mirror of
https://github.com/hpcaitech/Open-Sora.git
synced 2026-04-14 18:25:35 +02:00
439 lines
16 KiB
Python
439 lines
16 KiB
Python
import numpy as np
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.nn as nn
|
|
from einops import rearrange
|
|
from timm.models.layers import DropPath
|
|
from timm.models.vision_transformer import Mlp
|
|
|
|
from opensora.acceleration.checkpoint import auto_grad_checkpoint
|
|
from opensora.acceleration.communications import gather_forward_split_backward, split_forward_gather_backward
|
|
from opensora.acceleration.parallel_states import get_sequence_parallel_group
|
|
from opensora.models.layers.blocks import (
|
|
Attention,
|
|
CaptionEmbedder,
|
|
MultiHeadCrossAttention,
|
|
PatchEmbed3D,
|
|
SeqParallelAttention,
|
|
SeqParallelMultiHeadCrossAttention,
|
|
T2IFinalLayer,
|
|
TimestepEmbedder,
|
|
approx_gelu,
|
|
get_1d_sincos_pos_embed,
|
|
get_2d_sincos_pos_embed,
|
|
get_layernorm,
|
|
t2i_modulate,
|
|
)
|
|
from opensora.registry import MODELS
|
|
from opensora.utils.ckpt_utils import load_checkpoint
|
|
|
|
|
|
class STDiTBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
num_heads,
|
|
d_s=None,
|
|
d_t=None,
|
|
mlp_ratio=4.0,
|
|
drop_path=0.0,
|
|
enable_flash_attn=False,
|
|
enable_layernorm_kernel=False,
|
|
enable_sequence_parallelism=False,
|
|
):
|
|
super().__init__()
|
|
self.hidden_size = hidden_size
|
|
self.enable_flash_attn = enable_flash_attn
|
|
self._enable_sequence_parallelism = enable_sequence_parallelism
|
|
|
|
if enable_sequence_parallelism:
|
|
self.attn_cls = SeqParallelAttention
|
|
self.mha_cls = SeqParallelMultiHeadCrossAttention
|
|
else:
|
|
self.attn_cls = Attention
|
|
self.mha_cls = MultiHeadCrossAttention
|
|
|
|
self.norm1 = get_layernorm(hidden_size, eps=1e-6, affine=False, use_kernel=enable_layernorm_kernel)
|
|
self.attn = self.attn_cls(
|
|
hidden_size,
|
|
num_heads=num_heads,
|
|
qkv_bias=True,
|
|
enable_flash_attn=enable_flash_attn,
|
|
)
|
|
self.cross_attn = self.mha_cls(hidden_size, num_heads)
|
|
self.norm2 = get_layernorm(hidden_size, eps=1e-6, affine=False, use_kernel=enable_layernorm_kernel)
|
|
self.mlp = Mlp(
|
|
in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
|
|
)
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
|
self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
|
|
|
|
# temporal attention
|
|
self.d_s = d_s
|
|
self.d_t = d_t
|
|
|
|
if self._enable_sequence_parallelism:
|
|
sp_size = dist.get_world_size(get_sequence_parallel_group())
|
|
# make sure d_t is divisible by sp_size
|
|
assert d_t % sp_size == 0
|
|
self.d_t = d_t // sp_size
|
|
|
|
self.attn_temp = self.attn_cls(
|
|
hidden_size,
|
|
num_heads=num_heads,
|
|
qkv_bias=True,
|
|
enable_flash_attn=self.enable_flash_attn,
|
|
)
|
|
|
|
def t_mask_select(self, x, masked_x, x_mask):
|
|
# x: [B, (T, S), C]
|
|
# mased_x: [B, (T, S), C]
|
|
# x_mask: [B, T]
|
|
x = rearrange(x, "B (T S) C -> B T S C", T=self.d_t, S=self.d_s)
|
|
masked_x = rearrange(masked_x, "B (T S) C -> B T S C", T=self.d_t, S=self.d_s)
|
|
x = torch.where(x_mask[:, :, None, None], x, masked_x)
|
|
x = rearrange(x, "B T S C -> B (T S) C")
|
|
return x
|
|
|
|
def forward(self, x, y, t, mask=None, tpe=None, x_mask=None, t0=None):
|
|
B, N, C = x.shape
|
|
|
|
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
|
|
self.scale_shift_table[None] + t.reshape(B, 6, -1)
|
|
).chunk(6, dim=1)
|
|
x_m = t2i_modulate(self.norm1(x), shift_msa, scale_msa)
|
|
if x_mask is not None:
|
|
shift_msa_zero, scale_msa_zero, gate_msa_zero, shift_mlp_zero, scale_mlp_zero, gate_mlp_zero = (
|
|
self.scale_shift_table[None] + t0.reshape(B, 6, -1)
|
|
).chunk(6, dim=1)
|
|
x_m_zero = t2i_modulate(self.norm1(x), shift_msa_zero, scale_msa_zero)
|
|
x_m = self.t_mask_select(x_m, x_m_zero, x_mask)
|
|
|
|
# spatial branch
|
|
x_s = rearrange(x_m, "B (T S) C -> (B T) S C", T=self.d_t, S=self.d_s)
|
|
x_s = self.attn(x_s)
|
|
x_s = rearrange(x_s, "(B T) S C -> B (T S) C", T=self.d_t, S=self.d_s)
|
|
|
|
if x_mask is not None:
|
|
x_s_zero = gate_msa_zero * x_s
|
|
x_s = gate_msa * x_s
|
|
x_s = self.t_mask_select(x_s, x_s_zero, x_mask)
|
|
else:
|
|
x_s = gate_msa * x_s
|
|
|
|
x = x + self.drop_path(x_s)
|
|
|
|
# temporal branch
|
|
x_t = rearrange(x, "B (T S) C -> (B S) T C", T=self.d_t, S=self.d_s)
|
|
if tpe is not None:
|
|
x_t = x_t + tpe
|
|
x_t = self.attn_temp(x_t)
|
|
x_t = rearrange(x_t, "(B S) T C -> B (T S) C", T=self.d_t, S=self.d_s)
|
|
x = x + self.drop_path(gate_msa * x_t)
|
|
|
|
# cross attn
|
|
x = x + self.cross_attn(x, y, mask)
|
|
|
|
# mlp
|
|
x_m = t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)
|
|
if x_mask is not None:
|
|
x_m_zero = t2i_modulate(self.norm2(x), shift_mlp_zero, scale_mlp_zero)
|
|
x_m = self.t_mask_select(x_m, x_m_zero, x_mask)
|
|
|
|
x_mlp = self.mlp(x_m)
|
|
if x_mask is not None:
|
|
x_mlp_zero = gate_mlp_zero * x_mlp
|
|
x_mlp = gate_mlp * x_mlp
|
|
x_mlp = self.t_mask_select(x_mlp, x_mlp_zero, x_mask)
|
|
else:
|
|
x_mlp = gate_mlp * x_mlp
|
|
|
|
x = x + self.drop_path(x_mlp)
|
|
|
|
return x
|
|
|
|
|
|
@MODELS.register_module()
|
|
class STDiT(nn.Module):
|
|
def __init__(
|
|
self,
|
|
input_size=(1, 32, 32),
|
|
in_channels=4,
|
|
patch_size=(1, 2, 2),
|
|
hidden_size=1152,
|
|
depth=28,
|
|
num_heads=16,
|
|
mlp_ratio=4.0,
|
|
class_dropout_prob=0.1,
|
|
pred_sigma=True,
|
|
drop_path=0.0,
|
|
no_temporal_pos_emb=False,
|
|
caption_channels=4096,
|
|
model_max_length=120,
|
|
dtype=torch.float32,
|
|
space_scale=1.0,
|
|
time_scale=1.0,
|
|
freeze=None,
|
|
enable_flash_attn=False,
|
|
enable_layernorm_kernel=False,
|
|
enable_sequence_parallelism=False,
|
|
):
|
|
super().__init__()
|
|
self.pred_sigma = pred_sigma
|
|
self.in_channels = in_channels
|
|
self.out_channels = in_channels * 2 if pred_sigma else in_channels
|
|
self.hidden_size = hidden_size
|
|
self.patch_size = patch_size
|
|
self.input_size = input_size
|
|
num_patches = np.prod([input_size[i] // patch_size[i] for i in range(3)])
|
|
self.num_patches = num_patches
|
|
self.num_temporal = input_size[0] // patch_size[0]
|
|
self.num_spatial = num_patches // self.num_temporal
|
|
self.num_heads = num_heads
|
|
self.dtype = dtype
|
|
self.no_temporal_pos_emb = no_temporal_pos_emb
|
|
self.depth = depth
|
|
self.mlp_ratio = mlp_ratio
|
|
self.enable_flash_attn = enable_flash_attn
|
|
self.enable_layernorm_kernel = enable_layernorm_kernel
|
|
self.space_scale = space_scale
|
|
self.time_scale = time_scale
|
|
|
|
self.register_buffer("pos_embed", self.get_spatial_pos_embed())
|
|
self.register_buffer("pos_embed_temporal", self.get_temporal_pos_embed())
|
|
|
|
self.x_embedder = PatchEmbed3D(patch_size, in_channels, hidden_size)
|
|
self.t_embedder = TimestepEmbedder(hidden_size)
|
|
self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
|
|
self.y_embedder = CaptionEmbedder(
|
|
in_channels=caption_channels,
|
|
hidden_size=hidden_size,
|
|
uncond_prob=class_dropout_prob,
|
|
act_layer=approx_gelu,
|
|
token_num=model_max_length,
|
|
)
|
|
|
|
drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]
|
|
self.blocks = nn.ModuleList(
|
|
[
|
|
STDiTBlock(
|
|
self.hidden_size,
|
|
self.num_heads,
|
|
mlp_ratio=self.mlp_ratio,
|
|
drop_path=drop_path[i],
|
|
enable_flash_attn=self.enable_flash_attn,
|
|
enable_layernorm_kernel=self.enable_layernorm_kernel,
|
|
enable_sequence_parallelism=enable_sequence_parallelism,
|
|
d_t=self.num_temporal,
|
|
d_s=self.num_spatial,
|
|
)
|
|
for i in range(self.depth)
|
|
]
|
|
)
|
|
self.final_layer = T2IFinalLayer(
|
|
hidden_size,
|
|
np.prod(self.patch_size),
|
|
self.out_channels,
|
|
d_t=self.num_temporal,
|
|
d_s=self.num_spatial,
|
|
)
|
|
|
|
# init model
|
|
self.initialize_weights()
|
|
self.initialize_temporal()
|
|
if freeze is not None:
|
|
assert freeze in ["not_temporal", "text"]
|
|
if freeze == "not_temporal":
|
|
self.freeze_not_temporal()
|
|
elif freeze == "text":
|
|
self.freeze_text()
|
|
|
|
# sequence parallel related configs
|
|
self.enable_sequence_parallelism = enable_sequence_parallelism
|
|
if enable_sequence_parallelism:
|
|
self.sp_rank = dist.get_rank(get_sequence_parallel_group())
|
|
else:
|
|
self.sp_rank = None
|
|
|
|
def forward(self, x, timestep, y, mask=None, x_mask=None, **kwargs):
|
|
"""
|
|
Forward pass of STDiT.
|
|
Args:
|
|
x (torch.Tensor): latent representation of video; of shape [B, C, T, H, W]
|
|
timestep (torch.Tensor): diffusion time steps; of shape [B]
|
|
y (torch.Tensor): representation of prompts; of shape [B, 1, N_token, C]
|
|
mask (torch.Tensor): mask for selecting prompt tokens; of shape [B, N_token]
|
|
|
|
Returns:
|
|
x (torch.Tensor): output latent representation; of shape [B, C, T, H, W]
|
|
"""
|
|
dtype = self.x_embedder.proj.weight.dtype
|
|
x = x.to(dtype)
|
|
timestep = timestep.to(dtype)
|
|
y = y.to(dtype)
|
|
|
|
# embedding
|
|
x = self.x_embedder(x) # [B, N, C]
|
|
x = rearrange(x, "B (T S) C -> B T S C", T=self.num_temporal, S=self.num_spatial)
|
|
x = x + self.pos_embed
|
|
x = rearrange(x, "B T S C -> B (T S) C")
|
|
|
|
# shard over the sequence dim if sp is enabled
|
|
if self.enable_sequence_parallelism:
|
|
x = split_forward_gather_backward(x, get_sequence_parallel_group(), dim=1, grad_scale="down")
|
|
|
|
t = self.t_embedder(timestep, dtype=x.dtype) # [B, C]
|
|
t_mlp = self.t_block(t) # [B, C]
|
|
if x_mask is not None:
|
|
t0_timestep = torch.zeros_like(timestep)
|
|
t0 = self.t_embedder(t0_timestep, dtype=x.dtype)
|
|
t0_mlp = self.t_block(t0)
|
|
else:
|
|
t0 = None
|
|
t0_mlp = None
|
|
y = self.y_embedder(y, self.training) # [B, 1, N_token, C]
|
|
|
|
if mask is not None:
|
|
if mask.shape[0] != y.shape[0]:
|
|
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
|
|
mask = mask.squeeze(1).squeeze(1)
|
|
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
|
|
y_lens = mask.sum(dim=1).tolist()
|
|
else:
|
|
y_lens = [y.shape[2]] * y.shape[0]
|
|
y = y.squeeze(1).view(1, -1, x.shape[-1])
|
|
|
|
# blocks
|
|
for i, block in enumerate(self.blocks):
|
|
if i == 0:
|
|
if self.enable_sequence_parallelism:
|
|
tpe = torch.chunk(
|
|
self.pos_embed_temporal, dist.get_world_size(get_sequence_parallel_group()), dim=1
|
|
)[self.sp_rank].contiguous()
|
|
else:
|
|
tpe = self.pos_embed_temporal
|
|
else:
|
|
tpe = None
|
|
x = auto_grad_checkpoint(block, x, y, t_mlp, y_lens, tpe, x_mask, t0_mlp)
|
|
|
|
if self.enable_sequence_parallelism:
|
|
x = gather_forward_split_backward(x, get_sequence_parallel_group(), dim=1, grad_scale="up")
|
|
# x.shape: [B, N, C]
|
|
|
|
# final process
|
|
x = self.final_layer(x, t, x_mask, t0) # [B, N, C=T_p * H_p * W_p * C_out]
|
|
x = self.unpatchify(x) # [B, C_out, T, H, W]
|
|
|
|
# cast to float32 for better accuracy
|
|
x = x.to(torch.float32)
|
|
return x
|
|
|
|
def unpatchify(self, x):
|
|
"""
|
|
Args:
|
|
x (torch.Tensor): of shape [B, N, C]
|
|
|
|
Return:
|
|
x (torch.Tensor): of shape [B, C_out, T, H, W]
|
|
"""
|
|
|
|
N_t, N_h, N_w = [self.input_size[i] // self.patch_size[i] for i in range(3)]
|
|
T_p, H_p, W_p = self.patch_size
|
|
x = rearrange(
|
|
x,
|
|
"B (N_t N_h N_w) (T_p H_p W_p C_out) -> B C_out (N_t T_p) (N_h H_p) (N_w W_p)",
|
|
N_t=N_t,
|
|
N_h=N_h,
|
|
N_w=N_w,
|
|
T_p=T_p,
|
|
H_p=H_p,
|
|
W_p=W_p,
|
|
C_out=self.out_channels,
|
|
)
|
|
return x
|
|
|
|
def unpatchify_old(self, x):
|
|
c = self.out_channels
|
|
t, h, w = [self.input_size[i] // self.patch_size[i] for i in range(3)]
|
|
pt, ph, pw = self.patch_size
|
|
|
|
x = x.reshape(shape=(x.shape[0], t, h, w, pt, ph, pw, c))
|
|
x = rearrange(x, "n t h w r p q c -> n c t r h p w q")
|
|
imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))
|
|
return imgs
|
|
|
|
def get_spatial_pos_embed(self, grid_size=None):
|
|
if grid_size is None:
|
|
grid_size = self.input_size[1:]
|
|
pos_embed = get_2d_sincos_pos_embed(
|
|
self.hidden_size,
|
|
(grid_size[0] // self.patch_size[1], grid_size[1] // self.patch_size[2]),
|
|
scale=self.space_scale,
|
|
)
|
|
pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).requires_grad_(False)
|
|
return pos_embed
|
|
|
|
def get_temporal_pos_embed(self):
|
|
pos_embed = get_1d_sincos_pos_embed(
|
|
self.hidden_size,
|
|
self.input_size[0] // self.patch_size[0],
|
|
scale=self.time_scale,
|
|
)
|
|
pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).requires_grad_(False)
|
|
return pos_embed
|
|
|
|
def freeze_not_temporal(self):
|
|
for n, p in self.named_parameters():
|
|
if "attn_temp" not in n:
|
|
p.requires_grad = False
|
|
|
|
def freeze_text(self):
|
|
for n, p in self.named_parameters():
|
|
if "cross_attn" in n:
|
|
p.requires_grad = False
|
|
|
|
def initialize_temporal(self):
|
|
for block in self.blocks:
|
|
nn.init.constant_(block.attn_temp.proj.weight, 0)
|
|
nn.init.constant_(block.attn_temp.proj.bias, 0)
|
|
|
|
def initialize_weights(self):
|
|
# Initialize transformer layers:
|
|
def _basic_init(module):
|
|
if isinstance(module, nn.Linear):
|
|
torch.nn.init.xavier_uniform_(module.weight)
|
|
if module.bias is not None:
|
|
nn.init.constant_(module.bias, 0)
|
|
|
|
self.apply(_basic_init)
|
|
|
|
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
|
|
w = self.x_embedder.proj.weight.data
|
|
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
|
|
|
|
# Initialize timestep embedding MLP:
|
|
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
|
|
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
|
|
nn.init.normal_(self.t_block[1].weight, std=0.02)
|
|
|
|
# Initialize caption embedding MLP:
|
|
nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
|
|
nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)
|
|
|
|
# Zero-out adaLN modulation layers in PixArt blocks:
|
|
for block in self.blocks:
|
|
nn.init.constant_(block.cross_attn.proj.weight, 0)
|
|
nn.init.constant_(block.cross_attn.proj.bias, 0)
|
|
|
|
# Zero-out output layers:
|
|
nn.init.constant_(self.final_layer.linear.weight, 0)
|
|
nn.init.constant_(self.final_layer.linear.bias, 0)
|
|
|
|
|
|
@MODELS.register_module("STDiT-XL/2")
|
|
def STDiT_XL_2(from_pretrained=None, **kwargs):
|
|
model = STDiT(depth=28, hidden_size=1152, patch_size=(1, 2, 2), num_heads=16, **kwargs)
|
|
if from_pretrained is not None:
|
|
load_checkpoint(model, from_pretrained)
|
|
return model
|