diff --git a/flash_triton.py b/flash_triton.py new file mode 100644 index 0000000..0f6d485 --- /dev/null +++ b/flash_triton.py @@ -0,0 +1,1066 @@ +""" +*Experimental* implementation of FlashAttention in Triton. + +We use the FlashAttention implementation from Phil Tillet a starting point. +https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py + +Changes: +- Implement both causal and non-causal attention. +- Implement both self-attention and cross-attention. +- Support arbitrary seqlens (not just multiples of 128), for both forward and backward. +- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward. +- Support attention bias. +- Speed up the forward pass a bit, and only store the LSE instead of m and l. +- Make the backward for d=128 much faster by reducing register spilling. +- Optionally parallelize the backward pass across seqlen_k, to deal with the case of +small batch size * nheads. + +Caution: +- This is an *experimental* implementation. The forward pass should be quite robust but +I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler). +- This implementation has only been tested on A100. +- If you plan to use headdim other than 64 and 128, you should test for race conditions +(due to the Triton compiler), as done in tests/test_flash_attn.py +"test_flash_attn_triton_race_condition". I've tested and fixed many race conditions +for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident +that there are none left for other head dimensions. + +Differences between this Triton version and the CUDA version: +- Triton version doesn't support dropout. +- Triton forward is generally faster than CUDA forward, while Triton backward is +generally slower than CUDA backward. Overall Triton forward + backward is slightly slower +than CUDA forward + backward. +- Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor). +- Triton version supports attention bias, while CUDA version doesn't. +""" + +import math + +import torch +import triton +import triton.language as tl + + +# Disabling autotune for now, set num_warps=4 if headdim=64 and num_warps=8 if headdim=128 +# @triton.autotune( +# configs=[ +# triton.Config({"BLOCK_M": 128, "BLOCK_N": 128}, num_warps=4, num_stages=1), +# # This config has a race condition when EVEN_M == False, disabling it for now. +# # triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=1), +# ], +# key=['CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL', 'BLOCK_HEADDIM'] +# ) +@triton.heuristics( + { + "EVEN_M": lambda args: args["seqlen_q"] % args["BLOCK_M"] == 0, + "EVEN_N": lambda args: args["seqlen_k"] % args["BLOCK_N"] == 0, + "EVEN_HEADDIM": lambda args: args["headdim"] == args["BLOCK_HEADDIM"], + } +) +@triton.jit +def _fwd_kernel( + Q, + K, + V, + Bias, + Out, + Lse, + TMP, # NOTE: TMP is a scratchpad buffer to workaround a compiler bug + softmax_scale, + stride_qb, + stride_qh, + stride_qm, + stride_kb, + stride_kh, + stride_kn, + stride_vb, + stride_vh, + stride_vn, + stride_bb, + stride_bh, + stride_bm, + stride_ob, + stride_oh, + stride_om, + nheads, + seqlen_q, + seqlen_k, + seqlen_q_rounded, + headdim, + CACHE_KEY_SEQLEN_Q, + CACHE_KEY_SEQLEN_K, + BIAS_TYPE: tl.constexpr, + IS_CAUSAL: tl.constexpr, + BLOCK_HEADDIM: tl.constexpr, + EVEN_M: tl.constexpr, + EVEN_N: tl.constexpr, + EVEN_HEADDIM: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, +): + start_m = tl.program_id(0) + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + # off_b = tl.program_id(1) + # off_h = tl.program_id(2) + # off_hb = off_b * nheads + off_h + # initialize offsets + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_n = tl.arange(0, BLOCK_N) + offs_d = tl.arange(0, BLOCK_HEADDIM) + # Initialize pointers to Q, K, V + # Adding parenthesis around indexing might use int32 math instead of int64 math? + # https://github.com/openai/triton/issues/741 + # I'm seeing a tiny bit of difference (5-7us) + q_ptrs = Q + off_b * stride_qb + off_h * stride_qh + (offs_m[:, None] * stride_qm + offs_d[None, :]) + k_ptrs = K + off_b * stride_kb + off_h * stride_kh + (offs_n[:, None] * stride_kn + offs_d[None, :]) + v_ptrs = V + off_b * stride_vb + off_h * stride_vh + (offs_n[:, None] * stride_vn + offs_d[None, :]) + if BIAS_TYPE == "vector": + b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n + elif BIAS_TYPE == "matrix": + b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + (offs_m[:, None] * stride_bm + offs_n[None, :]) + # initialize pointer to m and l + t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m + lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") + acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32) + # load q: it will stay in SRAM throughout + # [2022-10-30] TD: Triton bug - in the case of EVEN_M=True and EVEN_N=False, if we just call + # tl.load(q_ptrs), we get the wrong output! + if EVEN_M & EVEN_N: + if EVEN_HEADDIM: + q = tl.load(q_ptrs) + else: + q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + else: + if EVEN_HEADDIM: + q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0) + else: + q = tl.load(q_ptrs, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + # loop over k, v and update accumulator + end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k) + for start_n in range(0, end_n, BLOCK_N): + start_n = tl.multiple_of(start_n, BLOCK_N) + # -- compute qk ---- + if EVEN_N & EVEN_M: # If we just do "if EVEN_N", there seems to be some race condition + if EVEN_HEADDIM: + k = tl.load(k_ptrs + start_n * stride_kn) + else: + k = tl.load(k_ptrs + start_n * stride_kn, mask=offs_d[None, :] < headdim, other=0.0) + else: + if EVEN_HEADDIM: + k = tl.load(k_ptrs + start_n * stride_kn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0) + else: + k = tl.load( + k_ptrs + start_n * stride_kn, + mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), + other=0.0, + ) + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + qk += tl.dot(q, k, trans_b=True) + # Trying to combine the two masks seem to make the result wrong + if not EVEN_N: # Need to mask out otherwise the softmax is wrong + qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float("-inf")) + if IS_CAUSAL: + qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float("-inf")) + if BIAS_TYPE != "none": + if BIAS_TYPE == "vector": + if EVEN_N: + bias = tl.load(b_ptrs + start_n).to(tl.float32) + else: + bias = tl.load(b_ptrs + start_n, mask=(start_n + offs_n) < seqlen_k, other=0.0).to(tl.float32) + bias = bias[None, :] + elif BIAS_TYPE == "matrix": + if EVEN_M & EVEN_N: + bias = tl.load(b_ptrs + start_n).to(tl.float32) + else: + bias = tl.load( + b_ptrs + start_n, + mask=(offs_m[:, None] < seqlen_q) & ((start_n + offs_n)[None, :] < seqlen_k), + other=0.0, + ).to(tl.float32) + # Slightly faster to multiply the softmax_scale in the tl.exp below since the compiler + # can then fuse the mult and add into an fma instruction. But if we have bias we need to + # to multiply with softmax_scale here. + qk = qk * softmax_scale + bias + m_ij = tl.maximum(tl.max(qk, 1), lse_i) + + m_ij = tl.where(m_ij == float("-inf"), 0, m_ij) + p = tl.exp(qk - m_ij[:, None]) + else: + m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i) + p = tl.exp(qk * softmax_scale - m_ij[:, None]) + l_ij = tl.sum(p, 1) + # p = tl.where(p==float("-inf"), 0, p) + # l_ij = tl.maximum(tl.sum(p, 1),-1e16) + # scale acc_o + acc_o_scale = tl.exp(m_i - m_ij) + # mask_sum = tl.sum(bias == float("-inf"), axis=1) == BLOCK_M + # acc_o_scale = tl.where(mask_sum, 0, acc_o_scale) + # # -- update output accumulator -- + # BUG: have to store and immediately load + tl.store(t_ptrs, acc_o_scale) + acc_o_scale = tl.load(t_ptrs) + acc_o = acc_o * acc_o_scale[:, None] + # update acc_o + if EVEN_N & EVEN_M: # If we just do "if EVEN_N", there seems to be some race condition + if EVEN_HEADDIM: + v = tl.load(v_ptrs + start_n * stride_vn) + else: + v = tl.load(v_ptrs + start_n * stride_vn, mask=offs_d[None, :] < headdim, other=0.0) + else: + if EVEN_HEADDIM: + v = tl.load(v_ptrs + start_n * stride_vn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0) + else: + v = tl.load( + v_ptrs + start_n * stride_vn, + mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), + other=0.0, + ) + p = p.to(v.dtype) + acc_o += tl.dot(p, v) + + # -- update statistics + m_i = m_ij + l_i_new = tl.exp(lse_i - m_ij) + l_ij + lse_i = m_ij + tl.log(l_i_new) + lse_i = tl.where(lse_i == float("-inf"), 0, lse_i) + o_scale = tl.exp(m_i - lse_i) + # BUG: have to store and immediately load + tl.store(t_ptrs, o_scale) + o_scale = tl.load(t_ptrs) + acc_o = acc_o * o_scale[:, None] + # rematerialize offsets to save registers + start_m = tl.program_id(0) + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + # write back l and m + lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m + tl.store(lse_ptrs, lse_i) + # initialize pointers to output + offs_d = tl.arange(0, BLOCK_HEADDIM) + out_ptrs = Out + off_b * stride_ob + off_h * stride_oh + (offs_m[:, None] * stride_om + offs_d[None, :]) + if EVEN_M: + if EVEN_HEADDIM: + tl.store(out_ptrs, acc_o) + else: + tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim) + else: + if EVEN_HEADDIM: + tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q) + else: + tl.store(out_ptrs, acc_o, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim)) + + +@triton.jit +def _bwd_preprocess_do_o_dot( + Out, + DO, + Delta, + stride_ob, + stride_oh, + stride_om, + stride_dob, + stride_doh, + stride_dom, + nheads, + seqlen_q, + seqlen_q_rounded, + headdim, + BLOCK_M: tl.constexpr, + BLOCK_HEADDIM: tl.constexpr, +): + start_m = tl.program_id(0) + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + # initialize offsets + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_d = tl.arange(0, BLOCK_HEADDIM) + # load + o = tl.load( + Out + off_b * stride_ob + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :], + mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), + other=0.0, + ).to(tl.float32) + do = tl.load( + DO + off_b * stride_dob + off_h * stride_doh + offs_m[:, None] * stride_dom + offs_d[None, :], + mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), + other=0.0, + ).to(tl.float32) + delta = tl.sum(o * do, axis=1) + # write-back + tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta) + + +@triton.jit +def _bwd_store_dk_dv( + dk_ptrs, + dv_ptrs, + dk, + dv, + offs_n, + offs_d, + seqlen_k, + headdim, + EVEN_M: tl.constexpr, + EVEN_N: tl.constexpr, + EVEN_HEADDIM: tl.constexpr, +): + # [2022-11-01] TD: Same bug. In the case of EVEN_N=True and EVEN_M=False, + # if we just call tl.store(dv_ptrs), there's a race condition + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + tl.store(dv_ptrs, dv) + tl.store(dk_ptrs, dk) + else: + tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim) + tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim) + else: + if EVEN_HEADDIM: + tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k) + tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k) + else: + tl.store(dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim)) + tl.store(dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim)) + + +@triton.jit +def _bwd_kernel_one_col_block( + start_n, + Q, + K, + V, + Bias, + DO, + DQ, + DK, + DV, + LSE, + D, + softmax_scale, + stride_qm, + stride_kn, + stride_vn, + stride_bm, + stride_dom, + stride_dqm, + stride_dkn, + stride_dvn, + seqlen_q, + seqlen_k, + headdim, + ATOMIC_ADD: tl.constexpr, + BIAS_TYPE: tl.constexpr, + IS_CAUSAL: tl.constexpr, + BLOCK_HEADDIM: tl.constexpr, + EVEN_M: tl.constexpr, + EVEN_N: tl.constexpr, + EVEN_HEADDIM: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, +): + # We need to make sure begin_m is a multiple of BLOCK_M (not BLOCK_N) + begin_m = 0 if not IS_CAUSAL else ((start_n * BLOCK_N) // BLOCK_M) * BLOCK_M + # initialize row/col offsets + offs_qm = begin_m + tl.arange(0, BLOCK_M) + offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N) + offs_m = tl.arange(0, BLOCK_M) + offs_d = tl.arange(0, BLOCK_HEADDIM) + # initialize pointers to value-like data + q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :]) + k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :]) + v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :]) + do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :]) + dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :]) + if BIAS_TYPE == "vector": + b_ptrs = Bias + offs_n + elif BIAS_TYPE == "matrix": + b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :]) + # initialize dv and dk + dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32) + dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32) + # There seems to be some problem with Triton pipelining that makes results wrong for + # headdim=64, seqlen=(113, 255), bias_type='matrix'. In this case the for loop + # may have zero step, and pipelining with the bias matrix could screw it up. + # So we just exit early. + if begin_m >= seqlen_q: + dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :]) + dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :]) + _bwd_store_dk_dv( + dk_ptrs, + dv_ptrs, + dk, + dv, + offs_n, + offs_d, + seqlen_k, + headdim, + EVEN_M=EVEN_M, + EVEN_N=EVEN_N, + EVEN_HEADDIM=EVEN_HEADDIM, + ) + return + # k and v stay in SRAM throughout + # [2022-10-30] TD: Same bug as the fwd. In the case of EVEN_N=True and EVEN_M=False, + # if we just call tl.load(k_ptrs), we get the wrong output! + if EVEN_N & EVEN_M: + if EVEN_HEADDIM: + k = tl.load(k_ptrs) + v = tl.load(v_ptrs) + else: + k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + else: + if EVEN_HEADDIM: + k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0) + v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0) + else: + k = tl.load(k_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + v = tl.load(v_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0) + # loop over rows + num_block_m = tl.cdiv(seqlen_q, BLOCK_M) + for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M): + start_m = tl.multiple_of(start_m, BLOCK_M) + offs_m_curr = start_m + offs_m + # load q, k, v, do on-chip + # Same bug as below. Otherwise gives wrong result for headdim=40, seqlen=(128, 117) + if EVEN_M & EVEN_HEADDIM: + q = tl.load(q_ptrs) + else: + if EVEN_HEADDIM: + q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0) + else: + q = tl.load(q_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + # recompute p = softmax(qk, dim=-1).T + qk = tl.dot(q, k, trans_b=True) + # Trying to combine the two masks seem to make the result wrong + if not EVEN_N: # Need to mask out otherwise the softmax is wrong + qk = tl.where(offs_n[None, :] < seqlen_k, qk, float("-inf")) + if IS_CAUSAL: + qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), qk, float("-inf")) + if BIAS_TYPE != "none": + tl.debug_barrier() # Race condition otherwise + if BIAS_TYPE == "vector": + if EVEN_N: + bias = tl.load(b_ptrs).to(tl.float32) + else: + bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to(tl.float32) + bias = bias[None, :] + elif BIAS_TYPE == "matrix": + if EVEN_M & EVEN_N: + bias = tl.load(b_ptrs).to(tl.float32) + else: + bias = tl.load( + b_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_n[None, :] < seqlen_k), other=0.0 + ).to(tl.float32) + qk = qk * softmax_scale + bias + # There seems to be a race condition when headdim=48/96, and dq, dk, dv are wrong. + # Also wrong for headdim=64. + if not (EVEN_M & EVEN_HEADDIM): + tl.debug_barrier() + lse_i = tl.load(LSE + offs_m_curr) + if BIAS_TYPE == "none": + p = tl.exp(qk * softmax_scale - lse_i[:, None]) + else: + p = tl.exp(qk - lse_i[:, None]) + # compute dv + # [2022-10-30] TD: A Triton bug: if EVEN_M=True and EVEN_HEADDIM=False, if we call + # do = tl.load(do_ptrs, mask=offs_d[None, :] < headdim, other=0.0), we get wrong outputs + # in the case of headdim=48/96, seqlen_q & seqlen_k >= 512. If headdim=40 or seqlen < 512, + # the output is correct. + if EVEN_M & EVEN_HEADDIM: + do = tl.load(do_ptrs) + else: + # [2022-11-01] TD: Triton bug, there's a race condition if we just use m_mask and not d_mask. + do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0) + # if EVEN_M: + # if EVEN_HEADDIM: + # do = tl.load(do_ptrs) + # else: + # do = tl.load(do_ptrs, mask=offs_d[None, :] < headdim, other=0.0) + # else: + # if EVEN_HEADDIM: + # do = tl.load(do_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0) + # else: + # do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) + # & (offs_d[None, :] < headdim), other=0.0) + dv += tl.dot(p.to(do.dtype), do, trans_a=True) + # compute dp = dot(v, do) + # There seems to be a race condition when headdim=48/96, and dq, dk are wrong. + # Also wrong for headdim=128, seqlen=(108, 256), and ATOMIC_ADD=True + # Also wrong for headdim=64, seqlen=(1023, 1024), and ATOMIC_ADD=False + if not (EVEN_M & EVEN_HEADDIM): + tl.debug_barrier() + dp = tl.dot(do, v, trans_b=True) + # There's a race condition for headdim=48 + if not EVEN_HEADDIM: + tl.debug_barrier() + # compute ds = p * (dp - delta[:, None]) + # Putting the subtraction after the dp matmul (instead of before) is slightly faster + Di = tl.load(D + offs_m_curr) + # Converting ds to q.dtype here reduces register pressure and makes it much faster + # for BLOCK_HEADDIM=128 + ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype) + # compute dk = dot(ds.T, q) + dk += tl.dot(ds, q, trans_a=True) + # compute dq + if not (EVEN_M & EVEN_HEADDIM): # Otherewise there's a race condition when BIAS_TYPE='matrix' + tl.debug_barrier() + if not ATOMIC_ADD: + if EVEN_M & EVEN_HEADDIM: # Race condition if we just do EVEN_M + dq = tl.load(dq_ptrs, eviction_policy="evict_last") + dq += tl.dot(ds, k) + tl.store(dq_ptrs, dq, eviction_policy="evict_last") + else: + if EVEN_HEADDIM: + dq = tl.load(dq_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0, eviction_policy="evict_last") + dq += tl.dot(ds, k) + tl.store(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q, eviction_policy="evict_last") + else: + dq = tl.load( + dq_ptrs, + mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), + other=0.0, + eviction_policy="evict_last", + ) + dq += tl.dot(ds, k) + tl.store( + dq_ptrs, + dq, + mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), + eviction_policy="evict_last", + ) + else: # If we're parallelizing across the seqlen_k dimension + dq = tl.dot(ds, k) + if EVEN_M & EVEN_HEADDIM: # Race condition if we just do EVEN_M + tl.atomic_add(dq_ptrs, dq) + else: + if EVEN_HEADDIM: + tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q) + else: + tl.atomic_add(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim)) + # increment pointers + dq_ptrs += BLOCK_M * stride_dqm + q_ptrs += BLOCK_M * stride_qm + do_ptrs += BLOCK_M * stride_dom + if BIAS_TYPE == "matrix": + b_ptrs += BLOCK_M * stride_bm + # write-back + dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :]) + dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :]) + _bwd_store_dk_dv( + dk_ptrs, + dv_ptrs, + dk, + dv, + offs_n, + offs_d, + seqlen_k, + headdim, + EVEN_M=EVEN_M, + EVEN_N=EVEN_N, + EVEN_HEADDIM=EVEN_HEADDIM, + ) + + +def init_to_zero(name): + return lambda nargs: nargs[name].zero_() + + +@triton.autotune( + configs=[ + triton.Config( + {"BLOCK_M": 128, "BLOCK_N": 128, "SEQUENCE_PARALLEL": False}, + num_warps=8, + num_stages=1, + pre_hook=init_to_zero("DQ"), + ), + triton.Config( + {"BLOCK_M": 128, "BLOCK_N": 128, "SEQUENCE_PARALLEL": True}, + num_warps=8, + num_stages=1, + pre_hook=init_to_zero("DQ"), + ), + # Other configs seem to give wrong results when seqlen_q % 128 != 0, disabling them for now + # # Kernel is buggy (give wrong result) if we set BLOCK_m=128, BLOCK_n=64, num_warps=*4* + # triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "SEQUENCE_PARALLEL": False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')), + # triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "SEQUENCE_PARALLEL": True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')), + # triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "SEQUENCE_PARALLEL": False}, num_warps=4, num_stages=1, pre_hook=init_to_zero('DQ')), + # triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "SEQUENCE_PARALLEL": True}, num_warps=4, num_stages=1, pre_hook=init_to_zero('DQ')), + ], + key=["CACHE_KEY_SEQLEN_Q", "CACHE_KEY_SEQLEN_K", "BIAS_TYPE", "IS_CAUSAL", "BLOCK_HEADDIM"], +) +@triton.heuristics( + { + "EVEN_M": lambda args: args["seqlen_q"] % args["BLOCK_M"] == 0, + "EVEN_N": lambda args: args["seqlen_k"] % args["BLOCK_N"] == 0, + "EVEN_HEADDIM": lambda args: args["headdim"] == args["BLOCK_HEADDIM"], + } +) +@triton.jit +def _bwd_kernel( + Q, + K, + V, + Bias, + DO, + DQ, + DK, + DV, + LSE, + D, + softmax_scale, + stride_qb, + stride_qh, + stride_qm, + stride_kb, + stride_kh, + stride_kn, + stride_vb, + stride_vh, + stride_vn, + stride_bb, + stride_bh, + stride_bm, + stride_dob, + stride_doh, + stride_dom, + stride_dqb, + stride_dqh, + stride_dqm, + stride_dkb, + stride_dkh, + stride_dkn, + stride_dvb, + stride_dvh, + stride_dvn, + nheads, + seqlen_q, + seqlen_k, + seqlen_q_rounded, + headdim, + CACHE_KEY_SEQLEN_Q, + CACHE_KEY_SEQLEN_K, + BIAS_TYPE: tl.constexpr, + IS_CAUSAL: tl.constexpr, + BLOCK_HEADDIM: tl.constexpr, + SEQUENCE_PARALLEL: tl.constexpr, + EVEN_M: tl.constexpr, + EVEN_N: tl.constexpr, + EVEN_HEADDIM: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, +): + off_hb = tl.program_id(1) + off_b = off_hb // nheads + off_h = off_hb % nheads + # offset pointers for batch/head + Q += off_b * stride_qb + off_h * stride_qh + K += off_b * stride_kb + off_h * stride_kh + V += off_b * stride_vb + off_h * stride_vh + DO += off_b * stride_dob + off_h * stride_doh + DQ += off_b * stride_dqb + off_h * stride_dqh + DK += off_b * stride_dkb + off_h * stride_dkh + DV += off_b * stride_dvb + off_h * stride_dvh + if BIAS_TYPE != "none": + Bias += off_b * stride_bb + off_h * stride_bh + # pointer to row-wise quantities in value-like data + D += off_hb * seqlen_q_rounded + LSE += off_hb * seqlen_q_rounded + if not SEQUENCE_PARALLEL: + num_block_n = tl.cdiv(seqlen_k, BLOCK_N) + for start_n in range(0, num_block_n): + _bwd_kernel_one_col_block( + start_n, + Q, + K, + V, + Bias, + DO, + DQ, + DK, + DV, + LSE, + D, + softmax_scale, + stride_qm, + stride_kn, + stride_vn, + stride_bm, + stride_dom, + stride_dqm, + stride_dkn, + stride_dvn, + seqlen_q, + seqlen_k, + headdim, + ATOMIC_ADD=False, + BIAS_TYPE=BIAS_TYPE, + IS_CAUSAL=IS_CAUSAL, + BLOCK_HEADDIM=BLOCK_HEADDIM, + EVEN_M=EVEN_M, + EVEN_N=EVEN_N, + EVEN_HEADDIM=EVEN_HEADDIM, + BLOCK_M=BLOCK_M, + BLOCK_N=BLOCK_N, + ) + else: + start_n = tl.program_id(0) + _bwd_kernel_one_col_block( + start_n, + Q, + K, + V, + Bias, + DO, + DQ, + DK, + DV, + LSE, + D, + softmax_scale, + stride_qm, + stride_kn, + stride_vn, + stride_bm, + stride_dom, + stride_dqm, + stride_dkn, + stride_dvn, + seqlen_q, + seqlen_k, + headdim, + ATOMIC_ADD=True, + BIAS_TYPE=BIAS_TYPE, + IS_CAUSAL=IS_CAUSAL, + BLOCK_HEADDIM=BLOCK_HEADDIM, + EVEN_M=EVEN_M, + EVEN_N=EVEN_N, + EVEN_HEADDIM=EVEN_HEADDIM, + BLOCK_M=BLOCK_M, + BLOCK_N=BLOCK_N, + ) + + +def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None): + # shape constraints + batch, seqlen_q, nheads, d = q.shape + _, seqlen_k, _, _ = k.shape + assert k.shape == (batch, seqlen_k, nheads, d) + assert v.shape == (batch, seqlen_k, nheads, d) + assert d <= 128, "FlashAttention only support head dimensions up to 128" + assert q.dtype == k.dtype == v.dtype, "All tensors must have the same type" + assert q.dtype in [torch.float16, torch.bfloat16], "Only support fp16 and bf16" + assert q.is_cuda and k.is_cuda and v.is_cuda + softmax_scale = softmax_scale or 1.0 / math.sqrt(d) + + has_bias = bias is not None + bias_type = "none" + if has_bias: + assert bias.dtype in [q.dtype, torch.float] + assert bias.is_cuda + assert bias.dim() == 4 + if bias.stride(-1) != 1: + bias = bias.contiguous() + if bias.shape[2:] == (1, seqlen_k): + bias_type = "vector" + elif bias.shape[2:] == (seqlen_q, seqlen_k): + bias_type = "matrix" + else: + raise RuntimeError("Last 2 dimensions of bias must be (1, seqlen_k)" " or (seqlen_q, seqlen_k)") + bias = bias.expand(batch, nheads, seqlen_q, seqlen_k) + bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0) + + seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128 + lse = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32) + tmp = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32) + o = torch.empty_like(q) + + BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16) + BLOCK = 128 + num_warps = 4 if d <= 64 else 8 + grid = lambda META: (triton.cdiv(seqlen_q, META["BLOCK_M"]), batch * nheads) + _fwd_kernel[grid]( + q, + k, + v, + bias, + o, + lse, + tmp, + softmax_scale, + q.stride(0), + q.stride(2), + q.stride(1), + k.stride(0), + k.stride(2), + k.stride(1), + v.stride(0), + v.stride(2), + v.stride(1), + *bias_strides, + o.stride(0), + o.stride(2), + o.stride(1), + nheads, + seqlen_q, + seqlen_k, + seqlen_q_rounded, + d, + seqlen_q // 32, + seqlen_k // 32, # key for triton cache (limit number of compilations) + # Can't use kwargs here because triton autotune expects key to be args, not kwargs + # IS_CAUSAL=causal, BLOCK_HEADDIM=d, + bias_type, + causal, + BLOCK_HEADDIM, + BLOCK_M=BLOCK, + BLOCK_N=BLOCK, + num_warps=num_warps, + num_stages=1, + ) + return o, lse, softmax_scale # softmax_scale could have been updated + + +def _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None): + # Make sure that the last dimension is contiguous + if do.stride(-1) != 1: + do = do.contiguous() + batch, seqlen_q, nheads, d = q.shape + _, seqlen_k, _, _ = k.shape + # assert d in {16, 32, 64, 128} + assert d <= 128 + seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128 + assert lse.shape == (batch, nheads, seqlen_q_rounded) + assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1 + assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1 + softmax_scale = softmax_scale or 1.0 / math.sqrt(d) + # dq_accum = torch.zeros_like(q, dtype=torch.float32) + dq_accum = torch.empty_like(q, dtype=torch.float32) + delta = torch.empty_like(lse) + # delta = torch.zeros_like(lse) + + BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16) + grid = lambda META: (triton.cdiv(seqlen_q, META["BLOCK_M"]), batch * nheads) + _bwd_preprocess_do_o_dot[grid]( + o, + do, + delta, + o.stride(0), + o.stride(2), + o.stride(1), + do.stride(0), + do.stride(2), + do.stride(1), + nheads, + seqlen_q, + seqlen_q_rounded, + d, + BLOCK_M=128, + BLOCK_HEADDIM=BLOCK_HEADDIM, + ) + + has_bias = bias is not None + bias_type = "none" + if has_bias: + assert bias.dtype in [q.dtype, torch.float] + assert bias.is_cuda + assert bias.dim() == 4 + assert bias.stride(-1) == 1 + if bias.shape[2:] == (1, seqlen_k): + bias_type = "vector" + elif bias.shape[2:] == (seqlen_q, seqlen_k): + bias_type = "matrix" + else: + raise RuntimeError("Last 2 dimensions of bias must be (1, seqlen_k)" " or (seqlen_q, seqlen_k)") + bias = bias.expand(batch, nheads, seqlen_q, seqlen_k) + bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0) + + # BLOCK_M = 128 + # BLOCK_N = 64 + # num_warps = 4 + grid = lambda META: (triton.cdiv(seqlen_k, META["BLOCK_N"]) if META["SEQUENCE_PARALLEL"] else 1, batch * nheads) + _bwd_kernel[grid]( + q, + k, + v, + bias, + do, + dq_accum, + dk, + dv, + lse, + delta, + softmax_scale, + q.stride(0), + q.stride(2), + q.stride(1), + k.stride(0), + k.stride(2), + k.stride(1), + v.stride(0), + v.stride(2), + v.stride(1), + *bias_strides, + do.stride(0), + do.stride(2), + do.stride(1), + dq_accum.stride(0), + dq_accum.stride(2), + dq_accum.stride(1), + dk.stride(0), + dk.stride(2), + dk.stride(1), + dv.stride(0), + dv.stride(2), + dv.stride(1), + nheads, + seqlen_q, + seqlen_k, + seqlen_q_rounded, + d, + seqlen_q // 32, + seqlen_k // 32, # key for triton cache (limit number of compilations) + # Can't use kwargs here because triton autotune expects key to be args, not kwargs + # IS_CAUSAL=causal, BLOCK_HEADDIM=d, + bias_type, + causal, + BLOCK_HEADDIM, + # SEQUENCE_PARALLEL=False, + # BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, + # num_warps=num_warps, + # num_stages=1, + ) + dq.copy_(dq_accum) + + +class FlashAttnQKVPackedFunc(torch.autograd.Function): + @staticmethod + def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None): + """ + qkv: (batch, seqlen, 3, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen). + ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen) + """ + # Make sure that the last dimension is contiguous + if qkv.stride(-1) != 1: + qkv = qkv.contiguous() + o, lse, ctx.softmax_scale = _flash_attn_forward( + qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], bias=bias, causal=causal, softmax_scale=softmax_scale + ) + ctx.save_for_backward(qkv, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + qkv, o, lse, bias = ctx.saved_tensors + assert not ctx.needs_input_grad[1], "FlashAttention does not support bias gradient yet" + # Triton's autotune causes the Tensor._version to change, and so Pytorch autograd + # does a memcpy. To avoid this we run in inference_mode, which doesn't track the version. + with torch.inference_mode(): + dqkv = torch.empty_like(qkv) + _flash_attn_backward( + do, + qkv[:, :, 0], + qkv[:, :, 1], + qkv[:, :, 2], + o, + lse, + dqkv[:, :, 0], + dqkv[:, :, 1], + dqkv[:, :, 2], + bias=bias, + causal=ctx.causal, + softmax_scale=ctx.softmax_scale, + ) + return dqkv, None, None, None + + +flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply + + +class FlashAttnKVPackedFunc(torch.autograd.Function): + @staticmethod + def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None): + """ + q: (batch, seqlen_q, nheads, headdim) + kv: (batch, seqlen_k, 2, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). + ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) + """ + # Make sure that the last dimension is contiguous + q, kv = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]] + o, lse, ctx.softmax_scale = _flash_attn_forward( + q, kv[:, :, 0], kv[:, :, 1], bias=bias, causal=causal, softmax_scale=softmax_scale + ) + ctx.save_for_backward(q, kv, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + q, kv, o, lse, bias = ctx.saved_tensors + assert not ctx.needs_input_grad[2], "FlashAttention does not support bias gradient yet" + # Triton's autotune causes the Tensor._version to change, and so Pytorch autograd + # does a memcpy. To avoid this we run in inference_mode, which doesn't track the version. + with torch.inference_mode(): + dq = torch.empty_like(q) + dkv = torch.empty_like(kv) + _flash_attn_backward( + do, + q, + kv[:, :, 0], + kv[:, :, 1], + o, + lse, + dq, + dkv[:, :, 0], + dkv[:, :, 1], + bias=bias, + causal=ctx.causal, + softmax_scale=ctx.softmax_scale, + ) + return dq, dkv, None, None, None + + +flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply + + +class FlashAttnFunc(torch.autograd.Function): + @staticmethod + def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None): + """ + q: (batch_size, seqlen_q, nheads, headdim) + k, v: (batch_size, seqlen_k, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). + ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) + """ + # Make sure that the last dimension is contiguous + q, k, v = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]] + o, lse, ctx.softmax_scale = _flash_attn_forward(q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale) + ctx.save_for_backward(q, k, v, o, lse, bias) + ctx.causal = causal + return o + + @staticmethod + def backward(ctx, do): + q, k, v, o, lse, bias = ctx.saved_tensors + assert not ctx.needs_input_grad[3], "FlashAttention does not support bias gradient yet" + # Triton's autotune causes the Tensor._version to change, and so Pytorch autograd + # does a memcpy. To avoid this we run in inference_mode, which doesn't track the version. + with torch.inference_mode(): + dq = torch.empty_like(q) + dk = torch.empty_like(k) + dv = torch.empty_like(v) + _flash_attn_backward( + do, q, k, v, o, lse, dq, dk, dv, bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale + ) + return dq, dk, dv, None, None, None + + +flash_attn_func = FlashAttnFunc.apply