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on
Zero
Running
on
Zero
| from typing import * | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from ...modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32 | |
| from ...modules.transformer import AbsolutePositionEmbedder | |
| from ...modules import sparse as sp | |
| from ...modules.sparse.transformer.modulated import ModulatedSparseTransformerCrossBlock | |
| from .dense_dit import TimestepEmbedder | |
| class SparseDiT(nn.Module): | |
| def __init__( | |
| self, | |
| resolution: int, | |
| in_channels: int, | |
| model_channels: int, | |
| cond_channels: int, | |
| out_channels: int, | |
| num_blocks: int, | |
| num_heads: Optional[int] = None, | |
| num_head_channels: Optional[int] = 64, | |
| num_kv_heads: Optional[int] = 2, | |
| compression_block_size: int = 4, | |
| selection_block_size: int = 8, | |
| topk: int = 8, | |
| compression_version: str = 'v2', | |
| mlp_ratio: float = 4, | |
| pe_mode: Literal["ape", "rope"] = "ape", | |
| use_fp16: bool = False, | |
| use_checkpoint: bool = False, | |
| share_mod: bool = False, | |
| qk_rms_norm: bool = False, | |
| qk_rms_norm_cross: bool = False, | |
| sparse_conditions: bool = False, | |
| factor: float = 1.0, | |
| window_size: Optional[int] = 8, | |
| use_shift: bool = True, | |
| ): | |
| super().__init__() | |
| self.resolution = resolution | |
| self.in_channels = in_channels | |
| self.model_channels = model_channels | |
| self.cond_channels = cond_channels | |
| self.out_channels = out_channels | |
| self.num_blocks = num_blocks | |
| self.num_heads = num_heads or model_channels // num_head_channels | |
| self.mlp_ratio = mlp_ratio | |
| self.pe_mode = pe_mode | |
| self.use_fp16 = use_fp16 | |
| self.use_checkpoint = use_checkpoint | |
| self.share_mod = share_mod | |
| self.qk_rms_norm = qk_rms_norm | |
| self.qk_rms_norm_cross = qk_rms_norm_cross | |
| self.dtype = torch.float16 if use_fp16 else torch.float32 | |
| self.sparse_conditions = sparse_conditions | |
| self.factor = factor | |
| self.compression_block_size = compression_block_size | |
| self.selection_block_size = selection_block_size | |
| self.t_embedder = TimestepEmbedder(model_channels) | |
| if share_mod: | |
| self.adaLN_modulation = nn.Sequential( | |
| nn.SiLU(), | |
| nn.Linear(model_channels, 6 * model_channels, bias=True) | |
| ) | |
| if sparse_conditions: | |
| self.cond_proj = sp.SparseLinear(cond_channels, cond_channels) | |
| self.pos_embedder_cond = AbsolutePositionEmbedder(model_channels, in_channels=3) | |
| if pe_mode == "ape": | |
| self.pos_embedder = AbsolutePositionEmbedder(model_channels) | |
| self.input_layer = sp.SparseLinear(in_channels, model_channels) | |
| self.blocks = nn.ModuleList([ | |
| ModulatedSparseTransformerCrossBlock( | |
| model_channels, | |
| cond_channels, | |
| num_heads=self.num_heads, | |
| num_kv_heads=num_kv_heads, | |
| compression_block_size=compression_block_size, | |
| selection_block_size=selection_block_size, | |
| topk=topk, | |
| mlp_ratio=self.mlp_ratio, | |
| attn_mode='full', | |
| compression_version=compression_version, | |
| use_checkpoint=self.use_checkpoint, | |
| use_rope=(pe_mode == "rope"), | |
| share_mod=self.share_mod, | |
| qk_rms_norm=self.qk_rms_norm, | |
| qk_rms_norm_cross=self.qk_rms_norm_cross, | |
| resolution=resolution, | |
| window_size=window_size, | |
| shift_window=window_size // 2 * (_ % 2) if use_shift else window_size // 2, | |
| ) | |
| for _ in range(num_blocks) | |
| ]) | |
| self.out_layer = sp.SparseLinear(model_channels, out_channels) | |
| self.initialize_weights() | |
| if use_fp16: | |
| self.convert_to_fp16() | |
| def device(self) -> torch.device: | |
| """ | |
| Return the device of the model. | |
| """ | |
| return next(self.parameters()).device | |
| def convert_to_fp16(self) -> None: | |
| """ | |
| Convert the torso of the model to float16. | |
| """ | |
| # self.blocks.apply(convert_module_to_f16) | |
| self.apply(convert_module_to_f16) | |
| def convert_to_fp32(self) -> None: | |
| """ | |
| Convert the torso of the model to float32. | |
| """ | |
| self.blocks.apply(convert_module_to_f32) | |
| def initialize_weights(self) -> None: | |
| # 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 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 adaLN modulation layers in DiT blocks: | |
| if self.share_mod: | |
| nn.init.constant_(self.adaLN_modulation[-1].weight, 0) | |
| nn.init.constant_(self.adaLN_modulation[-1].bias, 0) | |
| else: | |
| for block in self.blocks: | |
| nn.init.constant_(block.adaLN_modulation[-1].weight, 0) | |
| nn.init.constant_(block.adaLN_modulation[-1].bias, 0) | |
| # Zero-out output layers: | |
| nn.init.constant_(self.out_layer.weight, 0) | |
| nn.init.constant_(self.out_layer.bias, 0) | |
| def forward(self, x: sp.SparseTensor, t: torch.Tensor, cond: Union[torch.Tensor, sp.SparseTensor]) -> sp.SparseTensor: | |
| h = self.input_layer(x).type(self.dtype) | |
| t_emb = self.t_embedder(t) | |
| if self.share_mod: | |
| t_emb = self.adaLN_modulation(t_emb) | |
| t_emb = t_emb.type(self.dtype) | |
| cond = cond.type(self.dtype) | |
| if self.sparse_conditions: | |
| cond = self.cond_proj(cond) | |
| cond = cond + self.pos_embedder_cond(cond.coords[:, 1:]).type(self.dtype) | |
| if self.pe_mode == "ape": | |
| h = h + self.pos_embedder(h.coords[:, 1:], factor=self.factor).type(self.dtype) | |
| for block in self.blocks: | |
| h = block(h, t_emb, cond) | |
| h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:])) | |
| h = self.out_layer(h.type(x.dtype)) | |
| return h | |