# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # For a list of all contributors, visit: # https://github.com/fla-org/flash-linear-attention/graphs/contributors import torch import triton import triton.language as tl from kda._fla.ops.backends import dispatch from kda._fla.ops.utils.cache import fla_cache_autotune from kda._fla.ops.utils.index import prepare_chunk_indices from kda._fla.utils import autotune_cache_kwargs, check_shared_mem, input_guard BS_LIST = [32, 64] if check_shared_mem() else [16, 32] @triton.heuristics({ 'HAS_SCALE': lambda args: args['scale'] is not None, 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, }) @fla_cache_autotune( configs=[ triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8] ], key=['B', 'H', 'BT', 'IS_VARLEN', 'REVERSE'], **autotune_cache_kwargs, ) @triton.jit(do_not_specialize=['T']) def chunk_local_cumsum_scalar_kernel( s, o, scale, cu_seqlens, chunk_indices, T, B: tl.constexpr, H: tl.constexpr, BT: tl.constexpr, REVERSE: tl.constexpr, HAS_SCALE: tl.constexpr, IS_VARLEN: tl.constexpr, ): i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64) i_b, i_h = i_bh // H, i_bh % H if IS_VARLEN: i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) T = eos - bos else: bos, eos = i_b * T, i_b * T + T o_t = i_t * BT + tl.arange(0, BT) m_t = o_t < T p_s = s + bos*H + i_h + o_t * H p_o = o + bos*H + i_h + o_t * H # [BT] b_s = tl.load(p_s, mask=m_t, other=0.0).to(tl.float32) if REVERSE: b_o = tl.cumsum(b_s, axis=0, reverse=True) else: b_o = tl.cumsum(b_s, axis=0) if HAS_SCALE: b_o *= scale tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_t) @triton.heuristics({ 'HAS_SCALE': lambda args: args['scale'] is not None, 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, }) @fla_cache_autotune( configs=[ triton.Config({'BS': BS}, num_warps=num_warps) for BS in BS_LIST for num_warps in [2, 4, 8] ], key=['B', 'H', 'S', 'BT', 'IS_VARLEN', 'REVERSE'], **autotune_cache_kwargs, ) @triton.jit(do_not_specialize=['T']) def chunk_local_cumsum_vector_kernel( s, o, scale, cu_seqlens, chunk_indices, T, B: tl.constexpr, H: tl.constexpr, S: tl.constexpr, BT: tl.constexpr, BS: tl.constexpr, REVERSE: tl.constexpr, HAS_SCALE: tl.constexpr, IS_VARLEN: tl.constexpr, ): i_s, i_t, i_bh = tl.program_id(0), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) i_b, i_h = i_bh // H, i_bh % H if IS_VARLEN: i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) T = eos - bos else: bos, eos = i_b * T, i_b * T + T o_t = i_t * BT + tl.arange(0, BT) o_s = i_s * BS + tl.arange(0, BS) m_s = (o_t[:, None] < T) & (o_s[None, :] < S) p_s = s + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] p_o = o + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] # [BT, BS] b_s = tl.load(p_s, mask=m_s, other=0.0).to(tl.float32) if REVERSE: b_o = tl.cumsum(b_s, axis=0, reverse=True) else: b_o = tl.cumsum(b_s, axis=0) if HAS_SCALE: b_o *= scale tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_s) @triton.heuristics({ 'HAS_SCALE': lambda args: args['scale'] is not None, 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, }) @triton.autotune( configs=[ triton.Config({'BT': BT}, num_warps=num_warps, num_stages=num_stages) for BT in [32, 64, 128, 256] for num_warps in [2, 4, 8] for num_stages in [1, 2, 3, 4] ], key=['B', 'H', 'IS_VARLEN', 'REVERSE'], **autotune_cache_kwargs, ) @triton.jit(do_not_specialize=['T']) def chunk_global_cumsum_scalar_kernel( s, o, scale, cu_seqlens, T, B: tl.constexpr, H: tl.constexpr, BT: tl.constexpr, REVERSE: tl.constexpr, HAS_SCALE: tl.constexpr, IS_VARLEN: tl.constexpr, ): i_nh = tl.program_id(0).to(tl.int64) i_n, i_h = i_nh // H, i_nh % H if IS_VARLEN: bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) else: bos, eos = i_n * T, i_n * T + T T = eos - bos b_z = tl.zeros([], dtype=tl.float32) NT = tl.cdiv(T, BT) for i_c in range(NT): i_t = NT - 1 - i_c if REVERSE else i_c o_t = i_t * BT + tl.arange(0, BT) m_t = o_t < T p_s = s + bos*H + i_h + o_t * H p_o = o + bos*H + i_h + o_t * H b_s = tl.load(p_s, mask=m_t, other=0.0).to(tl.float32) if REVERSE: b_o = tl.cumsum(b_s, axis=0, reverse=True) else: b_o = tl.cumsum(b_s, axis=0) b_ss = tl.sum(b_s, 0) b_o += b_z if i_c >= 0: b_z += b_ss if HAS_SCALE: b_o *= scale tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_t) @triton.heuristics({ 'HAS_SCALE': lambda args: args['scale'] is not None, 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, }) @triton.autotune( configs=[ triton.Config({'BT': BT}, num_warps=num_warps, num_stages=num_stages) for BT in [16, 32, 64, 128] for num_warps in [2, 4, 8] for num_stages in [1, 2, 3, 4] ], key=['B', 'H', 'S', 'IS_VARLEN', 'REVERSE'], **autotune_cache_kwargs, ) @triton.jit(do_not_specialize=['T']) def chunk_global_cumsum_vector_kernel( s, o, scale, cu_seqlens, T, B: tl.constexpr, H: tl.constexpr, S: tl.constexpr, BT: tl.constexpr, BS: tl.constexpr, REVERSE: tl.constexpr, HAS_SCALE: tl.constexpr, IS_VARLEN: tl.constexpr, ): i_s, i_nh = tl.program_id(0), tl.program_id(1).to(tl.int64) i_n, i_h = i_nh // H, i_nh % H if IS_VARLEN: bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) else: bos, eos = i_n * T, i_n * T + T T = eos - bos b_z = tl.zeros([BS], dtype=tl.float32) NT = tl.cdiv(T, BT) for i_c in range(NT): i_t = NT - 1 - i_c if REVERSE else i_c o_t = i_t * BT + tl.arange(0, BT) o_s = i_s * BS + tl.arange(0, BS) m_s = (o_t[:, None] < T) & (o_s[None, :] < S) p_s = s + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] p_o = o + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] # [BT, BS] b_s = tl.load(p_s, mask=m_s, other=0.0).to(tl.float32) if REVERSE: b_c = b_z[None, :] + tl.cumsum(b_s, axis=0, reverse=True) else: b_c = b_z[None, :] + tl.cumsum(b_s, axis=0) if HAS_SCALE: b_c *= scale tl.store(p_o, b_c.to(p_o.dtype.element_ty), mask=m_s) b_z += tl.sum(b_s, 0) def chunk_local_cumsum_scalar( g: torch.Tensor, chunk_size: int, reverse: bool = False, scale: float = None, cu_seqlens: torch.Tensor | None = None, output_dtype: torch.dtype | None = torch.float, chunk_indices: torch.LongTensor | None = None, **kwargs, ) -> torch.Tensor: if 'head_first' in kwargs: raise DeprecationWarning( "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", ) B, T, H = g.shape assert chunk_size == 2**(chunk_size.bit_length()-1), "chunk_size must be a power of 2" BT = chunk_size if chunk_indices is None and cu_seqlens is not None: chunk_indices = prepare_chunk_indices(cu_seqlens, BT) NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype) grid = (NT, B * H) chunk_local_cumsum_scalar_kernel[grid]( s=g_org, o=g, scale=scale, cu_seqlens=cu_seqlens, chunk_indices=chunk_indices, T=T, B=B, H=H, BT=BT, REVERSE=reverse, ) return g def chunk_local_cumsum_vector( g: torch.Tensor, chunk_size: int, reverse: bool = False, scale: float = None, cu_seqlens: torch.Tensor | None = None, output_dtype: torch.dtype | None = torch.float, chunk_indices: torch.LongTensor | None = None, **kwargs, ) -> torch.Tensor: if 'head_first' in kwargs: raise DeprecationWarning( "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", ) B, T, H, S = g.shape BT = chunk_size if chunk_indices is None and cu_seqlens is not None: chunk_indices = prepare_chunk_indices(cu_seqlens, BT) NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) assert chunk_size == 2**(chunk_size.bit_length()-1), "chunk_size must be a power of 2" g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype) def grid(meta): return (triton.cdiv(meta['S'], meta['BS']), NT, B * H) # keep cummulative normalizer in fp32 # this kernel is equivalent to # g = g.view(B, H, NT, BT, -1).cumsum(-2).view(B, H, T, -1) chunk_local_cumsum_vector_kernel[grid]( s=g_org, o=g, scale=scale, cu_seqlens=cu_seqlens, chunk_indices=chunk_indices, T=T, B=B, H=H, S=S, BT=BT, REVERSE=reverse, ) return g @input_guard def chunk_global_cumsum_scalar( s: torch.Tensor, reverse: bool = False, cu_seqlens: torch.Tensor | None = None, scale: float = None, output_dtype: torch.dtype | None = torch.float, **kwargs, ) -> torch.Tensor: if 'head_first' in kwargs: raise DeprecationWarning( "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", ) B, T, H = s.shape N = len(cu_seqlens) - 1 if cu_seqlens is not None else B z = torch.empty_like(s, dtype=output_dtype or s.dtype) grid = (N * H,) chunk_global_cumsum_scalar_kernel[grid]( s=s, o=z, scale=scale, cu_seqlens=cu_seqlens, T=T, B=B, H=H, REVERSE=reverse, ) return z @input_guard def chunk_global_cumsum_vector( s: torch.Tensor, reverse: bool = False, cu_seqlens: torch.Tensor | None = None, scale: float = None, output_dtype: torch.dtype | None = torch.float, **kwargs, ) -> torch.Tensor: if 'head_first' in kwargs: raise DeprecationWarning( "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", ) B, T, H, S = s.shape N = len(cu_seqlens) - 1 if cu_seqlens is not None else B BS = min(32, triton.next_power_of_2(S)) z = torch.empty_like(s, dtype=output_dtype or s.dtype) grid = (triton.cdiv(S, BS), N * H) chunk_global_cumsum_vector_kernel[grid]( s=s, o=z, scale=scale, cu_seqlens=cu_seqlens, T=T, B=B, H=H, S=S, BS=BS, REVERSE=reverse, ) return z @input_guard @dispatch('utils') def chunk_global_cumsum( s: torch.Tensor, reverse: bool = False, cu_seqlens: torch.Tensor | None = None, scale: float = None, output_dtype: torch.dtype | None = torch.float, **kwargs, ) -> torch.Tensor: if 'head_first' in kwargs: raise DeprecationWarning( "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", ) if cu_seqlens is not None: assert s.shape[0] == 1, "Only batch size 1 is supported when cu_seqlens are provided" if len(s.shape) == 3: return chunk_global_cumsum_scalar( s=s, reverse=reverse, cu_seqlens=cu_seqlens, scale=scale, output_dtype=output_dtype, ) elif len(s.shape) == 4: return chunk_global_cumsum_vector( s=s, reverse=reverse, cu_seqlens=cu_seqlens, scale=scale, output_dtype=output_dtype, ) else: raise ValueError( f"Unsupported input shape {s.shape}, " f"which should be [B, T, H] or [B, T, H, D]", ) @input_guard @dispatch('utils') def chunk_local_cumsum( g: torch.Tensor, chunk_size: int, reverse: bool = False, scale: float = None, cu_seqlens: torch.Tensor | None = None, output_dtype: torch.dtype | None = torch.float, chunk_indices: torch.LongTensor | None = None, **kwargs, ) -> torch.Tensor: if 'head_first' in kwargs: raise DeprecationWarning( "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", ) if cu_seqlens is not None: assert g.shape[0] == 1, "Only batch size 1 is supported when cu_seqlens are provided" if len(g.shape) == 3: return chunk_local_cumsum_scalar( g=g, chunk_size=chunk_size, reverse=reverse, scale=scale, cu_seqlens=cu_seqlens, output_dtype=output_dtype, chunk_indices=chunk_indices, ) elif len(g.shape) == 4: return chunk_local_cumsum_vector( g=g, chunk_size=chunk_size, reverse=reverse, scale=scale, cu_seqlens=cu_seqlens, output_dtype=output_dtype, chunk_indices=chunk_indices, ) else: raise ValueError( f"Unsupported input shape {g.shape}, " f"which should be (B, T, H) or (B, T, H, D)", )