* Refactor elementwise kernels
* Instances fixes
* Fix cmake
* Fix max pool bwd test
* Update two stage gemm split k
* Restore elementwise scale for hiptensor backward compatiblity
* Fix Acc data type check in conv fwd multiple abd
* Disable conv fp64 fwd example
* Update grouped conv weight multi d
* File renaming and class renaming for device element-wise operation
* Add batchnorm-infer instances, external API and client example
* Add batchnorm-infer profiler module and gtests
* Remove file device_elementwise_extension.hpp and move NormalizeInInfer operation to element_wise_operation.hpp
* Remove the using of class aliasing for DeviceElementwiseForBatchNormInfer
* Rename class and file due to conflict from device_elementwise_2d.hpp
* Fix namespace in batcnnorm_infer_nhwc client example
* Rangify STL algorithms
This commit adapts rangified std::copy(), std::fill() & std::transform()
* Rangify check_err()
By rangifying check_err(), we can not only compare values between
std::vector<>s, but also compare any ranges which have same value
type.
* Allow constructing Tensor<> like a HostTensorDescriptor
* Simplify Tensor<> object construction logics
* Remove more unnecessary 'HostTensorDescriptor' objects
* Re-format example code
* Re-write more HostTensorDescriptor ctor call
* Move kernel implementation files under impl directory.
* Update examples paths.
* Update device kernel impl include paths.
* Update tensor operation instances include paths.
* Update profiler and tests include paths.
* Clang-format
* Update include paths for batched gemm reduce
* Refactor UnitTest ConvNDBwdWeight.
* Refactor fwd and bwd data convND UT.
* Fix used test macro.
* Fix include path.
* Fix include paths.
* Fix include paths in profiler and tests.
* Fix include paths.
Co-authored-by: Adam Osewski <aosewski@amd.com>
* Implement multiple-reduction in one kernel (kernels, device ops, examples)
* Add generic elementwise kernel and device interface
* Add generator for normal-distributed data initialization
* Add host refer implementation of batchnorm-forward and batchnorm-infer
* Add examples for implementing batchnorm-forward and batchnorm-infer using generic kernels
* Remove un-needed including in batchnorm example
* Renaming generic_elementwise to elementiwise in kernel and device classes/functions
* Change in gemm_layernorm examples to use DeviceElementwise instead of Device5AryElementwise
* Change in exampe 19_binary_elementwise to use DeviceElementwise instead of DeviceBinaryElementwise
* Change in device_cgemm_4gemm_xdl_cshuffle.hpp to use kernel_elementwise instead of kernel_binary_elementwise
* Add DeviceElementwiseBase and use it in device_normalize_instance.cpp
* Removing and renaming files
* Update to synchronize gemm_layernorm client example to the generic element-wise device op API
* Update to synchronize with the latest headers directory and HostTensorDescriptor interface renaming
* Merge two static member functions in device_elementwise.hpp
* Remove unary_elementwise_1d kernel and device
* Extract base class for elementwise
* Refactor interface of DeviceGemmReduce. Do not use tuple in interface
* [What] Rename d into reduce in gemm + reduction related code
[Why] Prepare to add d term for add
* Unify base class of gemm + reduce and gemm + bias + add + reduce
* 1. Rename gemm_bias_add_reduce for external api
2. Refine cmake
* Add normalize device operation
* [What] Reorder the argument
[Why] Because d0 is also the input of c.
* Add type string
* Add example of gemm_bias_add_layernorm via external api
* Refactor example code
* clang-format
* Fix compile error
* clang-format
* Add external api for gemm_add_add_layernorm and normalize
* Add client example
* clang-format
* 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()
* 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>
* Support different length of ScalarPerVector
* Add example of broadcast on fastest axis
* Typo
* Refine fastest example
* Add dimension check
* Modify fastest broadcast example to 3d
* Enforce users give scalarPerVector explicitely
* 1. Add CscalarPerVedctor
2. Not only broadcast on fastest need to set scalarPerVector to 1
* Rename var
* Move IsScalarPerVectorValid() inside IsSupportedArgument()
* Separate GridDesc_M0 into A, B and C
* rename var
* Rename var of length
Co-authored-by: rocking <chunylai@amd.com>
* 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>
