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import functools
from typing import Tuple
import torch
import torch.nn as nn
import math
from torch.nn.init import zeros_
from torch.nn.modules.module import T
# from torch.nn.attention.flex_attention import flex_attention, create_block_mask
from torch.nn.functional import scaled_dot_product_attention
def modulate(x, shift, scale):
return x * (1 + scale) + shift
class Embed(nn.Module):
def __init__(
self,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer = None,
bias: bool = True,
):
super().__init__()
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Linear(in_chans, embed_dim, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = self.proj(x)
x = self.norm(x)
return x
class TimestepEmbedder(nn.Module):
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10):
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
)
args = t[..., 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 forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class LabelEmbedder(nn.Module):
def __init__(self, num_classes, hidden_size):
super().__init__()
self.embedding_table = nn.Embedding(num_classes, hidden_size)
self.num_classes = num_classes
def forward(self, labels,):
embeddings = self.embedding_table(labels)
return embeddings
class FinalLayer(nn.Module):
def __init__(self, hidden_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, out_channels, bias=True)
self.adaLN_modulation = nn.Sequential(
nn.Linear(hidden_size, 2*hidden_size, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
class FeedForward(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
):
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
def forward(self, x):
x = self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x))
return x
def precompute_freqs_cis_2d(dim: int, height: int, width:int, theta: float = 10000.0, scale=16.0):
# assert H * H == end
# flat_patch_pos = torch.linspace(-1, 1, end) # N = end
x_pos = torch.linspace(0, scale, width)
y_pos = torch.linspace(0, scale, height)
y_pos, x_pos = torch.meshgrid(y_pos, x_pos, indexing="ij")
y_pos = y_pos.reshape(-1)
x_pos = x_pos.reshape(-1)
freqs = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) # Hc/4
x_freqs = torch.outer(x_pos, freqs).float() # N Hc/4
y_freqs = torch.outer(y_pos, freqs).float() # N Hc/4
x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs)
y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs)
freqs_cis = torch.cat([x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1) # N,Hc/4,2
freqs_cis = freqs_cis.reshape(height*width, -1)
return freqs_cis
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
freqs_cis = freqs_cis[None, :, None, :]
# xq : B N H Hc
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # B N H Hc/2
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) # B, N, H, Hc
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
class RAttention(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_norm: bool = True,
attn_drop: float = 0.,
proj_drop: float = 0.,
norm_layer: nn.Module = RMSNorm,
) -> None:
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.dim = dim
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x: torch.Tensor, pos, mask) -> torch.Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 1, 3, 4)
q, k, v = qkv[0], qkv[1], qkv[2] # B N H Hc
q = self.q_norm(q)
k = self.k_norm(k)
q, k = apply_rotary_emb(q, k, freqs_cis=pos)
q = q.view(B, -1, self.num_heads, C // self.num_heads).transpose(1, 2) # B, H, N, Hc
k = k.view(B, -1, self.num_heads, C // self.num_heads).transpose(1, 2).contiguous() # B, H, N, Hc
v = v.view(B, -1, self.num_heads, C // self.num_heads).transpose(1, 2).contiguous()
x = scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class DDTBlock(nn.Module):
def __init__(self, hidden_size, groups, mlp_ratio=4.0, ):
super().__init__()
self.norm1 = RMSNorm(hidden_size, eps=1e-6)
self.attn = RAttention(hidden_size, num_heads=groups, qkv_bias=False)
self.norm2 = RMSNorm(hidden_size, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
self.mlp = FeedForward(hidden_size, mlp_hidden_dim)
self.adaLN_modulation = nn.Sequential(
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c, pos, mask=None):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1)
x = x + gate_msa * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), pos, mask=mask)
x = x + gate_mlp * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class DDT(nn.Module):
def __init__(
self,
in_channels=4,
num_groups=12,
hidden_size=1152,
num_blocks=18,
num_encoder_blocks=4,
patch_size=2,
num_classes=1000,
learn_sigma=True,
deep_supervision=0,
weight_path=None,
load_ema=False,
):
super().__init__()
self.deep_supervision = deep_supervision
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels
self.hidden_size = hidden_size
self.num_groups = num_groups
self.num_blocks = num_blocks
self.num_encoder_blocks = num_encoder_blocks
self.patch_size = patch_size
self.x_embedder = Embed(in_channels*patch_size**2, hidden_size, bias=True)
self.s_embedder = Embed(in_channels*patch_size**2, hidden_size, bias=True)
self.t_embedder = TimestepEmbedder(hidden_size)
self.y_embedder = LabelEmbedder(num_classes+1, hidden_size)
self.final_layer = FinalLayer(hidden_size, in_channels*patch_size**2)
self.weight_path = weight_path
self.load_ema = load_ema
self.blocks = nn.ModuleList([
DDTBlock(self.hidden_size, self.num_groups) for _ in range(self.num_blocks)
])
self.initialize_weights()
self.precompute_pos = dict()
def fetch_pos(self, height, width, device):
if (height, width) in self.precompute_pos:
return self.precompute_pos[(height, width)].to(device)
else:
pos = precompute_freqs_cis_2d(self.hidden_size // self.num_groups, height, width).to(device)
self.precompute_pos[(height, width)] = pos
return pos
def initialize_weights(self):
# 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]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.s_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.s_embedder.proj.bias, 0)
# Initialize label embedding table:
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
# 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)
# Zero-out output layers:
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def forward(self, x, t, y, s=None, mask=None):
B, _, H, W = x.shape
pos = self.fetch_pos(H//self.patch_size, W//self.patch_size, x.device)
x = torch.nn.functional.unfold(x, kernel_size=self.patch_size, stride=self.patch_size).transpose(1, 2)
t = self.t_embedder(t.view(-1)).view(B, -1, self.hidden_size)
y = self.y_embedder(y).view(B, 1, self.hidden_size)
c = nn.functional.silu(t + y)
if s is None:
s = self.s_embedder(x)
for i in range(self.num_encoder_blocks):
s = self.blocks[i](s, c, pos, mask)
s = nn.functional.silu(t + s)
x = self.x_embedder(x)
for i in range(self.num_encoder_blocks, self.num_blocks):
x = self.blocks[i](x, s, pos, None)
x = self.final_layer(x, s)
x = torch.nn.functional.fold(x.transpose(1, 2).contiguous(), (H, W), kernel_size=self.patch_size, stride=self.patch_size)
return x, s |