GitLab

* add DEBUG_LOG macro to enable/disable debug output

* fix syntax

* fix syntax again

* fix syntax one more time

* remove balnk spaces

* use ifdefs

* add the Print argument

* move the definition of DEBUG_LOG to ck.hpp

* add the missign argument to Print()

* Let cmath included when compiling host codes in math_v2.hpp

* Remove including of cmath in device_base.hpp and device_permute.hpp

* add mnk padding, support m=0

* clean code

* clean code
Co-authored-by: Rostyslav Geyyer <46627076+geyyer@users.noreply.github.com>

* added 2d gridwise elementwise

* added 2d version of device elementwise

* added example file with updated device elementwise call

* added Cmake file

* changed NumDim into 2D

* fixed compiler issues

* fixed indexing for loop step

* fixed NumDim dimension error

* changed blockID to 2D

* updated Grid Desc

* updated kernel call

* fixed 2d thread indexing

* added dimensions for example file

* commented out unused code

* changed vector load

* removed extra code

* temporarily removing vector load on 2nd dim

* changed vector load back, still causing errors

* altered indexing

* changed isSupportedArgument for 2D

* changed indexing + do/while

* fixed isSupportedArgument

* changed dimension for debugging

* fixed

* added testing printouts

* testing change

* added variables to distribute threads through both dimensions

* testing changes

* integrated variable for thread distribution into device elementwise and added as parameter for gridwise elementwise

* removed most of the extraneous code, testing with different dimensions

* testing

* removed debugging print statements

* moved 2d elementwise permute into elementwise permute directory

* fixed formatting

* removed debugging comments from threadwise transfer
Co-authored-by: Jing Zhang <jizhan@amd.com>
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>

* apply new K-dimension check in gemm_xdl_cshuffle

* add K-dim check to gemm_xdl and batched_gemm_xdl

* fix syntax

* fix syntax

* clean-up the debug output

Co-authored-by: Chao Liu <chao.liu2@amd.com>

* fix bug where scaling may not be applied in some code path

* more test

* revert accidental example code changes

* start add example

* add multiple d fp16 example

* device transfer elementwiseop to gridwise

* gridwise add multiple d

* change example for multiple d

* fix spill registers

* fix for passthrough element op

* fix int8 overflow

* change example file name

* add instance for dl multiple d

* example add DsDataType

* remove grouped_convolution_forward_dl.hpp

* add head file(was deleted before)

* fix not support device issue

* format

* remove passthrough check
Co-authored-by: letaoqin <letaoqin@amd.com>

* wmma_op + unit test

* add arch limitation to wmma test

* change arch limitation

* Refactor + Add all type unit test(int4 compile failed)

* Add f32_16x16x16_bf16 unit test

* Remote int4 related

* delete deprecated test
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Use gemm_multiple_D instead

* Add gemm bias relu quantization example

* Add pure gemm quantization example

* Add quantization of perchannel conv + bias + relu example

* Refine the code

* Rename multiplier to requant_scale

* Rename the folder

* Remove redundant comment

* Rename the file. Prepare to add perchannel

* Add conv perchannel instance

* Move to quantization folder

* Add conv perchannel client example

* Apply Rangify constructor of HostTensorDescriptor & Tensor<>

* Fix merge error

* 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>