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import collections.abc
import math
from itertools import repeat
from typing import Callable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple(repeat(x, n))
return parse
to_2tuple = _ntuple(2)
class TimestepEmbedder(nn.Module):
def __init__(
self,
hidden_size: int,
frequency_embedding_size: int = 256,
*,
bias: bool = True,
timestep_scale: Optional[float] = None,
device: Optional[torch.device] = None,
):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=bias, device=device),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=bias, device=device),
)
self.frequency_embedding_size = frequency_embedding_size
self.timestep_scale = timestep_scale
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
half = dim // 2
freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
freqs.mul_(-math.log(max_period) / half).exp_()
# args = t[:, None].float() * freqs[None]
# embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
# if dim % 2:
# embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
args = t[:, :, None].float() * freqs[None] # TODO
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :, :1])], dim=-1) # TODO
return embedding
def forward(self, t):
if self.timestep_scale is not None:
t = t * self.timestep_scale
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class PooledCaptionEmbedder(nn.Module):
def __init__(
self,
caption_feature_dim: int,
hidden_size: int,
*,
bias: bool = True,
device: Optional[torch.device] = None,
):
super().__init__()
self.caption_feature_dim = caption_feature_dim
self.hidden_size = hidden_size
self.mlp = nn.Sequential(
nn.Linear(caption_feature_dim, hidden_size, bias=bias, device=device),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=bias, device=device),
)
def forward(self, x):
return self.mlp(x)
class FeedForward(nn.Module):
def __init__(
self,
in_features: int,
hidden_size: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float],
device: Optional[torch.device] = None,
):
super().__init__()
# keep parameter count and computation constant compared to standard FFN
hidden_size = int(2 * hidden_size / 3)
# custom dim factor multiplier
if ffn_dim_multiplier is not None:
hidden_size = int(ffn_dim_multiplier * hidden_size)
hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of)
self.hidden_dim = hidden_size
self.w1 = nn.Linear(in_features, 2 * hidden_size, bias=False, device=device)
self.w2 = nn.Linear(hidden_size, in_features, bias=False, device=device)
def forward(self, x):
x, gate = self.w1(x).chunk(2, dim=-1)
x = self.w2(F.silu(x) * gate)
return x
class PatchEmbed(nn.Module):
def __init__(
self,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten: bool = True,
bias: bool = True,
dynamic_img_pad: bool = False,
device: Optional[torch.device] = None,
):
super().__init__()
self.patch_size = to_2tuple(patch_size)
self.flatten = flatten
self.dynamic_img_pad = dynamic_img_pad
self.proj = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=patch_size,
stride=patch_size,
bias=bias,
device=device,
)
assert norm_layer is None
self.norm = norm_layer(embed_dim, device=device) if norm_layer else nn.Identity()
def forward(self, x):
B, _C, T, H, W = x.shape
if not self.dynamic_img_pad:
assert (
H % self.patch_size[0] == 0
), f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
assert (
W % self.patch_size[1] == 0
), f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
else:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
x = F.pad(x, (0, pad_w, 0, pad_h))
x = x.contiguous()
x = rearrange(x, "B C T H W -> (B T) C H W", B=B, T=T).contiguous()
x = self.proj(x)
# Flatten temporal and spatial dimensions.
if not self.flatten:
raise NotImplementedError("Must flatten output.")
x = x.contiguous()
x = rearrange(x, "(B T) C H W -> B (T H W) C", B=B, T=T).contiguous()
x = self.norm(x)
return x
class RMSNorm(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-5, device=None):
super().__init__()
self.eps = eps
# self.weight = torch.nn.Parameter(torch.empty(hidden_size, device=device))
self.weight = torch.nn.Parameter(torch.ones(hidden_size, device=device)*0.5) # TODO
self.register_parameter("bias", None)
def forward(self, x):
x_fp32 = x.float()
x_normed = x_fp32 * torch.rsqrt(x_fp32.pow(2).mean(-1, keepdim=True) + self.eps)
return (x_normed * self.weight).type_as(x)
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