* parse examples inside the add_example_executable function
* fix the example 64 cmake file
* add xdl flag to the gemm_bias_softmax_gemm_permute example
* add filtering of tests based on architecture type
* enable test_grouped_gemm for gfx9 only
* enable test_transpose only for gfx9
* only linnk test_transpose if it gets built
* split the gemm instances by architectures
* split gemm_bilinear,grouped_conv_bwd_weight instances by targets
* split instances by architecture
* split grouped_conv instances by architecture
* fix clang format
* fix the if-else logic in group_conv headers
* small fix for grouped convolution instances
* fix the grouped conv bwd weight dl instances
* fix client examples
* only enable client examples 3 and 4 on gfx9
* set the gfx9 macro
* make sure the architecture macros are set by cmake
* use separate set of xdl/wmma flags for host code
* sinmplify the main cmake file
* add conv_fwd_bf8 instance declaration
* save mean and inverse std in normalization
* Save mean and inverse std in splitK
* Vector save mean and inv std
* Modify instance for save mean and std
* simplify the layernorm example
* Save mean and std in groupnorm example
* Save mean and inv std in ckProfiler and test
* Remove compute data type from base class
* Save mean and inv std in client example
* Add changelog
* clang format
* Fix compile error
* Refine naming
* Avoid error in bf16
* revert changelog
* refactor cmake files for the tests
* refactor cmake files for examples
* fix cmake for gemm example
* fix the cmake file for all examples
* add splitting by data types in gemm_splitk instance header
* rename test to reflect only dl instances are used
* clean up CI workspace, update cmake for instances
* change the jenkinsfile syntax
* build all instances except DL on gfx11
* move workspace cleanup after stages
* clean up workspace after every stage
* isolate data types in grouped_conv_fwd header
* isolate dl instances for grouped_conv2d_fwd
* fix syntax
* fix cmake and batchnorm instances
* fix typo
* fix reduction instances
* fix grouped_conv headers
* fix syntax
* replace parsing logic for instances, replace bfp16 with bf16
* fix the client examples build
* clean up DTYPES from instances cmake files
* update the parsing logic in cmake files
* make an exception for reduction kernels
* update few remaining cmake files to handle DTYPES
* fix syntax
* fix cmake conflicts
* replace f8 with fp8 test name
* resolve conflicts for dpp instances
* properly split conv_nd_bwd_data instances
* split conv2d_fwd instance data types
* split the gemm, conv2d_fwd and batched_gemm_softamx_gemm
* split the tests by data types where possible
* filter examples by DTYPES
* split few remaining examples by DTYPES
* filter most instances by DTYPES
* add new lines at end of headers, fix grouped_gemm profiler
* fix syntax
* split the ckprofiler instances by DTYPES
* split the conv2d and quantization DL and XDL instances
* fix the splitting of conv2d DL instances
* split softmax and pool_fwd tests for fp16 and fp32 types
* fix syntax
* fix the dl_int8 quantization instances isolation
* enable gfx941/942 targets
* fix clang format
* fix the cmake logic for multiple targets
* fix cmake syntax for looping over targets
* add gfx941/942 support for gemm_xdl instances
* enable dl kernels on navi3
* do not build xdl tests and examples on Navi
* run tests before building everything on jenkins
* disable gemm_bilinear on gfx1030
* add gpu targets to installer on Navi
* put tests in the same order as before
* reduce the number of navi targets in CI
* build CI installed for gfx940 as well
* only build for MI300 during QA runs
* Add gemm + layernorm instance
* Add ckProfiler
* Add test
* Add client example
* Detect if user forger to set the workrspace
* Use literal in the example
* [What] use builtin function for sqrt
[Why] compiler will not use v_sqrt_f64_e64 if we use ::sqrt()
* check gemm vaildity in IsSupportedArgument
* Add more testcases
* Merge duplicated folder in client example
* Print more infomation
* Use better kernel parameter for MS problem size
* clang format
* Add constexpr for if condition and remove redundant include
* Remove cstdlib and add constexpr
* File renaming and class renaming for device element-wise operation
* Add batchnorm-infer instances, external API and client example
* Add batchnorm-infer profiler module and gtests
* Remove file device_elementwise_extension.hpp and move NormalizeInInfer operation to element_wise_operation.hpp
* Remove the using of class aliasing for DeviceElementwiseForBatchNormInfer
* Rename class and file due to conflict from device_elementwise_2d.hpp
* Fix namespace in batcnnorm_infer_nhwc client example
* Add device op of gemm layernorm
* [What] Rename F to H
[Why] F and G prepare for welford tensor
* Add gridwise gemm + welford
* Extract template parameter
* Rename kernel. Prepare to add second half kernel
* Extract var
* Add second kernel for gemm+layernorm
* Move to the gemm_layernorm folder
* Rename F and G to mean and var
* Do not use snakeCurved, it makes determination of padding for welford difficult
* Rewrite the device interface and rename some var
* Add welford count
* Update interface
* Sync code, prepare to test on MI200
* Clean the code
* Implement layernorm
* Add comment to mension hipFree
* Wrtie out the e for debug.
This could be remove and use h for instead
* 1. Allocate mean, var and count into by SetWorkSpacePointer.
2. Add GetWorkSpaceSize to calculate the space size
* Add gemm layernorm host code
* use reference layernorm
* Fix bug of blockwise welford for first kernel
* Fix bug of mean var padding for layernorm
* Use sgpr for shuffleM_index
* padding for GemmMeanVarCountGridDescriptor_M_NBlock
* Add layout parameter
* Check argument for gemm
* calculate max count for tail block
* Share E and H memory in device op
* Hard code the vector dim
* Refine the MakeDescriptor
* 1. Remove E parameter, because E is inside of device op
2. Check vector size
* [What] Rename MakeMeanVarDescriptor_M_N
[Why] Prepare to add count version of make descriptor
* Use 1D global memory for count
* Prevent redundant IO
* Update parameter
* Add pipeline v1/v2 selector
* Rename the example name
* Add base class for gemm layernorm
* Refine naming to distinguish naive and welford
* Add comment to explan in detail
* We don't need to pad in N dimension in gemm for mean/var/count. Set NPerTile 1
* Rewrite the 2st kernel, use multiple block along N dimension in layernorm kernel
* Share the vector size
* Refine var name
* [What] Force LayernormThreadSliceSize_N = vector size.
[Why] Memory coalesce
* Add comment
* Extract divisor out of the loop in reference layernorm
* Pad different size for E and H in layernorm kernel according to different block tile
* Refine naming
* Refine naming
* Prevent implicit cast
* [What] use ck::math::sqrt instead of __builtin_amdgcn_sqrtf
[Why] __builtin_amdgcn_sqrtf is only support float, double will cause casting
* Cast only constant
* Change of post shuffle thread descriptor
* Add EMeanVarDataType parameter.
* Merge the mean and var threadwise copy
* Add missing index
* Fix Typo
* Sync the variable with previous if
* 1. Declare e inside the host_gemm_layernorm()
2. Prevent implicit cast in reference code
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
* 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
* 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>
* Implement multiple-reduction in one kernel (kernels, device ops, examples)
* Add generic elementwise kernel and device interface
* Add generator for normal-distributed data initialization
* Add host refer implementation of batchnorm-forward and batchnorm-infer
* Add examples for implementing batchnorm-forward and batchnorm-infer using generic kernels
* Remove un-needed including in batchnorm example
* Renaming generic_elementwise to elementiwise in kernel and device classes/functions
* Change in gemm_layernorm examples to use DeviceElementwise instead of Device5AryElementwise
* Change in exampe 19_binary_elementwise to use DeviceElementwise instead of DeviceBinaryElementwise
* Change in device_cgemm_4gemm_xdl_cshuffle.hpp to use kernel_elementwise instead of kernel_binary_elementwise
* Add DeviceElementwiseBase and use it in device_normalize_instance.cpp
* Removing and renaming files
* Update to synchronize gemm_layernorm client example to the generic element-wise device op API
* Update to synchronize with the latest headers directory and HostTensorDescriptor interface renaming
* Merge two static member functions in device_elementwise.hpp
* Remove unary_elementwise_1d kernel and device
* 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>
* 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>
* Extract base class for elementwise
* Refactor interface of DeviceGemmReduce. Do not use tuple in interface
* [What] Rename d into reduce in gemm + reduction related code
[Why] Prepare to add d term for add
* Unify base class of gemm + reduce and gemm + bias + add + reduce
* 1. Rename gemm_bias_add_reduce for external api
2. Refine cmake
* Add normalize device operation
* [What] Reorder the argument
[Why] Because d0 is also the input of c.
* Add type string
* Add example of gemm_bias_add_layernorm via external api
* Refactor example code
* clang-format
* Fix compile error
* clang-format
* Add external api for gemm_add_add_layernorm and normalize
* Add client example
* clang-format
* Remove template from Reducton operation classes and add template to their operator() and GetIdentityValue() interfaces
* Change to unary elementwise operators and the reduce_unary_operator (class for mapping) and dependent variations in all host layers
* Remove the data type template parameter from reduce_binary_operator (class for mapping) and dependent variations in host layers
* Add InMemoryDataOperatonSupportedOnDataType to check the matching between data type and InMemoryDataOperation
* Use struct-scope operator template instantiation for binary and unary element-wise operations
* Change a few more elementwise operations to use template for operator()
* Tiny correction in Normalize operator
* Add static_assert to check the data type appliability for some reduction accumulator and element-wise operatons
* Correction in some examples with regard to using ReduceAccDataType
* Use static_assert for UnaryDivide
* Update to merged codes to use Element-wise operations and Reduction Accumulator operations correctly
* Tiny fix with regard to SetWorkSpacePointer()
* Copy "gemm reduce" to "gemm bias add reduce"
* Implement gemm bias add reduction
* Fix compiler error due to merge from develop
* Add tensor operation for gemm + bias + add + reduce
* Add gemm_bais_add_reduce to ckProfiler
* Add c1 functor
* Refine type
* Use reduceAccDataType instead of explicitly float
* Change to use check_err()
* Do relu in float32 instead of bhalf_t. Because bhalf_t is unsigned
* Refactor relu. using type_trait instead of overloading
* Rename DxsReduceAccElementwiseOperation to DxsReduceAccElementwiseOperation
* Fix denominator
* Refine nameing
* Fix denominator in host
* Remove useless include header
* Use AccDataType
* Fix static_cast order
* Refine type
* [What] Remove tuple type in the base class
[Why] External api depend on base class. if base class has relationship with type, we will need many class for different type
* Use the unified naming for math functions on host and HIP kernel
* Corresponding change/simplification in reduction host/profiler/examples due to unified math functions renaming
* Renaming GetReductionZeroVal() to GetIdentityValue()
* Tiny renaming in profile_reduce_impl.hpp
* More renaming in profile_reduce_impl.hpp
* Replace zeroVal by identiyVal
* Remove ck_ prefix in the naming of ck::math provided functions
* Implement reduction meand and reduction square mean
* Refine file name
* Add reduce mean and square mean
* Fix parameter name
* Add normalize device op (not implement invoker::run())
* Remove epislon
* Refine deviceop
* Add 5ary elementwise for normalization
* Add layernorm example
* layerNorm verication
* Fix compiler error due to merge from develop
* Fix typo
* Fix compile error
* Refine naming
* [What] Suport non pointer for invoker and argument
[Why] Snyc coding style with gemm
* Refine folder name
* Refine class name
* Evaluate perf of the kernel
* Fix compile error
* [What] Refine perf evaluation in example of gemm + reduction
[Why] evaluation of gemm + reduction may cause verification fail. Because evaluation will not initial global memory
* clang-format
