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Unverified Commit c99323be authored by zjing14's avatar zjing14 Committed by GitHub
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Revert "Grouped Gemm with looping over the tiles. (#788)" (#982) 

This reverts commit a4f72a31.
parent a4f72a31
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client_example/17_grouped_gemm_fastgelu/CMakeLists.txt
View file @ c99323be
add_executable(client_grouped_gemm_fastgelu grouped_gemm_fastgelu.cpp) add_executable(client_grouped_gemm_fastgelu grouped_gemm_fastgelu.cpp)
target_link_libraries(client_grouped_gemm_fastgelu PRIVATE composable_kernel::device_operations) target_link_libraries(client_grouped_gemm_fastgelu PRIVATE composable_kernel::device_operations)
\ No newline at end of file
include/ck/tensor_operation/gpu/device/device_grouped_gemm_splitk.hpp
View file @ c99323be
...@@ -8,57 +8,6 @@ namespace ck { ...@@ -8,57 +8,6 @@ namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
///
/// @brief Structure representing single GEMM problem arguments.
///
/// The pointer to the vector of those structures is passed
/// to the GroupedGEMM entry point kernel.
///
struct GroupedGemmKernelArguments
{
__host__ __device__ GroupedGemmKernelArguments(const void* p_a_grid_,
const void* p_b_grid_,
void* p_c_grid_,
index_t M_,
index_t N_,
index_t K_,
index_t StrideA_,
index_t StrideB_,
index_t StrideC_)
: p_a_grid{p_a_grid_},
p_b_grid{p_b_grid_},
p_c_grid{p_c_grid_},
M{M_},
N{N_},
K{K_},
StrideA{StrideA_},
StrideB{StrideB_},
StrideC{StrideC_}
{
}
const void* p_a_grid;
const void* p_b_grid;
void* p_c_grid;
index_t M;
index_t N;
index_t K;
index_t StrideA;
index_t StrideB;
index_t StrideC;
void Print() const
{
std::cout << "arg {"
<< "M:" << M << ", "
<< "N:" << N << ", "
<< "K:" << K << ", "
<< "SA:" << StrideA << ", "
<< "SB:" << StrideB << ", "
<< "SC:" << StrideC << "}" << std::endl;
}
};
template <typename ALayout, template <typename ALayout,
typename BLayout, typename BLayout,
typename DsLayout, typename DsLayout,
...@@ -82,28 +31,7 @@ struct DeviceGroupedGemmSplitK : public DeviceGroupedGemm<ALayout, ...@@ -82,28 +31,7 @@ struct DeviceGroupedGemmSplitK : public DeviceGroupedGemm<ALayout,
BElementwiseOperation, BElementwiseOperation,
CElementwiseOperation> CElementwiseOperation>
{ {
//---------------------------------------------------------------------------------------------- virtual void SetKBatchSize(BaseArgument* p_arg, index_t kbatch) const = 0;
/// @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([[maybe_unused]] BaseArgument* p_arg,
[[maybe_unused]] 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.
///
virtual void SetDeviceKernelArgs([[maybe_unused]] BaseArgument* p_arg,
[[maybe_unused]] const void* p_dev_kernel_args) const
{
}
}; };
} // namespace device } // namespace device
......
include/ck/tensor_operation/gpu/device/device_softmax.hpp
View file @ c99323be
...@@ -22,22 +22,22 @@ template <typename InDataType, ...@@ -22,22 +22,22 @@ template <typename InDataType,
index_t NumReduceDim> index_t NumReduceDim>
struct DeviceSoftmax : public BaseOperator struct DeviceSoftmax : public BaseOperator
{ {
/// //
/// @brief Makes a pointer to Argument class. // @brief Makes a pointer to Argument class.
/// //
/// @param[in] inLengths Input tensor extent(s) from high to low dimension // @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] inStrides Input tensor stride(s) from high to low dimension
/// @param[in] reduceDims The dimension(s) the normalization operation is applied // @param[in] reduceDims The dimension(s) the normalization operation is applied
/// @param[in] alpha double type value // @param[in] alpha double type value
/// @param[in] beta double type value // @param[in] beta double type value
/// @param[in] in_dev Typeless const pointer in device memory storing the input // @param[in] in_dev Typeless const pointer in device memory storing the input
/// tensor // tensor
/// @param out_dev Typeless pointer in device memory storing the output tensor // @param out_dev Typeless pointer in device memory storing the output tensor
/// @param[in] in_elementwise_op The input elementwise operation. // @param[in] in_elementwise_op The input elementwise operation.
/// @param[in] acc_elementwise_op The accumulation elementwise operation. // @param[in] acc_elementwise_op The accumulation elementwise operation.
/// //
/// @return Unique pointer to the Argument class. // @return Unique pointer to the Argument class.
/// //
virtual std::unique_ptr<BaseArgument> virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const std::vector<index_t> inLengths, MakeArgumentPointer(const std::vector<index_t> inLengths,
const std::vector<index_t> inStrides, const std::vector<index_t> inStrides,
......
include/ck/tensor_operation/gpu/device/impl/device_gemm_xdl_splitk_c_shuffle.hpp
View file @ c99323be
...@@ -168,7 +168,7 @@ struct DeviceGemmXdlSplitKCShuffle : public DeviceGemmSplitK<ALayout, ...@@ -168,7 +168,7 @@ struct DeviceGemmXdlSplitKCShuffle : public DeviceGemmSplitK<ALayout,
stream_config.stream_id_)); stream_config.stream_id_));
ave_time = launch_and_time_kernel( ave_time = launch_and_time_kernel(
stream_config, kernel, dim3(gdx, gdy, gdz), dim3(BlockSize), 0, karg); stream_config, kernel, dim3(gdx, gdy, gdz), dim3(BlockSize), 0, karg, b2c_map);
}; };
if(has_main_k0_block_loop) if(has_main_k0_block_loop)
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp
View file @ c99323be
...@@ -157,22 +157,22 @@ __global__ void ...@@ -157,22 +157,22 @@ __global__ void
} }
} // namespace } // namespace
/// //
/// @brief Device Convolution operation. // @brief Device Convolution operation.
/// //
/// Supports: // Supports:
/// @li Forward convolution with up to 3 spatial dimentions // @li Forward convolution with up to 3 spatial dimentions
/// @li Input tensor in GNWC data format // @li Input tensor in GNWC data format
/// @li Weight tensor in GKXC data format // @li Weight tensor in GKXC data format
/// @li Output tensor in GNWK data format // @li Output tensor in GNWK data format
/// //
/// 1D: // 1D:
/// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C] // out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
/// 2D: // 2D:
/// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C] // out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
/// 3D: // 3D:
/// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C] // out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
/// //
template <index_t NDimSpatial, template <index_t NDimSpatial,
typename ADataType, typename ADataType,
typename BDataType, typename BDataType,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_nhwc_kyxc_nhwk.hpp
View file @ c99323be
...@@ -154,22 +154,22 @@ __global__ void ...@@ -154,22 +154,22 @@ __global__ void
} // namespace } // namespace
/// //
/// @brief Device Convolution operation. // @brief Device Convolution operation.
/// //
/// Supports: // Supports:
/// @li Forward convolution with up to 3 spatial dimentions // @li Forward convolution with up to 3 spatial dimentions
/// @li Input tensor in GNWC data format // @li Input tensor in GNWC data format
/// @li Weight tensor in GKXC data format // @li Weight tensor in GKXC data format
/// @li Output tensor in GNWK data format // @li Output tensor in GNWK data format
/// //
/// 1D: // 1D:
/// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C] // out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
/// 2D: // 2D:
/// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C] // out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
/// 3D: // 3D:
/// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C] // out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
/// //
template < template <
index_t NDimSpatial, index_t NDimSpatial,
typename ADataType, typename ADataType,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_xdl_cshuffle.hpp
View file @ c99323be
...@@ -150,22 +150,22 @@ __global__ void ...@@ -150,22 +150,22 @@ __global__ void
} // namespace } // namespace
/// //
/// @brief Device Convolution operation. // @brief Device Convolution operation.
/// //
/// Supports: // Supports:
/// @li Forward convolution with up to 3 spatial dimentions // @li Forward convolution with up to 3 spatial dimentions
/// @li Input tensor in GNWC data format // @li Input tensor in GNWC data format
/// @li Weight tensor in GKXC data format // @li Weight tensor in GKXC data format
/// @li Output tensor in GNWK data format // @li Output tensor in GNWK data format
/// //
/// 1D: // 1D:
/// out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C] // out[N, Wo, K] = in[N, Wi, C] * wei[K, X, C]
/// 2D: // 2D:
/// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C] // out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
/// 3D: // 3D:
/// out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C] // out[N, Do, Ho, Wo, K] = in[N, Di, Hi, Wi, C] * wei[K, Z, Y, X, C]
/// //
template <index_t NDimSpatial, template <index_t NDimSpatial,
typename ALayout, typename ALayout,
typename BLayout, typename BLayout,
......
include/ck/tensor_operation/gpu/device/impl/device_softmax_impl.hpp
View file @ c99323be
...@@ -348,24 +348,24 @@ struct DeviceSoftmaxImpl : public DeviceSoftmax<InDataType, ...@@ -348,24 +348,24 @@ struct DeviceSoftmaxImpl : public DeviceSoftmax<InDataType,
acc_elementwise_op}; acc_elementwise_op};
}; };
/// //
/// @brief Makes a pointer to Argument class. // @brief Makes a pointer to Argument class.
/// //
/// @param[in] inLengths Input tensor extent(s) from high to low dimension // @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] inStrides Input tensor stride(s) from high to low dimension
/// @param[in] reduceDims The dimension(s) the normalization operation is applied // @param[in] reduceDims The dimension(s) the normalization operation is applied
/// @param[in] alpha Typeless pointer in host memory storing the alpha scaling // @param[in] alpha Typeless pointer in host memory storing the alpha scaling
/// value as type AccDataType // value as type AccDataType
/// @param[in] beta Typeless pointer in host memory storing the beta scaling // @param[in] beta Typeless pointer in host memory storing the beta scaling
/// value as type AccDataType // value as type AccDataType
/// @param[in] in_dev Typeless const pointer in device memory storing the input // @param[in] in_dev Typeless const pointer in device memory storing the input
/// tensor // tensor
/// @param out_dev Typeless pointer in device memory storing the output tensor // @param out_dev Typeless pointer in device memory storing the output tensor
/// @param[in] in_elementwise_op The input elementwise operation. // @param[in] in_elementwise_op The input elementwise operation.
/// @param[in] acc_elementwise_op The accumulation elementwise operation. // @param[in] acc_elementwise_op The accumulation elementwise operation.
/// //
/// @return Unique pointer to the Argument class. // @return Unique pointer to the Argument class.
/// //
std::unique_ptr<BaseArgument> MakeArgumentPointer(const std::vector<index_t> inLengths, std::unique_ptr<BaseArgument> MakeArgumentPointer(const std::vector<index_t> inLengths,
const std::vector<index_t> inStrides, const std::vector<index_t> inStrides,
const std::vector<int> reduceDims, const std::vector<int> reduceDims,
......
include/ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp
View file @ c99323be
...@@ -271,8 +271,7 @@ struct BlockToCTileMap_KSplit_M00_N0_M01Adapt ...@@ -271,8 +271,7 @@ struct BlockToCTileMap_KSplit_M00_N0_M01Adapt
{ {
} }
__host__ __device__ constexpr index_t __host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
{ {
const auto M0 = math::integer_divide_ceil(c_grid_desc_m_n.GetLength(I0), MPerBlock); const auto M0 = math::integer_divide_ceil(c_grid_desc_m_n.GetLength(I0), MPerBlock);
const auto N0 = math::integer_divide_ceil(c_grid_desc_m_n.GetLength(I1), NPerBlock); const auto N0 = math::integer_divide_ceil(c_grid_desc_m_n.GetLength(I1), NPerBlock);
...@@ -625,35 +624,23 @@ struct OffsettedBlockToCTileMap ...@@ -625,35 +624,23 @@ struct OffsettedBlockToCTileMap
index_t block_start_; index_t block_start_;
}; };
/// /**
/// @brief Simple tile mapping which creates 3D grid of block of threads. * @brief Simple tile mapping which creates 3D grid of block of threads.
/// *
/// @paragraph Description * @paragraph Description
/// This Block-to-C-tile-map creates a 3D grid (n_blocks, m_blocks, z_blocks) of thread * 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 * 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 * dimenions by corresponding tile size. The third dimension (Z) is a k-split dimension,
/// dimension, which denotes the number of blocks we use to divide work on GEMM K * which denotes the number of blocks we use to divide work on GEMM K dimension onto.
/// dimension onto. *
/// * @tparam MPerBlock Output block tile size in M dimension.
/// @tparam MPerBlock Output block tile size in M dimension. * @tparam NPerBlock Output block tile size in N dimension.
/// @tparam NPerBlock Output block tile size in N dimension. */
///
template <index_t MPerBlock, index_t NPerBlock> template <index_t MPerBlock, index_t NPerBlock>
struct BlockToCTileMap_3DGrid_KSplit struct BlockToCTileMap_3DGrid_KSplit
{ {
__host__ __device__ BlockToCTileMap_3DGrid_KSplit() = default;
/// __host__ __device__ BlockToCTileMap_3DGrid_KSplit() = default;
/// @brief Constructs a new instance.
///
/// @param[in] top_idx Swallow blockIdx.
///
/// @tparam TopIdx The type of block index.
///
template <typename TopIdx>
__host__ __device__ BlockToCTileMap_3DGrid_KSplit([[maybe_unused]] TopIdx top_idx)
{
}
__host__ __device__ constexpr auto __host__ __device__ constexpr auto
CalculateGridSize(index_t M, index_t N, index_t k_split) const CalculateGridSize(index_t M, index_t N, index_t k_split) const
...@@ -665,7 +652,8 @@ struct BlockToCTileMap_3DGrid_KSplit ...@@ -665,7 +652,8 @@ struct BlockToCTileMap_3DGrid_KSplit
return std::make_tuple(N0, M0, k_split); return std::make_tuple(N0, M0, k_split);
} }
__device__ constexpr auto CalculateBottomIndex() const template <typename TopIdx>
__device__ constexpr auto CalculateBottomIndex(const TopIdx&) const
{ {
return make_tuple(blockIdx.z, blockIdx.y, blockIdx.x); return make_tuple(blockIdx.z, blockIdx.y, blockIdx.x);
} }
...@@ -684,53 +672,6 @@ struct BlockToCTileMap_3DGrid_KSplit ...@@ -684,53 +672,6 @@ 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.
///
template <typename UnderlyingBlockToCTileMap>
struct LocalBlockToCTileMap
{
using underlying_type = UnderlyingBlockToCTileMap;
__host__ __device__ LocalBlockToCTileMap(UnderlyingBlockToCTileMap block_to_ctile_map,
index_t local_id)
: block_to_ctile_map_{block_to_ctile_map}, local_block_id_{local_id}
{
}
__host__ __device__ constexpr auto CalculateBottomIndex() const
{
return block_to_ctile_map_.CalculateBottomIndex(make_multi_index(local_block_id_));
}
template <typename CTileIdx, typename CTileDim>
__host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
const CTileDim& c_tile_dim) const
{
return block_to_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
}
template <typename CGridDesc_M_N>
__host__ bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
{
return block_to_ctile_map_.CheckValidity(c_grid_desc_m_n);
}
template <typename CGridDesc_M_N>
__host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
{
return block_to_ctile_map_.CalculateGridSize(c_grid_desc_m_n);
}
UnderlyingBlockToCTileMap block_to_ctile_map_;
index_t local_block_id_;
};
enum StreamKReductionStrategy enum StreamKReductionStrategy
{ {
Atomic = 0, // sk block use atomic to do reduction Atomic = 0, // sk block use atomic to do reduction
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp
View file @ c99323be
...@@ -4,8 +4,6 @@ ...@@ -4,8 +4,6 @@
#pragma once #pragma once
#include <iostream> #include <iostream>
#include <ostream>
#include <string>
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp" #include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v2.hpp" #include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v2.hpp"
...@@ -44,20 +42,4 @@ constexpr auto GridwiseGemmPipeline_Selector() ...@@ -44,20 +42,4 @@ constexpr auto GridwiseGemmPipeline_Selector()
} }
} }
inline std::string getPipelineVersionString(const PipelineVersion& pv)
{
switch(pv)
{
case PipelineVersion::v1: return "PipelineVersion::v1";
case PipelineVersion::v2: return "PipelineVersion::v2";
default: return "Unrecognized pipeline version!";
}
}
} // namespace ck } // namespace ck
inline std::ostream& operator<<(std::ostream& os, const ck::PipelineVersion pv)
{
os << ck::getPipelineVersionString(pv);
return os;
}
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r4r2.hpp
View file @ c99323be
...@@ -27,7 +27,8 @@ __global__ void ...@@ -27,7 +27,8 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS #if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU) __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif #endif
kernel_gemm_xdlops_v2r4r2_simplified(typename GridwiseGemm::Argument karg) kernel_gemm_xdlops_v2r4r2_simplified(typename GridwiseGemm::Argument karg,
const Block2CTileMap& b2c_map)
{ {
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \ #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)) defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
...@@ -35,12 +36,11 @@ __global__ void ...@@ -35,12 +36,11 @@ __global__ void
__shared__ uint8_t p_shared[shared_size]; __shared__ uint8_t p_shared[shared_size];
Block2CTileMap b2c_map{get_block_1d_id()};
GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation>( GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation>(
karg, static_cast<void*>(p_shared), b2c_map); karg, static_cast<void*>(p_shared), b2c_map);
#else #else
ignore = karg; ignore = karg;
ignore = b2c_map;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__)) #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
} }
...@@ -541,6 +541,15 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2 ...@@ -541,6 +541,15 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{})); make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
} }
// return block_id to C matrix tile idx (m0, n0) mapping
template <typename CGridDesc>
__host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
const CGridDesc& c_m_n_grid_desc, index_t /* M01 */, index_t /* N01 */, index_t KBatch)
{
return BlockToCTileMap_KSplit_M00_N0_M01Adapt<MPerBlock, NPerBlock, CGridDesc>(
c_m_n_grid_desc, 8, KBatch);
}
__host__ __device__ static constexpr auto __host__ __device__ static constexpr auto
GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock() GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
{ {
...@@ -566,28 +575,18 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2 ...@@ -566,28 +575,18 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
template <bool HasMainKBlockLoop, template <bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation, InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename Block2CTileMap> typename Block2CTileMap>
__device__ static void Run(const FloatA* p_a_grid, __device__ static void Run(const Argument& karg,
const FloatB* p_b_grid,
FloatC* p_c_grid,
index_t M,
index_t N,
index_t K,
index_t StrideA,
index_t StrideB,
index_t StrideC,
index_t MPadded,
index_t NPadded,
index_t KPadded,
index_t K0,
index_t k_batch,
void* __restrict__ p_shared_block, void* __restrict__ p_shared_block,
const Block2CTileMap& block_2_ctile_map) const Block2CTileMap& block_2_ctile_map)
{ {
const auto a_b_k0_m_k1_grid_desc = const FloatA* p_a_grid = karg.p_a_grid;
MakeAGridDescriptor_KBatch_K0_M_K1(M, MPadded, K, StrideA, k_batch, K0, KPadded); const FloatB* p_b_grid = karg.p_b_grid;
const auto b_b_k0_n_k1_grid_desc = FloatC* p_c_grid = karg.p_c_grid;
MakeBGridDescriptor_KBatch_K0_N_K1(K, NPadded, N, StrideB, k_batch, K0, KPadded); const auto a_b_k0_m_k1_grid_desc = MakeAGridDescriptor_KBatch_K0_M_K1(
const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N(M, N, StrideC); karg.M, karg.MPadded, karg.K, karg.StrideA, karg.k_batch, karg.K0, karg.KPadded);
const auto b_b_k0_n_k1_grid_desc = MakeBGridDescriptor_KBatch_K0_N_K1(
karg.K, karg.NPadded, karg.N, karg.StrideB, karg.k_batch, karg.K0, karg.KPadded);
const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N(karg.M, karg.N, karg.StrideC);
const auto c_grid_desc_mblock_mperblock_nblock_nperblock = const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n); MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n);
...@@ -603,7 +602,8 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2 ...@@ -603,7 +602,8 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize()); p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
// divide block work by [KBatch, M, N] // divide block work by [KBatch, M, N]
const auto block_work_idx = block_2_ctile_map.CalculateBottomIndex(); const auto block_work_idx =
block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
if(!block_2_ctile_map.ValidCTileIndex( if(!block_2_ctile_map.ValidCTileIndex(
block_work_idx, block_work_idx,
...@@ -1010,34 +1010,6 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2 ...@@ -1010,34 +1010,6 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
} }
} }
template <bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename Block2CTileMap>
__device__ static void Run(const Argument& karg,
void* __restrict__ p_shared_block,
const Block2CTileMap& block_2_ctile_map)
{
Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, Block2CTileMap>(karg.p_a_grid,
karg.p_b_grid,
karg.p_c_grid,
karg.M,
karg.N,
karg.K,
karg.StrideA,
karg.StrideB,
karg.StrideC,
karg.MPadded,
karg.NPadded,
karg.KPadded,
karg.K0,
karg.k_batch,
p_shared_block,
block_2_ctile_map);
}
static constexpr auto GetMPerBlock() { return MPerBlock; }
static constexpr auto GetNPerBlock() { return NPerBlock; }
static std::string GetTypeString() static std::string GetTypeString()
{ {
auto str = std::stringstream(); auto str = std::stringstream();
......
include/ck/utility/sequence.hpp
View file @ c99323be
...@@ -897,14 +897,3 @@ template <index_t NSize, index_t I> ...@@ -897,14 +897,3 @@ template <index_t NSize, index_t I>
using uniform_sequence_gen_t = typename uniform_sequence_gen<NSize, I>::type; using uniform_sequence_gen_t = typename uniform_sequence_gen<NSize, I>::type;
} // namespace ck } // namespace ck
template <ck::index_t... Is>
std::ostream& operator<<(std::ostream& os, const ck::Sequence<Is...>)
{
using S = ck::Sequence<Is...>;
os << "{";
ck::static_for<0, S::Size() - ck::Number<1>{}, 1>{}(
[&](auto i) { os << S::At(i).value << ", "; });
os << S::At(S::Size() - ck::Number<1>{}).value << "}";
return os;
}
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp
View file @ c99323be
...@@ -14,27 +14,27 @@ namespace ck { ...@@ -14,27 +14,27 @@ namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace host { namespace host {
/// //
/// @brief Reference implementation for forward convolution. // @brief Reference implementation for forward convolution.
/// //
/// @paragraph // @paragraph
/// Tensor descriptor in GNCHW/GKCXY/GNKHW dimensional order // Tensor descriptor in GNCHW/GKCXY/GNKHW dimensional order
/// Supports both GNCHW/NGCHW as well as GNHWC/NHWGC physical layout // Supports both GNCHW/NGCHW as well as GNHWC/NHWGC physical layout
/// as long as dimensions in tensor descriptor is in GNCHW order // as long as dimensions in tensor descriptor is in GNCHW order
/// //
/// @tparam InDataType Input tensor data type. // @tparam InDataType Input tensor data type.
/// @tparam WeiDataType Weights tensor data type. // @tparam WeiDataType Weights tensor data type.
/// @tparam OutDataType Output tensor data type. // @tparam OutDataType Output tensor data type.
/// @tparam InElementwiseOperation Functor for input tensor elementwise // @tparam InElementwiseOperation Functor for input tensor elementwise
/// operation. // operation.
/// @tparam WeiElementwiseOperation Functor for weights tensor elementwise // @tparam WeiElementwiseOperation Functor for weights tensor elementwise
/// operation. // operation.
/// @tparam NDimSpatial Number of spatial dimensions. // @tparam NDimSpatial Number of spatial dimensions.
/// //
/// input descriptor in [G, N, C, Do, Ho, Wo] order // input descriptor in [G, N, C, Do, Ho, Wo] order
/// weight descriptor in [G, K, C, Z, Y, X] order // weight descriptor in [G, K, C, Z, Y, X] order
/// output descriptor in [G, N, K, Di, Hi, Wi] order // output descriptor in [G, N, K, Di, Hi, Wi] order
/// phyiscal layout is irrelavent // phyiscal layout is irrelavent
template <ck::index_t NDimSpatial, template <ck::index_t NDimSpatial,
typename InDataType, typename InDataType,
typename WeiDataType, typename WeiDataType,
......
script/cmake-ck-dev.sh
View file @ c99323be
...@@ -12,9 +12,7 @@ cmake ...@@ -12,9 +12,7 @@ cmake
-save-temps=$PWD" \ -save-temps=$PWD" \
-D CMAKE_BUILD_TYPE=Release \ -D CMAKE_BUILD_TYPE=Release \
-D BUILD_DEV=ON \ -D BUILD_DEV=ON \
-D GPU_TARGETS="gfx90a" \ -D GPU_TARGETS="gfx908;gfx90a;gfx940" \
-D CMAKE_VERBOSE_MAKEFILE:BOOL=ON \ -D CMAKE_VERBOSE_MAKEFILE:BOOL=ON \
-D USE_BITINT_EXTENSION_INT4=OFF \ -D USE_BITINT_EXTENSION_INT4=OFF \
${MY_PROJECT_SOURCE} ${MY_PROJECT_SOURCE}
#-D GPU_TARGETS="gfx908;gfx90a;gfx940" \
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