Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,258 Bytes
db6a3b7 |
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 |
from typing import *
from enum import Enum
import torch
import math
from .. import SparseTensor
from .. import DEBUG, ATTN
if ATTN == 'xformers':
import xformers.ops as xops
elif ATTN == 'flash_attn':
import flash_attn
else:
raise ValueError(f"Unknown attention module: {ATTN}")
__all__ = [
'sparse_serialized_scaled_dot_product_self_attention',
]
class SerializeMode(Enum):
Z_ORDER = 0
Z_ORDER_TRANSPOSED = 1
HILBERT = 2
HILBERT_TRANSPOSED = 3
SerializeModes = [
SerializeMode.Z_ORDER,
SerializeMode.Z_ORDER_TRANSPOSED,
SerializeMode.HILBERT,
SerializeMode.HILBERT_TRANSPOSED
]
def calc_serialization(
tensor: SparseTensor,
window_size: int,
serialize_mode: SerializeMode = SerializeMode.Z_ORDER,
shift_sequence: int = 0,
shift_window: Tuple[int, int, int] = (0, 0, 0)
) -> Tuple[torch.Tensor, torch.Tensor, List[int]]:
"""
Calculate serialization and partitioning for a set of coordinates.
Args:
tensor (SparseTensor): The input tensor.
window_size (int): The window size to use.
serialize_mode (SerializeMode): The serialization mode to use.
shift_sequence (int): The shift of serialized sequence.
shift_window (Tuple[int, int, int]): The shift of serialized coordinates.
Returns:
(torch.Tensor, torch.Tensor): Forwards and backwards indices.
"""
fwd_indices = []
bwd_indices = []
seq_lens = []
seq_batch_indices = []
offsets = [0]
if 'vox2seq' not in globals():
import vox2seq
# Serialize the input
serialize_coords = tensor.coords[:, 1:].clone()
serialize_coords += torch.tensor(shift_window, dtype=torch.int32, device=tensor.device).reshape(1, 3)
if serialize_mode == SerializeMode.Z_ORDER:
code = vox2seq.encode(serialize_coords, mode='z_order', permute=[0, 1, 2])
elif serialize_mode == SerializeMode.Z_ORDER_TRANSPOSED:
code = vox2seq.encode(serialize_coords, mode='z_order', permute=[1, 0, 2])
elif serialize_mode == SerializeMode.HILBERT:
code = vox2seq.encode(serialize_coords, mode='hilbert', permute=[0, 1, 2])
elif serialize_mode == SerializeMode.HILBERT_TRANSPOSED:
code = vox2seq.encode(serialize_coords, mode='hilbert', permute=[1, 0, 2])
else:
raise ValueError(f"Unknown serialize mode: {serialize_mode}")
for bi, s in enumerate(tensor.layout):
num_points = s.stop - s.start
num_windows = (num_points + window_size - 1) // window_size
valid_window_size = num_points / num_windows
to_ordered = torch.argsort(code[s.start:s.stop])
if num_windows == 1:
fwd_indices.append(to_ordered)
bwd_indices.append(torch.zeros_like(to_ordered).scatter_(0, to_ordered, torch.arange(num_points, device=tensor.device)))
fwd_indices[-1] += s.start
bwd_indices[-1] += offsets[-1]
seq_lens.append(num_points)
seq_batch_indices.append(bi)
offsets.append(offsets[-1] + seq_lens[-1])
else:
# Partition the input
offset = 0
mids = [(i + 0.5) * valid_window_size + shift_sequence for i in range(num_windows)]
split = [math.floor(i * valid_window_size + shift_sequence) for i in range(num_windows + 1)]
bwd_index = torch.zeros((num_points,), dtype=torch.int64, device=tensor.device)
for i in range(num_windows):
mid = mids[i]
valid_start = split[i]
valid_end = split[i + 1]
padded_start = math.floor(mid - 0.5 * window_size)
padded_end = padded_start + window_size
fwd_indices.append(to_ordered[torch.arange(padded_start, padded_end, device=tensor.device) % num_points])
offset += valid_start - padded_start
bwd_index.scatter_(0, fwd_indices[-1][valid_start-padded_start:valid_end-padded_start], torch.arange(offset, offset + valid_end - valid_start, device=tensor.device))
offset += padded_end - valid_start
fwd_indices[-1] += s.start
seq_lens.extend([window_size] * num_windows)
seq_batch_indices.extend([bi] * num_windows)
bwd_indices.append(bwd_index + offsets[-1])
offsets.append(offsets[-1] + num_windows * window_size)
fwd_indices = torch.cat(fwd_indices)
bwd_indices = torch.cat(bwd_indices)
return fwd_indices, bwd_indices, seq_lens, seq_batch_indices
def sparse_serialized_scaled_dot_product_self_attention(
qkv: SparseTensor,
window_size: int,
serialize_mode: SerializeMode = SerializeMode.Z_ORDER,
shift_sequence: int = 0,
shift_window: Tuple[int, int, int] = (0, 0, 0)
) -> SparseTensor:
"""
Apply serialized scaled dot product self attention to a sparse tensor.
Args:
qkv (SparseTensor): [N, *, 3, H, C] sparse tensor containing Qs, Ks, and Vs.
window_size (int): The window size to use.
serialize_mode (SerializeMode): The serialization mode to use.
shift_sequence (int): The shift of serialized sequence.
shift_window (Tuple[int, int, int]): The shift of serialized coordinates.
shift (int): The shift to use.
"""
assert len(qkv.shape) == 4 and qkv.shape[1] == 3, f"Invalid shape for qkv, got {qkv.shape}, expected [N, *, 3, H, C]"
serialization_spatial_cache_name = f'serialization_{serialize_mode}_{window_size}_{shift_sequence}_{shift_window}'
serialization_spatial_cache = qkv.get_spatial_cache(serialization_spatial_cache_name)
if serialization_spatial_cache is None:
fwd_indices, bwd_indices, seq_lens, seq_batch_indices = calc_serialization(qkv, window_size, serialize_mode, shift_sequence, shift_window)
qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, seq_batch_indices))
else:
fwd_indices, bwd_indices, seq_lens, seq_batch_indices = serialization_spatial_cache
M = fwd_indices.shape[0]
T = qkv.feats.shape[0]
H = qkv.feats.shape[2]
C = qkv.feats.shape[3]
qkv_feats = qkv.feats[fwd_indices] # [M, 3, H, C]
if DEBUG:
start = 0
qkv_coords = qkv.coords[fwd_indices]
for i in range(len(seq_lens)):
assert (qkv_coords[start:start+seq_lens[i], 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch"
start += seq_lens[i]
if all([seq_len == window_size for seq_len in seq_lens]):
B = len(seq_lens)
N = window_size
qkv_feats = qkv_feats.reshape(B, N, 3, H, C)
if ATTN == 'xformers':
q, k, v = qkv_feats.unbind(dim=2) # [B, N, H, C]
out = xops.memory_efficient_attention(q, k, v) # [B, N, H, C]
elif ATTN == 'flash_attn':
out = flash_attn.flash_attn_qkvpacked_func(qkv_feats) # [B, N, H, C]
else:
raise ValueError(f"Unknown attention module: {ATTN}")
out = out.reshape(B * N, H, C) # [M, H, C]
else:
if ATTN == 'xformers':
q, k, v = qkv_feats.unbind(dim=1) # [M, H, C]
q = q.unsqueeze(0) # [1, M, H, C]
k = k.unsqueeze(0) # [1, M, H, C]
v = v.unsqueeze(0) # [1, M, H, C]
mask = xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens)
out = xops.memory_efficient_attention(q, k, v, mask)[0] # [M, H, C]
elif ATTN == 'flash_attn':
cu_seqlens = torch.cat([torch.tensor([0]), torch.cumsum(torch.tensor(seq_lens), dim=0)], dim=0) \
.to(qkv.device).int()
out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, cu_seqlens, max(seq_lens)) # [M, H, C]
out = out[bwd_indices] # [T, H, C]
if DEBUG:
qkv_coords = qkv_coords[bwd_indices]
assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch"
return qkv.replace(out)
|