action_module

class ActionModule(nn.Module):
    """
    action module from https://arxiv.org/pdf/2501.08325
    """
def __init__(
    self,
    mouse_dim_in: int = 2,
    keyboard_dim_in: int = 6,
    hidden_size: int = 128,
    img_hidden_size: int = 1536,
    keyboard_hidden_dim: int = 1024,
    mouse_hidden_dim: int = 1024,
    vae_time_compression_ratio: int = 4,
    windows_size: int = 3,
    heads_num: int = 16,
    patch_size: list | None = None,
    qk_norm: bool = True,
    qkv_bias: bool = False,
    rope_dim_list: list | None = None,
    rope_theta=256,
    mouse_qk_dim_list: list | None = None,
    enable_mouse=True,
    enable_keyboard=True,
    local_attn_size=6,
    sink_size=0,
    blocks: list | None = None,
):
    super().__init__()
    # Initialize mutable defaults
    patch_size = patch_size if patch_size is not None else [1, 2, 2]
    rope_dim_list = (rope_dim_list if rope_dim_list is not None else [8, 28, 28])
    mouse_qk_dim_list = (mouse_qk_dim_list if mouse_qk_dim_list is not None else [8, 28, 28])
    blocks = blocks if blocks is not None else []
    self.local_attn_size = local_attn_size
    self.sink_size = sink_size
    self.enable_mouse = enable_mouse
    self.enable_keyboard = enable_keyboard

    self.rope_dim_list = rope_dim_list
    self.rope_theta = rope_theta
    if self.enable_keyboard:
        self.keyboard_embed = nn.Sequential(
            nn.Linear(keyboard_dim_in, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )

    self.mouse_qk_dim_list = mouse_qk_dim_list
    self.heads_num = heads_num
    if self.enable_mouse:
        c = mouse_hidden_dim
        self.mouse_mlp = nn.Sequential(
            nn.Linear(
                mouse_dim_in * vae_time_compression_ratio * windows_size + img_hidden_size,
                c,
                bias=True,
            ),
            nn.GELU(approximate="tanh"),
            nn.Linear(c, c),
            FP32LayerNorm(c, elementwise_affine=True),
        )

        head_dim = c // heads_num
        self.t_qkv = ReplicatedLinear(c, c * 3, bias=qkv_bias)
        self.img_attn_q_norm = (RMSNorm(head_dim, eps=1e-6) if qk_norm else nn.Identity())
        self.img_attn_k_norm = (RMSNorm(head_dim, eps=1e-6) if qk_norm else nn.Identity())
        self.proj_mouse = ReplicatedLinear(c, img_hidden_size, bias=qkv_bias)

    if self.enable_keyboard:
        head_dim_key = keyboard_hidden_dim // heads_num
        self.key_attn_q_norm = (RMSNorm(head_dim_key, eps=1e-6) if qk_norm else nn.Identity())
        self.key_attn_k_norm = (RMSNorm(head_dim_key, eps=1e-6) if qk_norm else nn.Identity())

        self.mouse_attn_q = ReplicatedLinear(img_hidden_size, keyboard_hidden_dim, bias=qkv_bias)
        self.keyboard_attn_kv = ReplicatedLinear(
            hidden_size * windows_size * vae_time_compression_ratio,
            keyboard_hidden_dim * 2,
            bias=qkv_bias,
        )
        self.proj_keyboard = ReplicatedLinear(keyboard_hidden_dim, img_hidden_size, bias=qkv_bias)

    self.mouse_attn_layer = (LocalAttention(
        num_heads=heads_num,
        head_size=mouse_hidden_dim // heads_num,
        causal=False,
        supported_attention_backends=(
            AttentionBackendEnum.FLASH_ATTN,
            AttentionBackendEnum.TORCH_SDPA,
        ),
    ) if self.enable_mouse else None)

    self.keyboard_attn_layer = (LocalAttention(
        num_heads=heads_num,
        head_size=keyboard_hidden_dim // heads_num,
        causal=False,
        supported_attention_backends=(
            AttentionBackendEnum.FLASH_ATTN,
            AttentionBackendEnum.TORCH_SDPA,
        ),
    ) if self.enable_keyboard else None)

    self.vae_time_compression_ratio = vae_time_compression_ratio
    self.windows_size = windows_size
    self.patch_size = patch_size
    # Lazy initialization: freqs will be created on first forward pass
    self._freqs_cos = None
    self._freqs_sin = None

def patchify(self, x, patch_size):
    """
    x : (N C T H W)
    """
    pt, ph, pw = self.patch_size
    t, h, w = x.shape[2] // pt, x.shape[3] // ph, x.shape[4] // pw
    c = x.shape[1]
    x = x.reshape(shape=(x.shape[0], c, t, pt, h, ph, w, pw))
    x = torch.einsum("nctohpwq->nthwcopq", x)
    x = x.reshape(shape=(x.shape[0], t * h * w, c * pt * ph * pw))
    return x

def unpatchify(self, x, t, h, w, patch_size):
    """
    x: (N, T, patch_size**2 * C)
    imgs: (N, H, W, C)
    """
    c = x.shape[2] // patch_size  # self.unpatchify_channels
    pt, ph, pw = self.patch_size
    assert t * h * w == x.shape[1]

    x = x.reshape(shape=(x.shape[0], t, h, w, c, pt, ph, pw))
    x = torch.einsum("nthwcopq->nctohpwq", x)
    imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))

    return imgs

def get_rotary_pos_embed(
    self,
    video_length,
    height,
    width,
    head_dim,
    rope_dim_list=None,
    start_offset=0,
):
    target_ndim = 3
    ndim = 5 - 2

    latents_size = [video_length + start_offset, height, width]

    if isinstance(self.patch_size, int):
        assert all(s % self.patch_size == 0 for s in latents_size), (
            f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.patch_size}), "
            f"but got {latents_size}.")
        rope_sizes = [s // self.patch_size for s in latents_size]
    elif isinstance(self.patch_size, list):
        assert all(s % self.patch_size[idx] == 0 for idx, s in enumerate(latents_size)), (
            f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.patch_size}), "
            f"but got {latents_size}.")
        rope_sizes = [s // self.patch_size[idx] for idx, s in enumerate(latents_size)]

    if len(rope_sizes) != target_ndim:
        rope_sizes = [1] * (target_ndim - len(rope_sizes)) + rope_sizes  # time axis

    if rope_dim_list is None:
        rope_dim_list = [head_dim // target_ndim for _ in range(target_ndim)]
    assert sum(rope_dim_list) == head_dim, ("sum(rope_dim_list) should equal to head_dim of attention layer")
    # Use Matrix-Game wrapper for FastVideo's function
    freqs_cos, freqs_sin = _get_nd_rotary_pos_embed_matrixgame2(
        rope_dim_list,
        rope_sizes,
        theta=self.rope_theta,
        theta_rescale_factor=1,
    )
    return freqs_cos[-video_length * rope_sizes[1] * rope_sizes[2] //
                     self.patch_size[0]:], freqs_sin[-video_length * rope_sizes[1] * rope_sizes[2] //
                                                     self.patch_size[0]:]

def _forward_mouse(
    self,
    hidden_states: torch.Tensor,
    mouse_condition: torch.Tensor,
    *,
    is_causal: bool,
    kv_cache_mouse: dict[str, torch.Tensor] | None,
    pad_t: int,
    num_frame_per_block: int,
    block_mask_mouse: BlockMask | None,
    start_frame: int,
    freqs_cis: tuple[torch.Tensor, torch.Tensor],
    N_feats: int,
    B: int,
    C: int,
    tt: int,
    th: int,
    tw: int,
) -> torch.Tensor:
    pad = mouse_condition[:, 0:1, :].expand(-1, pad_t, -1)
    mouse_condition = torch.cat([pad, mouse_condition], dim=1)
    if is_causal and kv_cache_mouse is not None:
        mouse_condition = mouse_condition[
            :,
            self.vae_time_compression_ratio * (N_feats - num_frame_per_block - self.windows_size) + pad_t:,
            :,
        ]
        group_mouse = [
            mouse_condition[
                :,
                self.vae_time_compression_ratio * (i - self.windows_size) +
                pad_t:i * self.vae_time_compression_ratio + pad_t,
                :,
            ] for i in range(num_frame_per_block)
        ]
    else:
        local_num_frames = tt
        group_mouse = [
            mouse_condition[
                :,
                self.vae_time_compression_ratio * (i - self.windows_size) +
                pad_t:i * self.vae_time_compression_ratio + pad_t,
                :,
            ] for i in range(local_num_frames)
        ]

    group_mouse = torch.stack(group_mouse, dim=1)
    actual_num_frames = group_mouse.shape[1]  # Use actual stacked frame count

    S = th * tw
    group_mouse = group_mouse.unsqueeze(-1).expand(B, actual_num_frames, pad_t, C, S)
    group_mouse = group_mouse.permute(0, 4, 1, 2, 3).reshape(B * S, actual_num_frames, pad_t * C)

    group_mouse = torch.cat([hidden_states, group_mouse], dim=-1)
    group_mouse = self.mouse_mlp(group_mouse)
    # qkv
    mouse_qkv, _ = self.t_qkv(group_mouse)
    q, k, v = rearrange(mouse_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)  # BHW F H C
    q = self.img_attn_q_norm(q).to(v)
    k = self.img_attn_k_norm(k).to(v)
    # rope embd

    # freqs_cis = (self.freqs_cos, self.freqs_sin)

    q, k = _apply_rotary_emb_qk(q, k, freqs_cis[0], freqs_cis[1], start_offset=start_frame)
    ## TODO: adding cache here
    if is_causal:
        if kv_cache_mouse is None:
            assert (q.shape[0] == k.shape[0] and q.shape[0] % S == 0)  # == 880, f"{q.shape[0]},{k.shape[0]}"
            padded_length = math.ceil(q.shape[1] / 32) * 32 - q.shape[1]
            padded_q = _padding_q_k_v(q, padded_length)
            padded_k = _padding_q_k_v(k, padded_length)
            padded_v = _padding_q_k_v(v, padded_length)
            attn = flex_attention(
                query=padded_q.transpose(2, 1),  # after: B, HW, F, C
                key=padded_k.transpose(2, 1),
                value=padded_v.transpose(2, 1),
                block_mask=block_mask_mouse,
            )[:, :, :-padded_length].transpose(2, 1)
        else:
            attn = _update_kv_cache_and_attend(
                q,
                k,
                v,
                kv_cache_mouse,
                self.mouse_attn_layer,
                start_frame,
                num_frame_per_block,
                self.local_attn_size,
                self.sink_size,
                use_k_for_num_tokens=False,
            )
    else:
        attn = self.mouse_attn_layer(q, k, v)
    # Compute cu_squlens and max_seqlen for flash attention
    # qk norm
    attn = rearrange(attn, "(b S) T h d -> b (T S) (h d)", b=B)
    attn, _ = self.proj_mouse(attn)
    return attn

def _forward_keyboard(
    self,
    hidden_states: torch.Tensor,
    keyboard_condition: torch.Tensor,
    *,
    is_causal: bool,
    use_rope_keyboard: bool,
    kv_cache_keyboard: dict[str, torch.Tensor] | None,
    pad_t: int,
    num_frame_per_block: int,
    block_mask_keyboard: BlockMask | None,
    start_frame: int,
    freqs_cis: tuple[torch.Tensor, torch.Tensor],
    N_feats: int,
    B: int,
    tt: int,
    th: int,
    tw: int,
) -> torch.Tensor:
    pad = keyboard_condition[:, 0:1, :].expand(-1, pad_t, -1)
    keyboard_condition = torch.cat([pad, keyboard_condition], dim=1)
    if is_causal and kv_cache_keyboard is not None:
        keyboard_condition = keyboard_condition[
            :,
            self.vae_time_compression_ratio * (N_feats - num_frame_per_block - self.windows_size) + pad_t:,
            :,
        ]  # keyboard_condition[:, self.vae_time_compression_ratio*(start_frame - self.windows_size) + pad_t:start_frame * self.vae_time_compression_ratio + pad_t,:]
        keyboard_condition = self.keyboard_embed(keyboard_condition)
        group_keyboard = [
            keyboard_condition[
                :,
                self.vae_time_compression_ratio * (i - self.windows_size) +
                pad_t:i * self.vae_time_compression_ratio + pad_t,
                :,
            ] for i in range(num_frame_per_block)
        ]
    else:
        keyboard_condition = self.keyboard_embed(keyboard_condition)
        local_num_frames = tt
        group_keyboard = [
            keyboard_condition[
                :,
                self.vae_time_compression_ratio * (i - self.windows_size) +
                pad_t:i * self.vae_time_compression_ratio + pad_t,
                :,
            ] for i in range(local_num_frames)
        ]
    group_keyboard = torch.stack(group_keyboard, dim=1)  # B F RW C
    group_keyboard = group_keyboard.reshape(shape=(group_keyboard.shape[0], group_keyboard.shape[1], -1))
    # apply cross attn
    mouse_q, _ = self.mouse_attn_q(hidden_states)
    keyboard_kv, _ = self.keyboard_attn_kv(group_keyboard)

    B, L, HD = mouse_q.shape
    D = HD // self.heads_num
    q = mouse_q.view(B, L, self.heads_num, D)

    B, L, KHD = keyboard_kv.shape
    k, v = keyboard_kv.view(B, L, 2, self.heads_num, D).permute(2, 0, 1, 3, 4)

    # Compute cu_squlens and max_seqlen for flash attention
    # qk norm

    q = self.key_attn_q_norm(q).to(v)
    k = self.key_attn_k_norm(k).to(v)
    S = th * tw
    # assert S == 880
    # position embed
    if use_rope_keyboard:
        B, TS, H, D = q.shape
        T_ = TS // S
        q = q.view(B, T_, S, H, D).transpose(1, 2).reshape(B * S, T_, H, D)
        q, k = _apply_rotary_emb_qk(q, k, freqs_cis[0], freqs_cis[1], start_offset=start_frame)

        k = k.repeat_interleave(S, dim=0)
        v = v.repeat_interleave(S, dim=0)

        if is_causal:
            if kv_cache_keyboard is None:
                assert q.shape[0] == k.shape[0] and q.shape[0] % S == 0

                padded_length = math.ceil(q.shape[1] / 32) * 32 - q.shape[1]
                padded_q = _padding_q_k_v(q, padded_length)
                padded_k = _padding_q_k_v(k, padded_length)
                padded_v = _padding_q_k_v(v, padded_length)
                attn = flex_attention(
                    query=padded_q.transpose(2, 1),  # after: B, HW, F, C
                    key=padded_k.transpose(2, 1),
                    value=padded_v.transpose(2, 1),
                    block_mask=block_mask_keyboard,
                )[:, :, :-padded_length].transpose(2, 1)
            else:
                assert (k.shape[0] == S)  # BS == 1 or the cache should not be saved/ load method should be modified
                attn = _update_kv_cache_and_attend(
                    q,
                    k,
                    v,
                    kv_cache_keyboard,
                    self.keyboard_attn_layer,
                    start_frame,
                    num_frame_per_block,
                    self.local_attn_size,
                    self.sink_size,
                    use_k_for_num_tokens=True,
                    store_first_only=True,
                    repeat_factor=S,
                )
        else:
            attn = self.keyboard_attn_layer(q, k, v)
        attn = rearrange(attn, "(B S) T H D -> B (T S) (H D)", S=S)
    else:
        if is_causal:
            if kv_cache_keyboard is None:
                padded_length = math.ceil(q.shape[1] / 32) * 32 - q.shape[1]
                padded_q = _padding_q_k_v(q, padded_length)
                padded_k = _padding_q_k_v(k, padded_length)
                padded_v = _padding_q_k_v(v, padded_length)
                attn = flex_attention(
                    query=padded_q.transpose(2, 1),  # after: B, HW, F, C
                    key=padded_k.transpose(2, 1),
                    value=padded_v.transpose(2, 1),
                    block_mask=block_mask_keyboard,
                )[:, :, :-padded_length].transpose(2, 1)
            else:
                attn = _update_kv_cache_and_attend(
                    q,
                    k,
                    v,
                    kv_cache_keyboard,
                    self.keyboard_attn_layer,
                    start_frame,
                    num_frame_per_block,
                    self.local_attn_size,
                    self.sink_size,
                    use_k_for_num_tokens=True,
                )
        else:
            attn = self.keyboard_attn_layer(q, k, v)
        attn = rearrange(attn, "B L H D -> B L (H D)")
    attn, _ = self.proj_keyboard(attn)
    return attn

def forward(
    self,
    x: torch.Tensor,
    tt: int,
    th: int,
    tw: int,
    mouse_condition: torch.Tensor | None = None,
    keyboard_condition: torch.Tensor | None = None,
    block_mask_mouse: BlockMask | None = None,
    block_mask_keyboard: BlockMask | None = None,
    is_causal: bool = False,
    kv_cache_mouse: dict[str, torch.Tensor] | None = None,
    kv_cache_keyboard: dict[str, torch.Tensor] | None = None,
    start_frame: int = 0,
    use_rope_keyboard: bool = True,
    num_frame_per_block: int = 3,
):
    """
    hidden_states: B, tt*th*tw, C
    mouse_condition: B, N_frames, C1
    keyboard_condition: B, N_frames, C2
    """
    assert use_rope_keyboard

    target_device = x.device
    target_dtype = x.dtype
    if mouse_condition is not None:
        mouse_condition = mouse_condition.to(device=target_device, dtype=target_dtype)
    if keyboard_condition is not None:
        keyboard_condition = keyboard_condition.to(device=target_device, dtype=target_dtype)
    else:
        return x

    B, N_frames, C = keyboard_condition.shape
    assert tt * th * tw == x.shape[1]
    assert ((N_frames - 1) + self.vae_time_compression_ratio) % self.vae_time_compression_ratio == 0
    N_feats = int((N_frames - 1) / self.vae_time_compression_ratio) + 1

    # Lazy initialization of freqs on first forward pass
    if self._freqs_cos is None or self._freqs_sin is None:
        self._freqs_cos, self._freqs_sin = self.get_rotary_pos_embed(
            7500,
            self.patch_size[1],
            self.patch_size[2],
            64,
            self.mouse_qk_dim_list,
            start_offset=0,
        )

    # Defined freqs_cis early so it's available for both mouse and keyboard
    freqs_cis = (self._freqs_cos, self._freqs_sin)

    if is_causal:
        assert (N_feats == tt and kv_cache_mouse is None) or ((N_frames - 1) // self.vae_time_compression_ratio + 1
                                                              == start_frame + num_frame_per_block)
    # For non-causal (training), we trust that the caller provides correctly shaped inputs

    if self.enable_mouse and mouse_condition is not None:
        hidden_states = rearrange(x, "B (T S) C -> (B S) T C", T=tt,
                                  S=th * tw)  # 65*272*480 -> 17*(272//16)*(480//16) -> 8670
        B, N_frames, C = mouse_condition.shape
    else:
        hidden_states = x
    # padding

    pad_t = self.vae_time_compression_ratio * self.windows_size
    if self.enable_mouse and mouse_condition is not None:
        attn = self._forward_mouse(
            hidden_states,
            mouse_condition,
            is_causal=is_causal,
            kv_cache_mouse=kv_cache_mouse,
            pad_t=pad_t,
            num_frame_per_block=num_frame_per_block,
            block_mask_mouse=block_mask_mouse,
            start_frame=start_frame,
            freqs_cis=freqs_cis,
            N_feats=N_feats,
            B=B,
            C=C,
            tt=tt,
            th=th,
            tw=tw,
        )
        hidden_states = rearrange(x, "(B S) T C -> B (T S) C", B=B)

        hidden_states = hidden_states + attn

    if self.enable_keyboard and keyboard_condition is not None:
        attn = self._forward_keyboard(
            hidden_states,
            keyboard_condition,
            is_causal=is_causal,
            use_rope_keyboard=use_rope_keyboard,
            kv_cache_keyboard=kv_cache_keyboard,
            pad_t=pad_t,
            num_frame_per_block=num_frame_per_block,
            block_mask_keyboard=block_mask_keyboard,
            start_frame=start_frame,
            freqs_cis=freqs_cis,
            N_feats=N_feats,
            B=B,
            tt=tt,
            th=th,
            tw=tw,
        )
        hidden_states = hidden_states + attn
    return hidden_states</code></pre></details>
fastvideo.models.dits.matrixgame2.action_module.ActionModule.patchify
patchify(x, patch_size)
x : (N C T H W)
Source
def patchify(self, x, patch_size):
    """
    x : (N C T H W)
    """
    pt, ph, pw = self.patch_size
    t, h, w = x.shape[2] // pt, x.shape[3] // ph, x.shape[4] // pw
    c = x.shape[1]
    x = x.reshape(shape=(x.shape[0], c, t, pt, h, ph, w, pw))
    x = torch.einsum("nctohpwq->nthwcopq", x)
    x = x.reshape(shape=(x.shape[0], t * h * w, c * pt * ph * pw))
    return x
fastvideo.models.dits.matrixgame2.action_module.ActionModule.unpatchify
unpatchify(x, t, h, w, patch_size)
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
Source
def unpatchify(self, x, t, h, w, patch_size):
    """
    x: (N, T, patch_size**2 * C)
    imgs: (N, H, W, C)
    """
    c = x.shape[2] // patch_size  # self.unpatchify_channels
    pt, ph, pw = self.patch_size
    assert t * h * w == x.shape[1]
x = x.reshape(shape=(x.shape&#91;0&#93;, t, h, w, c, pt, ph, pw))
x = torch.einsum(&quot;nthwcopq-&gt;nctohpwq&quot;, x)
imgs = x.reshape(shape=(x.shape&#91;0&#93;, c, t * pt, h * ph, w * pw))

return imgs</code></pre></details>
fastvideo.models.dits.matrixgame2.action_module.ActionModule.get_rotary_pos_embed
get_rotary_pos_embed(video_length, height, width, head_dim, rope_dim_list = None, start_offset = 0)
Source
def get_rotary_pos_embed(
    self,
    video_length,
    height,
    width,
    head_dim,
    rope_dim_list=None,
    start_offset=0,
):
    target_ndim = 3
    ndim = 5 - 2
latents_size = &#91;video_length + start_offset, height, width&#93;

if isinstance(self.patch_size, int):
    assert all(s % self.patch_size == 0 for s in latents_size), (
        f&quot;Latent size(last &#123;ndim&#125; dimensions) should be divisible by patch size(&#123;self.patch_size&#125;), &quot;
        f&quot;but got &#123;latents_size&#125;.&quot;)
    rope_sizes = &#91;s // self.patch_size for s in latents_size&#93;
elif isinstance(self.patch_size, list):
    assert all(s % self.patch_size&#91;idx&#93; == 0 for idx, s in enumerate(latents_size)), (
        f&quot;Latent size(last &#123;ndim&#125; dimensions) should be divisible by patch size(&#123;self.patch_size&#125;), &quot;
        f&quot;but got &#123;latents_size&#125;.&quot;)
    rope_sizes = &#91;s // self.patch_size&#91;idx&#93; for idx, s in enumerate(latents_size)&#93;

if len(rope_sizes) != target_ndim:
    rope_sizes = &#91;1&#93; * (target_ndim - len(rope_sizes)) + rope_sizes  # time axis

if rope_dim_list is None:
    rope_dim_list = &#91;head_dim // target_ndim for _ in range(target_ndim)&#93;
assert sum(rope_dim_list) == head_dim, (&quot;sum(rope_dim_list) should equal to head_dim of attention layer&quot;)
# Use Matrix-Game wrapper for FastVideo&#x27;s function
freqs_cos, freqs_sin = _get_nd_rotary_pos_embed_matrixgame2(
    rope_dim_list,
    rope_sizes,
    theta=self.rope_theta,
    theta_rescale_factor=1,
)
return freqs_cos&#91;-video_length * rope_sizes&#91;1&#93; * rope_sizes&#91;2&#93; //
                 self.patch_size&#91;0&#93;:&#93;, freqs_sin&#91;-video_length * rope_sizes&#91;1&#93; * rope_sizes&#91;2&#93; //
                                                 self.patch_size&#91;0&#93;:&#93;</code></pre></details>
fastvideo.models.dits.matrixgame2.action_module.ActionModule.forward
forward(x: torch.Tensor, tt: int, th: int, tw: int, mouse_condition: torch.Tensor | None = None, keyboard_condition: torch.Tensor | None = None, block_mask_mouse: BlockMask | None = None, block_mask_keyboard: BlockMask | None = None, is_causal: bool = False, kv_cache_mouse: dict[str, torch.Tensor] | None = None, kv_cache_keyboard: dict[str, torch.Tensor] | None = None, start_frame: int = 0, use_rope_keyboard: bool = True, num_frame_per_block: int = 3)
hidden_states: B, ttthtw, C
mouse_condition: B, N_frames, C1
keyboard_condition: B, N_frames, C2
Source
def forward(
    self,
    x: torch.Tensor,
    tt: int,
    th: int,
    tw: int,
    mouse_condition: torch.Tensor | None = None,
    keyboard_condition: torch.Tensor | None = None,
    block_mask_mouse: BlockMask | None = None,
    block_mask_keyboard: BlockMask | None = None,
    is_causal: bool = False,
    kv_cache_mouse: dict[str, torch.Tensor] | None = None,
    kv_cache_keyboard: dict[str, torch.Tensor] | None = None,
    start_frame: int = 0,
    use_rope_keyboard: bool = True,
    num_frame_per_block: int = 3,
):
    """
    hidden_states: B, tt*th*tw, C
    mouse_condition: B, N_frames, C1
    keyboard_condition: B, N_frames, C2
    """
    assert use_rope_keyboard
target_device = x.device
target_dtype = x.dtype
if mouse_condition is not None:
    mouse_condition = mouse_condition.to(device=target_device, dtype=target_dtype)
if keyboard_condition is not None:
    keyboard_condition = keyboard_condition.to(device=target_device, dtype=target_dtype)
else:
    return x

B, N_frames, C = keyboard_condition.shape
assert tt * th * tw == x.shape&#91;1&#93;
assert ((N_frames - 1) + self.vae_time_compression_ratio) % self.vae_time_compression_ratio == 0
N_feats = int((N_frames - 1) / self.vae_time_compression_ratio) + 1

# Lazy initialization of freqs on first forward pass
if self._freqs_cos is None or self._freqs_sin is None:
    self._freqs_cos, self._freqs_sin = self.get_rotary_pos_embed(
        7500,
        self.patch_size&#91;1&#93;,
        self.patch_size&#91;2&#93;,
        64,
        self.mouse_qk_dim_list,
        start_offset=0,
    )

# Defined freqs_cis early so it&#x27;s available for both mouse and keyboard
freqs_cis = (self._freqs_cos, self._freqs_sin)

if is_causal:
    assert (N_feats == tt and kv_cache_mouse is None) or ((N_frames - 1) // self.vae_time_compression_ratio + 1
                                                          == start_frame + num_frame_per_block)
# For non-causal (training), we trust that the caller provides correctly shaped inputs

if self.enable_mouse and mouse_condition is not None:
    hidden_states = rearrange(x, &quot;B (T S) C -&gt; (B S) T C&quot;, T=tt,
                              S=th * tw)  # 65*272*480 -&gt; 17*(272//16)*(480//16) -&gt; 8670
    B, N_frames, C = mouse_condition.shape
else:
    hidden_states = x
# padding

pad_t = self.vae_time_compression_ratio * self.windows_size
if self.enable_mouse and mouse_condition is not None:
    attn = self._forward_mouse(
        hidden_states,
        mouse_condition,
        is_causal=is_causal,
        kv_cache_mouse=kv_cache_mouse,
        pad_t=pad_t,
        num_frame_per_block=num_frame_per_block,
        block_mask_mouse=block_mask_mouse,
        start_frame=start_frame,
        freqs_cis=freqs_cis,
        N_feats=N_feats,
        B=B,
        C=C,
        tt=tt,
        th=th,
        tw=tw,
    )
    hidden_states = rearrange(x, &quot;(B S) T C -&gt; B (T S) C&quot;, B=B)

    hidden_states = hidden_states + attn

if self.enable_keyboard and keyboard_condition is not None:
    attn = self._forward_keyboard(
        hidden_states,
        keyboard_condition,
        is_causal=is_causal,
        use_rope_keyboard=use_rope_keyboard,
        kv_cache_keyboard=kv_cache_keyboard,
        pad_t=pad_t,
        num_frame_per_block=num_frame_per_block,
        block_mask_keyboard=block_mask_keyboard,
        start_frame=start_frame,
        freqs_cis=freqs_cis,
        N_feats=N_feats,
        B=B,
        tt=tt,
        th=th,
        tw=tw,
    )
    hidden_states = hidden_states + attn
return hidden_states</code></pre></details>