live/pipelines/streamdiffusionv2/vendor/causvid/models/wan/causal_model.py

from .wan_base.modules.attention import attention
from .wan_base.modules.model import (
    WanRMSNorm,
    rope_apply,
    WanLayerNorm,
    WAN_CROSSATTENTION_CLASSES,
    Head,
    rope_params,
    MLPProj,
    sinusoidal_embedding_1d,
)
from torch.nn.attention.flex_attention import create_block_mask, flex_attention
from diffusers.configuration_utils import ConfigMixin, register_to_config
from torch.nn.attention.flex_attention import BlockMask
from diffusers.models.modeling_utils import ModelMixin
import torch.nn as nn
import torch
import math

# wan 1.3B model has a weird channel / head configurations and require max-autotune to work with flexattention
# see https://github.com/pytorch/pytorch/issues/133254
# change to default for other models
flex_attention = torch.compile(flex_attention, dynamic=False, mode="max-autotune")


def causal_rope_apply(x, grid_sizes, freqs, start_frame=0):
    n, c = x.size(2), x.size(3) // 2

    # split freqs
    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)

    # loop over samples
    output = []

    for i, (f, h, w) in enumerate(grid_sizes.tolist()):
        seq_len = f * h * w

        # precompute multipliers
        x_i = torch.view_as_complex(
            x[i, :seq_len].to(torch.float32).reshape(seq_len, n, -1, 2)
        )
        freqs_i = torch.cat(
            [
                freqs[0][start_frame : start_frame + f]
                .view(f, 1, 1, -1)
                .expand(f, h, w, -1),
                freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
                freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1),
            ],
            dim=-1,
        ).reshape(seq_len, 1, -1)

        # apply rotary embedding
        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
        x_i = torch.cat([x_i, x[i, seq_len:]])

        # append to collection
        output.append(x_i)
    return torch.stack(output).type_as(x)


class CausalWanSelfAttention(nn.Module):
    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6):
        assert dim % num_heads == 0
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.eps = eps
        self.sink_size = 3

        # layers
        self.q = nn.Linear(dim, dim)
        self.k = nn.Linear(dim, dim)
        self.v = nn.Linear(dim, dim)
        self.o = nn.Linear(dim, dim)
        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()

    def forward(
        self,
        x,
        seq_lens,
        grid_sizes,
        freqs,
        block_mask,
        kv_cache=None,
        current_start=0,
        current_end=0,
    ):
        r"""
        Args:
            x(Tensor): Shape [B, L, num_heads, C / num_heads]
            seq_lens(Tensor): Shape [B]
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
            block_mask (BlockMask)
        """
        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim

        # query, key, value function
        def qkv_fn(x):
            q = self.norm_q(self.q(x)).view(b, s, n, d)
            k = self.norm_k(self.k(x)).view(b, s, n, d)
            v = self.v(x).view(b, s, n, d)
            return q, k, v

        q, k, v = qkv_fn(x)

        if kv_cache is None:
            roped_query = rope_apply(q, grid_sizes, freqs).type_as(v)
            roped_key = rope_apply(k, grid_sizes, freqs).type_as(v)

            padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
            padded_roped_query = torch.cat(
                [
                    roped_query,
                    torch.zeros(
                        [q.shape[0], padded_length, q.shape[2], q.shape[3]],
                        device=q.device,
                        dtype=v.dtype,
                    ),
                ],
                dim=1,
            )

            padded_roped_key = torch.cat(
                [
                    roped_key,
                    torch.zeros(
                        [k.shape[0], padded_length, k.shape[2], k.shape[3]],
                        device=k.device,
                        dtype=v.dtype,
                    ),
                ],
                dim=1,
            )

            padded_v = torch.cat(
                [
                    v,
                    torch.zeros(
                        [v.shape[0], padded_length, v.shape[2], v.shape[3]],
                        device=v.device,
                        dtype=v.dtype,
                    ),
                ],
                dim=1,
            )

            x = flex_attention(
                query=padded_roped_query.transpose(2, 1),
                key=padded_roped_key.transpose(2, 1),
                value=padded_v.transpose(2, 1),
                block_mask=block_mask,
            )[:, :, :-padded_length].transpose(2, 1)
        else:
            frame_seqlen = math.prod(grid_sizes[0][1:]).item()
            sink_tokens = self.sink_size * frame_seqlen
            current_start_frame = current_start // frame_seqlen
            roped_query = causal_rope_apply(
                q, grid_sizes, freqs, start_frame=current_start_frame
            ).type_as(v)
            roped_key = causal_rope_apply(
                k, grid_sizes, freqs, start_frame=current_start_frame
            ).type_as(v)

            current_end = current_start + roped_query.shape[1]
            # If we are using local attention and the current KV cache size is larger than the local attention size, we need to truncate the KV cache
            kv_cache_size = kv_cache["k"].shape[1]
            num_new_tokens = roped_query.shape[1]
            if (current_end > kv_cache["global_end_index"].item()) and (
                num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size
            ):
                # Calculate the number of new tokens added in this step
                # Shift existing cache content left to discard oldest tokens
                # Clone the source slice to avoid overlapping memory error
                num_evicted_tokens = (
                    num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
                )
                num_rolled_tokens = (
                    kv_cache["local_end_index"].item()
                    - num_evicted_tokens
                    - sink_tokens
                )
                kv_cache["k"][:, sink_tokens : sink_tokens + num_rolled_tokens] = (
                    kv_cache["k"][
                        :,
                        sink_tokens + num_evicted_tokens : sink_tokens
                        + num_evicted_tokens
                        + num_rolled_tokens,
                    ].clone()
                )
                kv_cache["v"][:, sink_tokens : sink_tokens + num_rolled_tokens] = (
                    kv_cache["v"][
                        :,
                        sink_tokens + num_evicted_tokens : sink_tokens
                        + num_evicted_tokens
                        + num_rolled_tokens,
                    ].clone()
                )
                # Insert the new keys/values at the end
                local_end_index = (
                    kv_cache["local_end_index"].item()
                    + current_end
                    - kv_cache["global_end_index"].item()
                    - num_evicted_tokens
                )
                local_start_index = local_end_index - num_new_tokens
                kv_cache["k"][:, local_start_index:local_end_index] = roped_key
                kv_cache["v"][:, local_start_index:local_end_index] = v

            else:
                # Assign new keys/values directly up to current_end
                local_end_index = (
                    kv_cache["local_end_index"].item()
                    + current_end
                    - kv_cache["global_end_index"].item()
                )
                local_start_index = local_end_index - num_new_tokens
                kv_cache["k"][:, local_start_index:local_end_index] = roped_key
                kv_cache["v"][:, local_start_index:local_end_index] = v

            x = attention(
                roped_query,
                kv_cache["k"][:, :local_end_index],
                kv_cache["v"][:, :local_end_index],
            )

            kv_cache["global_end_index"].fill_(current_end)
            kv_cache["local_end_index"].fill_(local_end_index)

        # output
        x = x.flatten(2)
        x = self.o(x)
        return x


class CausalWanAttentionBlock(nn.Module):
    def __init__(
        self,
        cross_attn_type,
        dim,
        ffn_dim,
        num_heads,
        window_size=(-1, -1),
        qk_norm=True,
        cross_attn_norm=False,
        eps=1e-6,
    ):
        super().__init__()
        self.dim = dim
        self.ffn_dim = ffn_dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.cross_attn_norm = cross_attn_norm
        self.eps = eps

        # layers
        self.norm1 = WanLayerNorm(dim, eps)
        self.self_attn = CausalWanSelfAttention(
            dim, num_heads, window_size, qk_norm, eps
        )
        self.norm3 = (
            WanLayerNorm(dim, eps, elementwise_affine=True)
            if cross_attn_norm
            else nn.Identity()
        )
        self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](
            dim, num_heads, (-1, -1), qk_norm, eps
        )
        self.norm2 = WanLayerNorm(dim, eps)
        self.ffn = nn.Sequential(
            nn.Linear(dim, ffn_dim),
            nn.GELU(approximate="tanh"),
            nn.Linear(ffn_dim, dim),
        )

        # modulation
        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)

    def forward(
        self,
        x,
        e,
        seq_lens,
        grid_sizes,
        freqs,
        context,
        context_lens,
        block_mask,
        kv_cache=None,
        crossattn_cache=None,
        current_start=0,
        current_end=0,
    ):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
            e(Tensor): Shape [B, F, 6, C]
            seq_lens(Tensor): Shape [B], length of each sequence in batch
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """
        num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
        # assert e.dtype == torch.float32
        # with amp.autocast(dtype=torch.float32):
        e = (self.modulation.unsqueeze(1) + e).chunk(6, dim=2)
        # assert e[0].dtype == torch.float32

        # self-attention
        y = self.self_attn(
            (
                self.norm1(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen))
                * (1 + e[1])
                + e[0]
            ).flatten(1, 2),
            seq_lens,
            grid_sizes,
            freqs,
            block_mask,
            kv_cache,
            current_start,
            current_end,
        )

        # with amp.autocast(dtype=torch.float32):
        x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[2]).flatten(
            1, 2
        )

        # cross-attention & ffn function
        def cross_attn_ffn(x, context, context_lens, e, crossattn_cache=None):
            x = x + self.cross_attn(
                self.norm3(x), context, context_lens, crossattn_cache=crossattn_cache
            )
            y = self.ffn(
                (
                    self.norm2(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen))
                    * (1 + e[4])
                    + e[3]
                ).flatten(1, 2)
            )
            # with amp.autocast(dtype=torch.float32):
            x = x + (
                y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[5]
            ).flatten(1, 2)
            return x

        x = cross_attn_ffn(x, context, context_lens, e, crossattn_cache)
        return x


class CausalHead(nn.Module):
    def __init__(self, dim, out_dim, patch_size, eps=1e-6):
        super().__init__()
        self.dim = dim
        self.out_dim = out_dim
        self.patch_size = patch_size
        self.eps = eps

        # layers
        out_dim = math.prod(patch_size) * out_dim
        self.norm = WanLayerNorm(dim, eps)
        self.head = nn.Linear(dim, out_dim)

        # modulation
        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)

    def forward(self, x, e):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            e(Tensor): Shape [B, F, 1, C]
        """
        # assert e.dtype == torch.float32
        # with amp.autocast(dtype=torch.float32):
        num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
        e = (self.modulation.unsqueeze(1) + e).chunk(2, dim=2)
        x = self.head(
            self.norm(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1])
            + e[0]
        )
        return x


class CausalWanModel(ModelMixin, ConfigMixin):
    r"""
    Wan diffusion backbone supporting both text-to-video and image-to-video.
    """

    ignore_for_config = [
        "patch_size",
        "cross_attn_norm",
        "qk_norm",
        "text_dim",
        "window_size",
    ]
    _no_split_modules = ["WanAttentionBlock"]
    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        model_type="t2v",
        patch_size=(1, 2, 2),
        text_len=512,
        in_dim=16,
        dim=2048,
        ffn_dim=8192,
        freq_dim=256,
        text_dim=4096,
        out_dim=16,
        num_heads=16,
        num_layers=32,
        window_size=(-1, -1),
        qk_norm=True,
        cross_attn_norm=True,
        eps=1e-6,
    ):
        r"""
        Initialize the diffusion model backbone.

        Args:
            model_type (`str`, *optional*, defaults to 't2v'):
                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
            patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
                3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
            text_len (`int`, *optional*, defaults to 512):
                Fixed length for text embeddings
            in_dim (`int`, *optional*, defaults to 16):
                Input video channels (C_in)
            dim (`int`, *optional*, defaults to 2048):
                Hidden dimension of the transformer
            ffn_dim (`int`, *optional*, defaults to 8192):
                Intermediate dimension in feed-forward network
            freq_dim (`int`, *optional*, defaults to 256):
                Dimension for sinusoidal time embeddings
            text_dim (`int`, *optional*, defaults to 4096):
                Input dimension for text embeddings
            out_dim (`int`, *optional*, defaults to 16):
                Output video channels (C_out)
            num_heads (`int`, *optional*, defaults to 16):
                Number of attention heads
            num_layers (`int`, *optional*, defaults to 32):
                Number of transformer blocks
            window_size (`tuple`, *optional*, defaults to (-1, -1)):
                Window size for local attention (-1 indicates global attention)
            qk_norm (`bool`, *optional*, defaults to True):
                Enable query/key normalization
            cross_attn_norm (`bool`, *optional*, defaults to False):
                Enable cross-attention normalization
            eps (`float`, *optional*, defaults to 1e-6):
                Epsilon value for normalization layers
        """

        super().__init__()

        assert model_type in ["t2v", "i2v"]
        self.model_type = model_type

        self.patch_size = patch_size
        self.text_len = text_len
        self.in_dim = in_dim
        self.dim = dim
        self.ffn_dim = ffn_dim
        self.freq_dim = freq_dim
        self.text_dim = text_dim
        self.out_dim = out_dim
        self.num_heads = num_heads
        self.num_layers = num_layers
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.cross_attn_norm = cross_attn_norm
        self.eps = eps

        # embeddings
        self.patch_embedding = nn.Conv3d(
            in_dim, dim, kernel_size=patch_size, stride=patch_size
        )
        self.text_embedding = nn.Sequential(
            nn.Linear(text_dim, dim), nn.GELU(approximate="tanh"), nn.Linear(dim, dim)
        )

        self.time_embedding = nn.Sequential(
            nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)
        )
        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))

        # blocks
        cross_attn_type = "t2v_cross_attn" if model_type == "t2v" else "i2v_cross_attn"
        self.blocks = nn.ModuleList(
            [
                CausalWanAttentionBlock(
                    cross_attn_type,
                    dim,
                    ffn_dim,
                    num_heads,
                    window_size,
                    qk_norm,
                    cross_attn_norm,
                    eps,
                )
                for _ in range(num_layers)
            ]
        )

        # head
        self.head = CausalHead(dim, out_dim, patch_size, eps)

        # buffers (don't use register_buffer otherwise dtype will be changed in to())
        assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
        d = dim // num_heads
        self.freqs = torch.cat(
            [
                rope_params(1024, d - 4 * (d // 6)),
                rope_params(1024, 2 * (d // 6)),
                rope_params(1024, 2 * (d // 6)),
            ],
            dim=1,
        )

        if model_type == "i2v":
            self.img_emb = MLPProj(1280, dim)

        # initialize weights
        self.init_weights()

        self.gradient_checkpointing = False

        self.block_mask = None

        self.num_frame_per_block = 1

    def _set_gradient_checkpointing(self, module, value=False):
        self.gradient_checkpointing = value

    @staticmethod
    def _prepare_blockwise_causal_attn_mask(
        device: torch.device | str,
        num_frames: int = 21,
        frame_seqlen: int = 1560,
        num_frame_per_block=1,
    ) -> BlockMask:
        """
        we will divide the token sequence into the following format
        [1 latent frame] [1 latent frame] ... [1 latent frame]
        We use flexattention to construct the attention mask
        """
        total_length = num_frames * frame_seqlen

        # we do right padding to get to a multiple of 128
        padded_length = math.ceil(total_length / 128) * 128 - total_length

        ends = torch.zeros(
            total_length + padded_length, device=device, dtype=torch.long
        )

        # Block-wise causal mask will attend to all elements that are before the end of the current chunk
        frame_indices = torch.arange(
            start=0,
            end=total_length,
            step=frame_seqlen * num_frame_per_block,
            device=device,
        )

        for tmp in frame_indices:
            ends[tmp : tmp + frame_seqlen * num_frame_per_block] = (
                tmp + frame_seqlen * num_frame_per_block
            )

        def attention_mask(b, h, q_idx, kv_idx):
            return (kv_idx < ends[q_idx]) | (q_idx == kv_idx)
            # return ((kv_idx < total_length) & (q_idx < total_length))  | (q_idx == kv_idx) # bidirectional mask

        block_mask = create_block_mask(
            attention_mask,
            B=None,
            H=None,
            Q_LEN=total_length + padded_length,
            KV_LEN=total_length + padded_length,
            _compile=False,
            device=device,
        )

        import torch.distributed as dist

        if not dist.is_initialized() or dist.get_rank() == 0:
            print(
                f" cache a block wise causal mask with block size of {num_frame_per_block} frames"
            )
            print(block_mask)

        return block_mask

    def _forward_inference(
        self,
        x,
        t,
        context,
        seq_len,
        clip_fea=None,
        y=None,
        kv_cache: dict = None,
        crossattn_cache: dict = None,
        current_start: int = 0,
        current_end: int = 0,
    ):
        r"""
        Run the diffusion model with kv caching.
        See Algorithm 2 of CausVid paper https://arxiv.org/abs/2412.07772 for details.
        This function will be run for num_frame times.
        Process the latent frames one by one (1560 tokens each)

        Args:
            x (List[Tensor]):
                List of input video tensors, each with shape [C_in, F, H, W]
            t (Tensor):
                Diffusion timesteps tensor of shape [B]
            context (List[Tensor]):
                List of text embeddings each with shape [L, C]
            seq_len (`int`):
                Maximum sequence length for positional encoding
            clip_fea (Tensor, *optional*):
                CLIP image features for image-to-video mode
            y (List[Tensor], *optional*):
                Conditional video inputs for image-to-video mode, same shape as x

        Returns:
            List[Tensor]:
                List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
        """
        if self.model_type == "i2v":
            assert clip_fea is not None and y is not None
        # params
        device = self.patch_embedding.weight.device
        if self.freqs.device != device:
            self.freqs = self.freqs.to(device)

        if y is not None:
            x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]

        # embeddings
        x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
        grid_sizes = torch.stack(
            [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]
        )
        x = [u.flatten(2).transpose(1, 2) for u in x]
        seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
        assert seq_lens.max() <= seq_len
        x = torch.cat(x)
        """
        torch.cat([
            torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))],
                      dim=1) for u in x
        ])
        """

        # time embeddings
        # with amp.autocast(dtype=torch.float32):
        e = self.time_embedding(
            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x)
        )
        e0 = (
            self.time_projection(e)
            .unflatten(1, (6, self.dim))
            .unflatten(dim=0, sizes=t.shape)
        )
        # assert e.dtype == torch.float32 and e0.dtype == torch.float32

        # context
        context_lens = None
        context = self.text_embedding(
            torch.stack(
                [
                    torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
                    for u in context
                ]
            )
        )

        if clip_fea is not None:
            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
            context = torch.concat([context_clip, context], dim=1)

        # arguments
        kwargs = dict(
            e=e0,
            seq_lens=seq_lens,
            grid_sizes=grid_sizes,
            freqs=self.freqs,
            context=context,
            context_lens=context_lens,
            block_mask=self.block_mask,
        )

        def create_custom_forward(module):
            def custom_forward(*inputs, **kwargs):
                return module(*inputs, **kwargs)

            return custom_forward

        for block_index, block in enumerate(self.blocks):
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                assert False
            else:
                kwargs.update(
                    {
                        "kv_cache": kv_cache[block_index],
                        "crossattn_cache": crossattn_cache[block_index],
                        "current_start": current_start,
                        "current_end": current_end,
                    }
                )
                x = block(x, **kwargs)

        # head
        x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2))

        # unpatchify
        x = self.unpatchify(x, grid_sizes)
        return torch.stack(x)

    def _forward_train(
        self,
        x,
        t,
        context,
        seq_len,
        clip_fea=None,
        y=None,
    ):
        r"""
        Forward pass through the diffusion model

        Args:
            x (List[Tensor]):
                List of input video tensors, each with shape [C_in, F, H, W]
            t (Tensor):
                Diffusion timesteps tensor of shape [B]
            context (List[Tensor]):
                List of text embeddings each with shape [L, C]
            seq_len (`int`):
                Maximum sequence length for positional encoding
            clip_fea (Tensor, *optional*):
                CLIP image features for image-to-video mode
            y (List[Tensor], *optional*):
                Conditional video inputs for image-to-video mode, same shape as x

        Returns:
            List[Tensor]:
                List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
        """
        if self.model_type == "i2v":
            assert clip_fea is not None and y is not None
        # params
        device = self.patch_embedding.weight.device
        if self.freqs.device != device:
            self.freqs = self.freqs.to(device)

        # Construct blockwise causal attn mask
        if self.block_mask is None:
            self.block_mask = self._prepare_blockwise_causal_attn_mask(
                device,
                num_frames=x.shape[2],
                frame_seqlen=x.shape[-2]
                * x.shape[-1]
                // (self.patch_size[1] * self.patch_size[2]),
                num_frame_per_block=self.num_frame_per_block,
            )

        if y is not None:
            x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]

        # embeddings
        x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
        grid_sizes = torch.stack(
            [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]
        )
        x = [u.flatten(2).transpose(1, 2) for u in x]
        seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
        assert seq_lens.max() <= seq_len
        x = torch.cat(
            [
                torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1)
                for u in x
            ]
        )

        # time embeddings
        # with amp.autocast(dtype=torch.float32):
        e = self.time_embedding(
            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x)
        )
        e0 = (
            self.time_projection(e)
            .unflatten(1, (6, self.dim))
            .unflatten(dim=0, sizes=t.shape)
        )
        # assert e.dtype == torch.float32 and e0.dtype == torch.float32

        # context
        context_lens = None
        context = self.text_embedding(
            torch.stack(
                [
                    torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
                    for u in context
                ]
            )
        )

        if clip_fea is not None:
            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
            context = torch.concat([context_clip, context], dim=1)

        # arguments
        kwargs = dict(
            e=e0,
            seq_lens=seq_lens,
            grid_sizes=grid_sizes,
            freqs=self.freqs,
            context=context,
            context_lens=context_lens,
            block_mask=self.block_mask,
        )

        def create_custom_forward(module):
            def custom_forward(*inputs, **kwargs):
                return module(*inputs, **kwargs)

            return custom_forward

        for block_index, block in enumerate(self.blocks):
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                x = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    x,
                    **kwargs,
                    use_reentrant=False,
                )
            else:
                x = block(x, **kwargs)

        # head
        x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2))

        # unpatchify
        x = self.unpatchify(x, grid_sizes)
        return torch.stack(x)

    def forward(self, *args, **kwargs):
        if kwargs.get("kv_cache", None) is not None:
            return self._forward_inference(*args, **kwargs)
        else:
            return self._forward_train(*args, **kwargs)

    def unpatchify(self, x, grid_sizes):
        r"""
        Reconstruct video tensors from patch embeddings.

        Args:
            x (List[Tensor]):
                List of patchified features, each with shape [L, C_out * prod(patch_size)]
            grid_sizes (Tensor):
                Original spatial-temporal grid dimensions before patching,
                    shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)

        Returns:
            List[Tensor]:
                Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
        """

        c = self.out_dim
        out = []
        for u, v in zip(x, grid_sizes.tolist()):
            u = u[: math.prod(v)].view(*v, *self.patch_size, c)
            u = torch.einsum("fhwpqrc->cfphqwr", u)
            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
            out.append(u)
        return out

    def init_weights(self):
        r"""
        Initialize model parameters using Xavier initialization.
        """

        # basic init
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)

        # init embeddings
        nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
        for m in self.text_embedding.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, std=0.02)
        for m in self.time_embedding.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, std=0.02)

        # init output layer
        nn.init.zeros_(self.head.head.weight)