transformer/model.py

# Copyright 2024-2025 The Alibaba MatrixGameWan Team Authors. All rights reserved.
import math
import numpy as np
import torch
import torch.amp as amp
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
from diffusers.models.modeling_utils import ModelMixin
from einops import repeat, rearrange
from .action_module import ActionModule
from .attention import flash_attention

DISABLE_COMPILE = False  # get os env
__all__ = ["MatrixGameWanModel"]


def sinusoidal_embedding_1d(dim, position):
    # preprocess
    assert dim % 2 == 0
    half = dim // 2
    position = position.type(torch.float64)

    # calculation
    sinusoid = torch.outer(
        position, torch.pow(10000, -torch.arange(half).to(position).div(half))
    )
    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
    return x


# @amp.autocast(enabled=False)
def rope_params(max_seq_len, dim, theta=10000):
    assert dim % 2 == 0
    freqs = torch.outer(
        torch.arange(max_seq_len),
        1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float64).div(dim)),
    )
    freqs = torch.polar(torch.ones_like(freqs), freqs)
    return freqs


# @amp.autocast(enabled=False)
def rope_apply(x, grid_sizes, freqs):
    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 = []
    # print(grid_sizes.shape, len(grid_sizes.tolist()), grid_sizes.tolist()[0])
    f, h, w = grid_sizes.tolist()
    for i in range(len(x)):
        seq_len = f * h * w

        # precompute multipliers
        x_i = torch.view_as_complex(
            x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2)
        )
        freqs_i = torch.cat(
            [
                freqs[0][: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 MatrixGameWanRMSNorm(nn.Module):
    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.dim = dim
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
        """
        return self._norm(x.float()).type_as(x) * self.weight

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)


class MatrixGameWanLayerNorm(nn.LayerNorm):
    def __init__(self, dim, eps=1e-6, elementwise_affine=False):
        super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)

    def forward(self, x):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
        """
        return super().forward(x).type_as(x)


class MatrixGameWanSelfAttention(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

        # 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 = MatrixGameWanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
        self.norm_k = MatrixGameWanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()

    def forward(self, x, seq_lens, grid_sizes, freqs):
        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]
        """
        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)
        # print(k.shape, seq_lens)
        x = flash_attention(
            q=rope_apply(q, grid_sizes, freqs),
            k=rope_apply(k, grid_sizes, freqs),
            v=v,
            k_lens=seq_lens,
            window_size=self.window_size,
        )

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


# class MatrixGameWanT2VCrossAttention(MatrixGameWanSelfAttention):

#     def forward(self, x, context, context_lens, crossattn_cache=None):
#         r"""
#         Args:
#             x(Tensor): Shape [B, L1, C]
#             context(Tensor): Shape [B, L2, C]
#             context_lens(Tensor): Shape [B]
#             crossattn_cache (List[dict], *optional*): Contains the cached key and value tensors for context embedding.
#         """
#         b, n, d = x.size(0), self.num_heads, self.head_dim

#         # compute query, key, value
#         q = self.norm_q(self.q(x)).view(b, -1, n, d)

#         if crossattn_cache is not None:
#             if not crossattn_cache["is_init"]:
#                 crossattn_cache["is_init"] = True
#                 k = self.norm_k(self.k(context)).view(b, -1, n, d)
#                 v = self.v(context).view(b, -1, n, d)
#                 crossattn_cache["k"] = k
#                 crossattn_cache["v"] = v
#             else:
#                 k = crossattn_cache["k"]
#                 v = crossattn_cache["v"]
#         else:
#             k = self.norm_k(self.k(context)).view(b, -1, n, d)
#             v = self.v(context).view(b, -1, n, d)

#         # compute attention
#         x = flash_attention(q, k, v, k_lens=context_lens)

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


# class MatrixGameWanGanCrossAttention(MatrixGameWanSelfAttention):

#     def forward(self, x, context, crossattn_cache=None):
#         r"""
#         Args:
#             x(Tensor): Shape [B, L1, C]
#             context(Tensor): Shape [B, L2, C]
#             context_lens(Tensor): Shape [B]
#             crossattn_cache (List[dict], *optional*): Contains the cached key and value tensors for context embedding.
#         """
#         b, n, d = x.size(0), self.num_heads, self.head_dim

#         # compute query, key, value
#         qq = self.norm_q(self.q(context)).view(b, 1, -1, d)

#         kk = self.norm_k(self.k(x)).view(b, -1, n, d)
#         vv = self.v(x).view(b, -1, n, d)

#         # compute attention
#         x = flash_attention(qq, kk, vv)

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


class MatrixGameWanI2VCrossAttention(MatrixGameWanSelfAttention):
    def forward(self, x, context, crossattn_cache=None):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            context(Tensor): Shape [B, L2, C]
            context_lens(Tensor): Shape [B]
        """
        b, n, d = x.size(0), self.num_heads, self.head_dim

        # compute query, key, value
        q = self.norm_q(self.q(x)).view(b, -1, n, d)
        if crossattn_cache is not None:
            if not crossattn_cache["is_init"]:
                crossattn_cache["is_init"] = True
                k = self.norm_k(self.k(context)).view(b, -1, n, d)
                v = self.v(context).view(b, -1, n, d)
                crossattn_cache["k"] = k
                crossattn_cache["v"] = v
            else:
                k = crossattn_cache["k"]
                v = crossattn_cache["v"]
        else:
            k = self.norm_k(self.k(context)).view(b, -1, n, d)
            v = self.v(context).view(b, -1, n, d)
        # compute attention
        x = flash_attention(q, k, v, k_lens=None)

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


MatrixGameWan_CROSSATTENTION_CLASSES = {
    "i2v_cross_attn": MatrixGameWanI2VCrossAttention,
}


def mul_add(x, y, z):
    return x.float() + y.float() * z.float()


def mul_add_add(x, y, z):
    return x.float() * (1 + y) + z


class MatrixGameWanAttentionBlock(nn.Module):
    def __init__(
        self,
        cross_attn_type,
        dim,
        ffn_dim,
        num_heads,
        window_size=(-1, -1),
        qk_norm=True,
        cross_attn_norm=False,
        action_config={},
        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
        if len(action_config) != 0:
            self.action_model = ActionModule(**action_config)
        else:
            self.action_model = None
        # layers
        self.norm1 = MatrixGameWanLayerNorm(dim, eps)
        self.self_attn = MatrixGameWanSelfAttention(dim, num_heads, window_size, qk_norm, eps)
        self.norm3 = (
            MatrixGameWanLayerNorm(dim, eps, elementwise_affine=True)
            if cross_attn_norm
            else nn.Identity()
        )
        self.cross_attn = MatrixGameWan_CROSSATTENTION_CLASSES[cross_attn_type](
            dim, num_heads, (-1, -1), qk_norm, eps
        )
        self.norm2 = MatrixGameWanLayerNorm(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,
        mouse_cond=None,
        keyboard_cond=None,
        # context_lens,
    ):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
            e(Tensor): Shape [B, 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]
        """
        # assert e.dtype == torch.float32
        if e.dim() == 3:
            modulation = self.modulation
            # with amp.autocast(dtype=torch.float32):
            e = (self.modulation + e).chunk(6, dim=1)
        elif e.dim() == 4:
            modulation = self.modulation.unsqueeze(2)  # 1, 6, 1, dim
            # with amp.autocast("cuda", dtype=torch.float32):
            e = (modulation + e).chunk(6, dim=1)
            e = [ei.squeeze(1) for ei in e]
        # assert e[0].dtype == torch.float32

        # self-attention
        y = self.self_attn(
            self.norm1(x) * (1 + e[1]) + e[0], seq_lens, grid_sizes, freqs
        )
        # with amp.autocast(dtype=torch.float32):
        x = x + y * e[2]

        # cross-attention & ffn function
        def cross_attn_ffn(x, context, e, mouse_cond, keyboard_cond):
            dtype = context.dtype
            x = x + self.cross_attn(self.norm3(x.to(dtype)), context)
            if self.action_model is not None:
                assert mouse_cond is not None or keyboard_cond is not None
                x = self.action_model(
                    x.to(dtype),
                    grid_sizes[0],
                    grid_sizes[1],
                    grid_sizes[2],
                    mouse_cond,
                    keyboard_cond,
                )
            y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
            # with amp.autocast(dtype=torch.float32):
            x = x + y * e[5]
            return x

        x = cross_attn_ffn(x, context, e, mouse_cond, keyboard_cond)
        return x


class Head(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 = MatrixGameWanLayerNorm(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, C]
        """
        # assert e.dtype == torch.float32
        # with amp.autocast(dtype=torch.float32):
        if e.dim() == 2:
            modulation = self.modulation  # 1, 2, dim
            e = (modulation + e.unsqueeze(1)).chunk(2, dim=1)
        elif e.dim() == 3:
            modulation = self.modulation.unsqueeze(2)  # 1, 2, seq, dim
            e = (modulation + e.unsqueeze(1)).chunk(2, dim=1)
            e = [ei.squeeze(1) for ei in e]
        x = self.head(self.norm(x) * (1 + e[1]) + e[0])
        return x


class MLPProj(torch.nn.Module):
    def __init__(self, in_dim, out_dim):
        super().__init__()

        self.proj = torch.nn.Sequential(
            torch.nn.LayerNorm(in_dim),
            torch.nn.Linear(in_dim, in_dim),
            torch.nn.GELU(),
            torch.nn.Linear(in_dim, out_dim),
            torch.nn.LayerNorm(out_dim),
        )

    def forward(self, image_embeds):
        clip_extra_context_tokens = self.proj(image_embeds)
        return clip_extra_context_tokens


# class RegisterTokens(nn.Module):
#     def __init__(self, num_registers: int, dim: int):
#         super().__init__()
#         self.register_tokens = nn.Parameter(torch.randn(num_registers, dim) * 0.02)
#         self.rms_norm = MatrixGameWanRMSNorm(dim, eps=1e-6)

#     def forward(self):
#         return self.rms_norm(self.register_tokens)

#     def reset_parameters(self):
#         nn.init.normal_(self.register_tokens, std=0.02)


class MatrixGameWanModel(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
    r"""
    MatrixGameWan 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 = ["MatrixGameWanAttentionBlock"]
    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        model_type="i2v",
        patch_size=(1, 2, 2),
        text_len=512,
        in_dim=36,
        dim=1536,
        ffn_dim=8960,
        freq_dim=256,
        text_dim=4096,
        out_dim=16,
        num_heads=12,
        num_layers=30,
        window_size=(-1, -1),
        qk_norm=True,
        cross_attn_norm=True,
        inject_sample_info=False,
        action_config={},
        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 ["i2v"]
        self.model_type = model_type
        self.use_action_module = len(action_config) > 0
        assert self.use_action_module == True
        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
        self.local_attn_size = -1

        # 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 = "i2v_cross_attn"
        self.blocks = nn.ModuleList(
            [
                MatrixGameWanAttentionBlock(
                    cross_attn_type,
                    dim,
                    ffn_dim,
                    num_heads,
                    window_size,
                    qk_norm,
                    cross_attn_norm,
                    eps=eps,
                    action_config=action_config,
                )
                for _ in range(num_layers)
            ]
        )

        # head
        self.head = Head(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

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

    def forward(self, *args, **kwargs):
        # if kwargs.get('classify_mode', False) is True:
        # kwargs.pop('classify_mode')
        # return self._forward_classify(*args, **kwargs)
        # else:
        return self._forward(*args, **kwargs)

    def _forward(
        self,
        x,
        t,
        visual_context,
        cond_concat,
        mouse_cond=None,
        keyboard_cond=None,
        fps=None,
        # seq_len,
        # classify_mode=False,
        # concat_time_embeddings=False,
        # register_tokens=None,
        # cls_pred_branch=None,
        # gan_ca_blocks=None,
        # 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]
        """
        # params
        if mouse_cond is not None or keyboard_cond is not None:
            assert self.use_action_module == True
        device = self.patch_embedding.weight.device
        if self.freqs.device != device:
            self.freqs = self.freqs.to(device)

        x = torch.cat([x, cond_concat], dim=1)
        # embeddings
        x = self.patch_embedding(x)
        grid_sizes = torch.tensor(x.shape[2:], dtype=torch.long)
        x = x.flatten(2).transpose(1, 2)
        seq_lens = torch.tensor([u.size(0) for u in x], dtype=torch.long)
        # seq_len = seq_lens.max()
        # # 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):
        # assert t.ndim == 1
        e = self.time_embedding(
            sinusoidal_embedding_1d(self.freq_dim, t).type_as(x)
        )  # TODO: check if t ndim == 1

        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
        # 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 = self.img_emb(visual_context)

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

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

            return custom_forward

        for ii, 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)

        # unpatchify
        x = self.unpatchify(x, grid_sizes)

        return x.float()

    def unpatchify(self, x, grid_sizes):  # TODO check 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 [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
        bs = x.shape[0]
        x = x.view(bs, *grid_sizes, *self.patch_size, c)
        x = torch.einsum("bfhwpqrc->bcfphqwr", x)
        x = x.reshape(bs, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)])
        return x

    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.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)
        if self.use_action_module == True:
            for m in self.blocks:
                nn.init.zeros_(m.action_model.proj_mouse.weight)
                if m.action_model.proj_mouse.bias is not None:
                    nn.init.zeros_(m.action_model.proj_mouse.bias)
                nn.init.zeros_(m.action_model.proj_keyboard.weight)
                if m.action_model.proj_keyboard.bias is not None:
                    nn.init.zeros_(m.action_model.proj_keyboard.bias)