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Running on Zero
Running on Zero
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| import math | |
| from timm.models.vision_transformer import PatchEmbed, Mlp | |
| from timm.models.vision_transformer import Attention | |
| import torch.nn.functional as F | |
| def modulate(x, scale, shift): | |
| return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) | |
| class TimestepEmbedder(nn.Module): | |
| def __init__(self, dim, nfreq=256): | |
| super().__init__() | |
| self.mlp = nn.Sequential(nn.Linear(nfreq, dim), nn.SiLU(), nn.Linear(dim, dim)) | |
| self.nfreq = nfreq | |
| def timestep_embedding(t, dim, max_period=10000): | |
| half_dim = dim // 2 | |
| freqs = torch.exp( | |
| -math.log(max_period) | |
| * torch.arange(start=0, end=half_dim, dtype=torch.float32) | |
| / half_dim | |
| ).to(device=t.device) | |
| 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 = t * 1000 | |
| t_freq = self.timestep_embedding(t, self.nfreq) | |
| t_emb = self.mlp(t_freq) | |
| return t_emb | |
| class LabelEmbedder(nn.Module): | |
| def __init__(self, num_classes, dim): | |
| super().__init__() | |
| self.embedding = nn.Embedding(num_classes + 1, dim) | |
| self.num_classes = num_classes | |
| def forward(self, labels): | |
| embeddings = self.embedding(labels) | |
| return embeddings | |
| class RMSNorm(nn.Module): | |
| def __init__(self, dim, device=None, dtype=None): | |
| super().__init__() | |
| self.scale = dim**0.5 | |
| self.g = nn.Parameter(torch.ones(1, device=device, dtype=dtype)) | |
| def forward(self, x): | |
| return F.normalize(x, dim=-1) * self.scale * self.g | |
| class DiTBlock(nn.Module): | |
| def __init__(self, dim, num_heads, mlp_ratio=4.0): | |
| super().__init__() | |
| self.norm1 = RMSNorm(dim) | |
| self.attn = Attention( | |
| dim, num_heads=num_heads, qkv_bias=True, qk_norm=True, norm_layer=RMSNorm | |
| ) | |
| # Explicit attention supports JVP-based objectives. | |
| self.attn.fused_attn = False | |
| self.norm2 = RMSNorm(dim) | |
| mlp_dim = int(dim * mlp_ratio) | |
| approx_gelu = lambda: nn.GELU(approximate="tanh") | |
| self.mlp = Mlp(in_features=dim, hidden_features=mlp_dim, act_layer=approx_gelu, drop=0) | |
| self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim, 6 * dim)) | |
| def forward(self, x, c): | |
| shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation( | |
| c | |
| ).chunk(6, dim=-1) | |
| x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), scale_msa, shift_msa)) | |
| x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), scale_mlp, shift_mlp)) | |
| return x | |
| class FinalLayer(nn.Module): | |
| def __init__(self, dim, patch_size, out_dim): | |
| super().__init__() | |
| self.norm_final = RMSNorm(dim) | |
| self.linear = nn.Linear(dim, patch_size * patch_size * out_dim) | |
| self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim, 2 * dim)) | |
| 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 MFDiT(nn.Module): | |
| def __init__( | |
| self, | |
| input_size=32, | |
| patch_size=2, | |
| in_channels=4, | |
| dim=1152, | |
| depth=28, | |
| num_heads=16, | |
| mlp_ratio=4.0, | |
| num_register_tokens=4, | |
| num_classes=1000, | |
| ): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = in_channels | |
| self.patch_size = patch_size | |
| self.num_heads = num_heads | |
| self.num_classes = num_classes | |
| self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, dim) | |
| self.t_embedder = TimestepEmbedder(dim) | |
| self.r_embedder = TimestepEmbedder(dim) | |
| self.use_cond = num_classes is not None | |
| self.y_embedder = LabelEmbedder(num_classes, dim) if self.use_cond else None | |
| num_patches = self.x_embedder.num_patches | |
| self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, dim), requires_grad=True) | |
| self.blocks = nn.ModuleList([DiTBlock(dim, num_heads, mlp_ratio) for _ in range(depth)]) | |
| self.final_layer = FinalLayer(dim, patch_size, self.out_channels) | |
| self.initialize_weights() | |
| def initialize_weights(self): | |
| 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) | |
| pos_embed = get_2d_sincos_pos_embed( | |
| self.pos_embed.shape[-1], int(self.x_embedder.num_patches**0.5) | |
| ) | |
| self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) | |
| 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) | |
| if self.y_embedder is not None: | |
| nn.init.normal_(self.y_embedder.embedding.weight, std=0.02) | |
| nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) | |
| nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) | |
| for block in self.blocks: | |
| nn.init.constant_(block.adaLN_modulation[-1].weight, 0) | |
| nn.init.constant_(block.adaLN_modulation[-1].bias, 0) | |
| 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 unpatchify(self, x): | |
| """Reconstruct an NCHW tensor from flattened patch predictions.""" | |
| c = self.out_channels | |
| p = self.x_embedder.patch_size[0] | |
| h = w = int(x.shape[1] ** 0.5) | |
| assert h * w == x.shape[1] | |
| x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) | |
| x = torch.einsum("nhwpqc->nchpwq", x) | |
| imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p)) | |
| return imgs | |
| def forward_features(self, x, t, r, y=None, out_indices=[2, 5, 8, 11]): | |
| x = self.x_embedder(x) + self.pos_embed | |
| t = self.t_embedder(t) | |
| r = self.r_embedder(r) | |
| t = t + r | |
| c = t | |
| if self.use_cond and y is not None: | |
| y = self.y_embedder(y) | |
| c = c + y | |
| features = [] | |
| for i, block in enumerate(self.blocks): | |
| x = block(x, c) | |
| if i in out_indices: | |
| features.append(x) | |
| return features | |
| def forward(self, x, t, r, y=None): | |
| """Predict a velocity field with the same spatial shape as the input.""" | |
| H, W = x.shape[-2:] | |
| x = self.x_embedder(x) + self.pos_embed | |
| t = self.t_embedder(t) | |
| r = self.r_embedder(r) | |
| t = t + r | |
| c = t | |
| if self.use_cond: | |
| y = self.y_embedder(y) | |
| c = c + y | |
| for i, block in enumerate(self.blocks): | |
| x = block(x, c) | |
| x = self.final_layer(x, c) | |
| x = self.unpatchify(x) | |
| return x | |
| # Positional embeddings adapted from: | |
| # https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py | |
| def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0): | |
| """Return 2D sine-cosine embeddings with optional zero-valued prefix tokens.""" | |
| grid_h = np.arange(grid_size, dtype=np.float32) | |
| grid_w = np.arange(grid_size, dtype=np.float32) | |
| grid = np.meshgrid(grid_w, grid_h) | |
| grid = np.stack(grid, axis=0) | |
| grid = grid.reshape([2, 1, grid_size, grid_size]) | |
| pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) | |
| if cls_token and extra_tokens > 0: | |
| pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) | |
| return pos_embed | |
| def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): | |
| assert embed_dim % 2 == 0 | |
| emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) | |
| emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) | |
| emb = np.concatenate([emb_h, emb_w], axis=1) | |
| return emb | |
| def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): | |
| """Return sine-cosine embeddings for flattened positions.""" | |
| assert embed_dim % 2 == 0 | |
| omega = np.arange(embed_dim // 2, dtype=np.float64) | |
| omega /= embed_dim / 2.0 | |
| omega = 1.0 / 10000**omega | |
| pos = pos.reshape(-1) | |
| out = np.einsum("m,d->md", pos, omega) | |
| emb_sin = np.sin(out) | |
| emb_cos = np.cos(out) | |
| emb = np.concatenate([emb_sin, emb_cos], axis=1) | |
| return emb | |