[Feat] Support mma gemm with stride (#701)

* gemm_with_stride sm89

* fix offset issue

* bug fix

* format

* sm80 support

* add sm90

* add testing

* format

* add static_assert for wgmma

* Enhance error message for inner_box_dim validation in LowerBulkCopy

* lint fix

---------
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

src/op/bulk_copy.cc

@@ -273,7 +273,9 @@ Stmt Copy::LowerBulkCopy(const LowerArgs &T, arith::Analyzer *analyzer) const {
         << "inner_box_dim: " << *inner_box_dim << " is not divisible by 256";
     instruction_dim = 256;
   }
-  ICHECK((*inner_box_dim) % instruction_dim == 0);
+  ICHECK((*inner_box_dim) % instruction_dim == 0)
+      << "inner_box_dim: " << *inner_box_dim
+      << " is not divisible by instruction_dim: " << instruction_dim;
   desc.smem_box.Set(0, PrimExpr(instruction_dim));
 
   int inner_box_dim_ = instruction_dim * shared_tensor->dtype.bytes();

src/op/gemm.cc

@@ -47,14 +47,18 @@ Gemm::Gemm(Array<PrimExpr> args, BufferMap vmap) {
   K = args[7].as<IntImm>().value()->value;
   policy = static_cast<GemmWarpPolicy>(args[8].as<IntImm>().value()->value);
   clear_accum = args[9].as<Bool>().value();
-  if (args.size() > 10) {
-    kPack = args[10].as<IntImm>().value()->value;
+  stride_A = args[10].as<IntImm>().value()->value;
+  stride_B = args[11].as<IntImm>().value()->value;
+  offset_A = args[12].as<IntImm>().value()->value;
+  offset_B = args[13].as<IntImm>().value()->value;
+  if (args.size() > 14) {
+    kPack = args[14].as<IntImm>().value()->value;
     if (kPack != 1 && kPack != 2) {
       ICHECK(false) << "kPack must be 1 or 2";
     }
   }
-  if (args.size() > 11) {
-    wg_wait = args[11].as<IntImm>().value()->value;
+  if (args.size() > 15) {
+    wg_wait = args[15].as<IntImm>().value()->value;
   }
 }
 
@@ -284,6 +288,19 @@ bool Gemm::CheckWGMMA() const {
   }
 }
 
+static int GetArchInt(Target target) {
+  int arch_int = 0;
+  auto s = target->GetAttr<String>("arch");
+  ICHECK(s.defined());
+  const char *arch_str = s.value().c_str();
+  if (arch_str[0] == 's' && arch_str[1] == 'm' && arch_str[2] == '_') {
+    arch_int = atoi(&arch_str[3]);
+  } else {
+    arch_int = 0;
+  }
+  return arch_int;
+}
+
 Stmt Gemm::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
   auto block_size = *as_const_int(T.thread_bounds->extent);
   GemmInst gemm_inst = GetGemmInst(block_size, T.target);
@@ -301,6 +318,10 @@ Stmt Gemm::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
   ss << warp_m << ", " << warp_n << ", ";
   ss << trans_A << ", " << trans_B;
   ss << ", " << clear_accum;
+  if (TargetIsCuda(T.target) && (GetArchInt(T.target) >= 75)) {
+    ss << ", " << stride_A << ", " << stride_B;
+    ss << ", " << offset_A << ", " << offset_B;
+  }
   if (TargetIsCDNA(T.target)) {
     // for cdna gemm, we need to specify kPack
     ss << ", " << kPack;

src/op/gemm.h

@@ -45,6 +45,8 @@ private:
   PrimExpr Aptr, Bptr, Cptr;
   bool trans_A, trans_B;
   int M, N, K;
+  int stride_A, stride_B;
+  int offset_A, offset_B;
   bool clear_accum = false;
   // k_pack please ref to bitblas/tl/mfma_macro_generator.py::k_pack
   // only will be enabled under cdna mfma instructions

src/tl_templates/cuda/gemm_sm80.h

@@ -73,135 +73,143 @@ template <int N, int num_warp_n, bool transpose> struct SelectCopy {
       DefaultCopy>;
 };
 
-template <int Bits, int N, int K, bool K_inner, int num_warp_n,
+template <int Bits, int N, int K, bool K_inner, int num_warp_n, int leading_dim,
           typename Enable = void>
 struct OperandTraits {
   // Primary template, use padded layout and default copy
-  static constexpr int stride = K_inner ? K : N;
+  static constexpr int stride = leading_dim;
   static constexpr int padded =
       stride % (256 / Bits) == 0 ? stride + 128 / Bits : stride;
   using Layout = typename std::conditional<
-      K_inner, Layout<Shape<Int<N>, Int<K>>, Shape<Int<padded>, _1>>,
-      Layout<Shape<Int<N>, Int<K>>, Shape<_1, Int<padded>>>>::type;
+      K_inner, Layout<Shape<Int<N>, Int<leading_dim>>, Shape<Int<padded>, _1>>,
+      Layout<Shape<Int<leading_dim>, Int<K>>, Shape<_1, Int<padded>>>>::type;
   using Copy = DefaultCopy;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 64 == 32>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 32>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 3, 3>{}, Layout<Shape<_8, _32>, Stride<_32, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 64 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 3, 3>{}, Layout<Shape<_8, _64>, Stride<_64, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 64 == 32>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 32>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 3, 3>{}, Layout<Shape<_32, _8>, Stride<_1, _32>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = typename SelectCopy<N, num_warp_n, false>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 64 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 3, 3>{}, Layout<Shape<_64, _8>, Stride<_1, _64>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = typename SelectCopy<N, num_warp_n, false>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 32 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 2, 3>{}, Layout<Shape<_8, _32>, Stride<_32, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 32 == 16>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 16>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 3>{}, Layout<Shape<_8, _16>, Stride<_16, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 32 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 2, 3>{}, Layout<Shape<_32, _8>, Stride<_1, _32>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = UniversalCopy<tfloat32_t>;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 32 == 16>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 16>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 3>{}, Layout<Shape<_16, _8>, Stride<_1, _16>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = UniversalCopy<tfloat32_t>;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<8, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 128 == 64>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<8, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 128 == 64>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 4, 3>{}, Layout<Shape<_8, _64>, Stride<_64, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<8, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 128 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<8, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 128 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 4, 3>{}, Layout<Shape<_8, _128>, Stride<_128, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<64, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 16 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<64, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 16 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 0, 4>{}, Layout<Shape<_4, _16>, Stride<_16, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = DefaultCopy;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<64, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 16 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<64, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 16 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 2>{}, Layout<Shape<_16, _4>, Stride<_1, _16>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = DefaultCopy;
 };
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type_raw,
-          typename B_type_raw, typename C_type_raw>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type_raw, typename B_type_raw,
+          typename C_type_raw>
 class GemmTensorOp {
 public:
   using A_type =
@@ -215,10 +223,10 @@ public:
   using Instruction =
       DispatchInstruction<A_type, B_type, C_type, num_warp_m, num_warp_n, N>;
 
-  using OperandATraits =
-      OperandTraits<sizeof_bits<A_type>::value, M, K, !trans_A, num_warp_m>;
+  using OperandATraits = OperandTraits<sizeof_bits<A_type>::value, M, K,
+                                       !trans_A, num_warp_m, lda>;
   using OperandBTraits =
-      OperandTraits<sizeof_bits<B_type>::value, N, K, trans_B, num_warp_n>;
+      OperandTraits<sizeof_bits<B_type>::value, N, K, trans_B, num_warp_n, ldb>;
 
   using SmemLayoutA = typename OperandATraits::Layout;
   using SmemLayoutB = typename OperandBTraits::Layout;
@@ -244,12 +252,38 @@ public:
       return layout;
   }
 
+  template <int offset, int NN, int KK, bool trans, int lddim, typename Engine0,
+            typename Layout0>
+  static CUTE_DEVICE auto get_region_tensor(Tensor<Engine0, Layout0> &sa) {
+    if constexpr (offset == 0) {
+      return composition(
+          sa,
+          Layout<Shape<Int<NN>, Int<KK>>,
+                 Stride<_1, typename std::conditional<trans, Int<NN>,
+                                                      Int<lddim>>::type>>{});
+    } else {
+      if constexpr (trans) {
+        static_assert(offset % KK == 0, "Offset must be a multiple of K");
+        constexpr int offset_n = offset / KK;
+        return flat_divide(sa, Shape<Int<NN>, Int<KK>>{})(_, _, _0{},
+                                                          Int<offset_n>{});
+      } else {
+        static_assert(offset % NN == 0, "Offset must be a multiple of N");
+        constexpr int offset_n = offset / NN;
+        return flat_divide(sa, Shape<Int<NN>, Int<KK>>{})(_, _, Int<offset_n>{},
+                                                          _0{});
+      }
+    }
+  }
+
   static CUTE_DEVICE void body(A_type_raw *pA, B_type_raw *pB, C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sA = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
+    Tensor sA_all = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
                                 SmemLayoutA{});
-    Tensor sB = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
+    Tensor sB_all = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
                                 SmemLayoutB{});
+    Tensor sA = get_region_tensor<offset_a, M, K, !trans_A, lda>(sA_all);
+    Tensor sB = get_region_tensor<offset_b, N, K, trans_B, ldb>(sB_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_A = make_tiled_copy_A(SmemCopyA{}, tiled_mma);
@@ -287,8 +321,9 @@ public:
   static CUTE_DEVICE void body_rs(A_type_raw *pA, B_type_raw *pB,
                                   C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sB = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
+    Tensor sB_all = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
                                 SmemLayoutB{});
+    Tensor sB = get_region_tensor<offset_b, N, K, trans_B, ldb>(sB_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_B = make_tiled_copy_B(SmemCopyB{}, tiled_mma);
@@ -322,8 +357,9 @@ public:
   static CUTE_DEVICE void body_sr(A_type_raw *pA, B_type_raw *pB,
                                   C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sA = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
+    Tensor sA_all = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
                                 SmemLayoutA{});
+    Tensor sA = get_region_tensor<offset_a, M, K, !trans_A, lda>(sA_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_A = make_tiled_copy_A(SmemCopyA{}, tiled_mma);
@@ -360,29 +396,32 @@ public:
 namespace tl {
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_ss(A_type *pA, B_type *pB, C_type *accum) {
   using MMA = cute::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body(pA, pB, accum);
 }
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_rs(A_type *pA, B_type *pB, C_type *accum) {
   using MMA = cute::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body_rs(pA, pB, accum);
 }
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_sr(A_type *pA, B_type *pB, C_type *accum) {
   using MMA = cute::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body_sr(pA, pB, accum);
 }
 

src/tl_templates/cuda/gemm_sm89.h

@@ -91,135 +91,143 @@ template <int N, int num_warp_n, bool transpose> struct SelectCopy {
       DefaultCopy>;
 };
 
-template <int Bits, int N, int K, bool K_inner, int num_warp_n,
+template <int Bits, int N, int K, bool K_inner, int num_warp_n, int leading_dim,
           typename Enable = void>
 struct OperandTraits {
   // Primary template, use padded layout and default copy
-  static constexpr int stride = K_inner ? K : N;
+  static constexpr int stride = leading_dim;
   static constexpr int padded =
       stride % (256 / Bits) == 0 ? stride + 128 / Bits : stride;
   using Layout = typename std::conditional<
-      K_inner, Layout<Shape<Int<N>, Int<K>>, Shape<Int<padded>, _1>>,
-      Layout<Shape<Int<N>, Int<K>>, Shape<_1, Int<padded>>>>::type;
+      K_inner, Layout<Shape<Int<N>, Int<leading_dim>>, Shape<Int<padded>, _1>>,
+      Layout<Shape<Int<leading_dim>, Int<K>>, Shape<_1, Int<padded>>>>::type;
   using Copy = DefaultCopy;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 64 == 32>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 32>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 3, 3>{}, Layout<Shape<_8, _32>, Stride<_32, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 64 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 3, 3>{}, Layout<Shape<_8, _64>, Stride<_64, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 64 == 32>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 32>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 3, 3>{}, Layout<Shape<_32, _8>, Stride<_1, _32>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = typename SelectCopy<N, num_warp_n, false>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 64 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 3, 3>{}, Layout<Shape<_64, _8>, Stride<_1, _64>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = typename SelectCopy<N, num_warp_n, false>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 32 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 2, 3>{}, Layout<Shape<_8, _32>, Stride<_32, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 32 == 16>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 16>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 3>{}, Layout<Shape<_8, _16>, Stride<_16, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 32 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 2, 3>{}, Layout<Shape<_32, _8>, Stride<_1, _32>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = UniversalCopy<tfloat32_t>;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 32 == 16>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 16>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 3>{}, Layout<Shape<_16, _8>, Stride<_1, _16>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = UniversalCopy<tfloat32_t>;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<8, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 128 == 64>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<8, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 128 == 64>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 4, 3>{}, Layout<Shape<_8, _64>, Stride<_64, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<8, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 128 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<8, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 128 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 4, 3>{}, Layout<Shape<_8, _128>, Stride<_128, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<64, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 16 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<64, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 16 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 0, 4>{}, Layout<Shape<_4, _16>, Stride<_16, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = DefaultCopy;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<64, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 16 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<64, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 16 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 2>{}, Layout<Shape<_16, _4>, Stride<_1, _16>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = DefaultCopy;
 };
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type_raw,
-          typename B_type_raw, typename C_type_raw>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type_raw, typename B_type_raw,
+          typename C_type_raw>
 class GemmTensorOp {
 public:
   using A_type =
@@ -233,10 +241,10 @@ public:
   using Instruction =
       DispatchInstruction<A_type, B_type, C_type, num_warp_m, num_warp_n, N>;
 
-  using OperandATraits =
-      OperandTraits<sizeof_bits<A_type>::value, M, K, !trans_A, num_warp_m>;
+  using OperandATraits = OperandTraits<sizeof_bits<A_type>::value, M, K,
+                                       !trans_A, num_warp_m, lda>;
   using OperandBTraits =
-      OperandTraits<sizeof_bits<B_type>::value, N, K, trans_B, num_warp_n>;
+      OperandTraits<sizeof_bits<B_type>::value, N, K, trans_B, num_warp_n, ldb>;
 
   using SmemLayoutA = typename OperandATraits::Layout;
   using SmemLayoutB = typename OperandBTraits::Layout;
@@ -262,12 +270,44 @@ public:
       return layout;
   }
 
+  template <int offset, int NN, int KK, bool trans, int lddim, typename Engine0,
+            typename Layout0>
+  static CUTE_DEVICE auto get_region_tensor(Tensor<Engine0, Layout0> &sa) {
+    if constexpr (offset == 0) {
+      return composition(
+          sa,
+          Layout<Shape<Int<NN>, Int<KK>>,
+                 Stride<_1, typename std::conditional<trans, Int<NN>,
+                                                      Int<lddim>>::type>>{});
+    } else {
+      if constexpr (trans) {
+        static_assert(offset % KK == 0, "Offset must be a multiple of K");
+        constexpr int offset_n = offset / KK;
+        return flat_divide(sa, Shape<Int<NN>, Int<KK>>{})(_, _, _0{},
+                                                          Int<offset_n>{});
+      } else {
+        static_assert(offset % NN == 0, "Offset must be a multiple of N");
+        constexpr int offset_n = offset / NN;
+        return flat_divide(sa, Shape<Int<NN>, Int<KK>>{})(_, _, Int<offset_n>{},
+                                                          _0{});
+      }
+    }
+  }
+
   static CUTE_DEVICE void body(A_type_raw *pA, B_type_raw *pB, C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sA = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
+    Tensor sA_all = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
                                 SmemLayoutA{});
-    Tensor sB = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
+    Tensor sB_all = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
                                 SmemLayoutB{});
+    // Tensor sA = composition(sA_all, Layout<Shape<Int<M>, Int<K>>,
+    //   Stride<_1, typename std::conditional<trans_A, Int<lda>,
+    //   Int<M>>::type>>{});
+    // Tensor sB = composition(sB_all, Layout<Shape<Int<N>, Int<K>>,
+    //   Stride<_1, typename std::conditional<trans_B, Int<N>,
+    //   Int<ldb>>::type>>{});
+    Tensor sA = get_region_tensor<offset_a, M, K, !trans_A, lda>(sA_all);
+    Tensor sB = get_region_tensor<offset_b, N, K, trans_B, ldb>(sB_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_A = make_tiled_copy_A(SmemCopyA{}, tiled_mma);
@@ -306,8 +346,11 @@ public:
   static CUTE_DEVICE void body_rs(A_type_raw *pA, B_type_raw *pB,
                                   C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sB = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
+    Tensor sB_all = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
                                 SmemLayoutB{});
+    // Tensor sB = flat_divide(sB_all, Shape<Int<N>, Int<K>>{})(_, _, _0{},
+    // _0{});
+    Tensor sB = get_region_tensor<offset_b, N, K, trans_B, ldb>(sB_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_B = make_tiled_copy_B(SmemCopyB{}, tiled_mma);
@@ -342,8 +385,11 @@ public:
   static CUTE_DEVICE void body_sr(A_type_raw *pA, B_type_raw *pB,
                                   C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sA = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
+    Tensor sA_all = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
                                 SmemLayoutA{});
+    // Tensor sA = flat_divide(sA_all, Shape<Int<M>, Int<K>>{})(_, _, _0{},
+    // _0{});
+    Tensor sA = get_region_tensor<offset_a, M, K, !trans_A, lda>(sA_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_A = make_tiled_copy_A(SmemCopyA{}, tiled_mma);
@@ -380,29 +426,32 @@ public:
 namespace tl {
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_ss(A_type *pA, B_type *pB, C_type *accum) {
   using MMA = cute::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body(pA, pB, accum);
 }
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_rs(A_type *pA, B_type *pB, C_type *accum) {
   using MMA = cute::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body_rs(pA, pB, accum);
 }
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_sr(A_type *pA, B_type *pB, C_type *accum) {
   using MMA = cute::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body_sr(pA, pB, accum);
 }
 

src/tl_templates/cuda/gemm_sm90.h

@@ -194,16 +194,16 @@ struct DispatchInstruction<half_t, half_t, float, num_warp_m, num_warp_n, N> {
 };
 #endif
 
-template <int Bits, int N, int K, bool K_inner, int num_warp_n,
+template <int Bits, int N, int K, bool K_inner, int num_warp_n, int leading_dim,
           typename Enable = void>
 struct OperandTraits {
   // Primary template, use padded layout and default copy
-  static constexpr int stride = K_inner ? K : N;
+  static constexpr int stride = leading_dim;
   static constexpr int padded =
       stride % (256 / Bits) == 0 ? stride + 128 / Bits : stride;
   using Layout = typename std::conditional<
-      K_inner, Layout<Shape<Int<N>, Int<K>>, Shape<Int<padded>, _1>>,
-      Layout<Shape<Int<N>, Int<K>>, Shape<_1, Int<padded>>>>::type;
+      K_inner, Layout<Shape<Int<N>, Int<leading_dim>>, Shape<Int<padded>, _1>>,
+      Layout<Shape<Int<leading_dim>, Int<K>>, Shape<_1, Int<padded>>>>::type;
   using Copy = DefaultCopy;
 };
 
@@ -224,124 +224,132 @@ template <int N, int num_warp_n, bool transpose> struct SelectCopy {
       DefaultCopy>;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 64 == 32>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 32>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 3, 3>{}, Layout<Shape<_8, _32>, Stride<_32, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 64 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 3, 3>{}, Layout<Shape<_8, _64>, Stride<_64, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 64 == 32>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 32>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 3, 3>{}, Layout<Shape<_32, _8>, Stride<_1, _32>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = typename SelectCopy<N, num_warp_n, false>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<16, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 64 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<16, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 64 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 3, 3>{}, Layout<Shape<_64, _8>, Stride<_1, _64>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = typename SelectCopy<N, num_warp_n, false>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 32 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 2, 3>{}, Layout<Shape<_8, _32>, Stride<_32, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 32 == 16>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 16>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 3>{}, Layout<Shape<_8, _16>, Stride<_16, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename SelectCopy<N, num_warp_n, true>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 32 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 2, 3>{}, Layout<Shape<_32, _8>, Stride<_1, _32>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = UniversalCopy<tfloat32_t>;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<32, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 32 == 16>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<32, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 32 == 16>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 3>{}, Layout<Shape<_16, _8>, Stride<_1, _16>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = UniversalCopy<tfloat32_t>;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<8, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 128 == 64>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<8, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 128 == 64>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 4, 3>{}, Layout<Shape<_8, _64>, Stride<_64, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename std::conditional<N == 8 * num_warp_n, SM75_U32x2_LDSM_N,
                                          SM75_U32x4_LDSM_N>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<8, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 128 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<8, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 128 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<3, 4, 3>{}, Layout<Shape<_8, _128>, Stride<_128, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = typename std::conditional<N == 8 * num_warp_n, SM75_U32x2_LDSM_N,
                                          SM75_U32x4_LDSM_N>::type;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<64, N, K, true, num_warp_n,
-                     typename std::enable_if<K % 16 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<64, N, K, true, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 16 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 0, 4>{}, Layout<Shape<_4, _16>, Stride<_16, _1>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{}));
+  using Layout =
+      decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<leading_dim>>{}));
   using Copy = DefaultCopy;
 };
 
-template <int N, int K, int num_warp_n>
-struct OperandTraits<64, N, K, false, num_warp_n,
-                     typename std::enable_if<N % 16 == 0>::type> {
+template <int N, int K, int num_warp_n, int leading_dim>
+struct OperandTraits<64, N, K, false, num_warp_n, leading_dim,
+                     typename std::enable_if<leading_dim % 16 == 0>::type> {
   using LayoutAtom = decltype(composition(
       Swizzle<2, 2, 2>{}, Layout<Shape<_16, _4>, Stride<_1, _16>>{}));
-  using Layout = decltype(tile_to_shape(LayoutAtom{}, Shape<Int<N>, Int<K>>{},
-                                        Step<_2, _1>{}));
+  using Layout = decltype(tile_to_shape(
+      LayoutAtom{}, Shape<Int<leading_dim>, Int<K>>{}, Step<_2, _1>{}));
   using Copy = DefaultCopy;
 };
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type_raw,
-          typename B_type_raw, typename C_type_raw>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type_raw, typename B_type_raw,
+          typename C_type_raw>
 class GemmTensorOp {
 public:
   using A_type =
@@ -355,10 +363,11 @@ public:
   using Instruction =
       DispatchInstruction<A_type, B_type, C_type, num_warp_m, num_warp_n, N>;
 
-  using OperandATraits =
-      OperandTraits<sizeof_bits<A_type>::value, M, K, !trans_A, num_warp_m>;
+  using OperandATraits = OperandTraits<sizeof_bits<A_type>::value, M, K,
+                                       !trans_A, num_warp_m, lda>;
   using OperandBTraits =
-      OperandTraits<sizeof_bits<B_type>::value, N, K, trans_B, num_warp_n>;
+      OperandTraits<sizeof_bits<B_type>::value, N, K, trans_B, num_warp_n, ldb>;
+
   using SmemLayoutA = typename OperandATraits::Layout;
   using SmemLayoutB = typename OperandBTraits::Layout;
   using SmemCopyA = Copy_Atom<typename OperandATraits::Copy, A_type>;
@@ -383,12 +392,38 @@ public:
       return layout;
   }
 
+  template <int offset, int NN, int KK, bool trans, int lddim, typename Engine0,
+            typename Layout0>
+  static CUTE_DEVICE auto get_region_tensor(Tensor<Engine0, Layout0> &sa) {
+    if constexpr (offset == 0) {
+      return composition(
+          sa,
+          Layout<Shape<Int<NN>, Int<KK>>,
+                 Stride<_1, typename std::conditional<trans, Int<NN>,
+                                                      Int<lddim>>::type>>{});
+    } else {
+      if constexpr (trans) {
+        static_assert(offset % KK == 0, "Offset must be a multiple of K");
+        constexpr int offset_n = offset / KK;
+        return flat_divide(sa, Shape<Int<NN>, Int<KK>>{})(_, _, _0{},
+                                                          Int<offset_n>{});
+      } else {
+        static_assert(offset % NN == 0, "Offset must be a multiple of N");
+        constexpr int offset_n = offset / NN;
+        return flat_divide(sa, Shape<Int<NN>, Int<KK>>{})(_, _, Int<offset_n>{},
+                                                          _0{});
+      }
+    }
+  }
+
   static CUTE_DEVICE void body(A_type_raw *pA, B_type_raw *pB, C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sA = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
+    Tensor sA_all = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
                                 SmemLayoutA{});
-    Tensor sB = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
+    Tensor sB_all = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
                                 SmemLayoutB{});
+    Tensor sA = get_region_tensor<offset_a, M, K, !trans_A, lda>(sA_all);
+    Tensor sB = get_region_tensor<offset_b, N, K, trans_B, ldb>(sB_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_A = make_tiled_copy_A(SmemCopyA{}, tiled_mma);
@@ -426,8 +461,9 @@ public:
   static CUTE_DEVICE void body_rs(A_type_raw *pA, B_type_raw *pB,
                                   C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sB = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
+    Tensor sB_all = make_tensor(make_smem_ptr(reinterpret_cast<B_type *>(pB)),
                                 SmemLayoutB{});
+    Tensor sB = get_region_tensor<offset_b, N, K, trans_B, ldb>(sB_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_B = make_tiled_copy_B(SmemCopyB{}, tiled_mma);
@@ -461,8 +497,9 @@ public:
   static CUTE_DEVICE void body_sr(A_type_raw *pA, B_type_raw *pB,
                                   C_type_raw *pC) {
     const int tid = threadIdx.x;
-    Tensor sA = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
+    Tensor sA_all = make_tensor(make_smem_ptr(reinterpret_cast<A_type *>(pA)),
                                 SmemLayoutA{});
+    Tensor sA = get_region_tensor<offset_a, M, K, !trans_A, lda>(sA_all);
     TileMma tiled_mma;
     auto thr_mma = tiled_mma.get_thread_slice(tid);
     auto tiled_copy_A = make_tiled_copy_A(SmemCopyA{}, tiled_mma);
@@ -503,67 +540,86 @@ namespace tl {
 namespace tl_mma {
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_ss(A_type *pA, B_type *pB, C_type *accum) {
   using MMA =
       cute::tl_mma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body(pA, pB, accum);
 }
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_rs(A_type *pA, B_type *pB, C_type *accum) {
   using MMA =
       cute::tl_mma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body_rs(pA, pB, accum);
 }
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum, typename A_type, typename B_type,
-          typename C_type>
+          bool trans_B, bool clear_accum, int lda, int ldb, int offset_a,
+          int offset_b, typename A_type, typename B_type, typename C_type>
 CUTLASS_DEVICE void gemm_sr(A_type *pA, B_type *pB, C_type *accum) {
   using MMA =
       cute::tl_mma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
-                                 trans_B, clear_accum, A_type, B_type, C_type>;
+                                 trans_B, clear_accum, lda, ldb, offset_a,
+                                 offset_b, A_type, B_type, C_type>;
   MMA::body_sr(pA, pB, accum);
 }
 
 } // namespace tl_mma
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum = false, bool use_wgmma = true,
+          bool trans_B, bool clear_accum = false, int lda = 0, int ldb = 0,
+          int offset_a = 0, int offset_b = 0, bool use_wgmma = true,
           int wg_wait = 0, typename A_type, typename B_type, typename C_type>
 TL_DEVICE void gemm_ss(A_type *pA, B_type *pB, C_type *accum) {
   if constexpr (use_wgmma) {
+    static_assert((trans_A && lda == M) || (!trans_A && lda == K),
+                  "Hopper wgmma doesn't support custom stride for A");
+    static_assert((trans_B && ldb == K) || (!trans_B && ldb == N),
+                  "Hopper wgmma doesn't support custom stride for B");
+    static_assert(offset_a == 0 && offset_b == 0,
+                  "offset_a and offset_b must be zero for wgmma");
     using MMA = cute::tl_wgmma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n,
                                              trans_A, trans_B, clear_accum,
                                              A_type, B_type, C_type>;
     MMA::body<wg_wait>(pA, pB, accum);
   } else {
-    using MMA = cute::tl_mma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n,
-                                           trans_A, trans_B, clear_accum,
-                                           A_type, B_type, C_type>;
+    using MMA =
+        cute::tl_mma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
+                                   trans_B, clear_accum, lda, ldb, offset_a,
+                                   offset_b, A_type, B_type, C_type>;
     MMA::body(pA, pB, accum);
   }
 }
 
 template <int M, int N, int K, int num_warp_m, int num_warp_n, bool trans_A,
-          bool trans_B, bool clear_accum = false, bool use_wgmma = true,
+          bool trans_B, bool clear_accum = false, int lda = 0, int ldb = 0,
+          int offset_a = 0, int offset_b = 0, bool use_wgmma = true,
           int wg_wait = 0, typename A_type, typename B_type, typename C_type>
 TL_DEVICE void gemm_rs(A_type *pA, B_type *pB, C_type *accum) {
   if constexpr (use_wgmma) {
+    static_assert((trans_A && lda == M) || (!trans_A && lda == K),
+                  "Hopper wgmma doesn't support custom stride for A");
+    static_assert((trans_B && ldb == K) || (!trans_B && ldb == N),
+                  "Hopper wgmma doesn't support custom stride for B");
+    static_assert(offset_a == 0 && offset_b == 0,
+                  "offset_a and offset_b must be zero for wgmma");
     using MMA = cute::tl_wgmma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n,
                                              trans_A, trans_B, clear_accum,
                                              A_type, B_type, C_type>;
     MMA::body_rs<wg_wait>(pA, pB, accum);
   } else {
-    using MMA = cute::tl_mma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n,
-                                           trans_A, trans_B, clear_accum,
-                                           A_type, B_type, C_type>;
+    using MMA =
+        cute::tl_mma::GemmTensorOp<M, N, K, num_warp_m, num_warp_n, trans_A,
+                                   trans_B, clear_accum, lda, ldb, offset_a,
+                                   offset_b, A_type, B_type, C_type>;
     MMA::body_rs(pA, pB, accum);
   }
 }

testing/python/kernel/test_tilelang_kernel_gemm_with_stride.py

+import tilelang.testing
+import tilelang
+import tilelang.language as T
+import torch
+
+
+def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
+
+    @T.prim_func
+    def main(
+            A: T.Tensor((M, K), dtype),
+            B: T.Tensor((K, N), dtype),
+            C: T.Tensor((M, N), dtype),
+    ):
+        # Initialize Kernel Context
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
+            A_shared = T.alloc_shared((block_M, block_K * 2), dtype, scope="shared")
+            B_shared = T.alloc_shared((block_K, block_N * 2), dtype, scope="shared")
+            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+
+            # Clear local accumulation
+            T.clear(C_local)
+            T.clear(B_shared)
+            T.clear(A_shared)
+
+            for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=0):
+                # Copy tile of A
+                # T.copy(A[by * block_M, ko * block_K], A_shared)
+                for i, k in T.Parallel(block_M, block_K):
+                    A_shared[i, k + block_K] = A[by * block_M + i, ko * block_K + k]
+
+                # Copy tile of B
+                # T.copy(B[ko * block_K, bx * block_N], B_shared)
+                for i, k in T.Parallel(block_K, block_N):
+                    B_shared[i, k] = B[ko * block_K + i, bx * block_N + k]
+
+                # Perform a tile-level GEMM on the shared buffers
+                # Currently we dispatch to the cute/hip on Nvidia/AMD GPUs
+                T.gemm(A_shared[:, block_K:], B_shared[0:block_K, 0:block_N], C_local)
+
+            # Copy result back to global memory
+            T.copy(C_local, C[by * block_M, bx * block_N])
+
+    return main
+
+
+def run_gemm_with_stride_ss(M: int, N: int, K: int, block_M: int, block_N: int, block_K: int):
+    # 1. Define the kernel (matmul) and compile/lower it into an executable module
+    func = matmul(M, N, K, block_M, block_N, block_K)
+
+    # 2. Compile the kernel into a torch function
+    # out_idx specifies the index of the output buffer in the argument list
+    # if out_idx is specified, the tensor will be created during runtime
+    # target currently can be "cuda" or "hip" or "cpu".
+    jit_kernel = tilelang.compile(
+        func,
+        out_idx=[2],
+        target="cuda",
+        pass_configs={
+            tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
+            tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
+        })
+    # Create random input tensors on the GPU
+    a = torch.randn(M, K, device="cuda", dtype=torch.float16)
+    b = torch.randn(K, N, device="cuda", dtype=torch.float16)
+
+    # Run the kernel through the Profiler
+    c = jit_kernel(a, b)
+
+    print(c)
+    # Reference multiplication using PyTorch
+    ref_c = a @ b
+
+    # Validate correctness
+    torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
+    print("Kernel output matches PyTorch reference.")
+
+
+@tilelang.testing.requires_cuda
+@tilelang.testing.requires_cuda_compute_version_ge(7, 5)
+def test_tilelang_kernel_gemm_with_stride():
+    run_gemm_with_stride_ss(128, 128, 64, 32, 32, 32)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

tilelang/language/gemm.py

@@ -69,10 +69,32 @@ def gemm(
         else:
             raise ValueError(f"Unsupported argument type: {type(object)} for buffer {object}")
 
+    def retrieve_stride(object: Union[tir.Buffer, tir.BufferRegion]) -> List[int]:
+        if isinstance(object, tir.Buffer):
+            strides = []
+            stride = 1
+            for s in reversed(object.shape):
+                strides.insert(0, stride)
+                stride *= s
+            return strides
+        elif isinstance(object, tir.BufferRegion):
+            buffer, _ = object.buffer, object.region
+            strides = []
+            stride = 1
+            for s in reversed(buffer.shape):
+                strides.insert(0, stride)
+                stride *= s
+            return strides
+        else:
+            raise ValueError(f"Unsupported argument type: {type(object)} for buffer {object}")
+
     A_shape = retrieve_shape(A)
     B_shape = retrieve_shape(B)
     C_shape = retrieve_shape(C)
 
+    A_stride = retrieve_stride(A)
+    B_stride = retrieve_stride(B)
+
     assert len(C_shape) == 2, "current only support C as a 2D tensor"
     assert len(A_shape) >= 2, "current only support A as a 2D or higher-order tensor"
     assert len(B_shape) >= 2, "current only support B as a 2D or higher-order tensor"
@@ -90,6 +112,9 @@ def gemm(
     K_B = B_shape[-1] if transpose_B else B_shape[-2]
     assert K == K_B, f"T.gemm K shape check failed: K_A = {K}, K_B = {K_B}"
 
+    stride_a = A_stride[-2]
+    stride_b = B_stride[-2]
+
     def retrieve_ptr(object: Union[tir.Buffer, tir.BufferRegion],
                      access_type: str = "r") -> tir.PrimExpr:
         if isinstance(object, tir.Buffer):
@@ -105,12 +130,33 @@ def gemm(
                 strides.insert(0, stride)
                 stride *= s
             offset = 0
-            for i in range(len(indices)):
+            # not offset the last two dimension
+            for i in range(len(indices) - 2):
                 offset += indices[i] * strides[i]
             return buffer.access_ptr(access_mask=access_type, offset=offset)
         else:
             raise ValueError(f"Unsupported argument type: {type(object)} for buffer {object}")
 
+    def retrieve_offset(object: Union[tir.Buffer, tir.BufferRegion]) -> tir.PrimExpr:
+        """Retrieve the offset of the buffer or buffer region."""
+        if isinstance(object, tir.Buffer):
+            return [0] * len(object.shape)
+        elif isinstance(object, tir.BufferRegion):
+            _, region = object.buffer, object.region
+            indices = []
+            for r in region:
+                indices.append(r.min)
+            return indices
+        else:
+            raise ValueError(f"Unsupported argument type: {type(object)} for buffer {object}")
+
+    A_offset = retrieve_offset(A)
+    B_offset = retrieve_offset(B)
+    assert A_offset[-2] == 0, "The offset of the first dimension of A must be 0"
+    assert B_offset[-2] == 0, "The offset of the first dimension of B must be 0"
+    offset_a = A_offset[-1]
+    offset_b = B_offset[-1]
+
     Aptr = retrieve_ptr(A, "r")
     Bptr = retrieve_ptr(B, "r")
     Cptr = retrieve_ptr(C, "rw")
@@ -127,6 +173,10 @@ def gemm(
         K,
         policy,
         clear_accum,
+        stride_a,
+        stride_b,
+        offset_a,
+        offset_b,
         k_pack,
         wg_wait,
     )