minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

Revert "[Kernel Slimming] Migrate NVFP4 kernels to JIT" (#20005)

Browse Source
This commit is contained in:
Baizhou Zhang
2026-03-05 19:40:00 -08:00
committed by GitHub
parent 6e5a2de354
commit 51e5dc845a
27 changed files with 987 additions and 2456 deletions
Download Patch File Download Diff File Expand all files Collapse all files
sgl-kernel/csrc/common_extension.cc
+30
View File
@@ -157,9 +157,39 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
m.def("sgl_per_token_quant_fp8(Tensor input, Tensor! output_q, Tensor! output_s) -> ()");
m.impl("sgl_per_token_quant_fp8", torch::kCUDA, &sgl_per_token_quant_fp8);
m.def(
"cutlass_scaled_fp4_mm(Tensor! out, Tensor a, Tensor b,"
" Tensor block_scale_a, Tensor block_scale_b,"
" Tensor alpha) -> ()");
m.impl("cutlass_scaled_fp4_mm", torch::kCUDA, &cutlass_scaled_fp4_mm);
m.def(
"scaled_fp4_quant(Tensor! output, Tensor! input,"
" Tensor! output_scale, Tensor! input_scale) -> ()");
m.impl("scaled_fp4_quant", torch::kCUDA, &scaled_fp4_quant);
m.def("dsv3_fused_a_gemm(Tensor! output, Tensor mat_a, Tensor mat_b) -> ()");
m.impl("dsv3_fused_a_gemm", torch::kCUDA, &dsv3_fused_a_gemm);
// Compute NVFP4 experts quantization.
m.def(
"scaled_fp4_experts_quant(Tensor! output, Tensor! output_scale,"
"Tensor input, Tensor input_global_scale, Tensor input_offset_by_experts,"
"Tensor output_scale_offset_by_experts) -> ()");
m.impl("scaled_fp4_experts_quant", torch::kCUDA, &scaled_fp4_experts_quant);
m.def(
"silu_and_mul_scaled_fp4_experts_quant(Tensor! output, Tensor! output_scale,"
"Tensor input, Tensor input_global_scale, Tensor mask, bool use_silu_and_mul) -> ()");
m.impl("silu_and_mul_scaled_fp4_experts_quant", torch::kCUDA, &silu_and_mul_scaled_fp4_experts_quant);
m.def(
"cutlass_fp4_group_mm(Tensor! output, Tensor a, Tensor b,"
"Tensor a_blockscale, Tensor b_blockscale, Tensor alphas,"
"Tensor ab_strides, Tensor c_strides, Tensor problem_sizes,"
" Tensor expert_offsets, Tensor sf_offsets) -> ()");
m.impl("cutlass_fp4_group_mm", torch::kCUDA, &cutlass_fp4_group_mm);
m.def("dsv3_router_gemm(Tensor! output, Tensor mat_a, Tensor mat_b) -> ()");
m.impl("dsv3_router_gemm", torch::kCUDA, &dsv3_router_gemm);