| |
| |
|
|
| from typing import Optional |
|
|
| import torch |
| import triton |
| import triton.language as tl |
|
|
| from ...ops.utils import chunk_global_cumsum |
| from ...ops.utils.op import exp |
| from ...utils import autocast_custom_bwd, autocast_custom_fwd, input_guard |
|
|
|
|
| @triton.heuristics({ |
| 'USE_INITIAL_STATE': lambda args: args['h0'] is not None, |
| 'STORE_FINAL_STATE': lambda args: args['ht'] is not None, |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None |
| }) |
| @triton.autotune( |
| configs=[ |
| triton.Config({}, num_warps=num_warps) |
| for num_warps in [1, 2, 4] |
| ], |
| key=["BK", "BV", "USE_GK", "USE_GV", "USE_G"], |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def fused_recurrent_fwd_kernel( |
| q, |
| k, |
| v, |
| g, |
| gk, |
| gv, |
| o, |
| h0, |
| ht, |
| cu_seqlens, |
| scale, |
| T, |
| B: tl.constexpr, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr, |
| REVERSE: tl.constexpr, |
| USE_G: tl.constexpr, |
| USE_GK: tl.constexpr, |
| USE_GV: tl.constexpr, |
| USE_INITIAL_STATE: tl.constexpr, |
| STORE_FINAL_STATE: tl.constexpr, |
| IS_VARLEN: tl.constexpr |
| ): |
| i_v, i_k, i_nh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) |
| i_n, i_h = i_nh // H, i_nh % H |
| if IS_VARLEN: |
| bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) |
| all = T |
| T = eos - bos |
| else: |
| bos, eos = i_n * T, i_n * T + T |
| all = B * T |
|
|
| p_q = q + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| p_k = k + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| p_v = v + (bos + ((T-1) if REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
| p_o = o + ((i_k * all + bos) + ((T-1) if REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
| if USE_G: |
| p_g = g + (bos + ((T-1) if REVERSE else 0)) * H + i_h |
| if USE_GK: |
| p_gk = gk + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| if USE_GV: |
| p_gv = gv + (bos + ((T-1) if REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
|
|
| mask_k = (i_k * BK + tl.arange(0, BK)) < K |
| mask_v = (i_v * BV + tl.arange(0, BV)) < V |
| mask_h = mask_k[None, :] & mask_v[:, None] |
| b_h = tl.zeros([BV, BK], dtype=tl.float32) |
|
|
| if USE_INITIAL_STATE: |
| p_h0 = h0 + i_nh * K*V + (i_k * BK + tl.arange(0, BK)[None, :]) * V + (i_v * BV + tl.arange(0, BV)[:, None]) |
| b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32) |
|
|
| for _ in range(0, T): |
| b_q = tl.load(p_q, mask=mask_k, other=0).to(tl.float32) * scale |
| b_k = tl.load(p_k, mask=mask_k, other=0).to(tl.float32) |
| b_v = tl.load(p_v, mask=mask_v, other=0).to(tl.float32) |
| if USE_GK: |
| b_gk = tl.load(p_gk, mask=mask_k, other=0).to(tl.float32) |
| b_h = b_h * exp(b_gk[None, :]) |
| if USE_GV: |
| b_gv = tl.load(p_gv, mask=mask_v, other=0).to(tl.float32) |
| b_h = b_h * exp(b_gv[:, None]) |
| if USE_G: |
| b_g = tl.load(p_g).to(tl.float32) |
| b_h = b_h * exp(b_g) |
| b_h += b_k[None, :] * b_v[:, None] |
| b_o = b_h * b_q[None, :] |
| b_o = tl.sum(b_o, axis=1) |
| tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v) |
| p_q += (-1 if REVERSE else 1) * H*K |
| p_k += (-1 if REVERSE else 1) * H*K |
| p_v += (-1 if REVERSE else 1) * H*V |
| p_o += (-1 if REVERSE else 1) * H*V |
| if USE_GK: |
| p_gk += (-1 if REVERSE else 1) * H*K |
| if USE_GV: |
| p_gv += (-1 if REVERSE else 1) * H*V |
| if USE_G: |
| p_g += (-1 if REVERSE else 1) * H |
|
|
| if STORE_FINAL_STATE: |
| p_ht = ht + i_nh * K*V + (i_k * BK + tl.arange(0, BK)[None, :]) * V + (i_v * BV + tl.arange(0, BV)[:, None]) |
| tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h) |
|
|
|
|
| @triton.heuristics({ |
| 'USE_INITIAL_STATE': lambda args: args['h0'] is not None, |
| 'STORE_INITIAL_STATE_GRADIENT': lambda args: args['dh0'] is not None, |
| 'USE_FINAL_STATE_GRADIENT': lambda args: args['dht'] is not None, |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None |
| }) |
| @triton.autotune( |
| configs=[ |
| triton.Config({}, num_warps=num_warps) |
| for num_warps in [1, 2, 4] |
| ], |
| key=['BK', 'BV', 'USE_GK', 'USE_GV', 'USE_G'], |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def fused_recurrent_bwd_kernel( |
| q, |
| k, |
| v, |
| g, |
| gk, |
| gv, |
| h0, |
| do, |
| dq, |
| dk, |
| dv, |
| dht, |
| dh0, |
| cu_seqlens, |
| scale, |
| T, |
| B: tl.constexpr, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr, |
| REVERSE: tl.constexpr, |
| USE_G: tl.constexpr, |
| USE_GK: tl.constexpr, |
| USE_GV: tl.constexpr, |
| USE_INITIAL_STATE: tl.constexpr, |
| STORE_INITIAL_STATE_GRADIENT: tl.constexpr, |
| USE_FINAL_STATE_GRADIENT: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_v, i_k, i_nh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) |
| i_n, i_h = i_nh // H, i_nh % H |
| if IS_VARLEN: |
| bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) |
| all = T |
| T = eos - bos |
| else: |
| bos, eos = i_n * T, i_n * T + T |
| all = B * T |
|
|
| p_k = k + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| p_v = v + (bos + ((T-1) if REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
| p_do = do + (bos + ((T-1) if REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
| p_dq = dq + ((i_v * all + bos) + ((T-1) if REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| if USE_G: |
| p_g = g + (bos + ((T-1) if REVERSE else 0)) * H + i_h |
| if USE_GK: |
| p_gk = gk + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| if USE_GV: |
| p_gv = gv + (bos + ((T-1) if REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
|
|
| mask_k = i_k * BK + tl.arange(0, BK) < K |
| mask_v = i_v * BV + tl.arange(0, BV) < V |
| mask_h = mask_k[:, None] & mask_v[None, :] |
|
|
| b_h = tl.zeros([BK, BV], dtype=tl.float32) |
| if USE_INITIAL_STATE: |
| p_h0 = h0 + i_nh * K*V + (i_k * BK + tl.arange(0, BK)[:, None]) * V + (i_v * BV + tl.arange(0, BV)[None, :]) |
| b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32) |
|
|
| for _ in range(0, T): |
| b_k = tl.load(p_k, mask=mask_k, other=0).to(tl.float32) |
| b_v = tl.load(p_v, mask=mask_v, other=0).to(tl.float32) |
| b_do = tl.load(p_do, mask=mask_v, other=0).to(tl.float32) |
| if USE_G: |
| b_g = tl.load(p_g).to(tl.float32) |
| b_h = b_h * exp(b_g) |
| if USE_GK: |
| b_gk = tl.load(p_gk, mask=mask_k, other=0).to(tl.float32) |
| b_h = b_h * exp(b_gk[:, None]) |
| if USE_GV: |
| b_gv = tl.load(p_gv, mask=mask_v, other=0).to(tl.float32) |
| b_h = b_h * exp(b_gv[None, :]) |
| b_h += b_k[:, None] * b_v[None, :] |
| b_dq = b_h * b_do[None, :] |
| b_dq = tl.sum(b_dq, axis=1) * scale |
| tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), mask=mask_k) |
|
|
| p_k += (-1 if REVERSE else 1) * H*K |
| p_v += (-1 if REVERSE else 1) * H*V |
| p_do += (-1 if REVERSE else 1) * H*V |
| p_dq += (-1 if REVERSE else 1) * H*K |
| if USE_G: |
| p_g += (-1 if REVERSE else 1) * H |
| if USE_GK: |
| p_gk += (-1 if REVERSE else 1) * H*K |
| if USE_GV: |
| p_gv += (-1 if REVERSE else 1) * H*V |
|
|
| |
| tl.debug_barrier() |
|
|
| p_q = q + (bos + ((T - 1) if not REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| p_k = k + (bos + ((T - 1) if not REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| p_v = v + (bos + ((T - 1) if not REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
| p_do = do + (bos + ((T - 1) if not REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
| p_dk = dk + ((i_v * all + bos) + ((T - 1) if not REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| p_dv = dv + ((i_k * all + bos) + ((T - 1) if not REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
| if USE_G: |
| p_g = g + (bos + ((T - 1) if not REVERSE else 0)) * H + i_h |
| if USE_GK: |
| p_gk = gk + (bos + ((T - 1) if not REVERSE else 0)) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) |
| if USE_GV: |
| p_gv = gv + (bos + ((T - 1) if not REVERSE else 0)) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) |
|
|
| b_dh = tl.zeros([BK, BV], dtype=tl.float32) |
| if USE_FINAL_STATE_GRADIENT: |
| p_dht = dht + i_nh * K*V + (i_k * BK + tl.arange(0, BK)[:, None]) * V + (i_v * BV + tl.arange(0, BV)[None, :]) |
| b_dh += tl.load(p_dht, mask=mask_h, other=0).to(tl.float32) |
|
|
| for _ in range(T): |
| b_q = tl.load(p_q, mask=mask_k, other=0).to(tl.float32) * scale |
| b_k = tl.load(p_k, mask=mask_k, other=0).to(tl.float32) |
| b_v = tl.load(p_v, mask=mask_v, other=0).to(tl.float32) |
| b_do = tl.load(p_do, mask=mask_v, other=0).to(tl.float32) |
| b_dh += b_q[:, None] * b_do[None, :] |
| b_dk = tl.sum(b_dh * b_v[None, :], axis=1) |
| b_dv = tl.sum(b_dh * b_k[:, None], axis=0) |
| if USE_G: |
| b_g = tl.load(p_g).to(tl.float32) |
| b_dh *= exp(b_g) |
| if USE_GK: |
| b_gk = tl.load(p_gk, mask=mask_k, other=0).to(tl.float32) |
| b_dh *= exp(b_gk)[:, None] |
| if USE_GV: |
| b_gv = tl.load(p_gv, mask=mask_v, other=0).to(tl.float32) |
| b_dh *= exp(b_gv)[None, :] |
| tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), mask=mask_k) |
| tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), mask=mask_v) |
|
|
| p_q += (1 if REVERSE else -1) * H*K |
| p_k += (1 if REVERSE else -1) * H*K |
| p_v += (1 if REVERSE else -1) * H*V |
| p_do += (1 if REVERSE else -1) * H*V |
| p_dk += (1 if REVERSE else -1) * H*K |
| p_dv += (1 if REVERSE else -1) * H*V |
| if USE_G: |
| p_g += (1 if REVERSE else -1) * H |
| if USE_GK: |
| p_gk += (1 if REVERSE else -1) * H*K |
| if USE_GV: |
| p_gv += (1 if REVERSE else -1) * H*V |
|
|
| if STORE_INITIAL_STATE_GRADIENT: |
| p_dh0 = dh0 + i_nh * K*V + (i_k * BK + tl.arange(0, BK)[:, None]) * V + (i_v * BV + tl.arange(0, BV)[None, :]) |
| tl.store(p_dh0, b_dh.to(p_dh0.dtype.element_ty), mask=mask_h) |
|
|
|
|
| def fused_recurrent_fwd( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: Optional[torch.Tensor] = None, |
| gk: Optional[torch.Tensor] = None, |
| gv: Optional[torch.Tensor] = None, |
| scale: Optional[float] = None, |
| initial_state: Optional[torch.Tensor] = None, |
| output_final_state: bool = False, |
| reverse: bool = False, |
| cu_seqlens: Optional[torch.LongTensor] = None |
| ): |
| B, T, H, K, V = *k.shape, v.shape[-1] |
| N = B if cu_seqlens is None else len(cu_seqlens) - 1 |
| BK, BV = min(K, 64), min(V, 64) |
| NK, NV = triton.cdiv(K, BK), triton.cdiv(V, BV) |
|
|
| h0 = initial_state |
| ht = q.new_empty(N, H, K, V, dtype=torch.float32) if output_final_state else None |
| o = q.new_empty(NK, *v.shape, dtype=torch.float32) |
|
|
| grid = (NV, NK, N * H) |
| fused_recurrent_fwd_kernel[grid]( |
| q, |
| k, |
| v, |
| g, |
| gk, |
| gv, |
| o, |
| h0, |
| ht, |
| cu_seqlens, |
| scale, |
| T=T, |
| B=B, |
| H=H, |
| K=K, |
| V=V, |
| BK=BK, |
| BV=BV, |
| USE_G=g is not None, |
| USE_GK=gk is not None, |
| USE_GV=gv is not None, |
| REVERSE=reverse, |
| ) |
| o = o.sum(0) |
| return o, ht |
|
|
|
|
| def fused_recurrent_bwd( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: Optional[torch.Tensor] = None, |
| gk: Optional[torch.Tensor] = None, |
| gv: Optional[torch.Tensor] = None, |
| o: Optional[torch.Tensor] = None, |
| do: Optional[torch.Tensor] = None, |
| dht: Optional[torch.Tensor] = None, |
| scale: Optional[float] = None, |
| initial_state: Optional[torch.Tensor] = None, |
| reverse: bool = False, |
| cu_seqlens: Optional[torch.LongTensor] = None |
| ): |
| B, T, H, K, V = *k.shape, v.shape[-1] |
| N = B if cu_seqlens is None else len(cu_seqlens) - 1 |
|
|
| BK, BV = min(K, 64), min(V, 64) |
| NK, NV = triton.cdiv(K, BK), triton.cdiv(V, BV) |
|
|
| dq = q.new_empty(NV, *q.shape, dtype=torch.float32) |
| dk = q.new_empty(NV, *k.shape, dtype=torch.float32) |
| dv = q.new_empty(NK, *v.shape, dtype=torch.float32) |
| h0 = initial_state |
| dh0 = torch.empty_like(initial_state) if initial_state is not None else None |
|
|
| grid = (NV, NK, N * H) |
| fused_recurrent_bwd_kernel[grid]( |
| q, |
| k, |
| v, |
| g, |
| gk, |
| gv, |
| h0, |
| do, |
| dq, |
| dk, |
| dv, |
| dht, |
| dh0, |
| cu_seqlens, |
| scale, |
| B=B, |
| T=T, |
| H=H, |
| K=K, |
| V=V, |
| BK=BK, |
| BV=BV, |
| USE_G=g is not None, |
| USE_GK=gk is not None, |
| USE_GV=gv is not None, |
| REVERSE=reverse, |
| ) |
| dq = dq.sum(0) |
| dk = dk.sum(0) |
| dv = dv.sum(0) |
| dg, dgk, dgv = None, None, None |
| if g is not None: |
| dg = chunk_global_cumsum((dq * q.float() - dk * k.float()).sum(-1), reverse=not reverse, cu_seqlens=cu_seqlens) |
| if gk is not None: |
| dgk = chunk_global_cumsum(dq * q.float() - dk * k.float(), reverse=not reverse, cu_seqlens=cu_seqlens) |
| if gv is not None: |
| dgv = chunk_global_cumsum(do.float() * o.float() - dv * v.float(), reverse=not reverse, cu_seqlens=cu_seqlens) |
|
|
| return dq, dk, dv, dg, dgk, dgv, dh0 |
|
|
|
|
| class FusedRecurrentFunction(torch.autograd.Function): |
|
|
| @staticmethod |
| @input_guard |
| @autocast_custom_fwd |
| def forward( |
| ctx, |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: Optional[torch.Tensor] = None, |
| gk: Optional[torch.Tensor] = None, |
| gv: Optional[torch.Tensor] = None, |
| scale: Optional[float] = None, |
| initial_state: Optional[torch.Tensor] = None, |
| output_final_state: bool = False, |
| reverse: bool = False, |
| cu_seqlens: Optional[torch.LongTensor] = None |
| ): |
| o, ht = fused_recurrent_fwd( |
| q=q, |
| k=k, |
| v=v, |
| g=g, |
| gk=gk, |
| gv=gv, |
| scale=scale, |
| initial_state=initial_state, |
| output_final_state=output_final_state, |
| reverse=reverse, |
| cu_seqlens=cu_seqlens, |
| ) |
| ctx.save_for_backward(q, k, v, g, gk, gv, initial_state, o) |
| ctx.scale = scale |
| ctx.reverse = reverse |
| ctx.cu_seqlens = cu_seqlens |
| return o.to(q.dtype), ht |
|
|
| @staticmethod |
| @input_guard |
| @autocast_custom_bwd |
| def backward(ctx, do, dht): |
| q, k, v, g, gk, gv, initial_state, o = ctx.saved_tensors |
| |
| if dht is not None: |
| if not dht.eq(0).all(): |
| if g is not None: |
| assert g.requires_grad is False, "Cannot load final state gradient and use gates at the same time" |
| if gk is not None: |
| assert gk.requires_grad is False, "Cannot load final state gradient and use gates at the same time" |
| if gv is not None: |
| assert gv.requires_grad is False, "Cannot load final state gradient and use gates at the same time" |
| dq, dk, dv, dg, dgk, dgv, dh0 = fused_recurrent_bwd( |
| q=q, |
| k=k, |
| v=v, |
| g=g, |
| gk=gk, |
| gv=gv, |
| o=o, |
| do=do, |
| dht=dht, |
| scale=ctx.scale, |
| initial_state=initial_state, |
| reverse=ctx.reverse, |
| cu_seqlens=ctx.cu_seqlens, |
| ) |
| return dq.to(q.dtype), dk.to(k.dtype), dv.to(v.dtype), dg, dgk, dgv, None, dh0, None, None, None |
|
|
|
|
| def fused_recurrent( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: Optional[torch.Tensor] = None, |
| gk: Optional[torch.Tensor] = None, |
| gv: Optional[torch.Tensor] = None, |
| scale: Optional[float] = None, |
| initial_state: Optional[torch.Tensor] = None, |
| output_final_state: bool = False, |
| reverse: bool = False, |
| cu_seqlens: Optional[torch.LongTensor] = None, |
| ): |
| if scale is None: |
| scale = k.shape[-1] ** -0.5 |
| return FusedRecurrentFunction.apply( |
| q, |
| k, |
| v, |
| g, |
| gk, |
| gv, |
| scale, |
| initial_state, |
| output_final_state, |
| reverse, |
| cu_seqlens, |
| ) |
|
|
