| |
|
|
| |
|
|
| from typing import Optional, Tuple |
|
|
| import torch |
| import triton |
| import triton.language as tl |
|
|
| from ...ops.utils import chunk_global_reversed_cumsum, chunk_local_cumsum |
| from ...ops.common.chunk_h import chunk_fwd_h_fn, chunk_bwd_dh_fn |
| from ...utils import contiguous |
|
|
|
|
| @triton.jit |
| def chunk_gla_fwd_kernel_intra( |
| q, |
| k, |
| g, |
| A, |
| s_k_h, |
| s_k_t, |
| s_k_d, |
| scale, |
| T: tl.constexpr, |
| K: tl.constexpr, |
| BT: tl.constexpr, |
| BC: tl.constexpr, |
| BK: tl.constexpr, |
| NC: tl.constexpr |
| ): |
| i_k, i_c, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| i_t, i_i, i_j = i_c // (NC * NC), (i_c % (NC * NC)) // NC, (i_c % (NC * NC)) % NC |
| n_bh = tl.num_programs(2) |
|
|
| if i_i > i_j: |
| p_q = tl.make_block_ptr(q + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_g = tl.make_block_ptr(g + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_k = tl.make_block_ptr(k + i_bh * s_k_h, (K, T), (s_k_d, s_k_t), (i_k * BK, i_t * BT + i_j * BC), (BK, BC), (0, 1)) |
| p_gk = tl.make_block_ptr(g + i_bh * s_k_h, (K, T), (s_k_d, s_k_t), (i_k * BK, i_t * BT + i_j * BC), (BK, BC), (0, 1)) |
| p_gn = tl.make_block_ptr(g + i_bh * s_k_h, (T * K,), (s_k_d,), ((i_t * BT + i_i * BC) * K + i_k * BK,), (BK,), (0,)) |
| p_A = tl.make_block_ptr(A + (i_k*n_bh+i_bh)*T*BT, (T, BT), (BT, 1), (i_t * BT + i_i * BC, i_j * BC), (BC, BC), (1, 0)) |
| |
| b_gn = tl.load(p_gn, boundary_check=(0,)) |
| |
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_qg = (b_q * tl.exp(b_g - b_gn[None, :]) * scale).to(b_q.dtype) |
| |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_gk = tl.load(p_gk, boundary_check=(0, 1)) |
| b_kg = (b_k * tl.exp(b_gn[:, None] - b_gk)).to(b_k.dtype) |
| |
| b_A = tl.dot(b_qg, b_kg, allow_tf32=False) |
| tl.store(p_A, b_A.to(A.dtype.element_ty), boundary_check=(0, 1)) |
| elif i_i == i_j: |
| p_q = tl.make_block_ptr(q + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_g = tl.make_block_ptr(g + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_k = tl.make_block_ptr(k + i_bh * s_k_h, (T * K,), (s_k_d,), ((i_t * BT + i_j * BC) * K + i_k * BK,), (BK,), (0,)) |
| p_gk = tl.make_block_ptr(g + i_bh * s_k_h, (T * K,), (s_k_d,), ((i_t * BT + i_j * BC) * K + i_k * BK,), (BK,), (0,)) |
| |
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
|
|
| o_i = tl.arange(0, BC) |
| o_A = (i_bh + i_k * n_bh) * T * BT + (i_t * BT + i_i * BC + tl.arange(0, BC)) * BT + i_j * BC |
| m_A = (i_t * BT + i_i * BC + tl.arange(0, BC)) < T |
| for j in range(0, BC): |
| |
| b_k = tl.load(p_k, boundary_check=(0,)).to(tl.float32) |
| b_gk = tl.load(p_gk, boundary_check=(0,)).to(tl.float32) |
| |
| b_A = tl.sum(b_q * b_k[None, :] * tl.exp(b_g - b_gk[None, :]) * scale, 1) |
| b_A = tl.where(o_i >= j, b_A, 0.) |
| tl.store(A + o_A + j, b_A.to(b_q.dtype), mask=m_A) |
|
|
| p_k = tl.advance(p_k, (K,)) |
| p_gk = tl.advance(p_gk, (K,)) |
|
|
|
|
| @triton.jit |
| def chunk_gla_fwd_kernel_inter( |
| q, |
| v, |
| g, |
| h, |
| o, |
| A, |
| s_k_h, |
| s_k_t, |
| s_k_d, |
| s_v_h, |
| s_v_t, |
| s_v_d, |
| s_h_h, |
| s_h_t, |
| s_h_d, |
| scale, |
| T: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr |
| ): |
| i_v, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
|
|
| b_o = tl.zeros([BT, BV], dtype=tl.float32) |
| for i_k in range(tl.cdiv(K, BK)): |
| p_q = tl.make_block_ptr(q + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_g = tl.make_block_ptr(g + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_h = tl.make_block_ptr(h + i_bh * s_h_h + i_t * K * V, (K, V), (s_h_t, s_h_d), (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_g = tl.load(p_g, boundary_check=(0, 1)) |
| |
| b_qg = (b_q * tl.exp(b_g)).to(b_q.dtype) |
| |
| b_h = tl.load(p_h, boundary_check=(0, 1)) |
| |
| |
| if i_k >= 0: |
| b_o += tl.dot(b_qg, b_h, allow_tf32=False) |
| p_v = tl.make_block_ptr(v + i_bh * s_v_h, (T, V), (s_v_t, s_v_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_o = tl.make_block_ptr(o + i_bh * s_v_h, (T, V), (s_v_t, s_v_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_A = tl.make_block_ptr(A + i_bh * T * BT, (T, BT), (BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)) |
| |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| |
| b_A = tl.load(p_A, boundary_check=(0, 1)) |
| b_o += tl.dot(b_A, b_v, allow_tf32=False) |
| tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| @triton.jit |
| def chunk_gla_bwd_kernel_intra( |
| q, |
| k, |
| g, |
| dA, |
| dq, |
| dk, |
| dg, |
| s_k_h, |
| s_k_t, |
| s_k_d, |
| T: tl.constexpr, |
| K: tl.constexpr, |
| BT: tl.constexpr, |
| BC: tl.constexpr, |
| BK: tl.constexpr, |
| NC: tl.constexpr |
| ): |
| i_k, i_c, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| i_t, i_i = i_c // NC, i_c % NC |
|
|
| p_g = tl.make_block_ptr(g + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_gn = tl.make_block_ptr(g + i_bh * s_k_h, (T * K,), (s_k_d,), ((i_t * BT + i_i * BC) * K + i_k * BK,), (BK,), (0,)) |
| |
| b_gn = tl.load(p_gn, boundary_check=(0,)) |
| |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_dq = tl.zeros([BC, BK], dtype=tl.float32) |
| for i_j in range(0, i_i): |
| p_k = tl.make_block_ptr(k + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_gk = tl.make_block_ptr(g + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_dA = tl.make_block_ptr(dA + i_bh * T * BT, (T, BT), (BT, 1), (i_t * BT + i_i * BC, i_j * BC), (BC, BC), (1, 0)) |
| |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_gk = tl.load(p_gk, boundary_check=(0, 1)) |
| b_kg = (b_k * tl.exp(b_gn[None, :] - b_gk)).to(b_k.dtype) |
| |
| b_dA = tl.load(p_dA, boundary_check=(0, 1)) |
| |
| b_dq += tl.dot(b_dA, b_kg, allow_tf32=False) |
| b_dq *= tl.exp(b_g - b_gn[None, :]) |
|
|
| o_i = tl.arange(0, BC) |
| o_dA = i_bh * T * BT + (i_t * BT + i_i * BC + tl.arange(0, BC)) * BT + i_i * BC |
| m_dA = (i_t * BT + i_i * BC + tl.arange(0, BC)) < T |
| for j in range(0, BC): |
| p_kj = tl.make_block_ptr(k + i_bh * s_k_h, (T * K,), (1,), ((i_t * BT + i_i*BC+j) * K + i_k * BK,), (BK,), (0,)) |
| p_gkj = tl.make_block_ptr(g + i_bh * s_k_h, (T * K,), (1,), ((i_t * BT + i_i*BC+j) * K + i_k * BK,), (BK,), (0,)) |
| |
| b_dA = tl.load(dA + o_dA + j, mask=m_dA, other=0) |
| |
| b_kj = tl.load(p_kj, boundary_check=(0,)).to(tl.float32) |
| b_gkj = tl.load(p_gkj, boundary_check=(0,)).to(tl.float32) |
| |
| m_i = o_i[:, None] >= j |
| |
| b_dq += tl.where(m_i, b_dA[:, None] * b_kj[None, :] * tl.exp(b_g - b_gkj[None, :]), 0.) |
| p_dq = tl.make_block_ptr(dq + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
|
|
| b_dq = b_dq + tl.load(p_dq, boundary_check=(0, 1)) |
| tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| tl.debug_barrier() |
| p_k = tl.make_block_ptr(k + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_gk = tl.make_block_ptr(g + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_gn = tl.make_block_ptr(g + i_bh * s_k_h, (T*K,), (s_k_d,), ((i_t * BT + i_i * BC + BC - 1) * K + i_k * BK,), (BK,), (0,)) |
| |
| b_gn = tl.load(p_gn, boundary_check=(0,)) |
| |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_gk = tl.load(p_gk, boundary_check=(0, 1)) |
| b_dk = tl.zeros([BC, BK], dtype=tl.float32) |
| for i_j in range(i_i + 1, NC): |
| p_q = tl.make_block_ptr(q + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_g = tl.make_block_ptr(g + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_j * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_dA = tl.make_block_ptr(dA + i_bh * T * BT, (T, BT), (BT, 1), (i_t * BT + i_j * BC, i_i * BC), (BC, BC), (1, 0)) |
| |
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
| b_g = tl.load(p_g, boundary_check=(0, 1)) |
| b_qg = (b_q * tl.exp(b_g - b_gn[None, :])).to(b_q.dtype) |
| |
| b_dA = tl.load(p_dA, boundary_check=(0, 1)) |
| |
| b_dk += tl.dot(tl.trans(b_dA), b_qg, allow_tf32=False) |
| b_dk *= tl.exp(b_gn[None, :] - b_gk) |
|
|
| o_dA = i_bh * T * BT + (i_t * BT + i_i * BC) * BT + i_i * BC + tl.arange(0, BC) |
| for j in range(0, BC): |
| p_qj = tl.make_block_ptr(q + i_bh * s_k_h, (T * K,), (1,), ((i_t * BT + i_i * BC + j) * K + i_k * BK,), (BK,), (0,)) |
| p_gqj = tl.make_block_ptr(g + i_bh * s_k_h, (T * K,), (1,), ((i_t * BT + i_i * BC + j) * K + i_k * BK,), (BK,), (0,)) |
| |
| b_dA = tl.load(dA + o_dA + j * BT, mask=(i_t * BT + i_i * BC + j < T), other=0) |
| |
| b_qj = tl.load(p_qj, boundary_check=(0,)).to(tl.float32) |
| b_gqj = tl.load(p_gqj, boundary_check=(0,)).to(tl.float32) |
| |
| m_i = o_i[:, None] <= j |
| b_dk += tl.where(m_i, b_dA[:, None] * b_qj[None, :] * tl.exp(b_gqj[None, :] - b_gk), 0.) |
|
|
| p_q = tl.make_block_ptr(q + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_dk = tl.make_block_ptr(dk + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
| p_dg = tl.make_block_ptr(dg + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)) |
|
|
| b_q = tl.load(p_q, boundary_check=(0, 1)) |
| b_dk = b_dk + tl.load(p_dk, boundary_check=(0, 1)) |
| b_dg = b_q * b_dq - b_k * b_dk |
| tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1)) |
| tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0, 1)) |
|
|
|
|
| @triton.jit |
| def chunk_gla_bwd_kernel_inter( |
| k, |
| v, |
| h, |
| g, |
| A, |
| do, |
| dh, |
| dq, |
| dk, |
| dv, |
| dA, |
| s_k_h, |
| s_k_t, |
| s_k_d, |
| s_v_h, |
| s_v_t, |
| s_v_d, |
| s_h_h, |
| s_h_t, |
| s_h_d, |
| scale, |
| T: tl.constexpr, |
| K: tl.constexpr, |
| V: tl.constexpr, |
| BT: tl.constexpr, |
| BK: tl.constexpr, |
| BV: tl.constexpr |
| ): |
| i_k, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
| n_bh = tl.num_programs(2) |
|
|
| p_k = tl.make_block_ptr(k + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_gk = tl.make_block_ptr(g + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_gn = tl.make_block_ptr(g + i_bh * s_k_h, (T * K,), (s_k_d,), ((i_t * BT + BT - 1) * K + i_k * BK,), (BK,), (0,)) |
| p_A = tl.make_block_ptr(A + i_bh * T * BT, (BT, T), (1, BT), (0, i_t * BT), (BT, BT), (0, 1)) |
|
|
| |
| b_k = tl.load(p_k, boundary_check=(0, 1)) |
| b_gk = tl.load(p_gk, boundary_check=(0, 1)) |
| b_gn = tl.exp(tl.load(p_gn, boundary_check=(0,))[None, :] - b_gk) |
| b_k = (b_k * b_gn).to(b_k.dtype) |
| |
| b_A = tl.load(p_A, boundary_check=(0, 1)) |
|
|
| b_dq = tl.zeros([BT, BK], dtype=tl.float32) |
| b_dk = tl.zeros([BT, BK], dtype=tl.float32) |
| b_dA = tl.zeros([BT, BT], dtype=tl.float32) |
| for i_v in range(tl.cdiv(V, BV)): |
| p_v = tl.make_block_ptr(v + i_bh * s_v_h, (T, V), (s_v_t, s_v_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_h = tl.make_block_ptr(h + i_bh * s_h_h + i_t * V * K, (V, K), (s_h_d, s_h_t), (i_v * BV, i_k * BK), (BV, BK), (0, 1)) |
| p_do = tl.make_block_ptr(do + i_bh * s_v_h, (T, V), (s_v_t, s_v_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
| p_dh = tl.make_block_ptr(dh + i_bh * s_h_h + i_t * K*V, (K, V), (s_h_t, s_h_d), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
| p_dv = tl.make_block_ptr(dv + (i_k*n_bh+i_bh) * s_v_h, (T, V), (s_v_t, s_v_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0)) |
|
|
| |
| b_v = tl.load(p_v, boundary_check=(0, 1)) |
| |
| b_h = tl.load(p_h, boundary_check=(0, 1)) |
| |
| b_do = tl.load(p_do, boundary_check=(0, 1)) |
| |
| b_dh = tl.load(p_dh, boundary_check=(0, 1)) |
|
|
| |
| b_dv = tl.dot(b_k, b_dh, allow_tf32=False) |
| if i_k == 0: |
| b_dv += tl.dot(b_A, b_do, allow_tf32=False) |
| b_do = (b_do * scale).to(b_do.dtype) |
| tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1)) |
| |
| b_dA += tl.dot(b_do, tl.trans(b_v), allow_tf32=False) |
| |
| b_dq += tl.dot(b_do, b_h, allow_tf32=False) |
| |
| b_dk += tl.dot(b_v, tl.trans(b_dh), allow_tf32=False) |
| b_dq = b_dq * tl.exp(b_gk) |
| b_dk = b_dk * b_gn |
|
|
| p_dq = tl.make_block_ptr(dq + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dk = tl.make_block_ptr(dk + i_bh * s_k_h, (T, K), (s_k_t, s_k_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0)) |
| p_dA = tl.make_block_ptr(dA + i_bh * T * BT, (T, BT, ), (BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)) |
| 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)) |
|
|
| o_i = tl.arange(0, BT) |
| m_s = o_i[:, None] >= o_i[None, :] |
| |
| b_dA = tl.where(m_s, b_dA, 0.).to(b_k.dtype) |
| if i_k == 0: |
| tl.store(p_dA, b_dA.to(p_dA.dtype.element_ty), boundary_check=(0, 1)) |
|
|
| class ChunkGLAFunction(torch.autograd.Function): |
|
|
| @staticmethod |
| @contiguous |
| def forward(ctx, q, k, v, g, scale, initial_state, output_final_state, checkpoint_level): |
| B, H, T, K, V = *q.shape, v.shape[-1] |
| BT, BC = 64, 16 |
| BK = min(64, triton.next_power_of_2(K)) |
| BV = min(64, triton.next_power_of_2(V)) |
| NT, NC = triton.cdiv(T, BT), triton.cdiv(BT, BC) |
| NK = triton.cdiv(K, BK) |
| NV = triton.cdiv(V, BV) |
| num_warps = 4 if BK == 64 else 2 |
| num_stages = 1 |
|
|
| g_cumsum = chunk_local_cumsum(g, BT=BT) |
| g_org, g = g, g_cumsum |
|
|
| h, ht = chunk_fwd_h_fn( |
| k=k, v=v, g=None, gk=g, gv=None, BT=BT, h0=initial_state, output_final_state=output_final_state |
| ) |
| A = q.new_zeros(NK, B, H, T, BT) |
| grid = (NK, NT * NC * NC, B * H) |
| chunk_gla_fwd_kernel_intra[grid]( |
| q, k, g, A, |
| k.stride(1), k.stride(2), k.stride(3), |
| scale, |
| T=T, K=K, BT=BT, BC=BC, BK=BK, NC=NC, |
| num_warps=num_warps, |
| num_stages=num_stages |
| ) |
| A = A.sum(0, dtype=A.dtype) |
| o = torch.empty_like(v) |
| grid = (NV, NT, B * H) |
| chunk_gla_fwd_kernel_inter[grid]( |
| q, v, g, h, o, A, |
| k.stride(1), k.stride(2), k.stride(3), |
| v.stride(1), v.stride(2), v.stride(3), |
| h.stride(1), h.stride(2), h.stride(3), |
| scale, |
| T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, |
| num_warps=num_warps, |
| num_stages=num_stages |
| ) |
| if checkpoint_level >= 1: |
| del g |
| g = g_org |
| if checkpoint_level > 1: |
| del h |
| h = None |
|
|
| ctx.save_for_backward(q, k, v, g, h, initial_state, A) |
| ctx.BT = BT |
| ctx.scale = scale |
| ctx.checkpoint_level = checkpoint_level |
| return o, ht |
|
|
| @staticmethod |
| @contiguous |
| def backward(ctx, do, dht): |
| q, k, v, g, h, initial_state, A = ctx.saved_tensors |
| B, H, T, K, V = *q.shape, v.shape[-1] |
| BT, BC = ctx.BT, 16 |
| BK = min(64, triton.next_power_of_2(K)) |
| BV = min(64, triton.next_power_of_2(V)) |
| NT, NC = triton.cdiv(T, BT), triton.cdiv(BT, BC) |
| NK = triton.cdiv(K, BK) |
| num_warps = 4 if BK == 64 else 2 |
| num_stages = 1 |
|
|
| if ctx.checkpoint_level >= 1: |
| g_cumsum = chunk_local_cumsum(g, BT=BT) |
| g_org, g = g, g_cumsum |
|
|
| if h is None: |
| h, _ = chunk_fwd_h_fn( |
| k=k, v=v, g=None, gk=g, gv=None, BT=BT, h0=initial_state, output_final_state=False |
| ) |
|
|
| scale = ctx.scale |
| dh, dh0 = chunk_bwd_dh_fn(q=q, k=k, v=v, g=None, gk=g, gv=None, do=do, h0=initial_state, dht=dht, BT=BT, scale=scale) |
| dq = torch.empty_like(q) |
| dk = torch.empty_like(k) |
| dg = torch.empty_like(k, dtype=torch.float) |
| dv = v.new_empty(NK, *v.shape) |
| dA = q.new_zeros(B, H, T, BT) |
| grid = (NK, NT, B * H) |
| chunk_gla_bwd_kernel_inter[grid]( |
| k, v, h, g, A, do, dh, dq, dk, dv, dA, |
| k.stride(1), k.stride(2), k.stride(3), |
| v.stride(1), v.stride(2), v.stride(3), |
| h.stride(1), h.stride(2), h.stride(3), |
| scale, |
| T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, |
| num_warps=num_warps, |
| num_stages=num_stages |
| ) |
| dv = dv.sum(0, dtype=v.dtype) |
| grid = (NK, NT * NC, B * H) |
| chunk_gla_bwd_kernel_intra[grid]( |
| q, k, g, dA, dq, dk, dg, |
| k.stride(1), k.stride(2), k.stride(3), |
| T=T, K=K, BT=BT, BC=BC, BK=BK, NC=NC, |
| num_warps=num_warps, |
| num_stages=num_stages |
| ) |
| dg = chunk_global_reversed_cumsum(dg).to(k.dtype) |
| return dq, dk, dv, dg, None, dh0, None, None |
|
|
|
|
| def chunk_gla( |
| q: torch.Tensor, |
| k: torch.Tensor, |
| v: torch.Tensor, |
| g: torch.Tensor, |
| scale: Optional[int] = None, |
| initial_state: torch.Tensor = None, |
| output_final_state: bool = False, |
| checkpoint_level: Optional[int] = 2 |
| ) -> Tuple[torch.Tensor, torch.Tensor]: |
| r""" |
| Args: |
| q (torch.Tensor): |
| queries of shape `(B, H, T, K)` |
| k (torch.Tensor): |
| keys of shape `(B, H, T, K)` |
| v (torch.Tensor): |
| values of shape `(B, H, T, V)` |
| g (torch.Tensor): |
| Forget gates of shape `(B, H, T, K)` applied to keys. |
| scale (Optional[int]): |
| Scale factor for the GLA attention scores. |
| If not provided, it will default to `1 / sqrt(K)`. Default: `None`. |
| initial_state (Optional[torch.Tensor]): |
| Initial state of shape `(B, H, K, V)`. Default: `None`. |
| output_final_state (Optional[bool]): |
| Whether to output the final state of shape `(B, H, K, V)`. Default: `False`. |
| checkpoint_level (Optional[int]): |
| Checkpointing level; higher values will save more memories and do more recomputations during backward. |
| Default: `0`: |
| - 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. |
| """ |
| assert checkpoint_level in [0, 1, 2] |
| if scale is None: |
| scale = q.shape[-1] ** -0.5 |
| o, final_state = ChunkGLAFunction.apply(q, k, v, g, scale, initial_state, output_final_state, checkpoint_level) |
| return o, final_state |
|
|
