Merge branch 'develop' into amd-develop

test/wrapper/CMakeLists.txt

@@ -6,3 +6,9 @@ add_gtest_executable(test_copy test_copy.cpp)
 target_link_libraries(test_copy PRIVATE utility)
 add_gtest_executable(test_partition test_partition.cpp)
 target_link_libraries(test_partition PRIVATE utility)
+if(GPU_TARGETS MATCHES "gfx908" OR GPU_TARGETS MATCHES "gfx90a" OR
+   GPU_TARGETS MATCHES "gfx940" OR GPU_TARGETS MATCHES "gfx941" OR
+   GPU_TARGETS MATCHES "gfx942")
+    add_gtest_executable(test_gemm test_gemm.cpp)
+    target_link_libraries(test_gemm PRIVATE utility)
+endif()

test/wrapper/test_gemm.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <numeric>
+#include <cstdlib>
+#include <iostream>
+#include <initializer_list>
+#include <vector>
+#include <gtest/gtest.h>
+
+#include "ck/library/utility/host_tensor.hpp"
+
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+
+#include "ck/host_utility/kernel_launch.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/utility/common_header.hpp"
+#include "ck/library/utility/fill.hpp"
+#include "ck/wrapper/layout.hpp"
+#include "ck/wrapper/tensor.hpp"
+#include "ck/wrapper/operations/copy.hpp"
+#include "ck/wrapper/operations/gemm.hpp"
+
+template <typename DataType>
+void CheckResult(const std::vector<DataType>& a_data,
+                 const std::vector<DataType>& b_data,
+                 std::vector<DataType>& c_m_n_device_result,
+                 const ck::index_t M,
+                 const ck::index_t N,
+                 const ck::index_t K)
+{
+    using PassThrough           = ck::tensor_operation::element_wise::PassThrough;
+    using ReferenceGemmInstance = ck::tensor_operation::host::
+        ReferenceGemm<DataType, DataType, DataType, float, PassThrough, PassThrough, PassThrough>;
+
+    Tensor<DataType> a_m_k(HostTensorDescriptor({M, K}));
+    Tensor<DataType> b_k_n(HostTensorDescriptor({K, N}, {1, K}));
+    Tensor<DataType> c_m_n_host_result(HostTensorDescriptor({M, N}));
+
+    a_m_k.mData = a_data;
+    b_k_n.mData = b_data;
+
+    auto ref_op       = ReferenceGemmInstance{};
+    auto ref_invoker  = ref_op.MakeInvoker();
+    auto ref_argument = ref_op.MakeArgument(
+        a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
+
+    ref_invoker.Run(ref_argument);
+    EXPECT_TRUE(ck::utils::check_err(c_m_n_device_result, c_m_n_host_result.mData));
+}
+
+template <typename DataType,
+          typename GemmTraits,
+          ck::index_t scalar_per_vector,
+          typename BlockShape,
+          typename ThreadLayoutShape>
+__global__ void DeviceGemm(const void* p_a,
+                           const void* p_b,
+                           void* p_c,
+                           const ck::index_t M,
+                           const ck::index_t N,
+                           const ck::index_t K,
+                           const BlockShape tile_shape,
+                           const ThreadLayoutShape thread_layout)
+{
+    constexpr auto MPerBlock = ck::wrapper::size<0>(tile_shape);
+    constexpr auto NPerBlock = ck::wrapper::size<1>(tile_shape);
+    constexpr auto KPerBlock = ck::wrapper::size<2>(tile_shape);
+
+    const auto a_global_layout =
+        ck::wrapper::make_layout(ck::make_tuple(M, K), ck::make_tuple(K, 1));
+    const auto b_global_layout =
+        ck::wrapper::make_layout(ck::make_tuple(N, K), ck::make_tuple(K, 1));
+    const auto c_global_layout =
+        ck::wrapper::make_layout(ck::make_tuple(M, N), ck::make_tuple(N, 1));
+
+    constexpr auto a_tile_layout = ck::wrapper::make_layout(
+        ck::make_tuple(MPerBlock, KPerBlock), ck::make_tuple(KPerBlock, ck::Number<1>{}));
+    constexpr auto b_tile_layout = ck::wrapper::make_layout(
+        ck::make_tuple(NPerBlock, KPerBlock), ck::make_tuple(KPerBlock, ck::Number<1>{}));
+    constexpr auto c_tile_layout = ck::wrapper::make_layout(
+        ck::make_tuple(MPerBlock, NPerBlock), ck::make_tuple(NPerBlock, ck::Number<1>{}));
+
+    auto a_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
+        static_cast<const DataType*>(p_a), a_global_layout);
+    auto b_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
+        static_cast<const DataType*>(p_b), b_global_layout);
+    auto c_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
+        static_cast<DataType*>(p_c), c_global_layout);
+
+    auto a_padded_global_tensor = ck::wrapper::pad(a_global_tensor, shape(a_tile_layout));
+    auto b_padded_global_tensor = ck::wrapper::pad(b_global_tensor, shape(b_tile_layout));
+    auto c_padded_global_tensor = ck::wrapper::pad(c_global_tensor, shape(c_tile_layout));
+
+    __shared__ DataType lds_a[ck::wrapper::size(a_tile_layout)];
+    __shared__ DataType lds_b[ck::wrapper::size(b_tile_layout)];
+
+    auto a_lds_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
+        static_cast<DataType*>(lds_a), a_tile_layout);
+    auto b_lds_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
+        static_cast<DataType*>(lds_b), b_tile_layout);
+
+    const ck::index_t block_idx      = static_cast<ck::index_t>(blockIdx.x);
+    using DimAccessOrder             = ck::Tuple<ck::Number<0>, ck::Number<1>>;
+    constexpr ck::index_t vector_dim = 1;
+
+    auto c_global_local_tile = ck::wrapper::make_local_tile(
+        c_padded_global_tensor,
+        tile_shape,
+        block_idx,
+        make_tuple(ck::Number<1>{}, ck::Number<1>{}, ck::wrapper::slice(KPerBlock)));
+    auto c_global_local_partition =
+        ck::wrapper::make_blockwise_gemm_xdl_c_local_partition<DataType,
+                                                               decltype(a_tile_layout),
+                                                               decltype(b_tile_layout),
+                                                               ck::wrapper::size(thread_layout),
+                                                               GemmTraits>(c_global_local_tile);
+    auto c_vgpr_reg = ck::wrapper::make_blockwise_gemm_xdl_c_vgpr<DataType,
+                                                                  decltype(a_tile_layout),
+                                                                  decltype(b_tile_layout),
+                                                                  ck::wrapper::size(thread_layout),
+                                                                  GemmTraits>();
+    ck::wrapper::clear(c_vgpr_reg);
+
+    const ck::index_t num_loop = ck::math::integer_divide_ceil(K, KPerBlock);
+    ck::index_t i              = 0;
+    do
+    {
+        const auto k_slice = ck::wrapper::slice(i * KPerBlock, (i + 1) * KPerBlock);
+        auto a_padded_global_tensor_k_slice = a_padded_global_tensor(ck::wrapper::slice(), k_slice);
+        auto b_padded_global_tensor_k_slice = b_padded_global_tensor(ck::wrapper::slice(), k_slice);
+        auto a_global_local_tile            = ck::wrapper::make_local_tile(
+            a_padded_global_tensor_k_slice,
+            tile_shape,
+            block_idx,
+            make_tuple(ck::Number<1>{}, ck::wrapper::slice(N), ck::Number<1>{}));
+        auto b_global_local_tile = ck::wrapper::make_local_tile(
+            b_padded_global_tensor_k_slice,
+            tile_shape,
+            block_idx,
+            make_tuple(ck::wrapper::slice(M), ck::Number<1>{}, ck::Number<1>{}));
+
+        ck::wrapper::blockwise_copy<DimAccessOrder, vector_dim, scalar_per_vector>(
+            a_global_local_tile, a_lds_tensor, thread_layout);
+        ck::wrapper::blockwise_copy<DimAccessOrder, vector_dim, scalar_per_vector>(
+            b_global_local_tile, b_lds_tensor, thread_layout);
+        ck::block_sync_lds();
+        ck::wrapper::blockwise_gemm_xdl<DataType, ck::wrapper::size(thread_layout), GemmTraits>(
+            a_lds_tensor, b_lds_tensor, c_vgpr_reg);
+
+        ++i;
+    } while(i < num_loop);
+
+    ck::wrapper::copy(c_vgpr_reg, c_global_local_partition);
+}
+
+template <typename DataType,
+          typename GemmTraits,
+          ck::index_t scalar_per_vector,
+          typename BlockShape,
+          typename ThreadLayoutShape>
+void PerformGemm(const ck::index_t M,
+                 const ck::index_t N,
+                 const ck::index_t K,
+                 const BlockShape& tile_shape,
+                 const ThreadLayoutShape& thread_layout)
+{
+    // Global memory buffers
+    DeviceMem a_mem(M * K * sizeof(DataType));
+    DeviceMem b_mem(K * N * sizeof(DataType));
+    DeviceMem c_mem(M * N * sizeof(DataType));
+
+    std::vector<DataType> a_data(M * K);
+    std::vector<DataType> b_data(K * N);
+    ck::utils::FillUniformDistributionIntegerValue<DataType>{-5.f, 5.f}(a_data);
+    ck::utils::FillUniformDistributionIntegerValue<DataType>{-5.f, 5.f}(b_data);
+
+    a_mem.ToDevice(a_data.data());
+    b_mem.ToDevice(b_data.data());
+    c_mem.SetZero();
+
+    const ck::index_t grid_size =
+        ck::math::integer_divide_ceil(M, ck::wrapper::size<0>(tile_shape)) *
+        ck::math::integer_divide_ceil(N, ck::wrapper::size<1>(tile_shape));
+
+    const auto kernel =
+        DeviceGemm<DataType, GemmTraits, scalar_per_vector, BlockShape, ThreadLayoutShape>;
+    launch_and_time_kernel(StreamConfig{nullptr},
+                           kernel,
+                           dim3(grid_size),
+                           dim3(ck::wrapper::size(thread_layout)),
+                           0,
+                           a_mem.GetDeviceBuffer(),
+                           b_mem.GetDeviceBuffer(),
+                           c_mem.GetDeviceBuffer(),
+                           M,
+                           N,
+                           K,
+                           tile_shape,
+                           thread_layout);
+
+    std::vector<DataType> c_data(M * N);
+    c_mem.FromDevice(c_data.data());
+
+    CheckResult<DataType>(a_data, b_data, c_data, M, N, K);
+}
+
+TEST(TestGemm, Float)
+{
+    using DataType           = float;
+    const auto thread_layout = ck::make_tuple(ck::Number<16>{}, ck::Number<16>{});
+    const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 4>(
+        512, 512, 128, tile_shape, thread_layout);
+    // Irregular case
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 1>(
+        129, 129, 67, tile_shape, thread_layout);
+}
+
+TEST(TestGemm, Int8)
+{
+    using DataType           = int8_t;
+    const auto thread_layout = ck::make_tuple(ck::Number<64>{}, ck::Number<4>{});
+    const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_16K1, 16>(
+        512, 512, 128, tile_shape, thread_layout);
+    // Irregular case
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_16K1, 1>(
+        129, 129, 67, tile_shape, thread_layout);
+}
+
+TEST(TestGemm, Half)
+{
+    using DataType           = ck::half_t;
+    const auto thread_layout = ck::make_tuple(ck::Number<32>{}, ck::Number<8>{});
+    const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_8K1, 8>(
+        512, 512, 128, tile_shape, thread_layout);
+    // Irregular case
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_8K1, 1>(
+        129, 129, 67, tile_shape, thread_layout);
+}
+
+TEST(TestGemm, Float_2x4_4x2_XdlPerWave)
+{
+    using DataType                            = float;
+    const auto thread_layout_4x2_xdl_per_wave = ck::make_tuple(ck::Number<16>{}, ck::Number<8>{});
+    const auto thread_layout_2x4_xdl_per_wave = ck::make_tuple(ck::Number<8>{}, ck::Number<16>{});
+    const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_4x2XdlPerWave_4K1, 4>(
+        512, 512, 128, tile_shape, thread_layout_4x2_xdl_per_wave);
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x4XdlPerWave_4K1, 4>(
+        512, 512, 128, tile_shape, thread_layout_2x4_xdl_per_wave);
+}

test/wrapper/test_partition.cpp

@@ -29,17 +29,24 @@ TEST(TestPartition, LocalPartition)
     const auto tensor =
         ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(data.data(), layout);
 
-    const auto thread_steps  = ck::make_tuple(ck::Number<8>{}, ck::Number<1>{});
-    const auto thread_layout = ck::make_tuple(ck::Number<8>{}, ck::Number<1>{});
+    const auto thread_steps  = ck::make_tuple(ck::Number<1>{}, ck::Number<8>{}, ck::Number<1>{});
+    const auto thread_layout = ck::make_tuple(ck::Number<4>{}, ck::Number<8>{}, ck::Number<1>{});
+    // 3d partition on 2d shape (calculate partition on 3d thread layout, and then skip first dim)
+    const auto thread_projection =
+        ck::make_tuple(ck::wrapper::slice(4), ck::Number<1>{}, ck::Number<1>{});
+    constexpr ck::index_t projection_thread_length = ck::Number<4>{};
 
-    for(ck::index_t thread_id = 0; thread_id < ck::wrapper::size(thread_layout); thread_id++)
+    for(ck::index_t thread_id = 0;
+        thread_id < ck::wrapper::size(thread_layout) / projection_thread_length;
+        thread_id++)
     {
         const auto packed_partition =
-            ck::wrapper::make_local_partition(tensor, thread_layout, thread_id);
+            ck::wrapper::make_local_partition(tensor, thread_layout, thread_id, thread_projection);
 
         const auto expected_partition_size =
-            ck::wrapper::size(tensor) / ck::wrapper::size(thread_layout);
-        const auto expected_partition_first_val  = thread_id * ck::wrapper::size<0>(thread_steps);
+            ck::wrapper::size(tensor) /
+            (ck::wrapper::size(thread_layout) / projection_thread_length);
+        const auto expected_partition_first_val  = thread_id * ck::wrapper::size<1>(thread_steps);
         const auto expected_partition_second_val = expected_partition_first_val + 1;
         EXPECT_EQ(ck::wrapper::size(packed_partition), expected_partition_size);
         EXPECT_EQ(packed_partition(0), expected_partition_first_val);
@@ -58,8 +65,12 @@ TEST(TestPartition, LocalTile)
 
     const auto tensor =
         ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(data.data(), layout);
-
-    const auto block_shape = ck::make_tuple(ck::Number<2>{}, ck::Number<4>{}, ck::Number<2>{});
+    // 4d tile partitioning on 3d shape (calculate tile on 4d tile layout, and then skip last dim)
+    const auto block_shape =
+        ck::make_tuple(ck::Number<2>{}, ck::Number<4>{}, ck::Number<2>{}, ck::Number<2>{});
+    const auto block_projection =
+        ck::make_tuple(ck::Number<1>{}, ck::Number<1>{}, ck::Number<1>{}, ck::wrapper::slice(2));
+    constexpr ck::index_t projection_block_dim = ck::Number<2>{};
     const auto num_blocks =
         ck::make_tuple(ck::wrapper::size<0>(shape) / ck::wrapper::size<0>(block_shape),
                        ck::wrapper::size<1>(shape) / ck::wrapper::size<1>(block_shape),
@@ -69,9 +80,10 @@ TEST(TestPartition, LocalTile)
 
     for(auto block_idx : block_idxs)
     {
-        const auto packed_tile = ck::wrapper::make_local_tile(tensor, block_shape, block_idx);
+        const auto packed_tile =
+            ck::wrapper::make_local_tile(tensor, block_shape, block_idx, block_projection);
 
-        const auto expected_tile_size = ck::wrapper::size(block_shape);
+        const auto expected_tile_size = ck::wrapper::size(block_shape) / projection_block_dim;
         auto expected_tile_first_val  = (block_idx % ck::wrapper::size<2>(num_blocks)) *
                                        ck::wrapper::size<2>(block_shape) *
                                        ck::wrapper::size<2>(strides);