models/resampler.py

# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
import math

import torch
import torch.nn as nn

from diffusers.models.embeddings import Timesteps, TimestepEmbedding

def get_timestep_embedding(
    timesteps: torch.Tensor,
    embedding_dim: int,
    flip_sin_to_cos: bool = False,
    downscale_freq_shift: float = 1,
    scale: float = 1,
    max_period: int = 10000,
):
    """
    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.

    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
    embeddings. :return: an [N x dim] Tensor of positional embeddings.
    """
    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"

    half_dim = embedding_dim // 2
    exponent = -math.log(max_period) * torch.arange(
        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
    )
    exponent = exponent / (half_dim - downscale_freq_shift)

    emb = torch.exp(exponent)
    emb = timesteps[:, None].float() * emb[None, :]

    # scale embeddings
    emb = scale * emb

    # concat sine and cosine embeddings
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)

    # flip sine and cosine embeddings
    if flip_sin_to_cos:
        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)

    # zero pad
    if embedding_dim % 2 == 1:
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb


# FFN
def FeedForward(dim, mult=4):
    inner_dim = int(dim * mult)
    return nn.Sequential(
        nn.LayerNorm(dim),
        nn.Linear(dim, inner_dim, bias=False),
        nn.GELU(),
        nn.Linear(inner_dim, dim, bias=False),
    )

    
def reshape_tensor(x, heads):
    bs, length, width = x.shape
    #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
    x = x.view(bs, length, heads, -1)
    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
    x = x.transpose(1, 2)
    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
    x = x.reshape(bs, heads, length, -1)
    return x


class PerceiverAttention(nn.Module):
    def __init__(self, *, dim, dim_head=64, heads=8):
        super().__init__()
        self.scale = dim_head**-0.5
        self.dim_head = dim_head
        self.heads = heads
        inner_dim = dim_head * heads

        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)


    def forward(self, x, latents, shift=None, scale=None):
        """
        Args:
            x (torch.Tensor): image features
                shape (b, n1, D)
            latent (torch.Tensor): latent features
                shape (b, n2, D)
        """
        x = self.norm1(x)
        latents = self.norm2(latents)

        if shift is not None and scale is not None:
            latents = latents * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
        
        b, l, _ = latents.shape

        q = self.to_q(latents)
        kv_input = torch.cat((x, latents), dim=-2)
        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
        
        q = reshape_tensor(q, self.heads)
        k = reshape_tensor(k, self.heads)
        v = reshape_tensor(v, self.heads)

        # attention
        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
        weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
        out = weight @ v
        
        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)

        return self.to_out(out)


class Resampler(nn.Module):
    def __init__(
        self,
        dim=1024,
        depth=8,
        dim_head=64,
        heads=16,
        num_queries=8,
        embedding_dim=768,
        output_dim=1024,
        ff_mult=4,
        *args,
        **kwargs,
    ):
        super().__init__()
        
        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
        
        self.proj_in = nn.Linear(embedding_dim, dim)

        self.proj_out = nn.Linear(dim, output_dim)
        self.norm_out = nn.LayerNorm(output_dim)
        
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
                        FeedForward(dim=dim, mult=ff_mult),
                    ]
                )
            )

    def forward(self, x):
        
        latents = self.latents.repeat(x.size(0), 1, 1)
        
        x = self.proj_in(x)
        
        for attn, ff in self.layers:
            latents = attn(x, latents) + latents
            latents = ff(latents) + latents
            
        latents = self.proj_out(latents)
        return self.norm_out(latents)


class TimeResampler(nn.Module):
    def __init__(
        self,
        dim=1024,
        depth=8,
        dim_head=64,
        heads=16,
        num_queries=8,
        embedding_dim=768,
        output_dim=1024,
        ff_mult=4,
        timestep_in_dim=320,
        timestep_flip_sin_to_cos=True,
        timestep_freq_shift=0,
    ):
        super().__init__()
        
        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
        
        self.proj_in = nn.Linear(embedding_dim, dim)

        self.proj_out = nn.Linear(dim, output_dim)
        self.norm_out = nn.LayerNorm(output_dim)
        
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        # msa
                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
                        # ff
                        FeedForward(dim=dim, mult=ff_mult),
                        # adaLN
                        nn.Sequential(nn.SiLU(), nn.Linear(dim, 4 * dim, bias=True))
                    ]
                )
            )

        # time
        self.time_proj = Timesteps(timestep_in_dim, timestep_flip_sin_to_cos, timestep_freq_shift)
        self.time_embedding = TimestepEmbedding(timestep_in_dim, dim, act_fn="silu")

        # adaLN
        # self.adaLN_modulation = nn.Sequential(
        #     nn.SiLU(),
        #     nn.Linear(timestep_out_dim, 6 * timestep_out_dim, bias=True)
        # )


    def forward(self, x, timestep, need_temb=False):
        timestep_emb = self.embedding_time(x, timestep)  # bs, dim

        latents = self.latents.repeat(x.size(0), 1, 1)
        
        x = self.proj_in(x)
        x = x + timestep_emb[:, None]

        for attn, ff, adaLN_modulation in self.layers:
            shift_msa, scale_msa, shift_mlp, scale_mlp = adaLN_modulation(timestep_emb).chunk(4, dim=1)
            latents = attn(x, latents, shift_msa, scale_msa) + latents

            res = latents
            for idx_ff in range(len(ff)):
                layer_ff = ff[idx_ff]
                latents = layer_ff(latents)
                if idx_ff == 0 and isinstance(layer_ff, nn.LayerNorm):  # adaLN
                    latents = latents * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
            latents = latents + res

            # latents = ff(latents) + latents
            
        latents = self.proj_out(latents)
        latents = self.norm_out(latents)

        if need_temb:
            return latents, timestep_emb
        else:
            return latents



    def embedding_time(self, sample, timestep):

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
            if isinstance(timestep, float):
                dtype = torch.float32 if is_mps else torch.float64
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps.expand(sample.shape[0])

        t_emb = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=sample.dtype)

        emb = self.time_embedding(t_emb, None)
        return emb





if __name__ == '__main__':
    model = TimeResampler(
        dim=1280,
        depth=4,
        dim_head=64,
        heads=20,
        num_queries=16,
        embedding_dim=512,
        output_dim=2048,
        ff_mult=4,
        timestep_in_dim=320,
        timestep_flip_sin_to_cos=True,
        timestep_freq_shift=0,
        in_channel_extra_emb=2048,
    )