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import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
from .transformer_utils import BaseTemperalPointModel
import math
from einops_exts import check_shape, rearrange_many
from functools import partial
from rotary_embedding_torch import RotaryEmbedding
def exists(x):
return x is not None
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class RelativePositionBias(nn.Module):
def __init__(
self,
heads = 8,
num_buckets = 32,
max_distance = 128
):
super().__init__()
self.num_buckets = num_buckets
self.max_distance = max_distance
self.relative_attention_bias = nn.Embedding(num_buckets, heads)
@staticmethod
def _relative_position_bucket(relative_position, num_buckets = 32, max_distance = 128):
ret = 0
n = -relative_position
num_buckets //= 2
ret += (n < 0).long() * num_buckets
n = torch.abs(n)
max_exact = num_buckets // 2
is_small = n < max_exact
val_if_large = max_exact + (
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
).long()
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
ret += torch.where(is_small, n, val_if_large)
return ret
def forward(self, n, device):
q_pos = torch.arange(n, dtype = torch.long, device = device)
k_pos = torch.arange(n, dtype = torch.long, device = device)
rel_pos = rearrange(k_pos, 'j -> 1 j') - rearrange(q_pos, 'i -> i 1')
rp_bucket = self._relative_position_bucket(rel_pos, num_buckets = self.num_buckets, max_distance = self.max_distance)
values = self.relative_attention_bias(rp_bucket)
return rearrange(values, 'i j h -> h i j')
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, *args, **kwargs):
return self.fn(x, *args, **kwargs) + x
class LayerNorm(nn.Module):
def __init__(self, dim, eps = 1e-5):
super().__init__()
self.eps = eps
self.gamma = nn.Parameter(torch.ones(1, 1, dim))
self.beta = nn.Parameter(torch.zeros(1, 1, dim))
def forward(self, x):
var = torch.var(x, dim = -1, unbiased = False, keepdim = True)
mean = torch.mean(x, dim = -1, keepdim = True)
return (x - mean) / (var + self.eps).sqrt() * self.gamma + self.beta
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = LayerNorm(dim)
def forward(self, x, **kwargs):
x = self.norm(x)
return self.fn(x, **kwargs)
class EinopsToAndFrom(nn.Module):
def __init__(self, from_einops, to_einops, fn):
super().__init__()
self.from_einops = from_einops
self.to_einops = to_einops
self.fn = fn
def forward(self, x, **kwargs):
shape = x.shape
reconstitute_kwargs = dict(tuple(zip(self.from_einops.split(' '), shape)))
x = rearrange(x, f'{self.from_einops} -> {self.to_einops}')
x = self.fn(x, **kwargs)
x = rearrange(x, f'{self.to_einops} -> {self.from_einops}', **reconstitute_kwargs)
return x
class Attention(nn.Module):
def __init__(
self, dim, heads=4, attn_head_dim=None, casual_attn=False,rotary_emb = None):
super().__init__()
self.num_heads = heads
head_dim = dim // heads
self.casual_attn = casual_attn
if attn_head_dim is not None:
head_dim = attn_head_dim
all_head_dim = head_dim * self.num_heads
self.scale = head_dim ** -0.5
self.to_qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
self.proj = nn.Linear(all_head_dim, dim)
self.rotary_emb = rotary_emb
def forward(self, x, pos_bias = None):
N, device = x.shape[-2], x.device
qkv = self.to_qkv(x).chunk(3, dim = -1)
q, k, v = rearrange_many(qkv, '... n (h d) -> ... h n d', h=self.num_heads)
q = q * self.scale
if exists(self.rotary_emb):
q = self.rotary_emb.rotate_queries_or_keys(q)
k = self.rotary_emb.rotate_queries_or_keys(k)
sim = torch.einsum('... h i d, ... h j d -> ... h i j', q, k)
if exists(pos_bias):
sim = sim + pos_bias
if self.casual_attn:
mask = torch.tril(torch.ones(sim.size(-1), sim.size(-2))).to(device)
sim = sim.masked_fill(mask[..., :, :] == 0, float('-inf'))
attn = sim.softmax(dim = -1)
x = torch.einsum('... h i j, ... h j d -> ... h i d', attn, v)
x = rearrange(x, '... h n d -> ... n (h d)')
x = self.proj(x)
return x
class Block(nn.Module):
def __init__(self, dim, dim_out):
super().__init__()
self.proj = nn.Linear(dim, dim_out)
self.norm = LayerNorm(dim)
self.act = nn.SiLU()
def forward(self, x, scale_shift=None):
x = self.proj(x)
if exists(scale_shift):
x = self.norm(x)
scale, shift = scale_shift
x = x * (scale + 1) + shift
return self.act(x)
class ResnetBlock(nn.Module):
def __init__(self, dim, dim_out, cond_dim=None):
super().__init__()
self.mlp = nn.Sequential(
nn.SiLU(),
nn.Linear(cond_dim, dim_out * 2)
) if exists(cond_dim) else None
self.block1 = Block(dim, dim_out)
self.block2 = Block(dim_out, dim_out)
def forward(self, x, cond_emb=None):
scale_shift = None
if exists(self.mlp):
assert exists(cond_emb), 'time emb must be passed in'
cond_emb = self.mlp(cond_emb)
#cond_emb = rearrange(cond_emb, 'b f c -> b f 1 c')
scale_shift = cond_emb.chunk(2, dim=-1)
h = self.block1(x, scale_shift=scale_shift)
h = self.block2(h)
return h + x
class SimpleTransModel(BaseTemperalPointModel):
"""
A simple model that processes a point cloud by applying a series of MLPs to each point
individually, along with some pooled global features.
"""
def get_layers(self):
self.input_projection = nn.Linear(
in_features=70,
out_features=self.dim
)
cond_dim = 512 + self.timestep_embed_dim
num_head = self.dim//64
rotary_emb = RotaryEmbedding(min(32, num_head))
self.time_rel_pos_bias = RelativePositionBias(heads=num_head, max_distance=128) # realistically will not be able to generate that many frames of video... yet
temporal_casual_attn = lambda dim: Attention(dim, heads=num_head, casual_attn=False,rotary_emb=rotary_emb)
cond_block = partial(ResnetBlock, cond_dim=cond_dim)
layers = nn.ModuleList([])
for _ in range(self.num_layers):
layers.append(nn.ModuleList([
cond_block(self.dim, self.dim),
cond_block(self.dim, self.dim),
Residual(PreNorm(self.dim, temporal_casual_attn(self.dim)))
]))
return layers
def forward(self, inputs: torch.Tensor, timesteps: torch.Tensor, context=None):
"""
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
"""
# Prepare inputs
batch, num_frames, channels = inputs.size()
device = inputs.device
x = self.input_projection(inputs)
t_emb = self.time_mlp(timesteps) if exists(self.time_mlp) else None
t_emb = t_emb[:,None,:].expand(-1, num_frames, -1) # b f c
if context is not None:
t_emb = torch.cat([t_emb, context],-1)
time_rel_pos_bias = self.time_rel_pos_bias(num_frames, device=device)
for block1, block2, temporal_attn in self.layers:
x = block1(x, t_emb)
x = block2(x, t_emb)
x = temporal_attn(x, pos_bias=time_rel_pos_bias)
# Project
x = self.output_projection(x)
return x |