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[CPU] layernorm & fused add-layernorm kernels (#14074)

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This commit is contained in:
Zaili Wang
2025-12-12 08:58:23 +08:00
committed by GitHub
parent 9975acf50f
commit d6bd2d1126
5 changed files with 258 additions and 10 deletions
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17
.github/CI_PERMISSIONS.json vendored
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@@ -6,13 +6,6 @@
"reason": "custom override",
"can_rerun_stage": true
},
"MingxuZh": {
"can_tag_run_ci_label": true,
"can_rerun_failed_ci": true,
"cooldown_interval_minutes": 0,
"reason": "custom override",
"can_rerun_stage": true
},
"Alcanderian": {
"can_tag_run_ci_label": true,
"can_rerun_failed_ci": true,
@@ -167,6 +160,13 @@
"reason": "custom override",
"can_rerun_stage": true
},
"MingxuZh": {
"can_tag_run_ci_label": true,
"can_rerun_failed_ci": true,
"cooldown_interval_minutes": 0,
"reason": "custom override",
"can_rerun_stage": true
},
"Oasis-Git": {
"can_tag_run_ci_label": true,
"can_rerun_failed_ci": true,
@@ -227,7 +227,8 @@
"can_tag_run_ci_label": true,
"can_rerun_failed_ci": true,
"cooldown_interval_minutes": 0,
"reason": "custom override"
"reason": "custom override",
"can_rerun_stage": true
},
"XiaotongJiang": {
"can_tag_run_ci_label": true,

7
python/sglang/srt/layers/layernorm.py
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@@ -361,7 +361,12 @@ class LayerNorm(CustomOp):
self,
x: torch.Tensor,
) -> torch.Tensor:
return self.forward_native(x)
if _is_cpu_amx_available:
return torch.ops.sgl_kernel.layernorm_cpu(
x, self.weight.data, self.variance_epsilon
)
else:
return self.forward_native(x)
class GemmaRMSNorm(CustomOp):

173
sgl-kernel/csrc/cpu/norm.cpp
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@@ -302,6 +302,116 @@ void fused_rmsnorm_gated_kernel_impl(
} // anonymous namespace
template <typename scalar_t>
void fused_add_layernorm_kernel_impl(
scalar_t* __restrict__ input,
scalar_t* __restrict__ residual,
const scalar_t* __restrict__ weight,
float* __restrict__ buffer,
int64_t batch_size,
int64_t hidden_size,
int64_t input_strideN,
float eps = 1e-5) {
using bVec = at::vec::Vectorized<scalar_t>;
using fVec = at::vec::Vectorized<float>;
constexpr int kVecSize = bVec::size();
at::parallel_for(0, batch_size, 0, [&](int64_t begin, int64_t end) {
int tid = at::get_thread_num();
float* __restrict__ buffer_ptr = buffer + tid * hidden_size;
for (int64_t i = begin; i < end; ++i) {
scalar_t* __restrict__ input_ptr = input + i * input_strideN;
scalar_t* __restrict__ residual_ptr{(scalar_t*)nullptr};
if (residual != nullptr) {
residual_ptr = residual + i * hidden_size;
}
// First pass: compute mean and var
fVec sum_fvec{fVec(0.0)}, sum_sq_fvec{fVec(0.0)};
float sum_val{0.0}, sum_sq_val{0.0};
int64_t d{0};
#pragma GCC unroll 4
for (; d <= hidden_size - kVecSize; d += kVecSize) {
bVec x_bvec = bVec::loadu(input_ptr + d);
fVec x_fvec0, x_fvec1;
std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
if (residual_ptr != nullptr) {
bVec r_bvec = bVec::loadu(residual_ptr + d);
fVec r_fvec0, r_fvec1;
std::tie(r_fvec0, r_fvec1) = at::vec::convert_to_float(r_bvec);
x_fvec0 += r_fvec0;
x_fvec1 += r_fvec1;
bVec out_bvec = convert_from_float_ext<scalar_t>(x_fvec0, x_fvec1);
out_bvec.store(residual_ptr + d);
}
sum_fvec += x_fvec0;
sum_fvec += x_fvec1;
sum_sq_fvec += x_fvec0 * x_fvec0;
sum_sq_fvec += x_fvec1 * x_fvec1;
x_fvec0.store(buffer_ptr + d);
x_fvec1.store(buffer_ptr + d + fVec::size());
}
#pragma GCC unroll 4
for (; d < hidden_size; ++d) {
float x_val = static_cast<float>(input_ptr[d]);
if (residual_ptr != nullptr) {
float r_val = static_cast<float>(residual_ptr[d]);
x_val += r_val;
residual_ptr[d] = static_cast<scalar_t>(x_val);
}
sum_val += x_val;
sum_sq_val += x_val * x_val;
buffer_ptr[d] = x_val;
}
// Var(X) = E(X^2) - (E(X))^2
// Refer to FlashInfer impl:
// https://github.com/flashinfer-ai/flashinfer/blob/6bb01d19c2d9ab3b6a3a5e9e97448891a5ed2844/include/flashinfer/norm.cuh#L554
sum_val += vec_reduce_sum(sum_fvec);
sum_sq_val += vec_reduce_sum(sum_sq_fvec);
float mean{sum_val / hidden_size};
float mean_sq{sum_sq_val / hidden_size};
float variance{mean_sq - (mean * mean)};
float rsqrt_var{float(1) / std::sqrt(variance + eps)};
const fVec mean_fvec = fVec(mean);
const fVec scale_fvec = fVec(rsqrt_var);
// Second pass: apply normalization
#pragma GCC unroll 4
for (d = 0; d <= hidden_size - kVecSize; d += kVecSize) {
fVec x_fvec0 = fVec::loadu(buffer_ptr + d);
fVec x_fvec1 = fVec::loadu(buffer_ptr + d + fVec::size());
bVec w_bvec = bVec::loadu(weight + d);
fVec w_fvec0, w_fvec1;
std::tie(w_fvec0, w_fvec1) = at::vec::convert_to_float(w_bvec);
x_fvec0 = (x_fvec0 - mean_fvec) * scale_fvec * w_fvec0;
x_fvec1 = (x_fvec1 - mean_fvec) * scale_fvec * w_fvec1;
bVec x_bvec = convert_from_float_ext<scalar_t>(x_fvec0, x_fvec1);
x_bvec.store(input_ptr + d);
}
#pragma GCC unroll 4
for (; d < hidden_size; ++d) {
float normalized = (buffer_ptr[d] - mean) * rsqrt_var;
float x_val = normalized * static_cast<float>(weight[d]);
input_ptr[d] = static_cast<scalar_t>(x_val);
}
}
});
} // anonymous namespace
// input : {batch_size, hidden_size}
at::Tensor l2norm_cpu(at::Tensor& input, double eps) {
RECORD_FUNCTION("sgl-kernel::l2norm_cpu", std::vector<c10::IValue>({input}));
@@ -346,6 +456,35 @@ at::Tensor rmsnorm_cpu(at::Tensor& input, at::Tensor& weight, double eps) {
return output;
}
// input : {batch_size, hidden_size}
// weight: {hidden_size}
void layernorm_cpu(at::Tensor& input, at::Tensor& weight, double eps) {
RECORD_FUNCTION("sgl-kernel::layernorm_cpu", std::vector<c10::IValue>({input, weight}));
CHECK_LAST_DIM_CONTIGUOUS_INPUT(input);
CHECK_INPUT(weight);
CHECK_DIM(2, input);
CHECK_DIM(1, weight);
CHECK_EQ(input.size(1), weight.size(0));
int64_t batch_size = input.size(0);
int64_t hidden_size = input.size(1);
int64_t input_strideN = input.stride(0);
int64_t num_threads = at::get_num_threads();
at::Tensor buffer = at::empty({num_threads, hidden_size}, input.options().dtype(at::kFloat));
AT_DISPATCH_REDUCED_FLOATING_TYPES(input.scalar_type(), "layernorm_kernel", [&] {
fused_add_layernorm_kernel_impl<scalar_t>(
input.data_ptr<scalar_t>(),
nullptr,
weight.data_ptr<scalar_t>(),
buffer.data_ptr<float>(),
batch_size,
hidden_size,
input_strideN,
eps);
});
}
// input : {batch_size, hidden_size}
// weight: {hidden_size}
// gate: {batch_size, hidden_size}
@@ -415,3 +554,37 @@ void fused_add_rmsnorm_cpu(at::Tensor& input, at::Tensor& residual, at::Tensor&
eps);
});
}
// input : {batch_size, hidden_size}
// residual: {batch_size, hidden_size}
// weight : {hidden_size}
void fused_add_layernorm_cpu(at::Tensor& input, at::Tensor& residual, at::Tensor& weight, double eps) {
RECORD_FUNCTION("sgl-kernel::fused_add_layernorm_cpu", std::vector<c10::IValue>({input, residual, weight}));
CHECK_LAST_DIM_CONTIGUOUS_INPUT(input);
CHECK_INPUT(residual);
CHECK_INPUT(weight);
CHECK_DIM(2, input);
CHECK_DIM(2, residual);
CHECK_DIM(1, weight);
CHECK_EQ(input.size(0), residual.size(0));
CHECK_EQ(input.size(1), residual.size(1));
CHECK_EQ(input.size(1), weight.size(0));
int64_t batch_size = input.size(0);
int64_t hidden_size = input.size(1);
int64_t input_strideN = input.stride(0);
int64_t num_threads = at::get_num_threads();
at::Tensor buffer = at::empty({num_threads, hidden_size}, input.options().dtype(at::kFloat));
AT_DISPATCH_REDUCED_FLOATING_TYPES(input.scalar_type(), "fused_add_layernorm_kernel", [&] {
fused_add_layernorm_kernel_impl<scalar_t>(
input.data_ptr<scalar_t>(),
residual.data_ptr<scalar_t>(),
weight.data_ptr<scalar_t>(),
buffer.data_ptr<float>(),
batch_size,
hidden_size,
input_strideN,
eps);
});
}

10
sgl-kernel/csrc/cpu/torch_extension_cpu.cpp
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@@ -33,12 +33,18 @@ at::Tensor l2norm_cpu(at::Tensor& input, double eps);
// rmsnorm
at::Tensor rmsnorm_cpu(at::Tensor& input, at::Tensor& weight, double eps);
// layernorm
void layernorm_cpu(at::Tensor& input, at::Tensor& weight, double eps);
// qwen3_next_rmsnorm_gated
at::Tensor fused_rmsnorm_gated_cpu(at::Tensor& input, at::Tensor& weight, at::Tensor& gate, double eps);
// fused_add_rmsnorm
void fused_add_rmsnorm_cpu(at::Tensor& input, at::Tensor& residual, at::Tensor& weight, double eps);
// fused_add_layernorm
void fused_add_layernorm_cpu(at::Tensor& input, at::Tensor& residual, at::Tensor& weight, double eps);
// topk
std::tuple<at::Tensor, at::Tensor>
topk_sigmoid_cpu(at::Tensor& hidden_states, at::Tensor& gating_output, int64_t topk, bool renormalize);
@@ -324,12 +330,16 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
// norm
m.def("rmsnorm_cpu(Tensor input, Tensor weight, float eps) -> Tensor");
m.impl("rmsnorm_cpu", torch::kCPU, &rmsnorm_cpu);
m.def("layernorm_cpu(Tensor(a!) input, Tensor weight, float eps) -> ()");
m.impl("layernorm_cpu", torch::kCPU, &layernorm_cpu);
m.def("l2norm_cpu(Tensor input, float eps) -> Tensor");
m.impl("l2norm_cpu", torch::kCPU, &l2norm_cpu);
m.def("fused_rmsnorm_gated_cpu(Tensor input, Tensor weight, Tensor gate, float eps) -> Tensor");
m.impl("fused_rmsnorm_gated_cpu", torch::kCPU, &fused_rmsnorm_gated_cpu);
m.def("fused_add_rmsnorm_cpu(Tensor(a!) input, Tensor residual, Tensor weight, float eps) -> ()");
m.impl("fused_add_rmsnorm_cpu", torch::kCPU, &fused_add_rmsnorm_cpu);
m.def("fused_add_layernorm_cpu(Tensor(a!) input, Tensor residual, Tensor weight, float eps) -> ()");
m.impl("fused_add_layernorm_cpu", torch::kCPU, &fused_add_layernorm_cpu);
// topk
m.def("topk_sigmoid_cpu(Tensor hidden_states, Tensor gating_output, int topk, bool renormalize) -> (Tensor, Tensor)");

61
test/srt/cpu/test_norm.py
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@@ -3,7 +3,7 @@ import unittest
from typing import Optional, Tuple, Union
import torch
from utils import make_non_contiguous, precision
from utils import make_non_contiguous, parametrize, precision
from sglang.test.test_utils import CustomTestCase
@@ -131,5 +131,64 @@ class TestFusedRMSNormGated(CustomTestCase):
self._norm_test(*params)
class TestLayerNorm(CustomTestCase):
def _forward_native(
self,
x: torch.Tensor,
weight: torch.Tensor,
variance_epsilon: float,
residual: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
orig_dtype = x.dtype
x = x.to(torch.float32)
if residual is not None:
x = x + residual.to(torch.float32)
residual = x.to(orig_dtype)
(variance, mean) = torch.var_mean(x, dim=-1, keepdim=True, correction=0)
x = (x - mean) * torch.rsqrt(variance + variance_epsilon)
x = x.to(orig_dtype) * weight
if residual is None:
return x
else:
return x, residual
@parametrize(
m=[4096, 1024],
n=[4096, 4109],
dtype=[torch.float16, torch.bfloat16],
)
def test_norm(self, m: int, n: int, dtype: torch.dtype) -> None:
x_ln = torch.randn([m, n], dtype=dtype)
x_ln = make_non_contiguous(x_ln)
ref_x_ln = x_ln.clone()
hidden_size = x_ln.size(-1)
weight = torch.randn(hidden_size, dtype=dtype)
variance_epsilon = 1e-6
torch.ops.sgl_kernel.layernorm_cpu(x_ln, weight, variance_epsilon)
ref_ln_out = self._forward_native(ref_x_ln, weight, variance_epsilon)
atol = rtol = precision[ref_ln_out.dtype]
torch.testing.assert_close(x_ln, ref_ln_out, atol=atol, rtol=rtol)
x_add_ln = torch.randn([m, n], dtype=dtype)
x_add_ln = make_non_contiguous(x_add_ln)
ref_x_add_ln = x_add_ln.clone()
residual = torch.randn([m, hidden_size], dtype=dtype)
ref_residual = residual.clone()
torch.ops.sgl_kernel.fused_add_layernorm_cpu(
x_add_ln, residual, weight, variance_epsilon
)
ref_add_ln_out, ref_residual = self._forward_native(
ref_x_add_ln, weight, variance_epsilon, ref_residual
)
torch.testing.assert_close(x_add_ln, ref_add_ln_out, atol=atol, rtol=rtol)
torch.testing.assert_close(residual, ref_residual, atol=atol, rtol=rtol)
if __name__ == "__main__":
unittest.main()