            assert num_heads != -1, "Either num_heads or num_head_channels has to be set"

        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.dtype_ = torch.float16 if use_fp16 else torch.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        self.predict_codebook_ids = n_embed is not None

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        if self.num_classes is not None:
            self.label_emb = nn.Embedding(num_classes, time_embed_dim)

        self.input_blocks = nn.ModuleList(
            [TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))]
        )
        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                layers = [
                    ResnetBlock2D(
                        in_channels=ch,
                        out_channels=mult * model_channels,
                        dropout=dropout,
                        temb_channels=time_embed_dim,
                        eps=1e-5,
                        non_linearity="silu",
                        overwrite_for_ldm=True,
                    )
                ]
                ch = mult * model_channels
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        # num_heads = 1
                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
                    layers.append(
                        AttentionBlock(
                            ch,
                            num_heads=num_heads,
                            num_head_channels=dim_head,
                        )
                        if not use_spatial_transformer
                        else SpatialTransformer(
                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
                        )
                    )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        Downsample2D(ch, use_conv=conv_resample, out_channels=out_ch, padding=1, name="op")
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        if num_head_channels == -1:
            dim_head = ch // num_heads
        else:
            num_heads = ch // num_head_channels
            dim_head = num_head_channels
        if legacy:
            # num_heads = 1
            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels

        if dim_head < 0:
            dim_head = None
        self.mid = UNetMidBlock2D(
            in_channels=ch,
            dropout=dropout,
            temb_channels=time_embed_dim,
            resnet_eps=1e-5,
            resnet_act_fn="silu",
            resnet_time_scale_shift="scale_shift" if use_scale_shift_norm else "default",
            attention_layer_type="self" if not use_spatial_transformer else "spatial",
            attn_num_heads=num_heads,
            attn_num_head_channels=dim_head,
            attn_depth=transformer_depth,
            attn_encoder_channels=context_dim,
        )

        # TODO(Patrick) - delete after weight conversion
        # init to be able to overwrite `self.mid`
        self.middle_block = TimestepEmbedSequential(
            ResnetBlock2D(
                in_channels=ch,
                out_channels=None,
                dropout=dropout,
                temb_channels=time_embed_dim,
                eps=1e-5,
                non_linearity="silu",
                overwrite_for_ldm=True,
            ),
            AttentionBlock(
                ch,
                num_heads=num_heads,
                num_head_channels=dim_head,
            )
            if not use_spatial_transformer
            else SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim),
            ResnetBlock2D(
                in_channels=ch,
                out_channels=None,
                dropout=dropout,
                temb_channels=time_embed_dim,
                eps=1e-5,
                non_linearity="silu",
                overwrite_for_ldm=True,
            ),
        )
        self.mid.resnets[0] = self.middle_block[0]
        self.mid.attentions[0] = self.middle_block[1]
        self.mid.resnets[1] = self.middle_block[2]

        self._feature_size += ch

        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]:
            for i in range(num_res_blocks + 1):
                ich = input_block_chans.pop()
                layers = [
                    ResnetBlock2D(
                        in_channels=ch + ich,
                        out_channels=model_channels * mult,
                        dropout=dropout,
                        temb_channels=time_embed_dim,
                        eps=1e-5,
                        non_linearity="silu",
                        overwrite_for_ldm=True,
                    ),
                ]
                ch = model_channels * mult
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        # num_heads = 1
                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
                    layers.append(
                        AttentionBlock(
                            ch,
                            num_heads=num_heads_upsample,
                            num_head_channels=dim_head,
                        )
                        if not use_spatial_transformer
                        else SpatialTransformer(
                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
                        )
                    )
                if level and i == num_res_blocks:
                    out_ch = ch
                    layers.append(Upsample2D(ch, use_conv=conv_resample, out_channels=out_ch))
                    ds //= 2
                self.output_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch

        self.out = nn.Sequential(
            normalization(ch),
            nn.SiLU(),
            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
        )
        if self.predict_codebook_ids:
            self.id_predictor = nn.Sequential(
                normalization(ch),
                conv_nd(dims, model_channels, n_embed, 1),
                # nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
            )

    def convert_to_fp16(self):
        """
        Convert the torso of the model to float16.
        """
        self.input_blocks.apply(convert_module_to_f16)
        self.middle_block.apply(convert_module_to_f16)
        self.output_blocks.apply(convert_module_to_f16)

    def convert_to_fp32(self):
        """
        Convert the torso of the model to float32.
        """
        self.input_blocks.apply(convert_module_to_f32)
        self.middle_block.apply(convert_module_to_f32)
        self.output_blocks.apply(convert_module_to_f32)

    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
        """
        Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch
        of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if
        class-conditional. :return: an [N x C x ...] Tensor of outputs.
        """
        assert (y is not None) == (
            self.num_classes is not None
        ), "must specify y if and only if the model is class-conditional"
        hs = []
        if not torch.is_tensor(timesteps):
            timesteps = torch.tensor([timesteps], dtype=torch.long, device=x.device)
        t_emb = get_timestep_embedding(timesteps, self.model_channels, flip_sin_to_cos=True, downscale_freq_shift=0)
        emb = self.time_embed(t_emb)

        if self.num_classes is not None:
            assert y.shape == (x.shape[0],)
            emb = emb + self.label_emb(y)

        h = x.type(self.dtype_)
        for module in self.input_blocks:
            h = module(h, emb, context)
            hs.append(h)
        h = self.mid(h, emb, context)
        for module in self.output_blocks:
            h = torch.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context)
        h = h.type(x.dtype)
        if self.predict_codebook_ids:
            return self.id_predictor(h)
        else:
            return self.out(h)


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__()
        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 = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
        for block in self.transformer_blocks:
            x = block(x, context=context)
        x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
        x = self.proj_out(x)
        return x + x_in


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):
        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 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 reshape_heads_to_batch_dim(self, tensor):
        batch_size, seq_len, dim = tensor.shape
        head_size = self.heads
        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
        return tensor

    def reshape_batch_dim_to_heads(self, tensor):
        batch_size, seq_len, dim = tensor.shape
        head_size = self.heads
        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 forward(self, x, context=None, mask=None):
        batch_size, sequence_length, dim = x.shape

        h = self.heads

        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)

        q = self.reshape_heads_to_batch_dim(q)
        k = self.reshape_heads_to_batch_dim(k)
        v = self.reshape_heads_to_batch_dim(v)

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

        if exists(mask):
            mask = mask.reshape(batch_size, -1)
            max_neg_value = -torch.finfo(sim.dtype).max
            mask = mask[:, None, :].repeat(h, 1, 1)
            sim.masked_fill_(~mask, max_neg_value)

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

        out = torch.einsum("b i j, b j d -> b i d", attn, v)
        out = self.reshape_batch_dim_to_heads(out)
        return self.to_out(out)