Datasets:

ckadirt
/

vjxla

ArXiv:

Add files using upload-large-folder tool

7a8c992 verified over 1 year ago

22.5 kB

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path


def build_action_block_causal_attention_mask(T, H, W, add_tokens=1):
    N_T = add_tokens + (H * W)
    N = T * N_T
    mask = torch.zeros(N, N).bool()
    mask_block = torch.ones(N_T, N_T).bool()
    local_window_time = T

    for t1 in range(T):
        for t2 in range(max(0, t1 - local_window_time + 1), t1 + 1):
            mask[t1 * N_T : (t1 + 1) * N_T, t2 * N_T : (t2 + 1) * N_T] = mask_block

    return mask


def rotate_queries_or_keys(x, pos):
    B, num_heads, N, D = x.size()
    assert D % 2 == 0, "Embedding dimension must be a multiple of 2 for block matrix rotation"

    # -- compute angle for each position
    omega = torch.arange(D // 2, dtype=x.dtype, device=x.device)
    omega /= D / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)
    freq = torch.einsum("..., f -> ... f", pos, omega)  # (..., N, D/2), outer product

    # -- build rotation matrix and apply
    emb_sin = freq.sin()  # (..., N, D/2)
    emb_cos = freq.cos()  # (..., N, D/2)

    emb_sin = emb_sin.squeeze(-1).repeat(1, 1, 1, 2)
    emb_cos = emb_cos.squeeze(-1).repeat(1, 1, 1, 2)

    # --
    y = x.unflatten(-1, (-1, 2))
    y1, y2 = y.unbind(
        dim=-1,
    )
    y = torch.stack((-y2, y1), dim=-1)
    y = y.flatten(-2)
    return (x * emb_cos) + (y * emb_sin)


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return "p={}".format(self.drop_prob)


class MLP(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class SwiGLUFFN(nn.Module):
    def __init__(
        self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.0, wide_silu=True
    ):
        super().__init__()
        out_features = out_features or in_features
        swiglu_hidden_features = hidden_features = hidden_features or in_features
        if wide_silu:
            swiglu_hidden_features = int(2 * hidden_features / 3)
            align_as = 8
            swiglu_hidden_features = (swiglu_hidden_features + align_as - 1) // align_as * align_as
        self.fc1 = nn.Linear(in_features, swiglu_hidden_features)
        self.fc2 = nn.Linear(in_features, swiglu_hidden_features)
        self.act = act_layer()
        self.fc3 = nn.Linear(swiglu_hidden_features, out_features)

    def forward(self, x):
        x1 = self.fc1(x)
        x2 = self.fc2(x)
        hidden = F.silu(x1) * x2
        return self.fc3(hidden)


class ACRoPEAttention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        use_sdpa=True,
        is_causal=False,
        grid_size=16,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop_prob = proj_drop
        self.proj_drop = nn.Dropout(proj_drop)
        self.use_sdpa = use_sdpa
        # --
        self.d_dim = int(2 * ((head_dim // 3) // 2))
        self.h_dim = int(2 * ((head_dim // 3) // 2))
        self.w_dim = int(2 * ((head_dim // 3) // 2))
        self.grid_size = grid_size
        self.is_causal = is_causal

    def _get_frame_pos(self, ids, H_patches, W_patches):
        tokens_per_frame = int(H_patches * W_patches)
        return ids // tokens_per_frame

    def _get_height_pos(self, ids, H_patches, W_patches):
        # Remove frame component from ids
        tokens_per_frame = int(H_patches * W_patches)
        tokens_per_row = W_patches
        frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
        ids = ids - tokens_per_frame * frame_ids
        # --
        return ids // tokens_per_row

    def separate_positions(self, ids, H_patches, W_patches):
        tokens_per_frame = int(H_patches * W_patches)
        tokens_per_row = W_patches
        frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
        # --
        height_ids = self._get_height_pos(ids, H_patches, W_patches)
        # --
        # Remove frame component from ids (1st term) and height component (2nd term)
        width_ids = (ids - tokens_per_frame * frame_ids) - tokens_per_row * height_ids
        return 1.0 * frame_ids, 1.0 * height_ids, 1.0 * width_ids

    def forward(self, x, mask=None, attn_mask=None, T=None, H=None, W=None, action_tokens=0):
        B, N, C = x.size()

        # -- compute position of each frame token
        if mask is not None:
            mask = mask.unsqueeze(1).repeat(1, self.num_heads, 1)
            d_mask, h_mask, w_mask = self.separate_positions(mask, H, W)
        else:
            mask = torch.arange(int(T * H * W), device=x.device)
            d_mask, h_mask, w_mask = self.separate_positions(mask, H, W)

        # -- snap spatial positions to grid size
        h_mask *= self.grid_size / H
        w_mask *= self.grid_size / W

        # -- split out action tokens from sequence
        if action_tokens > 0:
            x = x.view(B, -1, action_tokens + H * W, C)  # [B, T, 1+H*W, D]

            action_q, action_k, action_v = [], [], []
            for i in range(action_tokens):
                a = x[:, :, i : i + 1, :].flatten(1, 2)
                # Note action tokens do not work with masking
                # -- compute qkv for action tokens and rotate
                qkv = self.qkv(a).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
                q, k, v = qkv[0], qkv[1], qkv[2]  # [B, num_heads, N, D]
                # --
                qd = rotate_queries_or_keys(q[..., : self.d_dim], pos=torch.arange(T, device=x.device))
                kd = rotate_queries_or_keys(k[..., : self.d_dim], pos=torch.arange(T, device=x.device))
                qr = q[..., self.d_dim :]
                kr = k[..., self.d_dim :]
                action_q += [torch.cat([qd, qr], dim=-1).view(B, self.num_heads, T, 1, -1)]
                action_k += [torch.cat([kd, kr], dim=-1).view(B, self.num_heads, T, 1, -1)]
                action_v += [v.view(B, self.num_heads, T, 1, -1)]

            action_q = torch.cat(action_q, dim=3).flatten(2, 3)
            action_k = torch.cat(action_k, dim=3).flatten(2, 3)
            action_v = torch.cat(action_v, dim=3).flatten(2, 3)
            x = x[:, :, action_tokens:, :].flatten(1, 2)

        # -- compute qkv for frame tokens and rotate
        qkv = self.qkv(x).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # [B, num_heads, N, D]

        s = 0
        # Rotate depth
        qd = rotate_queries_or_keys(q[..., s : s + self.d_dim], pos=d_mask)
        kd = rotate_queries_or_keys(k[..., s : s + self.d_dim], pos=d_mask)
        s += self.d_dim
        # Rotate height dim
        qh = rotate_queries_or_keys(q[..., s : s + self.h_dim], pos=h_mask)
        kh = rotate_queries_or_keys(k[..., s : s + self.h_dim], pos=h_mask)
        s += self.h_dim
        # Rotate width dim
        qw = rotate_queries_or_keys(q[..., s : s + self.w_dim], pos=w_mask)
        kw = rotate_queries_or_keys(k[..., s : s + self.w_dim], pos=w_mask)
        s += self.w_dim

        # Combine rotated dimension
        if s < self.head_dim:
            qr = q[..., s:]
            kr = k[..., s:]
            q = torch.cat([qd, qh, qw, qr], dim=-1)
            k = torch.cat([kd, kh, kw, kr], dim=-1)
        else:
            q = torch.cat([qd, qh, qw], dim=-1)
            k = torch.cat([kd, kh, kw], dim=-1)

        if action_tokens > 0:

            def merge_(tx, ta):
                """tx, tx in [B, num_heads, N, D]"""
                tx = tx.view(B, self.num_heads, T, H * W, -1)  # [B, T, H*W, D]
                ta = ta.view(B, self.num_heads, T, action_tokens, -1)  # [B, T, A, D]
                return torch.cat([ta, tx], dim=3).flatten(2, 3)

            q = merge_(q, action_q)
            k = merge_(k, action_k)
            v = merge_(v, action_v)

        if attn_mask is not None or self.use_sdpa:
            with torch.backends.cuda.sdp_kernel():
                x = F.scaled_dot_product_attention(
                    q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal, attn_mask=attn_mask
                )
                attn = None
        else:
            attn = (q @ k.transpose(-2, -1)) * self.scale  # [B, num_heads, D, D]
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = attn @ v

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class RoPEAttention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        use_sdpa=True,
        grid_size=14,
        is_causal=False,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop_prob = proj_drop
        self.proj_drop = nn.Dropout(proj_drop)
        self.use_sdpa = use_sdpa
        # --
        self.d_dim = int(2 * ((head_dim // 3) // 2))
        self.h_dim = int(2 * ((head_dim // 3) // 2))
        self.w_dim = int(2 * ((head_dim // 3) // 2))
        self.grid_size = grid_size
        self.is_causal = is_causal

    def _get_frame_pos(self, ids, H_patches=None, W_patches=None):
        if H_patches is None or W_patches is None:
            tokens_per_frame = int(self.grid_size * self.grid_size)
        else:
            tokens_per_frame = int(H_patches * W_patches)
        return ids // tokens_per_frame

    def _get_height_pos(self, ids, H_patches=None, W_patches=None):
        # Remove frame component from ids
        if H_patches is None or W_patches is None:
            tokens_per_frame = int(self.grid_size * self.grid_size)
            tokens_per_row = self.grid_size
        else:
            tokens_per_frame = int(H_patches * W_patches)
            tokens_per_row = W_patches
        frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
        ids = ids - tokens_per_frame * frame_ids
        # --
        return ids // tokens_per_row

    def separate_positions(self, ids, H_patches=None, W_patches=None):
        if H_patches is None or W_patches is None:
            tokens_per_frame = int(self.grid_size * self.grid_size)
            tokens_per_row = self.grid_size
        else:
            tokens_per_frame = int(H_patches * W_patches)
            tokens_per_row = W_patches
        frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
        # --
        height_ids = self._get_height_pos(ids, H_patches, W_patches)
        # --
        # Remove frame component from ids (1st term) and height component (2nd term)
        width_ids = (ids - tokens_per_frame * frame_ids) - tokens_per_row * height_ids
        return frame_ids, height_ids, width_ids

    def forward(self, x, mask=None, attn_mask=None, T=None, H_patches=None, W_patches=None):
        B, N, C = x.size()
        grid_depth = int(N // (self.grid_size * self.grid_size))

        qkv = self.qkv(x).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # [B, num_heads, N, D]

        if mask is not None:
            mask = mask.unsqueeze(1).repeat(1, self.num_heads, 1)
            d_mask, h_mask, w_mask = self.separate_positions(mask, H_patches, W_patches)
        else:
            if T is None or H_patches is None or W_patches is None:
                mask = torch.arange(int(grid_depth * self.grid_size * self.grid_size), device=x.device)
            else:
                mask = torch.arange(int(T * H_patches * W_patches), device=x.device)
            d_mask, h_mask, w_mask = self.separate_positions(mask, H_patches, W_patches)

        s = 0
        # Rotate depth
        qd = rotate_queries_or_keys(q[..., s : s + self.d_dim], pos=d_mask)
        kd = rotate_queries_or_keys(k[..., s : s + self.d_dim], pos=d_mask)
        s += self.d_dim
        # Rotate height dim
        qh = rotate_queries_or_keys(q[..., s : s + self.h_dim], pos=h_mask)
        kh = rotate_queries_or_keys(k[..., s : s + self.h_dim], pos=h_mask)
        s += self.h_dim
        # Rotate width dim
        qw = rotate_queries_or_keys(q[..., s : s + self.w_dim], pos=w_mask)
        kw = rotate_queries_or_keys(k[..., s : s + self.w_dim], pos=w_mask)
        s += self.w_dim

        # Combine rotated dimension
        if s < self.head_dim:
            qr = q[..., s:]
            kr = k[..., s:]
            q = torch.cat([qd, qh, qw, qr], dim=-1)
            k = torch.cat([kd, kh, kw, kr], dim=-1)
        else:
            q = torch.cat([qd, qh, qw], dim=-1)
            k = torch.cat([kd, kh, kw], dim=-1)

        if attn_mask is not None or self.use_sdpa:
            with torch.backends.cuda.sdp_kernel():
                x = F.scaled_dot_product_attention(
                    q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal, attn_mask=attn_mask
                )
                attn = None
        else:
            attn = (q @ k.transpose(-2, -1)) * self.scale  # [B, num_heads, D, D]
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = attn @ v

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Attention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        use_sdpa=True,
        is_causal=False,
    ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop_prob = proj_drop
        self.proj_drop = nn.Dropout(proj_drop)
        self.use_sdpa = use_sdpa
        self.is_causal = is_causal

    def forward(self, x, mask=None, attn_mask=None):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # [B, num_heads, N, D]

        if attn_mask is not None or self.use_sdpa:
            with torch.backends.cuda.sdp_kernel():
                x = F.scaled_dot_product_attention(
                    q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal, attn_mask=attn_mask
                )
                attn = None
        else:
            attn = (q @ k.transpose(-2, -1)) * self.scale  # [B, num_heads, D, D]
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = attn @ v

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class ACBlock(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        wide_silu=True,
        norm_layer=nn.LayerNorm,
        use_sdpa=True,
        is_causal=False,
        grid_size=16,
        use_rope=False,
        **kwargs,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        if use_rope:
            self.attn = ACRoPEAttention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                use_sdpa=use_sdpa,
                is_causal=is_causal,
                grid_size=grid_size,
                proj_drop=drop,
            )
        else:
            self.attn = Attention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                use_sdpa=use_sdpa,
                is_causal=is_causal,
                proj_drop=drop,
            )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        if act_layer is nn.SiLU:
            self.mlp = SwiGLUFFN(
                in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, wide_silu=wide_silu, drop=drop
            )
        else:
            self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x, mask=None, attn_mask=None, T=None, H=None, W=None, action_tokens=0):
        y = self.norm1(x)
        if isinstance(self.attn, ACRoPEAttention):
            y = self.attn(y, mask=mask, attn_mask=attn_mask, T=T, H=H, W=W, action_tokens=action_tokens)
        else:
            y = self.attn(y, mask=mask, attn_mask=attn_mask)
        x = x + self.drop_path(y)
        y = self.norm2(x)
        x = x + self.drop_path(self.mlp(y))
        return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        wide_silu=True,
        norm_layer=nn.LayerNorm,
        use_sdpa=True,
        is_causal=False,
        grid_size=16,
        use_rope=False,
        **kwargs,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        if use_rope:
            self.attn = RoPEAttention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                use_sdpa=use_sdpa,
                is_causal=is_causal,
                grid_size=grid_size,
                proj_drop=drop,
            )
        else:
            self.attn = Attention(
                dim,
                num_heads=num_heads,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                attn_drop=attn_drop,
                use_sdpa=use_sdpa,
                is_causal=is_causal,
                proj_drop=drop,
            )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        if act_layer is nn.SiLU:
            self.mlp = SwiGLUFFN(
                in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, wide_silu=wide_silu, drop=drop
            )
        else:
            self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x, mask=None, attn_mask=None, T=None, H_patches=None, W_patches=None):
        if isinstance(self.attn, RoPEAttention):
            y = self.attn(self.norm1(x), mask=mask, attn_mask=attn_mask, T=T, H_patches=H_patches, W_patches=W_patches)
        else:
            y = self.attn(self.norm1(x), mask=mask, attn_mask=attn_mask)
        x = x + self.drop_path(y)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class CrossAttention(nn.Module):
    def __init__(self, dim, num_heads=12, qkv_bias=False, use_sdpa=True):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim**-0.5
        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, int(dim * 2), bias=qkv_bias)
        # self.proj = nn.Linear(dim, dim)
        self.use_sdpa = use_sdpa

    def forward(self, q, x):
        B, n, C = q.shape
        q = self.q(q).reshape(B, n, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        B, N, C = x.shape
        kv = self.kv(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]  # (batch_size, num_heads, seq_len, feature_dim_per_head)

        if self.use_sdpa:
            with torch.backends.cuda.sdp_kernel():
                q = F.scaled_dot_product_attention(q, k, v)
        else:
            xattn = (q @ k.transpose(-2, -1)) * self.scale
            xattn = xattn.softmax(dim=-1)  # (batch_size, num_heads, query_len, seq_len)
            q = xattn @ v

        q = q.transpose(1, 2).reshape(B, n, C)
        return q


class CrossAttentionBlock(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4.0, qkv_bias=False, act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.xattn = CrossAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer)

    def forward(self, q, x):
        y = self.xattn(q, self.norm1(x))
        q = q + y
        q = q + self.mlp(self.norm2(q))
        return q

	# Copyright (c) Meta Platforms, Inc. and affiliates.
	#
	# This source code is licensed under the MIT license found in the
	# LICENSE file in the root directory of this source tree.

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from timm.models.layers import drop_path


	def build_action_block_causal_attention_mask(T, H, W, add_tokens=1):
	N_T = add_tokens + (H * W)
	N = T * N_T
	mask = torch.zeros(N, N).bool()
	mask_block = torch.ones(N_T, N_T).bool()
	local_window_time = T

	for t1 in range(T):
	for t2 in range(max(0, t1 - local_window_time + 1), t1 + 1):
	mask[t1 * N_T : (t1 + 1) * N_T, t2 * N_T : (t2 + 1) * N_T] = mask_block

	return mask


	def rotate_queries_or_keys(x, pos):
	B, num_heads, N, D = x.size()
	assert D % 2 == 0, "Embedding dimension must be a multiple of 2 for block matrix rotation"

	# -- compute angle for each position
	omega = torch.arange(D // 2, dtype=x.dtype, device=x.device)
	omega /= D / 2.0
	omega = 1.0 / 10000**omega # (D/2,)
	freq = torch.einsum("..., f -> ... f", pos, omega) # (..., N, D/2), outer product

	# -- build rotation matrix and apply
	emb_sin = freq.sin() # (..., N, D/2)
	emb_cos = freq.cos() # (..., N, D/2)

	emb_sin = emb_sin.squeeze(-1).repeat(1, 1, 1, 2)
	emb_cos = emb_cos.squeeze(-1).repeat(1, 1, 1, 2)

	# --
	y = x.unflatten(-1, (-1, 2))
	y1, y2 = y.unbind(
	dim=-1,
	)
	y = torch.stack((-y2, y1), dim=-1)
	y = y.flatten(-2)
	return (x * emb_cos) + (y * emb_sin)


	class DropPath(nn.Module):
	"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

	def __init__(self, drop_prob=None):
	super(DropPath, self).__init__()
	self.drop_prob = drop_prob

	def forward(self, x):
	return drop_path(x, self.drop_prob, self.training)

	def extra_repr(self) -> str:
	return "p={}".format(self.drop_prob)


	class MLP(nn.Module):
	def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
	super().__init__()
	out_features = out_features or in_features
	hidden_features = hidden_features or in_features
	self.fc1 = nn.Linear(in_features, hidden_features)
	self.act = act_layer()
	self.fc2 = nn.Linear(hidden_features, out_features)
	self.drop = nn.Dropout(drop)

	def forward(self, x):
	x = self.fc1(x)
	x = self.act(x)
	x = self.drop(x)
	x = self.fc2(x)
	x = self.drop(x)
	return x


	class SwiGLUFFN(nn.Module):
	def __init__(
	self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.0, wide_silu=True
	):
	super().__init__()
	out_features = out_features or in_features
	swiglu_hidden_features = hidden_features = hidden_features or in_features
	if wide_silu:
	swiglu_hidden_features = int(2 * hidden_features / 3)
	align_as = 8
	swiglu_hidden_features = (swiglu_hidden_features + align_as - 1) // align_as * align_as
	self.fc1 = nn.Linear(in_features, swiglu_hidden_features)
	self.fc2 = nn.Linear(in_features, swiglu_hidden_features)
	self.act = act_layer()
	self.fc3 = nn.Linear(swiglu_hidden_features, out_features)

	def forward(self, x):
	x1 = self.fc1(x)
	x2 = self.fc2(x)
	hidden = F.silu(x1) * x2
	return self.fc3(hidden)


	class ACRoPEAttention(nn.Module):
	def __init__(
	self,
	dim,
	num_heads=8,
	qkv_bias=False,
	qk_scale=None,
	attn_drop=0.0,
	proj_drop=0.0,
	use_sdpa=True,
	is_causal=False,
	grid_size=16,
	):
	super().__init__()
	self.num_heads = num_heads
	self.head_dim = head_dim = dim // num_heads
	self.scale = qk_scale or head_dim**-0.5
	self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
	self.attn_drop = nn.Dropout(attn_drop)
	self.proj = nn.Linear(dim, dim)
	self.proj_drop_prob = proj_drop
	self.proj_drop = nn.Dropout(proj_drop)
	self.use_sdpa = use_sdpa
	# --
	self.d_dim = int(2 * ((head_dim // 3) // 2))
	self.h_dim = int(2 * ((head_dim // 3) // 2))
	self.w_dim = int(2 * ((head_dim // 3) // 2))
	self.grid_size = grid_size
	self.is_causal = is_causal

	def _get_frame_pos(self, ids, H_patches, W_patches):
	tokens_per_frame = int(H_patches * W_patches)
	return ids // tokens_per_frame

	def _get_height_pos(self, ids, H_patches, W_patches):
	# Remove frame component from ids
	tokens_per_frame = int(H_patches * W_patches)
	tokens_per_row = W_patches
	frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
	ids = ids - tokens_per_frame * frame_ids
	# --
	return ids // tokens_per_row

	def separate_positions(self, ids, H_patches, W_patches):
	tokens_per_frame = int(H_patches * W_patches)
	tokens_per_row = W_patches
	frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
	# --
	height_ids = self._get_height_pos(ids, H_patches, W_patches)
	# --
	# Remove frame component from ids (1st term) and height component (2nd term)
	width_ids = (ids - tokens_per_frame * frame_ids) - tokens_per_row * height_ids
	return 1.0 * frame_ids, 1.0 * height_ids, 1.0 * width_ids

	def forward(self, x, mask=None, attn_mask=None, T=None, H=None, W=None, action_tokens=0):
	B, N, C = x.size()

	# -- compute position of each frame token
	if mask is not None:
	mask = mask.unsqueeze(1).repeat(1, self.num_heads, 1)
	d_mask, h_mask, w_mask = self.separate_positions(mask, H, W)
	else:
	mask = torch.arange(int(T * H * W), device=x.device)
	d_mask, h_mask, w_mask = self.separate_positions(mask, H, W)

	# -- snap spatial positions to grid size
	h_mask *= self.grid_size / H
	w_mask *= self.grid_size / W

	# -- split out action tokens from sequence
	if action_tokens > 0:
	x = x.view(B, -1, action_tokens + H * W, C) # [B, T, 1+H*W, D]

	action_q, action_k, action_v = [], [], []
	for i in range(action_tokens):
	a = x[:, :, i : i + 1, :].flatten(1, 2)
	# Note action tokens do not work with masking
	# -- compute qkv for action tokens and rotate
	qkv = self.qkv(a).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
	q, k, v = qkv[0], qkv[1], qkv[2] # [B, num_heads, N, D]
	# --
	qd = rotate_queries_or_keys(q[..., : self.d_dim], pos=torch.arange(T, device=x.device))
	kd = rotate_queries_or_keys(k[..., : self.d_dim], pos=torch.arange(T, device=x.device))
	qr = q[..., self.d_dim :]
	kr = k[..., self.d_dim :]
	action_q += [torch.cat([qd, qr], dim=-1).view(B, self.num_heads, T, 1, -1)]
	action_k += [torch.cat([kd, kr], dim=-1).view(B, self.num_heads, T, 1, -1)]
	action_v += [v.view(B, self.num_heads, T, 1, -1)]

	action_q = torch.cat(action_q, dim=3).flatten(2, 3)
	action_k = torch.cat(action_k, dim=3).flatten(2, 3)
	action_v = torch.cat(action_v, dim=3).flatten(2, 3)
	x = x[:, :, action_tokens:, :].flatten(1, 2)

	# -- compute qkv for frame tokens and rotate
	qkv = self.qkv(x).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
	q, k, v = qkv[0], qkv[1], qkv[2] # [B, num_heads, N, D]

	s = 0
	# Rotate depth
	qd = rotate_queries_or_keys(q[..., s : s + self.d_dim], pos=d_mask)
	kd = rotate_queries_or_keys(k[..., s : s + self.d_dim], pos=d_mask)
	s += self.d_dim
	# Rotate height dim
	qh = rotate_queries_or_keys(q[..., s : s + self.h_dim], pos=h_mask)
	kh = rotate_queries_or_keys(k[..., s : s + self.h_dim], pos=h_mask)
	s += self.h_dim
	# Rotate width dim
	qw = rotate_queries_or_keys(q[..., s : s + self.w_dim], pos=w_mask)
	kw = rotate_queries_or_keys(k[..., s : s + self.w_dim], pos=w_mask)
	s += self.w_dim

	# Combine rotated dimension
	if s < self.head_dim:
	qr = q[..., s:]
	kr = k[..., s:]
	q = torch.cat([qd, qh, qw, qr], dim=-1)
	k = torch.cat([kd, kh, kw, kr], dim=-1)
	else:
	q = torch.cat([qd, qh, qw], dim=-1)
	k = torch.cat([kd, kh, kw], dim=-1)

	if action_tokens > 0:

	def merge_(tx, ta):
	"""tx, tx in [B, num_heads, N, D]"""
	tx = tx.view(B, self.num_heads, T, H * W, -1) # [B, T, H*W, D]
	ta = ta.view(B, self.num_heads, T, action_tokens, -1) # [B, T, A, D]
	return torch.cat([ta, tx], dim=3).flatten(2, 3)

	q = merge_(q, action_q)
	k = merge_(k, action_k)
	v = merge_(v, action_v)

	if attn_mask is not None or self.use_sdpa:
	with torch.backends.cuda.sdp_kernel():
	x = F.scaled_dot_product_attention(
	q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal, attn_mask=attn_mask
	)
	attn = None
	else:
	attn = (q @ k.transpose(-2, -1)) * self.scale # [B, num_heads, D, D]
	attn = attn.softmax(dim=-1)
	attn = self.attn_drop(attn)
	x = attn @ v

	x = x.transpose(1, 2).reshape(B, N, C)
	x = self.proj(x)
	x = self.proj_drop(x)
	return x


	class RoPEAttention(nn.Module):
	def __init__(
	self,
	dim,
	num_heads=8,
	qkv_bias=False,
	qk_scale=None,
	attn_drop=0.0,
	proj_drop=0.0,
	use_sdpa=True,
	grid_size=14,
	is_causal=False,
	):
	super().__init__()
	self.num_heads = num_heads
	self.head_dim = head_dim = dim // num_heads
	self.scale = qk_scale or head_dim**-0.5
	self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
	self.attn_drop = nn.Dropout(attn_drop)
	self.proj = nn.Linear(dim, dim)
	self.proj_drop_prob = proj_drop
	self.proj_drop = nn.Dropout(proj_drop)
	self.use_sdpa = use_sdpa
	# --
	self.d_dim = int(2 * ((head_dim // 3) // 2))
	self.h_dim = int(2 * ((head_dim // 3) // 2))
	self.w_dim = int(2 * ((head_dim // 3) // 2))
	self.grid_size = grid_size
	self.is_causal = is_causal

	def _get_frame_pos(self, ids, H_patches=None, W_patches=None):
	if H_patches is None or W_patches is None:
	tokens_per_frame = int(self.grid_size * self.grid_size)
	else:
	tokens_per_frame = int(H_patches * W_patches)
	return ids // tokens_per_frame

	def _get_height_pos(self, ids, H_patches=None, W_patches=None):
	# Remove frame component from ids
	if H_patches is None or W_patches is None:
	tokens_per_frame = int(self.grid_size * self.grid_size)
	tokens_per_row = self.grid_size
	else:
	tokens_per_frame = int(H_patches * W_patches)
	tokens_per_row = W_patches
	frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
	ids = ids - tokens_per_frame * frame_ids
	# --
	return ids // tokens_per_row

	def separate_positions(self, ids, H_patches=None, W_patches=None):
	if H_patches is None or W_patches is None:
	tokens_per_frame = int(self.grid_size * self.grid_size)
	tokens_per_row = self.grid_size
	else:
	tokens_per_frame = int(H_patches * W_patches)
	tokens_per_row = W_patches
	frame_ids = self._get_frame_pos(ids, H_patches, W_patches)
	# --
	height_ids = self._get_height_pos(ids, H_patches, W_patches)
	# --
	# Remove frame component from ids (1st term) and height component (2nd term)
	width_ids = (ids - tokens_per_frame * frame_ids) - tokens_per_row * height_ids
	return frame_ids, height_ids, width_ids

	def forward(self, x, mask=None, attn_mask=None, T=None, H_patches=None, W_patches=None):
	B, N, C = x.size()
	grid_depth = int(N // (self.grid_size * self.grid_size))

	qkv = self.qkv(x).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
	q, k, v = qkv[0], qkv[1], qkv[2] # [B, num_heads, N, D]

	if mask is not None:
	mask = mask.unsqueeze(1).repeat(1, self.num_heads, 1)
	d_mask, h_mask, w_mask = self.separate_positions(mask, H_patches, W_patches)
	else:
	if T is None or H_patches is None or W_patches is None:
	mask = torch.arange(int(grid_depth * self.grid_size * self.grid_size), device=x.device)
	else:
	mask = torch.arange(int(T * H_patches * W_patches), device=x.device)
	d_mask, h_mask, w_mask = self.separate_positions(mask, H_patches, W_patches)

	s = 0
	# Rotate depth
	qd = rotate_queries_or_keys(q[..., s : s + self.d_dim], pos=d_mask)
	kd = rotate_queries_or_keys(k[..., s : s + self.d_dim], pos=d_mask)
	s += self.d_dim
	# Rotate height dim
	qh = rotate_queries_or_keys(q[..., s : s + self.h_dim], pos=h_mask)
	kh = rotate_queries_or_keys(k[..., s : s + self.h_dim], pos=h_mask)
	s += self.h_dim
	# Rotate width dim
	qw = rotate_queries_or_keys(q[..., s : s + self.w_dim], pos=w_mask)
	kw = rotate_queries_or_keys(k[..., s : s + self.w_dim], pos=w_mask)
	s += self.w_dim

	# Combine rotated dimension
	if s < self.head_dim:
	qr = q[..., s:]
	kr = k[..., s:]
	q = torch.cat([qd, qh, qw, qr], dim=-1)
	k = torch.cat([kd, kh, kw, kr], dim=-1)
	else:
	q = torch.cat([qd, qh, qw], dim=-1)
	k = torch.cat([kd, kh, kw], dim=-1)

	if attn_mask is not None or self.use_sdpa:
	with torch.backends.cuda.sdp_kernel():
	x = F.scaled_dot_product_attention(
	q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal, attn_mask=attn_mask
	)
	attn = None
	else:
	attn = (q @ k.transpose(-2, -1)) * self.scale # [B, num_heads, D, D]
	attn = attn.softmax(dim=-1)
	attn = self.attn_drop(attn)
	x = attn @ v

	x = x.transpose(1, 2).reshape(B, N, C)
	x = self.proj(x)
	x = self.proj_drop(x)
	return x


	class Attention(nn.Module):
	def __init__(
	self,
	dim,
	num_heads=8,
	qkv_bias=False,
	qk_scale=None,
	attn_drop=0.0,
	proj_drop=0.0,
	use_sdpa=True,
	is_causal=False,
	):
	super().__init__()
	self.num_heads = num_heads
	head_dim = dim // num_heads
	self.scale = qk_scale or head_dim**-0.5
	self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
	self.attn_drop = nn.Dropout(attn_drop)
	self.proj = nn.Linear(dim, dim)
	self.proj_drop_prob = proj_drop
	self.proj_drop = nn.Dropout(proj_drop)
	self.use_sdpa = use_sdpa
	self.is_causal = is_causal

	def forward(self, x, mask=None, attn_mask=None):
	B, N, C = x.shape
	qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
	q, k, v = qkv[0], qkv[1], qkv[2] # [B, num_heads, N, D]

	if attn_mask is not None or self.use_sdpa:
	with torch.backends.cuda.sdp_kernel():
	x = F.scaled_dot_product_attention(
	q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal, attn_mask=attn_mask
	)
	attn = None
	else:
	attn = (q @ k.transpose(-2, -1)) * self.scale # [B, num_heads, D, D]
	attn = attn.softmax(dim=-1)
	attn = self.attn_drop(attn)
	x = attn @ v

	x = x.transpose(1, 2).reshape(B, N, C)
	x = self.proj(x)
	x = self.proj_drop(x)
	return x


	class ACBlock(nn.Module):
	def __init__(
	self,
	dim,
	num_heads,
	mlp_ratio=4.0,
	qkv_bias=False,
	qk_scale=None,
	drop=0.0,
	attn_drop=0.0,
	drop_path=0.0,
	act_layer=nn.GELU,
	wide_silu=True,
	norm_layer=nn.LayerNorm,
	use_sdpa=True,
	is_causal=False,
	grid_size=16,
	use_rope=False,
	**kwargs,
	):
	super().__init__()
	self.norm1 = norm_layer(dim)
	if use_rope:
	self.attn = ACRoPEAttention(
	dim,
	num_heads=num_heads,
	qkv_bias=qkv_bias,
	qk_scale=qk_scale,
	attn_drop=attn_drop,
	use_sdpa=use_sdpa,
	is_causal=is_causal,
	grid_size=grid_size,
	proj_drop=drop,
	)
	else:
	self.attn = Attention(
	dim,
	num_heads=num_heads,
	qkv_bias=qkv_bias,
	qk_scale=qk_scale,
	attn_drop=attn_drop,
	use_sdpa=use_sdpa,
	is_causal=is_causal,
	proj_drop=drop,
	)

	self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
	self.norm2 = norm_layer(dim)
	mlp_hidden_dim = int(dim * mlp_ratio)
	if act_layer is nn.SiLU:
	self.mlp = SwiGLUFFN(
	in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, wide_silu=wide_silu, drop=drop
	)
	else:
	self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

	def forward(self, x, mask=None, attn_mask=None, T=None, H=None, W=None, action_tokens=0):
	y = self.norm1(x)
	if isinstance(self.attn, ACRoPEAttention):
	y = self.attn(y, mask=mask, attn_mask=attn_mask, T=T, H=H, W=W, action_tokens=action_tokens)
	else:
	y = self.attn(y, mask=mask, attn_mask=attn_mask)
	x = x + self.drop_path(y)
	y = self.norm2(x)
	x = x + self.drop_path(self.mlp(y))
	return x


	class Block(nn.Module):
	def __init__(
	self,
	dim,
	num_heads,
	mlp_ratio=4.0,
	qkv_bias=False,
	qk_scale=None,
	drop=0.0,
	attn_drop=0.0,
	drop_path=0.0,
	act_layer=nn.GELU,
	wide_silu=True,
	norm_layer=nn.LayerNorm,
	use_sdpa=True,
	is_causal=False,
	grid_size=16,
	use_rope=False,
	**kwargs,
	):
	super().__init__()
	self.norm1 = norm_layer(dim)
	if use_rope:
	self.attn = RoPEAttention(
	dim,
	num_heads=num_heads,
	qkv_bias=qkv_bias,
	qk_scale=qk_scale,
	attn_drop=attn_drop,
	use_sdpa=use_sdpa,
	is_causal=is_causal,
	grid_size=grid_size,
	proj_drop=drop,
	)
	else:
	self.attn = Attention(
	dim,
	num_heads=num_heads,
	qkv_bias=qkv_bias,
	qk_scale=qk_scale,
	attn_drop=attn_drop,
	use_sdpa=use_sdpa,
	is_causal=is_causal,
	proj_drop=drop,
	)

	self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
	self.norm2 = norm_layer(dim)
	mlp_hidden_dim = int(dim * mlp_ratio)
	if act_layer is nn.SiLU:
	self.mlp = SwiGLUFFN(
	in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, wide_silu=wide_silu, drop=drop
	)
	else:
	self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

	def forward(self, x, mask=None, attn_mask=None, T=None, H_patches=None, W_patches=None):
	if isinstance(self.attn, RoPEAttention):
	y = self.attn(self.norm1(x), mask=mask, attn_mask=attn_mask, T=T, H_patches=H_patches, W_patches=W_patches)
	else:
	y = self.attn(self.norm1(x), mask=mask, attn_mask=attn_mask)
	x = x + self.drop_path(y)
	x = x + self.drop_path(self.mlp(self.norm2(x)))
	return x


	class CrossAttention(nn.Module):
	def __init__(self, dim, num_heads=12, qkv_bias=False, use_sdpa=True):
	super().__init__()
	self.num_heads = num_heads
	head_dim = dim // num_heads
	self.scale = head_dim**-0.5
	self.q = nn.Linear(dim, dim, bias=qkv_bias)
	self.kv = nn.Linear(dim, int(dim * 2), bias=qkv_bias)
	# self.proj = nn.Linear(dim, dim)
	self.use_sdpa = use_sdpa

	def forward(self, q, x):
	B, n, C = q.shape
	q = self.q(q).reshape(B, n, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

	B, N, C = x.shape
	kv = self.kv(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
	k, v = kv[0], kv[1] # (batch_size, num_heads, seq_len, feature_dim_per_head)

	if self.use_sdpa:
	with torch.backends.cuda.sdp_kernel():
	q = F.scaled_dot_product_attention(q, k, v)
	else:
	xattn = (q @ k.transpose(-2, -1)) * self.scale
	xattn = xattn.softmax(dim=-1) # (batch_size, num_heads, query_len, seq_len)
	q = xattn @ v

	q = q.transpose(1, 2).reshape(B, n, C)
	return q


	class CrossAttentionBlock(nn.Module):
	def __init__(self, dim, num_heads, mlp_ratio=4.0, qkv_bias=False, act_layer=nn.GELU, norm_layer=nn.LayerNorm):
	super().__init__()
	self.norm1 = norm_layer(dim)
	self.xattn = CrossAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias)
	self.norm2 = norm_layer(dim)
	mlp_hidden_dim = int(dim * mlp_ratio)
	self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer)

	def forward(self, q, x):
	y = self.xattn(q, self.norm1(x))
	q = q + y
	q = q + self.mlp(self.norm2(q))
	return q