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from typing import *
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_windowed_scaled_dot_product_self_attention',
]
def calc_window_partition(
tensor: SparseTensor,
window_size: Union[int, Tuple[int, ...]],
shift_window: Union[int, Tuple[int, ...]] = 0
) -> Tuple[torch.Tensor, torch.Tensor, List[int], 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.
shift_window (Tuple[int, ...]): The shift of serialized coordinates.
Returns:
(torch.Tensor): Forwards indices.
(torch.Tensor): Backwards indices.
(List[int]): Sequence lengths.
(List[int]): Sequence batch indices.
"""
DIM = tensor.coords.shape[1] - 1
shift_window = (shift_window,) * DIM if isinstance(shift_window, int) else shift_window
window_size = (window_size,) * DIM if isinstance(window_size, int) else window_size
shifted_coords = tensor.coords.clone().detach()
shifted_coords[:, 1:] += torch.tensor(shift_window, device=tensor.device, dtype=torch.int32).unsqueeze(0)
MAX_COORDS = shifted_coords[:, 1:].max(dim=0).values.tolist()
NUM_WINDOWS = [math.ceil((mc + 1) / ws) for mc, ws in zip(MAX_COORDS, window_size)]
OFFSET = torch.cumprod(torch.tensor([1] + NUM_WINDOWS[::-1]), dim=0).tolist()[::-1]
shifted_coords[:, 1:] //= torch.tensor(window_size, device=tensor.device, dtype=torch.int32).unsqueeze(0)
shifted_indices = (shifted_coords * torch.tensor(OFFSET, device=tensor.device, dtype=torch.int32).unsqueeze(0)).sum(dim=1)
fwd_indices = torch.argsort(shifted_indices)
bwd_indices = torch.empty_like(fwd_indices)
bwd_indices[fwd_indices] = torch.arange(fwd_indices.shape[0], device=tensor.device)
seq_lens = torch.bincount(shifted_indices)
seq_batch_indices = torch.arange(seq_lens.shape[0], device=tensor.device, dtype=torch.int32) // OFFSET[0]
mask = seq_lens != 0
seq_lens = seq_lens[mask].tolist()
seq_batch_indices = seq_batch_indices[mask].tolist()
return fwd_indices, bwd_indices, seq_lens, seq_batch_indices
def sparse_windowed_scaled_dot_product_self_attention(
qkv: SparseTensor,
window_size: int,
shift_window: Tuple[int, int, int] = (0, 0, 0)
) -> SparseTensor:
"""
Apply windowed 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.
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'window_partition_{window_size}_{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_window_partition(qkv, window_size, 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)):
seq_coords = qkv_coords[start:start+seq_lens[i]]
assert (seq_coords[:, 0] == seq_batch_indices[i]).all(), f"SparseWindowedScaledDotProductSelfAttention: batch index mismatch"
assert (seq_coords[:, 1:].max(dim=0).values - seq_coords[:, 1:].min(dim=0).values < window_size).all(), \
f"SparseWindowedScaledDotProductSelfAttention: window size exceeded"
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)
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