148 lines
5.5 KiB
Python
148 lines
5.5 KiB
Python
import math
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import torch
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from .block_sparse_attn import block_sparse_attn
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from .triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
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from .triton_kernels.st_attn_triton import sliding_tile_attention_triton
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from .triton_kernels.index import map_to_index
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# Try to load the C++ extension
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try:
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from fastvideo_kernel._C import fastvideo_kernel_ops
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sta_fwd = getattr(fastvideo_kernel_ops, "sta_fwd", None)
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block_sparse_fwd = getattr(fastvideo_kernel_ops, "block_sparse_fwd", None)
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block_sparse_bwd = getattr(fastvideo_kernel_ops, "block_sparse_bwd", None)
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except ImportError:
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sta_fwd = None
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block_sparse_fwd = None
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block_sparse_bwd = None
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def sliding_tile_attention(
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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window_size: list,
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text_length: int,
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has_text: bool = True,
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seq_shape: str = "30x48x80",
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) -> torch.Tensor:
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# Check if the specific op is available
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if sta_fwd is None:
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return sliding_tile_attention_triton(
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q, k, v, window_size, text_length, has_text, seq_shape
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)
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seq_length = q.shape[2]
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shape_map = {"30x48x80": 1, "36x48x48": 2, "18x48x80": 3}
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if has_text:
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target_size = math.ceil(seq_length / 384) * 384
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pad_size = target_size - seq_length
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if pad_size > 0:
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q = torch.cat([q, q[:, :, -pad_size:]], dim=2)
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k = torch.cat([k, k[:, :, -pad_size:]], dim=2)
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v = torch.cat([v, v[:, :, -pad_size:]], dim=2)
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output = torch.empty_like(q)
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flag = shape_map[seq_shape]
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for head_idx, (t, h, w) in enumerate(window_size):
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sta_fwd(
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q[:, head_idx:head_idx + 1], k[:, head_idx:head_idx + 1],
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v[:, head_idx:head_idx + 1], output[:, head_idx:head_idx + 1],
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t, h, w, text_length, False, has_text, flag
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)
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if has_text:
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sta_fwd(q, k, v, output, 3, 3, 3, text_length, True, True, flag)
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return output[:, :, :seq_length]
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def video_sparse_attn(
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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variable_block_sizes: torch.Tensor,
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q_variable_block_sizes: torch.Tensor,
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topk: int,
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block_size: int | tuple = 64,
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compress_attn_weight: torch.Tensor = None,
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) -> torch.Tensor:
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if isinstance(block_size, int):
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block_size = (block_size, block_size, block_size)
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block_elements = block_size[0] * block_size[1] * block_size[2]
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batch, heads, q_seq_len, dim = q.shape
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kv_seq_len = k.shape[2]
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if v.shape[2] != kv_seq_len:
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raise ValueError(
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f"Expected k and v to have the same sequence length, got "
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f"k.shape[2]={kv_seq_len}, v.shape[2]={v.shape[2]}"
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)
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if k.shape[0] != batch or v.shape[0] != batch or k.shape[1] != heads or v.shape[1] != heads:
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raise ValueError("Expected q/k/v to have the same batch and head dimensions.")
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if q_seq_len % block_elements != 0 or kv_seq_len % block_elements != 0:
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raise ValueError(
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f"q_seq_len and kv_seq_len must be divisible by block_elements={block_elements}, "
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f"got q_seq_len={q_seq_len}, kv_seq_len={kv_seq_len}"
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)
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q_num_blocks = q_seq_len // block_elements
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kv_num_blocks = kv_seq_len // block_elements
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if variable_block_sizes.numel() != kv_num_blocks:
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raise ValueError(
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f"variable_block_sizes must have length kv_num_blocks={kv_num_blocks}, "
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f"got {variable_block_sizes.numel()}"
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)
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if q_variable_block_sizes.numel() != q_num_blocks:
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raise ValueError(
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f"q_variable_block_sizes must have length q_num_blocks={q_num_blocks}, "
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f"got {q_variable_block_sizes.numel()}"
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)
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# Compression branch
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q_c = q.view(batch, heads, q_num_blocks, block_elements, dim)
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k_c = k.view(batch, heads, kv_num_blocks, block_elements, dim)
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v_c = v.view(batch, heads, kv_num_blocks, block_elements, dim)
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q_c = (q_c.float().sum(dim=3) / q_variable_block_sizes.view(1, 1, -1, 1)).to(
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q.dtype)
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k_c = (k_c.float().sum(dim=3) / variable_block_sizes.view(1, 1, -1, 1)).to(
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k.dtype)
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v_c = (v_c.float().sum(dim=3) / variable_block_sizes.view(1, 1, -1, 1)).to(
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v.dtype)
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scores = torch.matmul(q_c, k_c.transpose(-2, -1)) / (dim**0.5)
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attn = torch.softmax(scores, dim=-1)
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out_c = torch.matmul(attn, v_c)
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out_c = out_c.view(batch, heads, q_num_blocks, 1, dim)
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out_c = out_c.repeat(1, 1, 1, block_elements,
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1).view(batch, heads, q_seq_len, dim)
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# Sparse branch
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topk_idx = torch.topk(scores, topk, dim=-1).indices
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mask = torch.zeros_like(scores,
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dtype=torch.bool).scatter_(-1, topk_idx, True)
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idx, num = map_to_index(mask)
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if block_sparse_fwd is not None:
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# Use autograd-enabled wrapper so backward works (and still uses SM90 kernel when available)
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out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]
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else:
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if q_seq_len != kv_seq_len:
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raise RuntimeError(
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"q/k have different lengths, but the compiled CUDA kernel (block_sparse_fwd) "
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"is not available. The Triton fallback currently requires q and k/v to have "
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"the same padded length."
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)
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# Triton-only forward (kept for environments without the wrapper deps)
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out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num, variable_block_sizes)
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if compress_attn_weight is not None:
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return out_c * compress_attn_weight + out_s
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return out_c + out_s
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