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)