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0df06e585cbcc20752cc090198c95a5fa2c345c2
composable_kernel/device_operation/include/device_reduce_multiblock_partial_reduce.hpp
Qianfeng 0df06e585c Reduction in Composable Kernel (#82) 
* Initial adding of generic reduction

* Initial adding of generic reduction ...

* Updates to make compiling done

* clang-format all files

* clang-format some files again

* Renaming in profiler/include/profile_reduce.hpp

* Updates and make BlockWise cases passed

* Updates and make ThreadWise and MultiBlockTwoCall cases passed

* Remove the support for MUL and NORM1 reduceOp from the profiler and the device instances

* Change to replace the dim0_max_vector_size/dim1_max_vector_size template argument in the device reduce classes

* format

* adding pooling

* added max and average pooling

* comment out cout and kernel timing

* Tiny simplification in profiler/reduce_profiler.cpp

* Add example for reduce_blockwise

* Tiny updates

* Change to pass the ElementWiseOp from device layer to kernel

* Fix the vectorDim and vectorSize in Device layer

* Enable vector load on both dim0 and dim1 for Threadwise method

* Tiny updates

* Change to let the user to pass the preUnaryOp and posUnaryOp

* Make pooling example work

* split device_reduce_instance into two libraries

* Tiny update

* Replace nanPropaOpt enum by boolean propagate_nan

* Simplification in DeviceReduce layer codes

* update build

* Change to clarify the difference between ck::half_t and half_float::half

* Renaming in all the reduction codes

* Add VectorSize as template parameter for device layer

* Add BetaIsZero as kernel template and as AccDataType for alpha

* print

* Small updates for pooling

* Updates for host_generic_reduction for reference

* Update to make AVG pooling pass

* Update to make MAX pooling with indices output pass

* fix

* add OutDst vector store to threadwise reduction and pooling

* tweak

* turn off check_indices that caused build issue

* refactor pooling

* clean up

* turn off check_indices for building issue for php-compiler

* add more tile size for odd C

* tweak conv for odd C

* update script

* clean up elementwise op

* add hack in reduction_operator.hpp to avoid compile error. To fix it, need to use element_wise_op in reduction op

* Add OutVectorSize as device and kernel tunable, also update to Elementwise Operations

* Move reduce operator mapping to host layer file reduction_operator_mapping.hpp from reduction_operator.hpp

* Change to the unary operators

* Move the definitions of unary operations to element_wise_operation.hpp

* re-org files

* Refine in device interfaces and multiblock kernels

* Split the reduction configurations into instances for specific methods

* Update in getTypeString() of device pool2d

* Renaming in host and kernel

* Tiny update in profiler/src/profiler.cpp

* Uncomment in device_operation/CMakeLists.txt to enable the building of all operations

* Make check_indices a templated function to remove some linking issue

* Renaming in the profiler reduce module

* Add support for double Reduction (but disable MultiblockAtomicAdd for double)

* Tiny correction of literal string

* Rename DevicePoolFwd to DevicePool2dFwd

* Split device_reduce_instance_xxx.cpp files according to the data types to speed up compiling

* Add comments for lists of configurations, lists of instances and references of add_reduce_instances_xxx

* Remove un-used header file gridwise_generic_reduction_wrapper_common.hpp

* Renaming and refining in the Reduction codes

* Tiny change in the unary operators

* Renaming symbols and files

* Renaming symbols in the kernels

* Move kernel kernel_set_buffer_value to separate file

* Add IndexDataType template parameter for kernels and use int32_t as index data type in device layer

* Tiny update in the kernels

* Remove definition of sqrtf()/isnan()/abs() for half_t due to some ADL issue

* Simplify a helper function in device layer

* Tiny adjustment in testing data initialization

* Renaming in kernel/device/host

* Add two testing scripts for reduction

* Refine the Unary operators in element_wise_operation.hpp

* Update in the reduce profiler module

* Update to the reduction testing scripts

* reduce compile parallelism

* change CI docker to rocm5.0

* remove unused variables

* fix build

Co-authored-by: Chao Liu <chao.liu2@amd.com>

[ROCm/composable_kernel commit: e17c0d8008]
2022-03-05 16:46:51 -06:00

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#ifndef DEVICE_REDUCE_MULTIBLOCK_PARTIAL_REDUCE_HPP
#define DEVICE_REDUCE_MULTIBLOCK_PARTIAL_REDUCE_HPP
#include <iostream>
#include <sstream>
#include "device.hpp"
#include "device_reduce.hpp"
#include "device_reduce_common.hpp"
#include "gridwise_2d_reduction_multiblock_partial_reduce.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <typename InDataType,
typename AccDataType,
typename OutDataType,
int Rank,
typename ReduceDims,
typename ReduceOperation,
typename InElementwiseOperation,
typename AccElementwiseOperation,
bool PropagateNan,
bool NeedIndices,
index_t BlockSize,
index_t MThreadClusterSize,
index_t KThreadClusterSize,
index_t MThreadSliceSize,
index_t KThreadSliceSize,
index_t InSrcVectorDim,
index_t InSrcVectorSize,
index_t OutDstVectorSize>
struct DeviceReduceMultiBlockPartialReduce
: public DeviceReduce<InElementwiseOperation, AccElementwiseOperation>
{
static_assert(Rank <= 6, "Bigger Rank size is not supported!");
static_assert(BlockSize == MThreadClusterSize * KThreadClusterSize,
"Invalid thread cluster size assignments!");
static_assert(OutDstVectorSize == 1, "OutDstVectorSize must be 1 for MultiBlockPartialReduce!");
using IndexDataType = int32_t;
using InvariantDims = decltype(get_invariant_dims<Rank, ReduceDims>());
static constexpr index_t srcDims = Rank;
static constexpr index_t dstDims = (InvariantDims::Size() == 0) ? 1 : InvariantDims::Size();
static constexpr bool reduceAllDims = (InvariantDims::Size() == 0);
static constexpr int M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
static constexpr int K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
size_t GetWorkspaceSizeInBytes(const std::vector<int>& inLengths) override
{
size_t invariant_total_length;
size_t reduce_total_length;
std::tie(invariant_total_length, reduce_total_length) =
get_2d_lengths<Rank, ReduceDims>(inLengths);
int iterations = 1;
while(true)
{
int testBlkGroupSize = (reduce_total_length + (K_BlockTileSize * iterations) - 1) /
(K_BlockTileSize * iterations);
// we want the blkGroupSize be not more than 128
if(testBlkGroupSize <= 128)
break;
iterations++;
};
int blkGroupSize = (reduce_total_length + (K_BlockTileSize * iterations) - 1) /
(K_BlockTileSize * iterations);
size_t workspace_size = invariant_total_length * blkGroupSize;
size_t wsSizeInBytes =
!NeedIndices ? workspace_size * sizeof(AccDataType)
: workspace_size * (sizeof(AccDataType) + sizeof(int)) + 64 + sizeof(int);
return (wsSizeInBytes);
};
bool HasFurtherCall() override { return (true); };
static auto MakeSrc2dDescriptor(const std::vector<int>& inLengths,
const std::vector<int>& inStrides,
int blkGroupSize,
int kBlockTileIterations)
{
const auto tupleSrcLengths = make_tuple_from_array(inLengths, Number<srcDims>{});
const auto tupleSrcStrides = make_tuple_from_array(inStrides, Number<srcDims>{});
const auto inDesc = make_naive_tensor_descriptor(tupleSrcLengths, tupleSrcStrides);
const auto in_grid_desc_m_k = [&]() {
if constexpr(reduceAllDims)
{
const auto one_dim_inDesc = transform_tensor_descriptor(
inDesc,
make_tuple(make_merge_transform(tupleSrcLengths)),
make_tuple(typename arithmetic_sequence_gen<0, srcDims, 1>::type{}),
make_tuple(Sequence<0>{}));
return transform_tensor_descriptor(one_dim_inDesc,
make_tuple(make_unmerge_transform(make_tuple(
1, one_dim_inDesc.GetLength(Number<0>{})))),
make_tuple(Sequence<0>{}),
make_tuple(Sequence<0, 1>{}));
}
else
{
const auto toReduceDimLengths =
make_tuple_from_array_and_index_seq(inLengths, ReduceDims{});
const auto invariantDimLengths =
make_tuple_from_array_and_index_seq(inLengths, InvariantDims{});
return transform_tensor_descriptor(
inDesc,
make_tuple(make_merge_transform(invariantDimLengths),
make_merge_transform(toReduceDimLengths)),
make_tuple(InvariantDims{}, ReduceDims{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}
}();
const auto outerLen = in_grid_desc_m_k.GetLength(Number<0>{});
const auto innerLen = in_grid_desc_m_k.GetLength(Number<1>{});
const int reduceSizePerBlock = K_BlockTileSize * kBlockTileIterations;
const auto inPad_M = math::integer_least_multiple(outerLen, M_BlockTileSize) - outerLen;
const auto inPad_K = reduceSizePerBlock * blkGroupSize - innerLen;
auto in_grid_desc_m_k_padded =
transform_tensor_descriptor(in_grid_desc_m_k,
make_tuple(make_right_pad_transform(outerLen, inPad_M),
make_right_pad_transform(innerLen, inPad_K)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return (in_grid_desc_m_k_padded);
};
static auto MakeWorkspace2dDescriptor(int outerLen, int blkGroupSize)
{
auto ws_desc_m_k = make_naive_tensor_descriptor_packed(make_tuple(outerLen, blkGroupSize));
const auto wsPad = math::integer_least_multiple(outerLen, M_BlockTileSize) - outerLen;
auto ws_desc_m_k_padded =
transform_tensor_descriptor(ws_desc_m_k,
make_tuple(make_right_pad_transform(outerLen, wsPad),
make_pass_through_transform(blkGroupSize)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return (ws_desc_m_k_padded);
};
struct Argument : public BaseArgument
{
Argument(const std::vector<index_t>& inLengths,
const std::vector<index_t>& inStrides,
const std::vector<index_t>& outLengths,
const std::vector<index_t>& outStrides,
float alpha,
float beta,
const InDataType* in_dev,
OutDataType* out_dev,
IndexDataType* out_indices_dev,
AccDataType* workspace_dev,
const InElementwiseOperation& in_elementwise_op,
const AccElementwiseOperation& acc_elementwise_op)
: in_dev_{in_dev},
out_dev_{out_dev},
out_indices_dev_{out_indices_dev},
workspace_dev_{workspace_dev}
{
inLengths_ = inLengths;
inStrides_ = inStrides;
outLengths_ = outLengths;
outStrides_ = outStrides;
in_elementwise_op_ = in_elementwise_op;
acc_elementwise_op_ = acc_elementwise_op;
alpha_ = static_cast<AccDataType>(alpha);
beta_ = static_cast<OutDataType>(beta);
std::tie(invariant_total_length, reduce_total_length) =
get_2d_lengths<Rank, ReduceDims>(inLengths);
if constexpr(InvariantDims::Size() == 0)
invariant_lowest_length = 1;
else
invariant_lowest_length = inLengths[InvariantDims::At(InvariantDims::Size() - 1)];
reduce_lowest_length = inLengths[ReduceDims::At(ReduceDims::Size() - 1)];
int iterations = 1;
while(true)
{
int testBlkGroupSize = (reduce_total_length + (K_BlockTileSize * iterations) - 1) /
(K_BlockTileSize * iterations);
// we want the blkGroupSize be not more than 128
if(testBlkGroupSize <= 128)
break;
iterations++;
};
blkGroupSize = (reduce_total_length + (K_BlockTileSize * iterations) - 1) /
(K_BlockTileSize * iterations);
kBlockTileIterations = iterations;
gridSize = math::integer_least_multiple(invariant_total_length, M_BlockTileSize) /
M_BlockTileSize * blkGroupSize;
size_t ws_buf2_bytes_offset = math::integer_least_multiple(
invariant_total_length * blkGroupSize * sizeof(AccDataType), 64);
if constexpr(NeedIndices)
workspace_indices_dev_ = reinterpret_cast<int*>(
reinterpret_cast<char*>(workspace_dev_) + ws_buf2_bytes_offset);
else
workspace_indices_dev_ = nullptr;
}
std::vector<int> inLengths_;
std::vector<int> inStrides_;
std::vector<int> outLengths_;
std::vector<int> outStrides_;
AccDataType alpha_;
OutDataType beta_;
const InDataType* in_dev_;
OutDataType* out_dev_;
IndexDataType* out_indices_dev_;
AccDataType* workspace_dev_;
IndexDataType* workspace_indices_dev_;
InElementwiseOperation in_elementwise_op_;
AccElementwiseOperation acc_elementwise_op_;
int invariant_lowest_length;
int reduce_lowest_length;
size_t invariant_total_length;
size_t reduce_total_length;
index_t blkGroupSize;
index_t kBlockTileIterations;
size_t gridSize;
};
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, int nrepeat = 1)
{
const auto in_grid_desc_m_k = DeviceReduceMultiBlockPartialReduce::MakeSrc2dDescriptor(
arg.inLengths_, arg.inStrides_, arg.blkGroupSize, arg.kBlockTileIterations);
const auto ws_desc_m_k = DeviceReduceMultiBlockPartialReduce::MakeWorkspace2dDescriptor(
arg.invariant_total_length, arg.blkGroupSize);
using InGridDesc_M_K = decltype(in_grid_desc_m_k);
using WorkspaceDesc_M_K = decltype(ws_desc_m_k);
using GridwiseReduce =
GridwiseReduction_mk_to_mk_multiblock_partial_reduce<InDataType,
AccDataType,
IndexDataType,
InGridDesc_M_K,
WorkspaceDesc_M_K,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
PropagateNan,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
InSrcVectorDim,
InSrcVectorSize,
OutDstVectorSize>;
float avg_time = 0;
const auto kernel = kernel_partial_reduce_multiblock<GridwiseReduce,
NeedIndices,
InDataType,
AccDataType,
IndexDataType,
InGridDesc_M_K,
WorkspaceDesc_M_K,
InElementwiseOperation,
AccElementwiseOperation>;
avg_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(arg.gridSize),
dim3(BlockSize),
0,
in_grid_desc_m_k,
ws_desc_m_k,
arg.in_elementwise_op_,
arg.acc_elementwise_op_,
arg.blkGroupSize,
arg.kBlockTileIterations,
arg.in_dev_,
arg.workspace_dev_,
arg.workspace_indices_dev_);
return (avg_time);
};
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
};
};
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
if constexpr(OutDstVectorSize != 1)
return (false);
if constexpr(InSrcVectorDim == 0)
{
if constexpr(InvariantDims::Size() == 0)
return (false);
if(pArg->inStrides_[InvariantDims::At(InvariantDims::Size() - 1)] != 1)
return (false);
if(pArg->invariant_lowest_length % InSrcVectorSize != 0)
return (false);
}
else
{
if(pArg->inStrides_[ReduceDims::At(ReduceDims::Size() - 1)] != 1)
return (false);
if(pArg->reduce_lowest_length % InSrcVectorSize != 0)
return (false);
};
// cases with small reduce_total_length should be handled by the BlockWise method
if(pArg->reduce_total_length <= BlockSize * KThreadSliceSize)
return (false);
return (true);
};
std::vector<int> GetWorkspace2dLengths(const BaseArgument* p_arg) override
{
const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
return (
std::vector<int>{static_cast<int>(pArg->invariant_total_length), pArg->blkGroupSize});
};
std::unique_ptr<BaseArgument>
MakeArgumentPointer(const std::vector<int>& inLengths,
const std::vector<int>& inStrides,
const std::vector<int>& outLengths,
const std::vector<int>& outStrides,
float alpha,
float beta,
const void* in_dev,
void* out_dev,
void* out_indices_dev,
void* workspace_dev,
const InElementwiseOperation& in_elementwise_op,
const AccElementwiseOperation& acc_elementwise_op) override
{
return std::make_unique<Argument>(inLengths,
inStrides,
outLengths,
outStrides,
alpha,
beta,
static_cast<const InDataType*>(in_dev),
static_cast<OutDataType*>(out_dev),
static_cast<IndexDataType*>(out_indices_dev),
static_cast<AccDataType*>(workspace_dev),
in_elementwise_op,
acc_elementwise_op);
};
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
{
return std::make_unique<Invoker>();
};
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceReduceMultiBlockPartialReduce<" << BlockSize << ",";
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
str << "InSrcVectorDim_" << InSrcVectorDim << "_InSrcVectorSize_" << InSrcVectorSize << "_OutDstVectorSize_" << OutDstVectorSize << ">";
// clang-format on
return str.str();
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
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