* Remove template from Reducton operation classes and add template to their operator() and GetIdentityValue() interfaces
* Change to unary elementwise operators and the reduce_unary_operator (class for mapping) and dependent variations in all host layers
* Remove the data type template parameter from reduce_binary_operator (class for mapping) and dependent variations in host layers
* Add InMemoryDataOperatonSupportedOnDataType to check the matching between data type and InMemoryDataOperation
* Use struct-scope operator template instantiation for binary and unary element-wise operations
* Change a few more elementwise operations to use template for operator()
* Tiny correction in Normalize operator
* Add static_assert to check the data type appliability for some reduction accumulator and element-wise operatons
* Correction in some examples with regard to using ReduceAccDataType
* Use static_assert for UnaryDivide
* Update to merged codes to use Element-wise operations and Reduction Accumulator operations correctly
* Tiny fix with regard to SetWorkSpacePointer()
* Copy "gemm reduce" to "gemm bias add reduce"
* Implement gemm bias add reduction
* Fix compiler error due to merge from develop
* Add tensor operation for gemm + bias + add + reduce
* Add gemm_bais_add_reduce to ckProfiler
* Add c1 functor
* Refine type
* Use reduceAccDataType instead of explicitly float
* Change to use check_err()
* Do relu in float32 instead of bhalf_t. Because bhalf_t is unsigned
* Refactor relu. using type_trait instead of overloading
* Rename DxsReduceAccElementwiseOperation to DxsReduceAccElementwiseOperation
* Fix denominator
* Refine nameing
* Fix denominator in host
* Remove useless include header
* Use AccDataType
* Fix static_cast order
* Refine type
* [What] Remove tuple type in the base class
[Why] External api depend on base class. if base class has relationship with type, we will need many class for different type
* add GetWorkSpaceSize to base arg and make an example on convnd_bwd_weight
* add bwd weight for bf16: init
* remove redundant compute
* use datatype and split k to check whether a workspace is used
* remove unused computation for work space size
* add some code for bfp16
* add device/grid unary op
* add unary type convert to bwd-weight example
* support bf16 splitk kernel for convnd bwd weight
* 1. remove comments. 2. add checkvalidity. 3. add gridsize computation
* add workspace size check
* fix format
* change function name
* Use the unified naming for math functions on host and HIP kernel
* Corresponding change/simplification in reduction host/profiler/examples due to unified math functions renaming
* Renaming GetReductionZeroVal() to GetIdentityValue()
* Tiny renaming in profile_reduce_impl.hpp
* More renaming in profile_reduce_impl.hpp
* Replace zeroVal by identiyVal
* Remove ck_ prefix in the naming of ck::math provided functions
* Reference CGEMM + test stub
* Format.
* Incomplete simple implementation
* Library instances
* Sketch of tests
* Test fixes.
* Example added
* Cosmetics
* Add elementwise operation kernel and example
* Add comment
* Add template argument of dim . Prepare to support multiple dimension
* Rename example
* Support 1 dimension
* Add static assert
* Add comment
* Second auxiliary buffer added
* Extract pad
* Remove redundant argument
* Support any dimension for elementwise operation
* Remove line
* Let it be the multiple number of CU
* Move thread per block to the parameter of constructor
* Consuming binary ops to do A+B / A-B
* Fix + cosmetics + bf16 test commented out temporarily
* Format
* Enabling bf16 test
* Revert "Enabling bf16 test"
This reverts commit f497e2ba44.
* Fix + test reenabled
* fix build
* Revert "fix build"
This reverts commit d73102384b.
* post PR #235 merge fix
* amend
* Single workspace for cgemm + helper
* Perf calc fix
* Review remarks: static_cast
* Review remarks: binary ops templated
* Cleaning
* Removal of instances and their tests
* Review remarks from aosew addressed
* Review remark: unnecessary attribute
* Post-merge fixes
* Restrict 4gemm to PassThrough + bug fix
* Review remarks
* update licence
* change cgemm example to fp16
Co-authored-by: rocking <chunylai@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Anthony Chang <ac.chang@outlook.com>
* Implement reduction meand and reduction square mean
* Refine file name
* Add reduce mean and square mean
* Fix parameter name
* Add normalize device op (not implement invoker::run())
* Remove epislon
* Refine deviceop
* Add 5ary elementwise for normalization
* Add layernorm example
* layerNorm verication
* Fix compiler error due to merge from develop
* Fix typo
* Fix compile error
* Refine naming
* [What] Suport non pointer for invoker and argument
[Why] Snyc coding style with gemm
* Refine folder name
* Refine class name
* Evaluate perf of the kernel
* Fix compile error
* [What] Refine perf evaluation in example of gemm + reduction
[Why] evaluation of gemm + reduction may cause verification fail. Because evaluation will not initial global memory
* clang-format
* Add elementwise operation kernel and example
* Add comment
* Add template argument of dim . Prepare to support multiple dimension
* Rename example
* Support 1 dimension
* Add static assert
* Add comment
* Extract pad
* Remove redundant argument
* Support any dimension for elementwise operation
* Remove line
* Let it be the multiple number of CU
* Move thread per block to the parameter of constructor
* rename threadPerBlock with blockSize
* Support double
* rename kernel function name
* remove redundant include header
* Refine type
* Need to the final dimension
* Refine variable name
* Refine type
* Use index_t instead of int in API
Co-authored-by: rocking <chunylai@amd.com>
* [Experimental] Change to gemm+reduce and batched-gemm+reduce
* Use threadwise-reduce function to improve the gridwise_gemm_reduce_xdl_cshuffle kernel
* Tiny fix in device_batched_gemm_xdl.hpp
* clang-format library/src/utility/conv_fwd_util.cpp
* Use thread cluster descriptor and explicit M_K 2d descriptor to simply Blockwise Reduction
* Change by replacing ReduceDims by NumReduceDims as Device Reduce interface template parameter
* Rename the folder name for the pool2d and reduce examples
* Update to reduction test scripts
* Add Readme for pool2d_fwd and reduce_blockwise examples
* Add support for int8_t reduction (ADD/AVG, MIN/MAX/AMAX)
* Tiny fix in reduce profiler and tiny update in reduce testing scripts
* Tiny fix in testing script profile_reduce_no_index.sh
* Tiny fix in testing script profile_reduce_no_index.sh
* Add support for bfp16 reduction (using bhalf_t = ushort)
* Tiny fix in amd_buffer_addressing.hpp
* Tiny change in script/profile_reduce_with_index.sh
* Use AccDataType for Beta value and use element_wise::PassThrough
* Use type_convert for type converting in host layer reduction
* Renaming and refining in Reduction profiler/device layer/examples
* Renaming and refining in Reduction profiler/device layer/examples
* Renaming all NumReduceDims to NumReduceDim
* Fix the leaked type_convert in ThreadwiseTensorSliceTransfer_v2
* Update to testing scripts to add bf16 support
* added more static_assert
* Remove buggy tunable configurations defined in device_reduce_instance_xxx.hpp
* Add static_assert to give compile-time warning for incorrect thread slice-size/vector-size configurations
* minor change
* Refine and fix (in GetWorkspaceSizeInBytes of MultiBlockPartialReduce) to make int8 completely pass
* Tiny renaming in gridwise_2d_reduction_multiblock_partial_reduce.hpp
* Tiny fix in script/profile_reduce_no_index.sh
* Refine in DeviceReduce layer with regard to using NumInvariantDim/NumReduceDim or InvariantDims/ReduceDims
* Generic renaming in host reduction and DeviceReduce layer
* Add support for 4-d all dimension reduction in the profiler and add_device_reduce_xxx instances
* Use multi-thread and simplification for host Reduction implementation
* Add ctest for reduction
* Update to clarify the using of data init method in produce_reduce/example_reduce/test_reduce/
* Update to the reduce CTest executables to enable default testing behavior when no command argument
* Renaming
Co-authored-by: Jianfeng yan <jfyan008@gmail.com>
