GitLab

* 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

* 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

* 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

* 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

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

* 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 threadwise and blockwise welford

* Rename gridwise op, prepare to add welford version

* implement welford and integrate welford into layernorm

* Take care of tail loop

* Fix buf when ThreadSliceK > 1

* Fix bug of merging of two empty set

* Rename clip to clamp

* 1. Fix type of count
2. Remove useless static_assert

* Do not inherit Reduction::Argument

* [What] replace __syncthreads() with block_sync_lds()
[Why] __syncthreads might wait both lgkmcnt(0) and vmcnt(0)

* Add y stride

* Rename.
DeviceLayernorm -> DeviceLayernormImpl
DeviceNormalization2 -> DeviceLayernorm

* Move literal ""_uz & ""_zu into namespace 'literals'

* Move namespace 'literals' as 'ck::literals'
Co-authored-by: Po-Yen, Chen <PoYen.Chen@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Refine parameter

* Add base class for layernorm

* Add layernorm instance

* Add layernorm to ckProfiler

* Remove redundant

* Add verification

* Fix compile error due to merge

* convnd_fwd fp16 example

* update example

* update example

* update instance

* updating refernce conv

* update reference conv

* update conv fwd profiler

* update conv 1d and 3d instance

* update include path

* clean

* update profiler for conv bwd data and weight

* update conv bwd weight

* clean

* update conv example

* update profiler for conv bwd weight

* update ckprofiler for conv bwd data

* fix reference conv bwd data bug; update conv bwd data test

* update examples

* fix initialization issue

* update test for conv fwd

* clean

* clean

* remove test case too sensitive to error threshhold

* fix test

* clean

* fix build

* adding conv multiple d

* adding conv multiple D

* add matrix padder

* add gemm padding to convnd

* adding group conv

* update gemm multi-d

* refactor

* refactor

* refactor

* clean

* clean

* refactor

* refactor

* reorg

* add ds

* add bias

* clean

* add G

* adding group

* adding group

* adding group

* update Tensor

* clean

* update example

* update DeviceGemmMultipleD_Xdl_CShuffle

* update conv bwd-data and bwd-weight

* upate contraction example

* update gemm and batch gemm with e permute

* fix example build

* instance for grouped conv1d

* update example

* adding group conv instance

* update gemm bilinear instance

* update gemm+add+add+fastgelu instance

* update profiler

* update profiler

* update test

* update test and client example

* clean

* add grouped conv into profiler

* update profiler

* clean

* add test grouped conv, update all conv test to gtest

* update test

* Implement layernorm kernel and deviceOp

* verify gpu kernel with host code

* 1. Separate gamma aand beta from affine
2. Check if argument is valid

* clean

* Sync the naming

* Support sweep once mode if we can put k dimension data inside one block

* [What] Get length from upper length.
[Why] if we get length directly, we may get length after padding.

* We only use one block in K dimension.
Hence, we can simplify the indexing of global R/W.

* Use 1d descriptor for gamma and beta

* Add accElementwiseOp

* Extract layernorm host code

* Support different YVectorDim in GridwiseLayernorm

* Rename XSrcVectorDim to XYSrcVectorDim. Because we use same parameter in deviceOp

* Gamma and beta can share the VGPR.

* Add test for fp32 and fp16

* Fix bug of concurrency and add test case which may fail orignally

* Propagate NaN for layernorm
Co-authored-by: Chao Liu <chao.liu2@amd.com>