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josc146
2024-05-28 22:35:47 +08:00
parent 3488d22d22
commit f05a4acb04
138 changed files with 29047 additions and 334 deletions
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5
finetune/lora/v6/fla/ops/simple_gla/README.md vendored Normal file
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- Simple GLA
Gating mechanism in https://arxiv.org/abs/2103.02143. Compared to GLA, the gating is head-wise instead of elementwise. As a result, we can adapt the RetNet kernel for training using matmul w/o numerical instability. It is faster than GLA but has less expressive power. I will use it as a baseline for the GLA.
$S_{t+1} = g_{t+1} \odot S_{t} + K_{t+1} V_{t+1}^{\top}$ where $g$ is a scalar.

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finetune/lora/v6/fla/ops/simple_gla/__init__.py vendored Normal file
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# -*- coding: utf-8 -*-
from .chunk import chunk_simple_gla
__all__ = [
'chunk_simple_gla'
]

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finetune/lora/v6/fla/ops/simple_gla/chunk.py vendored Normal file
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# -*- coding: utf-8 -*-
# Copyright (c) 2023, Yu Zhang, Songlin Yang
from typing import Tuple
import torch
import triton
import triton.language as tl
from torch.cuda.amp import custom_bwd, custom_fwd
from fla.utils import contiguous
@torch.jit.script
def normalize_output(q, k, o):
k = k.transpose(-2, -1)
k = k.cumsum(-1)
k = k.transpose(-2, -1)
z = (q * k).sum(-1, keepdim=True)
return o / (z + 1e-5)
@triton.jit
def chunk_simple_gla_fwd_kernel_h(
k,
v,
h,
g,
initial_state, # initial state of the chunk [B, H, D_head_K, D_head_V]
final_state, # final state of the chunk [B, H, D_head_K, D_head_V]
s_qk_h,
s_qk_t,
s_qk_d,
s_vo_h,
s_vo_t,
s_vo_d,
s_h_h,
s_h_t,
H: tl.constexpr,
T: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
NT: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr,
STORE_FINAL_STATE: tl.constexpr
):
i_k, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
# [BK, BV]
b_h = tl.zeros([BK, BV], dtype=tl.float32)
if USE_INITIAL_STATE:
p_h0 = tl.make_block_ptr(initial_state + i_bh * K * V,
(K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
b_h = tl.load(p_h0, boundary_check=(0, 1)).to(tl.float32)
for i_t in range(NT):
p_k = tl.make_block_ptr(
k + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
p_v = tl.make_block_ptr(
v + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_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 * K * V,
(K, V), (s_h_t, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_h, b_h.to(p_h.dtype.element_ty), boundary_check=(0, 1))
# [BK, BT]
b_k = tl.load(p_k, boundary_check=(0, 1))
# [BT, BV]
b_v = tl.load(p_v, boundary_check=(0, 1))
# [BK, BV]
b_g_last = tl.load(g + i_bh * T + i_t * BT + BT - 1)
b_h *= tl.math.exp2(b_g_last)
b_g = tl.load(g + i_bh * T + i_t * BT + tl.arange(0, BT))
b_h += tl.dot(b_k, (b_v * tl.math.exp2(b_g_last - b_g)[:, None]).to(b_k.dtype), allow_tf32=False)
if STORE_FINAL_STATE:
p_ht = tl.make_block_ptr(
final_state + i_bh * K * V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
@triton.jit
def chunk_simple_gla_fwd_kernel_o(
q,
k,
v,
h,
g,
o,
s_qk_h,
s_qk_t,
s_qk_d,
s_vo_h,
s_vo_t,
s_vo_d,
s_h_h,
s_h_t,
scale,
H: tl.constexpr,
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)
o_i = tl.arange(0, BT)
m_s = o_i[:, None] >= o_i[None, :]
b_o = tl.zeros([BT, BV], dtype=tl.float32)
b_s = tl.zeros([BT, BT], dtype=tl.float32)
for i_k in range(tl.cdiv(K, BK)):
p_q = tl.make_block_ptr(
q + i_bh * s_qk_h, (T, K), (s_qk_t, s_qk_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
p_k = tl.make_block_ptr(
k + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
p_h = tl.make_block_ptr(h + i_bh * s_h_h + i_t * K * V,
(K, V), (s_h_t, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
# [BT, BK]
b_q = tl.load(p_q, boundary_check=(0, 1))
# [BK, BT]
b_k = tl.load(p_k, boundary_check=(0, 1))
# [BT]
# [BK, BV]
b_h = tl.load(p_h, boundary_check=(0, 1))
b_o += tl.dot(b_q, b_h, allow_tf32=False)
b_s += tl.dot(b_q, b_k, allow_tf32=False)
p_g = g + i_bh * T + i_t * BT + tl.arange(0, BT)
b_g = tl.load(p_g)
b_o = b_o * tl.math.exp2(b_g)[:, None]
b_s = b_s * tl.math.exp2(b_g[:, None] - b_g[None, :])
b_s = tl.where(m_s, b_s, 0)
p_v = tl.make_block_ptr(v + i_bh * s_vo_h, (T, V),
(s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
b_v = tl.load(p_v, boundary_check=(0, 1))
b_o = (b_o + tl.dot(b_s.to(b_v.dtype), b_v, allow_tf32=False)) * scale
p_o = tl.make_block_ptr(o + i_bh * s_vo_h, (T, V),
(s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
@triton.jit
def chunk_simple_gla_bwd_kernel_dh(
q,
g,
do,
dh,
s_qk_h,
s_qk_t,
s_qk_d,
s_vo_h,
s_vo_t,
s_vo_d,
s_h_h,
s_h_t,
scale,
H: tl.constexpr,
T: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
NT: tl.constexpr
):
i_k, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
# [BK, BV]
b_dh = tl.zeros([BK, BV], dtype=tl.float32)
for i_t in range(NT - 1, -1, -1):
p_q = tl.make_block_ptr(
q + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
p_do = tl.make_block_ptr(
do + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_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, 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))
# [BK, BT]
b_q = tl.load(p_q, boundary_check=(0, 1))
b_q = (b_q * scale * tl.math.exp2(tl.load(g + i_bh * T +
i_t * BT + tl.arange(0, BT)))[None, :]).to(b_q.dtype)
# [BT, V]
b_do = tl.load(p_do, boundary_check=(0, 1))
# [BK, BV]
b_dh *= tl.math.exp2(tl.load(g + i_bh * T + i_t * BT + BT - 1))
b_dh += tl.dot(b_q, b_do.to(b_q.dtype), allow_tf32=False)
@triton.jit
def chunk_simple_gla_bwd_kernel_dqkv(
q,
k,
v,
h,
g,
do,
dh,
dq,
dk,
dv,
s_qk_h,
s_qk_t,
s_qk_d,
s_vo_h,
s_vo_t,
s_vo_d,
s_h_h,
s_h_t,
scale,
B: tl.constexpr,
H: tl.constexpr,
T: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
NT: 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)
o_i = tl.arange(0, BT)
p_q = tl.make_block_ptr(q + i_bh * s_qk_h, (K, T),
(s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
p_k = tl.make_block_ptr(k + i_bh * s_qk_h, (T, K),
(s_qk_t, s_qk_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
b_q = tl.load(p_q, boundary_check=(0, 1))
b_k = tl.load(p_k, boundary_check=(0, 1))
b_s = tl.dot(b_k, b_q, allow_tf32=False)
p_g = g + i_bh * T + i_t * BT + tl.arange(0, BT)
b_g = tl.load(p_g)
b_g_last = tl.load(g + i_bh * T + i_t * BT + BT - 1)
mask = tl.math.exp2(b_g[None, :] - b_g[:, None])
mask = tl.where(o_i[:, None] <= o_i[None, :], mask * scale, 0)
b_s = b_s * mask
b_dq = tl.zeros([BT, BK], dtype=tl.float32)
b_dk = tl.zeros([BT, BK], dtype=tl.float32)
b_ds = 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_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_h = tl.make_block_ptr(h + i_bh * s_h_h, (V, NT * K), (1, s_h_t),
(i_v * BV, i_t * K + i_k * BK), (BV, BK), (0, 1))
p_do = tl.make_block_ptr(
do + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_dh = tl.make_block_ptr(dh + i_bh * s_h_h, (NT * K, V),
(s_h_t, 1), (i_t * K + i_k * BK, i_v * BV), (BK, BV), (1, 0))
p_dv = tl.make_block_ptr(dv + (i_k*n_bh+i_bh)*s_vo_h, (T, V),
(s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
# [BT, BV]
b_v = tl.load(p_v, boundary_check=(0, 1))
b_do = tl.load(p_do, boundary_check=(0, 1))
# [BV, BK]
b_h = tl.load(p_h, boundary_check=(0, 1))
# [BK, BV]
b_dh = tl.load(p_dh, boundary_check=(0, 1))
# [BT, BT]
b_ds += tl.dot(b_do, tl.trans(b_v), allow_tf32=False)
# [BT, BK]
b_dq += tl.dot(b_do, b_h, allow_tf32=False) * scale
b_dk += tl.dot(b_v, tl.trans(b_dh), allow_tf32=False)
# [BT, BV]
b_dv = tl.dot(b_k, b_dh, allow_tf32=False) * tl.math.exp2(-b_g + b_g_last)[:, None] + \
tl.dot(b_s.to(b_q.dtype), b_do, allow_tf32=False)
tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1))
b_dq = b_dq * tl.math.exp2(b_g)[:, None]
b_dk = b_dk * tl.math.exp2(-b_g + b_g_last)[:, None]
b_ds = b_ds * tl.trans(mask)
b_ds = b_ds.to(b_k.dtype)
# [BT, BK]
b_dq += tl.dot(b_ds, b_k, allow_tf32=False)
b_dk += tl.trans(tl.dot(b_q, b_ds, allow_tf32=False))
p_dq = tl.make_block_ptr(dq + i_bh * s_qk_h, (T, K),
(s_qk_t, s_qk_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
p_dk = tl.make_block_ptr(dk + i_bh * s_qk_h, (T, K),
(s_qk_t, s_qk_d), (i_t * BT, i_k * BK), (BT, BK), (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))
class SimpleGLAFunction(torch.autograd.Function):
@staticmethod
@custom_fwd
@contiguous
def forward(ctx, q, k, v, g, initial_state, output_final_state):
B, H, T, K, V = *q.shape, v.shape[-1]
BT = 64
BK, BV = min(64, triton.next_power_of_2(K)), min(
64, triton.next_power_of_2(V))
NT, NK, NV = triton.cdiv(T, BT), triton.cdiv(K, BK), triton.cdiv(V, BV)
num_stages = 1
num_warps = 4 if BK == 64 else 2
scale = K ** -0.5
BT = 64
assert T % BT == 0, 'sequence length must be divisible by BT'
g = g.reshape(B, H, -1, BT)
g = g.cumsum(-1) * 1.44269504
g = g.reshape(B, H, -1)
final_state = None
if output_final_state:
final_state = q.new_empty(B, H, K, V, dtype=torch.float32, requires_grad=False)
h = q.new_empty(B, H, NT * K, V)
grid = (NK, NV, B * H)
chunk_simple_gla_fwd_kernel_h[grid](
k, v, h, g, initial_state, final_state,
q.stride(1), q.stride(2), q.stride(3),
v.stride(1), v.stride(2), v.stride(3),
h.stride(1), h.stride(2),
H=H, T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, NT=NT,
USE_INITIAL_STATE=initial_state is not None,
STORE_FINAL_STATE=output_final_state,
num_warps=num_warps,
num_stages=num_stages
)
grid = (NV, NT, B * H)
o = torch.empty_like(v)
chunk_simple_gla_fwd_kernel_o[grid](
q, k, v, h, g, o,
q.stride(1), q.stride(2), q.stride(3),
v.stride(1), v.stride(2), v.stride(3),
h.stride(1), h.stride(2),
scale,
H=H, T=T, K=K, V=V, BT=BT, BK=BK, BV=BV,
num_warps=num_warps,
num_stages=num_stages
)
ctx.save_for_backward(q, k, v, h, g)
return o.to(q.dtype), final_state
@staticmethod
@custom_bwd
@contiguous
def backward(ctx, do, d_ht=None):
q, k, v, h, g = ctx.saved_tensors
B, H, T, K, V = *q.shape, v.shape[-1]
BT = 64
BK, BV = min(32 if q.dtype == torch.float32 else 64, triton.next_power_of_2(K)), min(
32 if q.dtype == torch.float32 else 64, triton.next_power_of_2(V))
NT, NK, NV = triton.cdiv(T, BT), triton.cdiv(K, BK), triton.cdiv(V, BV)
num_stages = 1
num_warps = 4 if BK == 64 else 2
scale = K ** -0.5
dh = q.new_empty(B, H, NT * K, V)
grid = (NK, NV, B * H)
chunk_simple_gla_bwd_kernel_dh[grid](
q, g, do, dh,
q.stride(1), q.stride(2), q.stride(3),
v.stride(1), v.stride(2), v.stride(3),
dh.stride(1), dh.stride(2),
scale,
H=H, T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, NT=NT,
num_warps=num_warps,
num_stages=num_stages
)
grid = (NK, NT, B * H)
dq = torch.empty_like(q)
dk = torch.empty_like(k)
dv = v.new_empty(NK, *v.shape)
num_stages = 1
num_warps = 4 if BK == 64 else 2
chunk_simple_gla_bwd_kernel_dqkv[grid](
q, k, v, h, g, do, dh, dq, dk, dv,
q.stride(1), q.stride(2), q.stride(3),
v.stride(1), v.stride(2), v.stride(3),
dh.stride(1), dh.stride(2),
scale,
B=B, H=H, T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, NT=NT,
num_warps=num_warps,
num_stages=num_stages
)
dv = dv.sum(0)
dg = (dq * q - dk * k).sum(-1)
def rev_cumsum(x):
cumsum_x = x.cumsum(-1)
rev_cumsum_x = cumsum_x[..., -1, None] - cumsum_x
return rev_cumsum_x + x
dg = rev_cumsum(dg)
return dq.to(q.dtype), dk.to(k.dtype), dv.to(v.dtype), dg.to(g.dtype), None, None
def chunk_simple_gla(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor, # log decay
initial_state: torch.Tensor = None,
output_final_state: bool = False
) -> Tuple[torch.Tensor, torch.Tensor]:
if initial_state is not None:
initial_state = initial_state.detach()
g = g.float()
o, final_state = SimpleGLAFunction.apply(q, k, v, g, initial_state, output_final_state)
return o, final_state

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finetune/lora/v6/fla/ops/simple_gla/naive.py vendored Normal file
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# -*- coding: utf-8 -*-
import torch
from einops import rearrange
def torch_simple_gla(q, k, v, g, chunk_size=64):
q = rearrange(q, 'b h (n c) d -> b h n c d', c = chunk_size) * (q.shape[-1] ** -0.5)
k = rearrange(k, 'b h (n c) d -> b h n c d', c = chunk_size)
v = rearrange(v, 'b h (n c) d -> b h n c d', c = chunk_size)
g = rearrange(g, 'b h (n c) -> b h n c', c = chunk_size)
g = g.cumsum(-1)
kv = k.transpose(-1, -2) @ (v * (-g + g[:, :, :, -1, None]).exp()[..., None])
S = torch.zeros_like(kv)
for i in range(1, g.shape[-2]):
S[:, :, i] = S[:, :, i-1].clone() * g[:, :, i-1, -1, None, None].exp() + kv[:, :, i-1]
inter = (q * g[..., None].exp()) @ S
attn = q @ k.transpose(-1, -2)
attn = attn * (g[..., None] - g[..., None, :]).exp()
attn = attn.masked_fill(torch.triu(torch.ones(chunk_size, chunk_size, dtype=bool, device=q.device), diagonal=1), 0)
intra = attn @ v
o = inter + intra
return rearrange(o, 'b h n c d -> b h (n c) d')
def torch_simple_gla_recurrent(q, k, v, g, chunk_size=64):
# q = rearrange(q, 'b h (n c) d -> b h n c d', c = chunk_size) * (q.shape[-1] ** -0.5)
# k = rearrange(k, 'b h (n c) d -> b h n c d', c = chunk_size)
# v = rearrange(v, 'b h (n c) d -> b h n c d', c = chunk_size)
# g = rearrange(g, 'b h (n c) -> b h n c', c = chunk_size)
# g = g.cumsum(-1)
# kv = k.transpose(-1, -2) @ v
B, H, T, DK = q.shape
q = q * (DK ** -0.5)
_, _, _, DV = v.shape
S = torch.zeros(B, H, DK, DV).to(q)
o = torch.zeros(B, H, T, DV).to(q)
for i in range(T):
gate = g[:, :, i].exp()
key = k[:, :, i]
value = v[:, :, i]
kv = key.unsqueeze(-1) * value.unsqueeze(-2)
S = S.clone() * gate.unsqueeze(-1).unsqueeze(-1) + kv
q_i = q[:, :, i, :]
o_i = (q_i.unsqueeze(-1) * S).sum(-2)
o[:, :, i] = o_i
return o