GitLab

* Add license header.

* Reduce number of logged output. Add constant initialization.

* Add functional tests for grouped_gemm with different kbatch value.

* Add debug log informations + remove unused code.

* Don't pass kbatch to CalculateKPadded.

* Turn on logging in grouped gemm and gemm splitk profiler

* Debug: limit number of test cases to run;

* Log more information and initialize with constant value.

* Turn on DEBUG_LOG

* Add more debug log informations.

* Limit the number of instances to compile.

* Use GridwiseGemmPipeline

* Use KBatch to calculate K0

* Multiple DebugLog messages.

* Unit tests for multiple KBatch values.

* Refactoring

* Disable logging
* extract out of if statement KBatch update.

* Uncomment instances.

* Disable DebugLog.

* Use Kbatch when calculate KPadded.

* Fix CGridDesc padding.

* Use available helper functions.

* Uncomment code commented for debuggin.

* Remove unnecessary debug log messages.

* Uncomment previously commented code for debug purposes.

* Add KBatch info to profiler output summary log.

* Add gtests for gemm splitk using ckProfiler API.

* Add more test-cases for different data layout.

* Add more test cases for gemm splitk

* Remove old test.

* Unit tests for MKNK ggemm interface.

* Fix and add more unit-tests.

* Constepxr everything!

* Increase error threshold for fp16 and splitk.

Since we're using fp16 atomic add for splitk there's a
known precision loss.

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Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* Add instances for fp16/int8 Gemm kernels (Navi21)

* Extend instances with smaller tiles

* Fix SrcVectorTensor for km_kn_mn int8

* Expand the base class of pool2d, prepare to share base class with pool3d

* Add pool3d device op

* Add pool3d f16 example

* Refactor the base class. implement generic pooling in the future

* clang format

* get original index in max pooling

* Add outputindex to base class

* Fix dimension

* Add pooling instance

* Use indexType instead

* Remove useless header

* Extract IndexDataType to template

* Extract pooling reference code

* clang format

* clang format

* Fix typo

* Add tensor stride

* Add missing header

* Add index stride and output stride

* Refine naming

* Add type to base class

* Rename file

* Use proper size

* Fix typo

* Refine naming

* Modify the argument into vector.

* Add max pool profiler

* Refine naming

* Support f32 pool

* Fix typo

* Add avg pool2d fwd in profiler

* clang format

* Rename AccDatatype to ComputeDatatype

* Fix init

* test pool

* Extract variable

* Add client example

* Check the pooling dim

* clang format

* Connect argv and arg_parser

* Add found check

* Remove useless header

* Refine naming

* Adjust the order of device_pool_fwd

* simplify karg in device/grid split-k op

* fix mk_kn_mn instances

* add more instances

* B2C with 3D grid for KSplit

* Remove unused code.

* Use default B2C (3D grid) in grid gemm v2r4r2.

* Device gemm splitk use B2C map.

* Device GroupedGemmXdlSplitKCShuffle

* Example for GroupedGemm Xdl SplitK

* Introduce Device GroupedGemmSplitK

* Fix updating kbatch size.

* Add instance mk-nk-mn

* Enable set kbatch in profiler.

* Add GGemmSplitK mk-kn-mn instances

* Add more instances & split into multiple files.

* minor fix

* tuning

* clean

* disabled failed instances

* use pipe v2

* Ignore arg on not supported arch.

* fix warning

---------
Co-authored-by: carlushuang <carlus.huang@amd.com>
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>
Co-authored-by: Jing Zhang <jizhan@amd.com>
Co-authored-by: root <root@ctr-ubbsmc15.amd.com>

* [What] Remove pure conv int8 instance
[Why] We will never use pure int8 conv in AI, use int8 quantization instead

* Change layout

* Share the kernel parameter

* Support more type of NHWGC for group conv

* Revise client example of conv 2d, use NHWGC layout

* Add instance to cmake

* Revise layout of group conv quantization instance

* Revise layout of external api of group conv quantization

* Revise layout of group conv quantization client example

* Fix clang format

* Add comment to describe meaning of each parameter

* simplify karg in device/grid split-k op

* fix mk_kn_mn instances

* add more instances

* use name from tensor layout

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Co-authored-by: carlushuang <carlus.huang@amd.com>

* Rename to proper naming

* Add example of groupnorm + swish

* Extract duplicate code in example

* Add groupnorm + swish instances

* Ractor instance generation, split into multiple cpp file

* Add external api and client example

* Refine profiler message

* Use ck math version of exp

* Refine problem size in example

* Add host version of exp

This reverts commit bb5530af.

Co-authored-by: root <root@ctr-ubbsmc15.amd.com>

* simplify karg in device/grid split-k op

* fix mk_kn_mn instances

* add more instances

* use name from tensor layout

* Rename file. Prepare to support another activation

* Add comment for quantization

* Extract out_elementop

* Add tanh example

* Add conv + bias + tanh quantization instance

* Add missing parameter

* Refine cmake

* Add external api and client example

* Extract variable in example

* Fix the comment

---------
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* add workaround 637

* format

* change id

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Co-authored-by: zjing14 <zhangjing14@gmail.com>

* Add conv perlayer quantization

* Add gemm_dlops quantization

* Support int8 for innerproduct

* Refine gemm dlops int8 kernel parameter

* Support gfx908(MI100) and gfx90a(MI200)

* clang-format

* Rename example number

* Support different layout for d tensor

* Add conv dlops perchannel quantization example

* Move to example 40

* Extract the common code for different platform (dlops and xdlops)

* Move ot subfolder. Prepare to add other op of quantization

* Refine the quantization instance library

* Add conv dl instances and client example

* Remove unnecessary type

* Add gemm quantization instance

* Add external api and client example

* Refine num_bytes

* Separete different layout to different cpp

* Add more xdl instances

* Revert "Remove unnecessary type"

This reverts commit 82086918

.

* Remove CShuffleDataType in dlops
Let acc and CShuffleDataType be the same in xdlops

---------
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* Grouped gemm + Gelu instances.

* Device Instance Factory for GroupedGemm+Gelu

* Client example

* Rangify fill helper functions.

* Fix name clash.

* Profiler for grouped_gemm+gelu

* No need to use full namespace name.

* Add check for MRaw divisible by vector load.

* Ugly fix for big errors.

* Add grouped_gemm+gelu to profiler CMakelists.

* Store in argument additional info.

* Information about Mraw, Nraw, Kraw values.

* Use FastGelu instead of Gelu.

* Change client ex to use FastGelu

* Remove relaxed error precision.

* Remove duplicate output elementwise-op

---------
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* Sync the order of type string with template parameter

* Add more instances

* Check the vector size and remove redundant var

* Extract var to static, prepare to separate sweep once kernel

* Separate sweeponce flow and optimize the flow

* 1. Rename AccDatatype in normalization to computeData
2. Rename AccElementwiseOperation to YElementwiseOperation in normalization

* Remove useless code

* Update naive variance kernel

* Refine string

* Fix typo

* Support naive variance for device_normalization

* Check the blocksize

* Share the VGPR of x and y

* Share the VGPR of gamma and beta

* Add more instances

* Support fp16 sqrt for experiment

* Add CHANGELOG

* Fix typo

* clang-format

* Conv3d bwd weight client example.

* Update year in license

* Convolution bwd data 3D fp16/fp32 client example.

* Client example for convnd fwd fp16 fp32

* clang-format

* Review remarks.

* Fix compiler err.

* Update data layout to standard one.

* Add conv 3d fwd NDHWGC instances

* clang-format

* Conv3d fwd NDHWGC instances.

---------
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* Adding more bigger tiles.

* Remove failing instance.

* Remove instances which that don't improve perf.

---------
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* Add gemm + layernorm instance

* Add ckProfiler

* Add test

* Add client example

* Detect if user forger to set the workrspace

* Use literal in the example

* [What] use builtin function for sqrt
[Why] compiler will not use v_sqrt_f64_e64 if we use ::sqrt()

* check gemm vaildity in IsSupportedArgument

* Add more testcases

* Merge duplicated folder in client example

* Print more infomation

* Use better kernel parameter for MS problem size

* clang format

* Add constexpr for if condition and remove redundant include

* Remove cstdlib and add constexpr

* added instances for large N

* add instance for elementwise normlization

* added supported restrict in device_elementwise_normalization_impl.hpp

* add instance for gemm bias softmax gemm

* add client example

* change CGridDesc_G_M_N to CGridDesc_G_M_O

* add gridwise

* change c grid name

* device add d0s data

* fix 08 client_example

* add example 47_fused_attention

* example output correct

* add d0 to example

* add d0 element op

* rechange instance code

* change Acc0ElementwiseOperation to C0DEElementwiseOperation

* change example name

* update instance for cdeelementwiseop

* add bhalf_t ScaleAdd

* add test

* not surport geem1 bias

* remove some ignore

* fix test bug

Co-authored-by: Adam Osewski <aosewski@amd.com>

* 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

* start add example

* fix config

* fix showinfo bug

* add an elementop

* change to padding

* add xdl example

* change elementwiseop

* add instance

* add instance to profiler

* change file name

* fix deive not support issue

* add client example

* fix client gemm_add_multiply name

* change AddMultiply elementwiseop

* fix elementwiseop

* fix client example

* fix addmultiply op

* fix comments and fun name
Co-authored-by: letaoqin <letaoqin@amd.com>

* add example

* fix example

* add instance for gemm permute

* add to client example

* change configs

* change instance file name

* formate

* change client example file name and remove example

* 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>

* add mnk padding, support m=0

* clean code

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

Add padding device_gemm_add_add_fastgelu_xdl_c_shuffle instances to enable arbitrary problem size (#535)

* Add padding device_gemm_add_add_fastgelu_xdl_c_shuffle instances

* Add padding device_gemm_add_fastgelu_xdl_c_shuffle instances

* Add gemm_add_fastgelu profiler impl

* Add padding device_gemm_fastgelu_xdl_c_shuffle instances

* Add gemm_fastgelu profiler impl

Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* 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>

* 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()

Remove int8 from batchnorm-forward instances since it is not needed for forward training and could fail test (#516)

* 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>

* FastGelu support for more data types.

* AddFastGelu & FastGelu instances.

* Client example.

* clang-format

* Remove unused stride variable.

* Add new line at EOF.
Co-authored-by: Adam Osewski <aosewski@amd.com>

* fixed bug in softmax reference & add bf16 examples for batched_gemm_scale_softmax_gemm

* added bf16 tests for batched_gemm_softmax_gemm_permute

* changed format of device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instance.cpp

* changed format device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instance.cpp

* aligned annotations

* modified CMakeLists for examples

* add common example code of fp16/bf16 version for batched_gemm_scale_softmax_gemm_xdl

* use macro to control the instances

* added macro control into instances

* clang-format some files

* changed error tolerance for bf16

* changed index for 10_elementwise_normalization

* fixed xdlops code bug in amd_xdlops.hpp
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>

* start add example

* add device dl

* change launch kernel

* change init data method

* change example config

* add config valid check

* add instance for dl bwd

* add instance to ckProfiler

* reserver to profiler and cmakelist

* add instance to ckProfiler2

* change instance f32 config

* fix example return value
Co-authored-by: letaoqin <letaoqin@amd.com>
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>

We can use this template to eliminate duplicated iterator computing
logics. By providing return type to ck::accumulate_n(), we can avoid
type conversion operations.

* Remove redundant CMake setting

* Extract common code from files

* Rename folder 'convnd' to 'conv'

* Use std::array<> to accept compile-time kwnown # of arguments

* Fix compilation error of tuning parameter

* In example, use same setting as unit-test

* Remove no-longer used include directive

* Add interface for grouped conv bwd weight

* Add group support for conv bwd weight

* Add grouped conv bwd weight example

* Use group parameter in example

* Rename example folder

* Remove non-grouped version example source files

* Rename device op template

* Add group support to convolution backward weight

* Remove debug messages

* Use smaller group size in example

* Use named variable as loop terminate condition

* Prettify example output message

* Enlarge used grid size

* Allow real grid size exceeds expected grid size

* Rename interface file

* Add client example for grouped conv2d bwd weight

* Fix wrong include directive

* Rename client example folder