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General 2D Reduction Kernel (#2535)

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* General 2D Reduction Kernel

* Move the reduction kernel from the example
* Split the code and add the necessary policy, problem, shape files as
per ck_tile convention
* Add/modify the headers
* Modified the example to work with the 'new' kernel
* Added tests for the kernel
* N-D refernce reduce
* Added support for N-D input with transform to 2D
* Added padding to support various input sized tensors
* Bug fix in the thread buffer constructor
* Some comments to explain the reduce2d block kernel

* comments resolution

* clang-format

* comments resolution

* clang-format

* clang-format

* comments resolution

* clang-format

[ROCm/composable_kernel commit: 4750b293fe]
This commit is contained in:
Yashvardhan Agarwal
2025-08-06 16:36:59 +03:00
committed by GitHub
parent 4816a695a4
commit ae82a041bb
14 changed files with 905 additions and 199 deletions
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63
example/ck_tile/05_reduce/reduce.cpp
View File

@@ -1,16 +1,21 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck_tile/host.hpp"
#include "reduce.hpp"
#include "ck_tile/ops/reduce.hpp"
#include <cstring>
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "3328", "m dimension")
.insert("n", "4096", "n dimension")
arg_parser.insert("n", "32", "n dimension")
.insert("h", "7", "h dimension")
.insert("w", "7", "w dimension")
.insert("c", "512", "c dimension")
.insert("v", "1", "cpu validation or not")
.insert("prec", "fp16", "precision")
.insert("warmup", "5", "cold iter")
.insert("repeat", "20", "hot iter");
.insert("warmup", "0", "cold iter")
.insert("repeat", "1", "hot iter");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
@@ -23,15 +28,28 @@ bool run(const ck_tile::ArgParser& arg_parser)
using ComputeDataType = float;
using YDataType = DataType;
ck_tile::index_t m = arg_parser.get_int("m");
ck_tile::index_t n = arg_parser.get_int("n");
ck_tile::index_t N = arg_parser.get_int("n");
ck_tile::index_t H = arg_parser.get_int("h");
ck_tile::index_t W = arg_parser.get_int("w");
ck_tile::index_t C = arg_parser.get_int("c");
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
int repeat = arg_parser.get_int("repeat");
ck_tile::HostTensor<XDataType> x_host({m, n});
ck_tile::HostTensor<YDataType> y_host_ref({m});
ck_tile::HostTensor<YDataType> y_host_dev({m});
std::vector<ck_tile::index_t> problem_shape = {N, H, W, C};
std::vector<ck_tile::index_t> strides(4);
strides[0] = H * W * C;
strides[1] = W * C;
strides[2] = C;
strides[3] = 1;
// Define reduction specification:
constexpr auto kept_dim = ck_tile::sequence<0, 3>{}; // Which dimension to keep
constexpr auto reduce_dims = ck_tile::sequence<1, 2>{}; // Which dimensions to reduce
ck_tile::HostTensor<XDataType> x_host(problem_shape, strides);
ck_tile::HostTensor<YDataType> y_host_ref({N, C}, {C, 1});
ck_tile::HostTensor<YDataType> y_host_dev({N, C}, {C, 1});
ck_tile::FillUniformDistribution<XDataType>{-5.f, 5.f}(x_host);
@@ -54,7 +72,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
constexpr ck_tile::index_t kBlockSize = 256;
constexpr ck_tile::index_t kBlockPerCu = 1;
ck_tile::index_t kGridSize = (m / BlockTile::at(ck_tile::number<0>{}));
ck_tile::index_t kept_dim_len_prod = N * C;
ck_tile::index_t kGridSize = (kept_dim_len_prod + BlockTile::at(ck_tile::number<0>{}) - 1) /
BlockTile::at(ck_tile::number<0>{});
std::cout << "grid size " << kGridSize << std::endl;
using Shape = ck_tile::Reduce2dShape<BlockWarps, BlockTile, WarpTile, Vector>;
@@ -63,6 +83,17 @@ bool run(const ck_tile::ArgParser& arg_parser)
using Kernel = ck_tile::Reduce<Porblem>;
// Create input tensor shape and strides
auto input_shape =
ck_tile::make_tuple(problem_shape[0], problem_shape[1], problem_shape[2], problem_shape[3]);
auto input_strides = ck_tile::make_tuple(strides[0], strides[1], strides[2], strides[3]);
if(!Kernel::IsSupportedArgument(
C, input_strides)) // output tensor's continuous dimension and input strides
{
throw std::runtime_error("Wrong! Arguments not supported!\n");
}
float ave_time = launch_kernel(ck_tile::stream_config{nullptr, true, 0, warmup, repeat},
ck_tile::make_kernel<kBlockSize, kBlockPerCu>(
Kernel{},
@@ -71,10 +102,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
0,
static_cast<XDataType*>(x_buf.GetDeviceBuffer()),
static_cast<YDataType*>(y_buf.GetDeviceBuffer()),
m,
n));
input_shape,
input_strides,
kept_dim,
reduce_dims));
std::size_t num_btype = sizeof(XDataType) * m * n + sizeof(YDataType) * m;
std::size_t num_btype = sizeof(XDataType) * N * C * H * W + sizeof(YDataType) * N * C;
float gb_per_sec = num_btype / 1.E6 / ave_time;
@@ -86,7 +119,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
// reference
ck_tile::reference_reduce<XDataType, ComputeDataType, YDataType>(
x_host, y_host_ref, ReduceOp{});
x_host, y_host_ref, ReduceOp{}, kept_dim, reduce_dims);
y_buf.FromDevice(y_host_dev.mData.data());
pass = ck_tile::check_err(y_host_dev, y_host_ref);

164
example/ck_tile/05_reduce/reduce.hpp
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@@ -1,164 +0,0 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
#include "ck_tile/ops/reduce/block/block_reduce2d_default_policy.hpp"
namespace ck_tile {
template <typename BlockWarps, // num warps along seq<M, N>
typename BlockTile, // block size, seq<M, N>
typename WarpTile, // warp size, seq<M, N>
typename Vector> // contiguous pixels(vector size) along seq<M, N>
struct Reduce2dShape
{
static constexpr index_t Block_M = BlockTile::at(number<0>{});
static constexpr index_t Block_N = BlockTile::at(number<1>{});
static constexpr index_t Warp_M = WarpTile::at(number<0>{});
static constexpr index_t Warp_N = WarpTile::at(number<1>{});
static constexpr index_t Vector_M = Vector::at(number<0>{});
static constexpr index_t Vector_N = Vector::at(number<1>{});
static constexpr index_t WarpPerBlock_M = BlockWarps::at(number<0>{});
static constexpr index_t WarpPerBlock_N = BlockWarps::at(number<1>{});
static constexpr index_t ThreadPerWarp_M = Warp_M / Vector_M;
static constexpr index_t ThreadPerWarp_N = Warp_N / Vector_N;
static constexpr index_t Repeat_M = Block_M / (WarpPerBlock_M * Warp_M);
static constexpr index_t Repeat_N = Block_N / (WarpPerBlock_N * Warp_N);
static constexpr index_t BlockSize =
ck_tile::get_warp_size() * reduce_on_sequence(BlockWarps{}, multiplies{}, number<1>{});
};
template <typename XDataType_,
typename ComputeDataType_,
typename YDataType_,
typename BlockShape_,
typename ReduceOp_>
struct Reduce2dProblem
{
using XDataType = remove_cvref_t<XDataType_>;
using ComputeDataType = remove_cvref_t<ComputeDataType_>;
using YDataType = remove_cvref_t<YDataType_>;
using BlockShape = remove_cvref_t<BlockShape_>;
using ReduceOp = ReduceOp_;
static constexpr bool kNeedCrossLaneSync = BlockShape::ThreadPerWarp_N > 1;
static constexpr bool kNeedCrossWarpSync = BlockShape::WarpPerBlock_N > 1;
};
template <typename Problem_, typename Policy_ = BlockReduce2dDefaultPolicy>
struct Reduce
{
using Problem = ck_tile::remove_cvref_t<Problem_>;
using Policy = ck_tile::remove_cvref_t<Policy_>;
using XDataType = ck_tile::remove_cvref_t<typename Problem::XDataType>;
using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
using YDataType = ck_tile::remove_cvref_t<typename Problem::YDataType>;
#if 0
CK_TILE_DEVICE void operator()(const XDataType* p_x, YDataType* p_y, index_t M, index_t N)
const
{
using S = typename Problem::BlockShape;
const auto x_m_n = make_naive_tensor_view<address_space_enum::global>(
p_x, make_tuple(M, N), make_tuple(N, 1), number<S::Vector_N>{}, number<1>{});
const auto y_m = make_naive_tensor_view_packed<address_space_enum::global>(
p_y, make_tuple(M), number<1>{});
const auto iM = get_block_id() * S::Block_M;
auto x_window = make_tile_window(x_m_n,
make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
{iM, 0},
Policy::template MakeXBlockTileDistribution<Problem>());
auto y_window = make_tile_window(y_m, make_tuple(number<S::Block_M>{}), {iM});
const auto f_reduce = [](const auto& v0, const auto& v1) { return v0 + v1; };
const XDataType reduce_init_value = 0;
constexpr auto reduce_dims = sequence<1>{};
auto y_compute = decltype(block_tile_reduce<ComputeDataType>(
load_tile(x_window), reduce_dims, f_reduce, reduce_init_value)){};
set_tile(y_compute, reduce_init_value);
index_t num_n_tile_iteration =
__builtin_amdgcn_readfirstlane(integer_divide_ceil(N, S::Block_N));
for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
{
const auto x = load_tile(x_window);
block_tile_reduce(y_compute, x, reduce_dims, f_reduce);
move_tile_window(x_window, {0, S::Block_N});
}
block_tile_reduce_sync(y_compute, f_reduce);
store_tile(y_window, cast_tile<YDataType>(y_compute));
}
#else
CK_TILE_DEVICE void operator()(const XDataType* p_x, YDataType* p_y, index_t M, index_t N) const
{
using S = typename Problem::BlockShape;
const auto x_m_n = make_naive_tensor_view<address_space_enum::global>(
p_x, make_tuple(M, N), make_tuple(N, 1), number<S::Vector_N>{}, number<1>{});
const auto y_m = make_naive_tensor_view_packed<address_space_enum::global>(
p_y, make_tuple(M), number<1>{});
const auto iM = get_block_id() * S::Block_M;
auto x_window = make_tile_window(x_m_n,
make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
{iM, 0},
Policy::template MakeXBlockTileDistribution<Problem>());
auto y_window = make_tile_window(y_m, make_tuple(number<S::Block_M>{}), {iM});
__shared__ char smem[Policy::template GetSmemSize<Problem>()];
index_t num_n_tile_iteration =
__builtin_amdgcn_readfirstlane(integer_divide_ceil(N, S::Block_N));
auto reduce_func = typename Problem::ReduceOp{};
auto block_reduce2d = Policy::template GetBlockReduce2d<Problem>();
auto block_reduce2d_sync = Policy::template GetBlockReduce2dSync<Problem>();
auto block_reduce2d_cross_warp_sync =
Policy::template GetBlockReduce2dCrossWarpSync<Problem>();
using XTensorType = decltype(load_tile(x_window));
auto y_compute = block_reduce2d.template MakeYBlockTile<XTensorType>();
set_tile(y_compute, reduce_func.template GetIdentityValue<ComputeDataType>());
for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
{
const auto x = load_tile(x_window);
block_reduce2d(x, y_compute, reduce_func);
move_tile_window(x_window, {0, S::Block_N});
}
block_reduce2d_sync(y_compute, reduce_func);
block_reduce2d_cross_warp_sync(y_compute, smem, reduce_func);
store_tile(y_window, cast_tile<YDataType>(y_compute));
}
#endif
};
} // namespace ck_tile

6
include/ck_tile/core/container/thread_buffer.hpp
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@@ -42,7 +42,11 @@ struct thread_buffer {
// TODO: this ctor can't ignore
CK_TILE_HOST_DEVICE constexpr thread_buffer() : data{} {}
CK_TILE_HOST_DEVICE constexpr thread_buffer(const value_type & o) : data{o} {}
CK_TILE_HOST_DEVICE constexpr thread_buffer(const value_type & o) : data{} {
static_for<0, N, 1>{}(
[&](auto i) { data[i] = o; }
);
}
CK_TILE_HOST_DEVICE static constexpr auto size() { return N; }
CK_TILE_HOST_DEVICE auto & get() {return data; }

57
include/ck_tile/core/utility/reduce_operator.hpp
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@@ -26,7 +26,8 @@ struct Add
}
template <typename T,
typename = std::enable_if_t<std::is_same_v<T, half_t> || std::is_same_v<T, bf16_t>>>
typename = std::enable_if_t<std::is_same_v<T, half_t> || std::is_same_v<T, bf16_t> ||
std::is_same_v<T, fp8_t> || std::is_same_v<T, bf8_t>>>
CK_TILE_HOST_DEVICE constexpr T operator()(T& y, T x) const
{
float y_ = type_convert<float>(y);
@@ -34,6 +35,8 @@ struct Add
return type_convert<T>(y_ + x_);
}
static constexpr bool requires_special_combine = false;
};
struct SquareAdd
@@ -51,13 +54,47 @@ struct SquareAdd
{
return y + (x * x);
}
template <typename T,
typename = std::enable_if_t<std::is_same_v<T, half_t> || std::is_same_v<T, bf16_t> ||
std::is_same_v<T, fp8_t> || std::is_same_v<T, bf8_t>>>
CK_TILE_HOST_DEVICE constexpr T operator()(T& y, T x) const
{
float y_ = type_convert<float>(y);
float x_ = type_convert<float>(x);
return type_convert<T>(y_ + (x_ * x_));
}
// For combining partial results
template <typename T,
typename = std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double> ||
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>>>
CK_TILE_HOST_DEVICE constexpr T combine_partial_results(const T& partial1,
const T& partial2) const
{
return partial1 + partial2; // Just add the partial sums, don't square again
}
template <typename T,
typename = std::enable_if_t<std::is_same_v<T, half_t> || std::is_same_v<T, bf16_t> ||
std::is_same_v<T, fp8_t> || std::is_same_v<T, bf8_t>>>
CK_TILE_HOST_DEVICE constexpr T combine_partial_results(T& partial1, T& partial2) const
{
float partial1_ = type_convert<float>(partial1);
float partial2_ = type_convert<float>(partial2);
return type_convert<T>(partial1_ + partial2_);
}
static constexpr bool requires_special_combine = true;
};
struct Max
{
template <typename T,
typename = std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double> ||
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>>>
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t> ||
std::is_same_v<T, half_t> || std::is_same_v<T, bf16_t> ||
std::is_same_v<T, fp8_t> || std::is_same_v<T, bf8_t>>>
CK_TILE_HOST_DEVICE static constexpr T GetIdentityValue()
{
return numeric<T>::min();
@@ -65,18 +102,24 @@ struct Max
template <typename T,
typename = std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double> ||
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>>>
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t> ||
std::is_same_v<T, half_t> || std::is_same_v<T, bf16_t> ||
std::is_same_v<T, fp8_t> || std::is_same_v<T, bf8_t>>>
CK_TILE_HOST_DEVICE constexpr T operator()(const T& y, const T x) const
{
return max(y, x);
}
static constexpr bool requires_special_combine = false;
};
struct AbsMax
{
template <typename T,
typename = std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double> ||
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>>>
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t> ||
std::is_same_v<T, half_t> || std::is_same_v<T, bf16_t> ||
std::is_same_v<T, fp8_t> || std::is_same_v<T, bf8_t>>>
CK_TILE_HOST_DEVICE static constexpr T GetIdentityValue()
{
return numeric<T>::min();
@@ -84,11 +127,15 @@ struct AbsMax
template <typename T,
typename = std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double> ||
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>>>
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t> ||
std::is_same_v<T, half_t> || std::is_same_v<T, bf16_t> ||
std::is_same_v<T, fp8_t> || std::is_same_v<T, bf8_t>>>
CK_TILE_HOST_DEVICE constexpr T operator()(const T& y, const T x) const
{
return max(y, abs(x));
}
static constexpr bool requires_special_combine = false;
};
} // namespace ReduceOp

78
include/ck_tile/host/reference/reference_reduce.hpp
View File

@@ -30,4 +30,82 @@ reference_reduce(const HostTensor<XDataType>& x_m_n, HostTensor<YDataType>& y_m,
make_ParallelTensorFunctor(f, y_m.mDesc.get_lengths()[0])(std::thread::hardware_concurrency());
}
// Generic reference reduce for arbitrary dimensions
template <
typename XDataType,
typename ComputeDataType,
typename YDataType,
typename ReduceOp,
typename KeptDim, // Expected type: ck_tile::sequence<...> containing dimension indices to keep
typename ReduceDims> // Expected type: ck_tile::sequence<...> containing dimension indices to
// reduce
CK_TILE_HOST void reference_reduce(const HostTensor<XDataType>& x_tensor,
HostTensor<YDataType>& y_tensor,
ReduceOp reduce_op,
KeptDim kept_dim,
ReduceDims reduce_dims)
{
const auto& x_lengths = x_tensor.mDesc.get_lengths();
// Calculate total kept elements (product of all kept dimension lengths)
index_t total_kept_elements = 1;
static_for<0, kept_dim.size(), 1>{}(
[&](auto i) { total_kept_elements *= x_lengths[kept_dim.at(i)]; });
// Calculate total reduce elements (product of all reduce dimension lengths)
index_t total_reduce_elements = 1;
static_for<0, reduce_dims.size(), 1>{}(
[&](auto i) { total_reduce_elements *= x_lengths[reduce_dims.at(i)]; });
auto f = [&](auto linear_kept_idx) {
ComputeDataType v_acc = reduce_op.template GetIdentityValue<ComputeDataType>();
// Convert linear kept index to multi-dimensional kept indices
std::vector<index_t> kept_indices(kept_dim.size());
index_t temp_kept = linear_kept_idx;
static_for<0, kept_dim.size(), 1>{}([&](auto i) {
constexpr auto dim_idx = kept_dim.size() - 1 - i;
constexpr auto dim = kept_dim.at(dim_idx);
const auto len = x_lengths[dim];
kept_indices[dim_idx] = temp_kept % len;
temp_kept /= len;
});
for(index_t reduce_idx = 0; reduce_idx < total_reduce_elements; ++reduce_idx)
{
// Convert linear reduce index to multi-dimensional reduce indices
std::vector<index_t> reduce_indices(reduce_dims.size());
index_t temp_reduce = reduce_idx;
static_for<0, reduce_dims.size(), 1>{}([&](auto i) {
constexpr auto dim_idx = reduce_dims.size() - 1 - i;
constexpr auto dim = reduce_dims.at(dim_idx);
const auto len = x_lengths[dim];
reduce_indices[dim_idx] = temp_reduce % len;
temp_reduce /= len;
});
// Build full input tensor indices by combining kept and reduce indices
std::vector<std::size_t> full_indices(x_lengths.size(), 0);
static_for<0, kept_dim.size(), 1>{}(
[&](auto i) { full_indices[kept_dim.at(i)] = kept_indices[i]; });
static_for<0, reduce_dims.size(), 1>{}(
[&](auto i) { full_indices[reduce_dims.at(i)] = reduce_indices[i]; });
// Access input tensor element
const auto v_a = type_convert<ComputeDataType>(x_tensor(full_indices));
v_acc = reduce_op(v_acc, v_a);
}
// Calculate output tensor index using kept indices
// The output tensor has the same structure as the kept dimensions
std::vector<std::size_t> y_indices(kept_dim.size());
static_for<0, kept_dim.size(), 1>{}([&](auto i) { y_indices[i] = kept_indices[i]; });
y_tensor(y_indices) = type_convert<YDataType>(v_acc);
};
make_ParallelTensorFunctor(f, total_kept_elements)(std::thread::hardware_concurrency());
}
} // namespace ck_tile

5
include/ck_tile/ops/reduce.hpp
View File

@@ -5,8 +5,11 @@
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
#include "ck_tile/ops/reduce/block/block_reduce2d.hpp"
#include "ck_tile/ops/reduce/block/block_reduce2d_default_policy.hpp"
#include "ck_tile/ops/reduce/block/block_reduce2d_problem.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
#include "ck_tile/ops/reduce/kernel/reduce2d_kernel.hpp"
#include "ck_tile/ops/reduce/pipeline/reduce2d_default_policy.hpp"
#include "ck_tile/ops/reduce/pipeline/reduce2d_problem.hpp"
#include "ck_tile/ops/reduce/pipeline/reduce2d_shape.hpp"

72
include/ck_tile/ops/reduce/block/block_reduce2d.hpp
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@@ -7,20 +7,55 @@
namespace ck_tile {
// BlockReduce2d implements a hierarchical 2D reduction operator that reduces data along the second
// dimension using a user-specified reduction function.
//
// The reduction is performed in a three-stage hierarchical approach:
//
// STAGE 1: Thread-level reduction (BlockReduce2d)
// ===============================================
// - Each thread processes multiple elements from the input tensor within its assigned data
// partition
// - Reduction is performed locally within each thread by iterating over assigned elements
// - ReducePacksPerXDim controls how many elements sweep_tile processes in one iteration per
// dimension
// (e.g., {1,1} = 1 element at a time from each dimension, {2,4} = 2 from dim0, 4 from dim1)
// - Results are accumulated into a thread-local output tensor stored in registers
// - The output tensor distribution is derived from the input tensor's distribution using
// make_reduce_tile_distribution_encoding() to handle dimension reduction
//
// STAGE 2: Warp-level reduction (BlockReduce2dSync)
// ================================================
// - Performs inter-thread reduction within each warp
// - Uses warp shuffle operations to exchange data between threads in the same warp
// - Implements a tree-reduction pattern with power-of-2 stages
// - Only reduces along dimensions that map to lane IDs within the warp
//
// STAGE 3: Cross-warp reduction (BlockReduce2dCrossWarpSync)
// ========================================================
// - Performs reduction across multiple warps within the same thread block
// - Uses shared memory (LDS) to facilitate data exchange between warps
// - Each warp's lane-0 thread stores its partial results to shared memory
// - All threads participate in loading and reducing data from shared memory
// - Implements block-level synchronization to ensure memory consistency
// BlockReduce2d: Thread-level reduction (Stage 1)
template <typename Problem_, typename Policy_ = void>
struct BlockReduce2d
{
// in-thread reduction
// Thread-level reduction implementation
using Problem = remove_cvref_t<Problem_>;
using XDataType = typename Problem::XDataType;
using ComputeDataType = typename Problem::ComputeDataType;
CK_TILE_DEVICE constexpr BlockReduce2d() {}
template <typename XDistributedTensor_,
typename YDistributedTensor_,
typename ReduceFunc,
typename ReducePacksPerXDim = uniform_sequence_gen_t<2, 1>>
template <
typename XDistributedTensor_,
typename YDistributedTensor_,
typename ReduceFunc,
typename ReducePacksPerXDim =
uniform_sequence_gen_t<2, 1>> // {1,1} = process 1 element at a time from each dimension
CK_TILE_DEVICE void operator()(const XDistributedTensor_& x_tensor,
YDistributedTensor_& y_tensor,
const ReduceFunc& reduce_func,
@@ -33,6 +68,7 @@ struct BlockReduce2d
y_tensor(idx_0), ck_tile::type_convert<ComputeDataType>(x_tensor[idx_])...);
},
ReducePacksPerXDim{});
#if 0
constexpr auto I0 = number<0>{};
constexpr auto I1 = number<1>{};
@@ -75,6 +111,8 @@ struct BlockReduce2d
return tensor;
}
// uniform_sequence_gen_t<NSize, Value> generates sequence of NSize elements filled with Value
// e.g., uniform_sequence_gen_t<2, 1> → {1, 1} and uniform_sequence_gen_t<3, 4> → {4, 4, 4}
template <typename XDistributedTensor_,
typename ReduceFunc,
typename ReducePacksPerXDim = uniform_sequence_gen_t<2, 1>>
@@ -91,6 +129,7 @@ struct BlockReduce2d
}
};
// BlockReduce2dSync: Warp-level reduction (Stage 2)
template <typename Problem_, typename Policy_ = void>
struct BlockReduce2dSync
{
@@ -145,8 +184,15 @@ struct BlockReduce2dSync
// pull data from remote lane
const auto v_remote = warp_shuffle(v_local, src_lane);
// reduce
v_local = reduce_func(v_local, v_remote);
// For reduce, use combine_partial_results for operations that require it
if constexpr(ReduceFunc::requires_special_combine)
{
v_local = reduce_func.combine_partial_results(v_local, v_remote);
}
else
{
v_local = reduce_func(v_local, v_remote);
}
});
}
});
@@ -157,6 +203,7 @@ struct BlockReduce2dSync
}
};
// BlockReduce2dCrossWarpSync: Cross-warp reduction (Stage 3)
template <typename Problem_, typename Policy_ = void>
struct BlockReduce2dCrossWarpSync
{
@@ -263,8 +310,15 @@ struct BlockReduce2dCrossWarpSync
constexpr auto i_1 = number<i_1_n1 + 1>{};
const DataType v_remote = all_scratch[i_0 * num_reduce_warps + i_1];
// reduce
v_local = reduce_func(v_local, v_remote);
// For reduce, use combine_partial_results for operations that require it
if constexpr(ReduceFunc::requires_special_combine)
{
v_local = reduce_func.combine_partial_results(v_local, v_remote);
}
else
{
v_local = reduce_func(v_local, v_remote);
}
});
y_tensor.get_thread_buffer()(i_0) = v_local;

219
include/ck_tile/ops/reduce/kernel/reduce2d_kernel.hpp Normal file
View File

@@ -0,0 +1,219 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/reduce/block/block_reduce.hpp"
#include "ck_tile/ops/reduce/pipeline/reduce2d_default_policy.hpp"
// Reduce2d Kernel:
// =======================================
// This kernel implements a 2D reduction operation that reduces data along the second dimension
// of a matrix. The reduction is performed in multiple hierarchical stages.
namespace ck_tile {
template <typename Problem_, typename Policy_ = Reduce2dDefaultPolicy>
struct Reduce
{
using Problem = ck_tile::remove_cvref_t<Problem_>;
using Policy = ck_tile::remove_cvref_t<Policy_>;
using XDataType = ck_tile::remove_cvref_t<typename Problem::XDataType>;
using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
using YDataType = ck_tile::remove_cvref_t<typename Problem::YDataType>;
private:
// Helper function to calculate optimal vector size for input tensor
template <typename InputShape, typename ReduceDims>
static constexpr index_t CalculateInputVectorSize()
{
using S = typename Problem::BlockShape;
constexpr index_t memory_vector_size = 16 / sizeof(XDataType);
constexpr index_t thread_tile_vector_size = S::ThreadTile_N;
// Check if innermost reduce dimension is the last dimension (stride 1).
constexpr auto innermost_reduce_dim = ReduceDims{}.at(number<ReduceDims{}.size() - 1>{});
constexpr bool is_innermost_contiguous = (innermost_reduce_dim == InputShape{}.size() - 1);
// If innermost reduce dimension is not the last dim (not contiguous), limit vectorization
constexpr index_t stride_based_vector_size =
is_innermost_contiguous ? ck_tile::min(memory_vector_size, thread_tile_vector_size) : 1;
return stride_based_vector_size;
}
// Helper function to calculate optimal vector size for output tensor
static constexpr index_t CalculateOutputVectorSize()
{
using S = typename Problem::BlockShape;
constexpr index_t memory_vector_size = 16 / sizeof(YDataType);
constexpr index_t thread_tile_vector_size = S::ThreadTile_M;
constexpr index_t vector_size = ck_tile::min(memory_vector_size, thread_tile_vector_size);
return vector_size;
}
public:
template <typename InputShape, typename InputStrides, typename KeptDim, typename ReduceDims>
CK_TILE_DEVICE void operator()(const XDataType* p_x,
YDataType* p_y,
InputShape input_shape,
InputStrides input_strides,
KeptDim kept_dim,
ReduceDims reduce_dims) const
{
using S = typename Problem::BlockShape;
const auto iM = get_block_id() * S::Block_M;
static_assert(kept_dim.size() + reduce_dims.size() == InputShape::size(),
"Size of kept dimensions + reduced dimensions must equal input tensor rank");
// Extract lengths based on kept and reduced dimensions
const auto kept_lens = [&]() {
return generate_tuple([&](auto I) { return input_shape.at(number<kept_dim.at(I)>{}); },
number<kept_dim.size()>{});
}();
const auto reduce_lens = [&]() {
return generate_tuple(
[&](auto I) { return input_shape.at(number<reduce_dims.at(I)>{}); },
number<reduce_dims.size()>{});
}();
const auto kept_merge_transform = make_merge_transform(kept_lens);
const auto reduce_merge_transform = make_merge_transform(reduce_lens);
auto reduce_func = typename Problem::ReduceOp{};
const XDataType custom_padding_value =
type_convert<XDataType>(reduce_func.template GetIdentityValue<ComputeDataType>());
// Calculate optimal vector size for input tensor
constexpr auto x_tensor_vector_size = CalculateInputVectorSize<InputShape, ReduceDims>();
// Create input tensor view with custom padding value
auto desc = make_naive_tensor_descriptor(
input_shape, input_strides, number<x_tensor_vector_size>{}, number<1>{});
// Create buffer view with custom padding value
auto buffer_view = make_buffer_view<address_space_enum::global>(
p_x, desc.get_element_space_size(), custom_padding_value);
// Create tensor view with custom padding
const auto x_tensor = tensor_view<decltype(buffer_view), decltype(desc)>{buffer_view, desc};
const auto transformed_x_tensor = pad_tensor_view(
transform_tensor_view(x_tensor,
make_tuple(kept_merge_transform, reduce_merge_transform),
make_tuple(kept_dim, reduce_dims),
make_tuple(sequence<0>{}, sequence<1>{})),
make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
sequence<0, 1>{});
// Calculate strides for output tensor based on its own dimensions
const auto kept_strides = [&]() {
return generate_tuple(
[&](auto I) {
// Calculate stride for dimension I as product of all following dimensions
index_t stride = 1;
static_for<I + 1, kept_dim.size(), 1>{}(
[&](auto J) { stride *= kept_lens.at(number<J>{}); });
return stride;
},
number<kept_dim.size()>{});
}();
// Calculate optimal vector size for output tensor
constexpr auto y_tensor_vector_size = CalculateOutputVectorSize();
const auto y_m = make_naive_tensor_view<address_space_enum::global>(
p_y, kept_lens, kept_strides, number<y_tensor_vector_size>{}, number<1>{});
// Transform output tensor to 1D merged view
// This creates a view compatible with the 2D reduction pattern
const auto y_merged = transform_tensor_view(
y_m,
make_tuple(kept_merge_transform),
make_tuple(typename arithmetic_sequence_gen<0, kept_dim.size(), 1>::type{}),
make_tuple(sequence<0>{}));
auto x_window = make_tile_window(transformed_x_tensor,
make_tuple(number<S::Block_M>{}, number<S::Block_N>{}),
{iM, 0},
Policy::template MakeXBlockTileDistribution<Problem>());
auto y_window = make_tile_window(y_merged, make_tuple(number<S::Block_M>{}), {iM});
__shared__ char smem[Policy::template GetSmemSize<Problem>()];
// Get the merged dimension size from the transformed tensor
const auto merged_reduce_len =
transformed_x_tensor.get_tensor_descriptor().get_lengths().at(number<1>{});
index_t num_n_tile_iteration =
__builtin_amdgcn_readfirstlane(integer_divide_ceil(merged_reduce_len, S::Block_N));
auto block_reduce2d = Policy::template GetBlockReduce2d<Problem>();
auto block_reduce2d_sync = Policy::template GetBlockReduce2dSync<Problem>();
auto block_reduce2d_cross_warp_sync =
Policy::template GetBlockReduce2dCrossWarpSync<Problem>();
using XTensorType = decltype(load_tile(x_window));
auto y_compute = block_reduce2d.template MakeYBlockTile<XTensorType>();
set_tile(y_compute, reduce_func.template GetIdentityValue<ComputeDataType>());
for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
{
const auto x = load_tile(x_window);
block_reduce2d(x, y_compute, reduce_func);
move_tile_window(x_window, {0, S::Block_N});
}
block_reduce2d_sync(y_compute, reduce_func);
block_reduce2d_cross_warp_sync(y_compute, smem, reduce_func);
store_tile(y_window, cast_tile<YDataType>(y_compute));
}
/// @brief Validates if the given arguments are supported by the 2D reduction kernel.
///
/// @param y_continous_dim Size of the continuous dimension of the output tensor.
/// Must be a multiple of ThreadTile_N for proper thread mapping.
///
/// @param input_strides The stride configuration of the input tensor.
/// The last stride must be 1 to ensure contiguous memory access
/// and enable efficient vectorized loads.
///
/// @return true if the arguments are supported, false otherwise.
/// Error messages are logged when CK_TILE_LOGGING is enabled.
///
/// @note Requirements:
/// - y_continous_dim % ThreadTile_N == 0 (for proper thread distribution)
/// - input_strides[-1] == 1 (for contiguous memory access)
CK_TILE_HOST static bool IsSupportedArgument(index_t y_continous_dim, auto input_strides)
{
using S = typename Problem::BlockShape;
if(y_continous_dim % S::ThreadTile_N != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("Total reduction size should be a multiple of ThreadTile_N!");
}
return false;
}
if(input_strides.at(number<input_strides.size() - 1>{}) != 1)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR(
"Input tensor's last stride must be 1 to support correct vector access!");
}
return false;
}
return true;
}
};
} // namespace ck_tile

9
include/ck_tile/ops/reduce/block/block_reduce2d_default_policy.hpp → include/ck_tile/ops/reduce/pipeline/reduce2d_default_policy.hpp
View File

@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
@@ -9,7 +9,7 @@
namespace ck_tile {
struct BlockReduce2dDefaultPolicy
struct Reduce2dDefaultPolicy
{
template <typename Problem>
CK_TILE_DEVICE static constexpr auto MakeXBlockTileDistribution()
@@ -18,8 +18,9 @@ struct BlockReduce2dDefaultPolicy
return make_static_tile_distribution(
tile_distribution_encoding<
sequence<>,
tuple<sequence<S::Repeat_M, S::WarpPerBlock_M, S::ThreadPerWarp_M, S::Vector_M>,
sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
tuple<
sequence<S::Repeat_M, S::WarpPerBlock_M, S::ThreadPerWarp_M, S::ThreadTile_M>,
sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::ThreadTile_N>>,
tuple<sequence<1, 2>, sequence<1, 2>>,
tuple<sequence<1, 1>, sequence<2, 2>>,
sequence<1, 1, 2, 2>,

27
include/ck_tile/ops/reduce/pipeline/reduce2d_problem.hpp Normal file
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@@ -0,0 +1,27 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
namespace ck_tile {
template <typename XDataType_,
typename ComputeDataType_,
typename YDataType_,
typename BlockShape_,
typename ReduceOp_>
struct Reduce2dProblem
{
using XDataType = remove_cvref_t<XDataType_>;
using ComputeDataType = remove_cvref_t<ComputeDataType_>;
using YDataType = remove_cvref_t<YDataType_>;
using BlockShape = remove_cvref_t<BlockShape_>;
using ReduceOp = ReduceOp_;
static constexpr bool kNeedCrossLaneSync = BlockShape::ThreadPerWarp_N > 1;
static constexpr bool kNeedCrossWarpSync = BlockShape::WarpPerBlock_N > 1;
};
} // namespace ck_tile

37
include/ck_tile/ops/reduce/pipeline/reduce2d_shape.hpp Normal file
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@@ -0,0 +1,37 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
namespace ck_tile {
template <typename BlockWarps, // num warps along seq<M, N>
typename BlockTile, // block size, seq<M, N>
typename WarpTile, // warp size, seq<M, N>
typename ThreadTile> // contiguous pixels(vector size) along seq<M, N>
struct Reduce2dShape
{
static constexpr index_t Block_M = BlockTile::at(number<0>{});
static constexpr index_t Block_N = BlockTile::at(number<1>{});
static constexpr index_t Warp_M = WarpTile::at(number<0>{});
static constexpr index_t Warp_N = WarpTile::at(number<1>{});
static constexpr index_t ThreadTile_M = ThreadTile::at(number<0>{});
static constexpr index_t ThreadTile_N = ThreadTile::at(number<1>{});
static constexpr index_t WarpPerBlock_M = BlockWarps::at(number<0>{});
static constexpr index_t WarpPerBlock_N = BlockWarps::at(number<1>{});
static constexpr index_t ThreadPerWarp_M = Warp_M / ThreadTile_M;
static constexpr index_t ThreadPerWarp_N = Warp_N / ThreadTile_N;
static constexpr index_t Repeat_M = Block_M / (WarpPerBlock_M * Warp_M);
static constexpr index_t Repeat_N = Block_N / (WarpPerBlock_N * Warp_N);
static constexpr index_t BlockSize =
ck_tile::get_warp_size() * reduce_on_sequence(BlockWarps{}, multiplies{}, number<1>{});
};
} // namespace ck_tile

1
test/ck_tile/CMakeLists.txt
View File

@@ -21,3 +21,4 @@ add_subdirectory(add_rmsnorm2d_rdquant)
# add_subdirectory(layernorm2d)
# add_subdirectory(rmsnorm2d)
add_subdirectory(gemm_block_scale)
add_subdirectory(reduce)

7
test/ck_tile/reduce/CMakeLists.txt Normal file
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@@ -0,0 +1,7 @@
if(GPU_TARGETS MATCHES "gfx9")
add_gtest_executable(test_ck_tile_reduce2d test_reduce2d.cpp)
if(result EQUAL 0)
target_link_libraries(test_ck_tile_reduce2d PRIVATE utility)
endif()
endif()

359
test/ck_tile/reduce/test_reduce2d.cpp Normal file
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@@ -0,0 +1,359 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include <gtest/gtest.h>
#include <vector>
#include <cmath>
#include <tuple>
#include <iostream>
#include "ck_tile/core.hpp"
#include "ck_tile/host.hpp"
#include "ck_tile/ops/reduce.hpp"
#include "ck_tile/host/kernel_launch.hpp"
template <typename Tuple>
class TestCkTileReduce : public ::testing::Test
{
protected:
using XDataType = std::tuple_element_t<0, Tuple>;
using ComputeDataType = std::tuple_element_t<1, Tuple>;
using YDataType = std::tuple_element_t<2, Tuple>;
using ReduceOpType = std::tuple_element_t<3, Tuple>;
using BlockWarps_ = std::tuple_element_t<4, Tuple>;
using BlockTile_ = std::tuple_element_t<5, Tuple>;
using WarpTile_ = std::tuple_element_t<6, Tuple>;
using ThreadTile_ = std::tuple_element_t<7, Tuple>;
using TestReduce2dShape =
ck_tile::Reduce2dShape<BlockWarps_, BlockTile_, WarpTile_, ThreadTile_>;
template <std::size_t InputDim, typename KeptDimSeq, typename ReduceDimSeq>
void RunGenericTest(const std::vector<ck_tile::index_t>& input_shape,
const std::vector<ck_tile::index_t>& input_strides,
const std::vector<ck_tile::index_t>& output_shape,
const std::vector<ck_tile::index_t>& output_strides,
ck_tile::index_t kept_dim_len_prod,
ck_tile::index_t total_reduce_elements,
KeptDimSeq kept_dims,
ReduceDimSeq reduce_dims)
{
ck_tile::HostTensor<XDataType> h_x(input_shape, input_strides);
ck_tile::HostTensor<YDataType> h_y(output_shape, output_strides);
ck_tile::HostTensor<YDataType> h_y_ref(output_shape, output_strides);
ck_tile::FillUniformDistribution<XDataType>{-5.f, 5.f}(h_x);
h_y.SetZero();
h_y_ref.SetZero();
ck_tile::DeviceMem d_x_mem(h_x.get_element_space_size_in_bytes());
ck_tile::DeviceMem d_y_mem(h_y.get_element_space_size_in_bytes());
d_x_mem.ToDevice(h_x.data());
d_y_mem.ToDevice(h_y.data()); // Initialize device output buffer
// Problem and kernel setup
using Problem = ck_tile::
Reduce2dProblem<XDataType, ComputeDataType, YDataType, TestReduce2dShape, ReduceOpType>;
using Kernel = ck_tile::Reduce<Problem>;
// Launch configuration
constexpr ck_tile::index_t kBlockSize = 256;
constexpr ck_tile::index_t kBlockPerCu = 1;
ck_tile::index_t kGridSize =
(kept_dim_len_prod + TestReduce2dShape::Block_M - 1) / TestReduce2dShape::Block_M;
// Generic helper to create tuple from vector based on compile-time size
auto make_shape_tuple = []<std::size_t N>(const std::vector<ck_tile::index_t>& vec) {
return [&vec]<std::size_t... I>(std::index_sequence<I...>) {
return ck_tile::make_tuple(vec[I]...);
}(std::make_index_sequence<N>{});
};
auto input_shape_tuple = make_shape_tuple.template operator()<InputDim>(input_shape);
auto input_strides_tuple = make_shape_tuple.template operator()<InputDim>(input_strides);
if(!Kernel::IsSupportedArgument(
output_shape[output_shape.size() - 1],
input_strides_tuple)) // output tensor's continuous dimension
{
throw std::runtime_error("Wrong! Arguments not supported!\n");
}
ck_tile::launch_kernel(ck_tile::stream_config{nullptr, false, 0},
ck_tile::make_kernel<kBlockSize, kBlockPerCu>(
Kernel{},
kGridSize,
kBlockSize,
0,
static_cast<XDataType*>(d_x_mem.GetDeviceBuffer()),
static_cast<YDataType*>(d_y_mem.GetDeviceBuffer()),
input_shape_tuple,
input_strides_tuple,
kept_dims,
reduce_dims));
// Get results back
d_y_mem.FromDevice(h_y.data());
// Reference computation
ck_tile::reference_reduce<XDataType, ComputeDataType, YDataType>(
h_x, h_y_ref, ReduceOpType{}, kept_dims, reduce_dims);
// Calculate proper error thresholds based on data types and number of accumulations
const auto rtol = ck_tile::get_relative_threshold<XDataType, YDataType, ComputeDataType>(
total_reduce_elements);
const auto atol = ck_tile::get_absolute_threshold<XDataType, YDataType, ComputeDataType>(
5.0f, total_reduce_elements);
bool result =
ck_tile::check_err(h_y, h_y_ref, "Error: Incorrect reduce results!", rtol, atol);
EXPECT_TRUE(result);
}
// Convenience functions for specific dimensional patterns
void RunTest2D_KeepDim0_ReduceDim1(ck_tile::index_t dim0, ck_tile::index_t dim1)
{
constexpr auto kept_dims = ck_tile::sequence<0>{};
constexpr auto reduce_dims = ck_tile::sequence<1>{};
// Input shape and strides
std::vector<ck_tile::index_t> input_shape = {dim0, dim1};
std::vector<ck_tile::index_t> input_strides = {dim1, 1};
// Output shape and strides (keep dim0)
std::vector<ck_tile::index_t> output_shape = {dim0};
std::vector<ck_tile::index_t> output_strides = {1};
// Calculate products
ck_tile::index_t kept_dim_len_prod = dim0;
ck_tile::index_t total_reduce_elements = dim1;
RunGenericTest<2>(input_shape,
input_strides,
output_shape,
output_strides,
kept_dim_len_prod,
total_reduce_elements,
kept_dims,
reduce_dims);
}
void RunTest3D_KeepDim0_ReduceDim12(ck_tile::index_t dim0,
ck_tile::index_t dim1,
ck_tile::index_t dim2)
{
constexpr auto kept_dims = ck_tile::sequence<0>{};
constexpr auto reduce_dims = ck_tile::sequence<1, 2>{};
// Input shape and strides
std::vector<ck_tile::index_t> input_shape = {dim0, dim1, dim2};
std::vector<ck_tile::index_t> input_strides = {dim1 * dim2, dim2, 1};
// Output shape and strides (keep dim0)
std::vector<ck_tile::index_t> output_shape = {dim0};
std::vector<ck_tile::index_t> output_strides = {1};
// Calculate products
ck_tile::index_t kept_dim_len_prod = dim0; // product of kept dimensions
ck_tile::index_t total_reduce_elements = dim1 * dim2; // product of reduced dimensions
RunGenericTest<3>(input_shape,
input_strides,
output_shape,
output_strides,
kept_dim_len_prod,
total_reduce_elements,
kept_dims,
reduce_dims);
}
void RunTest3D_KeepDim01_ReduceDim2(ck_tile::index_t dim0,
ck_tile::index_t dim1,
ck_tile::index_t dim2)
{
constexpr auto kept_dims = ck_tile::sequence<0, 1>{};
constexpr auto reduce_dims = ck_tile::sequence<2>{};
// Input shape and strides
std::vector<ck_tile::index_t> input_shape = {dim0, dim1, dim2};
std::vector<ck_tile::index_t> input_strides = {dim1 * dim2, dim2, 1};
// Output shape and strides (keep dim0)
std::vector<ck_tile::index_t> output_shape = {dim0, dim1};
std::vector<ck_tile::index_t> output_strides = {dim1, 1};
// Calculate products
ck_tile::index_t kept_dim_len_prod = dim0 * dim1; // product of kept dimensions
ck_tile::index_t total_reduce_elements = dim2; // product of reduced dimensions
RunGenericTest<3>(input_shape,
input_strides,
output_shape,
output_strides,
kept_dim_len_prod,
total_reduce_elements,
kept_dims,
reduce_dims);
}
void RunTest4D_KeepDim01_ReduceDim23(ck_tile::index_t N,
ck_tile::index_t C,
ck_tile::index_t H,
ck_tile::index_t W)
{
constexpr auto kept_dims = ck_tile::sequence<0, 1>{};
constexpr auto reduce_dims = ck_tile::sequence<2, 3>{};
// Input shape and strides
std::vector<ck_tile::index_t> input_shape = {N, C, H, W};
std::vector<ck_tile::index_t> input_strides = {C * H * W, H * W, W, 1};
// Output shape and strides (keep dim0, dim1)
std::vector<ck_tile::index_t> output_shape = {N, C};
std::vector<ck_tile::index_t> output_strides = {C, 1};
// Calculate products
ck_tile::index_t kept_dim_len_prod = N * C; // product of kept dimensions
ck_tile::index_t total_reduce_elements = H * W; // product of reduced dimensions
RunGenericTest<4>(input_shape,
input_strides,
output_shape,
output_strides,
kept_dim_len_prod,
total_reduce_elements,
kept_dims,
reduce_dims);
}
void RunTest4D_KeepDim03_ReduceDim12(ck_tile::index_t N,
ck_tile::index_t H,
ck_tile::index_t W,
ck_tile::index_t C)
{
constexpr auto kept_dims = ck_tile::sequence<0, 3>{};
constexpr auto reduce_dims = ck_tile::sequence<1, 2>{};
// Input shape and strides
std::vector<ck_tile::index_t> input_shape = {N, H, W, C};
std::vector<ck_tile::index_t> input_strides = {H * W * C, W * C, C, 1};
// Output shape and strides (keep dim0, dim1)
std::vector<ck_tile::index_t> output_shape = {N, C};
std::vector<ck_tile::index_t> output_strides = {C, 1};
// Calculate products
ck_tile::index_t kept_dim_len_prod = N * C; // product of kept dimensions
ck_tile::index_t total_reduce_elements = H * W; // product of reduced dimensions
RunGenericTest<4>(input_shape,
input_strides,
output_shape,
output_strides,
kept_dim_len_prod,
total_reduce_elements,
kept_dims,
reduce_dims);
}
};
// Shape parameters for different test configurations
using Shape1_BlockWarps = ck_tile::sequence<4, 1>;
using Shape1_BlockTile = ck_tile::sequence<128, 128>;
using Shape1_WarpTile = ck_tile::sequence<32, 128>;
using Shape1_ThreadTile = ck_tile::sequence<8, 8>;
using Shape2_BlockWarps = ck_tile::sequence<2, 2>; // Cross-warp reduction test
using Shape2_BlockTile = ck_tile::sequence<2, 1024>;
using Shape2_WarpTile = ck_tile::sequence<1, 512>;
using Shape2_ThreadTile = ck_tile::sequence<1, 8>;
// Test configurations for different data types and operations
using TestConfig_F32_Add = std::tuple<float,
float,
float,
ck_tile::ReduceOp::Add,
Shape1_BlockWarps,
Shape1_BlockTile,
Shape1_WarpTile,
Shape1_ThreadTile>;
using TestConfig_F16_Add = std::tuple<ck_tile::half_t,
float,
ck_tile::half_t,
ck_tile::ReduceOp::Add,
Shape1_BlockWarps,
Shape1_BlockTile,
Shape1_WarpTile,
Shape1_ThreadTile>;
using TestConfig_F32_CrossWarp = std::tuple<float,
float,
float,
ck_tile::ReduceOp::Add,
Shape2_BlockWarps,
Shape2_BlockTile,
Shape2_WarpTile,
Shape2_ThreadTile>;
using TestConfig_F32_Max = std::tuple<float,
float,
float,
ck_tile::ReduceOp::Max,
Shape1_BlockWarps,
Shape1_BlockTile,
Shape1_WarpTile,
Shape1_ThreadTile>;
using TestConfig_F32_SquareAdd = std::tuple<float,
float,
float,
ck_tile::ReduceOp::SquareAdd,
Shape1_BlockWarps,
Shape1_BlockTile,
Shape1_WarpTile,
Shape1_ThreadTile>;
using TestTypes = ::testing::Types<TestConfig_F32_Add,
TestConfig_F16_Add,
TestConfig_F32_CrossWarp,
TestConfig_F32_Max,
TestConfig_F32_SquareAdd>;
TYPED_TEST_SUITE(TestCkTileReduce, TestTypes);
// 2D Tests - Keep dim0, reduce dim1
TYPED_TEST(TestCkTileReduce, Test2D_KeepDim0_ReduceDim1_64x32)
{
this->RunTest2D_KeepDim0_ReduceDim1(64, 32);
}
TYPED_TEST(TestCkTileReduce, Test2D_KeepDim0_ReduceDim1_1024x512)
{
this->RunTest2D_KeepDim0_ReduceDim1(1024, 512);
}
// 3D Tests - Keep dim0, reduce dim1,2
TYPED_TEST(TestCkTileReduce, Test3D_KeepDim0_ReduceDim12_128x128x1)
{
this->RunTest3D_KeepDim0_ReduceDim12(128, 128, 8);
}
// 3D Tests - Keep dim0,1, reduce dim1
TYPED_TEST(TestCkTileReduce, Test3D_KeepDim01_ReduceDim2_512x1024x16)
{
this->RunTest3D_KeepDim01_ReduceDim2(512, 1024, 16);
}
// 4D Tests - Keep dim0,1, reduce dim2,3 (NCHW -> NC)
TYPED_TEST(TestCkTileReduce, Test4D_KeepDim01_ReduceDim23_32x256x16x16)
{
this->RunTest4D_KeepDim01_ReduceDim23(32, 256, 16, 16);
}
// 4D Tests - Keep dim0,3, reduce dim1,2 (NHWC -> NC)
TYPED_TEST(TestCkTileReduce, Test4D_KeepDim03_ReduceDim12_16x32x32x128)
{
this->RunTest4D_KeepDim03_ReduceDim12(16, 32, 32, 128);
}