EngineX-Hygon/sglang
5
0
Fork 0
Code Issues Pull Requests Actions 7 Projects Releases Wiki Activity

Revert "[1/2] sgl-kernel: Fuse routed scaling factor into select_experts" (#8706)

Browse Source
This commit is contained in:
Liangsheng Yin
2025-08-02 20:14:30 +08:00
committed by GitHub
parent ac6962ccd6
commit f9f0138f80
5 changed files with 12 additions and 38 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
sgl-kernel/csrc/common_extension.cc
View File

@@ -174,7 +174,7 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
m.def(
"moe_fused_gate(Tensor input, Tensor bias, int num_expert_group, int topk_group, int topk, int "
"num_fused_shared_experts, float routed_scaling_factor, bool apply_routed_scaling_factor_on_output) -> "
"num_fused_shared_experts, float routed_scaling_factor) -> "
"(Tensor[])");
m.impl("moe_fused_gate", torch::kCUDA, &moe_fused_gate);
m.def(

27
sgl-kernel/csrc/moe/moe_fused_gate.cu
View File

@@ -59,7 +59,6 @@ __device__ void moe_fused_gate_impl(
int64_t topk,
int64_t num_fused_shared_experts,
double routed_scaling_factor,
bool apply_routed_scaling_factor_on_output,
Params params) {
int tidx = threadIdx.x;
int64_t thread_row =
@@ -249,9 +248,6 @@ __device__ void moe_fused_gate_impl(
for (int ii = 0; ii < topk; ++ii) {
int64_t const idx = topk * thread_row + ii;
output_ptr[idx] = output_ptr[idx] / output_sum;
if (apply_routed_scaling_factor_on_output) {
output_ptr[idx] *= routed_scaling_factor;
}
}
}
}
@@ -286,8 +282,7 @@ __global__ void moe_fused_gate_kernel(
int64_t topk_group,
int64_t topk,
int64_t num_fused_shared_experts,
double routed_scaling_factor,
bool apply_routed_scaling_factor_on_output) {
double routed_scaling_factor) {
KernelParams<VPT, NUM_EXPERTS, THREADS_PER_ROW, ROWS_PER_WARP, ROWS_PER_CTA, WARPS_PER_CTA> params;
moe_fused_gate_impl<T>(
input,
@@ -299,7 +294,6 @@ __global__ void moe_fused_gate_kernel(
topk,
num_fused_shared_experts,
routed_scaling_factor,
apply_routed_scaling_factor_on_output,
params);
}
@@ -320,8 +314,7 @@ __global__ void moe_fused_gate_kernel(
topk_group, \
topk, \
num_fused_shared_experts, \
routed_scaling_factor, \
apply_routed_scaling_factor_on_output); \
routed_scaling_factor); \
dispatched = true; \
} while (0)
@@ -349,8 +342,7 @@ __global__ void moe_fused_gate_kernel_dynamic(
int64_t topk_group,
int64_t topk,
int64_t num_fused_shared_experts,
double routed_scaling_factor,
bool apply_routed_scaling_factor_on_output) {
double routed_scaling_factor) {
KernelParamsDynamic params;
params.NUM_EXPERTS = num_experts; // e.g, for deepseek v3, this is 256
params.VPT = num_experts / num_expert_group; // e.g., for deepseek v3, this is 256 / 8 = 32
@@ -369,7 +361,6 @@ __global__ void moe_fused_gate_kernel_dynamic(
topk,
num_fused_shared_experts,
routed_scaling_factor,
apply_routed_scaling_factor_on_output,
params);
}
@@ -383,8 +374,7 @@ std::vector<at::Tensor> moe_fused_gate(
int64_t topk_group,
int64_t topk,
int64_t num_fused_shared_experts,
double routed_scaling_factor,
bool apply_routed_scaling_factor_on_output) {
double routed_scaling_factor) {
int64_t num_rows = input.size(0);
int32_t num_experts = input.size(1);
auto options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA);
@@ -483,8 +473,7 @@ std::vector<at::Tensor> moe_fused_gate(
topk_group,
topk,
num_fused_shared_experts,
routed_scaling_factor,
apply_routed_scaling_factor_on_output);
routed_scaling_factor);
} else if (input.scalar_type() == at::kHalf) {
moe_fused_gate_kernel_dynamic<float16_t><<<num_blocks, block_dim, 0, stream>>>(
input.data_ptr(),
@@ -497,8 +486,7 @@ std::vector<at::Tensor> moe_fused_gate(
topk_group,
topk,
num_fused_shared_experts,
routed_scaling_factor,
apply_routed_scaling_factor_on_output);
routed_scaling_factor);
} else if (input.scalar_type() == at::kFloat) {
moe_fused_gate_kernel_dynamic<float32_t><<<num_blocks, block_dim, 0, stream>>>(
input.data_ptr(),
@@ -511,8 +499,7 @@ std::vector<at::Tensor> moe_fused_gate(
topk_group,
topk,
num_fused_shared_experts,
routed_scaling_factor,
apply_routed_scaling_factor_on_output);
routed_scaling_factor);
} else {
TORCH_CHECK(false, "Unsupported data type for moe_fused_gate");
}

3
sgl-kernel/include/sgl_kernel_ops.h
View File

@@ -243,8 +243,7 @@ std::vector<at::Tensor> moe_fused_gate(
int64_t topk_group,
int64_t topk,
int64_t num_fused_shared_experts,
double routed_scaling_factor,
bool apply_routed_scaling_factor_on_output);
double routed_scaling_factor);
void fp8_blockwise_scaled_grouped_mm(
torch::Tensor& output,

11
sgl-kernel/python/sgl_kernel/moe.py
View File

@@ -44,7 +44,6 @@ def moe_fused_gate(
topk,
num_fused_shared_experts=0,
routed_scaling_factor=0,
apply_routed_scaling_factor_on_output=False,
):
# This fused kernel function is used to select topk expert in a hierarchical 2-layer fashion
# it split group of expert into num_expert_group, and use top2 expert weight sum in each group
@@ -52,13 +51,8 @@ def moe_fused_gate(
# the #experts is decided by the input tensor shape and we currently only support power of 2 #experts
# and #experts should be divisible by num_expert_group. #expert/num_expert_group <= 32 is limited for now.
# for non-supported case, we suggest to use the biased_grouped_topk func in sglang.srt.layers.moe.topk
# num_fused_shared_experts: if > 0, the last several experts will be
# replaced with shared experts. the shared experts will be divided by the
# routed_scaling_factor - this is intended to cancel out later when routed+shared
# output is scaled so that shared experts are not scaled.
# routed_scaling_factor: if > 0, the experts will be scaled by this factor
# apply_routed_scaling_factor_on_output: if true, output will be
# scaled by the routed_scaling_factor
# num_fused_shared_experts: if > 0, the last several experts will be replaced with shared experts
# routed_scaling_factor: if > 0, the shared experts will be scaled by this factor
return torch.ops.sgl_kernel.moe_fused_gate.default(
input_tensor,
bias,
@@ -67,7 +61,6 @@ def moe_fused_gate(
topk,
num_fused_shared_experts,
routed_scaling_factor,
apply_routed_scaling_factor_on_output,
)

7
sgl-kernel/tests/test_moe_fused_gate.py
View File

@@ -19,10 +19,7 @@ from sglang.srt.layers.moe.topk import biased_grouped_topk
],
)
@pytest.mark.parametrize("num_fused_shared_experts", [0, 1, 2])
@pytest.mark.parametrize("apply_routed_scaling_factor_on_output", [True, False])
def test_moe_fused_gate_combined(
seq_length, params, num_fused_shared_experts, apply_routed_scaling_factor_on_output
):
def test_moe_fused_gate_combined(seq_length, params, num_fused_shared_experts):
num_experts, num_expert_group, topk_group, topk = params
dtype = torch.float32
@@ -40,7 +37,6 @@ def test_moe_fused_gate_combined(
topk=topk,
num_fused_shared_experts=num_fused_shared_experts,
routed_scaling_factor=2.5,
apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
)
ref_output, ref_indices = biased_grouped_topk(
scores,
@@ -52,7 +48,6 @@ def test_moe_fused_gate_combined(
topk_group=topk_group,
num_fused_shared_experts=num_fused_shared_experts,
routed_scaling_factor=2.5,
apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
)
# When num_fused_shared_experts > 0, ignore the comparison of the last topk dimension