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https://github.com/ROCm/composable_kernel.git
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b13f7b1861
* 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]
82 lines
2.3 KiB
C++
82 lines
2.3 KiB
C++
#ifndef DEVICE_REDUCE_COMMON_HPP
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#define DEVICE_REDUCE_COMMON_HPP
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#include <vector>
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#include "common_header.hpp"
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#include "reduction_enums.hpp"
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#include "reduction_operator.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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// template <typename preUnaryOpType, typename posUnaryOpType>
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// using DeviceReducePtr = std::unique_ptr<DeviceReduce<preUnaryOpType, posUnaryOpType>>;
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template <int Rank, typename ReduceDims>
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std::pair<size_t, size_t> get_2d_lengths(const std::vector<int>& inLengths)
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{
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static_assert(Rank <= 6, "bigger Rank size not supported!");
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size_t tensor_total_length = 1;
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size_t reduce_total_length = 1;
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static_for<0, ReduceDims::Size(), 1>{}(
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[&](auto i) { reduce_total_length *= inLengths[ReduceDims::At(i)]; });
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static_for<0, Rank, 1>{}([&](auto i) { tensor_total_length *= inLengths[i.value]; });
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return std::make_pair(tensor_total_length / reduce_total_length, reduce_total_length);
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};
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template <int x, typename Seq>
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constexpr bool belong()
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{
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bool inside = false;
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static_for<0, Seq::Size(), 1>{}([&](auto i) { inside = (inside || (x == Seq::At(i))); });
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return (inside);
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};
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template <int Rank, typename ReduceDims, int start = 0>
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constexpr auto get_invariant_dims()
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{
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static_assert(Rank <= 6, "bigger Rank size not supported!");
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if constexpr(start >= Rank)
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return Sequence<>{};
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else
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{
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if constexpr(!belong<start, ReduceDims>())
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return merge_sequences(Sequence<start>{},
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get_invariant_dims<Rank, ReduceDims, start + 1>());
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else
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return get_invariant_dims<Rank, ReduceDims, start + 1>();
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};
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};
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// helper functions using variadic template arguments
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template <index_t... Ns>
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static auto make_tuple_from_array_and_index_seq(const std::vector<int>& lengths, Sequence<Ns...>)
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{
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return make_tuple(static_cast<index_t>(lengths[Ns])...);
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};
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template <index_t arraySize>
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static auto make_tuple_from_array(const std::vector<int>& lengths, Number<arraySize>)
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{
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static_assert(arraySize >= 1 && arraySize <= 6, "The tensor should have 1 to 6 dimensions");
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constexpr auto index_seq = typename arithmetic_sequence_gen<0, arraySize, 1>::type{};
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return make_tuple_from_array_and_index_seq(lengths, index_seq);
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};
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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#endif
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