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 DiTBlock(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 DiT(nn.Module): def __init__( self, in_channels=4, num_groups=12, hidden_size=1152, num_blocks=18, 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.patch_size = patch_size self.x_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([ DiTBlock(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, dtype): if (height, width) in self.precompute_pos: return self.precompute_pos[(height, width)].to(device, dtype) else: pos = precompute_freqs_cis_2d(self.hidden_size // self.num_groups, height, width).to(device, dtype) 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 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, masks=None): if masks is None: masks = [None, ]*self.num_blocks if isinstance(masks, torch.Tensor): masks = masks.unbind(0) if isinstance(masks, (tuple, list)) and len(masks) < self.num_blocks: masks = masks + [None]*(self.num_blocks-len(masks)) B, _, H, W = x.shape x = torch.nn.functional.unfold(x, kernel_size=self.patch_size, stride=self.patch_size).transpose(1, 2) x = self.x_embedder(x) pos = self.fetch_pos(H // self.patch_size, W // self.patch_size, x.device, x.dtype) B, L, C = x.shape t = self.t_embedder(t.view(-1)).view(B, -1, C) y = self.y_embedder(y).view(B, 1, C) condition = nn.functional.silu(t + y) for i, block in enumerate(self.blocks): x = block(x, condition, pos, masks[i]) x = self.final_layer(x, condition) x = torch.nn.functional.fold(x.transpose(1, 2).contiguous(), (H, W), kernel_size=self.patch_size, stride=self.patch_size) return x