--- title: action_module ---

fastvideo.models.dits.matrixgame2.action_module.LocalAttention

LocalAttention(num_heads: int, head_size: int, num_kv_heads: int | None = None, softmax_scale: float | None = None, causal: bool = False, supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None, default_backend: AttentionBackendEnum | None = None, **extra_impl_args)

Attention layer.

fastvideo.models.dits.matrixgame2.action_module.LocalAttention.forward

forward(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None) -> torch.Tensor

Apply local attention between query, key and value tensors.

Parameters

q: torch.Tensor

Query tensor of shape [batch_size, seq_len, num_heads, head_dim]

k: torch.Tensor

Key tensor of shape [batch_size, seq_len, num_heads, head_dim]

v: torch.Tensor

Value tensor of shape [batch_size, seq_len, num_heads, head_dim]

Returns

torch.Tensor

torch.Tensor: Output tensor after local attention

fastvideo.models.dits.matrixgame2.action_module.ReplicatedLinear

ReplicatedLinear(input_size: int, output_size: int, bias: bool = True, skip_bias_add: bool = False, params_dtype: torch.dtype | None = None, quant_config: QuantizationConfig | None = None, prefix: str = '')

Replicated linear layer.

Parameters

input_size: int

input dimension of the linear layer.

output_size: int

output dimension of the linear layer.

bias: bool

If true, add bias.

skip_bias_add: bool

If true, skip adding bias but instead return it.

params_dtype: torch.dtype | None

Data type for the parameters.

quant_config: QuantizationConfig | None

Quantization configure.

prefix: str

The name of the layer in the state dict, including all parents (e.g. model.layers.0.qkv_proj)

fastvideo.models.dits.matrixgame2.action_module.ReplicatedLinear.weight_loader

weight_loader(param: Parameter, loaded_weight: torch.Tensor) -> None

fastvideo.models.dits.matrixgame2.action_module.ReplicatedLinear.forward

forward(x: torch.Tensor) -> tuple[torch.Tensor, Parameter | None]

fastvideo.models.dits.matrixgame2.action_module.ReplicatedLinear.extra_repr

extra_repr() -> str

fastvideo.models.dits.matrixgame2.action_module.ReplicatedLinear.get_shape_mapping

get_shape_mapping() -> dict

Get the mapping from (input_shape, output_shape) to layer types.

fastvideo.models.dits.matrixgame2.action_module.ReplicatedLinear.reset_shape_tracking

reset_shape_tracking() -> None

Clear tracked shapes and layer type mappings.

fastvideo.models.dits.matrixgame2.action_module.ReplicatedLinear.print_shape_summary

print_shape_summary() -> None

Log a summary of all unique shapes and their layer types.

fastvideo.models.dits.matrixgame2.action_module.FP32LayerNorm

FP32LayerNorm()

fastvideo.models.dits.matrixgame2.action_module.FP32LayerNorm.forward

forward(inputs: torch.Tensor) -> torch.Tensor

fastvideo.models.dits.matrixgame2.action_module.RMSNorm

RMSNorm(hidden_size: int, eps: float = 1e-06, dtype: torch.dtype = torch.float32, var_hidden_size: int | None = None, has_weight: bool = True)

Root mean square normalization.

Computes x -> w * x / sqrt(E[x^2] + eps) where w is the learned weight. Refer to https://arxiv.org/abs/1910.07467

fastvideo.models.dits.matrixgame2.action_module.RMSNorm.forward_native

forward_native(x: torch.Tensor, residual: torch.Tensor | None = None) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]

PyTorch-native implementation equivalent to forward().

fastvideo.models.dits.matrixgame2.action_module.RMSNorm.extra_repr

extra_repr() -> str

fastvideo.models.dits.matrixgame2.action_module.AttentionBackendEnum

AttentionBackendEnum()

fastvideo.models.dits.matrixgame2.action_module.retain_kv_with_sink

retain_kv_with_sink(k: torch.Tensor, v: torch.Tensor, target_len: int, sink_size: int) -> tuple[torch.Tensor, torch.Tensor]

fastvideo.models.dits.matrixgame2.action_module.ActionModule

ActionModule(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)

action module from https://arxiv.org/pdf/2501.08325

Source
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

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[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

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 = [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]:]

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[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