modeling_stdit2.py

import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from einops import rearrange
from .configuration_stdit2 import STDiT2Config
from .layers import (
    STDiT2Block,
    CaptionEmbedder,
    PatchEmbed3D,
    T2IFinalLayer,
    TimestepEmbedder,
    SizeEmbedder,
    PositionEmbedding2D
)
from rotary_embedding_torch import RotaryEmbedding
from .utils import (
    get_2d_sincos_pos_embed,
    approx_gelu
)
from transformers import PreTrainedModel


class STDiT2(PreTrainedModel):

    config_class = STDiT2Config

    def __init__(
        self,
        config: STDiT2Config
    ):
        super().__init__(config)
        self.pred_sigma = config.pred_sigma
        self.in_channels = config.in_channels
        self.out_channels = config.in_channels * 2 if config.pred_sigma else config.in_channels
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_heads
        self.no_temporal_pos_emb = config.no_temporal_pos_emb
        self.depth = config.depth
        self.mlp_ratio = config.mlp_ratio
        self.enable_flash_attn = config.enable_flash_attn
        self.enable_layernorm_kernel = config.enable_layernorm_kernel
        self.enable_sequence_parallelism = config.enable_sequence_parallelism

        # support dynamic input
        self.patch_size = config.patch_size
        self.input_size = config.input_size
        self.input_sq_size = config.input_sq_size
        self.pos_embed = PositionEmbedding2D(config.hidden_size)

        self.x_embedder = PatchEmbed3D(config.patch_size, config.in_channels, config.hidden_size)
        self.t_embedder = TimestepEmbedder(config.hidden_size)
        self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(config.hidden_size, 6 * config.hidden_size, bias=True))
        self.t_block_temp = nn.Sequential(nn.SiLU(), nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=True))  # new
        self.y_embedder = CaptionEmbedder(
            in_channels=config.caption_channels,
            hidden_size=config.hidden_size,
            uncond_prob=config.class_dropout_prob,
            act_layer=approx_gelu,
            token_num=config.model_max_length,
        )

        drop_path = [x.item() for x in torch.linspace(0, config.drop_path, config.depth)]
        self.rope = RotaryEmbedding(dim=self.hidden_size // self.num_heads)  # new
        self.blocks = nn.ModuleList(
            [
                STDiT2Block(
                    self.hidden_size,
                    self.num_heads,
                    mlp_ratio=self.mlp_ratio,
                    drop_path=drop_path[i],
                    enable_flash_attn=self.enable_flash_attn,
                    enable_layernorm_kernel=self.enable_layernorm_kernel,
                    enable_sequence_parallelism=self.enable_sequence_parallelism,
                    rope=self.rope.rotate_queries_or_keys,
                    qk_norm=config.qk_norm,
                )
                for i in range(self.depth)
            ]
        )
        self.final_layer = T2IFinalLayer(config.hidden_size, np.prod(self.patch_size), self.out_channels)

        # multi_res
        assert self.hidden_size % 3 == 0, "hidden_size must be divisible by 3"
        self.csize_embedder = SizeEmbedder(self.hidden_size // 3)
        self.ar_embedder = SizeEmbedder(self.hidden_size // 3)
        self.fl_embedder = SizeEmbedder(self.hidden_size)  # new
        self.fps_embedder = SizeEmbedder(self.hidden_size)  # new

        # init model
        self.initialize_weights()
        self.initialize_temporal()
        if config.freeze is not None:
            assert config.freeze in ["not_temporal", "text"]
            if config.freeze == "not_temporal":
                self.freeze_not_temporal()
            elif config.freeze == "text":
                self.freeze_text()

        # sequence parallel related configs
        if self.enable_sequence_parallelism:
            self.sp_rank = dist.get_rank(get_sequence_parallel_group())
        else:
            self.sp_rank = None

    def get_dynamic_size(self, x):
        _, _, T, H, W = x.size()
        if T % self.patch_size[0] != 0:
            T += self.patch_size[0] - T % self.patch_size[0]
        if H % self.patch_size[1] != 0:
            H += self.patch_size[1] - H % self.patch_size[1]
        if W % self.patch_size[2] != 0:
            W += self.patch_size[2] - W % self.patch_size[2]
        T = T // self.patch_size[0]
        H = H // self.patch_size[1]
        W = W // self.patch_size[2]
        return (T, H, W)

    def forward(
        self, x, timestep, y, mask=None, x_mask=None, num_frames=None, height=None, width=None, ar=None, fps=None
    ):
        """
        Forward pass of STDiT.
        Args:
            x (torch.Tensor): latent representation of video; of shape [B, C, T, H, W]
            timestep (torch.Tensor): diffusion time steps; of shape [B]
            y (torch.Tensor): representation of prompts; of shape [B, 1, N_token, C]
            mask (torch.Tensor): mask for selecting prompt tokens; of shape [B, N_token]

        Returns:
            x (torch.Tensor): output latent representation; of shape [B, C, T, H, W]
        """
        B = x.shape[0]
        x = x.to(self.final_layer.linear.weight.dtype)
        timestep = timestep.to(self.final_layer.linear.weight.dtype)
        y = y.to(self.final_layer.linear.weight.dtype)


        # === process data info ===
        # 1. get dynamic size
        hw = torch.cat([height[:, None], width[:, None]], dim=1)
        rs = (height[0].item() * width[0].item()) ** 0.5
        csize = self.csize_embedder(hw, B)

        # 2. get aspect ratio
        ar = ar.unsqueeze(1)
        ar = self.ar_embedder(ar, B)
        data_info = torch.cat([csize, ar], dim=1)

        # 3. get number of frames
        fl = num_frames.unsqueeze(1)
        fps = fps.unsqueeze(1)
        fl = self.fl_embedder(fl, B)
        fl = fl + self.fps_embedder(fps, B)

        # === get dynamic shape size ===
        _, _, Tx, Hx, Wx = x.size()
        T, H, W = self.get_dynamic_size(x)
        S = H * W
        scale = rs / self.input_sq_size
        base_size = round(S**0.5)
        pos_emb = self.pos_embed(x, H, W, scale=scale, base_size=base_size)

        # embedding
        x = self.x_embedder(x)  # [B, N, C]
        x = rearrange(x, "B (T S) C -> B T S C", T=T, S=S)
        x = x + pos_emb
        x = rearrange(x, "B T S C -> B (T S) C")

        # shard over the sequence dim if sp is enabled
        if self.enable_sequence_parallelism:
            x = split_forward_gather_backward(x, get_sequence_parallel_group(), dim=1, grad_scale="down")

        # prepare adaIN
        t = self.t_embedder(timestep, dtype=x.dtype)  # [B, C]
        t_spc = t + data_info  # [B, C]
        t_tmp = t + fl  # [B, C]
        t_spc_mlp = self.t_block(t_spc)  # [B, 6*C]
        t_tmp_mlp = self.t_block_temp(t_tmp)  # [B, 3*C]
        if x_mask is not None:
            t0_timestep = torch.zeros_like(timestep)
            t0 = self.t_embedder(t0_timestep, dtype=x.dtype)
            t0_spc = t0 + data_info
            t0_tmp = t0 + fl
            t0_spc_mlp = self.t_block(t0_spc)
            t0_tmp_mlp = self.t_block_temp(t0_tmp)
        else:
            t0_spc = None
            t0_tmp = None
            t0_spc_mlp = None
            t0_tmp_mlp = None

        # prepare y
        y = self.y_embedder(y, self.training)  # [B, 1, N_token, C]

        if mask is not None:
            if mask.shape[0] != y.shape[0]:
                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
            mask = mask.squeeze(1).squeeze(1)
            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
            y_lens = mask.sum(dim=1).tolist()
        else:
            y_lens = [y.shape[2]] * y.shape[0]
            y = y.squeeze(1).view(1, -1, x.shape[-1])

        # blocks
        for _, block in enumerate(self.blocks):
            x = block(
                x,
                y,
                t_spc_mlp,
                t_tmp_mlp,
                y_lens,
                x_mask,
                t0_spc_mlp,
                t0_tmp_mlp,
                T,
                S,
            )

        if self.enable_sequence_parallelism:
            x = gather_forward_split_backward(x, get_sequence_parallel_group(), dim=1, grad_scale="up")
        # x.shape: [B, N, C]

        # final process
        x = self.final_layer(x, t, x_mask, t0_spc, T, S)  # [B, N, C=T_p * H_p * W_p * C_out]
        x = self.unpatchify(x, T, H, W, Tx, Hx, Wx)  # [B, C_out, T, H, W]

        # cast to float32 for better accuracy
        x = x.to(torch.float32)
        return x

    def unpatchify(self, x, N_t, N_h, N_w, R_t, R_h, R_w):
        """
        Args:
            x (torch.Tensor): of shape [B, N, C]

        Return:
            x (torch.Tensor): of shape [B, C_out, T, H, W]
        """

        # N_t, N_h, N_w = [self.input_size[i] // self.patch_size[i] for i in range(3)]
        T_p, H_p, W_p = self.patch_size
        x = rearrange(
            x,
            "B (N_t N_h N_w) (T_p H_p W_p C_out) -> B C_out (N_t T_p) (N_h H_p) (N_w W_p)",
            N_t=N_t,
            N_h=N_h,
            N_w=N_w,
            T_p=T_p,
            H_p=H_p,
            W_p=W_p,
            C_out=self.out_channels,
        )
        # unpad
        x = x[:, :, :R_t, :R_h, :R_w]
        return x

    def unpatchify_old(self, x):
        c = self.out_channels
        t, h, w = [self.input_size[i] // self.patch_size[i] for i in range(3)]
        pt, ph, pw = self.patch_size

        x = x.reshape(shape=(x.shape[0], t, h, w, pt, ph, pw, c))
        x = rearrange(x, "n t h w r p q c -> n c t r h p w q")
        imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))
        return imgs

    def get_spatial_pos_embed(self, H, W, scale=1.0, base_size=None):
        pos_embed = get_2d_sincos_pos_embed(
            self.hidden_size,
            (H, W),
            scale=scale,
            base_size=base_size,
        )
        pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).requires_grad_(False)
        return pos_embed

    def freeze_not_temporal(self):
        for n, p in self.named_parameters():
            if "attn_temp" not in n:
                p.requires_grad = False

    def freeze_text(self):
        for n, p in self.named_parameters():
            if "cross_attn" in n:
                p.requires_grad = False

    def initialize_temporal(self):
        for block in self.blocks:
            nn.init.constant_(block.attn_temp.proj.weight, 0)
            nn.init.constant_(block.attn_temp.proj.bias, 0)

    def initialize_weights(self):
        # 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 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]))

        # 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)
        nn.init.normal_(self.t_block[1].weight, std=0.02)
        nn.init.normal_(self.t_block_temp[1].weight, std=0.02)

        # Initialize caption embedding MLP:
        nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
        nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)

        # Zero-out adaLN modulation layers in PixArt blocks:
        for block in self.blocks:
            nn.init.constant_(block.cross_attn.proj.weight, 0)
            nn.init.constant_(block.cross_attn.proj.bias, 0)

        # Zero-out output layers:
        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)