Rename files in sgl kernel to avoid nested folder structure (#4213)

Co-authored-by: zhyncs <me@zhyncs.com>

sgl-kernel/src/sgl-kernel/torch_extension.cc → sgl-kernel/csrc/torch_extension.cc

@@ -16,33 +16,9 @@ limitations under the License.
 #include <ATen/core/dispatch/Dispatcher.h>
 #include <torch/library.h>
 
-#include "sgl_kernels_ops.h"
-
-TORCH_LIBRARY_EXPAND(sgl_kernels, m) {
-  /*
-   * From csrc/activation
-   */
-  m.def("rmsnorm(Tensor! output, Tensor input, Tensor weight, float eps, int cuda_stream) -> ()");
-  m.impl("rmsnorm", torch::kCUDA, &rmsnorm);
-
-  m.def("fused_add_rmsnorm(Tensor! input, Tensor! residual, Tensor weight, float eps) -> ()");
-  m.impl("fused_add_rmsnorm", torch::kCUDA, &sgl_fused_add_rmsnorm);
-
-  m.def("gemma_rmsnorm(Tensor! output, Tensor input, Tensor weight, float eps, int cuda_stream) -> ()");
-  m.impl("gemma_rmsnorm", torch::kCUDA, &gemma_rmsnorm);
-
-  m.def("gemma_fused_add_rmsnorm(Tensor! input, Tensor! residual, Tensor weight, float eps, int cuda_stream) -> ()");
-  m.impl("gemma_fused_add_rmsnorm", torch::kCUDA, &gemma_fused_add_rmsnorm);
-
-  m.def("silu_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
-  m.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);
-
-  m.def("gelu_tanh_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
-  m.impl("gelu_tanh_and_mul", torch::kCUDA, &gelu_tanh_and_mul);
-
-  m.def("gelu_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
-  m.impl("gelu_and_mul", torch::kCUDA, &gelu_and_mul);
+#include "sgl_kernel_ops.h"
 
+TORCH_LIBRARY_EXPAND(sgl_kernel, m) {
   /*
    * From csrc/allreduce
    */
@@ -67,6 +43,30 @@ TORCH_LIBRARY_EXPAND(sgl_kernels, m) {
    */
   m.impl("lightning_attention_decode", torch::kCUDA, &lightning_attention_decode);
 
+  /*
+   * From csrc/elementwise
+   */
+  m.def("rmsnorm(Tensor! output, Tensor input, Tensor weight, float eps, int cuda_stream) -> ()");
+  m.impl("rmsnorm", torch::kCUDA, &rmsnorm);
+
+  m.def("fused_add_rmsnorm(Tensor! input, Tensor! residual, Tensor weight, float eps) -> ()");
+  m.impl("fused_add_rmsnorm", torch::kCUDA, &sgl_fused_add_rmsnorm);
+
+  m.def("gemma_rmsnorm(Tensor! output, Tensor input, Tensor weight, float eps, int cuda_stream) -> ()");
+  m.impl("gemma_rmsnorm", torch::kCUDA, &gemma_rmsnorm);
+
+  m.def("gemma_fused_add_rmsnorm(Tensor! input, Tensor! residual, Tensor weight, float eps, int cuda_stream) -> ()");
+  m.impl("gemma_fused_add_rmsnorm", torch::kCUDA, &gemma_fused_add_rmsnorm);
+
+  m.def("silu_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
+  m.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);
+
+  m.def("gelu_tanh_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
+  m.impl("gelu_tanh_and_mul", torch::kCUDA, &gelu_tanh_and_mul);
+
+  m.def("gelu_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
+  m.impl("gelu_and_mul", torch::kCUDA, &gelu_and_mul);
+
   /*
    * From csrc/gemm
    */
@@ -93,6 +93,9 @@ TORCH_LIBRARY_EXPAND(sgl_kernels, m) {
   m.def("sgl_per_tensor_quant_fp8(Tensor input, Tensor output_q, Tensor output_s, bool is_static) -> ()");
   m.impl("sgl_per_tensor_quant_fp8", torch::kCUDA, &sgl_per_tensor_quant_fp8);
 
+  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(
       "cublas_grouped_gemm(Tensor[] inputs, Tensor[] weights, Tensor[] outputs,"
       " ScalarType out_dtype, int cublas_handle, int cuda_stream) -> ()");
@@ -171,9 +174,6 @@ TORCH_LIBRARY_EXPAND(sgl_kernels, m) {
       "apply_rope_pos_ids_cos_sin_cache(Tensor q, Tensor k, Tensor! q_rope, Tensor! k_rope, Tensor cos_sin_cache, "
       "Tensor pos_ids, bool interleave, int cuda_stream) -> ()");
   m.impl("apply_rope_pos_ids_cos_sin_cache", torch::kCUDA, &apply_rope_pos_ids_cos_sin_cache);
-
-  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);
 }
 
-REGISTER_EXTENSION(_kernels)
+REGISTER_EXTENSION(common_ops)

sgl-kernel/src/sgl-kernel/torch_extension_rocm.cc → sgl-kernel/csrc/torch_extension_rocm.cc

@@ -16,9 +16,9 @@ limitations under the License.
 #include <ATen/core/dispatch/Dispatcher.h>
 #include <torch/library.h>
 
-#include "sgl_kernels_ops.h"
+#include "sgl_kernel_ops.h"
 
-TORCH_LIBRARY_EXPAND(sgl_kernels, m) {
+TORCH_LIBRARY_EXPAND(sgl_kernel, m) {
   /*
    * From csrc/allreduce
    */

sgl-kernel/src/sgl-kernel/include/sgl_kernels_ops.h → sgl-kernel/include/sgl_kernel_ops.h

@@ -36,18 +36,6 @@ limitations under the License.
 
 using fptr_t = int64_t;
 
-/*
- * From csrc/activation
- */
-void rmsnorm(at::Tensor& output, at::Tensor& input, at::Tensor& weight, double eps, int64_t cuda_stream);
-void sgl_fused_add_rmsnorm(torch::Tensor input, torch::Tensor residual, torch::Tensor weight, double eps);
-void gemma_rmsnorm(at::Tensor& output, at::Tensor& input, at::Tensor& weight, double eps, int64_t cuda_stream);
-void gemma_fused_add_rmsnorm(
-    at::Tensor& input, at::Tensor& residual, at::Tensor& weight, double eps, int64_t cuda_stream);
-void silu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);
-void gelu_tanh_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);
-void gelu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);
-
 /*
  * From csrc/allreduce
  */
@@ -88,6 +76,30 @@ void register_graph_buffers(
     fptr_t _fa, const std::vector<std::vector<int64_t>>& handles, const std::vector<std::vector<int64_t>>& offsets);
 #endif
 
+/*
+ * From csrc/attention
+ */
+void lightning_attention_decode(
+    const torch::Tensor& q,
+    const torch::Tensor& k,
+    const torch::Tensor& v,
+    const torch::Tensor& past_kv,
+    const torch::Tensor& slope,
+    torch::Tensor output,
+    torch::Tensor new_kv);
+
+/*
+ * From csrc/elementwise
+ */
+void rmsnorm(at::Tensor& output, at::Tensor& input, at::Tensor& weight, double eps, int64_t cuda_stream);
+void sgl_fused_add_rmsnorm(torch::Tensor input, torch::Tensor residual, torch::Tensor weight, double eps);
+void gemma_rmsnorm(at::Tensor& output, at::Tensor& input, at::Tensor& weight, double eps, int64_t cuda_stream);
+void gemma_fused_add_rmsnorm(
+    at::Tensor& input, at::Tensor& residual, at::Tensor& weight, double eps, int64_t cuda_stream);
+void silu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);
+void gelu_tanh_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);
+void gelu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);
+
 /*
  * From csrc/gemm
  */
@@ -120,6 +132,7 @@ void sgl_per_token_group_quant_fp8(
     double fp8_min,
     double fp8_max);
 void sgl_per_tensor_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s, bool is_static);
+void sgl_per_token_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s);
 void cublas_grouped_gemm(
     const std::vector<torch::Tensor>& inputs,
     const std::vector<torch::Tensor>& weights,
@@ -254,18 +267,3 @@ void apply_rope_pos_ids_cos_sin_cache(
     at::Tensor pos_ids,
     bool interleave,
     int64_t cuda_stream);
-
-/*
- * Other
- */
-void lightning_attention_decode(
-    const torch::Tensor& q,
-    const torch::Tensor& k,
-    const torch::Tensor& v,
-    const torch::Tensor& past_kv,
-    const torch::Tensor& slope,
-    torch::Tensor output,
-    torch::Tensor new_kv);
-
-// sgl_per_token_quant_fp8
-void sgl_per_token_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s);

sgl-kernel/pyproject.toml

@@ -20,10 +20,6 @@ dependencies = []
 "Homepage" = "https://github.com/sgl-project/sglang/tree/main/sgl-kernel"
 "Bug Tracker" = "https://github.com/sgl-project/sglang/issues"
 
-[tool.setuptools]
-package-dir = {"sgl_kernel" = "src/sgl-kernel"}
-packages = ["sgl_kernel", "sgl_kernel.ops", "sgl_kernel.csrc"]
-
 [tool.wheel]
 exclude = [
   "dist*",

sgl-kernel/src/sgl-kernel/__init__.py → sgl-kernel/python/sgl_kernel/__init__.py

@@ -9,7 +9,10 @@ if os.path.exists("/usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.12"):
         mode=ctypes.RTLD_GLOBAL,
     )
 
-from sgl_kernel.ops.activation import (
+from sgl_kernel import common_ops
+from sgl_kernel.allreduce import *
+from sgl_kernel.attention import lightning_attention_decode
+from sgl_kernel.elementwise import (
     apply_rope_with_cos_sin_cache_inplace,
     fused_add_rmsnorm,
     gelu_and_mul,
@@ -19,9 +22,7 @@ from sgl_kernel.ops.activation import (
     rmsnorm,
     silu_and_mul,
 )
-from sgl_kernel.ops.allreduce import *
-from sgl_kernel.ops.attention import lightning_attention_decode
-from sgl_kernel.ops.gemm import (
+from sgl_kernel.gemm import (
     bmm_fp8,
     cublas_grouped_gemm,
     fp8_blockwise_scaled_mm,
@@ -31,15 +32,15 @@ from sgl_kernel.ops.gemm import (
     sgl_per_token_group_quant_fp8,
     sgl_per_token_quant_fp8,
 )
-from sgl_kernel.ops.moe import moe_align_block_size
-from sgl_kernel.ops.sampling import (
+from sgl_kernel.moe import moe_align_block_size
+from sgl_kernel.sampling import (
     min_p_sampling_from_probs,
     top_k_renorm_prob,
     top_k_top_p_sampling_from_probs,
     top_p_renorm_prob,
     top_p_sampling_from_probs,
 )
-from sgl_kernel.ops.speculative import (
+from sgl_kernel.speculative import (
     build_tree_kernel,
     build_tree_kernel_efficient,
     tree_speculative_sampling_target_only,

sgl-kernel/src/sgl-kernel/ops/allreduce.py → sgl-kernel/python/sgl_kernel/allreduce.py

 from typing import List, Tuple
 
-import sgl_kernel.ops._kernels
 import torch
 
 if torch.version.hip is not None:
@@ -13,49 +12,49 @@ if torch.version.hip is not None:
         rank: int,
         full_nvlink: bool,
     ) -> int:
-        return torch.ops.sgl_kernels.init_custom_ar(
+        return torch.ops.sgl_kernel.init_custom_ar(
             meta, rank_data, handles, offsets, rank, full_nvlink
         )
 
     def all_reduce_reg(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
-        torch.ops.sgl_kernels.all_reduce_reg(fa, inp, out)
+        torch.ops.sgl_kernel.all_reduce_reg(fa, inp, out)
 
     def all_reduce_unreg(
         fa: int, inp: torch.Tensor, reg_buffer: torch.Tensor, out: torch.Tensor
     ) -> None:
-        torch.ops.sgl_kernels.all_reduce_unreg(fa, inp, reg_buffer, out)
+        torch.ops.sgl_kernel.all_reduce_unreg(fa, inp, reg_buffer, out)
 
     def dispose(fa: int) -> None:
-        torch.ops.sgl_kernels.dispose(fa)
+        torch.ops.sgl_kernel.dispose(fa)
 
     def meta_size() -> int:
-        return torch.ops.sgl_kernels.meta_size()
+        return torch.ops.sgl_kernel.meta_size()
 
     def register_buffer(
         fa: int, t: torch.Tensor, handles: List[str], offsets: List[int]
     ) -> None:
-        return torch.ops.sgl_kernels.register_buffer(fa, t, handles, offsets)
+        return torch.ops.sgl_kernel.register_buffer(fa, t, handles, offsets)
 
     def get_graph_buffer_ipc_meta(fa: int) -> Tuple[torch.Tensor, List[int]]:
-        return torch.ops.sgl_kernels.get_graph_buffer_ipc_meta(fa)
+        return torch.ops.sgl_kernel.get_graph_buffer_ipc_meta(fa)
 
     def register_graph_buffers(
         fa: int, handles: List[str], offsets: List[List[int]]
     ) -> None:
-        torch.ops.sgl_kernels.register_graph_buffers(fa, handles, offsets)
+        torch.ops.sgl_kernel.register_graph_buffers(fa, handles, offsets)
 
     def allocate_meta_buffer(size: int) -> torch.Tensor:
-        return torch.ops.sgl_kernels.allocate_meta_buffer(size)
+        return torch.ops.sgl_kernel.allocate_meta_buffer(size)
 
     def get_meta_buffer_ipc_handle(inp: torch.Tensor) -> torch.Tensor:
-        return torch.ops.sgl_kernels.get_meta_buffer_ipc_handle(inp)
+        return torch.ops.sgl_kernel.get_meta_buffer_ipc_handle(inp)
 
 else:
     # TRTLLM custom allreduce
     def init_custom_reduce(
         rank_id, num_devices, rank_data, buffers, tmp_buffers, barrier_in, barrier_out
     ):
-        return torch.ops.sgl_kernels.init_custom_ar(
+        return torch.ops.sgl_kernel.init_custom_ar(
             rank_id,
             num_devices,
             rank_data,
@@ -66,13 +65,13 @@ else:
         )
 
     def custom_dispose(fa):
-        torch.ops.sgl_kernels.dispose(fa)
+        torch.ops.sgl_kernel.dispose(fa)
 
     def custom_reduce(fa, inp, out):
-        torch.ops.sgl_kernels.all_reduce(fa, inp, out)
+        torch.ops.sgl_kernel.all_reduce(fa, inp, out)
 
     def get_graph_buffer_ipc_meta(fa):
-        return torch.ops.sgl_kernels.get_graph_buffer_ipc_meta(fa)
+        return torch.ops.sgl_kernel.get_graph_buffer_ipc_meta(fa)
 
     def register_graph_buffers(fa, handles, offsets):
-        torch.ops.sgl_kernels.register_graph_buffers(fa, handles, offsets)
+        torch.ops.sgl_kernel.register_graph_buffers(fa, handles, offsets)

sgl-kernel/src/sgl-kernel/ops/attention.py → sgl-kernel/python/sgl_kernel/attention.py

-import sgl_kernel.ops._kernels
 import torch
 
 
 def lightning_attention_decode(q, k, v, past_kv, slope, output, new_kv):
-    torch.ops.sgl_kernels.lightning_attention_decode(
+    torch.ops.sgl_kernel.lightning_attention_decode(
         q, k, v, past_kv, slope, output, new_kv
     )

sgl-kernel/src/sgl-kernel/ops/activation.py → sgl-kernel/python/sgl_kernel/elementwise.py

 from typing import Optional
 
-import sgl_kernel.ops._kernels
 import torch
-from sgl_kernel.ops.utils import get_cuda_stream
+from sgl_kernel.utils import get_cuda_stream
 
 
 # These implementations extensively draw from and build upon the FlashInfer project https://github.com/flashinfer-ai/flashinfer
@@ -15,14 +14,14 @@ def rmsnorm(
 ) -> torch.Tensor:
     if out is None:
         out = torch.empty_like(input)
-    torch.ops.sgl_kernels.rmsnorm(out, input, weight, eps, get_cuda_stream())
+    torch.ops.sgl_kernel.rmsnorm(out, input, weight, eps, get_cuda_stream())
     return out
 
 
 def fused_add_rmsnorm(
     input: torch.Tensor, residual: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6
 ) -> None:
-    torch.ops.sgl_kernels.fused_add_rmsnorm(input, residual, weight, eps)
+    torch.ops.sgl_kernel.fused_add_rmsnorm(input, residual, weight, eps)
 
 
 def gemma_rmsnorm(
@@ -33,14 +32,14 @@ def gemma_rmsnorm(
 ) -> torch.Tensor:
     if out is None:
         out = torch.empty_like(input)
-    torch.ops.sgl_kernels.gemma_rmsnorm(out, input, weight, eps, get_cuda_stream())
+    torch.ops.sgl_kernel.gemma_rmsnorm(out, input, weight, eps, get_cuda_stream())
     return out
 
 
 def gemma_fused_add_rmsnorm(
     input: torch.Tensor, residual: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6
 ) -> None:
-    torch.ops.sgl_kernels.gemma_fused_add_rmsnorm(
+    torch.ops.sgl_kernel.gemma_fused_add_rmsnorm(
         input, residual, weight, eps, get_cuda_stream()
     )
 
@@ -66,7 +65,7 @@ def silu_and_mul(input: torch.Tensor, out: torch.Tensor = None) -> torch.Tensor:
             device=input.device,
             dtype=input.dtype,
         )
-    torch.ops.sgl_kernels.silu_and_mul(out, input, get_cuda_stream())
+    torch.ops.sgl_kernel.silu_and_mul(out, input, get_cuda_stream())
     return out
 
 
@@ -81,7 +80,7 @@ def gelu_tanh_and_mul(input: torch.Tensor, out: torch.Tensor = None) -> torch.Te
             device=input.device,
             dtype=input.dtype,
         )
-    torch.ops.sgl_kernels.gelu_tanh_and_mul(out, input, get_cuda_stream())
+    torch.ops.sgl_kernel.gelu_tanh_and_mul(out, input, get_cuda_stream())
     return out
 
 
@@ -96,7 +95,7 @@ def gelu_and_mul(input: torch.Tensor, out: torch.Tensor = None) -> torch.Tensor:
             device=input.device,
             dtype=input.dtype,
         )
-    torch.ops.sgl_kernels.gelu_and_mul(out, input, get_cuda_stream())
+    torch.ops.sgl_kernel.gelu_and_mul(out, input, get_cuda_stream())
     return out
 
 
@@ -141,7 +140,7 @@ def apply_rope_with_cos_sin_cache_inplace(
         raise ValueError("cos_sin_cache should be float32")
 
     positions = positions.int()
-    torch.ops.sgl_kernels.apply_rope_pos_ids_cos_sin_cache(
+    torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache(
         q=query.view(query.shape[0], -1, head_size),
         k=key.view(key.shape[0], -1, head_size),
         q_rope=query.view(query.shape[0], -1, head_size),

sgl-kernel/src/sgl-kernel/ops/gemm.py → sgl-kernel/python/sgl_kernel/gemm.py

 from typing import List, Optional
 
-import sgl_kernel.ops._kernels
 import torch
-from sgl_kernel.ops.utils import _get_cache_buf, get_cuda_stream
+from sgl_kernel.utils import _get_cache_buf, get_cuda_stream
 
 
 def int8_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype, bias=None):
-    return torch.ops.sgl_kernels.int8_scaled_mm(
+    return torch.ops.sgl_kernel.int8_scaled_mm(
         mat_a,
         mat_b,
         scales_a,
@@ -17,7 +16,7 @@ def int8_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype, bias=None):
 
 
 def fp8_blockwise_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype):
-    return torch.ops.sgl_kernels.fp8_blockwise_scaled_mm(
+    return torch.ops.sgl_kernel.fp8_blockwise_scaled_mm(
         mat_a,
         mat_b,
         scales_a,
@@ -27,7 +26,7 @@ def fp8_blockwise_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype):
 
 
 def fp8_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype, bias=None):
-    return torch.ops.sgl_kernels.fp8_scaled_mm(
+    return torch.ops.sgl_kernel.fp8_scaled_mm(
         mat_a,
         mat_b,
         scales_a,
@@ -46,7 +45,7 @@ def _bmm_fp8_internal(
     B_scale: torch.Tensor,
 ) -> None:
     cublas_handle = torch.cuda.current_blas_handle()
-    torch.ops.sgl_kernels.bmm_fp8(
+    torch.ops.sgl_kernel.bmm_fp8(
         A,
         B,
         D,
@@ -86,7 +85,7 @@ def sgl_per_token_group_quant_fp8(
     fp8_min: float,
     fp8_max: float,
 ) -> None:
-    torch.ops.sgl_kernels.sgl_per_token_group_quant_fp8(
+    torch.ops.sgl_kernel.sgl_per_token_group_quant_fp8(
         input, output_q, output_s, group_size, eps, fp8_min, fp8_max
     )
 
@@ -97,7 +96,7 @@ def sgl_per_tensor_quant_fp8(
     output_s: torch.Tensor,
     is_static: bool,
 ) -> None:
-    torch.ops.sgl_kernels.sgl_per_tensor_quant_fp8(input, output_q, output_s, is_static)
+    torch.ops.sgl_kernel.sgl_per_tensor_quant_fp8(input, output_q, output_s, is_static)
 
 
 def cublas_grouped_gemm(
@@ -110,7 +109,7 @@ def cublas_grouped_gemm(
         len(inputs) > 0 and len(weights) > 0 and len(outputs) > 0
     ), "Inputs/weights/outputs should not be empty!"
     cublas_handle = torch.cuda.current_blas_handle()
-    torch.ops.sgl_kernels.cublas_grouped_gemm(
+    torch.ops.sgl_kernel.cublas_grouped_gemm(
         inputs,
         weights,
         outputs,
@@ -125,4 +124,4 @@ def sgl_per_token_quant_fp8(
     output_q: torch.Tensor,
     output_s: torch.Tensor,
 ) -> None:
-    torch.ops.sgl_kernels.sgl_per_token_quant_fp8(input, output_q, output_s)
+    torch.ops.sgl_kernel.sgl_per_token_quant_fp8(input, output_q, output_s)

sgl-kernel/src/sgl-kernel/ops/moe.py → sgl-kernel/python/sgl_kernel/moe.py

-import sgl_kernel.ops._kernels
 import torch
 
 
@@ -12,7 +11,7 @@ def moe_align_block_size(
     token_cnts_buffer,
     cumsum_buffer,
 ):
-    torch.ops.sgl_kernels.moe_align_block_size(
+    torch.ops.sgl_kernel.moe_align_block_size(
         topk_ids,
         num_experts,
         block_size,