File size: 7,383 Bytes
f72219a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
from typing import Optional, Tuple
import torch
import triton
import triton.language as tl
from fla.ops.common.utils import prepare_chunk_offsets
from fla.ops.utils.op import exp
from fla.utils import check_shared_mem, use_cuda_graph
@triton.heuristics({
'USE_FINAL_STATE_GRADIENT': lambda args: args['dht'] is not None,
'USE_INITIAL_STATE': lambda args: args['dh0'] is not None,
'USE_OFFSETS': lambda args: args['offsets'] is not None,
})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [2, 4, 8, 16, 32]
for num_stages in [2, 3, 4]
],
key=['BT', 'BK', 'BV', "V"],
use_cuda_graph=use_cuda_graph,
)
@triton.jit(do_not_specialize=['T'])
def chunk_dplr_bwd_kernel_dhu(
qg,
bg,
w,
gk,
dht,
dh0,
do,
dh,
dv,
dv2,
offsets,
chunk_offsets,
T,
H: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BC: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
USE_FINAL_STATE_GRADIENT: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr,
USE_OFFSETS: tl.constexpr,
HEAD_FIRST: tl.constexpr
):
i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
i_n, i_h = i_nh // H, i_nh % H
if USE_OFFSETS:
bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32)
T = eos - bos
NT = tl.cdiv(T, BT)
boh = tl.load(chunk_offsets + i_n).to(tl.int32)
else:
bos, eos = i_n * T, i_n * T + T
NT = tl.cdiv(T, BT)
boh = i_n * NT
# [BK, BV]
b_dh = tl.zeros([BK, BV], dtype=tl.float32)
if USE_FINAL_STATE_GRADIENT:
p_dht = tl.make_block_ptr(dht + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
b_dh += tl.load(p_dht, boundary_check=(0, 1))
mask_k = tl.arange(0, BK) < K
for i_t in range(NT - 1, -1, -1):
if HEAD_FIRST:
p_dh = tl.make_block_ptr(dh + (i_nh * NT + i_t) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
else:
p_dh = tl.make_block_ptr(dh + ((boh+i_t) * H + i_h) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_dh, b_dh.to(p_dh.dtype.element_ty), boundary_check=(0, 1))
b_dh_tmp = tl.zeros([BK, BV], dtype=tl.float32)
for i_c in range(tl.cdiv(BT, BC) - 1, -1, -1):
if HEAD_FIRST:
p_qg = tl.make_block_ptr(qg + i_nh * T*K, (K, T), (1, K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
p_bg = tl.make_block_ptr(bg + i_nh * T*K, (T, K), (K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0))
p_w = tl.make_block_ptr(w + i_nh * T*K, (K, T), (1, K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
p_dv = tl.make_block_ptr(dv + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
p_do = tl.make_block_ptr(do + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
p_dv2 = tl.make_block_ptr(dv2 + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
else:
p_qg = tl.make_block_ptr(qg+(bos*H+i_h)*K, (K, T), (1, H*K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
p_bg = tl.make_block_ptr(bg+(bos*H+i_h)*K, (T, K), (H*K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0))
p_w = tl.make_block_ptr(w+(bos*H+i_h)*K, (K, T), (1, H*K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1))
p_dv = tl.make_block_ptr(dv+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
p_do = tl.make_block_ptr(do+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
p_dv2 = tl.make_block_ptr(dv2+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0))
# [BK, BT]
b_qg = tl.load(p_qg, boundary_check=(0, 1))
# [BT, BK]
b_bg = tl.load(p_bg, boundary_check=(0, 1))
b_w = tl.load(p_w, boundary_check=(0, 1))
# [BT, V]
b_do = tl.load(p_do, boundary_check=(0, 1))
b_dv = tl.load(p_dv, boundary_check=(0, 1))
b_dv2 = b_dv + tl.dot(b_bg, b_dh.to(b_bg.dtype))
tl.store(p_dv2, b_dv2.to(p_dv.dtype.element_ty), boundary_check=(0, 1))
# [BK, BV]
b_dh_tmp += tl.dot(b_qg, b_do.to(b_qg.dtype))
b_dh_tmp += tl.dot(b_w, b_dv2.to(b_qg.dtype))
last_idx = min((i_t + 1) * BT, T) - 1
if HEAD_FIRST:
bg_last = tl.load(gk + (i_nh * T + last_idx) * K + tl.arange(0, BK), mask=mask_k)
else:
bg_last = tl.load(gk + ((bos + last_idx) * H + i_h) * K + tl.arange(0, BK), mask=mask_k)
b_dh *= exp(bg_last)[:, None]
b_dh += b_dh_tmp
if USE_INITIAL_STATE:
p_dh0 = tl.make_block_ptr(dh0 + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_dh0, b_dh.to(p_dh0.dtype.element_ty), boundary_check=(0, 1))
def chunk_dplr_bwd_dhu(
qg: torch.Tensor,
bg: torch.Tensor,
w: torch.Tensor,
gk: torch.Tensor,
h0: torch.Tensor,
dht: Optional[torch.Tensor],
do: torch.Tensor,
dv: torch.Tensor,
offsets: Optional[torch.LongTensor] = None,
indices: Optional[torch.LongTensor] = None,
head_first: bool = True,
chunk_size: int = 64
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
if head_first:
B, H, T, K, V = *qg.shape, do.shape[-1]
else:
B, T, H, K, V = *qg.shape, do.shape[-1]
BT = min(chunk_size, max(triton.next_power_of_2(T), 16))
BK = triton.next_power_of_2(K)
assert BK <= 256, "current kernel does not support head dimension being larger than 256."
# H100
if check_shared_mem('hopper', qg.device.index):
BV = 64
BC = 64 if K <= 128 else 32
elif check_shared_mem('ampere', qg.device.index): # A100
BV = 32
BC = 32
else: # Etc: 4090
BV = 16
BC = 16
# N: the actual number of sequences in the batch with either equal or variable lengths
if offsets is None:
N, NT, chunk_offsets = B, triton.cdiv(T, BT), None
else:
N, NT, chunk_offsets = len(offsets) - 1, len(indices), prepare_chunk_offsets(offsets, BT)
BC = min(BT, BC)
NK, NV = triton.cdiv(K, BK), triton.cdiv(V, BV)
assert NK == 1, 'NK > 1 is not supported because it involves time-consuming synchronization'
if head_first:
dh = qg.new_empty(B, H, NT, K, V)
else:
dh = qg.new_empty(B, NT, H, K, V)
dh0 = torch.empty_like(h0, dtype=torch.float32) if h0 is not None else None
dv2 = torch.zeros_like(dv)
grid = (NK, NV, N * H)
chunk_dplr_bwd_kernel_dhu[grid](
qg=qg,
bg=bg,
w=w,
gk=gk,
dht=dht,
dh0=dh0,
do=do,
dh=dh,
dv=dv,
dv2=dv2,
offsets=offsets,
chunk_offsets=chunk_offsets,
T=T,
H=H,
K=K,
V=V,
BT=BT,
BC=BC,
BK=BK,
BV=BV,
HEAD_FIRST=head_first
)
return dh, dh0, dv2
|