Add Ampere half-float example

examples/sycl/ampere/ampere_gemm_fp16_fp16_fp32_tensor_op_fp32.cu

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+/***************************************************************************************************
+ * Copyright (c) 2024 - 2024 Codeplay Software Ltd. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
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+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
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+ * this list of conditions and the following disclaimer in the documentation
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+ *
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+ *
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+ *
+ **************************************************************************************************/
+
+#include "../common/example_runner.hpp"
+#include "gemm_configuration.hpp"
+
+int main(int argc, const char** argv)
+{
+  //
+  // Parse options
+  //
+
+  Options options;
+
+  options.parse(argc, argv);
+
+  if (options.help) {
+    options.print_usage(std::cout) << std::endl;
+    return 0;
+  }
+
+  if (options.error) {
+    std::cerr << "Aborting execution." << std::endl;
+    return -1;
+  }
+
+  //
+  // Run examples
+  //
+
+  // The KernelHardwareInfo struct holds the number of EUs on the GPU with a given device ID. This
+  // information is used by the underlying kernel.
+  cutlass::KernelHardwareInfo hw_info;
+
+  // Change device_id to another value if you are running on a machine with multiple GPUs and wish
+  // to use a GPU other than that with device ID 0.
+  hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
+
+  bool passed;
+
+// The code section below describes datatype for input, output matrices and computation between
+// elements in input matrices.
+  using ElementAccumulator = float;                   // <- data type of accumulator
+  using ElementComputeEpilogue = float;  // <- data type of epilogue operations
+  using ElementInputA = half_t;                        // <- data type of elements in input matrix A
+  using ElementInputB = half_t;                        // <- data type of elements in input matrix B
+  using ElementOutput = float;                        // <- data type of elements in output matrix D
+
+  using LayoutA = cutlass::layout::ColumnMajor;
+  using LayoutB = cutlass::layout::RowMajor;
+  using LayoutC = cutlass::layout::ColumnMajor;
+  using LayoutD = cutlass::layout::ColumnMajor;
+
+  using TileShape = Shape<_128, _128, _32>;
+
+  using TiledMma = TiledMMA<
+          MMA_Atom<SM80_16x8x16_F32F16F16F32_TN>,
+          Layout<Shape<_2,_2,_1>>, // 2x2x1 thread group
+          Tile<_32,_32,_16>>;                           // 32x32x8 MMA for LDSM, 1x2x1 value group
+
+  static constexpr int kAlignmentA = 8;
+  using DefaultOperandA = DefaultGemm_TensorOpSm80_OperandA<
+          ElementInputA, LayoutA, kAlignmentA, 32>;
+  using SmemLayoutAtomA = typename DefaultOperandA::SmemLayoutAtom; // M, K
+  using SmemCopyAtomA = typename DefaultOperandA::SmemCopyAtom;
+  using GmemTiledCopyA = typename DefaultOperandA::GmemTiledCopy;
+
+  static constexpr int kAlignmentB = 8;
+  using DefaultOperandB = DefaultGemm_TensorOpSm80_OperandB<
+          ElementInputB, LayoutB, kAlignmentB, 32>;
+  using SmemLayoutAtomB = typename DefaultOperandB::SmemLayoutAtom; // N, K
+  using SmemCopyAtomB = typename DefaultOperandB::SmemCopyAtom;
+  using GmemTiledCopyB = typename DefaultOperandB::GmemTiledCopy;
+
+  using Stages = Int<3>;
+
+  // This code section describes the epilogue part of the kernel
+  using EpilogueOp = cutlass::epilogue::thread::LinearCombination<
+          ElementOutput,                                     // <- data type of output matrix
+          128 / cutlass::sizeof_bits<ElementOutput>::value,  // <- the number of elements per vectorized
+          // memory access. For a byte, it's 16
+          // elements. This becomes the vector width of
+          // math instructions in the epilogue too
+          ElementAccumulator,                                // <- data type of accumulator
+          ElementComputeEpilogue>;  // <- data type for alpha/beta in linear combination function
+
+  using DispatchPolicy = cutlass::gemm::MainloopSm80CpAsync<Stages{}>;
+
+  // Define strides (mixed)
+  using StrideA = cutlass::detail::TagToStrideA_t<LayoutA>;
+  using StrideB = cutlass::detail::TagToStrideB_t<LayoutB>;
+  using StrideC = cutlass::detail::TagToStrideC_t<LayoutC>;
+  using StrideD = cutlass::detail::TagToStrideC_t<LayoutD>;
+
+  using CollectiveEpilogue = cutlass::epilogue::collective::DefaultEpilogue<
+          StrideC,
+          StrideD,
+          EpilogueOp,
+          cutlass::gemm::EpilogueDefault>;
+
+  // Mainloop
+  using CollectiveMainloop = cutlass::gemm::collective::CollectiveMma<
+          DispatchPolicy,
+          TileShape,
+          ElementInputA,
+          StrideA,
+          ElementInputB,
+          StrideB,
+          TiledMma,
+          GmemTiledCopyA, SmemLayoutAtomA, SmemCopyAtomA, cute::identity,  // A
+          GmemTiledCopyB, SmemLayoutAtomB, SmemCopyAtomB, cute::identity   // B
+  >;
+
+  using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
+          Shape<int, int, int, int>,
+          CollectiveMainloop,
+          CollectiveEpilogue
+  >;
+
+  using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
+
+  ExampleRunner<Gemm> runner;
+
+  runner.run(options, hw_info);
+
+  return 0;
+}