| |
|
|
| import torch |
| import triton |
| import triton.language as tl |
|
|
| from fla.ops.utils import get_max_num_splits, prepare_chunk_indices |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| }) |
| @triton.jit(do_not_specialize=['T']) |
| def transform_q_fwd_kernel( |
| q, |
| q_new, |
| w1, |
| w2, |
| cu_seqlens, |
| indices, |
| T, |
| S: tl.constexpr, |
| G: tl.constexpr, |
| HQ: tl.constexpr, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| BT: tl.constexpr, |
| BS: tl.constexpr, |
| BK: tl.constexpr, |
| NUM_BLOCKS: tl.constexpr, |
| IS_VARLEN: 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(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32) |
| T = eos - bos |
| else: |
| i_n = i_b |
| bos, eos = i_n * T, i_n * T + T |
| |
| p_q = tl.make_block_ptr(q + (bos * HQ + i_hq) * K, (T, K), (HQ*K, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| b_q = tl.zeros([BT, BK], dtype=tl.float32) |
| b_q += tl.load(p_q, boundary_check=(0, 1)) |
|
|
| if BS == BT: |
| if (i_t * BT) % S == 0: |
| p_q_new = tl.make_block_ptr(q_new + ((bos * NUM_BLOCKS + (i_t * BT // S)) * HQ + i_hq) * K, |
| (T, K), (HQ*K*NUM_BLOCKS, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| tl.store(p_q_new, b_q.to(q_new.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| for offset in range((i_t + 1) * BT - 2 * BS, S-BS, -BS): |
| p_w1 = tl.make_block_ptr(w1 + (bos * H + i_h) * K, (K, T), (1, K*H), (0, offset), (BK, BS), (0, 1)) |
| p_w2 = tl.make_block_ptr(w2 + (bos * H + i_h) * K, (T, K), (K*H, 1), (offset, 0), (BS, BK), (1, 0)) |
| b_w1 = tl.load(p_w1, boundary_check=(0, 1)) |
| b_w2 = tl.load(p_w2, boundary_check=(0, 1)) |
| m_s = i_t * BT + tl.arange(0, BT) >= (offset + BS) |
| b_s2 = tl.dot(b_q.to(b_w1.dtype), b_w1) |
| b_s2 = tl.where(m_s[:, None], b_s2, 0) |
| b_q -= tl.dot(b_s2.to(b_w2.dtype), b_w2) |
|
|
| if offset % S == 0: |
| p_q_new = tl.make_block_ptr(q_new + ((bos * NUM_BLOCKS + (offset // S)) * HQ + i_hq) * K, |
| (T, K), (HQ*K*NUM_BLOCKS, 1), (i_t * BT, 0), (BT, BK), (1, 0)) |
| tl.store(p_q_new, b_q.to(q_new.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| def transform_q_fwd_fn( |
| q, |
| w1, |
| w2, |
| cu_seqlens, |
| BT, |
| BS, |
| S, |
| ): |
| B, T, HQ, K = q.shape |
| H = w1.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) |
|
|
| num_blocks = triton.cdiv(T, S) if cu_seqlens is None else get_max_num_splits(cu_seqlens, S) |
| q_new = torch.zeros(B, T, num_blocks, HQ, K, dtype=q.dtype, device=q.device) |
| transform_q_fwd_kernel[(NT, B * HQ)]( |
| q=q, |
| q_new=q_new, |
| w1=w1, |
| w2=w2, |
| cu_seqlens=cu_seqlens, |
| indices=indices, |
| T=T, |
| K=K, |
| BK=triton.next_power_of_2(K), |
| G=G, |
| HQ=HQ, |
| H=H, |
| BS=BS, |
| BT=BT, |
| S=S, |
| NUM_BLOCKS=num_blocks, |
| num_warps=8 if (BT == 128 and K == 128) else 4, |
| ) |
| return q_new |
|
|
