GitLab

* 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

* 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

* 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

* 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

* 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>

* 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

* Use smaller tensor size in test

* Use even more smaller tensor size

* Touch only failing test case inputs

* 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>

* Re-structure ckProfiler source files

* Rename profiler.cpp to main.cpp

* Modularize ckProfiler operations

* Add description for profiler operations

* Use longer name to avoid name collision

* Use macro to delay expansion

* Use std::move() to avoid object copying

* Prohibit users from calling dtor

* Use macro to eliminate redundant code

* Make friend function hidden

* Add missing include directive <iostream>

* Fix wrong include directives

* Remove int8 from batchnorm-forward instances since it is not needed for forward training and could fail test
Co-authored-by: Qianfeng Zhang <Qianfeng.Zhang@amd.com>

* 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()

* properly return error flag; reveals bug in split-k gemm

* fix bug in split k

* update split-k test case
Co-authored-by: Chao Liu <chao.liu2@amd.com>

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>

* workaround bf16 atten fwd issue on gfx908

* typo

* 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>

* 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

* 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 group...

* add fused addition lyernorm

* add fused addition lyernorm

* changed CMakelist

* removed annotates

* modified descriptor of C

* fixed bug in gridwise add layernorm

* format the files

* modified name from add&layernorm into elementwise&layernorm

* created fused elementwise layernorm branch

* change input into tuple type

* add sweep once to reduce load & read of C from global memory

* modified Argument api

* modified way to malloc c in global memory

* changed gamma and beta to m_k_desc

* fixed bug when sweep once and move CDataType when define device level struct

* add src dim for gamma and beta

* implement optimization for coalesced

* delete a annotation line

* fixed some bug to meet the requirements of ck

* add bandwidth computing in example, and fixed the time unit

* move device_elementwise_layernorm_impl.hpp into device/impl

* fixed bug in device_elementwise_layernorm_impl.hpp

* changed name from layernorm into normalization

* clang-format the changed files

* changed the names

* moved immidiate results into lds, it become faster in non-sweeponce cases

* changed naming of C into X to make the defination more clear

* changed naming in example

* add tests for elementwise normalization

* move example_elementwise_layernorm_blockwise into folder 44_elementwise_normalization

* move test_elementwise_layernorm_fp16 into new folder

* move elementwise_normalization_instances into a new folder

* add more tests in test_elementwise_layernorm_fp16.cpp

* added some corner cases in test

* fixed method to compute lds size for matrix X

* changed name of 44_elementwise_normalization into 45_elementwise_normalization

* modified some comments

* modified some other confused comments

* reduce redundant tests in test_elementwise_layernorm_fp16.cpp

* disable gtest discovery to run tests per-program not per-case

* register cmake target to ctest

* Sync the naming

* Sync the test of layernorm with groupnorm

* Sync the naming

* Minor change for comment and log

* [What] Add saveMean and SaveInvVariance in the interface.
[Why] These can optimize the backward

* Add reduction across all dims cases.

* host softmax: handle all reduce

* Test cases when reduced dim is not innermost axis.

* Fix syntax.

* Test non innermost dim for fp32 and int8

* Group test suites wrt NumReduceDim.

* Additionally test failing cases.

* Throw error when Rank or NumReduceDims doesn't match arguments.

* Check reducedDims has correct values

* Move don't reuse DeviceReduceMultiblock IsSupportedArgument method.
Instead implement own. (in fact just get rid of one check to enable
reduction across inner dimensions).

* Reorganize unit tests to better cover use scenarios.

* Test input validation
* Test reduction of inner dimensions with custom op instances.

* Refactor fp32 and int8 unit tests.

* Fix FP32 instance template parameters.

* Add more instances.

* Instances with InSrcVectorDim=0.

* Do not initialize and copy data when arg not supported.

* ckProfiler Softmax use instance factory.

* Refactor device softmax IsSupported.

* Additionally add non-polymorphic api functions

* Split softmax instances into multiple files.

* Fix profiler.

* Reorganize tests to reuse profiler and cover edge cases.

* Clang-format

* I8 Softmax instances along with UT.

* Reuse type alias definitions from instance factory header.

* Clean included headers

* Fix variable names.

* Add missing checks in Argument constructor.
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Anthony Chang <ac.chang@outlook.com>

* reopen masking att instance due to CI is upgraded

* re-enable instances previously failed on 9110

* enable ksize-kpadding pair validity test

* add non-masked attention+permute test; expose masking boolean to attention kernel handles

* disable bench

* fix test

* move files

* bulk rename batched_gemm_masking_scale_softmax_gemm_permute to batched_gemm_softmax_gemm_permute

* format

* amend rename

* disable bench in test

* add mask/no-mask test for non-permute attention kernels

* disable broken kernel instance

* example working

add non-permuted problem statement

evaluating whether overhead comes from permutation or the extra kernel arg

* interface for bias addition without implementing it

* test and profiler running

* tidy

* mask type determined by enum class

* unify example code

* move masking specialization to its own header

* align formats

* extract helper functions

* experiment merging dims for attn w/ permute; shows perf parity with attn wo/ permute

* add tensor specialization to template args

since tensor spec packed shows perf parity when permutation isn't needed

remove redundant template args

comment on 'packed' tensor specialization

* grouped attention with input/output permute example

* format

* clean up

* refactor acc0 tile visitor
Co-authored-by: shaojiewang <wsjmessi@163.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Fix for lwpck-425, update BlockTransferSrcVectorDim

* Revert "Fix for lwpck-425, update BlockTransferSrcVectorDim"

This reverts commit fd24e280e28ff238b452cfdde58a988affd46461.

* Add Batched Gemm int8 test, expect it to fail

* Format

* Re-add the fix

* 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

* Apply suggestions from code review
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>

This reverts commit efbcc6ed.

* add fused addition lyernorm

* add fused addition lyernorm

* changed CMakelist

* removed annotates

* modified descriptor of C

* fixed bug in gridwise add layernorm

* format the files

* modified name from add&layernorm into elementwise&layernorm

* created fused elementwise layernorm branch

* change input into tuple type

* add sweep once to reduce load & read of C from global memory

* modified Argument api

* modified way to malloc c in global memory

* changed gamma and beta to m_k_desc

* fixed bug when sweep once and move CDataType when define device level struct

* add src dim for gamma and beta

* implement optimization for coalesced

* delete a annotation line

* fixed some bug to meet the requirements of ck

* add bandwidth computing in example, and fixed the time unit

* move device_elementwise_layernorm_impl.hpp into device/impl

* fixed bug in device_elementwise_layernorm_impl.hpp

* changed name from layernorm into normalization

* clang-format the changed files

* changed the names

* moved immidiate results into lds, it become faster in non-sweeponce cases

* changed naming of C into X to make the defination more clear

* changed naming in example

* add tests for elementwise normalization

* move example_elementwise_layernorm_blockwise into folder 44_elementwise_normalization

* move test_elementwise_layernorm_fp16 into new folder

* move elementwise_normalization_instances into a new folder

* add more tests in test_elementwise_layernorm_fp16.cpp

* added some corner cases in test

* fixed method to compute lds size for matrix X

* changed name of 44_elementwise_normalization into 45_elementwise_normalization

* modified some comments

* modified some other confused comments

* reduce redundant tests in test_elementwise_layernorm_fp16.cpp

* 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>

* Fix bug of profiler for layernorm

* 1. Rename layernorm into normalization
2. Decouple softmax from normalization

* clang-format

* use another instance to check the efficiency

* optimize group layer norm

* 1. coalesce load/store data for gridwise layer norm welford. 2. move a sqrt and divison into a outer static loop

* add more instances to layernorm

* add 2 more test cases

* remove ignore in generating tuple of vector
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* 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>