* 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
* Use dim 0 as faster dim for writing mean/var/count workspace in batchnorm multiblock method [performance]
* Add CountDataType as template parameter in blockwise_welford
* Add utility/get_shift.hpp
* Add BatchNorm multiblock single-kernel implementation
* Add smem inline assembly based implementation of gms_init/gms_barrier/gms_reset for gfx90a
* Renaming in device_batchnorm_forward_impl.hpp
* Tiny fix in the batchnorm_fwd profiler
* Revert "Add smem inline assembly based implementation of gms_init/gms_barrier/gms_reset for gfx90a"
This reverts commit d16d00919c.
* Use the old two-kernel batchnorm multiblock method for gfx1030
* Use the old two-kernel batchnorm multiblock method for gfx908
* use the single-kernel batchnorm multiblock method only for gfx90a
* Remove get_wave_id() from utility/get_id.hpp since it is not used
* Set true for testing running mean/variance and saving mean/invvariance in the examples
* Fix to copy-right words
* Remove un-needed including in utility/get_id.hpp
* Add comments to workgroup_synchronization.hpp
* Remove un-used codes in gridwise_multiblock_batchnorm_forward.hpp
* Renaming in the kernels
* Remove un-used kernel file
* 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
* Refine the device batchnorm-backward base API templates and data type assignments
* Remove duplicated kernel file
* Add batchnorm backward instances and external API
* Add batchnorm-backward profiler and tests
* Add client example which uses batchnorm backward external API
* Merge test/batchnorm_fwd and test/batchnorm_bwd into one directory
* Loose the threshold for batchnorm-backward check_err()
* Implemented batchnorm-backward Blockwise and Multiblock kernels
* Add batchnorm-backward device op
* Add batchnorm-backward host-reference op
* Add batchnorm-backward example
* Parameters renaming in batchnorm backward kernels and device op
* Change in the example to loose the threshold for ScaleDiff checking
* Add comments to explain the implementation of batchnorm-backward
* Parameters renaming again in batchnorm backward kernels
* Improve the expression calculation for performance
* Add batchnorm backward to README
* Add comments to explain inv-variance in batchnorm forward and backward
* Renaming the batchnorm forward training and inferring examples
* Add/update the comments for batchnorm-backward kernels
* Renaming again
* Add block_sync_lds between two consecutive blockwise reductions
* Move common expression 1/N out of the static_for loops
* Add dy_elementwise_op
* Renaming in backward example again
* Add checking for reduceDims in reference_batchnorm_backward
* Update to comments and codes format
* Rename in the comments
* Remove common expression out of the loop in reference_batchnorm_backward_nhwc_c
* Add block_sync_lds() between blockwise reduction again
* Fix comments again
* Remove int8 from batchnorm-forward instances since it is not needed for forward training and could fail test
* Update to device_batchnorm_forward base class to include all template parameters for problem description
* Add batchnorm forward instances and external api
* Add batchnorm forward profiler module which uses the external api
* Add some comments in batchnorm_forward example to explain the dimensions in lengths[]
* Replace the reference_batchnorm_forward_nhwc_c by generic reference_batchnorm_forward
* Improvement to the batchnorm infer base API
* Add batchnorm forward client example which shows using the batchnorm forward external API
* Add test for batchnorm forward
* Tuning the batchnorm profiler initialized values and error threshold
* Add support for bhalf_t in instances/external api/tests
* Add support for int8_t in instances/external api/tests
* Add support for double in instances/external api/tests
* Let ScaleDataType and BiasDataType be same as XDataType and YDataType when creating instances
* Checking before running best instance in batchnorm_fwd_nhwc client example
* Add checking for YElementwiseOp in batchnorm_forward external API
* Add more types in batchnorm forward profiler
* Add more test lengths
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
* 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
* Update to the batchnorm-forward API and base class
* Fix leeked header including in gridwise_set_buffer_value.hpp
* Add kernels and device file for batchnorm-forward welford supporting both blockwise and multi-block reduction
* Update to the batchnorm-forward example to use the new batchnorm-forward device interface
* Change the batchnorm-forward reference to use sequential welford method
* Change to assign the workspace into four buffers in the host layer
* Use GetReduceCountPerThread functor to replace the initial count for Blockwise and Multiblock welford
* Tiny correction and remove un-used file under example/34_batchnorm
* Renaming in the kernel arguments
* Explicitly use ck::math::sqrt in batchnorm-forward kernels
* Add some comments to some kernels
* Tiny fix
* Generalize the data types in reference_batchnorm_forward_nhwc_c
* Use ck::ignore to mark un-used parameters
* Move GetReduceCountPerThread functor codes from kernel to device
* Remove some un-used codes in device_batchnorm_forward_impl.hpp
* Tiny fix in batchnorm_forward example
* Move GetReduceCountPerThread() to welford_helper.hpp
* Use seperate data type for Scale and Bias
* Renaming in device Op
* Tiny fix in forward example
* Updata to batchnorm-infer (type spliting, renaming)
* Add time and bandwidth measurement to the batchnorm-forward example
* Add support of elementwise operation for batchnorm forward output
* Reduce object copying by passing object as reference type
* Tiny change for performance
* Updates for performance again
* Some Renamings
* Add GetActualVariance template parameter for ThreadwiseWelfordMerge
* Tiny update in reference batchnorm forward nhwc/c
* Move batchnorm multiblock kernel files to grid/batchnorm_multiblock sub-directory
* Fuse mean and bias in the normalization calculation
Co-authored-by: root <root@dc-smc-18.amd.com>
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
* 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
