* replace obsolete offload-arch flags with GPU_TARGETS

* fix a build error for client app

* replace commma with semicolon in GPU_TARGETS

* build CK only once, use deb package in all subsequent stages

* update jenkins file

* change prefix for build_CK stage

* update writing deb metadata to control file

* update ubuntu source for docker, script syntax for deb package metadata

* try different way to create deb metadata

* clean up DEBIAN before creating one

* fix the CI folder names, fix splitK qa

* use correct docker in all stages, separate tests for splitK verification and performance

* clean old comments, change dir before packaging

* use different package syntax

* change packaging syntax

* package with cmake

* remove unnecessary build prefix

* get rid of unnecessary paths

* change paths during unpacking

* change script syntax while unpacking

* get rid of unneccesary steps

* get rid of comments in the scripts

* use double quotes for scripts

* add ccache during build, try dpkg -x

* pull and install each package separately

* use full package names

* try to use stashing for packages

* change stash/unstash syntax

* move unstash out of shell, run tests on any gpu node

* unpack each package separately

* try re-using existing workspace

* merge the build and test stages, only stash ckProfiler

* merge the build and test stages, only stash zipped ckProfiler

* fix syntax

* add GPU check before build and test, rename docker to usual name

* fix build

* fix build

* add lower triangle bmm

* init code for tile skipping

* functionality right with lower triangle mask

* add decoder lower triangular mask calculation

* use 7*13 group

* fix n2 compute error

* attention with lower triangle mask with tile skipping

* add template to distinguish masking kernel

* rename template and remove default template value

* remove lower triangle gemm reference struct

* add some comments on example

* add 10 instance for masking bmm + scale + softmax + bmm + permute kernels

* add test

* add test file

* add gtest for bmm masking scale softmax bmm permute

* clang-format

* fix compile error

* check lef bottom corner for tile skipping

* fix error: check left bottom corner for tile skipping

* add k padding

* add test and instance for MNK padding

* passing a mask struct

* fix instances

* delete used comments

* format
Co-authored-by: danyao12 <yaodan@dc-smc-13.amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Add groupnorm example by layernorm
1.  Reference is not ready
2. shape of gamma and beta need to be fix

* Let shape of gamma and beta can be same as x

* Modify test, instance and client example

* [What] Fix bug of layernorm for greater than 2 dimension.
[Why] We need to get upper length from merge transform instead of embed transform.

* Add reference for groupnorm

* Fuse sigmoid after groupnorm

* [What] Rename original layernorm into layernorm2d
[Why] Prepare to add groupnorm using layernorm5d

* clang-format

* Add groupnorm test

* Refine error message

* Add groupnorm ckProfiler

* Test groupnorm kernel from device_instance

* update example

* upadte profiler

* Fix test naming

* Fix argc number

* Move descriptor and sweeponce to argument for quick debugging
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Add example folder for 'DeviceElementwise'

* Re-structure example files

* Move common parts into common.hpp

* Use more strict input

* Add more helper methods in 'DeviceElementwise'

* Use more specific method to write example

* Allow specify problem through command line argument

* Allow specify problem 'axes' through command line argument

* Add check to template type argument

* Add transpose_shape() to generalize shape permute

* Generalize transpose utility functions

* Use better name for tensor indices

* Add checks in helper functions

* Remove debug messages

* Refine error message for check_err()

* Generalize variable naming in example code

* Add device op 'DevicePermute'

This device op is clone of 'DeviceElementwise'

* Use 'DevicePermute' device op in example

* Remove 'elementwise' from identifiers

* Remove 'elementwise' from file paths

* Remove base class of 'DevicePermute'

* Let 'DevicePermute' inherit from 'BaseOperator'

* Add simple type traits to validate device op type

* Add static_assert() to check type constraints

* Create 'DevicePermuteBase' to generate methods

* Use indirect base type to generate methods

* Remove 'is_device_op<>' type traits

* Only accept single-input-single-output for 'DervicePermute'

* Simplify 'DevicePermute' interface

* Re-format 'DeviceElementwise'

* Use CRTP to generate overridden virtual method

* Remove unnecessary include directives

* Distinguish input & output shape in 'DevicePermute'

* Passing 'axes' to 'DevicePermute'

* Use more reasonable return value for Invoker::Run()

* Add 'GridwisePermute' kernel

This kernel is a clone of 'GridwiseElementwise_1D'

* Remove no-longer used type argument

* Check if input/output shape meet the requirement

* Remove no-longer used method

* Remove never-entered-if-clause

* Change problem description for 'DevicePermute'

* Transform descriptor into 3 dimensions

* Add debug code the verify result

* Add comment to indicate template argument location

* Add N/H/WPerBlock template parameter to 'DevicePermute'

* Rename 'GridwisePermute' to 'GridwiseCopy'

* Check tensor descriptor dimensions in 'GridwiseElementwise_1D'

* Add missing include directive

* Add 'BlockSize' parameter to 'DevicePermute'

* Remove no-longer used method

* Add 'BlockToTileMap' for 'GridwiseCopy'

* Use the normal Block2TileMap convention

* Rename 'BlockToTileMap' as 'Block2TileMap'

* Fix most of compilation errors

* Let 'Block2TileMap' map block to 2d coordinate

* Allow data transfer in 'GridwiseCopy'

* Fix wrong output descriptor for 2nd blockwise copy

* Rename 'GridwiseCopy' as 'GridwisePermute'

* Remove '1d' in identifiers

* Remove commented-out codes

* Remove 'MPerThread' template parameter

* Seperate template parameters

* Unify variable namming convention

* Use more verbose way to create expressions

* Add template parameter 'InBlockLdsExtraW'

* Release the constraint on In/OutGridDesc

* Use date type directly as template argument

* Re-arrange template arguments for blockwise copy

* Remove no-longer used template parameters

* Embed layout in the variable names

* Add GridwisePermute::CheckValidity()

* Extract local types as template parameters

* Rename local type alias

* Add more template parameters (vector width related)

* Calculate new SrcVectorDim/DstVectorDim after merge descriptor dimensions

* Fill tensor values start from 1

* Re-formate example code

* Avoid too-large block id

* Add comment

* Make sure 'SrcVectorDim' is not same as 'DstVectorDim'

* Add check for the 'VectorDim' & 'ScalarPerVector' template params

* Let 'DstVectorDim' equals 'SrcVectorDim' after transpose out grid desc

* Remove no-longer used template parameter 'NPerBlock'

* Fix wrong descriptor creation logics

* Specify problem in each examples

* Use better example name

* Add new example 'example_permute_NxHxW_fp32'

* Add example for demonstrating bundle multiple elems in tensor

* Add support to permute multiple elements together

* Change the default problem size

* Add span<> class template

* Use span<> to generalize check_err() interface

* Fix ambiguous ctor call

* Avoid create necessary objects

* Use helper functions to simplify example code

* Add example for 4xfp16 permute

* Disable failed-to-compile example

* Add check for the NUM_ELEMS_IN_BUNDLE

* Remove redundant parameter in helper lambda function

* Add check for the input tensor type's byte-size

* Check scalar-per-vector with padded length

* Use more verbose name to avoid name collision

* Use fixed 'VectorDim' & 'ScalarPerVector' for LDS

* Embed shape info in name of descriptor constructor

* Rename example folder '36_permute' into '37_permute'

* Avoid using too-large LDS in kernel code

* Remove redundant example

* Usw switch() to group similar codes

* Add const to the span<> type arguement

* Simply initialize tensor with floating point values

* Use fp16 as data type in all examples

* Enlarge tensor size in example

* Enalrge N-dim in example

* Add check for the bundled type in example

* Use more stricter error threshold

* Remove global load/store loop in kernel code

* Measure execution time by default

* Use faster device op config for example 'NxHxW_fp16'

* Use faster device op config for example '1xHxW_fp16'

* Use faster device op config for example 'HxWx4_fp16'

* Remove cmd arg parsing logics

* Rename functions

* Extract bundle permutation logic out

* Simplify permute bundle example

* Add Tensor<>::GetElementSpaceSizeInBytes()

* Add Tensor<>::data()

* Use new methods to simplify code

* Use type alias to replace duplicated code

* Use existing method to shorten code

* Allow FillUniformDistribution accept range arugment

* Intialize random values in range

* Add Tensor<>::size()

* Use more meaningful names in permute bundle example

* Use more meaningful names in permute element examples

* Use rangified copy() to copy elements

* Use function return value directly to eliminate variables

* Add to_array() conversion tool to eliminate more variables

* Add Tensor<>::AsSpan<>() to create view of tensor values

* Use AsSpan() to shorten check_err() calls

* Remove no-longer-used 'using' directives

* Move 'using' directive to proper code position

* Remove redudant variables

* Remove useless static_assert()

* Add check for range types

* Declare variable right before first use

* Move long return type as tailing return type

* Add BaseInvokerCRTP<> class template to generate method

* Create new base type for 'DervicePermute' implementations

* Move 'NumDim' template param to the first

* Rename 'DevicePermute' to 'DevicePermuteImpl'

* Add 'noexcept' specifier to CRTP generated method

* Move 'Block2TileMap' definition into 'GridwisePermute'

* Use type alias to reduce code

* Unify naming style in 'DevicePermute'

* Add comments in 'GridwisePermute'

* Rename permute example folder

* Use std::cerr to report error

* Use larger shape in examples

* Rename '38_permute' to '39_permute'

* Make sure we use unsigned type for shape & indices

* Remove opt-ed out assertion

* Remove template BaseInvokerCRTP<>

* sanity check

* add attribution

* add irrgular k tile size for batched attention

* format

* grouped attn without batch validates; now move toward grouped batched attn

* grouped batched attention

* working

* remove debug logging

clean up

clean up

* reintroduce g_ prefix back to host tensor variables

* format

* rename file

* restore old file

* rename

* consolidate padded/non-padded attention example

* harmonize padding specialization in attn examples

* init commit of convnd bwd data

* begin compiling example

* have a first version that produce a right result

* refine device level launch kernel code

* add more instances in example and get right results

* clang-format

* format example file

* add more instances

* fix instances

* adding conv_bwd_data multile_d

* adding conv_bwd_data multile_d

* adding conv_bwd multiple d

* adding conv_bwd multiple d

* adding conv_bwd multiple d

* refactor

* refactor

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* refactor

* update conv fwd's bias impl

* refactor

* reorg file

* clean up cmake

* clean

* clean

* clean
Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* refactor

* start

* add device gemm file

* add BatchStrideD0

* add stridd0

* add gridwise file

* add d0 parameters to gridwise gemm

* add c layout transformer

* add d0 threadwise copy

* init kernel

* init kernel

* regular code

* nm desc put to out

* kernel parameter can not use reference

* host add bias+gelu

* run right for bias+gelu

* change AddFastGelu into another file

* interface add d1 bias parameters

* add d1 parameter to argument

* add d1 parameter to gridwise

* first all code,not verify

* gelu change to relu and GetElementSpaceSize bug

* add instance

* start add to ckprofiler

* ckprofiler finish code

* change input parameter for ckProfiler

* fix host bias+gelu bug

* show help for ckProfiler

* fix bug for lunch kernel ignore parametes

* add pad and fix about bug

* mutiple d0

* add dynamic d0_element_op

* change profiler and  instance to mutiple d0

* example have 2 d0

* remove some comments not using

* change 2 d0 have self  parameters

* change d element_op name

* change class name(multiple_d)

* fix bug

* fix bug that don't find file

* update profiler

* refactor

* update profiler

* clean

* revert example change

* add gon layout

* optimize parameter for gno

* add gon to gemm+gemm

* change helping input parameters

* change to GemmPadder_v2

* using ForEach

* fix gb_per_sec
Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: ltqin <letaoqin@amd.com>

* upgrade the OS and ROCM versions in CK docker

* add cxx flags to link code with rocm5.2 and ck-9110 compiler

* rename the docker image

* run ONNX gemms using init=1

* add gridwise/device sparse embedding

* update code

* update code

* remove useless makefile

* code fix

* workable

* work properly

* emb add

* add more instance

* format

* remove useless code

* fix format

* fix clang-tidy

* clean

* fix a compile error
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Chao Liu <lc.roy86@gmail.com>

* fix example; make padding on by default in example; fix argument checks

* fix Gemm1KPacK which has since regressed from PR #399

* add processing for the onng_gemm and splitK_gemm

* add profile_onnx_gemm.sh

* add stderr to logfiles, add splitK and onnx gemm parsing

* enable splitK gemm wresults posting to db

* modify comment

* trim unnecessary check

* add gemm spec in kernel name

* add TNTT gemm_gemm + atten kernel instances

* refactor attention padding to better fit in unit tests

This streamlines usage where "ResetNaNToMinusInf" is now hidden from user facing device op.
Also added compile-time conditionals that load OOB value as NaN only after padding is enabled

* add adhoc padding test for atten

* shrink input value range for attention kernel validation to avoid occasional error by 1e-3

Still unsure whether this kind of deterministic floating point accurary issue is expected
or not. May want to try exact same approach as the GPU kernel in the host reference
GEMM+Softmax+GEMM function to see if the accuracy discrepancy goes away. Until then,
shrink the input value range as it is less likely to produce errors of around ~1e-3.

* attention kernel proper granular padding for all 4 dims

* IsSupportedArgument checks

* test more padded cases

* block PadK specialization in attention kernels

* workaround clang crash for gfx908

(gfx908 only) workaround for compiler crash in fused kernels on mainline #9110; #10738 seems ok
error message was "fatal error: error in backend: Error while trying to spill VGPR0 from class
VGPR_32: Cannot scavenge register without an emergency spill slot!"
this fall back to less ideal way of handle NPadding in fused attention kernel

* comment out kernels giving wrong results on MI100; MI200 doesn't seem affected

* add gemm_gemm TNNT instance

* sanitize Gemm1KPack

* disable instances that failed validation on mi100

* Update Softmax device operation interface.

* Update ckProfiler.

* Update Softmax UT.

* Update example.

* Client example.

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

* add scripts

* fixed splitK_gemm_fp32

* clean

* clean

* use gemm_xdl_splitK_c_shuffle into profiler

* remove device_gemm_xdl_splitk.hpp

* refactor

* refactor

* adding int4/int8/fp16/bf16 for conv+conv and gemm+gemm

* adding int4/int8/fp16/bf16 for conv+conv and gemm+gemm

* clean

* Refactor the design of DeviceGemmMultipleDMultipleR_Xdl_CShuffle

* Add 'DeviceGroupedConvFwdMultipleDMultipleR' interface

* Add DeviceGroupedConvFwdMultipleDMultipleR_Xdl_CShuffle

* Remove 'GridwiseConvFwdMultipleDMultipleR_xdl_cshuffle'

* Add 'TransformConvFwdToGemm<>' utility class (from Chao)

* Use 'TransformConvFwdToGemm<>' to shorten code

* Fix ill-formed method declaration

* Re-implement MakeRGridDescriptor_M() function

* Change problem description

* Use macro to define layout types

* Define K-reduced output tensor layout types

* Let user to decide R output tensor layout

* Rename variables

* Add padding to the reduced output tensor if necessary

* Extract common code as helper method

* Remove debug message

* Add missing include directive

* Add partial fp16 Conv + Reduction example

* Add example verification code for 2D Conv problem

* Use type alias to simplify code

* Share code across different-dimension Conv problems

* Rename file/functions from run_conv_fwd* to run_convnd_fwd*

* Make example code more verbose

* Add code to support 1D & 3D Conv + Reduction on host

* Add more examples for data type: bf16, fp32

* Add example for int8

* Add custom target to group examples

* Use more general custom target name

* Change the description in error message

* Disable testing for example other than fp32

* Add examplel for int4 (just copy from int8)

* Fix wrong data type

* Use larger data type for intermediate tensors

* Finish int4 example

* Undefine macro PP_DEFINE_LAYOUT_TYPE() after use

* Use named variables to replace magic numbers

* Remove debug messages

* Use same A/B data type for host Conv in int4 example

* Add check for the 'RLayout' type argument

* Group same-dim-layouts together in 'LayoutSetting<>'

* Add 'final' specifier to utility classes

* Use different initialization method for examples

* Remove macro PP_DEFINE_LAYOUT_TYPE()

* Fix code-comment mismatch

* Use more reasonable initialization value for all data types

* Default use init_method=1 for all examples

* Remove never-used code

* Remove confusing out-of-date comments

* clean
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Chao Liu <lc.roy86@gmail.com>

* refactor conv

* add conv+conv example, 1x1 only

* 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>

* add padding algo for bmm+scale+softmax+bmm. Version for verification

* remove verification code

* remove comments

* add padded bmm scale softmax bmm example

* format

* refactor

* add comments for usages of padding bmm+scale+softmax+bmm
Co-authored-by: Chao Liu <lc.roy86@gmail.com>

* avoid potential hazard; flaky test issue persists

* pin down the random seed to avoid flakiness

* fix the performance of the batched gemm verification

* fix tabs

* replace hipcc compiler with clang++

* build client app with hipcc

* build client app with clang

* add an option to build with hipcc ro clang

* fix the environment for client app

* fix setting up compiler in cmake_build

* change the way the compiler is set

* add scripts

* fixed splitK_gemm_fp32

* clean

* clean

* More int4 UT.

* Disable BitwiseRepresentation UT.

* Add UT with static_cast

* Surround cout statements with #if
Co-authored-by: Adam Osewski <aosewski@amd.com>

* Grouped GEmm int4.

* Formatting + fix K dimension for int8.

* Batched Gemm int4 example.

* CGEMM int4 example.

* Include inc filese in clang-format.

* SplitK int4 example

* Refactoring of performance measurement.

* Fix #ifdef statements.
Co-authored-by: Adam Osewski <aosewski@amd.com>