| import torch |
| import triton |
| import triton.language as tl |
|
|
| from fla.ops.utils import prepare_chunk_indices |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['offsets'] is not None, |
| 'USE_GATE': lambda args: args['g_cumsum'] is not None, |
| }) |
| @triton.jit(do_not_specialize=['T']) |
| def parallel_path_bwd_intra_chunk_kernel( |
| q, k, v, g_cumsum, w1, w2, |
| L, D, |
| dq, dq_new, dk, dv, dw1, dw2, do, dg_cumsum, |
| offsets, indices, |
| T, scale, |
| G: tl.constexpr, HQ: tl.constexpr, H: tl.constexpr, |
| K: tl.constexpr, V: tl.constexpr, BK: tl.constexpr, BV: tl.constexpr, |
| BT: tl.constexpr, S: tl.constexpr, |
| IS_VARLEN: tl.constexpr, USE_GATE: tl.constexpr, |
| ): |
| i_t, i_bh = tl.program_id(0), tl.program_id(1) |
| i_b, i_hq = i_bh // HQ, i_bh % HQ |
| i_h = i_hq // G |
|
|
| if IS_VARLEN: |
| i_n, i_t = tl.load(indices + i_t * 2).to(tl.int32), tl.load(indices + i_t * 2 + 1).to(tl.int32) |
| bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32) |
| T = eos - bos |
| else: |
| i_n = i_b |
| bos, eos = i_n * T, i_n * T + T |
|
|
| |
| k += (bos * H + i_h) * K |
| v += (bos * H + i_h) * V |
| w1 += (bos * H + i_h) * K |
| w2 += (bos * H + i_h) * K |
|
|
| q += (bos * HQ + i_hq) * K |
| dq += (bos * HQ + i_hq) * K |
| dq_new += (bos * HQ + i_hq) * K |
| dk += (bos * HQ + i_hq) * K |
| dv += (bos * HQ + i_hq) * V |
| do += (bos * HQ + i_hq) * V |
| dw1 += (bos * HQ + i_hq) * K |
| dw2 += (bos * HQ + i_hq) * K |
| L += (bos * HQ + i_hq) |
| D += (bos * HQ + i_hq) |
| if USE_GATE: |
| g_cumsum += (bos * HQ + i_hq) |
| dg_cumsum += (bos * HQ + i_hq) |
|
|
| |
| sm_scale = scale * 1.44269504 |
|
|
| p_do = tl.make_block_ptr(do, (T, V), (HQ*V, 1), (i_t * BT, 0), (BT, BV), (1, 0)) |
| |
| b_do = tl.load(p_do, boundary_check=(0, 1)) |
|
|
| p_delta = tl.make_block_ptr(D, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, )) |
| b_delta = tl.load(p_delta, boundary_check=(0, )) |
| p_l = tl.make_block_ptr(L, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, )) |
| b_l = tl.load(p_l, boundary_check=(0, )) |
|
|
| b_dq = tl.zeros([BT, BK], dtype=tl.float32) |
| p_dq = tl.make_block_ptr(dq, (T, K), (HQ*K, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| b_dq += tl.load(p_dq, boundary_check=(0, 1)) |
| p_q = tl.make_block_ptr(q, (T, K), (HQ*K, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
|
|
| if USE_GATE: |
| p_gq_cumsum = tl.make_block_ptr(g_cumsum, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, )) |
| b_gq_cumsum = tl.load(p_gq_cumsum, boundary_check=(0, )) |
| b_dgq = tl.zeros([BT ], dtype=tl.float32) |
| else: |
| b_dgq = None |
|
|
| curr_start = (tl.floor(i_t * BT / S).to(tl.int32) * S).to(tl.int32) |
|
|
| for offset in range(curr_start, i_t * BT, BT): |
| mask = offset + tl.arange(0, BT) < T |
| p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (offset, 0), (BT, BK), (1, 0)) |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_q_tmp = tl.zeros([BT, BK], dtype=tl.float32) |
| b_q_tmp += b_q |
| for i_t_small in range(i_t * BT - BT, offset, -BT): |
| p_w1 = tl.make_block_ptr(w1, (T, K), (H*K, 1), (i_t_small, 0), (BT, BK), (1, 0)) |
| b_w1 = tl.load(p_w1, boundary_check=(0, 1)) |
| p_w2 = tl.make_block_ptr(w2, (T, K), (H*K, 1), (i_t_small, 0), (BT, BK), (1, 0)) |
| b_w2 = tl.load(p_w2, boundary_check=(0, 1)) |
| b_A_tmp = tl.dot(b_q_tmp.to(b_w1.dtype), tl.trans(b_w1)) |
| b_q_tmp -= tl.dot(b_A_tmp.to(b_w1.dtype), b_w2) |
| b_q2 = b_q_tmp.to(b_k.dtype) |
| b_A = tl.dot(b_q2, tl.trans(b_k)) |
| if USE_GATE: |
| p_gk_cumsum = tl.make_block_ptr(g_cumsum, (T, ), (HQ, ), (offset, ), (BT, ), (0, )) |
| b_gk_cumsum = tl.load(p_gk_cumsum, boundary_check=(0, )) |
| b_A = b_A + b_gq_cumsum[:, None] - b_gk_cumsum[None, :] |
| b_A = tl.where((i_t * BT + tl.arange(0, BT) < T)[:, None], b_A, float("-inf")) |
| b_A_softmax = tl.math.exp2(b_A * sm_scale - b_l[:, None]) |
| b_dv = tl.dot(tl.trans(b_A_softmax.to(b_do.dtype)), b_do) |
| tl.atomic_add( |
| dv + ((offset + tl.arange(0, BT)) * HQ * V)[:, None] + tl.arange(0, BV)[None, :], |
| b_dv.to(dv.dtype.element_ty), |
| mask=mask[:, None], |
| sem='relaxed', |
| ) |
| p_v = tl.make_block_ptr(v, (T, V), (V*H, 1), (offset, 0), (BT, BV), (1, 0)) |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| b_dp = tl.dot(b_do, tl.trans(b_v)) |
| b_dA = ((b_dp - b_delta[:, None]) * b_A_softmax * scale) |
| if USE_GATE: |
| b_dgk = -tl.sum(b_dA, axis=0) |
| tl.atomic_add(dg_cumsum + (offset + tl.arange(0, BT)) * HQ, b_dgk, mask=mask, sem='relaxed') |
| b_dgq += tl.sum(b_dA, axis=1) |
| b_dA = b_dA.to(b_v.dtype) |
| b_dk = tl.dot(tl.trans(b_dA), b_q2) |
| tl.atomic_add(dk + (offset + tl.arange(0, BT))[:, None] * HQ*K + tl.arange(0, |
| BK)[None, :], b_dk, mask=mask[:, None], sem='relaxed') |
| p_w1 = tl.make_block_ptr(w1, (T, K), (H*K, 1), (offset, 0), (BT, BK), (1, 0)) |
| b_w1 = tl.load(p_w1, boundary_check=(0, 1)) |
| p_w2 = tl.make_block_ptr(w2, (T, K), (H*K, 1), (offset, 0), (BT, BK), (1, 0)) |
| b_w2 = tl.load(p_w2, boundary_check=(0, 1)) |
| b_dA2 = tl.dot(b_dq.to(b_w2.dtype), tl.trans(b_w2)).to(b_v.dtype) |
| b_A2 = tl.dot(b_q2.to(b_w1.dtype), tl.trans(b_w1)).to(b_v.dtype) |
| b_dw2 = -tl.dot(tl.trans(b_A2), b_dq.to(b_v.dtype)) |
| tl.atomic_add(dw2 + (offset + tl.arange(0, BT))[:, None] * HQ*K + tl.arange(0, |
| BK)[None, :], b_dw2, mask=mask[:, None], sem='relaxed') |
| b_dw1 = -tl.dot(tl.trans(b_dA2), b_q2.to(b_v.dtype)) |
| tl.atomic_add(dw1 + (offset + tl.arange(0, BT))[:, None] * HQ*K + tl.arange(0, |
| BK)[None, :], b_dw1, mask=mask[:, None], sem='relaxed') |
| b_dq -= tl.dot(b_dA2, b_w1.to(b_v.dtype)) |
| b_dq += tl.dot(b_dA.to(b_k.dtype), b_k) |
|
|
| p_dq_new = tl.make_block_ptr(dq_new, (T, K), (HQ*K, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| tl.store(p_dq_new, b_dq.to(dq_new.dtype.element_ty), boundary_check=(0, 1)) |
| mask = i_t * BT + tl.arange(0, BT) < T |
| if USE_GATE: |
| tl.atomic_add(dg_cumsum + (i_t * BT + tl.arange(0, BT)) * HQ, b_dgq, mask=mask, sem='relaxed') |
|
|
|
|
| def parallel_path_bwd_intra_chunk_fn( |
| q, k, v, g_cumsum, w1, w2, |
| dq, dk, dv, dg_cumsum, dw1, dw2, do, |
| scale, L, D, |
| cu_seqlens, |
| S, BT, |
| ): |
| assert dk.dtype == dv.dtype == dw1.dtype == dw2.dtype == torch.float32, 'atomic_add requires float32' |
| B, T, HQ, K = q.shape |
| assert dk.shape == dq.shape |
|
|
| V = v.shape[-1] |
| H = k.shape[-2] |
| G = HQ // H |
| indices = prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None |
| NT = triton.cdiv(T, BT) if cu_seqlens is None else len(indices) |
| dq_new = torch.empty_like(dq, dtype=q.dtype) |
| parallel_path_bwd_intra_chunk_kernel[(NT, B*HQ)]( |
| q=q, k=k, v=v, g_cumsum=g_cumsum, |
| w1=w1, w2=w2, L=L, D=D, |
| dq=dq, dq_new=dq_new, dk=dk, dv=dv, dw1=dw1, dw2=dw2, |
| do=do, dg_cumsum=dg_cumsum, |
| offsets=cu_seqlens, indices=indices, |
| T=T, S=S, BT=BT, scale=scale, |
| G=G, HQ=HQ, H=H, K=K, V=V, |
| BK=triton.next_power_of_2(K), BV=triton.next_power_of_2(V), |
| ) |
| return dq_new, dk, dv, dw1, dw2, dg_cumsum |
|
|
