* 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>
* [What] Separate fixpoint gemm from gemm example
[Why] let example of gemm_int8 be pure gemm.
[What]
1. Add gemm_requant_relu_requant,
2. Let CDataType be int32 in pure gemm, because no one use int8 CDataType. It is also part of gemm_requant_relu_requant
* Fix path
* Revise cmakelist due to merge develop
* Add gemm fp16 test
* Extract PrepareGemmTensor
* Extract TestGemm
* Add test for different layout
* Add 4 layouts of shuffle version of fp32
* Add 4 layouts of shuffle version of int8
* Add 4 layouts of shuffle version of bf16
* replace all DeviceGemmPtr_ with DeviceGemmNoOpPtr to fit naming convension
* Add test for non-shuffle verstion of gemm
* Fix typo
* Print kernel information
* Add rest of the fp32 kernel to the test
* 1. Add rest of the fp16 device iop.
2. Mark the invalid device operation
Co-authored-by: rocking <chunylai@amd.com>
* [What] Separate fixpoint gemm from gemm example
[Why] let example of gemm_int8 be pure gemm.
[What]
1. Add gemm_requant_relu_requant,
2. Let CDataType be int32 in pure gemm, because no one use int8 CDataType. It is also part of gemm_requant_relu_requant
* Fix path
* Revise cmakelist due to merge develop
Co-authored-by: rocking <chunylai@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
* 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>
* add wrw reference
* start device
* raw not split version
* run simple example
* start to use atomic add
* simple transform result correct
* first version that can run
* fix atomic and set operator choice
* add check split-k
* format
* change input parameter
* add pad for t total
* rename example index
Co-authored-by: ltqin <letaoqin@amd.com>
* Add int8 of mk_nk_mn to the ckProfiler
* Add example of int8 gemm
* Fix typo, use ushort instead of half_t for bfloat16
* replace ushortXXX_t to bhalfXXX_t
* rename ushort to bhalf_t
* Add bf16 example
* Add bf16 gemm to ckProfiler
* Fix alignment
* Fix typo
* Add unit test for gemm_xdl int8
* Add gemm_xdl fp32 unit test
* Add gemm_xdl bf16 unit test
* fix build
* fix build issue due to merge conflict
* Fix build
* Fix build error
Co-authored-by: rocking <chunylai@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* add space_filling_curve
* cleanup and move space_filling_curve into test
* WIP: start refactoring threadwise_transfer_v1r3
* threadwise_copy works but needs further refactoring
* add some comments
* add SpaceFillingCurve::GetIndices()
* minor changes
* removed GetIndices; refactored GetDstCoordinateResetStep
* add DynamicBuffer::Transfer, but Add is not tested
* rebased agaist develop
* threadwise_copy_v6r1/v6r2/v6r3 using space-filling curve start to work
* minor changes
* refactored threadcopy v3r1, v2; removed old implementations
* clang-format
* cleanup
* fix a typo in v6r3
* format
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* start conv2d bwd api
* kernel running
* add bwd reference
* change to no shuffle
* fix bwd reference
* pass verification
* add Filter1x1Stride1Pad0 and start testing
* change some tuning parameter
* fix test error
* add fp16 tuning parameter
* add bf16 tuning parameter
* add int8 tuning parameters
* change fp32 tuning parameter
* add bwd to profiler
* fix bug for bwd profiler
* fix ckProfiler bug
* change conv2d_bwd_xdl to fp16
* fix bug in comments
* fix precompile id
* fix enum conv name
* chage _bwd_ to _bwd_data_
* change conv2d_bwd example id
* bwd to bwd data
* fix prehead
* fix MakeDefaultBlock2CTileMap ,import form merge develop
* format bwd instance
* bwd to bwd data
* change name bwd to bwd data
* change name bwd to bwd data in example
* formate code
* change conv2d bwd data id in example
* rewrite readme for example
* fix CalculateMagicNumbers about div zero
* add workaround CK_WORKAROUND_SWDEV_325164
* change test_conf2d_bwd_data show info
* format
* fix bug for workaround:CK_WORKAROUND_SWDEV_325164
* formate tuning parameters
* formate tuning parameters again
* formate tuning parameters 3
* formate tuning parameters 4
* remove add function template
* format
* update comment
Co-authored-by: ltqin <letaoqin@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* add gitignore
* host tensor: allow generating sequentially increasing value in a given dimension
* gridwise gemm v3r1: allow distinct K0/K1 values for A/B block descriptor
- remove dangling header include
- modify example gemm_xdl accordingly
- infer KPack value from M/NPerXdl
- device conv2d fwd: update parameters accordingly for the underlying gridwise gemm v3r1
(API for conv2d fwd stays the same for now until we decide to expose individual K0s for activation and weight)
* add LDS data dump utility
* profiler: reflect API change for distinct K0/K1 for A/B matrices
* profiler: add conflict-free LDS write FP16 kernel instances
* fix accidental perf regression
* address feedback; cosmetic changes
* clang-format for new files
* format
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* 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.
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* conv3d compiles but has memory error
* conv3d works
* fix performance issue by using __builtin_amdgc_readfirstlane
* change MakeBlock2CTileMap to MakeDefaultBlock2CTileMap; change c_blockid_to* to cblockid_to*
* clang-format
* remove CK_EXPERIMENTAL_PASS_TENSOR_DECRIPTOR_BY_*; moved wrapper into DeviceConv3d
* format
* remove useless marc
* add comment
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* [What] Refactor verification of gemm alpha_beta, move to reference operation
[Why] Sync with other verification
* Profile mk_nk for gemm bias 2d
* Support bias 2d with mn * kn in profiler
* Support bias 2d with km*kn and km*nk in profiler
* Support fp32 bias 2d in profiler
* format
* format
Co-authored-by: rocking <chunylai@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* [What] Add 2d version of bias, prepare to implement alpha / beta scaling
* Add alpha / beta functor
* Refine parameter of example
* [What] Use real type instead of template
[Why] Prevent implicit cast
* Rename parameter for general operator
* Remove redundant comment
* Fix compile error
Co-authored-by: rocking <chunylai@amd.com>
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
* 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
