* Do not hardcode the function parameter, use template instead.
* [What] Remove AThreadTransferSrcResetCoordinateAfterRun and BThreadTransferSrcResetCoordinateAfterRun in host API
[Why] "C_Shuffle" version is supposed to be similar to the vanilla one
* Fix typo
Let DeviceGemmXdl_C_Shuffle use kernel_gemm_xdlops_v3r1
* [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
* fix relu
* clean up
* clean up
* adding 1x1 conv
* adding 1x1 conv
* added 1x1 conv
* refactor
* refactor
* refactor
* added profiler for conv+bias+relu+add
* clean up
* adding conv+bias+relu
* adding conv+bias+relu
* added conv+bias+relu
* Update README.md
* update cpu verification
* adding c shuffle
* update static_tensor for dealing with invalid element
* adding c shuffle
* debugging
* fix bug
* convert to fp16 before shuffle
* shuffle more than one M/NRepeat
* clean up
* remove coordinate step hack from GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r1
* clean up
* remove coordinate step hack from all gridwise gemm xdl
* clean up coordinate step hack
* clean up coordinate step hack
* ThreadwiseTensorSliceTransfer_v3r2 support pointwise op on both src and dst
* adding output shuffle in conv+bias+relu+add
* update
* added conv+bias+relu+add with c shuffle
* added conv+bias+relu+add with c shuffle
* fix forward_sweep bugs in threadwise copy
* clean up
* refactor
* clean up
* clean up
* added conv_c_shuffle+bias_relu
* clean up
* added conv+bias+relu+atomic_add
* clean up
* clean up
* clean up
* clean up
* clean up
* clean up
* misc fixes; add 1x1 specialization
* clean up
* delete unused device op
* clean up
* add support for odd C value
* Bug in BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
* Bug in ThreadwiseTensorSliceTransfer_v1r3 logic for calculating "forward_sweep"
* add add new algorithm from v4r4r2
* program once issue
* add split k functiion
* redefine code
* add a matrix unmerge
* add b matrix unmerge k0
* trans a and b to gridegemm
* nhwc init
* no hacks and vector load
* add hacks
* modify some parameter
* fix tuning prometer for fp32
* fix tuning prometer for fp16
* start change gridwise k split
* init ok
* revome a b matrix k0mk1 desc in grid
* carewrite lculate gridsize
* add kbatch to CalculateBottomIndex
* remove some unused funtion
* add clear data function before call kernel
* out hacks
* in hacks
* rename device convolution file and function name
* modify kBatch value
* fix some tuning code
* start from v4r4 nhwc
* nhwc atomic is able to run
* just for fp32
* enable nchw atomic
* tweak
* tweak
* re-arrange gridwise gemm hot loop for wrw
* add wrw v4r5
* v4r4r5 fp16
* v4r4r4 fp16
* v4r4r2 fp16
* V4R4R4XDLNHWC fp16
* V4R4R2XDLATOMICNCHW fp16
* adjust for fp16
* input gridsize
* change kbatch to gridsize
* testing wrw
* clean up
* k_batch to gridsize
* fix bug
* wrw v4r4r4 kbatch change to gride size
* wrw v4r4r2 kbatch change to gride size
* after merge , change gridwise gemm v2r4
* change MakeCBlockClusterAdaptor
* other method use new gridwise gemm
* clean up
* chapad method nge to make_right_pad_transform
* kbatch out from transform function
* clean up and fix bug
* fix bug
* using function type reduce template parameters
* using auto replace define fuction type
* clean up
Co-authored-by: ltqin <letaoqin@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Jing Zhang <jizhan@amd.com>
* Tiny fix in using data type template parameters in blockwise and direct_threadwise kernel
* Fix with regard to implementing GetZeroVal() in both kernel and host
* Avoid convert to compType from dstDataType before writting the output value
* Add half_t support to NumericLimits and make constexpr GetZeroVal() of binary operator
* Add CONSTANT decorator for descriptor read buffer
* Use get_thread_local_1d_id() for thread local Id
* Rename GetZeroVal() to GetReductionZeroVal() in the kernels
* Remove constexpr from initialized zeroVal and tiny fix in reduction_operator.hpp
* Occasional tiny simplification and update in the kernel files
* Update to re-order tensor dimensions on the host, split second_call kernel wrapper files and simplify reduce_all kernel wrappers
* Update to remove OpenCL tidy checking failures
* Update for better readability
* Remove unused codes and not-needed template parameters in the kernel wrappers
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* use cast_pointer_to_generic_address_space() in v6r1 kernel wrapper, DynamcBuffer and buffer_load take customized invalid-element-value, add buffer_load/store for fp64
* use remove_cvref_t
* add solver ConvIgemmFwdV6r1DlopsNchwKcyxNkhw; rename static ck source files
* make inner product compatible on gfx900
* Update src/include/miopen/solver/ck_utility_common.hpp
* compiler parameter use stream
* use int instead of index_t in kernel wrapper
* DynamicBuffer, StaticBuffer, amd_buffer_load support customized value for invalid element
* Add dynamic generic reduction kernel layer (kernel wrappers, kernel implementations and utilities)
* Some updates to dynamic composable kernel facility for the need of dynamic generic reduction
* Update to generic reduction C++ host interface layer to support dynamic generic reduction
* Update to remove tidy complaints in host interface layer
* Change the unary operator form from void op(T &x) to T op(T x)
* Update to pass single workspace pointer for all kernels (fix for OpenCL backend)
* Use cppcheck-suppress to prevent some strange warnings
* Re-use operator [] and () for DynamicBuffer and update to depending codes
* Remove useless codes in first call threadwise/warpwise/blockwise kernel wrappers
* [performance] Remove un-needed local buffer initialization
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: JD <Jehandad.Khan@amd.com>
* add f32/i32 atomicAdd support into dynamicBuffer, and enable it in v1r3
* fixed
* fixed
* update comment
Co-authored-by: Chao Liu <chao.liu2@amd.com>
