| |
|
|
|
|
| import torch |
| import triton |
| import triton.language as tl |
|
|
| from fla.ops.utils import prepare_chunk_indices |
| from fla.ops.utils.op import exp |
| from fla.utils import autotune_cache_kwargs, check_shared_mem |
|
|
| BK_LIST = [32, 64] if check_shared_mem() else [16, 32] |
| BV_LIST = [64, 128] if check_shared_mem('ampere') else [16, 32] |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| }) |
| @triton.autotune( |
| configs=[ |
| triton.Config({'BK': BK, 'BV': BV}, num_warps=num_warps, num_stages=num_stages) |
| for BK in BK_LIST |
| for BV in BV_LIST |
| for num_warps in [2, 4, 8] |
| for num_stages in [2, 3, 4] |
| ], |
| key=['BT'], |
| **autotune_cache_kwargs, |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def chunk_kda_bwd_kernel_inter( |
| q, |
| k, |
| v, |
| g, |
| h, |
| do, |
| dh, |
| dq, |
| dk, |
| dv, |
| dw, |
| dg, |
| cu_seqlens, |
| chunk_indices, |
| scale, |
| T, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_k, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| i_b, i_h = i_bh // H, i_bh % H |
| if IS_VARLEN: |
| i_tg = i_t |
| i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32) |
| bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32) |
| T = eos - bos |
| NT = tl.cdiv(T, BT) |
| else: |
| NT = tl.cdiv(T, BT) |
| i_tg = i_b * NT + i_t |
| bos, eos = i_b * T, i_b * T + T |
| o_k = i_k * BK + tl.arange(0, BK) |
| m_k = o_k < K |
|
|
| q += (bos * H + i_h) * K |
| k += (bos * H + i_h) * K |
| v += (bos * H + i_h) * V |
| g += (bos * H + i_h) * K |
| h += (i_tg * H + i_h) * K*V |
| do += (bos * H + i_h) * V |
| dh += (i_tg * H + i_h) * K*V |
| dq += (bos * H + i_h) * K |
| dk += (bos * H + i_h) * K |
| dw += (bos * H + i_h) * K |
| dv += (bos * H + i_h) * V |
| dg += (bos * H + i_h) * K |
|
|
| p_g = tl.make_block_ptr(g, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| p_gn = g + (min(T, i_t * BT + BT) - 1) * H*K + o_k |
| b_gn = tl.load(p_gn, mask=m_k, other=0) |
| b_dq = tl.zeros([BT, BK], dtype=tl.float32) |
| b_dk = tl.zeros([BT, BK], dtype=tl.float32) |
| b_dw = tl.zeros([BT, BK], dtype=tl.float32) |
| b_dgk = tl.zeros([BK], dtype=tl.float32) |
|
|
| for i_v in range(tl.cdiv(V, BV)): |
| p_v = tl.make_block_ptr(v, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_do = tl.make_block_ptr(do, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_h = tl.make_block_ptr(h, (V, K), (1, V), (i_v * BV, i_k * BK), (BV, BK), (0, 1)) |
| p_dh = tl.make_block_ptr(dh, (V, K), (1, V), (i_v * BV, i_k * BK), (BV, BK), (0, 1)) |
| |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| b_do = tl.load(p_do, boundary_check=(0, 1)) |
| |
| b_h = tl.load(p_h, boundary_check=(0, 1)) |
| b_dh = tl.load(p_dh, boundary_check=(0, 1)) |
|
|
| |
| b_dgk += tl.sum(b_h * b_dh, axis=0) |
| |
| b_dq += tl.dot(b_do, b_h.to(b_do.dtype)) |
| b_dk += tl.dot(b_v, b_dh.to(b_v.dtype)) |
|
|
| p_dv = tl.make_block_ptr(dv, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| b_dv = tl.load(p_dv, boundary_check=(0, 1)) |
| b_dw += tl.dot(b_dv.to(b_v.dtype), b_h.to(b_v.dtype)) |
|
|
| p_dw = tl.make_block_ptr(dw, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| tl.store(p_dw, -b_dw.to(p_dw.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| b_dgk *= exp(b_gn) |
| b_dq *= scale |
| b_dq = b_dq * exp(b_g) |
| b_dk = b_dk * exp(b_gn[None, :] - b_g) |
|
|
| p_q = tl.make_block_ptr(q, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dq = tl.make_block_ptr(dq, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dk = tl.make_block_ptr(dk, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dg = tl.make_block_ptr(dg, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_dgk += tl.sum(b_dk * b_k, axis=0) |
| b_dg = b_q * b_dq - b_k * b_dk |
| b_dg = b_dg - tl.cumsum(b_dg, axis=0) + tl.sum(b_dg, axis=0)[None, :] + b_dgk[None, :] |
|
|
| tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), boundary_check=(0, 1)) |
| tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1)) |
| tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| def chunk_kda_bwd_dqkwg( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| w: torch.Tensor, |
| v: torch.Tensor, |
| h: torch.Tensor, |
| g: torch.Tensor, |
| do: torch.Tensor, |
| dh: torch.Tensor, |
| dv: torch.Tensor, |
| scale: float | None = None, |
| cu_seqlens: torch.LongTensor | None = None, |
| chunk_size: int = 64, |
| ): |
| B, T, H, K, V = *k.shape, v.shape[-1] |
| BT = min(chunk_size, max(16, triton.next_power_of_2(T))) |
|
|
| chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) if cu_seqlens is not None else None |
| NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) |
|
|
| dq = torch.empty_like(q, dtype=torch.float) |
| dk = torch.empty_like(k, dtype=torch.float) |
| dw = torch.empty_like(w) |
| dg = torch.empty_like(g) |
| def grid(meta): return (triton.cdiv(K, meta['BK']), NT, B * H) |
| chunk_kda_bwd_kernel_inter[grid]( |
| q=q, |
| k=k, |
| v=v, |
| g=g, |
| h=h, |
| do=do, |
| dh=dh, |
| dq=dq, |
| dk=dk, |
| dv=dv, |
| dw=dw, |
| dg=dg, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| scale=scale, |
| T=T, |
| H=H, |
| K=K, |
| V=V, |
| BT=BT, |
| ) |
| return dq, dk, dw, dg |
|
|
