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* 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>

* format

* improving pipeline

* fix typo

* format

* adding thread group

* adding thread group

* adding thread group

* adding gemm pipeline

* tweak

* refactor

* refactor

* add missing type convert

* refactor

* refactor

* refactor

* clean

* fix build

* refactor

* format

* clean up

* use remove_cvref_t

* clean

* use pipeline_v2 for gemm kernel

* Remove inconsistent indent

* Fix compilation errors due to incomplete merge process

* Add missing include directives

* Fix compilation errors in currently unused files

* Add license in newly added files

* Re-format touched files by clang-format-10

* Fix wrong template argument count of DeviceGemm<>

* Use language construct to choose between types

* Use language construct to choose GEMM example instance

* Fix compilation error due to interface change

* Re-use type alias to avoid duplication

* Unify type alias usage in source file

* Only use v2 pipeline in one gridwise GEMM type

* Remove no-longer used include directives

* Add static_assert() to check pipeline type requirements

* Revert "Add static_assert() to check pipeline type requirements"

This reverts commit f0985f0a132671a1caaea92810c9f30dcf062bde.

* clean

* clean

* clean

* clean
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: shaojiewang <wsjmessi@163.com>

* adding contraction

* add contraction example

* update examle

* update example

* format

* update readme

* clean header

* clean header

* contraction with multiple D

* rename

* fix naming issue; add instances for contraction+bilinear

* change assumed virtual layout of contraction; add client example

* update example

* update

* contraction+scale

* use type_convert

* rename

* init commit

* add c_permute

* add mnk padding

* fixed comments

* Fixed comments
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* refactor

* update example

* update example

* gemm bilinear

* clean

* update

* dump lds content in appropriate precision type

* add squared add reduction op; allows sq sum

* initial stub from regular gemm impl

* layernorm example code & host verification

* initial layernorm implementation

* tidy up

* make C0 precision type consistent with C

* clang-tidy and additional comments

* tighten up example code

* account for extra flops/bytes from normalization

* clang-format

* c0 bias/beta/gamma now have its own precision type

* AccElemOp for gemm outputs prior to feeding to layernorm

* update workgroup mapping

* rename kernel template param to reflect its dual use

* use LDS mem pool for reduction workspace

* change cshuffle precision type to f16; clean up

* clang-format

* correct naming

* explicit cast

* fully implemented gemm + bias + activation + add + norm

* activation in correct order

* reflect reduction API's recent change

* amend

* clean up; add comment

* keep up with recent changes in reduction API

* format

* resolve merge conflicts
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* init commit

* add desc

* finished c permute

* fixed vector lens

* use 'sweep once' softmax kernel where applicable

* threadwise copy's dst buffer can specify invalid element value

* add int8 in/out float compute softmax support

give a bit of leeway for int absolute tolerance as there's a single data point of all test cases showing off-by-1 error

* format

* softmax inherits DeviceNormalization

* softmax profiler stub

* tighten up reference softmax interface

* example prints tensor dimension

* add fp32 to softmax profiler

* rename header

* hook with ckProfiler

* format

* resolve merge conflict

* resolve merge conflicts

* update normalization profiler help string

* resolve conflict

* typo

* remove residual

* softmax profiler: address feedback

* test for mixed precision input/output

* fully qualify ck::math::isnan

* add comment for device normalization interface

* revise wording

* constness for alpha/beta scaler pointer