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import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
import einops
from einops import rearrange
def trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn(
"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2,
)
with torch.no_grad():
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
tensor.uniform_(2 * l - 1, 2 * u - 1)
tensor.erfinv_()
tensor.mul_(std * math.sqrt(2.0))
tensor.add_(mean)
tensor.clamp_(min=a, max=b)
return tensor
def film_modulate(x, shift, scale):
return x * (1 + scale) + shift
def timestep_embedding(timesteps, dim, max_period=10000):
half = dim // 2
freqs = torch.exp(
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32)
/ half
).to(device=timesteps.device)
args = timesteps[:, 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
)
return embedding
def unpatchify(x, channels=3, input_type="2d", img_size=None):
if input_type == "2d":
patch_size = int((x.shape[2] // channels) ** 0.5)
h, w = img_size[0] // patch_size, img_size[1] // patch_size
x = rearrange(
x,
"B (h w) (p1 p2 C) -> B C (h p1) (w p2)",
h=h,
p1=patch_size,
p2=patch_size,
)
elif input_type == "1d":
patch_size = int(x.shape[2] // channels)
h = x.shape[1]
x = rearrange(x, "B h (p1 C) -> B C (h p1)", h=h, p1=patch_size)
return x
class TimestepEmbedder(nn.Module):
def __init__(self, hidden_size, frequency_embedding_size=256, out_size=None):
super().__init__()
if out_size is None:
out_size = hidden_size
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, out_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
def forward(self, t):
t_freq = timestep_embedding(t, self.frequency_embedding_size).type(
self.mlp[0].weight.dtype
)
t_emb = self.mlp(t_freq)
return t_emb
class PatchEmbed(nn.Module):
def __init__(self, patch_size, in_chans=3, embed_dim=768, input_type="2d"):
super().__init__()
self.patch_size = patch_size
self.input_type = input_type
if input_type == "2d":
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=True
)
elif input_type == "1d":
self.proj = nn.Conv1d(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=True
)
def forward(self, x):
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class PE_wrapper(nn.Module):
def __init__(self, dim=768, method="abs", length=None, **kwargs):
super().__init__()
self.method = method
if method == "abs":
self.length = length
self.abs_pe = nn.Parameter(torch.zeros(1, length, dim))
trunc_normal_(self.abs_pe, mean=0.0, std=0.02, a=-0.04, b=0.04)
elif method == "none":
self.id = nn.Identity()
else:
raise NotImplementedError
def forward(self, x):
if self.method == "abs":
_, L, _ = x.shape
assert L <= self.length
x = x + self.abs_pe[:, :L, :]
elif self.method == "none":
x = self.id(x)
return x
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
return output * self.weight
class GELU(nn.Module):
def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out, bias=bias)
self.approximate = approximate
def gelu(self, gate: torch.Tensor) -> torch.Tensor:
if gate.device.type != "mps":
return F.gelu(gate, approximate=self.approximate)
return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(
dtype=gate.dtype
)
def forward(self, hidden_states):
hidden_states = self.proj(hidden_states)
hidden_states = self.gelu(hidden_states)
return hidden_states
class GEGLU(nn.Module):
def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)
def gelu(self, gate: torch.Tensor) -> torch.Tensor:
if gate.device.type != "mps":
return F.gelu(gate)
return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
def forward(self, hidden_states):
hidden_states = self.proj(hidden_states)
hidden_states, gate = hidden_states.chunk(2, dim=-1)
return hidden_states * self.gelu(gate)
class FeedForward(nn.Module):
def __init__(
self,
dim,
dim_out=None,
mult=4,
dropout=0.0,
activation_fn="geglu",
final_dropout=False,
inner_dim=None,
bias=True,
):
super().__init__()
if inner_dim is None:
inner_dim = int(dim * mult)
dim_out = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
act_fn = GELU(dim, inner_dim, bias=bias)
elif activation_fn == "gelu-approximate":
act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias)
elif activation_fn == "geglu":
act_fn = GEGLU(dim, inner_dim, bias=bias)
else:
raise NotImplementedError
self.net = nn.ModuleList([])
self.net.append(act_fn)
self.net.append(nn.Dropout(dropout))
self.net.append(nn.Linear(inner_dim, dim_out, bias=bias))
if final_dropout:
self.net.append(nn.Dropout(dropout))
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
for module in self.net:
hidden_states = module(hidden_states)
return hidden_states
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