Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,172 Bytes
bb65ef0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
# SPDX-License-Identifier: Apache-2.0
import functools
import math
from dataclasses import dataclass
import torch
from vsa import video_sparse_attn
from typing import Any
VSA_TILE_SIZE = (4, 4, 4)
@functools.lru_cache(maxsize=10)
def get_tile_partition_indices(
dit_seq_shape: tuple[int, int, int],
tile_size: tuple[int, int, int],
device: torch.device,
) -> torch.LongTensor:
T, H, W = dit_seq_shape
ts, hs, ws = tile_size
indices = torch.arange(T * H * W, device=device,
dtype=torch.long).reshape(T, H, W)
ls = []
for t in range(math.ceil(T / ts)):
for h in range(math.ceil(H / hs)):
for w in range(math.ceil(W / ws)):
ls.append(indices[t * ts:min(t * ts + ts, T),
h * hs:min(h * hs + hs, H),
w * ws:min(w * ws + ws, W)].flatten())
index = torch.cat(ls, dim=0)
return index
@functools.lru_cache(maxsize=10)
def get_reverse_tile_partition_indices(
dit_seq_shape: tuple[int, int, int],
tile_size: tuple[int, int, int],
device: torch.device,
) -> torch.LongTensor:
return torch.argsort(
get_tile_partition_indices(dit_seq_shape, tile_size, device))
@functools.lru_cache(maxsize=10)
def construct_variable_block_sizes(
dit_seq_shape: tuple[int, int, int],
num_tiles: tuple[int, int, int],
device: torch.device,
) -> torch.LongTensor:
"""
Compute the number of valid (non‑padded) tokens inside every
(ts_t × ts_h × ts_w) tile after padding ‑‑ flattened in the order
(t‑tile, h‑tile, w‑tile) that `rearrange` uses.
Returns
-------
torch.LongTensor # shape: [∏ full_window_size]
"""
# unpack
t, h, w = dit_seq_shape
ts_t, ts_h, ts_w = VSA_TILE_SIZE
n_t, n_h, n_w = num_tiles
def _sizes(dim_len: int, tile: int, n_tiles: int) -> torch.LongTensor:
"""Vector with the size of each tile along one dimension."""
sizes = torch.full((n_tiles, ), tile, dtype=torch.int, device=device)
# size of last (possibly partial) tile
remainder = dim_len - (n_tiles - 1) * tile
sizes[-1] = remainder if remainder > 0 else tile
return sizes
t_sizes = _sizes(t, ts_t, n_t) # [n_t]
h_sizes = _sizes(h, ts_h, n_h) # [n_h]
w_sizes = _sizes(w, ts_w, n_w) # [n_w]
# broadcast‑multiply to get voxels per tile, then flatten
block_sizes = (
t_sizes[:, None, None] # [n_t, 1, 1]
* h_sizes[None, :, None] # [1, n_h, 1]
* w_sizes[None, None, :] # [1, 1, n_w]
).reshape(-1) # [n_t * n_h * n_w]
return block_sizes
@functools.lru_cache(maxsize=10)
def get_non_pad_index(
variable_block_sizes: torch.LongTensor,
max_block_size: int,
):
n_win = variable_block_sizes.shape[0]
device = variable_block_sizes.device
starts_pad = torch.arange(n_win, device=device) * max_block_size
index_pad = starts_pad[:, None] + torch.arange(max_block_size,
device=device)[None, :]
index_mask = torch.arange(
max_block_size, device=device)[None, :] < variable_block_sizes[:, None]
return index_pad[index_mask]
@dataclass
class VideoSparseAttentionMetadata():
current_timestep: int
dit_seq_shape: list[int]
VSA_sparsity: float
num_tiles: list[int]
total_seq_length: int
tile_partition_indices: torch.LongTensor
reverse_tile_partition_indices: torch.LongTensor
variable_block_sizes: torch.LongTensor
non_pad_index: torch.LongTensor
def build(
current_timestep: int,
raw_latent_shape: tuple[int, int, int],
patch_size: tuple[int, int, int],
VSA_sparsity: float,
device: torch.device,
**kwargs: dict[str, Any],
) -> VideoSparseAttentionMetadata:
patch_size = patch_size
dit_seq_shape = (raw_latent_shape[0] // patch_size[0],
raw_latent_shape[1] // patch_size[1],
raw_latent_shape[2] // patch_size[2])
num_tiles = (math.ceil(dit_seq_shape[0] / VSA_TILE_SIZE[0]),
math.ceil(dit_seq_shape[1] / VSA_TILE_SIZE[1]),
math.ceil(dit_seq_shape[2] / VSA_TILE_SIZE[2]))
total_seq_length = math.prod(dit_seq_shape)
tile_partition_indices = get_tile_partition_indices(
dit_seq_shape, VSA_TILE_SIZE, device)
reverse_tile_partition_indices = get_reverse_tile_partition_indices(
dit_seq_shape, VSA_TILE_SIZE, device)
variable_block_sizes = construct_variable_block_sizes(
dit_seq_shape, num_tiles, device)
non_pad_index = get_non_pad_index(variable_block_sizes,
math.prod(VSA_TILE_SIZE))
return VideoSparseAttentionMetadata(
current_timestep=current_timestep,
dit_seq_shape=dit_seq_shape, # type: ignore
VSA_sparsity=VSA_sparsity, # type: ignore
num_tiles=num_tiles, # type: ignore
total_seq_length=total_seq_length, # type: ignore
tile_partition_indices=tile_partition_indices, # type: ignore
reverse_tile_partition_indices=reverse_tile_partition_indices,
variable_block_sizes=variable_block_sizes,
non_pad_index=non_pad_index)
class VideoSparseAttentionImpl():
def __init__(
self,
num_heads: int,
head_size: int,
causal: bool,
softmax_scale: float,
num_kv_heads: int | None = None,
prefix: str = "",
**extra_impl_args,
) -> None:
self.prefix = prefix
def tile(self, x: torch.Tensor, num_tiles: list[int],
tile_partition_indices: torch.LongTensor,
non_pad_index: torch.LongTensor) -> torch.Tensor:
t_padded_size = num_tiles[0] * VSA_TILE_SIZE[0]
h_padded_size = num_tiles[1] * VSA_TILE_SIZE[1]
w_padded_size = num_tiles[2] * VSA_TILE_SIZE[2]
x_padded = torch.zeros(
(x.shape[0], t_padded_size * h_padded_size * w_padded_size,
x.shape[-2], x.shape[-1]),
device=x.device,
dtype=x.dtype)
x_padded[:, non_pad_index] = x[:, tile_partition_indices]
return x_padded
def untile(self, x: torch.Tensor,
reverse_tile_partition_indices: torch.LongTensor,
non_pad_index: torch.LongTensor) -> torch.Tensor:
x = x[:, non_pad_index][:, reverse_tile_partition_indices]
return x
def preprocess_qkv(
self,
qkv: torch.Tensor,
attn_metadata: VideoSparseAttentionMetadata,
) -> torch.Tensor:
return self.tile(qkv, attn_metadata.num_tiles,
attn_metadata.tile_partition_indices,
attn_metadata.non_pad_index)
def postprocess_output(
self,
output: torch.Tensor,
attn_metadata: VideoSparseAttentionMetadata,
) -> torch.Tensor:
return self.untile(output, attn_metadata.reverse_tile_partition_indices,
attn_metadata.non_pad_index)
def forward( # type: ignore[override]
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_metadata: VideoSparseAttentionMetadata,
) -> torch.Tensor:
query = query.transpose(1, 2).contiguous()
key = key.transpose(1, 2).contiguous()
value = value.transpose(1, 2).contiguous()
VSA_sparsity = attn_metadata.VSA_sparsity
cur_topk = math.ceil(
(1 - VSA_sparsity) *
(attn_metadata.total_seq_length / math.prod(VSA_TILE_SIZE)))
hidden_states = video_sparse_attn(
query,
key,
value,
variable_block_sizes=attn_metadata.variable_block_sizes,
topk=cur_topk,
block_size=VSA_TILE_SIZE,
compress_attn_weight=None).transpose(1, 2)
return hidden_states
|