* Add maxpool instances
* Rename index pool to max pool.
* Add maxpool bwd bf16 instances
* Add avg pool bwd instances
* Rename avgpool and maxpool to avg_pool3d and max_pool
* Add bf16 pool fwd instances
* Add max pool bwd to ckProfiler
* Add avg pool3d bwd to ckProfiler
* Add avg pool bwd test
* Fix bug of reference pool fwd (dilation)
* Fix bug of max pool bwd (dilation and initZero)
* Support bf16 compute data type
* Force compute type be f32. Because atomicAdd only support f32
* Add max pool bwd test
* Rename folder
* Rename pool
* Add max pool bwd client example
* Add avg pool bwd client example
* Add missing workspace
* clang format
* Rename macro
* remove useless header
* remove useless layout
* Add basic fp8 definitions and prn-generator
* Format
* Add fp8<->fp32 type_convert
* Format
* Split type_convert and cast_to/from_f8
* Format
* Minor fix
* Minor fix
* Move fp8 utils to a separate header
* Add elementwise ops
* Add fp8_convert_sr
* Format
* Add element op
* Eliminate magic numbers
* Split f8_convert_sr in host and device
* Format
* Add some constexpr
* Add a datatype test
* Format
* Another format
* Add fp8<->fp16 tests
* Update type_converts
* Format
* Add fp16 casting functions
* Format
* Use seed as a runtime arg
* Use element location for PRNG
* Format
* Add fp8<->fp16 to PassThrough element op
* Clean up
* Merge host and device implementations
* Add comments on rounding modes
* Remove leftover code
* Put type_converts into a separate header
* Put random number gen to a separate header
* Rearrange f8_utils' namespaces
* Refactor type_convert.hpp
* Move f8_t definition
* Change to the DeviceReduce base class template to include all problem description information
* Add external api for reduction
* Add client example to test the reduction external api
* Spelling correction
* Re-implement the host_reduction to follow the DeviceReduce base API format
* Change the reduce profiler to call the external API for collecting device instances
* Rename reduce client example directory from 08_reduce to 12_reduce
* Remove (void) before the functional call
* Tiny update in reduce client example
* Tiny update in profile_reduce_impl.hpp
* Rename the reduce client example directory
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
* GEMM + Reduce max fp16+fp32
* GEmm + Max bf16 + int8
* Refactor common definitions.
* Refactor common func of mean meansquare example.
* More examples for mean meansquare.
* Update int8 examples and skip them cause of random errors.
* Int4 examples.
* Fix examples for max int4/8
* Tensor conversion for int4 input data for mean meansquare example.
* Remove int4 mean_meansquare example
* Fix int8 mean_meansquare example.
-All ReductionAccData and R<N>DataType have to be F32. The INT32 data
type is giving wrong results.
* Guard int4 with ifdef
* Change int8 example to add_addsquare due to div rounding err.
* Clang format
* Change the return type of common function.
* Get back int8 example with division.
* Remove int8 mean meansquare.
* Use proper cast for BF16 data type.
* Use ck::literals.
* Use proper data type for host tensors & reference.
- Use ReduceAccDataType for reference gemm output data type.
- Cast host reference output tensor to EDataType
- Fix ifdefs for int4.
Co-authored-by: Adam Osewski <aosewski@amd.com>
* dump lds content in appropriate precision type
* add squared add reduction op; allows sq sum
* initial stub from regular gemm impl
* layernorm example code & host verification
* initial layernorm implementation
* tidy up
* make C0 precision type consistent with C
* clang-tidy and additional comments
* tighten up example code
* account for extra flops/bytes from normalization
* clang-format
* c0 bias/beta/gamma now have its own precision type
* AccElemOp for gemm outputs prior to feeding to layernorm
* update workgroup mapping
* rename kernel template param to reflect its dual use
* use LDS mem pool for reduction workspace
* change cshuffle precision type to f16; clean up
* clang-format
* correct naming
* explicit cast
* fully implemented gemm + bias + activation + add + norm
* activation in correct order
* reflect reduction API's recent change
* amend
* clean up; add comment
* keep up with recent changes in reduction API
* format
* resolve merge conflicts
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* 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()
* 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
* Tiny fix in dynamic_buffer.hpp to support vectorized AtomicAdd for double type
* Update to host layer and host reduction
* Merge and remove reduction kernels
* Merge and remove reduction device interfaces and update pooling device interface
* Merge and remove useless reduction device instances
* Update to reduction profiler and reduction ctests
* Update to reduction and pooling examples and add one reduction example
* Change to reduction examples to let them testable by ctest
* Add explicit pass checking for reduction and pooling examples
* Explicit assignment of tensor shapes in example reduce_blockwise_two_call
* Use atomic_add to repace atomicAdd and add atomic_add for double type
* Add reduce ctest support for double data type
* Replace to_int_vector() by using c++ std::vector::assign()
* Keep DeviceReduceThreadWise separated from DeviceReduceBlockWise
* Merge DeviceReduceBlockWise and DeviceReduceMultiBlockAtomicAdd into DeviceReduceMultiBlock
* Add GetAtomicOperationZeroValue() support for AtomicMax
* Tiny change to reduce example README.md
* Fix some tiny issues due to branch merging
* Revoke previous change in dynamic_buffer.hpp and add atomic_add for double2_t
* Add reduce multiblock_atomic_add instances for fp64 to verify vectorized atomic_add on fp64
* Renaming
* Clean the header includings in device_reduce instances header files
