| |
|
|
|
|
| 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 |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| 'USE_G': lambda args: args['g'] is not None, |
| }) |
| @triton.autotune( |
| configs=[ |
| triton.Config({'BK': BK}, num_warps=num_warps, num_stages=num_stages) |
| for BK in [32, 64, 128] |
| for num_warps in [2, 4, 8] |
| for num_stages in [2, 3, 4] |
| ], |
| key=['H', 'K', 'BT', 'IS_VARLEN', 'USE_G'], |
| **autotune_cache_kwargs, |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def chunk_scaled_dot_comba_pkt_fwd_kernel( |
| k, |
| p, |
| beta, |
| g0, |
| g, |
| A, |
| cu_seqlens, |
| chunk_indices, |
| T, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| BT: tl.constexpr, |
| BK: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| USE_G: tl.constexpr, |
| ): |
| i_t, i_bh = tl.program_id(0), tl.program_id(1) |
| i_b, i_h = i_bh // H, i_bh % H |
| if IS_VARLEN: |
| 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 |
| else: |
| bos, eos = i_b * T, i_b * T + T |
| o_t = i_t * BT + tl.arange(0, BT) |
| m_t = o_t < T |
|
|
| p_beta = tl.make_block_ptr(beta + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| b_beta = tl.load(p_beta, boundary_check=(0,)) |
|
|
| b_A = tl.zeros([BT, BT], dtype=tl.float32) |
| for i_k in range(tl.cdiv(K, BK)): |
| p_k = tl.make_block_ptr(k + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_p = tl.make_block_ptr(p + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_p = tl.load(p_p, boundary_check=(0, 1)) |
| b_pb = b_p * b_beta[:, None] |
| b_A += tl.dot(b_pb.to(b_k.dtype), tl.trans(b_k)) |
|
|
| if USE_G: |
| p_g0 = tl.make_block_ptr(g0 + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| p_g = tl.make_block_ptr(g + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| b_g0 = tl.load(p_g0, boundary_check=(0,)) |
| b_g = tl.load(p_g, boundary_check=(0,)) |
| b_A = b_A * exp(b_g0[:, None] - b_g[None, :]) |
|
|
| m_A = (o_t[:, None] > o_t[None, :]) & (m_t[:, None] & m_t) |
| b_A = tl.where(m_A, b_A, 0) |
| p_A = tl.make_block_ptr(A + (bos*H + i_h) * BT, (T, BT), (BT*H, 1), (i_t * BT, 0), (BT, BT), (1, 0)) |
| tl.store(p_A, b_A.to(p_A.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| def chunk_scaled_dot_comba_pkt_fwd( |
| k: torch.Tensor, |
| p: torch.Tensor, |
| beta: torch.Tensor, |
| g0: torch.Tensor | None = None, |
| g: torch.Tensor | None = None, |
| cu_seqlens: torch.LongTensor | None = None, |
| chunk_size: int = 64, |
| output_dtype: torch.dtype = torch.float32, |
| ) -> torch.Tensor: |
| r""" |
| Compute beta \mathcal{A}(i-1/j) * P * K^T. |
| |
| Args: |
| k (torch.Tensor): |
| The key tensor of shape `[B, T, H, K]`. |
| p (torch.Tensor): |
| The auxiliary key tensor of shape `[B, T, H, K]`. |
| beta (torch.Tensor): |
| The beta tensor of shape `[B, T, H]`. |
| g0 (torch.Tensor): |
| The cumulative sum minus the original one of the gate tensor of shape `[B, T, H]`. |
| Default: None |
| g (torch.Tensor): |
| The cumulative sum of the gate tensor of shape `[B, T, H]`. |
| Default: None |
| cu_seqlens (torch.LongTensor): |
| The cumulative sequence lengths of the input tensor. |
| Default: None |
| chunk_size (int): |
| The chunk size. Default: 64. |
| output_dtype (torch.dtype): |
| The dtype of the output tensor. Default: `torch.float32` |
| |
| Returns: |
| beta * K * K^T of shape `[B, T, H, BT]` where `BT` is the chunk size. |
| """ |
| B, T, H, K = k.shape |
| BT = chunk_size |
| chunk_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(chunk_indices) |
| A = torch.empty(B, T, H, BT, device=k.device, dtype=output_dtype) |
| chunk_scaled_dot_comba_pkt_fwd_kernel[(NT, B * H)]( |
| k=k, |
| p=p, |
| beta=beta, |
| g0=g0, |
| g=g, |
| A=A, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| T=T, |
| H=H, |
| K=K, |
| BT=BT, |
| ) |
| return A |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| }) |
| @triton.autotune( |
| configs=[ |
| triton.Config({}, num_warps=num_warps, num_stages=num_stages) |
| for num_warps in [2, 4] |
| for num_stages in [2, 3, 4] |
| ], |
| key=['H', 'K', 'V', 'BT', 'BK', 'BV', 'IS_VARLEN'], |
| **autotune_cache_kwargs, |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def prepare_wy_repr_bwd_kernel( |
| k, |
| v, |
| p, |
| beta, |
| g0, |
| g, |
| A, |
| dw, |
| du, |
| dk, |
| dv, |
| dp, |
| dbeta, |
| dg0, |
| dg, |
| cu_seqlens, |
| chunk_indices, |
| T, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_t, i_bh = tl.program_id(0), tl.program_id(1) |
| i_b, i_h = i_bh // H, i_bh % H |
| if IS_VARLEN: |
| 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 |
| else: |
| bos, eos = i_b * T, i_b * T + T |
|
|
| p_beta = tl.make_block_ptr(beta + (bos*H + i_h), (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| p_g0 = tl.make_block_ptr(g0 + (bos*H + i_h), (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| p_g = tl.make_block_ptr(g + (bos*H + i_h), (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| p_A = tl.make_block_ptr(A + (bos*H + i_h) * BT, (BT, T), (1, H*BT), (0, i_t * BT), (BT, BT), (0, 1)) |
|
|
| b_A = tl.load(p_A, boundary_check=(0, 1)) |
| b_beta = tl.load(p_beta, boundary_check=(0,)) |
| b_g0 = tl.load(p_g0, boundary_check=(0,)) |
| b_g0_exp = tl.exp(b_g0) |
| b_g = tl.load(p_g, boundary_check=(0,)) |
|
|
| b_dbeta = tl.zeros([BT], dtype=tl.float32) |
| b_dA = tl.zeros([BT, BT], dtype=tl.float32) |
| b_dg0 = tl.zeros([BT], dtype=tl.float32) |
|
|
| for i_k in range(tl.cdiv(K, BK)): |
| p_p = tl.make_block_ptr(p + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dp = tl.make_block_ptr(dp + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dw = tl.make_block_ptr(dw + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| b_p = tl.load(p_p, boundary_check=(0, 1)) |
| b_p_beta_g0 = (b_p * b_beta[:, None] * b_g0_exp[:, None]).to(b_p.dtype) |
| b_dw = tl.load(p_dw, boundary_check=(0, 1)) |
| b_dA += tl.dot(b_dw, tl.trans(b_p_beta_g0)) |
| b_dp_beta_g0 = tl.dot(b_A, b_dw) |
| b_dp = b_dp_beta_g0 * b_beta[:, None] * b_g0_exp[:, None] |
| b_dbeta += tl.sum(b_dp_beta_g0 * b_p * b_g0_exp[:, None], 1) |
| b_dg0 += tl.sum(b_dp * b_p, 1) |
| tl.store(p_dp, b_dp.to(p_dp.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| for i_v in range(tl.cdiv(V, BV)): |
| p_v = tl.make_block_ptr(v + (bos*H + i_h) * V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_dv = tl.make_block_ptr(dv + (bos*H + i_h) * V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_du = tl.make_block_ptr(du + (bos*H + i_h) * V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| b_v_beta = (b_v * b_beta[:, None]).to(b_v.dtype) |
| b_du = tl.load(p_du, boundary_check=(0, 1)) |
| b_dA += tl.dot(b_du, tl.trans(b_v_beta)) |
| b_dv_beta = tl.dot(b_A, b_du) |
| b_dv = b_dv_beta * b_beta[:, None] |
| b_dbeta += tl.sum(b_dv_beta * b_v, 1) |
| tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| o_t = i_t * BT + tl.arange(0, BT) |
| m_t = o_t < T |
| m_A = (o_t[:, None] > o_t[None, :]) & (m_t[:, None] & m_t) |
| b_dA = tl.where(m_A, b_dA, 0) |
| b_dA = tl.dot(b_dA.to(b_A.dtype), b_A) |
| b_dA = tl.dot(b_A, b_dA.to(b_A.dtype)) |
| b_dA = tl.where(m_A, -b_dA * exp(b_g0[:, None] - b_g[None, :]), 0).to(k.dtype.element_ty) |
| b_dA = b_dA.to(k.dtype.element_ty) |
| b_A = tl.zeros([BT, BT], dtype=tl.float32) |
|
|
| for i_k in range(tl.cdiv(K, BK)): |
| p_k = tl.make_block_ptr(k + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_p = tl.make_block_ptr(p + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dk = tl.make_block_ptr(dk + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dp = tl.make_block_ptr(dp + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_p = tl.load(p_p, boundary_check=(0, 1)) |
| b_dp = tl.load(p_dp, boundary_check=(0, 1)) |
| b_p_beta = (b_p * b_beta[:, None]).to(b_p.dtype) |
| b_A += tl.dot(b_p_beta, tl.trans(b_k)) |
| b_dp_beta = tl.dot(b_dA, b_k) |
| b_dbeta += tl.sum(b_dp_beta * b_p, 1) |
| b_dk = tl.dot(tl.trans(b_dA), b_p_beta) |
| b_dp += b_dp_beta * b_beta[:, None] |
| tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1)) |
| tl.store(p_dp, b_dp.to(p_dp.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| b_dA_A = b_dA * b_A |
| b_dg0 += tl.sum(b_dA_A, axis=1) |
| b_dg = - tl.sum(b_dA_A, axis=0) |
| p_dg = tl.make_block_ptr(dg + (bos*H + i_h), (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| p_dg0 = tl.make_block_ptr(dg0 + (bos*H + i_h), (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| p_dbeta = tl.make_block_ptr(dbeta + (bos*H + i_h), (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0,)) |
| tl.store(p_dg0, b_dg0.to(p_dg0.dtype.element_ty), boundary_check=(0,)) |
| tl.store(p_dbeta, b_dbeta.to(p_dbeta.dtype.element_ty), boundary_check=(0,)) |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| }) |
| @triton.autotune( |
| configs=[ |
| triton.Config({}, num_warps=num_warps, num_stages=num_stages) |
| for num_warps in [2, 4, 8] |
| for num_stages in [2, 3, 4] |
| ], |
| key=['H', 'K', 'V', 'BT', 'BK', 'BV', 'IS_VARLEN'], |
| **autotune_cache_kwargs, |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def recompute_w_u_fwd_kernel( |
| k, |
| v, |
| beta, |
| w, |
| u, |
| A, |
| g, |
| cu_seqlens, |
| chunk_indices, |
| T, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_t, i_bh = tl.program_id(0), tl.program_id(1) |
| i_b, i_h = i_bh // H, i_bh % H |
| if IS_VARLEN: |
| 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 |
| else: |
| bos, eos = i_b * T, i_b * T + T |
| p_beta = tl.make_block_ptr(beta + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| p_g = tl.make_block_ptr(g + (bos*H + i_h), (T,), (H,), (i_t * BT,), (BT,), (0,)) |
| p_A = tl.make_block_ptr(A + (bos*H + i_h) * BT, (T, BT), (H*BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)) |
| b_beta = tl.load(p_beta, boundary_check=(0,)) |
| b_A = tl.load(p_A, boundary_check=(0, 1)) |
| b_g = tl.exp(tl.load(p_g, boundary_check=(0,))) |
|
|
| for i_v in range(tl.cdiv(V, BV)): |
| p_v = tl.make_block_ptr(v + (bos*H + i_h) * V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_u = tl.make_block_ptr(u + (bos*H + i_h) * V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| b_vb = (b_v * b_beta[:, None]).to(b_v.dtype) |
| b_u = tl.dot(b_A, b_vb, allow_tf32=False) |
| tl.store(p_u, b_u.to(p_u.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| for i_k in range(tl.cdiv(K, BK)): |
| p_k = tl.make_block_ptr(k + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_w = tl.make_block_ptr(w + (bos*H + i_h) * K, (T, K), (H*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_kb = (b_k * b_beta[:, None] * b_g[:, None]).to(b_k.dtype) |
| b_w = tl.dot(b_A, b_kb) |
| tl.store(p_w, b_w.to(p_w.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| def recompute_w_u_fwd( |
| k: torch.Tensor, |
| v: torch.Tensor, |
| beta: torch.Tensor, |
| g_cumsum: torch.Tensor, |
| A: torch.Tensor, |
| cu_seqlens: torch.LongTensor | None, |
| ) -> tuple[torch.Tensor, torch.Tensor]: |
| B, T, H, K, V = *k.shape, v.shape[-1] |
| BT = A.shape[-1] |
|
|
| chunk_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(chunk_indices) |
| BK = 64 |
| BV = 64 |
|
|
| u = torch.empty_like(v) |
| w = torch.empty_like(k) |
| recompute_w_u_fwd_kernel[(NT, B*H)]( |
| k=k, |
| v=v, |
| beta=beta, |
| w=w, |
| u=u, |
| A=A, |
| g=g_cumsum, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| T=T, |
| H=H, |
| K=K, |
| V=V, |
| BT=BT, |
| BK=BK, |
| BV=BV, |
| ) |
| return w, u |
|
|
|
|
| def prepare_wy_repr_bwd( |
| k: torch.Tensor, |
| v: torch.Tensor, |
| p: torch.Tensor, |
| g0: torch.Tensor, |
| g: torch.Tensor, |
| beta: torch.Tensor, |
| A: torch.Tensor, |
| dw: torch.Tensor, |
| du: torch.Tensor, |
| cu_seqlens: torch.LongTensor | None, |
| ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
| B, T, H, K, V = *k.shape, v.shape[-1] |
| BT = 64 |
| chunk_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(chunk_indices) |
| CONST_TILING = 64 if check_shared_mem() else 32 |
| BK = min(max(triton.next_power_of_2(K), 16), CONST_TILING) |
| BV = min(max(triton.next_power_of_2(V), 16), CONST_TILING) |
|
|
| dk = torch.empty_like(k) |
| dv = torch.empty_like(v) |
| dp = torch.empty_like(p) |
| dbeta = torch.empty_like(beta) |
| dg0 = torch.empty_like(g0) |
| dg = torch.empty_like(g) |
| prepare_wy_repr_bwd_kernel[(NT, B * H)]( |
| k=k, |
| v=v, |
| p=p, |
| beta=beta, |
| g0=g0, |
| g=g, |
| A=A, |
| dw=dw, |
| du=du, |
| dk=dk, |
| dv=dv, |
| dp=dp, |
| dbeta=dbeta, |
| dg0=dg0, |
| dg=dg, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| T=T, |
| H=H, |
| K=K, |
| V=V, |
| BT=BT, |
| BK=BK, |
| BV=BV, |
| ) |
| return dk, dv, dp, dbeta, dg0, dg |
|
|
