* add instance for gemm bias softmax gemm
* add client example
* change CGridDesc_G_M_N to CGridDesc_G_M_O
* add gridwise
* change c grid name
* device add d0s data
* fix 08 client_example
* add example 47_fused_attention
* example output correct
* add d0 to example
* add d0 element op
* rechange instance code
* change Acc0ElementwiseOperation to C0DEElementwiseOperation
* change example name
* update instance for cdeelementwiseop
* add bhalf_t ScaleAdd
* add test
* not surport geem1 bias
* remove some ignore
* fix test bug
* 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
* Use double as alpha/beta values type in reduce device op api
* Use double as alpha/beta values type in softmax device op api
* Use double as alpha/beta values type in multiple-reduce device op api
* Use double as epsilon value type in normalization/elementwise-normalization device op api
* add example
* fix example
* add instance for gemm permute
* add to client example
* change configs
* change instance file name
* formate
* change client example file name and remove example
* Change to the DeviceReduce base class template to include all problem description information
* Add external api for reduction
* Add client example to test the reduction external api
* Spelling correction
* Re-implement the host_reduction to follow the DeviceReduce base API format
* Change the reduce profiler to call the external API for collecting device instances
* Rename reduce client example directory from 08_reduce to 12_reduce
* Remove (void) before the functional call
* Tiny update in reduce client example
* Tiny update in profile_reduce_impl.hpp
* Rename the reduce client example directory
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
* 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>
* start add example
* add multiple d fp16 example
* device transfer elementwiseop to gridwise
* gridwise add multiple d
* change example for multiple d
* fix spill registers
* fix for passthrough element op
* fix int8 overflow
* change example file name
* add instance for dl multiple d
* example add DsDataType
* remove grouped_convolution_forward_dl.hpp
* add head file(was deleted before)
* fix not support device issue
* format
* remove passthrough check
Co-authored-by: letaoqin <letaoqin@amd.com>
* Refine the device batchnorm-backward base API templates and data type assignments
* Remove duplicated kernel file
* Add batchnorm backward instances and external API
* Add batchnorm-backward profiler and tests
* Add client example which uses batchnorm backward external API
* Merge test/batchnorm_fwd and test/batchnorm_bwd into one directory
* Loose the threshold for batchnorm-backward check_err()
* Implemented batchnorm-backward Blockwise and Multiblock kernels
* Add batchnorm-backward device op
* Add batchnorm-backward host-reference op
* Add batchnorm-backward example
* Parameters renaming in batchnorm backward kernels and device op
* Change in the example to loose the threshold for ScaleDiff checking
* Add comments to explain the implementation of batchnorm-backward
* Parameters renaming again in batchnorm backward kernels
* Improve the expression calculation for performance
* Add batchnorm backward to README
* Add comments to explain inv-variance in batchnorm forward and backward
* Renaming the batchnorm forward training and inferring examples
* Add/update the comments for batchnorm-backward kernels
* Renaming again
* Add block_sync_lds between two consecutive blockwise reductions
* Move common expression 1/N out of the static_for loops
* Add dy_elementwise_op
* Renaming in backward example again
* Add checking for reduceDims in reference_batchnorm_backward
* Update to comments and codes format
* Rename in the comments
* Remove common expression out of the loop in reference_batchnorm_backward_nhwc_c
* Add block_sync_lds() between blockwise reduction again
* Fix comments again
* Remove int8 from batchnorm-forward instances since it is not needed for forward training and could fail test
* Update to device_batchnorm_forward base class to include all template parameters for problem description
* Add batchnorm forward instances and external api
* Add batchnorm forward profiler module which uses the external api
* Add some comments in batchnorm_forward example to explain the dimensions in lengths[]
* Replace the reference_batchnorm_forward_nhwc_c by generic reference_batchnorm_forward
* Improvement to the batchnorm infer base API
* Add batchnorm forward client example which shows using the batchnorm forward external API
* Add test for batchnorm forward
* Tuning the batchnorm profiler initialized values and error threshold
* Add support for bhalf_t in instances/external api/tests
* Add support for int8_t in instances/external api/tests
* Add support for double in instances/external api/tests
* Let ScaleDataType and BiasDataType be same as XDataType and YDataType when creating instances
* Checking before running best instance in batchnorm_fwd_nhwc client example
* Add checking for YElementwiseOp in batchnorm_forward external API
* Add more types in batchnorm forward profiler
* Add more test lengths
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
* FastGelu support for more data types.
* AddFastGelu & FastGelu instances.
* Client example.
* clang-format
* Remove unused stride variable.
* Add new line at EOF.
Co-authored-by: Adam Osewski <aosewski@amd.com>
* fixed bug in softmax reference & add bf16 examples for batched_gemm_scale_softmax_gemm
* added bf16 tests for batched_gemm_softmax_gemm_permute
* changed format of device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instance.cpp
* changed format device_batched_gemm_softmax_gemm_permute_xdl_cshuffle_bf16_bf16_bf16_bf16_gmk_gnk_gno_gmo_instance.cpp
* aligned annotations
* modified CMakeLists for examples
* add common example code of fp16/bf16 version for batched_gemm_scale_softmax_gemm_xdl
* use macro to control the instances
* added macro control into instances
* clang-format some files
* changed error tolerance for bf16
* changed index for 10_elementwise_normalization
* fixed xdlops code bug in amd_xdlops.hpp
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
We can use this template to eliminate duplicated iterator computing
logics. By providing return type to ck::accumulate_n(), we can avoid
type conversion operations.
* 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
* add client example for elementwise_normalization
* clang format elementwise_layernorm2d.cpp
* changed some naming to make it more understandable
* changed naming of input into ab_input
* fixed bug for threadwise_x_store
* add elementwise operation to reference
* Rename example folder for GroupedConvFwdMultipleD
* Unify example codes
* Change target names
* Add fp16 example for multiple d instance
* Re-format common.hpp
* Add interface 'DeviceGroupedConvFwd'
* Use simpler interface
* Move common conv params out
* Rename conv fwd client example folder
* Add missing include directive
* Update grouped conv instance implementations
* Simplify ckProfiler (grouped conv forward)
* Use GroupedConvFwd to implement client example
* Use greater groupe count in example
* Add custom target to group examples
* Add extra tag param to instance factory function
* Use tag to differentiate factory functions
* Add missing tag argument for factory function
* Remove inheritance relationship
* Remove no-longer used include directive
* Add license in front of file
* Remove redundant CMake setting
* Extract common code from files
* Rename folder 'convnd' to 'conv'
* Use std::array<> to accept compile-time kwnown # of arguments
* Fix compilation error of tuning parameter
* In example, use same setting as unit-test
* Remove no-longer used include directive
* Add interface for grouped conv bwd weight
* Add group support for conv bwd weight
* Add grouped conv bwd weight example
* Use group parameter in example
* Rename example folder
* Remove non-grouped version example source files
* Rename device op template
* Add group support to convolution backward weight
* Remove debug messages
* Use smaller group size in example
* Use named variable as loop terminate condition
* Prettify example output message
* Enlarge used grid size
* Allow real grid size exceeds expected grid size
* Rename interface file
* Add client example for grouped conv2d bwd weight
* Fix wrong include directive
* Rename client example folder
* add fused addition lyernorm
* add fused addition lyernorm
* changed CMakelist
* removed annotates
* modified descriptor of C
* fixed bug in gridwise add layernorm
* format the files
* modified name from add&layernorm into elementwise&layernorm
* created fused elementwise layernorm branch
* change input into tuple type
* add sweep once to reduce load & read of C from global memory
* modified Argument api
* modified way to malloc c in global memory
* changed gamma and beta to m_k_desc
* fixed bug when sweep once and move CDataType when define device level struct
* add src dim for gamma and beta
* implement optimization for coalesced
* delete a annotation line
* fixed some bug to meet the requirements of ck
* add bandwidth computing in example, and fixed the time unit
* move device_elementwise_layernorm_impl.hpp into device/impl
* fixed bug in device_elementwise_layernorm_impl.hpp
* changed name from layernorm into normalization
* clang-format the changed files
* changed the names
* moved immidiate results into lds, it become faster in non-sweeponce cases
* changed naming of C into X to make the defination more clear
* changed naming in example
* add tests for elementwise normalization
* move example_elementwise_layernorm_blockwise into folder 44_elementwise_normalization
* move test_elementwise_layernorm_fp16 into new folder
* move elementwise_normalization_instances into a new folder
* add more tests in test_elementwise_layernorm_fp16.cpp
* added some corner cases in test
* fixed method to compute lds size for matrix X
* changed name of 44_elementwise_normalization into 45_elementwise_normalization
* modified some comments
* modified some other confused comments
* reduce redundant tests in test_elementwise_layernorm_fp16.cpp
* Improve example reusability
* Remove no-longer used file
* Rename folder of grouped_conv_bwd_data example
* Add normal grouped conv bwd example
* Add interface 'DeviceGroupedConvBwdData'
* Prettify comment of device op type arguments
* Add grouped conv2d/conv3d backward data fp16 instances
* Fix wrong template argument
* Add grouped_conv2d_bwd_data client example
* Use simpler expression to calculate memory size
* Fix formating
* Remove grouped_conv3d_bw_data instances
Underlying device operator is not ready to handle 3D input
* Remove no-longer necessary include directive
* Add missing include directive
* Use more realistic conv param in example
* Add gridwise gemm pipeline v1/v2 selector
* Pipeline selector working, test-wise add pipeline options to one instance
* Add gemm instances
* Add debug info to DeviceGemmXdl
* Add debug info to DeviceGemmXdl_CShuffle
* Add debug info to DeviceGemmXdl_CShuffle and instances to gemm_add_add_fastgelu
* Minor fix
* Add debug info to DeviceBatchedGemmXdl and instances to batched_gemm
* set up inter-wave configuration
* use defualt loop scheduling for supported gemm ops
for blanket-applying interwave scheduling for all supported gemm ops, define macro CK_EXPERIMENTAL_DEFAULT_TO_INTER_WAVE_SCHEDULING=1. this should be discouraged though as it is not covered by CI
* Add enum PipelineVersion
* Update instances
* Format
* Fix the merge conflict
* Add flags to disable added instances
* Test disable flag check
* Disable flag check
* Enable the instances
Co-authored-by: Anthony Chang <ac.chang@outlook.com>
* Add reduction across all dims cases.
* host softmax: handle all reduce
* Test cases when reduced dim is not innermost axis.
* Fix syntax.
* Test non innermost dim for fp32 and int8
* Group test suites wrt NumReduceDim.
* Additionally test failing cases.
* Throw error when Rank or NumReduceDims doesn't match arguments.
* Check reducedDims has correct values
* Move don't reuse DeviceReduceMultiblock IsSupportedArgument method.
Instead implement own. (in fact just get rid of one check to enable
reduction across inner dimensions).
* Reorganize unit tests to better cover use scenarios.
* Test input validation
* Test reduction of inner dimensions with custom op instances.
* Refactor fp32 and int8 unit tests.
* Fix FP32 instance template parameters.
* Add more instances.
* Instances with InSrcVectorDim=0.
* Do not initialize and copy data when arg not supported.
* ckProfiler Softmax use instance factory.
* Refactor device softmax IsSupported.
* Additionally add non-polymorphic api functions
* Split softmax instances into multiple files.
* Fix profiler.
* Reorganize tests to reuse profiler and cover edge cases.
* Clang-format
* I8 Softmax instances along with UT.
* Reuse type alias definitions from instance factory header.
* Clean included headers
* Fix variable names.
* Add missing checks in Argument constructor.
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Anthony Chang <ac.chang@outlook.com>
* Add conv2d requant example
* Fix bash error
* Rename example
* 1. Rename gemm quantization
2. shares the requantization lambda function with conv
* Refine declare type
* Add conv bias relu quantization exmaple
* clang format
* Fix compile error due to merge develop
* Fix CI error
* Extract quantization post operation into another file
* Support quantization for non piecewise linear function
* Add instance for conv quantization
* Add convolution quantization factory
* Add convolution quantization client example
* Add more instances with different template parameters
* clang format
* Sync the naming with the develop
* add device of dl
* fix k1 of GridwiseGemmDl_km_kn_mn_v1r3
* init version for dl conv
* add example(init)
* result right
* disable elementwise operation
* check parameters
* add fp32,int8 example and change check code
* change deive file and class name
* add check vector access of C
* add instance
* add to ckProfiler
* add Filter1x1Pad0 instances
* fix ignore error
* fix for CI
Co-authored-by: letaoqin <letaoqin@amd.com>
* Update to the batchnorm-forward API and base class
* Fix leeked header including in gridwise_set_buffer_value.hpp
* Add kernels and device file for batchnorm-forward welford supporting both blockwise and multi-block reduction
* Update to the batchnorm-forward example to use the new batchnorm-forward device interface
* Change the batchnorm-forward reference to use sequential welford method
* Change to assign the workspace into four buffers in the host layer
* Use GetReduceCountPerThread functor to replace the initial count for Blockwise and Multiblock welford
* Tiny correction and remove un-used file under example/34_batchnorm
* Renaming in the kernel arguments
* Explicitly use ck::math::sqrt in batchnorm-forward kernels
* Add some comments to some kernels
* Tiny fix
* Generalize the data types in reference_batchnorm_forward_nhwc_c
* Use ck::ignore to mark un-used parameters
* Move GetReduceCountPerThread functor codes from kernel to device
* Remove some un-used codes in device_batchnorm_forward_impl.hpp
* Tiny fix in batchnorm_forward example
* Move GetReduceCountPerThread() to welford_helper.hpp
* Use seperate data type for Scale and Bias
* Renaming in device Op
* Tiny fix in forward example
* Updata to batchnorm-infer (type spliting, renaming)
* Add time and bandwidth measurement to the batchnorm-forward example
* Add support of elementwise operation for batchnorm forward output
* Reduce object copying by passing object as reference type
* Tiny change for performance
* Updates for performance again
* Some Renamings
* Add GetActualVariance template parameter for ThreadwiseWelfordMerge
* Tiny update in reference batchnorm forward nhwc/c
* Move batchnorm multiblock kernel files to grid/batchnorm_multiblock sub-directory
* Fuse mean and bias in the normalization calculation
Co-authored-by: root <root@dc-smc-18.amd.com>
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
* reopen masking att instance due to CI is upgraded
* re-enable instances previously failed on 9110
* enable ksize-kpadding pair validity test
* add non-masked attention+permute test; expose masking boolean to attention kernel handles
* disable bench
* fix test
* move files
* bulk rename batched_gemm_masking_scale_softmax_gemm_permute to batched_gemm_softmax_gemm_permute
* format
* amend rename
* disable bench in test
* add mask/no-mask test for non-permute attention kernels
* disable broken kernel instance
* example working
add non-permuted problem statement
evaluating whether overhead comes from permutation or the extra kernel arg
* interface for bias addition without implementing it
* test and profiler running
* tidy
* mask type determined by enum class
* unify example code
* move masking specialization to its own header
* align formats
* extract helper functions
* experiment merging dims for attn w/ permute; shows perf parity with attn wo/ permute
* add tensor specialization to template args
since tensor spec packed shows perf parity when permutation isn't needed
remove redundant template args
comment on 'packed' tensor specialization
* grouped attention with input/output permute example
* format
* clean up
* refactor acc0 tile visitor
Co-authored-by: shaojiewang <wsjmessi@163.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* Fix for lwpck-425, update BlockTransferSrcVectorDim
* Revert "Fix for lwpck-425, update BlockTransferSrcVectorDim"
This reverts commit fd24e280e2.
* Add Batched Gemm int8 test, expect it to fail
* Format
* Re-add the fix
* Simplify the macros for declaring and defining the add_device_reduce_instance_xxxx() instances
* Change the types of lengths and strides from std::vector to std::array for the reduction device interfaces
* Remove DeviceSoftmaxImpl's depending on DeviceReduceMultiblock
* Split the cpp and hpp files for reduction instances to enable more parallel compiling
* Remove the using of macros for declaring reduction instances and instance references
* Update to add_device_reduce_instance_xxxx templated functions
* Use ReduceOperation+InElementwiseOp+AccElementwiseOp to repace the ReduceOpId in defining add_reduce_instance_xxxx() templates
* Change return format
* add fused addition lyernorm
* add fused addition lyernorm
* changed CMakelist
* removed annotates
* modified descriptor of C
* fixed bug in gridwise add layernorm
* format the files
* modified name from add&layernorm into elementwise&layernorm
* created fused elementwise layernorm branch
* change input into tuple type
* add sweep once to reduce load & read of C from global memory
* modified Argument api
* modified way to malloc c in global memory
* changed gamma and beta to m_k_desc
* fixed bug when sweep once and move CDataType when define device level struct
* add src dim for gamma and beta
* implement optimization for coalesced
* delete a annotation line
* fixed some bug to meet the requirements of ck
* add bandwidth computing in example, and fixed the time unit
* move device_elementwise_layernorm_impl.hpp into device/impl
* fixed bug in device_elementwise_layernorm_impl.hpp
* changed name from layernorm into normalization
* clang-format the changed files
* changed the names
* moved immidiate results into lds, it become faster in non-sweeponce cases
* changed naming of C into X to make the defination more clear
* changed naming in example
* add tests for elementwise normalization
* move example_elementwise_layernorm_blockwise into folder 44_elementwise_normalization
* move test_elementwise_layernorm_fp16 into new folder
* move elementwise_normalization_instances into a new folder
* add more tests in test_elementwise_layernorm_fp16.cpp
* added some corner cases in test
* fixed method to compute lds size for matrix X
* changed name of 44_elementwise_normalization into 45_elementwise_normalization
* modified some comments
* modified some other confused comments
* reduce redundant tests in test_elementwise_layernorm_fp16.cpp
* 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>
* use another instance to check the efficiency
* optimize group layer norm
* 1. coalesce load/store data for gridwise layer norm welford. 2. move a sqrt and divison into a outer static loop
* add more instances to layernorm
* add 2 more test cases
* remove ignore in generating tuple of vector
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* fix device instance library to add all instances
* remove cppcheck from requirements.txt
Co-authored-by: Jun Liu <Liu.Jun@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* Add groupnorm example by layernorm
1. Reference is not ready
2. shape of gamma and beta need to be fix
* Let shape of gamma and beta can be same as x
* Modify test, instance and client example
* [What] Fix bug of layernorm for greater than 2 dimension.
[Why] We need to get upper length from merge transform instead of embed transform.
* Add reference for groupnorm
* Fuse sigmoid after groupnorm
* [What] Rename original layernorm into layernorm2d
[Why] Prepare to add groupnorm using layernorm5d
* clang-format
* Add groupnorm test
* Refine error message
* Add groupnorm ckProfiler
* Test groupnorm kernel from device_instance
* update example
* upadte profiler
* Fix test naming
* Fix argc number
* Move descriptor and sweeponce to argument for quick debugging
Co-authored-by: Chao Liu <chao.liu2@amd.com>
