|
|
| import torch |
| import triton |
| import triton.language as tl |
|
|
| from fla.ops.utils import prepare_chunk_indices |
|
|
|
|
| @triton.heuristics({ |
| "USE_G": lambda args: args['g_cumsum'] is not None, |
| "IS_VARLEN": lambda args: args['offsets'] is not None, |
| }) |
| @triton.jit(do_not_specialize=['T']) |
| def intra_chunk_preprocess_fwd_kernel( |
| q, |
| k, |
| v, |
| w, |
| beta, |
| g_cumsum, |
| o, |
| A, |
| L, |
| M, |
| w2, |
| q_new, |
| k_new, |
| scale, |
| indices, |
| offsets, |
| T, |
| H: tl.constexpr, |
| G: tl.constexpr, |
| HQ: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr, |
| BT: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| USE_G: tl.constexpr, |
| ): |
| i_t, i_nh = tl.program_id(0), tl.program_id(1) |
| i_n, i_hq = i_nh // HQ, i_nh % 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: |
| bos, eos = i_n * T, i_n * T + T |
|
|
| sm_scale = scale * 1.44269504 |
| |
| A += (bos*H + i_h) * BT |
| q += (bos*HQ + i_hq) * K |
| q_new += (bos*HQ + i_hq) * K |
| k += (bos*H + i_h) * K |
| k_new += (bos*H + i_h) * K |
| w2 += (bos*H + i_h) * K |
| w += (bos*H + i_h) * K |
| v += (bos*H + i_h) * V |
| o += (bos*HQ + i_hq) * V |
| beta += (bos*H + i_h) |
| if USE_G: |
| g_cumsum += (bos*HQ + i_hq) |
| L += (bos*HQ + i_hq) |
| M += (bos*HQ + i_hq) |
|
|
| p_q = tl.make_block_ptr(q, (T, K), (HQ*K, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| p_k = tl.make_block_ptr(k, (K, T), (1, H*K), (0, i_t * BT), (BK, BT), (0, 1)) |
| p_w = tl.make_block_ptr(w, (T, K), (H*K, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| p_v = tl.make_block_ptr(v, (T, V), (H*V, 1), (i_t * BT, 0), (BT, BV), (1, 0)) |
| p_beta = tl.make_block_ptr(beta, (T, ), (H, ), (i_t * BT, ), (BT, ), (0, )) |
| p_T = tl.make_block_ptr(A, (T, BT), (BT*H, 1), (i_t * BT, 0), (BT, BT), (1, 0)) |
|
|
| b_beta = tl.load(p_beta, boundary_check=(0, )) |
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
| b_kt = tl.load(p_k, boundary_check=(0, 1)) |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| b_w = tl.load(p_w, boundary_check=(0, 1)) |
| b_T = tl.load(p_T, boundary_check=(0, 1)) |
| b_T = b_T * b_beta[None, :] |
|
|
| o_i = tl.arange(0, BT) |
| m_t = o_i[:, None] >= o_i[None, :] |
|
|
| b_qw = tl.where(m_t, tl.dot(b_q, tl.trans(b_w.to(b_q.dtype))), 0).to(b_q.dtype) |
| b_qwT = tl.dot(b_qw, b_T.to(b_q.dtype)).to(b_q.dtype) |
| b_wbk = tl.where(o_i[:, None] > o_i[None, :], tl.dot(b_w.to(b_q.dtype), b_kt), 0).to(b_q.dtype) |
| b_A = tl.where(m_t, tl.dot(b_q, b_kt) - tl.dot(b_qwT.to(b_q.dtype), b_wbk), 0) |
|
|
| b_q = b_q.to(tl.float32) - tl.dot(b_qwT, b_w.to(b_q.dtype)) |
| p_q_new = tl.make_block_ptr(q_new, (T, K), (K*HQ, 1), (i_t * BT, 0), (BT, K), (1, 0)) |
| tl.store(p_q_new, b_q.to(p_q_new.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| if i_hq % G == 0: |
| b_Twb = tl.dot(b_T, b_w) |
| p_w2 = tl.make_block_ptr(w2, (T, K), (K*H, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| tl.store(p_w2, b_Twb.to(p_w2.dtype.element_ty), boundary_check=(0, 1)) |
| b_T_wbk = tl.dot(b_T.to(b_kt.dtype), b_wbk).to(b_kt.dtype) |
| p_k_new = tl.make_block_ptr(k_new, (K, T), (1, K*H), (0, i_t * BT), (BK, BT), (0, 1)) |
| tl.store(p_k_new, (b_kt - tl.dot(tl.trans(b_w.to(b_kt.dtype)), b_T_wbk)).to(p_k_new.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| if USE_G: |
| p_g_cumsum = tl.make_block_ptr(g_cumsum, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, )) |
| b_g_cumsum = tl.load(p_g_cumsum, boundary_check=(0, )) |
| b_A = b_A + (b_g_cumsum[:, None] - b_g_cumsum[None, :]) |
| b_A = tl.where((i_t * BT + tl.arange(0, BT) < T)[:, None], b_A, float("-inf")) |
|
|
| b_qkT_softmax = tl.where(o_i[:, None] >= o_i[None, :], b_A * sm_scale, float("-inf")) |
| m_i = tl.max(b_qkT_softmax, 1) |
| b_qkT_softmax = tl.math.exp2(b_qkT_softmax - m_i[:, None]) |
| l_i = tl.sum(b_qkT_softmax, 1) |
| b_o = tl.dot(b_qkT_softmax.to(b_v.dtype), b_v) |
| p_o = tl.make_block_ptr(o, (T, V), (V*HQ, 1), (i_t * BT, 0), (BT, BV), (1, 0)) |
| tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1)) |
| p_l = tl.make_block_ptr(L, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, )) |
| p_m = tl.make_block_ptr(M, (T, ), (HQ, ), (i_t * BT, ), (BT, ), (0, )) |
| tl.store(p_m, m_i.to(p_m.dtype.element_ty), boundary_check=(0,)) |
| tl.store(p_l, l_i.to(p_l.dtype.element_ty), boundary_check=(0,)) |
|
|
|
|
|
|
| def intra_chunk_preprocess_fwd_fn(q, k, v, w, beta, g_cumsum, A, scale, BT, cu_seqlens): |
| HQ = q.shape[-2] |
| B, T, H, K = k.shape |
| V = v.shape[-1] |
| q_new = torch.empty_like(q, dtype=torch.float32) |
| k_new = torch.empty_like(k) |
| o = torch.empty(B, T, HQ, V, device=q.device, dtype=torch.float32) |
|
|
| 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) |
| grid = (NT, B*HQ) |
| L = torch.empty(B, T, HQ, dtype=torch.float32, device=q.device) |
| M = torch.empty(B, T, HQ, dtype=torch.float32, device=q.device) |
| w2 = torch.empty_like(w) |
| G = HQ//H |
|
|
| intra_chunk_preprocess_fwd_kernel[grid]( |
| q=q, |
| k=k, |
| v=v, |
| w=w, |
| beta=beta, |
| g_cumsum=g_cumsum, |
| o=o, |
| A=A, |
| L=L, |
| M=M, |
| w2=w2, |
| q_new=q_new, |
| k_new=k_new, |
| scale=scale, |
| offsets=cu_seqlens, |
| indices=indices, |
| T=T, |
| H=H, |
| G=G, |
| HQ=HQ, |
| K=K, |
| V=V, |
| BK=triton.next_power_of_2(K), |
| BV=triton.next_power_of_2(V), |
| BT=BT, |
| num_warps=4 if BT == 64 else 2, |
| ) |
| return q_new, k_new, w2, o, L, M |
|
|
