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from typing import *
import torch
import math
from .. import SparseTensor
from .. import config
__all__ = [
'sparse_windowed_scaled_dot_product_self_attention',
'sparse_windowed_scaled_dot_product_cross_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.
(torch.Tensor): Sequence lengths.
(dict): Attn func args.
"""
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 = [i + j for i, j in zip(tensor.spatial_shape, shift_window)]
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)
mask = seq_lens != 0
seq_lens = seq_lens[mask]
if config.ATTN == 'xformers':
if 'xops' not in globals():
import xformers.ops as xops
attn_func_args = {
'attn_bias': xops.fmha.BlockDiagonalMask.from_seqlens(seq_lens)
}
elif config.ATTN == 'flash_attn':
attn_func_args = {
'cu_seqlens': torch.cat([torch.tensor([0], device=tensor.device), torch.cumsum(seq_lens, dim=0)], dim=0).int(),
'max_seqlen': torch.max(seq_lens)
}
return fwd_indices, bwd_indices, seq_lens, attn_func_args
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.
Returns:
(SparseTensor): [N, *, H, C] sparse tensor containing the output features.
"""
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'windowed_attention_{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, attn_func_args = calc_window_partition(qkv, window_size, shift_window)
qkv.register_spatial_cache(serialization_spatial_cache_name, (fwd_indices, bwd_indices, seq_lens, attn_func_args))
else:
fwd_indices, bwd_indices, seq_lens, attn_func_args = serialization_spatial_cache
qkv_feats = qkv.feats[fwd_indices] # [M, 3, H, C]
if config.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[:, 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 config.ATTN == 'xformers':
if 'xops' not in globals():
import xformers.ops as xops
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]
out = xops.memory_efficient_attention(q, k, v, **attn_func_args)[0] # [M, H, C]
elif config.ATTN == 'flash_attn':
if 'flash_attn' not in globals():
import flash_attn
out = flash_attn.flash_attn_varlen_qkvpacked_func(qkv_feats, **attn_func_args) # [M, H, C]
out = out[bwd_indices] # [T, H, C]
if config.DEBUG:
qkv_coords = qkv_coords[bwd_indices]
assert torch.equal(qkv_coords, qkv.coords), "SparseWindowedScaledDotProductSelfAttention: coordinate mismatch"
return qkv.replace(out)
def sparse_windowed_scaled_dot_product_cross_attention(
q: SparseTensor,
kv: SparseTensor,
q_window_size: int,
kv_window_size: int,
q_shift_window: Tuple[int, int, int] = (0, 0, 0),
kv_shift_window: Tuple[int, int, int] = (0, 0, 0),
) -> SparseTensor:
"""
Apply windowed scaled dot product cross attention to two sparse tensors.
Args:
q (SparseTensor): [N, *, H, C] sparse tensor containing Qs.
kv (SparseTensor): [N, *, 2, H, C] sparse tensor containing Ks and Vs.
q_window_size (int): The window size to use for Qs.
kv_window_size (int): The window size to use for Ks and Vs.
q_shift_window (Tuple[int, int, int]): The shift of serialized coordinates for Qs.
kv_shift_window (Tuple[int, int, int]): The shift of serialized coordinates for Ks and Vs.
Returns:
(SparseTensor): [N, *, H, C] sparse tensor containing the output features.
"""
assert len(q.shape) == 3, f"Invalid shape for q, got {q.shape}, expected [N, *, H, C]"
assert len(kv.shape) == 4 and kv.shape[1] == 2, f"Invalid shape for kv, got {kv.shape}, expected [N, *, 2, H, C]"
q_serialization_spatial_cache_name = f'windowed_attention_{q_window_size}_{q_shift_window}'
q_serialization_spatial_cache = q.get_spatial_cache(q_serialization_spatial_cache_name)
if q_serialization_spatial_cache is None:
q_fwd_indices, q_bwd_indices, q_seq_lens, q_attn_func_args = calc_window_partition(q, q_window_size, q_shift_window)
q.register_spatial_cache(q_serialization_spatial_cache_name, (q_fwd_indices, q_bwd_indices, q_seq_lens, q_attn_func_args))
else:
q_fwd_indices, q_bwd_indices, q_seq_lens, q_attn_func_args = q_serialization_spatial_cache
kv_serialization_spatial_cache_name = f'windowed_attention_{kv_window_size}_{kv_shift_window}'
kv_serialization_spatial_cache = kv.get_spatial_cache(kv_serialization_spatial_cache_name)
if kv_serialization_spatial_cache is None:
kv_fwd_indices, kv_bwd_indices, kv_seq_lens, kv_attn_func_args = calc_window_partition(kv, kv_window_size, kv_shift_window)
kv.register_spatial_cache(kv_serialization_spatial_cache_name, (kv_fwd_indices, kv_bwd_indices, kv_seq_lens, kv_attn_func_args))
else:
kv_fwd_indices, kv_bwd_indices, kv_seq_lens, kv_attn_func_args = kv_serialization_spatial_cache
assert len(q_seq_lens) == len(kv_seq_lens), "Number of sequences in q and kv must match"
q_feats = q.feats[q_fwd_indices] # [M, H, C]
kv_feats = kv.feats[kv_fwd_indices] # [M, 2, H, C]
if config.ATTN == 'xformers':
if 'xops' not in globals():
import xformers.ops as xops
k, v = kv_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(q_seq_lens, kv_seq_lens)
out = xops.memory_efficient_attention(q, k, v, attn_bias=mask)[0] # [M, H, C]
elif config.ATTN == 'flash_attn':
if 'flash_attn' not in globals():
import flash_attn
out = flash_attn.flash_attn_varlen_kvpacked_func(q_feats, kv_feats,
cu_seqlens_q=q_attn_func_args['cu_seqlens'], cu_seqlens_k=kv_attn_func_args['cu_seqlens'],
max_seqlen_q=q_attn_func_args['max_seqlen'], max_seqlen_k=kv_attn_func_args['max_seqlen'],
) # [M, H, C]
out = out[q_bwd_indices] # [T, H, C]
return q.replace(out)
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