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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 |