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from typing import Optional, Tuple |
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import torch |
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import triton |
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import triton.language as tl |
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from fla.ops.utils.op import exp |
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from fla.utils import autocast_custom_bwd, autocast_custom_fwd, input_guard, use_cuda_graph |
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@triton.heuristics({ |
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'USE_INITIAL_STATE': lambda args: args['h0'] is not None, |
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'STORE_FINAL_STATE': lambda args: args['ht'] is not None, |
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'USE_OFFSETS': lambda args: args['offsets'] is not None |
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}) |
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@triton.autotune( |
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configs=[ |
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triton.Config({'BV': BV}, num_warps=num_warps, num_stages=num_stages) |
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for BV in [16, 32, 64] |
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for num_warps in [2, 4, 8, 16] |
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for num_stages in [2, 3, 4] |
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], |
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key=['BK'], |
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use_cuda_graph=use_cuda_graph, |
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) |
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@triton.jit(do_not_specialize=['T']) |
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def fused_recurrent_dplr_delta_rule_fwd_kernel( |
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q, |
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k, |
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v, |
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a, |
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b, |
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gk, |
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o, |
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h0, |
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ht, |
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offsets, |
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scale, |
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T, |
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B: tl.constexpr, |
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H: tl.constexpr, |
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K: tl.constexpr, |
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V: tl.constexpr, |
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BK: tl.constexpr, |
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BV: tl.constexpr, |
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REVERSE: tl.constexpr, |
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USE_INITIAL_STATE: tl.constexpr, |
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STORE_FINAL_STATE: tl.constexpr, |
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USE_OFFSETS: tl.constexpr, |
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HEAD_FIRST: tl.constexpr |
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): |
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i_v, i_nh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64) |
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i_n, i_h = i_nh // H, i_nh % H |
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if USE_OFFSETS: |
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bos, eos = tl.load(offsets + i_n).to(tl.int64), tl.load(offsets + i_n + 1).to(tl.int64) |
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T = eos - bos |
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else: |
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bos, eos = i_n * T, i_n * T + T |
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o_k = tl.arange(0, BK) |
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o_v = i_v * BV + tl.arange(0, BV) |
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if HEAD_FIRST: |
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p_q = q + i_nh * T*K + ((T-1) * K if REVERSE else 0) + o_k |
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p_k = k + i_nh * T*K + ((T-1) * K if REVERSE else 0) + o_k |
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p_a = a + i_nh * T*K + ((T-1) * K if REVERSE else 0) + o_k |
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p_b = b + i_nh * T*K + ((T-1) * K if REVERSE else 0) + o_k |
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p_gk = gk + i_nh * T*K + ((T-1) * K if REVERSE else 0) + o_k |
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p_v = v + i_nh * T*V + ((T-1) * V if REVERSE else 0) + o_v |
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p_o = o + i_nh * T*V + ((T-1) * V if REVERSE else 0) + o_v |
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else: |
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p_q = q + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + o_k |
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p_k = k + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + o_k |
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p_a = a + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + o_k |
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p_b = b + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + o_k |
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p_gk = gk + (bos + ((T-1) if REVERSE else 0)) * H*K + i_h * K + o_k |
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p_v = v + (bos + ((T-1) if REVERSE else 0)) * H*V + i_h * V + o_v |
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p_o = o + (bos + ((T-1) if REVERSE else 0)) * H*V + i_h * V + o_v |
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mask_k = o_k < K |
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mask_v = o_v < V |
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mask_h = mask_k[None, :] & mask_v[:, None] |
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b_h = tl.zeros([BV, BK], dtype=tl.float32) |
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if USE_INITIAL_STATE: |
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p_h0 = h0 + i_nh * K*V + o_k[None, :] * V + o_v[:, None] |
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b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32) |
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for _ in range(0, T): |
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b_q = tl.load(p_q, mask=mask_k, other=0).to(tl.float32) * scale |
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b_k = tl.load(p_k, mask=mask_k, other=0).to(tl.float32) |
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b_a = tl.load(p_a, mask=mask_k, other=0).to(tl.float32) |
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b_b = tl.load(p_b, mask=mask_k, other=0).to(tl.float32) |
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b_gk = tl.load(p_gk, mask=mask_k, other=0).to(tl.float32) |
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b_v = tl.load(p_v, mask=mask_v, other=0).to(tl.float32) |
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tmp = tl.sum(b_h * b_a[None, :], axis=1) |
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b_h = exp(b_gk)[None, :] * b_h + (tmp[:, None] * b_b[None, :] + b_k[None, :] * b_v[:, None]) |
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b_o = tl.sum(b_h * b_q[None, :], axis=1) |
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tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v) |
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p_q += (-1 if REVERSE else 1) * (1 if HEAD_FIRST else H) * K |
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p_k += (-1 if REVERSE else 1) * (1 if HEAD_FIRST else H) * K |
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p_a += (-1 if REVERSE else 1) * (1 if HEAD_FIRST else H) * K |
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p_b += (-1 if REVERSE else 1) * (1 if HEAD_FIRST else H) * K |
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p_gk += (-1 if REVERSE else 1) * (1 if HEAD_FIRST else H) * K |
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p_v += (-1 if REVERSE else 1) * (1 if HEAD_FIRST else H) * V |
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p_o += (-1 if REVERSE else 1) * (1 if HEAD_FIRST else H) * V |
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if STORE_FINAL_STATE: |
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p_ht = ht + i_nh * K*V + o_k[None, :] * V + o_v[:, None] |
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tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h) |
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def fused_recurrent_dplr_delta_rule_fwd( |
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q: torch.Tensor, |
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k: torch.Tensor, |
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v: torch.Tensor, |
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a: torch.Tensor, |
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b: torch.Tensor, |
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gk: torch.Tensor, |
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scale: Optional[float] = 1.0, |
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initial_state: Optional[torch.Tensor] = None, |
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output_final_state: bool = False, |
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reverse: bool = False, |
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offsets: Optional[torch.LongTensor] = None, |
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head_first: bool = True |
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): |
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if head_first: |
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B, H, T, K, V = *k.shape, v.shape[-1] |
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else: |
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B, T, H, K, V = *k.shape, v.shape[-1] |
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N = B if offsets is None else len(offsets) - 1 |
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BK = triton.next_power_of_2(K) |
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h0 = initial_state |
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if output_final_state: |
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ht = q.new_empty(N, H, K, V, dtype=torch.float32) |
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else: |
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ht = None |
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o = torch.empty_like(v) |
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def grid(meta): return (triton.cdiv(V, meta['BV']), N * H) |
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fused_recurrent_dplr_delta_rule_fwd_kernel[grid]( |
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q, |
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k, |
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v, |
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a, |
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b, |
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gk, |
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o, |
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h0, |
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ht, |
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offsets, |
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scale, |
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T=T, |
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B=B, |
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H=H, |
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K=K, |
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V=V, |
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BK=BK, |
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REVERSE=reverse, |
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HEAD_FIRST=head_first |
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) |
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return o, ht |
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class FusedRecurrentDPLRDeltaRuleFunction(torch.autograd.Function): |
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@staticmethod |
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@input_guard |
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@autocast_custom_fwd |
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def forward( |
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ctx, |
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q: torch.Tensor, |
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k: torch.Tensor, |
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v: torch.Tensor, |
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a: torch.Tensor, |
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b: torch.Tensor, |
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gk: torch.Tensor, |
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scale: Optional[float] = 1.0, |
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initial_state: Optional[torch.Tensor] = None, |
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output_final_state: bool = False, |
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reverse: bool = False, |
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offsets: Optional[torch.LongTensor] = None, |
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head_first: bool = False |
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): |
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o, ht = fused_recurrent_dplr_delta_rule_fwd( |
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q=q, |
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k=k, |
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v=v, |
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a=a, |
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b=b, |
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gk=gk, |
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scale=scale, |
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initial_state=initial_state, |
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output_final_state=output_final_state, |
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reverse=reverse, |
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offsets=offsets, |
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head_first=head_first |
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) |
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return o, ht |
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@staticmethod |
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@input_guard |
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@autocast_custom_bwd |
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def backward(ctx, do, dht): |
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raise NotImplementedError( |
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"Backward pass for fused_recurrent_dplr_delta_rule is not implemented and will not be supported. " |
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"This kernel is only for inference. " |
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"For training, please use `chunk_dplr_delta_rule`." |
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) |
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def fused_recurrent_dplr_delta_rule( |
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q: torch.Tensor, |
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k: torch.Tensor, |
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v: torch.Tensor, |
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a: torch.Tensor, |
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b: torch.Tensor, |
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gk: torch.Tensor, |
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scale: Optional[float] = 1.0, |
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initial_state: Optional[torch.Tensor] = None, |
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output_final_state: bool = False, |
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reverse: bool = False, |
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cu_seqlens: Optional[torch.Tensor] = None, |
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head_first: bool = False |
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) -> Tuple[torch.Tensor, torch.Tensor]: |
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r""" |
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This function computes the recurrence S_t = S_t @ (I + a_t b_t^T) + v_t k_t^T in a recurrent manner. |
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Args: |
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q (torch.Tensor): |
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queries of shape `[B, H, T, K]` |
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k (torch.Tensor): |
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keys of shape `[B, H, T, K]` |
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v (torch.Tensor): |
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values of shape `[B, H, T, V]` |
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a (torch.Tensor): |
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as of shape `[B, H, T, K]` |
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b (torch.Tensor): |
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bs of shape `[B, H, T, K]` |
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gk (torch.Tensor): |
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gk of shape `[B, H, T, K]` |
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scale (Optional[int]): |
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Scale factor for the RetNet attention scores. |
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If None, it will default to `1 / sqrt(K)`. Default: `1.0`. |
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initial_state (Optional[torch.Tensor]): |
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Initial state of shape `[B, H, K, V]`. Default: `None`. |
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output_final_state (Optional[bool]): |
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Whether to output the final state of shape `[B, H, K, V]`. Default: `False`. |
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reverse (Optional[bool]): |
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If `True`, process the state passing in reverse order. Default: `False`. |
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cu_seqlens (Optional[torch.Tensor]): |
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Cumulative sequence lengths of shape `[N + 1]` used for variable-length training, |
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consistent with the FlashAttention API. |
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head_first (Optional[bool]): |
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Whether the inputs are in the head-first format, which is not supported for variable-length inputs. |
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Default: `False`. |
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""" |
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if cu_seqlens is not None: |
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if q.shape[0] != 1: |
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raise ValueError(f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`." |
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f"Please flatten variable-length inputs before processing.") |
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if head_first: |
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raise RuntimeError("Sequences with variable lengths are not supported for head-first mode") |
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if initial_state is not None and initial_state.shape[0] != len(cu_seqlens) - 1: |
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raise ValueError(f"The number of initial states is expected to be equal to the number of input sequences, " |
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f"i.e., {len(cu_seqlens) - 1} rather than {initial_state.shape[0]}.") |
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if scale is None: |
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scale = q.shape[-1] ** -0.5 |
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else: |
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assert scale > 0, "scale must be positive" |
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o, final_state = FusedRecurrentDPLRDeltaRuleFunction.apply( |
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q, |
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k, |
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v, |
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a, |
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b, |
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gk, |
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scale, |
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initial_state, |
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output_final_state, |
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reverse, |
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cu_seqlens, |
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head_first |
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) |
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return o, final_state |
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