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import numbers
from typing import Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from diffusers.utils import is_torch_version
if is_torch_version(">=", "2.1.0"):
LayerNorm = nn.LayerNorm
else:
# Has optional bias parameter compared to torch layer norm
# TODO: replace with torch layernorm once min required torch version >= 2.1
class LayerNorm(nn.Module):
def __init__(self, dim, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True):
super().__init__()
self.eps = eps
if isinstance(dim, numbers.Integral):
dim = (dim,)
self.dim = torch.Size(dim)
if elementwise_affine:
self.weight = nn.Parameter(torch.ones(dim))
self.bias = nn.Parameter(torch.zeros(dim)) if bias else None
else:
self.weight = None
self.bias = None
def forward(self, input):
return F.layer_norm(input, self.dim, self.weight, self.bias, self.eps)
class RMSNorm(nn.Module):
def __init__(self, dim, eps: float, elementwise_affine: bool = True):
super().__init__()
self.eps = eps
if isinstance(dim, numbers.Integral):
dim = (dim,)
self.dim = torch.Size(dim)
if elementwise_affine:
self.weight = nn.Parameter(torch.ones(dim))
else:
self.weight = None
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
if self.weight is not None:
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
hidden_states = hidden_states * self.weight
hidden_states = hidden_states.to(input_dtype)
return hidden_states
class AdaLayerNormContinuous(nn.Module):
def __init__(
self,
embedding_dim: int,
conditioning_embedding_dim: int,
# NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters
# because the output is immediately scaled and shifted by the projected conditioning embeddings.
# Note that AdaLayerNorm does not let the norm layer have scale and shift parameters.
# However, this is how it was implemented in the original code, and it's rather likely you should
# set `elementwise_affine` to False.
elementwise_affine=True,
eps=1e-5,
bias=True,
norm_type="layer_norm",
):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
if norm_type == "layer_norm":
self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
elif norm_type == "rms_norm":
self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)
else:
raise ValueError(f"unknown norm_type {norm_type}")
def forward_with_pad(self, x: torch.Tensor, conditioning_embedding: torch.Tensor, hidden_length=None) -> torch.Tensor:
assert hidden_length is not None
emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
batch_emb = torch.zeros_like(x).repeat(1, 1, 2)
i_sum = 0
num_stages = len(hidden_length)
for i_p, length in enumerate(hidden_length):
batch_emb[:, i_sum:i_sum+length] = emb[i_p::num_stages][:,None]
i_sum += length
batch_scale, batch_shift = torch.chunk(batch_emb, 2, dim=2)
x = self.norm(x) * (1 + batch_scale) + batch_shift
return x
def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor, hidden_length=None) -> torch.Tensor:
# convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT)
if hidden_length is not None:
return self.forward_with_pad(x, conditioning_embedding, hidden_length)
emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
scale, shift = torch.chunk(emb, 2, dim=1)
x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
return x
class AdaLayerNormZero(nn.Module):
r"""
Norm layer adaptive layer norm zero (adaLN-Zero).
Parameters:
embedding_dim (`int`): The size of each embedding vector.
num_embeddings (`int`): The size of the embeddings dictionary.
"""
def __init__(self, embedding_dim: int, num_embeddings: Optional[int] = None):
super().__init__()
self.emb = None
self.silu = nn.SiLU()
self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
def forward_with_pad(
self,
x: torch.Tensor,
timestep: Optional[torch.Tensor] = None,
class_labels: Optional[torch.LongTensor] = None,
hidden_dtype: Optional[torch.dtype] = None,
emb: Optional[torch.Tensor] = None,
hidden_length: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
# x: [bs, seq_len, dim]
if self.emb is not None:
emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)
emb = self.linear(self.silu(emb))
batch_emb = torch.zeros_like(x).repeat(1, 1, 6)
i_sum = 0
num_stages = len(hidden_length)
for i_p, length in enumerate(hidden_length):
batch_emb[:, i_sum:i_sum+length] = emb[i_p::num_stages][:,None]
i_sum += length
batch_shift_msa, batch_scale_msa, batch_gate_msa, batch_shift_mlp, batch_scale_mlp, batch_gate_mlp = batch_emb.chunk(6, dim=2)
x = self.norm(x) * (1 + batch_scale_msa) + batch_shift_msa
return x, batch_gate_msa, batch_shift_mlp, batch_scale_mlp, batch_gate_mlp
def forward(
self,
x: torch.Tensor,
timestep: Optional[torch.Tensor] = None,
class_labels: Optional[torch.LongTensor] = None,
hidden_dtype: Optional[torch.dtype] = None,
emb: Optional[torch.Tensor] = None,
hidden_length: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
if hidden_length is not None:
return self.forward_with_pad(x, timestep, class_labels, hidden_dtype, emb, hidden_length)
if self.emb is not None:
emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)
emb = self.linear(self.silu(emb))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1)
x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp |