Fix typos and unify size(s)/stride(s) API calls (#8799)

sgl-kernel/csrc/attention/cutlass_mla_kernel.cu

@@ -105,10 +105,10 @@ typename T::Fmha::Arguments args_from_options(
   hw_info.device_id = q_nope.device().index();
   hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
 
-  int batches = q_nope.sizes()[0];
-  int page_count_per_seq = page_table.sizes()[1];
-  int page_count_total = kv_c_and_k_pe_cache.sizes()[0];
-  int page_size = kv_c_and_k_pe_cache.sizes()[1];
+  int batches = q_nope.size(0);
+  int page_count_per_seq = page_table.size(1);
+  int page_count_total = kv_c_and_k_pe_cache.size(0);
+  int page_size = kv_c_and_k_pe_cache.size(1);
   int max_seq_len = page_size * page_count_per_seq;
   using TileShapeH = typename T::TileShapeH;
   using TileShapeD = typename T::TileShapeD;
@@ -220,7 +220,7 @@ void cutlass_mla_decode(
   auto in_dtype = q_nope.dtype();
   at::cuda::CUDAGuard device_guard{(char)q_nope.get_device()};
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream(q_nope.get_device());
-  const int page_size = kv_c_and_k_pe_cache.sizes()[1];
+  const int page_size = kv_c_and_k_pe_cache.size(1);
 
   // NOTE(alcanderian): IsPersistent has bug with manual split_kv.
   // Kernel will hang if batch is too large with large num_kv_splits. (for example bs=8, num_kv_splits=8)

sgl-kernel/csrc/gemm/dsv3_fused_a_gemm.cu

@@ -640,9 +640,9 @@ void dsv3_fused_a_gemm(torch::Tensor& output, torch::Tensor const& mat_a, torch:
   TORCH_CHECK(output.size(0) == num_tokens, "required output.shape[0] == mat_a.shape[0]")
   TORCH_CHECK(output.size(1) == hd_out, "required output.shape[1] == mat_b.shape[1]")
 
-  TORCH_CHECK(mat_a.strides()[1] == 1);   // Row-major
-  TORCH_CHECK(output.strides()[1] == 1);  // Row-major
-  TORCH_CHECK(mat_b.strides()[0] == 1);   // Column-major
+  TORCH_CHECK(mat_a.stride(1) == 1, "mat_a must be a row major tensor");     // Row-major
+  TORCH_CHECK(output.stride(1) == 1, "output must be a row major tensor");   // Row-major
+  TORCH_CHECK(mat_b.stride(0) == 1, "mat_b must be a column major tensor");  // Column-major
 
   auto const data_type = mat_a.scalar_type();
   TORCH_CHECK(

sgl-kernel/csrc/gemm/fp8_blockwise_gemm_kernel.cu

@@ -353,7 +353,7 @@ torch::Tensor fp8_blockwise_scaled_mm(
   TORCH_CHECK(mat_a.dim() == 2, "mat_a must be a 2D tensor");
   TORCH_CHECK(mat_b.dim() == 2, "mat_b must be a 2D tensor");
   TORCH_CHECK(mat_a.stride(1) == 1, "mat_a must be a row major tensor");
-  TORCH_CHECK(mat_b.stride(0) == 1, "mat_a must be a column major tensor");
+  TORCH_CHECK(mat_b.stride(0) == 1, "mat_b must be a column major tensor");
   TORCH_CHECK(mat_a.size(1) == mat_b.size(0), "mat_a and mat_b shapes cannot be multiplied");
 
   TORCH_CHECK(

sgl-kernel/csrc/gemm/fp8_gemm_kernel.cu

@@ -1080,7 +1080,7 @@ torch::Tensor fp8_scaled_mm(
   TORCH_CHECK(mat_a.dim() == 2, "mat_a must be a 2D tensor");
   TORCH_CHECK(mat_b.dim() == 2, "mat_b must be a 2D tensor");
   TORCH_CHECK(mat_a.stride(1) == 1, "mat_a must be a row major tensor");
-  TORCH_CHECK(mat_b.stride(0) == 1, "mat_a must be a column major tensor");
+  TORCH_CHECK(mat_b.stride(0) == 1, "mat_b must be a column major tensor");
   TORCH_CHECK(mat_a.size(1) == mat_b.size(0), "mat_a and mat_b shapes cannot be multiplied");
 
   TORCH_CHECK(

sgl-kernel/csrc/gemm/int8_gemm_kernel.cu

@@ -672,7 +672,7 @@ torch::Tensor int8_scaled_mm(
   TORCH_CHECK(mat_a.dim() == 2, "mat_a must be a 2D tensor");
   TORCH_CHECK(mat_b.dim() == 2, "mat_b must be a 2D tensor");
   TORCH_CHECK(mat_a.stride(1) == 1, "mat_a must be a row major tensor");
-  TORCH_CHECK(mat_b.stride(0) == 1, "mat_a must be a column major tensor");
+  TORCH_CHECK(mat_b.stride(0) == 1, "mat_b must be a column major tensor");
   TORCH_CHECK(mat_a.size(1) == mat_b.size(0), "mat_a and mat_b shapes cannot be multiplied");
   TORCH_CHECK(mat_a.size(1) % 16 == 0, "mat_a.size(1) must be multiple of 16 for memory alignment");
   TORCH_CHECK(mat_b.size(0) % 16 == 0, "mat_b.size(0) must be multiple of 16 for memory alignment");

sgl-kernel/csrc/gemm/nvfp4_scaled_mm_kernels.cu

@@ -273,20 +273,20 @@ void cutlass_scaled_fp4_mm_sm100a(
   TORCH_CHECK(A.dim() == 2, "a must be a matrix");
   TORCH_CHECK(B.dim() == 2, "b must be a matrix");
   TORCH_CHECK(
-      A.sizes()[1] == B.sizes()[1],
+      A.size(1) == B.size(1),
       "a and b shapes cannot be multiplied (",
-      A.sizes()[0],
+      A.size(0),
       "x",
-      A.sizes()[1],
+      A.size(1),
       " and ",
-      B.sizes()[0],
+      B.size(0),
       "x",
-      B.sizes()[1],
+      B.size(1),
       ")");
 
-  auto const m = A.sizes()[0];
-  auto const n = B.sizes()[0];
-  auto const k = A.sizes()[1] * 2;
+  auto const m = A.size(0);
+  auto const n = B.size(0);
+  auto const k = A.size(1) * 2;
 
   constexpr int alignment = 32;
   TORCH_CHECK(
@@ -294,9 +294,9 @@ void cutlass_scaled_fp4_mm_sm100a(
       "Expected k to be divisible by ",
       alignment,
       ", but got a shape: (",
-      A.sizes()[0],
+      A.size(0),
       "x",
-      A.sizes()[1],
+      A.size(1),
       "), k: ",
       k,
       ".");
@@ -305,9 +305,9 @@ void cutlass_scaled_fp4_mm_sm100a(
       "Expected n to be divisible by ",
       alignment,
       ", but got b shape: (",
-      B.sizes()[0],
+      B.size(0),
       "x",
-      B.sizes()[1],
+      B.size(1),
       ").");
 
   auto round_up = [](int x, int y) { return (x + y - 1) / y * y; };
@@ -320,37 +320,37 @@ void cutlass_scaled_fp4_mm_sm100a(
   TORCH_CHECK(A_sf.dim() == 2, "scale_a must be a matrix");
   TORCH_CHECK(B_sf.dim() == 2, "scale_b must be a matrix");
   TORCH_CHECK(
-      A_sf.sizes()[1] == B_sf.sizes()[1],
+      A_sf.size(1) == B_sf.size(1),
       "scale_a and scale_b shapes cannot be multiplied (",
-      A_sf.sizes()[0],
+      A_sf.size(0),
       "x",
-      A_sf.sizes()[1],
+      A_sf.size(1),
       " and ",
-      B_sf.sizes()[0],
+      B_sf.size(0),
       "x",
-      B_sf.sizes()[1],
+      B_sf.size(1),
       ")");
   TORCH_CHECK(
-      A_sf.sizes()[0] == rounded_m && A_sf.sizes()[1] == rounded_k,
+      A_sf.size(0) == rounded_m && A_sf.size(1) == rounded_k,
       "scale_a must be padded and swizzled to a shape (",
       rounded_m,
       "x",
       rounded_k,
       "), but got a shape (",
-      A_sf.sizes()[0],
+      A_sf.size(0),
       "x",
-      A_sf.sizes()[1],
+      A_sf.size(1),
       ")");
   TORCH_CHECK(
-      B_sf.sizes()[0] == rounded_n && B_sf.sizes()[1] == rounded_k,
+      B_sf.size(0) == rounded_n && B_sf.size(1) == rounded_k,
       "scale_b must be padded and swizzled to a shape (",
       rounded_n,
       "x",
       rounded_k,
       "), but got a shape (",
-      B_sf.sizes()[0],
+      B_sf.size(0),
       "x",
-      B_sf.sizes()[1],
+      B_sf.size(1),
       ")");
 
   auto out_dtype = D.dtype();