Add typo checker in pre-commit (#6179)

Co-authored-by: Brayden Zhong <b8zhong@uwaterloo.ca>

sgl-kernel/benchmark/bench_per_token_group_quant_8bit.py

@@ -20,7 +20,7 @@ def _per_token_group_quant_8bit(
     y_s_ptr,
     # Stride of input
     y_stride,
-    # Collums of input
+    # Columns of input
     N,
     # Avoid to divide zero
     eps,

sgl-kernel/csrc/cpu/common.h

@@ -49,7 +49,7 @@ namespace {
 
 #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
 #define CHECK_LAST_DIM_CONTIGUOUS(x) \
-  TORCH_CHECK(x.strides()[x.strides().size() - 1] == 1, #x "must be contiguous at last dimention")
+  TORCH_CHECK(x.strides()[x.strides().size() - 1] == 1, #x "must be contiguous at last dimension")
 
 #define CHECK_INPUT(x) \
   CHECK_CPU(x);        \

sgl-kernel/csrc/cpu/decode.cpp

@@ -718,7 +718,7 @@ void decode_attention_kernel_impl(
 
         m_prime = m_i;
 
-        // caculate V' <- s_delta @ V + V' * m_delta
+        // calculate V' <- s_delta @ V + V' * m_delta
         index_gemm_kernel_nn<scalar_t, index_t>(
             /* A   */ s_delta,
             /* B   */ v_buffer + head_id * v_strideH,
@@ -925,7 +925,7 @@ void decode_attention_grouped_kernel_impl(
           m_prime[h] = m_i;
         }
 
-        // caculate V' <- s_delta @ V + V' * m_delta
+        // calculate V' <- s_delta @ V + V' * m_delta
         index_gemm_kernel_nn<scalar_t, index_t>(
             /* A   */ s_delta,
             /* B   */ v_buffer + head_kv_id * v_strideH,

sgl-kernel/csrc/cpu/extend.cpp

@@ -323,7 +323,7 @@ void extend_attention_kernel_impl(
             /* ld_src */ v_strideN,
             /* ld_dst */ head_size_v);
 
-        // caculate V' <- s_delta @ V + V'
+        // calculate V' <- s_delta @ V + V'
         at::native::cpublas::brgemm(
             /* M     */ m_size,
             /* N     */ head_size_v,
@@ -434,7 +434,7 @@ void extend_attention_kernel_impl(
             /* ld_src */ ve_strideN,
             /* ld_dst */ head_size_v);
 
-        // caculate V' <- s_delta @ V + V'
+        // calculate V' <- s_delta @ V + V'
         at::native::cpublas::brgemm(
             /* M     */ m_size,
             /* N     */ head_size_v,

sgl-kernel/csrc/cpu/gemm.h

@@ -79,7 +79,7 @@ void fused_experts_int8_kernel_impl(
     int64_t topk,
     int64_t num_tokens_post_pad);
 
-// shared expert implememntation for int8 w8a8
+// shared expert implementation for int8 w8a8
 template <typename scalar_t>
 void shared_expert_int8_kernel_impl(
     scalar_t* __restrict__ output,

sgl-kernel/csrc/cpu/gemm_int8.cpp

@@ -51,7 +51,7 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
     __m512 vd0;
     __m512 vd1[COLS];
 
-    // oops! 4x4 spills but luckly we use 4x2
+    // oops! 4x4 spills but we use 4x2
     __m512 vbias[COLS];
 
     // [NOTE]: s8s8 igemm compensation in avx512-vnni

sgl-kernel/csrc/speculative/packbit.cu

-// This is only a pluggin used for flashinfer 0.1.6. The new version does not need it.
+// This is only a plugin used for flashinfer 0.1.6. The new version does not need it.
 /*
  * Copyright (c) 2025 by SGLang team.
  * Copyright (c) 2025 by FlashInfer team.

sgl-kernel/include/sgl_kernel_torch_shim.h

@@ -20,16 +20,16 @@ limitations under the License.
 #include <torch/library.h>
 
 /**
- * Unforunately, the type signatures of the flash_attn ops are not compatible
+ * Unfortunately, the type signatures of the flash_attn ops are not compatible
  * with the PyTorch library bindings. To get around that we use
- * `make_pytorch_shim` which creates a lambda that exponses the API using
+ * `make_pytorch_shim` which creates a lambda that exposes the API using
  * PyTorch compatible types to the types, then converts them to the types
  * expected by the flash_attn ops. This shims allows us to make minimal changes
  * to `flash_api.cpp` making it easier to synchronize with upstream changes.
  *
  * The `pytorch_library_compatible_type` struct is used to map from the
  * flash_attn ops types to a PyTorch library compatible one. The main issues is
- * that the following types are not support by PyTorch libary bindings:
+ * that the following types are not support by PyTorch library bindings:
  *  - `int`
  *  - `float`
  *  - `std::optional<T> &`

sgl-kernel/python/sgl_kernel/elementwise.py

@@ -229,7 +229,7 @@ def apply_rope_with_cos_sin_cache_inplace(
         Whether to use Neox style RoPE, default: ``True``.
 
         * If ``True``, the last dimension of the query/key tensor is not interleaved, i.e.,
-          we rorate the first half dimensions ``([..., :head_dim//2])`` and the second half
+          we rotate the first half dimensions ``([..., :head_dim//2])`` and the second half
           dimensions ``([..., head_dim//2:])``.
 
         * If ``False``, the last dimension of the query/key tensor is interleaved, i.e.,

sgl-kernel/python/sgl_kernel/flash_attn.py

@@ -17,7 +17,7 @@ def is_fa3_supported(device=None) -> bool:
     #  Between sm80/sm87 and sm86/sm89 is the shared memory size. you can follow the link below for more information
     #  https://docs.nvidia.com/cuda/cuda-c-programming-guide/#shared-memory-8-x
     #  And for sgl-kernel right now, we can build fa3 on sm80/sm86/sm89/sm90a.
-    #  Thats mean if you use A100/A*0/L20/L40/L40s/4090 you can use fa3.
+    #  That means if you use A100/A*0/L20/L40/L40s/4090 you can use fa3.
     return (
         torch.cuda.get_device_capability(device)[0] == 9
         or torch.cuda.get_device_capability(device)[0] == 8

sgl-kernel/python/sgl_kernel/moe.py

@@ -45,10 +45,10 @@ def moe_fused_gate(
 ):
     # 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
-    # as the group weight to select exerpt groups and then select topk experts within the selected groups
+    # as the group weight to select expert groups and then select topk experts within the selected groups
     # 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 limitted for now.
-    # for non-supported case, we suggestion to use the biased_grouped_topk func in sglang.srt.layers.moe.topk
+    # 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
     # n_share_experts_fusion: if > 0, the last expert will be replaced with a round-robin shared expert
     # routed_scaling_factor: if > 0, the last expert will be scaled by this factor
     return torch.ops.sgl_kernel.moe_fused_gate.default(

sgl-kernel/tests/test_flash_attention.py

@@ -24,7 +24,7 @@ def is_fa3_supported(device=None) -> bool:
     #  Between sm80/sm87 and sm86/sm89 is the shared memory size. you can follow the link below for more information
     #  https://docs.nvidia.com/cuda/cuda-c-programming-guide/#shared-memory-8-x
     #  And for sgl-kernel right now, we can build fa3 on sm80/sm86/sm89/sm90a.
-    #  Thats mean if you use A100/A*0/L20/L40/L40s/4090 you can use fa3.
+    #  That means if you use A100/A*0/L20/L40/L40s/4090 you can use fa3.
     return (
         torch.cuda.get_device_capability(device)[0] == 9
         or torch.cuda.get_device_capability(device)[0] == 8

sgl-kernel/tests/test_per_token_group_quant_8bit.py

@@ -21,7 +21,7 @@ def _per_token_group_quant_fp8(
     y_s_ptr,
     # Stride of input
     y_stride,
-    # Collums of input
+    # Columns of input
     N,
     # Avoid to divide zero
     eps,

sgl-router/src/tree.rs

@@ -1070,7 +1070,7 @@ mod tests {
 
     #[test]
     fn test_utf8_split_seq() {
-        // The string should be indexed and splitted by a utf-8 value basis instead of byte basis
+        // The string should be indexed and split by a utf-8 value basis instead of byte basis
         // use .chars() to get the iterator of the utf-8 value
         let tree = Arc::new(Tree::new());
 

test/srt/test_openai_server.py

@@ -433,7 +433,7 @@ class TestOpenAIServer(CustomTestCase):
                             )
 
     def test_completion_stream(self):
-        # parallel sampling adn list input are not supported in streaming mode
+        # parallel sampling and list input are not supported in streaming mode
         for echo in [False, True]:
             for logprobs in [None, 5]:
                 for use_list_input in [True, False]:

test/srt/test_session_control.py

@@ -161,7 +161,7 @@ class TestSessionControl(CustomTestCase):
             ]
         )
 
-        # query with a non-existing rid (the last one should be disappeared becuase of backtrack), should see abort
+        # query with a non-existing rid (the last one should be disappeared because of backtrack), should see abort
         response = requests.post(
             self.base_url + "/generate",
             json={
@@ -668,7 +668,7 @@ class TestSessionControlVision(CustomTestCase):
         ).json()
         outputs_from_session.append(response["text"])
 
-        # query with a non-existing rid (the last one should be disappeared becuase of backtrack), should see abort
+        # query with a non-existing rid (the last one should be disappeared because of backtrack), should see abort
         response = requests.post(
             self.base_url + "/generate",
             json={

test/srt/test_srt_endpoint.py

@@ -295,7 +295,7 @@ class TestSRTEndpoint(CustomTestCase):
         print(f"{output_top_logprobs=}")
 
         # Parse results
-        # This is becaues the grammar constraint allows all prefix tokens
+        # This is because the grammar constraint allows all prefix tokens
         logprobs = [None] * 2
         for i in range(len(output_top_logprobs)):
             try:

test/srt/test_srt_engine_with_quant_args.py

@@ -8,7 +8,7 @@ class TestSRTEngineWithQuantArgs(CustomTestCase):
 
     def test_1_quantization_args(self):
 
-        # we only test fp8 because other methods are currenly depend on vllm. We can add other methods back to test after vllm depency is resolved.
+        # we only test fp8 because other methods are currently dependent on vllm. We can add other methods back to test after vllm dependency is resolved.
         quantization_args_list = [
             # "awq",
             "fp8",

test/srt/test_vlm_accuracy.py

@@ -116,7 +116,7 @@ class VisionLLMLogitsBase(unittest.IsolatedAsyncioTestCase):
         }},
         {{
           "type": "text",
-          "text": "Whats in this picture?"
+          "text": "What's in this picture?"
         }}
       ]
     }}