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Commit d254ed90 authored by Adam Osewski's avatar Adam Osewski
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Update comments to conform with doxygen style.

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include/ck/tensor_operation/gpu/device/device_grouped_gemm_splitk.hpp
View file @ d254ed90
......@@ -82,22 +82,22 @@ struct DeviceGroupedGemmSplitK : public DeviceGroupedGemm<ALayout,
BElementwiseOperation,
CElementwiseOperation>
{
//------------------------------------------------------------------------//
// @brief Sets the k batch size.
//
// @param p_arg Pointer to the Argument we're going to change.
// @param[in] kbatch The kbatch value.
//
///------------------------------------------------------------------------//
/// @brief Sets the k batch size.
///
/// @param p_arg Pointer to the Argument we're going to change.
/// @param[in] kbatch The kbatch value.
///
virtual void SetKBatchSize(BaseArgument* /*p_arg*/, index_t /*kbatch*/) const {}
//------------------------------------------------------------------------//
//
// @brief Sets the device kernel arguments pointer.
//
// @param p_arg The pointer to the Argument we're going to update.
// @param[in] p_dev_kernel_args The pointer to the device memory which contains kernel
// arguments.
//
///------------------------------------------------------------------------//
///
/// @brief Sets the device kernel arguments pointer.
///
/// @param p_arg The pointer to the Argument we're going to update.
/// @param[in] p_dev_kernel_args The pointer to the device memory which contains kernel
/// arguments.
///
virtual void SetDeviceKernelArgs(BaseArgument* /*p_arg*/,
const void* /*p_dev_kernel_args*/) const
{
......
include/ck/tensor_operation/gpu/device/device_softmax.hpp
View file @ d254ed90
......@@ -22,22 +22,22 @@ template <typename InDataType,
index_t NumReduceDim>
struct DeviceSoftmax : public BaseOperator
{
//
// @brief Makes a pointer to Argument class.
//
// @param[in] inLengths Input tensor extent(s) from high to low dimension
// @param[in] inStrides Input tensor stride(s) from high to low dimension
// @param[in] reduceDims The dimension(s) the normalization operation is applied
// @param[in] alpha double type value
// @param[in] beta double type value
// @param[in] in_dev Typeless const pointer in device memory storing the input
// tensor
// @param out_dev Typeless pointer in device memory storing the output tensor
// @param[in] in_elementwise_op The input elementwise operation.
// @param[in] acc_elementwise_op The accumulation elementwise operation.
//
// @return Unique pointer to the Argument class.
//
///
/// @brief Makes a pointer to Argument class.
///
/// @param[in] inLengths Input tensor extent(s) from high to low dimension
/// @param[in] inStrides Input tensor stride(s) from high to low dimension
/// @param[in] reduceDims The dimension(s) the normalization operation is applied
/// @param[in] alpha double type value
/// @param[in] beta double type value
/// @param[in] in_dev Typeless const pointer in device memory storing the input
/// tensor
/// @param out_dev Typeless pointer in device memory storing the output tensor
/// @param[in] in_elementwise_op The input elementwise operation.
/// @param[in] acc_elementwise_op The accumulation elementwise operation.
///
/// @return Unique pointer to the Argument class.
///
virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const std::vector<index_t> inLengths,
const std::vector<index_t> inStrides,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp
View file @ d254ed90
......@@ -201,22 +201,22 @@ __global__ void
}
} // namespace
//
// @brief Device Convolution operation.
//
// Supports:
// @li Forward convolution with up to 3 spatial dimentions
// @li Input tensor in GNWC data format
// @li Weight tensor in GKXC data format
// @li Output tensor in GNWK data format
//
// 1D:
// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
// 2D:
// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
// 3D:
// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
//
///
/// @brief Device Convolution operation.
///
/// Supports:
/// @li Forward convolution with up to 3 spatial dimentions
/// @li Input tensor in GNWC data format
/// @li Weight tensor in GKXC data format
/// @li Output tensor in GNWK data format
///
/// 1D:
/// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
/// 2D:
/// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
/// 3D:
/// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
///
template <index_t NDimSpatial,
typename ADataType,
typename BDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_nhwc_kyxc_nhwk.hpp
View file @ d254ed90
......@@ -154,22 +154,22 @@ __global__ void
} // namespace
//
// @brief Device Convolution operation.
//
// Supports:
// @li Forward convolution with up to 3 spatial dimentions
// @li Input tensor in GNWC data format
// @li Weight tensor in GKXC data format
// @li Output tensor in GNWK data format
//
// 1D:
// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
// 2D:
// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
// 3D:
// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
//
///
/// @brief Device Convolution operation.
///
/// Supports:
/// @li Forward convolution with up to 3 spatial dimentions
/// @li Input tensor in GNWC data format
/// @li Weight tensor in GKXC data format
/// @li Output tensor in GNWK data format
///
/// 1D:
/// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
/// 2D:
/// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
/// 3D:
/// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
///
template <
index_t NDimSpatial,
typename ADataType,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_wmma_cshuffle.hpp
View file @ d254ed90
......@@ -76,23 +76,23 @@ struct ComputePtrOffsetOfStridedBatch
} // namespace
//
// @brief Device Convolution operation.
//
// Supports:
// @li Forward convolution with up to 3 spatial dimentions
// @li Input tensor in GNWC data format
// @li Weight tensor in GKXC data format
// @li Output tensor in GNWK data format
//
// 1D:
// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
// 2D:
// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
// 3D:
// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
// Assume:
// AK1 == BK1
///
/// @brief Device Convolution operation.
///
/// Supports:
/// @li Forward convolution with up to 3 spatial dimentions
/// @li Input tensor in GNWC data format
/// @li Weight tensor in GKXC data format
/// @li Output tensor in GNWK data format
///
/// 1D:
/// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
/// 2D:
/// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
/// 3D:
/// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
/// Assume:
/// AK1 == BK1
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp
View file @ d254ed90
......@@ -194,22 +194,22 @@ __global__ void
} // namespace
//
// @brief Device Convolution operation.
//
// Supports:
// @li Forward convolution with up to 3 spatial dimentions
// @li Input tensor in GNWC data format
// @li Weight tensor in GKXC data format
// @li Output tensor in GNWK data format
//
// 1D:
// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
// 2D:
// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
// 3D:
// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
//
///
/// @brief Device Convolution operation.
///
/// Supports:
/// @li Forward convolution with up to 3 spatial dimentions
/// @li Input tensor in GNWC data format
/// @li Weight tensor in GKXC data format
/// @li Output tensor in GNWK data format
///
/// 1D:
/// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
/// 2D:
/// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
/// 3D:
/// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
///
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_splitk_cshuffle_tile_loop.hpp
View file @ d254ed90
......@@ -25,23 +25,23 @@ namespace ck {
namespace tensor_operation {
namespace device {
//
// @brief Entry point kernel for device-wide Grouped GEMM operation.
//
// @param[in] gemm_desc_const The pointer to the array of GEMM descriptor structures.
// @param[in] tile_count The overall number of output tiles we divided all groups
// into.
// @param[in] k_batch The number of batches we split the K dimension into.
//
// @tparam GridwiseGemm The specific GridwiseGEMM algorithm implementation.
// @tparam GemmDesc The structure holding all necessary descriptors and other
// data needed for groupd gemm calculation and work
// distribution.
// @tparam HasMainKBlockLoop Flag indicating whether all GEMM problem configurations
// need to loop over tiles in K dimension.
// @tparam CGlobalMemoryDataOperation The functor used to store data in output C matrix.
// In example could be: AtomicAdd or Store.
//
///
/// @brief Entry point kernel for device-wide Grouped GEMM operation.
///
/// @param[in] gemm_desc_const The pointer to the array of GEMM descriptor structures.
/// @param[in] tile_count The overall number of output tiles we divided all groups
/// into.
/// @param[in] k_batch The number of batches we split the K dimension into.
///
/// @tparam GridwiseGemm The specific GridwiseGEMM algorithm implementation.
/// @tparam GemmDesc The structure holding all necessary descriptors and
/// other data needed for groupd gemm calculation and work
/// distribution.
/// @tparam HasMainKBlockLoop Flag indicating whether all GEMM problem configurations
/// need to loop over tiles in K dimension.
/// @tparam CGlobalMemoryDataOperation The functor used to store data in output C matrix.
/// In example could be: AtomicAdd or Store.
///
template <typename GridwiseGemm,
typename GemmDesc,
typename FloatA,
......@@ -383,18 +383,18 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
// Assume we want to have at most 2 waves per SIMD
static constexpr int CU_BLOCKS = math::integer_divide_floor(2 * CU_SIMDS, BLOCK_WAVES);
//
// @brief Launch Grouped Gemm kernel.
//
// @note This function overload is using user provided device buffer for kernel
// arguments.
//
// @param[in] arg The structure containing kernel arguments (in host memory).
// @param[in] dev_gemm_args The point to device memory with kernel arguments.
// @param[in] stream_config The device stream configuration.
//
// @return The average kernel execution time (if time measurement is enabled.)
//
///
/// @brief Launch Grouped Gemm kernel.
///
/// @note This function overload is using user provided device buffer for kernel
/// arguments.
///
/// @param[in] arg The structure containing kernel arguments (in host memory).
/// @param[in] dev_gemm_args The point to device memory with kernel arguments.
/// @param[in] stream_config The device stream configuration.
///
/// @return The average kernel execution time (if time measurement is enabled.)
///
float Run(const Argument& arg,
const void* dev_gemm_args,
const StreamConfig& stream_config = StreamConfig{})
......@@ -451,18 +451,18 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
return ave_time;
}
//
// @brief Launch Grouped Gemm kernel.
//
// @note This function overload is using device workspace buffer for kernel arguments.
// The user should call @see GetWorkSpaceSize and @see SetWorkSpacePointer on
// arg parameter to properly allocate this buffer.
//
// @param[in] arg The structure containing kernel arguments (in host memory).
// @param[in] stream_config The device stream configuration.
//
// @return The average kernel execution time (if time measurement is enabled.)
//
///
/// @brief Launch Grouped Gemm kernel.
///
/// @note This function overload is using device workspace buffer for kernel
/// arguments. The user should call @see GetWorkSpaceSize and @see
/// SetWorkSpacePointer on arg parameter to properly allocate this buffer.
///
/// @param[in] arg The structure containing kernel arguments (in host memory).
/// @param[in] stream_config The device stream configuration.
///
/// @return The average kernel execution time (if time measurement is enabled.)
///
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
if(arg.p_workspace_ != nullptr)
......
include/ck/tensor_operation/gpu/device/impl/device_softmax_impl.hpp
View file @ d254ed90
......@@ -348,24 +348,24 @@ struct DeviceSoftmaxImpl : public DeviceSoftmax<InDataType,
acc_elementwise_op};
};
//
// @brief Makes a pointer to Argument class.
//
// @param[in] inLengths Input tensor extent(s) from high to low dimension
// @param[in] inStrides Input tensor stride(s) from high to low dimension
// @param[in] reduceDims The dimension(s) the normalization operation is applied
// @param[in] alpha Typeless pointer in host memory storing the alpha scaling
// value as type AccDataType
// @param[in] beta Typeless pointer in host memory storing the beta scaling
// value as type AccDataType
// @param[in] in_dev Typeless const pointer in device memory storing the input
// tensor
// @param out_dev Typeless pointer in device memory storing the output tensor
// @param[in] in_elementwise_op The input elementwise operation.
// @param[in] acc_elementwise_op The accumulation elementwise operation.
//
// @return Unique pointer to the Argument class.
//
///
/// @brief Makes a pointer to Argument class.
///
/// @param[in] inLengths Input tensor extent(s) from high to low dimension
/// @param[in] inStrides Input tensor stride(s) from high to low dimension
/// @param[in] reduceDims The dimension(s) the normalization operation is applied
/// @param[in] alpha Typeless pointer in host memory storing the alpha scaling
/// value as type AccDataType
/// @param[in] beta Typeless pointer in host memory storing the beta scaling
/// value as type AccDataType
/// @param[in] in_dev Typeless const pointer in device memory storing the input
/// tensor
/// @param out_dev Typeless pointer in device memory storing the output tensor
/// @param[in] in_elementwise_op The input elementwise operation.
/// @param[in] acc_elementwise_op The accumulation elementwise operation.
///
/// @return Unique pointer to the Argument class.
///
std::unique_ptr<BaseArgument> MakeArgumentPointer(const std::vector<index_t> inLengths,
const std::vector<index_t> inStrides,
const std::vector<int> reduceDims,
......
include/ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp
View file @ d254ed90
......@@ -622,30 +622,31 @@ struct OffsettedBlockToCTileMap
index_t block_start_;
};
//
// @brief Simple tile mapping which creates 3D grid of block of threads.
//
// @paragraph Description
// This Block-to-C-tile-map creates a 3D grid (n_blocks, m_blocks, z_blocks) of thread
// blocks. The first 2D are regular 2D tiles created by division of output GEMM
// dimenions by corresponding tile size. The third dimension (Z) is a k-split dimension,
// which denotes the number of blocks we use to divide work on GEMM K dimension onto.
//
// @tparam MPerBlock Output block tile size in M dimension.
// @tparam NPerBlock Output block tile size in N dimension.
//
///
/// @brief Simple tile mapping which creates 3D grid of block of threads.
///
/// @paragraph Description
/// This Block-to-C-tile-map creates a 3D grid (n_blocks, m_blocks, z_blocks) of thread
/// blocks. The first 2D are regular 2D tiles created by division of output GEMM
/// dimenions by corresponding tile size. The third dimension (Z) is a k-split
/// dimension, which denotes the number of blocks we use to divide work on GEMM K
/// dimension onto.
///
/// @tparam MPerBlock Output block tile size in M dimension.
/// @tparam NPerBlock Output block tile size in N dimension.
///
template <index_t MPerBlock, index_t NPerBlock>
struct BlockToCTileMap_3DGrid_KSplit
{
__host__ __device__ BlockToCTileMap_3DGrid_KSplit() = default;
//
// @brief Constructs a new instance.
//
// @param <unnamed> Swallow blockIdx.
//
// @tparam TopIdx The type of block index.
//
///
/// @brief Constructs a new instance.
///
/// @param <unnamed> Swallow blockIdx.
///
/// @tparam TopIdx The type of block index.
///
template <typename TopIdx>
__host__ __device__ BlockToCTileMap_3DGrid_KSplit(TopIdx)
{
......@@ -680,14 +681,14 @@ struct BlockToCTileMap_3DGrid_KSplit
}
};
//
// @brief Block to CTile Map which foster external mechanism for setting up local block id.
//
// In example this type can be easily used to implement tile looping work distribution
// scheme.
//
// @tparam UnderlyingBlockToCTileMap The type of the local tile mapp.
//
///
/// @brief Block to CTile Map which foster external mechanism for setting up local block id.
///
/// In example this type can be easily used to implement tile looping work distribution
/// scheme.
///
/// @tparam UnderlyingBlockToCTileMap The type of the local tile mapp.
///
template <typename UnderlyingBlockToCTileMap>
struct LocalBlockToCTileMap
{
......
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp
View file @ d254ed90
......@@ -14,27 +14,27 @@ namespace ck {
namespace tensor_operation {
namespace host {
//
// @brief Reference implementation for forward convolution.
//
// @paragraph
// Tensor descriptor in GNCHW/GKCXY/GNKHW dimensional order
// Supports both GNCHW/NGCHW as well as GNHWC/NHWGC physical layout
// as long as dimensions in tensor descriptor is in GNCHW order
//
// @tparam InDataType Input tensor data type.
// @tparam WeiDataType Weights tensor data type.
// @tparam OutDataType Output tensor data type.
// @tparam InElementwiseOperation Functor for input tensor elementwise
// operation.
// @tparam WeiElementwiseOperation Functor for weights tensor elementwise
// operation.
// @tparam NDimSpatial Number of spatial dimensions.
//
// input descriptor in [G, N, C, Do, Ho, Wo] order
// weight descriptor in [G, K, C, Z, Y, X] order
// output descriptor in [G, N, K, Di, Hi, Wi] order
// phyiscal layout is irrelavent
///
/// @brief Reference implementation for forward convolution.
///
/// @paragraph
/// Tensor descriptor in GNCHW/GKCXY/GNKHW dimensional order
/// Supports both GNCHW/NGCHW as well as GNHWC/NHWGC physical layout
/// as long as dimensions in tensor descriptor is in GNCHW order
///
/// @tparam InDataType Input tensor data type.
/// @tparam WeiDataType Weights tensor data type.
/// @tparam OutDataType Output tensor data type.
/// @tparam InElementwiseOperation Functor for input tensor elementwise
/// operation.
/// @tparam WeiElementwiseOperation Functor for weights tensor elementwise
/// operation.
/// @tparam NDimSpatial Number of spatial dimensions.
///
/// input descriptor in [G, N, C, Do, Ho, Wo] order
/// weight descriptor in [G, K, C, Z, Y, X] order
/// output descriptor in [G, N, K, Di, Hi, Wi] order
/// phyiscal layout is irrelavent
template <ck::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
......
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