Improve the cutlass gemm

mlx/backend/cuda/CMakeLists.txt

@@ -26,6 +26,8 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/gemms/gemv.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cutlass_gemm.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simple_gemm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/jit_module.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
@@ -88,6 +90,9 @@ target_include_directories(mlx PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/gen")
 target_compile_options(mlx
                        PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>")
 
+# Keep ptx around for inspection
+target_compile_options(mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--keep>")
+
 # Enable calling host constexpr functions from device. This is needed because
 # the constexpr version of isnan is host only.
 target_compile_options(
@@ -173,3 +178,12 @@ target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
 # Install CCCL headers for JIT.
 install(DIRECTORY ${cccl_SOURCE_DIR}/include/cuda
         DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cccl)
+
+# Fetch and make available cutlass
+FetchContent_Declare(
+  cutlass
+  GIT_REPOSITORY https://github.com/NVIDIA/cutlass.git
+  GIT_TAG v4.1.0)
+FetchContent_Populate(cutlass)
+target_include_directories(
+  mlx PRIVATE $<BUILD_INTERFACE:${cutlass_SOURCE_DIR}/include>)

mlx/backend/cuda/gemms/cutlass_gemm.cu

@@ -0,0 +1,396 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/dtype_utils.h"
+
+#include <cute/tensor.hpp>
+#include <cutlass/arch/arch.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/gemm/device/gemm.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/numeric_types.h>
+
+#include <iostream>
+
+namespace mlx::core::cu {
+
+namespace {
+
+using namespace cute;
+using bf16 = cute::bfloat16_t;
+
+template <typename Kernel>
+void configure_matmul(Kernel kernel, int smem_size) {
+  static bool initialized = false;
+  if (!initialized) {
+    initialized = true;
+    cudaFuncSetAttribute(
+        kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);
+  }
+}
+
+template <bool transpose, typename Tiler>
+constexpr int get_feature_size(Tiler smem) {
+  int feature_size = (transpose) ? size<0>(smem) : size<1>(smem);
+  return (feature_size >= 64) ? 64 : feature_size;
+}
+
+constexpr int constexpr_log2(int x) {
+  return (x > 0) ? 1 + constexpr_log2(x >> 1) : -1;
+}
+
+template <int feature_size, int itemsize, int copy_bits>
+constexpr int get_swizzle_bits() {
+  constexpr int swizzle_bits =
+      constexpr_log2(feature_size * itemsize / copy_bits);
+  return (swizzle_bits > 3) ? 3 : swizzle_bits;
+}
+
+template <int itemsize, bool transpose, int copy_bits, typename Tiler>
+constexpr auto make_smem_layout(Tiler smem) {
+  constexpr int feature_size = get_feature_size<transpose>(smem);
+  constexpr int swizzle_bits =
+      get_swizzle_bits<feature_size, itemsize, copy_bits>();
+
+  using F = Int<feature_size>;
+  using BaseLayout = std::conditional_t<
+      transpose,
+      Layout<cute::Shape<F, _8>, cute::Stride<_1, F>>,
+      Layout<cute::Shape<_8, F>, cute::Stride<F, _1>>>;
+
+  auto swizzled =
+      make_composed_layout(Swizzle<swizzle_bits, 3, 3>{}, 0, BaseLayout{});
+
+  return tile_to_shape(swizzled, smem);
+}
+
+template <int itemsize, bool transpose, int copy_bits, typename Tiler>
+constexpr auto make_result_smem_layout(Tiler smem) {
+  constexpr int feature_size = get_feature_size<transpose>(smem);
+  constexpr int swizzle_bits =
+      get_swizzle_bits<feature_size, itemsize, copy_bits>();
+
+  using F = Int<feature_size>;
+  using BaseLayout = std::conditional_t<
+      transpose,
+      Layout<cute::Shape<F, _8>, cute::Stride<_1, F>>,
+      Layout<cute::Shape<_8, F>, cute::Stride<F, _1>>>;
+
+  auto swizzled = make_composed_layout(
+      Swizzle<transpose ? 0 : swizzle_bits, 3, 4>{}, 0, BaseLayout{});
+
+  return tile_to_shape(swizzled, smem);
+}
+
+template <
+    int num_threads,
+    int itemsize,
+    bool transpose,
+    int copy_bits,
+    typename Copier,
+    typename Tiler>
+constexpr auto make_tiled_copy(Copier copy_op, Tiler smem) {
+  constexpr int num_elements = copy_bits / itemsize;
+  constexpr int feature_size = transpose ? size<0>(smem) : size<1>(smem);
+  constexpr int copies_per_feature = feature_size / num_elements;
+
+  using E = Int<num_elements>;
+  using C = Int<copies_per_feature>;
+  using R = Int<num_threads / copies_per_feature>;
+
+  using ThreadLayout = std::conditional_t<
+      transpose,
+      Layout<cute::Shape<C, R>, cute::Stride<_1, C>>,
+      Layout<cute::Shape<R, C>, cute::Stride<C, _1>>>;
+  using ValueLayout = std::conditional_t<
+      transpose,
+      Layout<cute::Shape<E, _1>>,
+      Layout<cute::Shape<_1, E>>>;
+
+  return make_tiled_copy(copy_op, ThreadLayout{}, ValueLayout{});
+}
+
+template <int rasterization_factor>
+__device__ inline int2 raster_tile(int x, int y) {
+  return {
+      x / rasterization_factor,
+      (x % rasterization_factor) + y * rasterization_factor};
+}
+
+template <
+    typename T,
+    typename SLayoutA,
+    typename SLayoutB,
+    typename SLayoutC,
+    typename CopyA,
+    typename CopyB,
+    typename CopyC,
+    typename MMA,
+    int rasterization_factor>
+__global__ static __launch_bounds__(decltype(size(MMA{}))::value) void matmul_kernel(
+    const T* __restrict__ A,
+    const T* __restrict__ B,
+    T* __restrict__ C,
+    SLayoutA SA,
+    SLayoutB SB,
+    SLayoutC SC,
+    CopyA copy_a,
+    CopyB copy_b,
+    CopyC copy_c,
+    MMA mma,
+    int M,
+    int N,
+    int K) {
+  constexpr auto BM = size<0>(SA);
+  constexpr auto BN = size<0>(SB);
+  constexpr auto BK = size<1>(SA);
+  constexpr auto PIPE = size<2>(SA);
+
+  const int2 tile = raster_tile<rasterization_factor>(blockIdx.x, blockIdx.y);
+  const int blocks_m = ceil_div(M, BM);
+  const int blocks_n = ceil_div(N, BN);
+
+  // Exit early if the tile is OOB
+  if (tile.x >= blocks_m || tile.y >= blocks_n) {
+    return;
+  }
+
+  // Make the full tensors
+  Tensor full_A =
+      make_tensor(make_gmem_ptr(A), make_shape(M, K), make_stride(K, _1{}));
+  Tensor full_B =
+      make_tensor(make_gmem_ptr(B), make_shape(N, K), make_stride(K, _1{}));
+  Tensor full_C =
+      make_tensor(make_gmem_ptr(C), make_shape(M, N), make_stride(N, _1{}));
+
+  // Partition the tensors into tiles and select the ones for this threadblock
+  Tensor local_A =
+      local_tile(full_A, make_shape(BM, BK), make_coord(tile.x, _));
+  Tensor local_B =
+      local_tile(full_B, make_shape(BN, BK), make_coord(tile.y, _));
+  Tensor local_C =
+      local_tile(full_C, make_shape(BM, BN), make_coord(tile.x, tile.y));
+
+  // Make shared memory tensors
+  extern __shared__ char shared_memory[];
+  T* shared_A_ptr = reinterpret_cast<T*>(shared_memory);
+  T* shared_B_ptr =
+      reinterpret_cast<T*>(shared_memory + cosize(SA) * sizeof(T));
+  T* shared_C_ptr = reinterpret_cast<T*>(shared_memory);
+  Tensor shared_A = make_tensor(make_smem_ptr(shared_A_ptr), SA);
+  Tensor shared_B = make_tensor(make_smem_ptr(shared_B_ptr), SB);
+  Tensor shared_C = make_tensor(make_smem_ptr(shared_C_ptr), SC);
+
+  // Get the copies that correspond to this thread
+  auto thread_copy_a = copy_a.get_slice(threadIdx.x);
+  Tensor local_A_src = thread_copy_a.partition_S(local_A);
+  Tensor local_A_dst = thread_copy_a.partition_D(shared_A);
+  auto thread_copy_b = copy_b.get_slice(threadIdx.x);
+  Tensor local_B_src = thread_copy_a.partition_S(local_B);
+  Tensor local_B_dst = thread_copy_a.partition_D(shared_B);
+  auto thread_copy_c = copy_c.get_slice(threadIdx.x);
+  Tensor local_C_src = thread_copy_c.partition_S(shared_C);
+  Tensor local_C_dst = thread_copy_c.partition_D(local_C);
+
+  // Start fetches
+  int k_tile_count = size<2>(local_A);
+  int k_tile_next = 0;
+  CUTE_UNROLL
+  for (int k = 0; k < PIPE - 1; k++) {
+    copy(copy_a, local_A_src(_, _, _, k_tile_next), local_A_dst(_, _, _, k));
+    copy(copy_b, local_B_src(_, _, _, k_tile_next), local_B_dst(_, _, _, k));
+    cp_async_fence();
+    k_tile_count--;
+    k_tile_next += (k_tile_count > 0);
+  }
+
+  // Get the MMA that corresponds to this thread and allocate registers
+  auto thread_mma = mma.get_slice(threadIdx.x);
+  Tensor mma_shared_A = thread_mma.partition_A(shared_A);
+  Tensor mma_shared_B = thread_mma.partition_B(shared_B);
+  Tensor mma_shared_C = thread_mma.partition_C(shared_C);
+  Tensor mma_global_C = thread_mma.partition_C(local_C);
+  Tensor mma_frag_A = mma.make_fragment_A(mma_shared_A(_, _, _, 0));
+  Tensor mma_frag_B = mma.make_fragment_B(mma_shared_B(_, _, _, 0));
+  Tensor mma_frag_C = mma.make_fragment_C(mma_global_C);
+  clear(mma_frag_C);
+
+  // Make shared to register copies
+  Copy_Atom<SM75_U32x4_LDSM_N, bf16> s2r_atom_a;
+  Copy_Atom<SM75_U32x4_LDSM_N, bf16> s2r_atom_b;
+  auto s2r_copy_a = make_tiled_copy_A(s2r_atom_a, mma);
+  auto s2r_thread_copy_a = s2r_copy_a.get_slice(threadIdx.x);
+  auto s2r_copy_b = make_tiled_copy_B(s2r_atom_b, mma);
+  auto s2r_thread_copy_b = s2r_copy_b.get_slice(threadIdx.x);
+  Tensor mma_A_src = s2r_thread_copy_a.partition_S(shared_A);
+  Tensor mma_A_dst = s2r_thread_copy_a.retile_D(mma_frag_A);
+  Tensor mma_B_src = s2r_thread_copy_b.partition_S(shared_B);
+  Tensor mma_B_dst = s2r_thread_copy_b.retile_D(mma_frag_B);
+
+  constexpr auto RPIPE = size<2>(mma_shared_A);
+  int smem_read = 0;
+  int smem_write = PIPE - 1;
+  Tensor mma_A_src_p = mma_A_src(_, _, _, smem_read);
+  Tensor mma_B_src_p = mma_B_src(_, _, _, smem_read);
+
+  // Start the register pipeline
+  if constexpr (RPIPE > 1) {
+    cp_async_wait<PIPE - 2>();
+    __syncthreads();
+    copy(s2r_copy_a, mma_A_src_p(_, _, Int<0>{}), mma_A_dst(_, _, Int<0>{}));
+    copy(s2r_copy_b, mma_B_src_p(_, _, Int<0>{}), mma_B_dst(_, _, Int<0>{}));
+  }
+
+  CUTE_NO_UNROLL
+  while (k_tile_count > -(PIPE - 1)) {
+    CUTE_UNROLL
+    for (int k_block = 0; k_block < RPIPE; k_block++) {
+      if (k_block == RPIPE - 1) {
+        mma_A_src_p = mma_A_src(_, _, _, smem_read);
+        mma_B_src_p = mma_B_src(_, _, _, smem_read);
+        cp_async_wait<PIPE - 2>();
+        __syncthreads();
+      }
+
+      // Load the next register tile
+      auto k_block_next = (k_block + 1) % RPIPE;
+      copy(
+          s2r_copy_a,
+          mma_A_src_p(_, _, k_block_next),
+          mma_A_dst(_, _, k_block_next));
+      copy(
+          s2r_copy_b,
+          mma_B_src_p(_, _, k_block_next),
+          mma_B_dst(_, _, k_block_next));
+
+      if (k_block == 0) {
+        copy(
+            copy_a,
+            local_A_src(_, _, _, k_tile_next),
+            local_A_dst(_, _, _, smem_write));
+        copy(
+            copy_b,
+            local_B_src(_, _, _, k_tile_next),
+            local_B_dst(_, _, _, smem_write));
+        cp_async_fence();
+        k_tile_count--;
+        k_tile_next += (k_tile_count > 0);
+        smem_write = smem_read;
+        smem_read = (smem_read == PIPE - 1) ? 0 : (smem_read + 1);
+      }
+
+      gemm(
+          mma,
+          mma_frag_A(_, _, k_block),
+          mma_frag_B(_, _, k_block),
+          mma_frag_C);
+    }
+  }
+
+  copy(mma_frag_C, mma_shared_C);
+  __syncthreads();
+  copy(copy_c, local_C_src, local_C_dst);
+
+  // if (threadIdx.x == 0) {
+  //   print("fC: "); print(mma_frag_C); print("\n");
+  //   print("sC: "); print(mma_shared_C); print("\n");
+  //   print("dC: "); print(local_C_dst); print("\n");
+  //
+  //   print(s2r_atom_a); print("\n");
+  // }
+}
+
+} // namespace
+
+void cutlass_gemm(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    cu::CommandEncoder& enc) {
+  enc.set_input_array(a);
+  enc.set_input_array(b);
+  enc.set_output_array(out);
+  dispatch_float_types(a.dtype(), "simple_gemm", [&](auto type_tag) {
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    if constexpr (std::is_same_v<DataType, __nv_bfloat16>) {
+      using namespace cute;
+
+      // Tile definitions
+      auto BM = Int<128>{};
+      auto BN = Int<128>{};
+      auto BK = Int<64>{};
+      auto BP = Int<3>{};
+      auto GM = Int<8>{};
+
+      // Thread definitions
+      using TM = Int<2>;
+      using TN = Int<2>;
+      using TK = Int<1>;
+      constexpr int num_threads = TM::value * TN::value * 32;
+
+      auto SA = make_smem_layout<16, false, 128>(make_shape(BM, BK, BP));
+      auto SB = make_smem_layout<16, false, 128>(make_shape(BN, BK, BP));
+      auto SC = make_result_smem_layout<16, false, 128>(make_shape(BM, BN));
+
+      constexpr auto smem_size = (cosize(SA) + cosize(SB)) * sizeof(bf16);
+
+      auto async_copy_op =
+          Copy_Atom<SM80_CP_ASYNC_CACHEALWAYS<uint128_t>, bf16>{};
+      auto tiled_copy_a = make_tiled_copy<num_threads, 16, false, 128>(
+          async_copy_op, make_shape(BM, BK));
+      auto tiled_copy_b = make_tiled_copy<num_threads, 16, false, 128>(
+          async_copy_op, make_shape(BN, BK));
+
+      auto sync_copy_op = Copy_Atom<UniversalCopy<uint128_t>, bf16>{};
+      auto tiled_copy_c = make_tiled_copy<num_threads, 16, false, 128>(
+          sync_copy_op, make_shape(BM, BN));
+
+      auto mma_op = SM80_16x8x16_F32BF16BF16F32_TN{};
+      auto tiled_mma = make_tiled_mma(
+          mma_op, Layout<cute::Shape<TM, TN, TK>>{}, Tile<_32, _32, _16>{});
+
+      auto kernel = matmul_kernel<
+          bf16,
+          decltype(SA),
+          decltype(SB),
+          decltype(SC),
+          decltype(tiled_copy_a),
+          decltype(tiled_copy_b),
+          decltype(tiled_copy_c),
+          decltype(tiled_mma),
+          GM.value>;
+      configure_matmul(kernel, smem_size);
+
+      dim3 block(size(tiled_mma));
+      dim3 grid(
+          size(ceil_div(M, BM) * GM), size(ceil_div(ceil_div(N, BN), GM)));
+
+      enc.add_kernel_node(
+          kernel,
+          grid,
+          block,
+          smem_size,
+          a.data<bf16>(),
+          b.data<bf16>(),
+          out.data<bf16>(),
+          SA,
+          SB,
+          SC,
+          tiled_copy_a,
+          tiled_copy_b,
+          tiled_copy_c,
+          tiled_mma,
+          M,
+          N,
+          K);
+    } else {
+      throw std::runtime_error("Only bfloat16 supported");
+    }
+  });
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cutlass_gemm.h

@@ -0,0 +1,18 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/device.h"
+
+namespace mlx::core::cu {
+
+void cutlass_gemm(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    cu::CommandEncoder& enc);
+
+}

mlx/backend/cuda/gemms/simple_gemm.cu

@@ -0,0 +1,69 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/steel/gemm.cuh"
+#include "mlx/dtype_utils.h"
+
+#include <iostream>
+
+namespace mlx::core::cu {
+
+namespace {
+
+template <typename Kernel>
+static void configure_smem(Kernel kernel, int SM) {
+  static bool done = false;
+  if (done) {
+    return;
+  }
+  std::cout << "configuring" << std::endl;
+  cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, SM);
+  cudaFuncSetAttribute(
+      kernel,
+      cudaFuncAttributePreferredSharedMemoryCarveout,
+      cudaSharedmemCarveoutMaxShared);
+  done = true;
+}
+
+} // namespace
+
+void simple_gemm(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    cu::CommandEncoder& enc) {
+  enc.set_input_array(a);
+  enc.set_input_array(b);
+  enc.set_output_array(out);
+  dispatch_float_types(a.dtype(), "simple_gemm", [&](auto type_tag) {
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    constexpr int BM = 128;
+    constexpr int BN = 128;
+    constexpr int BK = 32;
+    constexpr int PIPE = 3;
+    constexpr int SM = PIPE * sizeof(DataType) * (BM * BK + BN * BK);
+    constexpr int WM = 2;
+    constexpr int WN = 4;
+
+    auto kernel = ab_t_aligned<DataType, BM, BN, BK, WM, WN, PIPE>;
+    configure_smem(kernel, SM);
+
+    dim3 grid(N / BN, M / BM);
+    enc.add_kernel_node(
+        kernel,
+        grid,
+        WM * WN * WARP_SIZE,
+        SM,
+        a.data<DataType>(),
+        b.data<DataType>(),
+        out.data<DataType>(),
+        N,
+        K);
+  });
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/simple_gemm.h

@@ -0,0 +1,18 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/device.h"
+
+namespace mlx::core::cu {
+
+void simple_gemm(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    cu::CommandEncoder& enc);
+
+}

mlx/backend/cuda/matmul.cpp

@@ -3,7 +3,9 @@
 #include "mlx/backend/common/matmul.h"
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/gemms/cublas_gemm.h"
+#include "mlx/backend/cuda/gemms/cutlass_gemm.h"
 #include "mlx/backend/cuda/gemms/gemv.h"
+#include "mlx/backend/cuda/gemms/simple_gemm.h"
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/primitives.h"
 
@@ -11,8 +13,14 @@
 #include <numeric>
 
 namespace mlx::core {
+
 namespace {
 
+int get_test_gemm() {
+  static int t = env::get_var("MLX_ENABLE_TEST_GEMM", 0);
+  return t;
+}
+
 std::tuple<bool, int64_t, array>
 check_transpose(cu::CommandEncoder& enc, const Stream& s, const array& arr) {
   auto stx = arr.strides()[arr.ndim() - 2];
@@ -95,6 +103,18 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
+  if (M % 512 == 0 && N % 512 == 0 && K % 512 == 0 && !a_transposed &&
+      b_transposed && batch_count == 1 && get_test_gemm() == 1) {
+    cu::simple_gemm(a, b, out, M, N, K, encoder);
+    return;
+  }
+
+  if (M % 512 == 0 && N % 512 == 0 && K % 512 == 0 && !a_transposed &&
+      b_transposed && batch_count == 1 && get_test_gemm() == 2) {
+    cu::cutlass_gemm(a, b, out, M, N, K, encoder);
+    return;
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Invoke cublasLt
   CublasGemm gemm(

mlx/backend/cuda/steel/gemm.cuh

@@ -4,95 +4,189 @@
 
 namespace mlx::core::cu {
 
+template <typename T, int BM, int BN, int BK, int WM, int WN>
+__device__ inline void gemm_ab_t(
+    RegisterTile<float, BM / WM, BN / WN>& C,
+    SharedTile<T, BM, BK>& As,
+    SharedTile<T, BN, BK>& Bs,
+    RegisterTileLoader<SharedTile<T, BM, BK>>& rloader_a,
+    RegisterTileLoader<SharedTile<T, BN, BK>>& rloader_b) {
+  RegisterTile<T, BM / WM, 16> A[2];
+  RegisterTile<T, BN / WN, 16> B[2];
+
+  rloader_a.load(A[0], As.base_addr(), 0);
+  rloader_b.load(B[0], Bs.base_addr(), 0);
+
+  MLX_UNROLL
+  for (int k = 1; k < BK / 16; k++) {
+    rloader_a.load(A[k & 1], As.base_addr(), k);
+    rloader_b.load(B[k & 1], Bs.base_addr(), k);
+
+    mma_t(C, A[(k - 1) & 1], B[(k - 1) & 1]);
+  }
+  mma_t(C, A[(BK / 16 - 1) & 1], B[(BK / 16 - 1) & 1]);
+}
+
 /**
  * An example gemm written with the utils.
  *
  * Computes A @ B.T when A and B are all aligned with the block sizes.
  */
-template <typename T, int BM, int BN, int BK>
+// template <typename T, int BM, int BN, int BK, int WM, int WN, int PIPE>
-__global__ void ab_t_aligned(const T* a, const T* b, T* y, int N, int K) {
+//__global__ __launch_bounds__(WM * WN * WARP_SIZE, 1)
-  constexpr int WARPS_M = 2;
+// void ab_t_aligned(const T* a, const T* b, T* y, int N, int K) {
-  constexpr int WARPS_N = 2;
+//   constexpr int NUM_WARPS = WM * WN;
-  constexpr int NUM_WARPS = WARPS_M * WARPS_N;
+//   constexpr int WARP_STEP_M = BM / WM;
-  constexpr int WARP_STEP_M = BM / WARPS_M;
+//   constexpr int WARP_STEP_N = BN / WN;
-  constexpr int WARP_STEP_N = BN / WARPS_N;
+//
+//   // Precompute some offsets for each thread
+//   const int warpid = threadIdx.x / 32;
+//   const int laneid = threadIdx.x % 32;
+//   const int wm = warpid / WN;
+//   const int wn = warpid % WN;
+//   const int offset_m = wm * WARP_STEP_M;
+//   const int offset_n = wn * WARP_STEP_N;
+//
+//   // Allocate shared memory
+//   extern __shared__ char shmem[];
+//   SharedTile<T, BM, BK>(&as)[PIPE] =
+//       *(SharedTile<T, BM, BK>(*)[PIPE])(&shmem[0]);
+//   SharedTile<T, BN, BK>(&bs)[PIPE] =
+//       *(SharedTile<T, BN, BK>(*)[PIPE])(&shmem[sizeof(T) * PIPE * BM * BK]);
+//
+//   // Move the global pointers to the tile
+//   a += blockIdx.y * BM * K;
+//   b += blockIdx.x * BN * K;
+//   y += blockIdx.y * BM * N + blockIdx.x * BN;
+//
+//   // Make the loaders to/from SMEM
+//   SharedTileLoader<NUM_WARPS, SharedTile<T, BM, BK>> sloader_a(a, K);
+//   SharedTileLoader<NUM_WARPS, SharedTile<T, BN, BK>> sloader_b(b, K);
+//   RegisterTileLoader<SharedTile<T, BM, BK>> rloader_a(offset_m, laneid);
+//   RegisterTileLoader<SharedTile<T, BN, BK>> rloader_b(offset_n, laneid);
+//
+//   // Start the SM pipeline
+//   MLX_UNROLL
+//   for (int i = 0; i < PIPE - 1; i++) {
+//     sloader_a.load_async(as[i].base_addr());
+//     sloader_b.load_async(bs[i].base_addr());
+//     cp_async_commit();
+//     sloader_a.next();
+//     sloader_b.next();
+//   }
+//
+//   // Allocate and zero the MMA accumulator
+//   RegisterTile<float, BM / WM, BN / WN> C;
+//   C.fill(0);
+//
+//   // Matmul loop
+//   int num_blocks = K / BK;
+//   int sread = 0;
+//   int swrite = PIPE - 1;
+//   for (int i = 0; i < num_blocks; i++) {
+//     cp_async_wait<PIPE - 1>();
+//
+//     gemm_ab_t<T, BM, BN, BK, WM, WN>(
+//         C, as[sread], bs[sread], rloader_a, rloader_b);
+//
+//     sloader_a.load_async(as[swrite].base_addr());
+//     sloader_b.load_async(bs[swrite].base_addr());
+//     cp_async_commit();
+//     sloader_a.next(i + PIPE < num_blocks);
+//     sloader_b.next(i + PIPE < num_blocks);
+//
+//     swrite = sread;
+//     sread = (sread + 1) % PIPE;
+//   }
+//
+//   C.store_global(y, N, offset_m, offset_n);
+// }
+
+template <typename T, int BM, int BN, int BK, int WM, int WN, int PIPE>
+__global__ __launch_bounds__(
+    WM* WN* WARP_SIZE,
+    1) void ab_t_aligned(const T* a, const T* b, T* y, int N, int K) {
+  constexpr int NUM_WARPS = WM * WN;
+  constexpr int WARP_STEP_M = BM / WM;
+  constexpr int WARP_STEP_N = BN / WN;
 
   // Precompute some offsets for each thread
   const int warpid = threadIdx.x / 32;
   const int laneid = threadIdx.x % 32;
-  const int wm = warpid / WARPS_N;
+  const int wm = warpid / WN;
-  const int wn = warpid % WARPS_N;
+  const int wn = warpid % WN;
   const int offset_m = wm * WARP_STEP_M;
   const int offset_n = wn * WARP_STEP_N;
 
   // Allocate shared memory
   extern __shared__ char shmem[];
-  SharedTile<T, BM, BK>(&as)[2] = *(SharedTile<T, BM, BK>(*)[2])(&shmem[0]);
+  SharedTile<T, BM, BK>(&as)[PIPE] =
-  SharedTile<T, BN, BK>(&bs)[2] =
+      *(SharedTile<T, BM, BK>(*)[PIPE])(&shmem[0]);
-      *(SharedTile<T, BN, BK>(*)[2])(&shmem[sizeof(T) * 2 * BM * BK]);
+  SharedTile<T, BN, BK>(&bs)[PIPE] =
-
+      *(SharedTile<T, BN, BK>(*)[PIPE])(&shmem[sizeof(T) * PIPE * BM * BK]);
-  // Allocate registers for the MMA
-  RegisterTile<float, BM / WARPS_M, BN / WARPS_N> C;
-  RegisterTile<T, BM / WARPS_M, 16> A;
-  RegisterTile<T, BN / WARPS_N, 16> B;
 
   // Move the global pointers to the tile
   a += blockIdx.y * BM * K;
   b += blockIdx.x * BN * K;
   y += blockIdx.y * BM * N + blockIdx.x * BN;
 
-  // Zero the accumulators
+  // Make the loaders to/from SMEM
-  C.fill(0);
+  using sloader = SharedTileLoader<NUM_WARPS, SharedTile<T, BM, BK>>;
+  constexpr int SSTEP = sloader::STEP_ROWS * sizeof(T) * BK;
+  const int srow = threadIdx.x / sloader::NUM_LOADS_PER_ROW;
+  const int scol =
+      (threadIdx.x % sloader::NUM_LOADS_PER_ROW) * sloader::ELEMENTS_PER_LOAD;
+  a += srow * K + scol;
+  b += srow * K + scol;
+  uint32_t sm_offsets[PIPE][2];
+  MLX_UNROLL
+  for (int s = 0; s < PIPE; s++) {
+    sm_offsets[s][0] = as[s].loc(as[s].base_addr(), srow, scol);
+    sm_offsets[s][1] = bs[s].loc(bs[s].base_addr(), srow, scol);
+  }
+  RegisterTileLoader<SharedTile<T, BM, BK>> rloader_a(offset_m, laneid);
+  RegisterTileLoader<SharedTile<T, BN, BK>> rloader_b(offset_n, laneid);
 
   // Start the SM pipeline
-  load_async<NUM_WARPS>(as[0], as[0].base_addr(), a, K);
+  MLX_UNROLL
-  load_async<NUM_WARPS>(bs[0], bs[0].base_addr(), b, K);
+  for (int s = 0; s < PIPE - 1; s++) {
-  cp_async_commit();
-
-  int tic = 0;
-  for (int k_block = BK; k_block < K; k_block += BK) {
-    load_async<NUM_WARPS>(as[tic ^ 1], as[tic ^ 1].base_addr(), a + k_block, K);
-    load_async<NUM_WARPS>(bs[tic ^ 1], bs[tic ^ 1].base_addr(), b + k_block, K);
-    cp_async_commit();
-    cp_async_wait<1>();
-    __syncthreads();
-
     MLX_UNROLL
-    for (int k = 0; k < BK / 16; k++) {
+    for (int l = 0; l < sloader::NUM_LOADS_PER_THREAD; l++) {
-      A.load(
+      cp_async<16>(sm_offsets[s][0] + l * SSTEP, a);
-          as[tic],
+      cp_async<16>(sm_offsets[s][1] + l * SSTEP, b);
-          as[tic].base_addr(),
+      a += sloader::STEP_ROWS * K;
-          offset_m + laneid % 16,
+      b += sloader::STEP_ROWS * K;
-          k * 16 + laneid / 16 * 8);
-      B.load(
-          bs[tic],
-          bs[tic].base_addr(),
-          offset_n + laneid % 16,
-          k * 16 + laneid / 16 * 8);
-
-      mma_t(C, A, B);
     }
-
+    cp_async_commit();
-    tic ^= 1;
   }
 
-  // Empty the pipeline
+  // Allocate and zero the MMA accumulator
-  cp_async_wait_all();
+  RegisterTile<float, BM / WM, BN / WN> C;
-  __syncthreads();
+  C.fill(0);
-  MLX_UNROLL
-  for (int k = 0; k < BK / 16; k++) {
-    A.load(
-        as[tic],
-        as[tic].base_addr(),
-        offset_m + laneid % 16,
-        k * 16 + laneid / 16 * 8);
-    B.load(
-        bs[tic],
-        bs[tic].base_addr(),
-        offset_n + laneid % 16,
-        k * 16 + laneid / 16 * 8);
 
-    mma_t(C, A, B);
+  // Matmul loop
+  int num_blocks = K / BK;
+  int sread = 0;
+  int swrite = PIPE - 1;
+  for (int i = 0; i < num_blocks; i++) {
+    cp_async_wait<PIPE - 1>();
+
+    gemm_ab_t<T, BM, BN, BK, WM, WN>(
+        C, as[sread], bs[sread], rloader_a, rloader_b);
+
+    if (false) {
+      MLX_UNROLL
+      for (int l = 0; l < sloader::NUM_LOADS_PER_THREAD; l++) {
+        cp_async<16>(sm_offsets[swrite][0] + l * SSTEP, a);
+        cp_async<16>(sm_offsets[swrite][1] + l * SSTEP, b);
+        a += sloader::STEP_ROWS * K;
+        b += sloader::STEP_ROWS * K;
+      }
+    }
+    cp_async_commit();
+
+    swrite = sread;
+    sread = (sread + 1) % PIPE;
   }
 
   C.store_global(y, N, offset_m, offset_n);

mlx/backend/cuda/steel/tiles.cuh

@@ -223,59 +223,10 @@ struct RegisterTile {
   }
 };
 
-/**
- * A simple container of multiple Tile16x16.
- *
- * Provides utility functions for loading and manipulating collections of basic
- * tiles.
- */
-template <typename T, int ROWS_, int COLS_>
-struct RegisterTile {
-  static constexpr int ROWS = ROWS_;
-  static constexpr int COLS = COLS_;
-  static constexpr int TILES_X = COLS / 16;
-  static constexpr int TILES_Y = ROWS / 16;
-
-  Tile16x16<T> data[TILES_X * TILES_Y];
-
-  __device__ inline void fill(T v) {
-    MLX_UNROLL
-    for (int i = 0; i < TILES_Y; i++) {
-      MLX_UNROLL
-      for (int j = 0; j < TILES_X; j++) {
-        data[i * TILES_X + j].fill(v);
-      }
-    }
-  }
-
-  template <typename Tile>
-  __device__ inline void
-  load(Tile& tile, uint32_t base_address, int row, int col) {
-    MLX_UNROLL
-    for (int i = 0; i < TILES_Y; i++) {
-      MLX_UNROLL
-      for (int j = 0; j < TILES_X; j++) {
-        data[i * TILES_X + j].load(
-            tile.loc(base_address, row + i * 16, col + j * 16));
-      }
-    }
-  }
-
-  template <typename U>
-  __device__ inline void store_global(U* x, int N, int row, int col) {
-    MLX_UNROLL
-    for (int i = 0; i < TILES_Y; i++) {
-      MLX_UNROLL
-      for (int j = 0; j < TILES_X; j++) {
-        data[i * TILES_X + j].store_global(
-            x + (row + i * 16) * N + col + j * 16, N);
-      }
-    }
-  }
-};
-
 template <typename T, int ROWS_, int COLS_>
 struct SharedTile {
+  using value_type = T;
+
   static constexpr int ROWS = ROWS_;
   static constexpr int COLS = COLS_;
   static constexpr int TILES_X = COLS / 16;
@@ -317,23 +268,26 @@ struct SharedTile {
     }
   }
 
-  // Return the location of the element at (row, col) using the swizzle.
+  __device__ static inline uint32_t offset(int row, int col) {
-  __device__ static inline uint32_t loc(uint32_t ptr, int row, int col) {
     if constexpr (swizzle_bytes > 0) {
       static constexpr int swizzle_repeat = swizzle_bytes * 8;
       static constexpr int subtile_cols = swizzle_bytes / sizeof(T);
       const int outer_idx = col / subtile_cols;
-      const uint32_t addr = ptr +
+      const uint32_t addr = sizeof(T) *
-          sizeof(T) *
+          (outer_idx * ROWS * subtile_cols + row * subtile_cols +
-              (outer_idx * ROWS * subtile_cols + row * subtile_cols +
+           col % subtile_cols);
-               col % subtile_cols);
       const int swizzle = ((addr % swizzle_repeat) >> 7) << 4;
       return (addr ^ swizzle);
     } else {
-      return ptr + sizeof(T) * (row * COLS + col);
+      return sizeof(T) * (row * COLS + col);
     }
   }
 
+  // Return the location of the element at (row, col) using the swizzle.
+  __device__ static inline uint32_t loc(uint32_t ptr, int row, int col) {
+    return ptr + offset(row, col);
+  }
+
   // Convenience functions to edit elements going through the swizzle.
   __device__ inline T& operator()(int row, int col) {
     return *ptr(data, row, col);
@@ -364,6 +318,76 @@ struct SharedTile {
   }
 };
 
+template <int NUM_WARPS, typename Tile>
+struct SharedTileLoader {
+  using T = typename Tile::value_type;
+
+  static constexpr int NUM_THREADS = NUM_WARPS * 32;
+  static constexpr int ELEMENTS_PER_LOAD = sizeof(float4) / sizeof(T);
+  static constexpr int NUM_LOADS = Tile::NUMEL / ELEMENTS_PER_LOAD;
+  static constexpr int NUM_LOADS_PER_THREAD = NUM_LOADS / NUM_THREADS;
+  static constexpr int NUM_LOADS_PER_ROW = Tile::COLS / ELEMENTS_PER_LOAD;
+  static constexpr int STEP_ROWS = NUM_THREADS / NUM_LOADS_PER_ROW;
+
+  const T* x_;
+  int N_;
+  uint32_t offset_;
+
+  __device__ SharedTileLoader(const T* x, int N) : x_(x), N_(N) {
+    const int row = threadIdx.x / NUM_LOADS_PER_ROW;
+    const int col = threadIdx.x % NUM_LOADS_PER_ROW;
+
+    x_ += row * N + col * ELEMENTS_PER_LOAD;
+    offset_ = Tile::offset(row, col * ELEMENTS_PER_LOAD);
+  }
+
+  __device__ inline void load_async(uint32_t base_address) {
+    MLX_UNROLL
+    for (int i = 0; i < NUM_LOADS_PER_THREAD; i++) {
+      cp_async<16>(
+          base_address + offset_ + i * STEP_ROWS * sizeof(T) * Tile::COLS,
+          x_ + i * STEP_ROWS * N_);
+    }
+  }
+
+  __device__ inline void next() {
+    x_ += Tile::COLS;
+  }
+};
+
+template <typename Tile>
+struct RegisterTileLoader {
+  using T = typename Tile::value_type;
+
+  uint32_t offset_[Tile::COLS / 16];
+
+  __device__ RegisterTileLoader(int offset_row, int laneid) {
+    const int row = offset_row + laneid & 15;
+    const int col = (laneid >> 4) << 3;
+
+    MLX_UNROLL
+    for (int i = 0; i < Tile::COLS / 16; i++) {
+      offset_[i] = Tile::offset(row, col + i * 16);
+    }
+  }
+
+  template <typename T, int ROWS, int COLS>
+  __device__ inline void
+  load(RegisterTile<T, ROWS, COLS>& x, uint32_t base_address, int col) {
+    constexpr int TILES_Y = RegisterTile<T, ROWS, COLS>::TILES_Y;
+    constexpr int TILES_X = RegisterTile<T, ROWS, COLS>::TILES_X;
+
+    MLX_UNROLL
+    for (int i = 0; i < TILES_Y; i++) {
+      MLX_UNROLL
+      for (int j = 0; j < TILES_X; j++) {
+        x.data[i * TILES_X + j].load(
+            base_address + offset_[j + col] + i * 16 * Tile::COLS * sizeof(T));
+      }
+    }
+  }
+};
+
 /**
  * Load the tile from global memory by loading 16 bytes at a time and storing
  * them immediately.

mlx/backend/cuda/steel/utils.cuh

@@ -21,15 +21,15 @@ __device__ inline void cp_async(uint32_t row_address, const T* x) {
 #if defined(MLX_CUDA_SM_80_ENABLED)
   if constexpr (N == 16) {
     asm volatile(
-        "cp.async.ca.shared::cta.global [%0], [%1], 16;\n" ::"r"(row_address),
+        "cp.async.cg.shared::cta.global [%0], [%1], 16;\n" ::"r"(row_address),
         "l"(reinterpret_cast<const int4*>(x)));
   } else if constexpr (N == 8) {
     asm volatile(
-        "cp.async.ca.shared::cta.global [%0], [%1], 8;\n" ::"r"(row_address),
+        "cp.async.cg.shared::cta.global [%0], [%1], 8;\n" ::"r"(row_address),
         "l"(reinterpret_cast<const int2*>(x)));
   } else if constexpr (N == 4) {
     asm volatile(
-        "cp.async.ca.shared::cta.global [%0], [%1], 4;\n" ::"r"(row_address),
+        "cp.async.cg.shared::cta.global [%0], [%1], 4;\n" ::"r"(row_address),
         "l"(reinterpret_cast<const int*>(x)));
   }
 #endif