| |
|
|
| import warnings |
|
|
| import torch |
| import triton |
| import triton.language as tl |
| from einops import reduce |
|
|
| from fla.ops.common.chunk_h import chunk_bwd_dh, chunk_fwd_h |
| from fla.ops.gla.chunk import chunk_gla_bwd, chunk_gla_fwd |
| from fla.ops.utils import prepare_chunk_indices |
| from fla.ops.utils.cumsum import chunk_local_cumsum |
| from fla.ops.utils.op import exp |
| from fla.ops.utils.softmax import softmax_bwd, softmax_fwd |
| from fla.utils import autotune_cache_kwargs, input_guard |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| }) |
| @triton.autotune( |
| configs=[ |
| triton.Config({'BK': BK, 'BV': BV}, num_warps=num_warps, num_stages=num_stages) |
| for BK in [32, 64] |
| for BV in [32, 64] |
| for num_warps in [2, 4, 8] |
| for num_stages in [2, 3, 4] |
| ], |
| key=['BT'], |
| **autotune_cache_kwargs, |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def chunk_gsa_fwd_k_kernel_inter( |
| q, |
| k, |
| h, |
| g, |
| o, |
| A, |
| cu_seqlens, |
| chunk_indices, |
| scale, |
| T, |
| HQ: tl.constexpr, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr, |
| NG: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_v, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| i_b, i_hq = i_bh // HQ, i_bh % HQ |
| i_h = i_hq // NG |
| if IS_VARLEN: |
| i_tg = i_t |
| 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 |
| NT = tl.cdiv(T, BT) |
| else: |
| NT = tl.cdiv(T, BT) |
| i_tg = i_b * NT + i_t |
| bos, eos = i_b * T, i_b * T + T |
|
|
| o_i = tl.arange(0, BT) |
| m_s = o_i[:, None] >= o_i[None, :] |
|
|
| b_o = tl.zeros([BT, BV], dtype=tl.float32) |
| b_A = tl.zeros([BT, BT], dtype=tl.float32) |
| for i_k in range(tl.cdiv(K, BK)): |
| p_q = tl.make_block_ptr(q + (bos * HQ + i_hq) * K, (T, K), (HQ*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_k = tl.make_block_ptr(k + (bos * H + i_h) * K, (K, T), (1, H*K), (i_k * BK, i_t * BT), (BK, BT), (0, 1)) |
| p_h = tl.make_block_ptr(h + (i_tg * H + i_h) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
|
|
| |
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
| b_q = (b_q * scale).to(b_q.dtype) |
| |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| |
| b_h = tl.load(p_h, boundary_check=(0, 1)) |
| |
| b_o += tl.dot(b_q, b_h) |
| |
| b_A += tl.dot(b_q, b_k) |
| p_g = tl.make_block_ptr(g + (bos * H + i_h) * V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_o = tl.make_block_ptr(o + (bos * HQ + i_hq) * V, (T, V), (HQ*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_A = tl.make_block_ptr(A + (bos * HQ + i_hq) * BT, (T, BT), (HQ*BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)) |
| |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_o = b_o * exp(b_g) |
| tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| |
| b_A = tl.where(m_s, b_A, 0.) |
| if i_v == 0: |
| tl.store(p_A, b_A.to(p_A.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| }) |
| @triton.jit(do_not_specialize=['T']) |
| def chunk_gsa_fwd_k_kernel_intra( |
| v, |
| g, |
| o, |
| A, |
| cu_seqlens, |
| chunk_indices, |
| T, |
| HQ: tl.constexpr, |
| H: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BC: tl.constexpr, |
| BV: tl.constexpr, |
| NC: tl.constexpr, |
| NG: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_v, i_c, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| i_b, i_hq = i_bh // HQ, i_bh % HQ |
| i_h = i_hq // NG |
| i_t, i_i = i_c // NC, i_c % NC |
| 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_v = i_v * BV + tl.arange(0, BV) |
| m_v = o_v < V |
|
|
| if i_t * BT + i_i * BC >= T: |
| return |
|
|
| p_g = tl.make_block_ptr(g + (bos * H + i_h) * V, (T, V), (H*V, 1), (i_t * BT + i_i * BC, i_v * BV), (BC, BV), (1, 0)) |
| p_gn = g + (bos + min(i_t * BT + i_i * BC, T)) * H*V + i_h * V + o_v |
| |
| b_gn = tl.load(p_gn, mask=m_v, other=0) |
| |
| b_o = tl.zeros([BC, BV], dtype=tl.float32) |
| for i_j in range(0, i_i): |
| p_A = tl.make_block_ptr(A + (bos*HQ+i_hq) * BT, (T, BT), (HQ*BT, 1), (i_t*BT+i_i*BC, i_j * BC), (BC, BC), (1, 0)) |
| p_v = tl.make_block_ptr(v + (bos*H+i_h) * V, (T, V), (H*V, 1), (i_t * BT + i_j * BC, i_v * BV), (BC, BV), (1, 0)) |
| p_gv = tl.make_block_ptr(g + (bos*H+i_h) * V, (T, V), (H*V, 1), (i_t * BT + i_j * BC, i_v * BV), (BC, BV), (1, 0)) |
| |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| b_gv = tl.load(p_gv, boundary_check=(0, 1)) |
| b_vg = (b_v * exp(b_gn[None, :] - b_gv)).to(b_v.dtype) |
| |
| b_A = tl.load(p_A, boundary_check=(0, 1)) |
| b_o += tl.dot(b_A, b_vg) |
| |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_o *= exp(b_g - b_gn[None, :]) |
|
|
| o_i = tl.arange(0, BC) |
| o_A = (bos + i_t * BT + i_i * BC + tl.arange(0, BC)) * HQ*BT + i_hq * BT + i_i * BC |
| m_A = (i_t * BT + i_i * BC + tl.arange(0, BC)) < T |
| for j in range(0, min(BC, T - i_t * BT - i_i * BC)): |
| p_v = v + (bos + i_t * BT + i_i * BC + j) * H*V + i_h * V + o_v |
| p_gv = g + (bos + i_t * BT + i_i * BC + j) * H*V + i_h * V + o_v |
| |
| b_A = tl.load(A + o_A + j, mask=m_A, other=0) |
| |
| b_v = tl.load(p_v, mask=m_v, other=0).to(tl.float32) |
| b_gv = tl.load(p_gv, mask=m_v, other=0).to(tl.float32) |
| |
| b_vg = b_v[None, :] * exp(b_g - b_gv[None, :]) |
| |
| b_o += tl.where(o_i[:, None] >= j, b_A[:, None] * b_vg, 0.) |
| p_o = tl.make_block_ptr(o + (bos*HQ + i_hq) * V, (T, V), (HQ*V, 1), (i_t * BT + i_i * BC, i_v * BV), (BC, BV), (1, 0)) |
| b_o += tl.load(p_o, boundary_check=(0, 1)) |
| tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| }) |
| @triton.autotune( |
| configs=[ |
| triton.Config({}, num_warps=num_warps) |
| for num_warps in [2, 4, 8] |
| ], |
| key=["BT"], |
| **autotune_cache_kwargs, |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def chunk_gsa_bwd_k_kernel_dA( |
| v, |
| g, |
| do, |
| dA, |
| chunk_indices, |
| cu_seqlens, |
| scale, |
| T, |
| B: tl.constexpr, |
| HQ: tl.constexpr, |
| H: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BC: tl.constexpr, |
| BV: tl.constexpr, |
| NC: tl.constexpr, |
| NG: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_v, i_c, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| i_b, i_hq = i_bh // HQ, i_bh % HQ |
| i_h = i_hq // NG |
| i_t, i_i, i_j = i_c // (NC * NC), (i_c % (NC * NC)) // NC, (i_c % (NC * NC)) % NC |
| 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) |
| all = T |
| T = eos - bos |
| else: |
| bos, eos = i_b * T, i_b * T + T |
| all = B * T |
|
|
| o_v = i_v * BV + tl.arange(0, BV) |
| m_v = o_v < V |
|
|
| if i_t * BT + i_i * BC >= T: |
| return |
|
|
| p_dA = tl.make_block_ptr(dA+((i_v*all+bos)*HQ+i_hq)*BT, (T, BT), (HQ*BT, 1), (i_t*BT+i_i*BC, i_j*BC), (BC, BC), (1, 0)) |
|
|
| |
| b_dA = tl.zeros([BC, BC], dtype=tl.float32) |
| if i_i > i_j: |
| p_v = tl.make_block_ptr(v + (bos*H+i_h) * V, (V, T), (1, H*V), (i_v * BV, i_t*BT + i_j*BC), (BV, BC), (0, 1)) |
| p_gv = tl.make_block_ptr(g + (bos*H+i_h) * V, (V, T), (1, H*V), (i_v * BV, i_t*BT + i_j*BC), (BV, BC), (0, 1)) |
| p_gn = g + (bos + i_t*BT + i_i*BC) * H*V + i_h * V + o_v |
| p_g = tl.make_block_ptr(g + (bos*H+i_h) * V, (T, V), (H*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
| p_do = tl.make_block_ptr(do + (bos*HQ+i_hq) * V, (T, V), (HQ*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
| |
| b_gn = tl.load(p_gn, mask=m_v, other=0.) |
| |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_do = tl.load(p_do, boundary_check=(0, 1)) |
| b_do = (b_do * exp(b_g - b_gn[None, :]) * scale).to(b_do.dtype) |
| |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| b_gv = tl.load(p_gv, boundary_check=(0, 1)) |
| b_vg = (b_v * exp(b_gn[:, None] - b_gv)).to(b_v.dtype) |
| |
| b_dA = tl.dot(b_do, b_vg) |
| elif i_i == i_j: |
| p_g = tl.make_block_ptr(g + (bos*H + i_h) * V, (T, V), (H*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
| p_do = tl.make_block_ptr(do + (bos*HQ + i_hq) * V, (T, V), (HQ*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
| p_v = v + (bos + i_t*BT + i_j*BC) * H*V + i_h * V + o_v |
| p_gv = g + (bos + i_t*BT + i_j*BC) * H*V + i_h * V + o_v |
| |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_do = tl.load(p_do, boundary_check=(0, 1)) * scale |
| m_v = o_v < V |
|
|
| o_i = tl.arange(0, BC) |
| |
| m_dA = o_i[:, None] >= o_i[None, :] |
| for j in range(0, min(BC, T - i_t * BT - i_j * BC)): |
| |
| b_v = tl.load(p_v, mask=m_v, other=0).to(tl.float32) |
| b_gv = tl.load(p_gv, mask=m_v, other=0).to(tl.float32) |
| |
| b_dAj = tl.sum(b_do * b_v[None, :] * exp(b_g - b_gv[None, :]), 1) |
| b_dA = tl.where((o_i == j)[None, :], b_dAj[:, None], b_dA) |
|
|
| p_v += H*V |
| p_gv += H*V |
| b_dA = tl.where(m_dA, b_dA, 0.) |
| tl.store(p_dA, b_dA.to(dA.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| @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=['BT'], |
| **autotune_cache_kwargs, |
| ) |
| @triton.jit(do_not_specialize=['T']) |
| def chunk_gsa_bwd_k_kernel_dqkvg( |
| q, |
| k, |
| v, |
| h, |
| g, |
| A, |
| do, |
| dh, |
| dq, |
| dk, |
| dv, |
| dg, |
| dgv, |
| dA, |
| cu_seqlens, |
| chunk_indices, |
| scale, |
| T, |
| B: tl.constexpr, |
| HQ: tl.constexpr, |
| H: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr, |
| NG: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_k, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| i_b, i_hq = i_bh // HQ, i_bh % HQ |
| i_h = i_hq // NG |
| if IS_VARLEN: |
| i_tg = i_t |
| 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) |
| all = T |
| T = eos - bos |
| NT = tl.cdiv(T, BT) |
| else: |
| NT = tl.cdiv(T, BT) |
| i_tg = i_b * NT + i_t |
| bos, eos = i_b * T, i_b * T + T |
| all = B * T |
|
|
| o_i = tl.arange(0, BT) |
| o_t = min(i_t * BT + BT, T) |
| m_s = o_i[:, None] >= o_i[None, :] |
|
|
| p_q = tl.make_block_ptr(q + (bos*HQ+i_hq) * K, (T, K), (HQ*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| 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_A = tl.make_block_ptr(A + ((i_k*all+bos)*HQ+i_hq)*BT, (T, BT), (HQ*BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)) |
|
|
| |
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| |
| b_A = tl.dot((b_q * scale).to(b_q.dtype), tl.trans(b_k)) |
| b_A = tl.where(m_s, b_A, 0.) |
| tl.store(p_A, b_A.to(p_A.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| b_dq = tl.zeros([BT, BK], dtype=tl.float32) |
| b_dk = tl.zeros([BT, BK], dtype=tl.float32) |
| for i_v in range(tl.cdiv(V, BV)): |
| o_v = i_v * BV + tl.arange(0, 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_g = tl.make_block_ptr(g + (bos*H+i_h)*V, (T, V), (H*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_gn = g + (bos + o_t - 1) * H*V + i_h * V + o_v |
| p_do = tl.make_block_ptr(do + (bos*HQ+i_hq)*V, (T, V), (HQ*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_dv = tl.make_block_ptr(dv + ((i_k*all+bos)*HQ+i_hq)*V, (T, V), (HQ*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_dg = tl.make_block_ptr(dg + (bos*HQ+i_hq)*V, (T, V), (HQ*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_dgv = tl.make_block_ptr(dgv+((i_k*all+bos)*HQ+i_hq)*V, (T, V), (HQ*V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_h = tl.make_block_ptr(h + (i_tg * H + i_h) * K*V, (V, K), (1, V), (i_v * BV, i_k * BK), (BV, BK), (0, 1)) |
| p_dh = tl.make_block_ptr(dh + (i_tg * HQ + i_hq) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
| m_v = o_v < V |
|
|
| |
| b_gn = tl.load(p_gn, mask=m_v, other=0) |
| |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_gv = exp(b_gn[None, :] - b_g) |
| |
| b_h = tl.load(p_h, boundary_check=(0, 1)) |
| |
| b_do = tl.load(p_do, boundary_check=(0, 1)) |
| b_do = (b_do * exp(b_g) * scale).to(b_do.dtype) |
| |
| b_dh = tl.load(p_dh, boundary_check=(0, 1)) |
| |
| b_dg = tl.sum(tl.trans(b_h) * b_dh, 0) * exp(b_gn) |
|
|
| b_dh = b_dh.to(b_k.dtype) |
| |
| b_dq += tl.dot(b_do, b_h.to(b_k.dtype)) |
| b_dk += tl.dot((b_v * b_gv).to(b_v.dtype), tl.trans(b_dh)) |
| |
| b_dv = tl.dot(b_k, b_dh) * b_gv |
| |
| b_dg += tl.sum(b_dv * b_v, 0) |
|
|
| if i_k == 0: |
| b_dgv = tl.load(p_dg, boundary_check=(0, 1)) + b_dg[None, :] |
| else: |
| b_dgv = tl.zeros([BT, BV], dtype=tl.float32) + b_dg[None, :] |
|
|
| tl.store(p_dgv, b_dgv.to(p_dgv.dtype.element_ty), boundary_check=(0, 1)) |
| tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1)) |
| p_dA = tl.make_block_ptr(dA + (bos*HQ + i_hq) * BT, (T, BT), (HQ*BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)) |
| p_dq = tl.make_block_ptr(dq + (bos*HQ + i_hq) * K, (T, K), (HQ*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dk = tl.make_block_ptr(dk + (bos*HQ + i_hq) * K, (T, K), (HQ*K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| |
| b_dA = tl.load(p_dA, boundary_check=(0, 1)) |
| |
| b_dq += tl.dot(b_dA, b_k) |
| b_dk += tl.dot(tl.trans(b_dA).to(b_k.dtype), b_q) |
|
|
| tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), boundary_check=(0, 1)) |
| tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| @triton.heuristics({ |
| 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, |
| }) |
| @triton.jit(do_not_specialize=['T']) |
| def chunk_gsa_bwd_k_kernel_intra_dvg( |
| v, |
| g, |
| o, |
| A, |
| do, |
| dv, |
| dg, |
| cu_seqlens, |
| chunk_indices, |
| T, |
| HQ: tl.constexpr, |
| H: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BC: tl.constexpr, |
| BV: tl.constexpr, |
| NC: tl.constexpr, |
| NG: tl.constexpr, |
| IS_VARLEN: tl.constexpr, |
| ): |
| i_v, i_c, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| i_b, i_hq = i_bh // HQ, i_bh % HQ |
| i_h = i_hq // NG |
| i_t, i_i = i_c // NC, i_c % NC |
| 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_v = i_v * BV + tl.arange(0, BV) |
| m_v = o_v < V |
|
|
| if i_t * BT + i_i * BC >= T: |
| return |
|
|
| p_gv = tl.make_block_ptr(g + (bos*H+i_h)*V, (T, V), (H*V, 1), (i_t * BT + i_i * BC, i_v * BV), (BC, BV), (1, 0)) |
| p_gn = g + (bos + min(i_t * BT + i_i * BC + BC, T)-1)*H*V + i_h*V + o_v |
| |
| b_gn = tl.load(p_gn, mask=m_v, other=0) |
| |
| b_gv = tl.load(p_gv, boundary_check=(0, 1)) |
| b_dv = tl.zeros([BC, BV], dtype=tl.float32) |
| for i_j in range(i_i + 1, NC): |
| p_g = tl.make_block_ptr(g + (bos*H+i_h) * V, (T, V), (H*V, 1), (i_t * BT + i_j * BC, i_v * BV), (BC, BV), (1, 0)) |
| p_A = tl.make_block_ptr(A + (bos*HQ+i_hq) * BT, (BT, T), (1, HQ*BT), (i_i*BC, i_t*BT + i_j*BC), (BC, BC), (0, 1)) |
| p_do = tl.make_block_ptr(do + (bos*HQ+i_hq) * V, (T, V), (HQ*V, 1), (i_t*BT + i_j*BC, i_v*BV), (BC, BV), (1, 0)) |
| |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_do = tl.load(p_do, boundary_check=(0, 1)) * exp(b_g - b_gn[None, :]) |
| |
| b_A = tl.load(p_A, boundary_check=(0, 1)) |
| |
| b_dv += tl.dot(b_A, b_do.to(b_A.dtype)) |
| b_dv *= exp(b_gn[None, :] - b_gv) |
|
|
| o_i = tl.arange(0, BC) |
| o_c = i_i * BC + tl.arange(0, BC) |
|
|
| p_g = g + (bos + i_t * BT + i_i * BC) * H*V + i_h * V + o_v |
| p_A = A + (bos + i_t*BT + i_i*BC) * HQ*BT + i_hq * BT + o_c |
| p_do = do + (bos + i_t*BT + i_i*BC) * HQ*V + i_hq * V + o_v |
| for j in range(0, min(BC, T - i_t * BT - i_i * BC)): |
| |
| b_A = tl.load(p_A) |
| |
| b_g = tl.load(p_g, mask=m_v, other=0) |
| b_do = tl.load(p_do, mask=m_v, other=0) |
| |
| m_i = o_i[:, None] <= j |
| b_dv += tl.where(m_i, exp(b_g[None, :] - b_gv) * b_A[:, None] * b_do[None, :], 0.) |
|
|
| p_g += H * V |
| p_A += HQ * BT |
| p_do += HQ * V |
| p_o = tl.make_block_ptr(o + (bos*HQ+i_hq)*V, (T, V), (HQ*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
| p_v = tl.make_block_ptr(v + (bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
| p_do = tl.make_block_ptr(do + (bos*HQ+i_hq)*V, (T, V), (HQ*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
| p_dv = tl.make_block_ptr(dv + (bos*HQ+i_hq)*V, (T, V), (HQ*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
| p_dg = tl.make_block_ptr(dg + (bos*HQ+i_hq)*V, (T, V), (HQ*V, 1), (i_t*BT + i_i*BC, i_v*BV), (BC, BV), (1, 0)) |
|
|
| b_o = tl.load(p_o, boundary_check=(0, 1)).to(tl.float32) |
| b_v = tl.load(p_v, boundary_check=(0, 1)).to(tl.float32) |
| b_do = tl.load(p_do, boundary_check=(0, 1)).to(tl.float32) |
| b_dv = b_dv + tl.load(p_dv, boundary_check=(0, 1)).to(tl.float32) |
| b_dg = b_o * b_do - b_v * b_dv |
| tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1)) |
| tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| def chunk_gsa_fwd_v( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: torch.Tensor, |
| scale: float = 1., |
| initial_state: torch.Tensor | None = None, |
| output_final_state: bool = False, |
| cu_seqlens: torch.LongTensor | None = None, |
| chunk_size: int = 64, |
| ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
| _, A, h, ht, o = chunk_gla_fwd( |
| q=q, |
| k=k, |
| v=v, |
| g=None, |
| g_cumsum=g, |
| scale=scale, |
| initial_state=initial_state, |
| output_final_state=output_final_state, |
| cu_seqlens=cu_seqlens, |
| chunk_size=chunk_size, |
| ) |
| return A, h, ht, o |
|
|
|
|
| def chunk_gsa_fwd_k( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: torch.Tensor, |
| h0: torch.Tensor | None = None, |
| output_final_state: bool = False, |
| scale: float = 1., |
| cu_seqlens: torch.LongTensor | None = None, |
| chunk_size: int = 64, |
| ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
| B, T, H, K, V = *k.shape, v.shape[-1] |
| BT = chunk_size |
| BC = min(16, BT) |
| BV = min(64, triton.next_power_of_2(V)) |
| HQ = q.shape[2] |
|
|
| 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) |
| NC = triton.cdiv(BT, BC) |
| NG = HQ // H |
|
|
| h, ht = chunk_fwd_h( |
| k=k, |
| v=v, |
| g=None, |
| gk=None, |
| gv=g, |
| h0=h0, |
| output_final_state=output_final_state, |
| cu_seqlens=cu_seqlens, |
| chunk_size=BT, |
| states_in_fp32=False, |
| ) |
| o = v.new_empty(B, T, HQ, V) |
| A = q.new_empty(B, T, HQ, BT) |
| def grid(meta): return (triton.cdiv(V, meta['BV']), NT, B * HQ) |
| chunk_gsa_fwd_k_kernel_inter[grid]( |
| q, |
| k, |
| h, |
| g, |
| o, |
| A, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| scale=scale, |
| T=T, |
| HQ=HQ, |
| H=H, |
| K=K, |
| V=V, |
| BT=BT, |
| NG=NG, |
| ) |
|
|
| def grid(meta): return (triton.cdiv(V, meta['BV']), NT * NC, B * HQ) |
| chunk_gsa_fwd_k_kernel_intra[grid]( |
| v, |
| g, |
| o, |
| A, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| T=T, |
| HQ=HQ, |
| H=H, |
| V=V, |
| BT=BT, |
| BC=BC, |
| BV=BV, |
| NC=NC, |
| NG=NG, |
| num_warps=4, |
| num_stages=2, |
| ) |
| return A, h, ht, o |
|
|
|
|
| def chunk_gsa_bwd_v( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: torch.Tensor, |
| h0: torch.Tensor, |
| h: torch.Tensor, |
| A: torch.Tensor, |
| do: torch.Tensor, |
| dht: torch.Tensor, |
| dg: torch.Tensor, |
| scale: float = 1., |
| cu_seqlens: torch.LongTensor | None = None, |
| chunk_size: int = 64, |
| ): |
| dq, dk, dv, dg, dh0 = chunk_gla_bwd( |
| q=q, |
| k=k, |
| v=v, |
| g=None, |
| g_cumsum=g, |
| scale=scale, |
| initial_state=h0, |
| h=h, |
| A=A, |
| do=do, |
| dht=dht, |
| cu_seqlens=cu_seqlens, |
| chunk_size=chunk_size, |
| ) |
| return dq, dk, dv, dg, dh0 |
|
|
|
|
| def chunk_gsa_bwd_k( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: torch.Tensor, |
| h: torch.Tensor, |
| h0: torch.Tensor, |
| o: torch.Tensor, |
| do: torch.Tensor, |
| dht: torch.Tensor, |
| dg: torch.Tensor, |
| scale: float = 1., |
| cu_seqlens: torch.LongTensor | None = None, |
| chunk_size: int = 64, |
| ): |
| B, T, H, K, V = *k.shape, v.shape[-1] |
| BT = chunk_size |
| BC = min(16, BT) |
| BK = min(64, triton.next_power_of_2(K)) |
| BV = min(64, triton.next_power_of_2(V)) |
| HQ = q.shape[2] |
|
|
| 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) |
| NC = triton.cdiv(BT, BC) |
| NK = triton.cdiv(K, BK) |
| NV = triton.cdiv(V, BV) |
| NG = HQ // H |
|
|
| if h is None: |
| h, _ = chunk_fwd_h( |
| k=k, |
| v=v, |
| g=None, |
| gk=None, |
| gv=g, |
| h0=h0, |
| output_final_state=False, |
| cu_seqlens=cu_seqlens, |
| chunk_size=BT, |
| states_in_fp32=False, |
| ) |
| dh, dh0 = chunk_bwd_dh( |
| q=q, |
| k=k, |
| v=v, |
| g=None, |
| gk=None, |
| gv=g, |
| do=do, |
| h0=h0, |
| dht=dht, |
| scale=scale, |
| cu_seqlens=cu_seqlens, |
| chunk_size=BT, |
| states_in_fp32=True, |
| ) |
| dA = q.new_empty(NV, B, T, HQ, BT) |
| grid = (NV, NT * NC * NC, B * HQ) |
| chunk_gsa_bwd_k_kernel_dA[grid]( |
| v, |
| g, |
| do, |
| dA, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| scale=scale, |
| T=T, |
| B=B, |
| HQ=HQ, |
| H=H, |
| V=V, |
| BT=BT, |
| BC=BC, |
| BV=BV, |
| NC=NC, |
| NG=NG, |
| ) |
| dA = dA.sum(0, dtype=dA.dtype) |
|
|
| A = do.new_empty(NK, B, T, HQ, BT) |
| dq = torch.empty_like(q) |
| dk = k.new_empty(B, T, HQ, K) |
| dv = v.new_empty(NK, B, T, HQ, V) |
| dgv = g.new_empty(NK, B, T, HQ, V, dtype=torch.float) |
| grid = (NK, NT, B * HQ) |
| chunk_gsa_bwd_k_kernel_dqkvg[grid]( |
| q, |
| k, |
| v, |
| h, |
| g, |
| A, |
| do, |
| dh, |
| dq, |
| dk, |
| dv, |
| dg, |
| dgv, |
| dA, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| scale=scale, |
| T=T, |
| B=B, |
| HQ=HQ, |
| H=H, |
| K=K, |
| V=V, |
| BT=BT, |
| BK=BK, |
| BV=BV, |
| NG=NG, |
| ) |
| A = A.sum(0, dtype=A.dtype) |
| dv = dv.sum(0, dtype=dv.dtype) |
| dgv = dgv.sum(0, dtype=dgv.dtype) |
|
|
| def grid(meta): return (triton.cdiv(V, meta['BV']), NT * NC, B * HQ) |
| chunk_gsa_bwd_k_kernel_intra_dvg[grid]( |
| v, |
| g, |
| o, |
| A, |
| do, |
| dv, |
| dg, |
| cu_seqlens=cu_seqlens, |
| chunk_indices=chunk_indices, |
| T=T, |
| HQ=HQ, |
| H=H, |
| V=V, |
| BT=BT, |
| BC=BC, |
| BV=BV, |
| NC=NC, |
| NG=NG, |
| num_warps=4, |
| num_stages=2, |
| ) |
| dg = dgv.add_(chunk_local_cumsum(dg, chunk_size=BT, reverse=True, cu_seqlens=cu_seqlens)) |
|
|
| return dq, dk, dv, dg, dh0 |
|
|
|
|
| def chunk_gsa_fwd( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| s: torch.Tensor, |
| g: torch.Tensor, |
| initial_state: tuple[torch.Tensor, torch.Tensor] | None = None, |
| output_final_state: bool = False, |
| scale: float = 1., |
| cu_seqlens: torch.LongTensor | None = None, |
| chunk_size: int = 64, |
| ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
| hk0, hv0 = None, None |
| if initial_state is not None: |
| hk0, hv0 = initial_state |
| Ak, hk, hkt, ok = chunk_gsa_fwd_k( |
| q=q, |
| k=k, |
| v=s, |
| g=g, |
| h0=hk0, |
| output_final_state=output_final_state, |
| scale=scale, |
| cu_seqlens=cu_seqlens, |
| chunk_size=chunk_size, |
| ) |
|
|
| |
| p = softmax_fwd(ok, dtype=torch.float) |
|
|
| qv = p.to(q.dtype) |
| Av, hv, hvt, ov = chunk_gsa_fwd_v( |
| q=qv, |
| k=s, |
| v=v, |
| g=g, |
| scale=1., |
| initial_state=hv0, |
| output_final_state=output_final_state, |
| cu_seqlens=cu_seqlens, |
| chunk_size=chunk_size, |
| ) |
| return Ak, hk, hkt, ok, p, Av, hv, hvt, ov |
|
|
|
|
| def chunk_gsa_bwd( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| s: torch.Tensor, |
| g: torch.Tensor, |
| ok: torch.Tensor, |
| p: torch.Tensor, |
| A: tuple[torch.Tensor, torch.Tensor], |
| h: tuple[torch.Tensor, torch.Tensor], |
| initial_state: tuple[torch.Tensor, torch.Tensor] | None, |
| scale: float, |
| do: torch.Tensor, |
| dht: tuple[torch.Tensor, torch.Tensor], |
| cu_seqlens: torch.LongTensor | None = None, |
| chunk_size: int = 64, |
| ): |
| hk0, hv0 = None, None |
| if initial_state is not None: |
| hk0, hv0 = initial_state |
|
|
| _, Av = A |
| hk, hv = h |
| dhkt, dhvt = dht |
|
|
| qv = p.to(q.dtype) |
| dqv, dsv, dv, dg, dhv0 = chunk_gsa_bwd_v( |
| q=qv, |
| k=s, |
| v=v, |
| g=g, |
| h0=hv0, |
| h=hv, |
| A=Av, |
| do=do, |
| dht=dhvt, |
| dg=None, |
| scale=1., |
| cu_seqlens=cu_seqlens, |
| chunk_size=chunk_size, |
| ) |
|
|
| |
| |
| dok = softmax_bwd(p, dqv, dtype=ok.dtype) |
|
|
| dq, dk, dsk, dg, dhk0 = chunk_gsa_bwd_k( |
| q=q, |
| k=k, |
| v=s, |
| g=g, |
| h0=hk0, |
| h=hk, |
| o=ok, |
| do=dok, |
| dht=dhkt, |
| dg=dg, |
| scale=scale, |
| cu_seqlens=cu_seqlens, |
| chunk_size=chunk_size, |
| ) |
|
|
| ds = dsv.add_(dsk) |
| if q.shape[1] != k.shape[1]: |
| dk, dv, ds, dg = map(lambda x: reduce(x, 'b (h g) ... -> b h ...', 'sum', h=k.shape[1]), (dk, dv, ds, dg)) |
| dg = dg.to(s.dtype) |
| return dq, dk, dv, ds, dg, dhk0, dhv0 |
|
|
|
|
| class ChunkGSAFunction(torch.autograd.Function): |
|
|
| @staticmethod |
| @input_guard |
| def forward( |
| ctx, |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| s: torch.Tensor, |
| g: torch.Tensor, |
| scale: float, |
| hk0: torch.Tensor | None, |
| hv0: torch.Tensor | None, |
| output_final_state: bool, |
| checkpoint_level: int, |
| cu_seqlens: torch.LongTensor | None, |
| ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: |
| chunk_size = min(64, max(16, triton.next_power_of_2(q.shape[1]))) |
|
|
| g_org, g = g, chunk_local_cumsum(g, chunk_size, cu_seqlens=cu_seqlens) |
| Ak, hk, hkt, ok, p, Av, hv, hvt, ov = chunk_gsa_fwd( |
| q=q, |
| k=k, |
| v=v, |
| s=s, |
| g=g, |
| initial_state=(hk0, hv0), |
| output_final_state=output_final_state, |
| scale=scale, |
| cu_seqlens=cu_seqlens, |
| chunk_size=chunk_size, |
| ) |
|
|
| if checkpoint_level >= 1: |
| del g |
| g = g_org |
| if checkpoint_level > 1: |
| del hk |
| del hv |
| hk, hv = None, None |
| else: |
| hk0, hv0 = None, None |
|
|
| ctx.save_for_backward(q, k, v, s, g, ok, p, Av, hk0, hv0, hk, hv) |
| ctx.checkpoint_level = checkpoint_level |
| ctx.scale = scale |
| ctx.cu_seqlens = cu_seqlens |
| ctx.chunk_size = chunk_size |
| return ov, hkt, hvt |
|
|
| @staticmethod |
| @input_guard |
| def backward(ctx, dov, dhkt=None, dhvt=None): |
| q, k, v, s, g, ok, p, Av, hk0, hv0, hk, hv = ctx.saved_tensors |
| scale = ctx.scale |
| cu_seqlens = ctx.cu_seqlens |
| chunk_size = ctx.chunk_size |
|
|
| if ctx.checkpoint_level >= 1: |
| g = chunk_local_cumsum(g, chunk_size, cu_seqlens=cu_seqlens) |
| dq, dk, dv, ds, dg, dhk0, dhv0 = chunk_gsa_bwd( |
| q=q, |
| k=k, |
| v=v, |
| s=s, |
| g=g, |
| ok=ok, |
| p=p, |
| A=(None, Av), |
| h=(hk, hv), |
| initial_state=(hk0, hv0), |
| scale=scale, |
| do=dov, |
| dht=(dhkt, dhvt), |
| cu_seqlens=cu_seqlens, |
| chunk_size=chunk_size, |
| ) |
| return dq, dk, dv, ds, dg, None, dhk0, dhv0, None, None, None, None |
|
|
|
|
| @torch.compiler.disable |
| def chunk_gsa( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| s: torch.Tensor, |
| g: torch.Tensor | None = None, |
| scale: int | None = None, |
| initial_state: tuple[torch.Tensor] | None = None, |
| output_final_state: bool | None = False, |
| checkpoint_level: int | None = 2, |
| cu_seqlens: torch.LongTensor | None = None, |
| head_first: bool | None = False, |
| ) -> tuple[torch.Tensor, torch.Tensor]: |
| r""" |
| Args: |
| q (torch.Tensor): |
| queries of shape `[B, T, HQ, K]`.. |
| k (torch.Tensor): |
| keys of shape `[B, T, H, K]`. |
| GQA is performed if `H` is not equal to `HQ`. |
| v (torch.Tensor): |
| values of shape `[B, T, H, V]`. |
| s (torch.Tensor): |
| slot representations of shape `[B, T, H, M]`.. |
| g (torch.Tensor): |
| Forget gates of shape `[B, T, H, M]` applied to keys. |
| If not provided, this function is equivalent to vanilla ABC. |
| scale (Optional[float]): |
| Scale factor for attention scores. |
| If not provided, it will default to `1 / sqrt(K)`. Default: `None`. |
| initial_state (Optional[Tuple[torch.Tensor]]): |
| Initial state tuple having tensors of shape `[N, H, K, M]` and `[N, H, M, V]` for `N` input sequences. |
| For equal-length input sequences, `N` equals the batch size `B`. |
| Default: `None`. |
| output_final_state (Optional[bool]): |
| Whether to output the final state tuple, having tensors of shape `[N, H, K, M]` and `[N, H, M, V]`. |
| Default: `False`. |
| checkpoint_level (Optional[int]): |
| Checkpointing level; higher values will save more memories and do more recomputations during backward. |
| Default: `2`: |
| - Level `0`: no memory saved, no recomputation. |
| - Level `1`: recompute the fp32 cumulative values during backward. |
| - Level `2`: recompute the fp32 cumulative values and forward hidden states during backward. |
| cu_seqlens (torch.LongTensor): |
| Cumulative sequence lengths of shape `[N+1]` used for variable-length training, |
| consistent with the FlashAttention API. |
| head_first (Optional[bool]): |
| Whether the inputs are in the head-first format. Default: `False`. |
| This argument has been deprecated. |
| |
| Returns: |
| o (torch.Tensor): |
| Outputs of shape `[B, T, H, V]`. |
| final_state (Tuple[torch.Tensor]): |
| Final state tuple having tensors of shape `[N, H, K, M]` and `[N, H, M, V]` if `output_final_state=True`. |
| `None` otherwise. |
| |
| Examples:: |
| >>> import torch |
| >>> import torch.nn.functional as F |
| >>> from einops import rearrange |
| >>> from fla.ops.gsa import fused_recurrent_gsa |
| # inputs with equal lengths |
| >>> B, T, H, K, V, M = 4, 2048, 4, 512, 512, 64 |
| >>> q = torch.randn(B, T, H, K, device='cuda') |
| >>> k = torch.randn(B, T, H, K, device='cuda') |
| >>> v = torch.randn(B, T, H, V, device='cuda') |
| >>> s = torch.randn(B, T, H, M, device='cuda') |
| >>> g = F.logsigmoid(torch.randn(B, T, H, M, device='cuda')) |
| >>> h0 = (torch.randn(B, H, K, M, device='cuda'), torch.randn(B, H, M, V, device='cuda')) |
| >>> o, (hk, hv) = chunk_gsa( |
| q, k, v, s, g, |
| initial_state=h0, |
| output_final_state=True |
| ) |
| # for variable-length inputs, the batch size `B` is expected to be 1 and `cu_seqlens` is required |
| >>> q, k, v, s, g = map(lambda x: rearrange(x, 'b t h d -> 1 (b t) h d'), (q, k, v, s, g)) |
| # for a batch with 4 sequences, `cu_seqlens` with 5 start/end positions are expected |
| >>> cu_seqlens = q.new_tensor([0, 2048, 4096, 6144, 8192], dtype=torch.long) |
| >>> o_var, (hk_var, hv_var) = chunk_gsa( |
| q, k, v, s, g, |
| initial_state=h0, |
| output_final_state=True, |
| cu_seqlens=cu_seqlens |
| ) |
| >>> assert o.allclose(o_var.view(o.shape)) |
| >>> assert hk.allclose(hk_var) |
| >>> assert hv.allclose(hv_var) |
| """ |
| if head_first: |
| raise DeprecationWarning( |
| "head_first is deprecated and will be removed in a future version. " |
| "Please use head_first=False for now instead.", |
| ) |
| if not head_first and q.shape[1] < q.shape[2]: |
| warnings.warn( |
| f"Input tensor shape suggests potential format mismatch: seq_len ({q.shape[1]}) < num_heads ({q.shape[2]}). " |
| "This may indicate the inputs were passed in head-first format [B, H, T, ...] " |
| "when head_first=False was specified. " |
| "Please verify your input tensor format matches the expected shape [B, T, H, ...].", |
| ) |
| if cu_seqlens is not None: |
| if q.shape[0] != 1: |
| raise ValueError( |
| f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`." |
| f"Please flatten variable-length inputs before processing.", |
| ) |
| if initial_state is not None and initial_state[0].shape[0] != len(cu_seqlens) - 1: |
| raise ValueError( |
| f"The number of initial states is expected to be equal to the number of input sequences, " |
| f"i.e., {len(cu_seqlens) - 1} rather than {initial_state[0].shape[0]}.", |
| ) |
| assert checkpoint_level in [0, 1, 2] |
| if g is None: |
| |
| z = s.float().logcumsumexp(2) |
| g = torch.cat((z[:, :, :1], z[:, :, :-1]), 1) - z |
| s = torch.exp(s - z).to(k.dtype) |
| if scale is None: |
| scale = q.shape[-1] ** -0.5 |
|
|
| hk0, hv0 = None, None |
| if initial_state is not None: |
| hk0, hv0 = initial_state |
| o, *final_state = ChunkGSAFunction.apply( |
| q, |
| k, |
| v, |
| s, |
| g, |
| scale, |
| hk0, |
| hv0, |
| output_final_state, |
| checkpoint_level, |
| cu_seqlens, |
| ) |
| return o, final_state |
|
|
