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import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.attention.flex_attention import flex_attention
from .utils import get_freqs, nablaT_v2
if torch.cuda.get_device_capability()[0] >= 9:
try:
from flash_attn import flash_attn_func as FA
print("FlashAttention 2 is found")
except:
FA = None
try:
from flash_attn_interface import flash_attn_func as FA
print("FlashAttention 3 is found")
except:
FA = FA
else:
try:
from flash_attn import flash_attn_func as FA
print("FlashAttention 2 is found")
except:
FA = None
@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
def sdpa(q, k, v):
query = q.transpose(1, 2).contiguous()
key = k.transpose(1, 2).contiguous()
value = v.transpose(1, 2).contiguous()
out = (
F.scaled_dot_product_attention(
query,
key,
value
)
.transpose(1, 2)
.contiguous()
)
return out
if FA is None:
print("FlashAttention is not found. Using SDPA instead.")
FA = sdpa
@torch.compile()
@torch.autocast(device_type="cuda", dtype=torch.float32)
def apply_scale_shift_norm(norm, x, scale, shift):
return (norm(x) * (scale + 1.0) + shift).to(torch.bfloat16)
@torch.compile()
@torch.autocast(device_type="cuda", dtype=torch.float32)
def apply_gate_sum(x, out, gate):
return (x + gate * out).to(torch.bfloat16)
@torch.compile()
@torch.autocast(device_type="cuda", enabled=False)
def apply_rotary(x, rope):
x_ = x.reshape(*x.shape[:-1], -1, 1, 2).to(torch.float32)
x_out = (rope * x_).sum(dim=-1)
return x_out.reshape(*x.shape).to(torch.bfloat16)
class TimeEmbeddings(nn.Module):
def __init__(self, model_dim, time_dim, max_period=10000.0):
super().__init__()
assert model_dim % 2 == 0
self.model_dim = model_dim
self.max_period = max_period
self.register_buffer(
"freqs", get_freqs(model_dim // 2, max_period), persistent=False
)
self.in_layer = nn.Linear(model_dim, time_dim, bias=True)
self.activation = nn.SiLU()
self.out_layer = nn.Linear(time_dim, time_dim, bias=True)
@torch.autocast(device_type="cuda", dtype=torch.float32)
def forward(self, time):
args = torch.outer(time, self.freqs.to(device=time.device))
time_embed = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
time_embed = self.out_layer(self.activation(self.in_layer(time_embed)))
return time_embed
class TextEmbeddings(nn.Module):
def __init__(self, text_dim, model_dim):
super().__init__()
self.in_layer = nn.Linear(text_dim, model_dim, bias=True)
self.norm = nn.LayerNorm(model_dim, elementwise_affine=True)
def forward(self, text_embed):
text_embed = self.in_layer(text_embed)
return self.norm(text_embed).type_as(text_embed)
class VisualEmbeddings(nn.Module):
def __init__(self, visual_dim, model_dim, patch_size):
super().__init__()
self.patch_size = patch_size
self.in_layer = nn.Linear(math.prod(patch_size) * visual_dim, model_dim)
def forward(self, x):
duration, height, width, dim = x.shape
x = (
x.view(
duration // self.patch_size[0],
self.patch_size[0],
height // self.patch_size[1],
self.patch_size[1],
width // self.patch_size[2],
self.patch_size[2],
dim,
)
.permute(0, 2, 4, 1, 3, 5, 6)
.flatten(3, 6)
)
return self.in_layer(x)
class RoPE1D(nn.Module):
def __init__(self, dim, max_pos=1024, max_period=10000.0):
super().__init__()
self.max_period = max_period
self.dim = dim
self.max_pos = max_pos
freq = get_freqs(dim // 2, max_period)
pos = torch.arange(max_pos, dtype=freq.dtype)
self.register_buffer(f"args", torch.outer(pos, freq), persistent=False)
@torch.autocast(device_type="cuda", enabled=False)
def forward(self, pos):
args = self.args[pos]
cosine = torch.cos(args)
sine = torch.sin(args)
rope = torch.stack([cosine, -sine, sine, cosine], dim=-1)
rope = rope.view(*rope.shape[:-1], 2, 2)
return rope.unsqueeze(-4)
class RoPE3D(nn.Module):
def __init__(self, axes_dims, max_pos=(128, 128, 128), max_period=10000.0):
super().__init__()
self.axes_dims = axes_dims
self.max_pos = max_pos
self.max_period = max_period
for i, (axes_dim, ax_max_pos) in enumerate(zip(axes_dims, max_pos)):
freq = get_freqs(axes_dim // 2, max_period)
pos = torch.arange(ax_max_pos, dtype=freq.dtype)
self.register_buffer(f"args_{i}", torch.outer(pos, freq), persistent=False)
@torch.autocast(device_type="cuda", enabled=False)
def forward(self, shape, pos, scale_factor=(1.0, 1.0, 1.0)):
duration, height, width = shape
args_t = self.args_0[pos[0]] / scale_factor[0]
args_h = self.args_1[pos[1]] / scale_factor[1]
args_w = self.args_2[pos[2]] / scale_factor[2]
args = torch.cat(
[
args_t.view(duration, 1, 1, -1).repeat(1, height, width, 1),
args_h.view(1, height, 1, -1).repeat(duration, 1, width, 1),
args_w.view(1, 1, width, -1).repeat(duration, height, 1, 1),
],
dim=-1,
)
cosine = torch.cos(args)
sine = torch.sin(args)
rope = torch.stack([cosine, -sine, sine, cosine], dim=-1)
rope = rope.view(*rope.shape[:-1], 2, 2)
return rope.unsqueeze(-4)
class Modulation(nn.Module):
def __init__(self, time_dim, model_dim, num_params):
super().__init__()
self.activation = nn.SiLU()
self.out_layer = nn.Linear(time_dim, num_params * model_dim)
self.out_layer.weight.data.zero_()
self.out_layer.bias.data.zero_()
@torch.compile()
@torch.autocast(device_type="cuda", dtype=torch.float32)
def forward(self, x):
return self.out_layer(self.activation(x))
class MultiheadSelfAttentionEnc(nn.Module):
def __init__(self, num_channels, head_dim):
super().__init__()
assert num_channels % head_dim == 0
self.num_heads = num_channels // head_dim
self.to_query = nn.Linear(num_channels, num_channels, bias=True)
self.to_key = nn.Linear(num_channels, num_channels, bias=True)
self.to_value = nn.Linear(num_channels, num_channels, bias=True)
self.query_norm = nn.RMSNorm(head_dim)
self.key_norm = nn.RMSNorm(head_dim)
self.out_layer = nn.Linear(num_channels, num_channels, bias=True)
@torch.compile()
def get_qkv(self, x):
query = self.to_query(x)
key = self.to_key(x)
value = self.to_value(x)
shape = query.shape[:-1]
query = query.reshape(*shape, self.num_heads, -1)
key = key.reshape(*shape, self.num_heads, -1)
value = value.reshape(*shape, self.num_heads, -1)
return query, key, value
@torch.compile()
def norm_qk(self, q, k):
q = self.query_norm(q.float()).type_as(q)
k = self.key_norm(k.float()).type_as(k)
return q, k
@torch.compile()
def scaled_dot_product_attention(self, query, key, value):
out = FA(q=query.unsqueeze(0), k=key.unsqueeze(0), v=value.unsqueeze(0))[0].flatten(-2, -1)
return out
@torch.compile()
def out_l(self, x):
return self.out_layer(x)
def forward(self, x, rope):
query, key, value = self.get_qkv(x)
query, key = self.norm_qk(query, key)
query = apply_rotary(query, rope).type_as(query)
key = apply_rotary(key, rope).type_as(key)
out = self.scaled_dot_product_attention(query, key, value)
out = self.out_l(out)
return out
class MultiheadSelfAttentionDec(nn.Module):
def __init__(self, num_channels, head_dim):
super().__init__()
assert num_channels % head_dim == 0
self.num_heads = num_channels // head_dim
self.to_query = nn.Linear(num_channels, num_channels, bias=True)
self.to_key = nn.Linear(num_channels, num_channels, bias=True)
self.to_value = nn.Linear(num_channels, num_channels, bias=True)
self.query_norm = nn.RMSNorm(head_dim)
self.key_norm = nn.RMSNorm(head_dim)
self.out_layer = nn.Linear(num_channels, num_channels, bias=True)
@torch.compile()
def get_qkv(self, x):
query = self.to_query(x)
key = self.to_key(x)
value = self.to_value(x)
shape = query.shape[:-1]
query = query.reshape(*shape, self.num_heads, -1)
key = key.reshape(*shape, self.num_heads, -1)
value = value.reshape(*shape, self.num_heads, -1)
return query, key, value
@torch.compile()
def norm_qk(self, q, k):
q = self.query_norm(q.float()).type_as(q)
k = self.key_norm(k.float()).type_as(k)
return q, k
@torch.compile()
def attention(self, query, key, value):
out = FA(q=query.unsqueeze(0), k=key.unsqueeze(0), v=value.unsqueeze(0))[0].flatten(-2, -1)
return out
@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
def nabla(self, query, key, value, sparse_params=None):
query = query.unsqueeze(0).transpose(1, 2).contiguous()
key = key.unsqueeze(0).transpose(1, 2).contiguous()
value = value.unsqueeze(0).transpose(1, 2).contiguous()
block_mask = nablaT_v2(
query,
key,
sparse_params["sta_mask"],
thr=sparse_params["P"],
)
out = (
flex_attention(
query,
key,
value,
block_mask=block_mask
)
.transpose(1, 2)
.squeeze(0)
.contiguous()
)
out = out.flatten(-2, -1)
return out
@torch.compile()
def out_l(self, x):
return self.out_layer(x)
def forward(self, x, rope, sparse_params=None):
query, key, value = self.get_qkv(x)
query, key = self.norm_qk(query, key)
query = apply_rotary(query, rope).type_as(query)
key = apply_rotary(key, rope).type_as(key)
if sparse_params is not None:
out = self.nabla(query, key, value, sparse_params=sparse_params)
else:
out = self.attention(query, key, value)
out = self.out_l(out)
return out
class MultiheadCrossAttention(nn.Module):
def __init__(self, num_channels, head_dim):
super().__init__()
assert num_channels % head_dim == 0
self.num_heads = num_channels // head_dim
self.to_query = nn.Linear(num_channels, num_channels, bias=True)
self.to_key = nn.Linear(num_channels, num_channels, bias=True)
self.to_value = nn.Linear(num_channels, num_channels, bias=True)
self.query_norm = nn.RMSNorm(head_dim)
self.key_norm = nn.RMSNorm(head_dim)
self.out_layer = nn.Linear(num_channels, num_channels, bias=True)
@torch.compile()
def get_qkv(self, x, cond):
query = self.to_query(x)
key = self.to_key(cond)
value = self.to_value(cond)
shape, cond_shape = query.shape[:-1], key.shape[:-1]
query = query.reshape(*shape, self.num_heads, -1)
key = key.reshape(*cond_shape, self.num_heads, -1)
value = value.reshape(*cond_shape, self.num_heads, -1)
return query, key, value
@torch.compile()
def norm_qk(self, q, k):
q = self.query_norm(q.float()).type_as(q)
k = self.key_norm(k.float()).type_as(k)
return q, k
@torch.compile()
def attention(self, query, key, value):
out = FA(q=query.unsqueeze(0), k=key.unsqueeze(0), v=value.unsqueeze(0))[0].flatten(-2, -1)
return out
@torch.compile()
def out_l(self, x):
return self.out_layer(x)
def forward(self, x, cond):
query, key, value = self.get_qkv(x, cond)
query, key = self.norm_qk(query, key)
out = self.attention(query, key, value)
out = self.out_l(out)
return out
class FeedForward(nn.Module):
def __init__(self, dim, ff_dim):
super().__init__()
self.in_layer = nn.Linear(dim, ff_dim, bias=False)
self.activation = nn.GELU()
self.out_layer = nn.Linear(ff_dim, dim, bias=False)
@torch.compile()
def forward(self, x):
return self.out_layer(self.activation(self.in_layer(x)))
class OutLayer(nn.Module):
def __init__(self, model_dim, time_dim, visual_dim, patch_size):
super().__init__()
self.patch_size = patch_size
self.modulation = Modulation(time_dim, model_dim, 2)
self.norm = nn.LayerNorm(model_dim, elementwise_affine=False)
self.out_layer = nn.Linear(
model_dim, math.prod(patch_size) * visual_dim, bias=True
)
def forward(self, visual_embed, text_embed, time_embed):
shift, scale = torch.chunk(self.modulation(time_embed), 2, dim=-1)
visual_embed = apply_scale_shift_norm(
self.norm,
visual_embed,
scale[:, None, None],
shift[:, None, None],
).type_as(visual_embed)
x = self.out_layer(visual_embed)
duration, height, width, _ = x.shape
x = (
x.view(
duration,
height,
width,
-1,
self.patch_size[0],
self.patch_size[1],
self.patch_size[2],
)
.permute(0, 4, 1, 5, 2, 6, 3)
.flatten(0, 1)
.flatten(1, 2)
.flatten(2, 3)
)
return x
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