GitLab

* interface for GEMM and GEMM+add+add+fastgelu

* rename namespace

* instance factory

* fix build

* fix build; add GEMM client example

* clean

* UniforFill with integer values.

* Log tested instance type string.

* Add UT for all convolution specializations.

* debugging conv

* Fix dangling reference bug.

* Small refinements.

* Fix call to error checking function.

* Small refinements to tests.

* Configure error tolerance
* Change problem size.
* Remove OddC case from types that do not support it.

* Add helper traits for AccumulatorDataType.

* Print first 5 errs in check_err for integral types.

* Rename FillUniform to FillUniformDistribution

* Refactor

* Do not use typed tests.
* Instead use plain fixture class with templatized member functions.
* Initialize tensors with integer values.

* Refine test instances.

* Properly set accumulator data type.
* Add another "big" instance.

* Refactor convolution tests.

* Revert "debugging conv"

This reverts commit b109516455631ff8fd6dce99cf7c14bf8e323ebb.

* Add pragma once + format + small refinement.

* Fix some unwanted changes.

* Clang-format

* Fix profile_convnd to use renamed tensor initializer.

* Add instances for ConvFWDND kernel case 2D

* Helpers to get ConvNDFwd 2D instances.

* Refactoring.

* Remove "small block" instance as it was generating compiler errors.
* Remove default template parameters values.

* Refine and fix test.

* Fix problem with default template parameter types.
* Adjust error thresholds for floating point values test.
* Use integer values initialization for instances test.
* Add tests for ConvNDFwd 2D case.

* Remove AccumulatorDataType type trait.

* Update unit-tests.

* Remove operator<< overload.

* Unlock conv1d/3d nd fwd instances.

* Enable skipping calculating reference using flag.

* Fix number of channels for first ResNet50 layer.

* Clang-format.
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* use pre-built docker instead of building a new one

* try docker.image.pull

* change syntax in docker.image()

* add 30 min timeout

* increase timeout to 3 hours

* move performance tests to first stage for testing

* set image variable to the new container name

* update image name

* check available images

* check available images in both places

* try different image name

* use image ID to refer to image

* run performance on gfx90a

* fix the gpu_arch labeling, add parameter

* move env vars out of stages

* add stand-alone performance script, MI200 tests, CU numbers

* dos2unix for run_perf_tests.sh

* try the new git credentials

* use env var for git credentials

* use pre-built docker instead of building a new one

* try docker.image.pull

* change syntax in docker.image()

* add 30 min timeout

* increase timeout to 3 hours

* move performance tests to first stage for testing

* set image variable to the new container name

* update image name

* check available images

* check available images in both places

* try different image name

* use image ID to refer to image

* run performance on gfx90a

* fix the gpu_arch labeling, add parameter

* move env vars out of stages

* add stand-alone performance script, MI200 tests, CU numbers

* add resnet50 test to performance tests

* add blanks before gpu_arch in log files

* add resnet50 test with N=4 and process its results

* add ROCM and HIP versions to test tables

* uncomment the sql queries

* fix script syntax in jenkinsfile

* Tiny fix in dynamic_buffer.hpp to support vectorized AtomicAdd for double type

* Update to host layer and host reduction

* Merge and remove reduction kernels

* Merge and remove reduction device interfaces and update pooling device interface

* Merge and remove useless reduction device instances

* Update to reduction profiler and reduction ctests

* Update to reduction and pooling examples and add one reduction example

* Change to reduction examples to let them testable by ctest

* Add explicit pass checking for reduction and pooling examples

* Explicit assignment of tensor shapes in example reduce_blockwise_two_call

* Use atomic_add to repace atomicAdd and add atomic_add for double type

* Add reduce ctest support for double data type

* Replace to_int_vector() by using c++ std::vector::assign()

* Keep DeviceReduceThreadWise separated from DeviceReduceBlockWise

* Merge DeviceReduceBlockWise and DeviceReduceMultiBlockAtomicAdd into DeviceReduceMultiBlock

* Add GetAtomicOperationZeroValue() support for AtomicMax

* Tiny change to reduce example README.md

* Fix some tiny issues due to branch merging

* Revoke previous change in dynamic_buffer.hpp and add atomic_add for double2_t

* Add reduce multiblock_atomic_add instances for fp64 to verify vectorized atomic_add on fp64

* Renaming

* Clean the header includings in device_reduce instances header files

* modify ckProfiler_gemm output

* fix syntax

* change ckProfiler output and return 0

* fix syntax

* output datatype

* fix syntax

* output datatype in another way

* fix syntax

* fix syntax

* test return values of ckProfiler

* add layout info and tests, make sure ckprofiler returns 0

* fix syntax

* change layout output

* fix syntax

* fix syntax again

* update script to process perf results

* rearrange jenkins stages

* fix typo

* add python packages to Docker file

* adding setuptools-rust package

* modify parsing for new test parameters

* test db credentials on jenkins

* fix syntax

* update python script to handle incomplete lines

* ungrade python to 3.8 and write the gemm_params table

* add sqlalchemy package to docker

* move perf data processing to master node

* move the master node inside a steps region

* add new stage for result processing

* move results processing to separate stage

* reduce number of tests to speedup debugging

* pass config to processPerfResults stage

* run script on master in a docker container

* replace show_node_info

* try loading docker on master node again

* use ansible node instead of master

* get rid of pymysql package

* try ssh connection using paramiko

* put back pymysql

* put the perf data processing back on the gpu node

* put back artifact definition

* archive the perf_log before parsing

* clean up jenkinsfile, fix parsing

* fix typo

* enable all perf tests

* put all stages in original order, finalize script

* fix gpu_arch version

* update parsing script

* remove obsolete file causing merge conflict

* add performance test to jenkins pipeline

* fix typo

* fix the syntax in conv_fwd_util.cpp

* fix the error message syntax spacing

* fix the error message syntax spacing again

* run profile_gemm and archive results

* fix typo

* try to figure out the paths

* try to figure out the paths one more time

* skip the copying step

* build ckProfiler release only once

* change directory using dir

* fix dir syntax

* change the gemm parameters

* do not pipe script output to file

* try running ckProfiler directly

* fix typo

* use set +e

* run profile_gemm.sh || true

* run multiple gemms and parse results

* fix typo in jenkinsfile

* fix syntax

* add new gemm sizes, update scripts

* put all jenkins steps in original order
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Chao Liu <lc.roy86@gmail.com>

* compile ck for all targets

* update the target criteria

* change the target condition

* fixed some typos

* fixed missed file

* revert changes in README

* revert device_conv3d_fwd_xdl_...

* update device_conv3d_fwd_xdl_...

* update device_batched_gemm_reduce...

* test the unused arguments fix

* test the warning suppression

* try suppress warnings in device_batched_gemm_reduce_xdl...

* fix the last warnings

* replace UNUSED with std::ignore

* fix a typo

* replaced std::ignore with ignore

* add igonre header to common_header

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

* adding compilation for multiple targets

* fix build

* clean

* update Jekinsfile

* update readme

* update Jenkins

* use ck::half_t instead of ushort for bf16

* rename enum classes

* clean

* rename

* clean

* Convolution ND

* Code unification across dimensions for generating tensor descriptors.
* Example
* Instances

* Move convnd f32 instance file to comply with repo structure.

* Conv 1D tensor layouts.

* Formatting and use ReferenceConv

* Reference ConvFwd supporting 1D and 2D convolution.

* Debug printing TensorLayout name.

* Conv fwd 1D instance f32

* Refactor conv ND example.

Needed to support various conv dimensio.

Needed to support various conv dimensions

* Rename conv nd example director to prevent conflicts.

* Refactor some common utility to single file.

Plus some tests.

* Refactor GetHostTensorDescriptor + UT.

* Add 1D test case.

* Test reference convolution 1d/2d

* Remove some leftovers.

* Fix convolution example error for 1D

* Refactor test check errors utility function.

* Test Conv2D Fwd XDL

* More UT for 1D case.

* Parameterize input & weight initializers.

* Rename example to prevent conflicts.

* Split convnd instance into separate files for 1d/2d

* Address review comments.

* Fix data type for flops/gbytes calculations.

* Assign example number 11.

* 3D cases for convolution utility functions.

* 3D reference convolution.

* Add support for 3D convolution.

* Check for inputs bigger than  2GB.

* Formatting

* Support for bf16/f16/f32/i8 - conv instances + UT.

* Use check_err from test_util.hpp.

* Split convnd test into separate files for each dim.

* Fix data generation and use proper instances.

* Formatting

* Skip tensor initialization if not necessary.

* Fix CMakefiles.

* Remove redundant conv2d_fwd test.

* Lower problem size for conv3D UT.

* 3D case for convnd example.

* Remove leftovers after merge.

* Add Conv Specialization string to GetTypeString

* Skip instance causing numerical errors.

* Small fixes.

* Remove redundant includes.

* Fix namespace name error.

* Script for automatic testing and logging convolution fwd UTs

* Comment out numactl cmd.

* Refine weights initalization and relax rtol for fp16

* Fix weights initialization for int8.

* Add type_convert when store output in ref conv 1D.

* Get back old conv2d_fwd_xdl operation.

* Silence conv debug print.

* format

* clean

* clean

* Fix merge.

* Fix namespace for check_err
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Use thread cluster descriptor and explicit M_K 2d descriptor to simply Blockwise Reduction

* Change by replacing ReduceDims by NumReduceDims as Device Reduce interface template parameter

* Rename the folder name for the pool2d and reduce examples

* Update to reduction test scripts

* Add Readme for pool2d_fwd and reduce_blockwise examples

* Add support for int8_t reduction (ADD/AVG, MIN/MAX/AMAX)

* Tiny fix in reduce profiler and tiny update in reduce testing scripts

* Tiny fix in testing script profile_reduce_no_index.sh

* Tiny fix in testing script profile_reduce_no_index.sh

* Add support for bfp16 reduction (using bhalf_t = ushort)

* Tiny fix in amd_buffer_addressing.hpp

* Tiny change in script/profile_reduce_with_index.sh

* Use AccDataType for Beta value and use element_wise::PassThrough

* Use type_convert for type converting in host layer reduction

* Renaming and refining in Reduction profiler/device layer/examples

* Renaming and refining in Reduction profiler/device layer/examples

* Renaming all NumReduceDims to NumReduceDim

* Fix the leaked type_convert in ThreadwiseTensorSliceTransfer_v2

* Update to testing scripts to add bf16 support

* added more static_assert

* Remove buggy tunable configurations defined in device_reduce_instance_xxx.hpp

* Add static_assert to give compile-time warning for incorrect thread slice-size/vector-size configurations

* minor change

* Refine and fix (in GetWorkspaceSizeInBytes of MultiBlockPartialReduce) to make int8 completely pass

* Tiny renaming in gridwise_2d_reduction_multiblock_partial_reduce.hpp

* Tiny fix in script/profile_reduce_no_index.sh

* Refine in DeviceReduce layer with regard to using NumInvariantDim/NumReduceDim or InvariantDims/ReduceDims

* Generic renaming in host reduction and DeviceReduce layer

* Add support for 4-d all dimension reduction in the profiler and add_device_reduce_xxx instances

* Use multi-thread and simplification for host Reduction implementation

* Add ctest for reduction

* Update to clarify the using of data init method in produce_reduce/example_reduce/test_reduce/

* Update to the reduce CTest executables to enable default testing behavior when no command argument

* Renaming
Co-authored-by: Jianfeng yan <jfyan008@gmail.com>

* Use thread cluster descriptor and explicit M_K 2d descriptor to simply Blockwise Reduction

* Change by replacing ReduceDims by NumReduceDims as Device Reduce interface template parameter

* Rename the folder name for the pool2d and reduce examples

* Update to reduction test scripts

* Add Readme for pool2d_fwd and reduce_blockwise examples

* Tiny fix in reduce profiler and tiny update in reduce testing scripts

* Tiny fix in testing script profile_reduce_no_index.sh

* Tiny change in script/profile_reduce_with_index.sh

* Renaming and refining in Reduction profiler/device layer/examples

* Renaming and refining in Reduction profiler/device layer/examples

* Renaming all NumReduceDims to NumReduceDim

* Initial adding of generic reduction

* Initial adding of generic reduction ...

* Updates to make compiling done

* clang-format all files

* clang-format some files again

* Renaming in profiler/include/profile_reduce.hpp

* Updates and make BlockWise cases passed

* Updates and make ThreadWise and MultiBlockTwoCall cases passed

* Remove the support for MUL and NORM1 reduceOp from the profiler and the device instances

* Change to replace the dim0_max_vector_size/dim1_max_vector_size template argument in the device reduce classes

* format

* adding pooling

* added max and average pooling

* comment out cout and kernel timing

* Tiny simplification in profiler/reduce_profiler.cpp

* Add example for reduce_blockwise

* Tiny updates

* Change to pass the ElementWiseOp from device layer to kernel

* Fix the vectorDim and vectorSize in Device layer

* Enable vector load on both dim0 and dim1 for Threadwise method

* Tiny updates

* Change to let the user to pass the preUnaryOp and posUnaryOp

* Make pooling example work

* split device_reduce_instance into two libraries

* Tiny update

* Replace nanPropaOpt enum by boolean propagate_nan

* Simplification in DeviceReduce layer codes

* update build

* Change to clarify the difference between ck::half_t and half_float::half

* Renaming in all the reduction codes

* Add VectorSize as template parameter for device layer

* Add BetaIsZero as kernel template and as AccDataType for alpha

* print

* Small updates for pooling

* Updates for host_generic_reduction for reference

* Update to make AVG pooling pass

* Update to make MAX pooling with indices output pass

* fix

* add OutDst vector store to threadwise reduction and pooling

* tweak

* turn off check_indices that caused build issue

* refactor pooling

* clean up

* turn off check_indices for building issue for php-compiler

* add more tile size for odd C

* tweak conv for odd C

* update script

* clean up elementwise op

* add hack in reduction_operator.hpp to avoid compile error. To fix it, need to use element_wise_op in reduction op

* Add OutVectorSize as device and kernel tunable, also update to Elementwise Operations

* Move reduce operator mapping to host layer file reduction_operator_mapping.hpp from reduction_operator.hpp

* Change to the unary operators

* Move the definitions of unary operations to element_wise_operation.hpp

* re-org files

* Refine in device interfaces and multiblock kernels

* Split the reduction configurations into instances for specific methods

* Update in getTypeString() of device pool2d

* Renaming in host and kernel

* Tiny update in profiler/src/profiler.cpp

* Uncomment in device_operation/CMakeLists.txt to enable the building of all operations

* Make check_indices a templated function to remove some linking issue

* Renaming in the profiler reduce module

* Add support for double Reduction (but disable MultiblockAtomicAdd for double)

* Tiny correction of literal string

* Rename DevicePoolFwd to DevicePool2dFwd

* Split device_reduce_instance_xxx.cpp files according to the data types to speed up compiling

* Add comments for lists of configurations, lists of instances and references of add_reduce_instances_xxx

* Remove un-used header file gridwise_generic_reduction_wrapper_common.hpp

* Renaming and refining in the Reduction codes

* Tiny change in the unary operators

* Renaming symbols and files

* Renaming symbols in the kernels

* Move kernel kernel_set_buffer_value to separate file

* Add IndexDataType template parameter for kernels and use int32_t as index data type in device layer

* Tiny update in the kernels

* Remove definition of sqrtf()/isnan()/abs() for half_t due to some ADL issue

* Simplify a helper function in device layer

* Tiny adjustment in testing data initialization

* Renaming in kernel/device/host

* Add two testing scripts for reduction

* Refine the Unary operators in element_wise_operation.hpp

* Update in the reduce profiler module

* Update to the reduction testing scripts

* reduce compile parallelism

* change CI docker to rocm5.0

* remove unused variables

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

* tweak conv for odd C

* update script

* clean up elementwise op

* fix build

* clean up

* added example for gemm+bias+relu+add

* added example for gemm+bias+relu

* add profiler for gemm_s_shuffle; re-org files

* add profiler

* fix build

* clean up

* clean up

* clean up

* fix build

* [What]
1. Add DeviceGemmXdl_C_Shuffle
2. Revise example of gemm_xdl
[Why] Prepare to add shuffle version of D = alpha * (A * B) + beta * C
[How] Imitate DeviceGemmXdl and device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp

* start fixing 16bit data packing

* adding StaticTensor

* adding StaticTensor

* adding StaticTensor

* add missing constexpr

* adding static tensor

* adding static tensor

* adding transpose

* add inline asm for transpose 2x2 of half_t

* add general transpose_vectors(), but have unnecessary register initialization using v_mov

* fix unnecessary register initialization in transpose_vector by using more pass-by-reference

* add hardcoded logic for NHWC wrw

* improve asm for v_pack

* make ThreadwiseTensorSliceTransfer_v3r2 support any tensor

* tweak

* reorganize file

* add DeviceGemmXdl

* update script

* fix naming issue

* fix comment

* output HostTensorDescriptor

* rename

* padded GEMM for fwd v4r4r4 nhwc

* refactor

* refactor

* refactor

* adding ckProfiler

* adding ckProfiler

* refactor

* fix tuning parameter bug

* add more gemm instances

* add more fp16 GEMM instances

* fix profiler driver

* fix bug in tuning parameter

* add fp32 gemm instances

* small fix

* refactor

* rename

* refactor gemm profiler; adding DeviceConv and conv profiler

* refactor

* fix

* add conv profiler

* refactor

* adding more GEMM and Conv instance

* Create README.md

Add build instruction for ckProfiler

* Create README.md

Add Readme for gemm_xdl example

* Update README.md

Remove build instruction from top most folder

* Update README.md

* clean up

* add parameters

* tweak gemm

* tweak

* update conv

* update script

* adding bwd 1x1

* update script

* adding 1x1 bwd

* debugging bwd 1x1 failure

* update script

* update script

* test

* test v100

* clean up

* add gemm driver

* tweak

* add gemm kernel: mk_kn_mn and km_kn_mn

* tweak

* add GEMM km_nk_mn

* fix comment

* Squashed 'src/composable_kernel/' content from commit f6edda61

git-subtree-dir: src/composable_kernel
git-subtree-split: f6edda61

* add solver ConvIgemmFwdV6r1DlopsNchwKcyxNkhw; rename static ck source files

* Squashed 'src/composable_kernel/' changes from f6edda61..5781adf5

5781adf5 Update develop (#5) (#6)
97e6d514 Merge pull request #4 from ROCmSoftwarePlatform/separate_online_compile
7b1ec41e refactor
49c33aae refactor
54b3e73d rename

git-subtree-dir: src/composable_kernel
git-subtree-split: 5781adf5



* fix

* refactor

* remove online compilation from CK

* refactor

* fix

* add ctest

* add c-style pointer cast

* vector/scalar pointer cast use c-style pointer cast instead of reinterpret_cast

* fix clang warning suppression

* tidy

* suppress cppcheck

* fix enum issue

* revert chagnes to hip build

* fix kernel filename

* update CK build script

* rename

* rename

* make innner product compatiable on gfx900

* Update src/include/miopen/solver/ck_utility_common.hpp
Co-authored-by: JD <Jehandad.Khan@amd.com>

* compiler parameter use stream

* use int instead of index_t in kernel wrapper

* DynamicBuffer, StaticBuffer, amd_buffer_load support customized value for invalid element

* refactor

* refactor

* change cmakelist

* change ck common utility

* fix
Co-authored-by: JD <Jehandad.Khan@amd.com>

* update online kernel wrapper bundle all descriptors in a tuple

* change __CONSTANT__ to CONSTANT

* rename

* adding tuning

* added IsValidCompileParameter

* reorginze

* adding tunable for fp16 and int8

* fix kernel compile warning and bug fixes

* suppress warning about cast CONSTANT (address space 4) pointer

* fix building issue

* change olc cmake

* adding online compile to fwd-v4r5r2

* update scripts

* remane fwd-v4r5r2 to fwd-v6r1

* clean up

* deprecate static kernels

* create files for xdlops

* working on blockwise_gemm_xdlops

* add KReduction

* add m/n repeats

* add 2x2 pipeline

* added 128x128 wavegemm

* use StaticBuffer of vector_type

* break vector type to blk_size

* add kpack into xldops_gemm and blockwise_gemm

* abroadcast only

* add fp32 mfma instructions

* adding fp16 mfma

* pack half4_t

* rename kperwave to kpack

* add 32x32x8fp16

* add fp16 mfma

* clean code

* clean code

* V4r4 xdlops kpack (#35)

* add kpack with incorrect results

* bug fix for make_dynamic_naive_tensor_descriptor_aligned_v2

* add 1x1 kernel

* add gridwise_gemm_v2 - single_buffer

* enabled dwordx4 for fp16
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* refactor fwd-v4r4-xdlops

* add v4r4-nhwc-xdlop

* improve some perf of nhwc and nchw by tuning parameters, and change scheuduling in gridwise-gemm loop

* tweak scheduling in gridwise gemm

* add v4r3 with a single output copy

* init commit: output with slice win

* adding sliceWin

* add multiple repeats pattern

* starting adding bwd-v4r1-xdlops

* use tuple as SrcBuffer

* adding bwd-data v4r1 nhwc xdlops

* fix bug in make_dynamic_naive_tensor_descriptor_aligned_v2()

* fix bug in host bwd-data conv

* initial implementation of bwd-data v4r1 nhwc xdlops

* add launch bound flags

* enable launch bound

* add m/nrepeat=4

* tweak bwd-data v4r1 nhwc xdlops

* added bwd-data v4r1 nhwc xlops with output A and weight B

* add fwd-v4r4 nhwc xdlops, A input, B weight, C output
Co-authored-by: Chao Liu <chao.liu2@amd.com>

* Add online-compiling facility

* Synchronize from fwd-v4r5 and implement host interfaces to call conv-fwd v4r4/v4r5 using on-line compiling method

* Tiny adjustment to time reporting

* Use object assignment to replace explicit bytes copying in the first kernel of v4r4/v4r5

* Use single thread to assign descriptor object to device memory

* Adjust to the workload assignment of the two kernels of v4r4 (experimental)

* Revert "Adjust to the workload assignment of the two kernels of v4r4 (experimental)"

This reverts commit eb38461456bb0c82b6c0d32cdd616e181907e20c.

* Update to make constexpr for generating descriptor types in kernel 2 of dynamic conv-fwd v4r4

* Update to dynamic conv-fwd v4r4 online-compiling

* Update to dynamic conv-fwd v4r5 online-compiling (result not accurate)

* Tiny update to driver/CMakeLists.txt

* clang-format

* Tiny comments change

* Add env OLC_DUMP_SAVE_TMP_DIR to support saving of temperary dir

* Fwd v4r5 olc perf (#39)

* added hip-clang flags that fix perf issue of online compilation

* fix bug for olc fwd-v4r5-nchw

* Move constexpr and type reference statements out of the function body in conv-fwd v4r4/v4r5 kernel wrapper

* Remove printing in hip_build_utils.cpp

* Update to root CMakeLists.txt

* Revert "Move constexpr and type reference statements out of the function body in conv-fwd v4r4/v4r5 kernel wrapper"

This reverts commit 3d2c5d8ecdd8298b72d127110500ed5b38d9835c.
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: root <root@dc-smc-18.amd.com>

* experimenting magic number division

* overhauling fwd-v4r4 to clearly reflect transformation graph

* added fwd-v4r5

* bug fix for make_dynamic_naive_tensor_descriptor_aligned_v2

* bug fix and added sanity-check in transform_dynamic_tensor_descriptor

* added conv_driver_v2

* Use DynamicBuffer to hold raw pointer (to global and LDS memory)

* add workaround for compiler issue (inefficient ISA) of ds_write for int8x4, int8x8, int8x16

* Replace most raw index calculation to coordinate transformation
* Overhaul blockwise and threadwise GEMM
* Overhaul driver for gridwies GEMM kernel
Co-authored-by: Jing Zhang <jizhan@amd.com>

* support dynamic tensor descriptor

* use buffer load OOB feature for padding case

* add navi support

* add int8x4 inference kernel
Co-authored-by: Chao Liu <chao@ixt-rack-81.local.lan>
Co-authored-by: Jing Zhang <jizhan@amd.com>

* tuning para,

* testing on v100

* add fp16

* remove deprecated tensor descriptor

* sync with miopen

* update build script
Co-authored-by: Jing Zhang <jizhan@amd.com>

* Added bwd data v3r1: breaking down compute into a series of load balanced GEMM, and launch in a single kernel
* Added bwd data v4r1: like v3r1, but launch GEMMs in multiple kernels
* Tweaked v1r1  and v1r2 (atomic) on AMD GPU

* enabled atomic add in tensor copy
* added gridwise GEMM
* added backward data conv using GEMM + atomic
* added backward data conv using GEMM, no atomic

Refactor, so can bring multi-index transformation and padding support into MIOpen