GitLab

* enable dl kernels on navi3

* do not build xdl tests and examples on Navi

* run tests before building everything on jenkins

* disable gemm_bilinear on gfx1030

* add gpu targets to installer on Navi

* put tests in the same order as before

* reduce the number of navi targets in CI

* build CI installed for gfx940 as well

* only build for MI300 during QA runs

* Add contraction profiler and tests

* Build and style fixes

* Allow to use any elementwise operator for ref_contraction

* Introduce profile_contraction_scale and profile_contraction_bilinear

* Make ref_contraction generic and extend interface tests

* Stylistic minor fixes

* Extend test_contraction_interface

* replace amd_buffer_atomic_add with hip_atomic_add

* fix grouped_gemm_splitk kernels on mi300

* fix syntax

* revert experimental atomic_add changes

---------
Co-authored-by: Jing Zhang <jizhan@amd.com>

* enable gfx940

* switch between intrinsic mfma routines on mi100/200 and mi300

* fix mfma_int8 on MI300

* disable 2 int8 examples on MI300

* Update cmake-ck-dev.sh

* restore gitignore file

* modify Jenkinsfile to the internal repo

---------
Co-authored-by: Jing Zhang <jizha@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* 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

* 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

* Add type_convert implementations for bf16

* Add the fix for conv_fwd

* Add the fix for conv_bwd_data

* Add the fix for conv_bwd_weight

* Format

* Format

* Another format

* Add a macro to use workaround on MI200 only

* Format

---------
Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* 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 CMake Option "USE_OPT_NAVI3X"

* fix bug

* 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 820869182f6a8f62b2c9004101ba6bf76b96be14.

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

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

* Pass shared mem pointer as pointer to void.

* Device Op GroupedGEMM Multiple D

* Example for grouped gemm multiple d.

* Add MI200 to supported archs.

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

* enable building on Nav31

* fix syntax

* replace GPU_TARGETS with offload-arch

* add gfx1102 rachitecture

* fix typo

* update changelog

* fix a bug blocking wmma_gemm_multipleD

* Utilize matrix padder in device_wmma_op

* cosmetic change for gemmpadding format

* clang format

* Change gridwise gemm from FIFO to KMN loop fashion

* clean up

* fast gelu using builtin function

* clean

* clean

* clean

* clean:

* clean

* fix compilation

* clean

* clean

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

* Add DeviceOp and examples

* Format DeviceOp template arguments

* Remove bf16 example

* Format

* Format

* Update MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N

* Refactor argument preparation

* Update conv_bwd_weight_dl to grouped_conv_bwd_weight_dl

* Rename device op file

* Update include directive in the example file

* Update descriptor preparation for grouped op

* Update the argument

* Update batch handling

* Add gridwise gemm supporting batched input

* Update blockwise indexing, working version

* Update copyright year

* Update check if argument is supported

* Refactor and make consistent with xdl examples

* Update check if argument is supported

* Add changelog entry

* Added comments on Dl op split_k>1 support

---------
Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* add contraction_bilinear

* add contraction_scale_xdl_fp64

* reduce tile size to avoid register spill

---------
Co-authored-by: root <root@ctr-ubbsmc16.amd.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

* 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

* tempsave

* tempsave

* tempsave

* runtime bug, cannot find symbol

* workaround for incorrect HIP warpSize return value

* debugging

* tempsave

* Correctness OK, waiting for optimization

* Tidy up + format

* temp save

* temp save, reproduce the v_bfi_b32 issue

* add inline asm for wmmaop test

* tidy up

* clean some debug purpose code

* discard some codes

* clang format

* clang format

* compiler issue fixed + increase tile size

* navi3x_multipleD+example

* temp save

* workable

* batchedgemm[OK], groupconv[debug]

* groupconv: Sanity check[OK], Performance[Bad]

* navi3x_groupconv_need_optimization

* format

* Add arch limitation to all wmma examples

* fix bug: example30 input conv args

* 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>

* 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

* 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

* 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

* Use double as alpha/beta values type in reduce device op api

* Use double as alpha/beta values type in softmax device op api

* Use double as alpha/beta values type in multiple-reduce device op api

* Use double as epsilon value type in normalization/elementwise-normalization device op api

* wavelet gemm programming model support for CK

* GEMM pipeline update for wavelet progrmmaing model

* Updated wavelet programming pipeline

* fixes for global-write for math-wave

* fixed bug in global writes

* Updated comments for better readability

* fixed clang format errors

* added block_lds without barrier sync

* clean

* clean

* clean

* clean

* refactor

* prototype

4 layouts

fix default stride

all problem sizes

tidy

move file

update build script

restore old file

fix build

* refactor standalone test to use gemm test harness

* simplify gemm test

* update build script

* remove redundant

* early return when cmd arg doesn't match

* tidy

* report failure when result not validated

* tidy

* Add comment depicting B2C mapping pattern.

* Formatting & comments.

* Comparison with custom B2C mapping pattern.

* Example for wavelet gemm.

* Add wavelet to Gemm standalone test.

* Remove debug code.

* Remove dangling #endif directive.

Co-authored-by: root <Raman Jana>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Anthony Chang <ac.chang@outlook.com>
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>

* 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 multi embeddings support

* fix format

* optimize sqrt

* add reduce operation

* change to elementwise op

* fix name

* rename

* run ci cd

* format example

* format code

* format code

* 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 device op of gemm layernorm

* [What] Rename F to H
[Why] F and G prepare for welford tensor

* Add gridwise gemm + welford

* Extract template parameter

* Rename kernel. Prepare to add second half kernel

* Extract var

* Add second kernel for gemm+layernorm

* Move to the gemm_layernorm folder

* Rename F and G to mean and var

* Do not use snakeCurved, it makes determination of padding  for welford difficult

* Rewrite the device interface and rename some var

* Add welford count

* Update interface

* Sync code, prepare to test on MI200

* Clean the code

* Implement layernorm

* Add comment to mension hipFree

* Wrtie out the e for debug.
This could be remove and use h for instead

* 1. Allocate mean, var and count into by SetWorkSpacePointer.
2. Add GetWorkSpaceSize to calculate the space size

* Add gemm layernorm host code

* use reference layernorm

* Fix bug of blockwise welford for first kernel

* Fix bug of mean var padding for layernorm

* Use sgpr for shuffleM_index

* padding for GemmMeanVarCountGridDescriptor_M_NBlock

* Add layout parameter

* Check argument for gemm

* calculate max count for tail block

* Share E and H memory in device op

* Hard code the vector dim

* Refine the MakeDescriptor

* 1. Remove E parameter, because E is inside of device op
2. Check vector size

* [What] Rename MakeMeanVarDescriptor_M_N
[Why] Prepare to add count version of make descriptor

* Use 1D global memory for count

* Prevent redundant IO

* Update parameter

* Add pipeline v1/v2 selector

* Rename the example name

* Add base class for gemm layernorm

* Refine naming to distinguish naive and welford

* Add comment to explan in detail

* We don't need to pad in N dimension in gemm for mean/var/count. Set NPerTile 1

* Rewrite the 2st kernel, use multiple block along N dimension in layernorm kernel

* Share the vector size

* Refine var name

* [What] Force LayernormThreadSliceSize_N = vector size.
[Why] Memory coalesce

* Add comment

* Extract divisor out of the loop in reference layernorm

* Pad different size for E and H in layernorm kernel according to different block tile

* Refine naming

* Refine naming

* Prevent implicit cast

* [What] use ck::math::sqrt instead of __builtin_amdgcn_sqrtf
[Why] __builtin_amdgcn_sqrtf is only support float, double will cause casting

* Cast only constant

* Change of post shuffle thread descriptor

* Add EMeanVarDataType parameter.

* Merge the mean and var threadwise copy

* Add missing index

* Fix Typo

* Sync the variable with previous if

* 1. Declare e inside the host_gemm_layernorm()
2. Prevent implicit cast in reference code
Co-authored-by: Po Yen Chen <PoYen.Chen@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

* tempsave

* tempsave

* tempsave

* runtime bug, cannot find symbol

* workaround for incorrect HIP warpSize return value

* debugging

* tempsave

* Correctness OK, waiting for optimization

* Tidy up + format

* temp save

* temp save, reproduce the v_bfi_b32 issue

* add inline asm for wmmaop test

* tidy up

* clean some debug purpose code

* discard some codes

* clang format

* clang format

* compiler issue fixed + increase tile size

* 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>

* 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()

* 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>

* 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.