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)