composable_kernel/test/wrapper/test_wrapper_partition.cpp

// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT

#include <numeric>
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <vector>
#include <gtest/gtest.h>

#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/wrapper/layout.hpp"
#include "ck/wrapper/tensor.hpp"

TEST(TestPartition, LocalPartition)
{
    const auto shape =
        ck::make_tuple(ck::make_tuple(ck::Number<16>{}, ck::Number<4>{}), ck::Number<4>{});
    const auto strides =
        ck::make_tuple(ck::make_tuple(ck::Number<1>{}, ck::Number<16>{}), ck::Number<64>{});
    const auto layout = ck::wrapper::make_layout(shape, strides);

    std::vector<ck::index_t> data(ck::wrapper::size(layout));
    std::iota(data.begin(), data.end(), 0);

    const auto tensor =
        ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(data.data(), layout);

    const auto thread_steps = ck::make_tuple(ck::Number<1>{}, ck::Number<8>{}, ck::Number<1>{});
    // row-major thread layout
    const auto thread_layout =
        ck::wrapper::make_layout(ck::make_tuple(ck::Number<4>{}, ck::Number<8>{}, ck::Number<1>{}),
                                 ck::make_tuple(ck::Number<8>{}, ck::Number<1>{}, 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) / projection_thread_length;
        thread_id++)
    {
        const auto packed_partition =
            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) / 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);
        EXPECT_EQ(packed_partition(1), expected_partition_second_val);
    }
}

TEST(TestPartition, LocalTile)
{
    const auto shape   = ck::make_tuple(ck::Number<16>{}, ck::Number<4>{}, ck::Number<4>{});
    const auto strides = ck::make_tuple(ck::Number<1>{}, ck::Number<16>{}, ck::Number<64>{});
    const auto layout  = ck::wrapper::make_layout(shape, strides);

    std::vector<ck::index_t> data(ck::wrapper::size(layout));
    std::iota(data.begin(), data.end(), 0);

    const auto tensor =
        ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(data.data(), layout);
    // 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));

    const auto grid_shape =
        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),
                       ck::wrapper::size<2>(shape) / ck::wrapper::size<2>(block_shape));
    std::vector<ck::Tuple<ck::index_t, ck::index_t, ck::index_t, ck::index_t>> block_idxs;
    for(int i = 0; i < ck::wrapper::size<0>(grid_shape); i++)
    {
        for(int j = 0; j < ck::wrapper::size<1>(grid_shape); j++)
        {
            for(int k = 0; k < ck::wrapper::size<2>(grid_shape); k++)
            {
                block_idxs.emplace_back(i, j, k, 0);
            }
        }
    }

    for(auto block_idx : block_idxs)
    {
        constexpr ck::index_t projection_block_dim = ck::Number<2>{};
        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) / projection_block_dim;
        auto expected_tile_first_val  = ck::wrapper::size<2>(block_idx) *
                                       ck::wrapper::size<2>(block_shape) *
                                       ck::wrapper::size<2>(strides);
        expected_tile_first_val += ck::wrapper::size<1>(block_idx) *
                                   ck::wrapper::size<1>(block_shape) *
                                   ck::wrapper::size<1>(strides);
        expected_tile_first_val += ck::wrapper::size<0>(block_idx) *
                                   ck::wrapper::size<0>(block_shape) *
                                   ck::wrapper::size<0>(strides);

        const auto expected_tile_second_val = expected_tile_first_val + 1;
        EXPECT_EQ(ck::wrapper::size(packed_tile), expected_tile_size);
        EXPECT_EQ(packed_tile(0), expected_tile_first_val);
        EXPECT_EQ(packed_tile(1), expected_tile_second_val);
    }
}