File size: 4,215 Bytes
7aec9bd | 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 | # -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
from typing import Optional
import torch
import triton
import triton.language as tl
from fla.utils import input_guard
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps)
for num_warps in [1, 2, 4, 8, 16, 32]
],
key=['N']
)
@triton.jit
def l2norm_fwd_kernel(
X,
Y,
N,
eps,
BLOCK_N: tl.constexpr,
):
i_m = tl.program_id(0)
X += i_m * N
Y += i_m * N
# Compute mean and variance
cols = tl.arange(0, BLOCK_N)
mask = cols < N
x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32)
xbar = tl.where(mask, x, 0.0)
var = tl.sum(xbar * xbar, axis=0)
rstd = 1 / tl.sqrt(var + eps)
# tl.store(Rstd + i_m, rstd)
# Normalize and apply linear transformation
y = x * rstd
# Write output
tl.store(Y + cols, y, mask=mask)
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps)
for num_warps in [1, 2, 4, 8, 16, 32]
],
key=['N']
)
@triton.jit
def l2norm_bwd_kernel(
X,
DY,
DX,
N,
eps,
BLOCK_N: tl.constexpr,
):
i_m = tl.program_id(0)
X += i_m * N
DX += i_m * N
DY += i_m * N
# Y += i_m * stride_y_row
cols = tl.arange(0, BLOCK_N)
mask = cols < N
x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32)
x = tl.where(mask, x, 0.0)
var = tl.sum(x * x)
rstd = 1 / tl.sqrt(var + eps)
# tl.store(Rstd + i_m, rstd)
# Normalize and apply linear transformation
# y = x * rstd
dy = tl.load(DY + cols, mask=mask, other=0.0).to(tl.float32)
dy = tl.where(mask, dy, 0.0)
dx = dy * rstd - tl.sum(dy * x) * (1 / (var+eps)) * rstd * x
tl.store(DX + cols, dx, mask=mask)
def l2norm_fwd(
x: torch.Tensor,
eps: float = 1e-6,
output_dtype: Optional[torch.dtype] = None
):
x_shape_og = x.shape
x = x.reshape(-1, x.shape[-1])
# allocate output
if output_dtype is None:
y = torch.empty_like(x)
else:
y = torch.empty_like(x, dtype=output_dtype)
assert y.stride(-1) == 1
N = x.shape[-1]
M = x.shape[0]
# rstd = torch.empty((M,), dtype=torch.float32, device=x.device)
# Less than 64KB per feature: enqueue fused kernel
MAX_FUSED_SIZE = 65536 // x.element_size()
BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
if N > BLOCK_N:
raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
# heuristics for number of warps
l2norm_fwd_kernel[(M,)](
x,
y,
N,
eps,
BLOCK_N,
)
return y.reshape(x_shape_og)
def l2norm_bwd(
x: torch.Tensor,
dy: torch.Tensor,
eps: float = 1e-5
):
x_shape_og = x.shape
x = x.reshape(-1, dy.shape[-1])
dy = dy.reshape(-1, dy.shape[-1])
if dy.stride(-1) != 1:
dy = dy.contiguous()
assert dy.shape == x.shape
# allocate output
dx = torch.empty_like(x)
M = x.shape[0]
N = x.shape[-1]
# rstd = torch.empty((M,), dtype=torch.float32, device=x.device)
# Less than 64KB per feature: enqueue fused kernel
MAX_FUSED_SIZE = 65536 // x.element_size()
BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
if N > BLOCK_N:
raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
# heuristics for number of warps
l2norm_bwd_kernel[(M,)](
x,
dy,
dx,
N,
eps,
BLOCK_N,
)
return dx.reshape(x_shape_og)
class L2NormFunction(torch.autograd.Function):
@staticmethod
@input_guard
def forward(
ctx,
x,
eps=1e-6,
output_dtype=None
):
y = l2norm_fwd(x, eps, output_dtype)
ctx.eps = eps
ctx.x_dtype = x.dtype
ctx.save_for_backward(x)
return y
@staticmethod
@input_guard
def backward(ctx, dy):
x, = ctx.saved_tensors
dx = l2norm_bwd(x, dy, ctx.eps)
return dx, None, None
def l2_norm(
x: torch.Tensor,
eps: float = 1e-6,
output_dtype: Optional[torch.dtype] = None
) -> torch.Tensor:
return L2NormFunction.apply(x, eps, output_dtype)
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