* add multi embeddings support
* fix format
* optimize sqrt
* add reduce operation
* change to elementwise op
* fix name
* rename
* run ci cd
* format example
* format code
* format code
* 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>
* wmma_op + unit test
* add arch limitation to wmma test
* change arch limitation
* Refactor + Add all type unit test(int4 compile failed)
* Add f32_16x16x16_bf16 unit test
* tempsave
* tempsave
* tempsave
* runtime bug, cannot find symbol
* workaround for incorrect HIP warpSize return value
* debugging
* tempsave
* Correctness OK, waiting for optimization
* Tidy up + format
* temp save
* temp save, reproduce the v_bfi_b32 issue
* add inline asm for wmmaop test
* tidy up
* clean some debug purpose code
* discard some codes
* clang format
* clang format
* compiler issue fixed + increase tile size
* 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>
* 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
* 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
* Sync the naming
* Sync the test of layernorm with groupnorm
* Sync the naming
* Minor change for comment and log
* [What] Add saveMean and SaveInvVariance in the interface.
[Why] These can optimize the backward
* 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 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>
* 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>
* start split k
* add base device class
* add example after merge develop
* add gridwise gemm
* add b matrix split k
* split=1
* change name for kb
* not bias result right
* bias only add once
* fix register spill
* regular code
* add fp32 example
* fix for 64bit index
* fix CheckValidity of gridwise
* 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>
* 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>
* Add example folder for 'DeviceElementwise'
* Re-structure example files
* Move common parts into common.hpp
* Use more strict input
* Add more helper methods in 'DeviceElementwise'
* Use more specific method to write example
* Allow specify problem through command line argument
* Allow specify problem 'axes' through command line argument
* Add check to template type argument
* Add transpose_shape() to generalize shape permute
* Generalize transpose utility functions
* Use better name for tensor indices
* Add checks in helper functions
* Remove debug messages
* Refine error message for check_err()
* Generalize variable naming in example code
* Add device op 'DevicePermute'
This device op is clone of 'DeviceElementwise'
* Use 'DevicePermute' device op in example
* Remove 'elementwise' from identifiers
* Remove 'elementwise' from file paths
* Remove base class of 'DevicePermute'
* Let 'DevicePermute' inherit from 'BaseOperator'
* Add simple type traits to validate device op type
* Add static_assert() to check type constraints
* Create 'DevicePermuteBase' to generate methods
* Use indirect base type to generate methods
* Remove 'is_device_op<>' type traits
* Only accept single-input-single-output for 'DervicePermute'
* Simplify 'DevicePermute' interface
* Re-format 'DeviceElementwise'
* Use CRTP to generate overridden virtual method
* Remove unnecessary include directives
* Distinguish input & output shape in 'DevicePermute'
* Passing 'axes' to 'DevicePermute'
* Use more reasonable return value for Invoker::Run()
* Add 'GridwisePermute' kernel
This kernel is a clone of 'GridwiseElementwise_1D'
* Remove no-longer used type argument
* Check if input/output shape meet the requirement
* Remove no-longer used method
* Remove never-entered-if-clause
* Change problem description for 'DevicePermute'
* Transform descriptor into 3 dimensions
* Add debug code the verify result
* Add comment to indicate template argument location
* Add N/H/WPerBlock template parameter to 'DevicePermute'
* Rename 'GridwisePermute' to 'GridwiseCopy'
* Check tensor descriptor dimensions in 'GridwiseElementwise_1D'
* Add missing include directive
* Add 'BlockSize' parameter to 'DevicePermute'
* Remove no-longer used method
* Add 'BlockToTileMap' for 'GridwiseCopy'
* Use the normal Block2TileMap convention
* Rename 'BlockToTileMap' as 'Block2TileMap'
* Fix most of compilation errors
* Let 'Block2TileMap' map block to 2d coordinate
* Allow data transfer in 'GridwiseCopy'
* Fix wrong output descriptor for 2nd blockwise copy
* Rename 'GridwiseCopy' as 'GridwisePermute'
* Remove '1d' in identifiers
* Remove commented-out codes
* Remove 'MPerThread' template parameter
* Seperate template parameters
* Unify variable namming convention
* Use more verbose way to create expressions
* Add template parameter 'InBlockLdsExtraW'
* Release the constraint on In/OutGridDesc
* Use date type directly as template argument
* Re-arrange template arguments for blockwise copy
* Remove no-longer used template parameters
* Embed layout in the variable names
* Add GridwisePermute::CheckValidity()
* Extract local types as template parameters
* Rename local type alias
* Add more template parameters (vector width related)
* Calculate new SrcVectorDim/DstVectorDim after merge descriptor dimensions
* Fill tensor values start from 1
* Re-formate example code
* Avoid too-large block id
* Add comment
* Make sure 'SrcVectorDim' is not same as 'DstVectorDim'
* Add check for the 'VectorDim' & 'ScalarPerVector' template params
* Let 'DstVectorDim' equals 'SrcVectorDim' after transpose out grid desc
* Remove no-longer used template parameter 'NPerBlock'
* Fix wrong descriptor creation logics
* Specify problem in each examples
* Use better example name
* Add new example 'example_permute_NxHxW_fp32'
* Add example for demonstrating bundle multiple elems in tensor
* Add support to permute multiple elements together
* Change the default problem size
* Add span<> class template
* Use span<> to generalize check_err() interface
* Fix ambiguous ctor call
* Avoid create necessary objects
* Use helper functions to simplify example code
* Add example for 4xfp16 permute
* Disable failed-to-compile example
* Add check for the NUM_ELEMS_IN_BUNDLE
* Remove redundant parameter in helper lambda function
* Add check for the input tensor type's byte-size
* Check scalar-per-vector with padded length
* Use more verbose name to avoid name collision
* Use fixed 'VectorDim' & 'ScalarPerVector' for LDS
* Embed shape info in name of descriptor constructor
* Rename example folder '36_permute' into '37_permute'
* Avoid using too-large LDS in kernel code
* Remove redundant example
* Usw switch() to group similar codes
* Add const to the span<> type arguement
* Simply initialize tensor with floating point values
* Use fp16 as data type in all examples
* Enlarge tensor size in example
* Enalrge N-dim in example
* Add check for the bundled type in example
* Use more stricter error threshold
* Remove global load/store loop in kernel code
* Measure execution time by default
* Use faster device op config for example 'NxHxW_fp16'
* Use faster device op config for example '1xHxW_fp16'
* Use faster device op config for example 'HxWx4_fp16'
* Remove cmd arg parsing logics
* Rename functions
* Extract bundle permutation logic out
* Simplify permute bundle example
* Add Tensor<>::GetElementSpaceSizeInBytes()
* Add Tensor<>::data()
* Use new methods to simplify code
* Use type alias to replace duplicated code
* Use existing method to shorten code
* Allow FillUniformDistribution accept range arugment
* Intialize random values in range
* Add Tensor<>::size()
* Use more meaningful names in permute bundle example
* Use more meaningful names in permute element examples
* Use rangified copy() to copy elements
* Use function return value directly to eliminate variables
* Add to_array() conversion tool to eliminate more variables
* Add Tensor<>::AsSpan<>() to create view of tensor values
* Use AsSpan() to shorten check_err() calls
* Remove no-longer-used 'using' directives
* Move 'using' directive to proper code position
* Remove redudant variables
* Remove useless static_assert()
* Add check for range types
* Declare variable right before first use
* Move long return type as tailing return type
* Add BaseInvokerCRTP<> class template to generate method
* Create new base type for 'DervicePermute' implementations
* Move 'NumDim' template param to the first
* Rename 'DevicePermute' to 'DevicePermuteImpl'
* Add 'noexcept' specifier to CRTP generated method
* Move 'Block2TileMap' definition into 'GridwisePermute'
* Use type alias to reduce code
* Unify naming style in 'DevicePermute'
* Add comments in 'GridwisePermute'
* Rename permute example folder
* Use std::cerr to report error
* Use larger shape in examples
* Rename '38_permute' to '39_permute'
* Make sure we use unsigned type for shape & indices
* Remove opt-ed out assertion
* Remove template BaseInvokerCRTP<>
* init commit of convnd bwd data
* begin compiling example
* have a first version that produce a right result
* refine device level launch kernel code
* add more instances in example and get right results
* clang-format
* format example file
* add more instances
* fix instances
* adding conv_bwd_data multile_d
* adding conv_bwd_data multile_d
* adding conv_bwd multiple d
* adding conv_bwd multiple d
* adding conv_bwd multiple d
* refactor
* refactor
* adding conv bwd data multiple d
* adding conv bwd data multiple d
* adding conv bwd data multiple d
* adding conv bwd data multiple d
* adding conv bwd data multiple d
* adding conv bwd data multiple d
* adding conv bwd data multiple d
* refactor
* update conv fwd's bias impl
* refactor
* reorg file
* clean up cmake
* clean
* clean
* clean
Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* modify comment
* trim unnecessary check
* add gemm spec in kernel name
* add TNTT gemm_gemm + atten kernel instances
* refactor attention padding to better fit in unit tests
This streamlines usage where "ResetNaNToMinusInf" is now hidden from user facing device op.
Also added compile-time conditionals that load OOB value as NaN only after padding is enabled
* add adhoc padding test for atten
* shrink input value range for attention kernel validation to avoid occasional error by 1e-3
Still unsure whether this kind of deterministic floating point accurary issue is expected
or not. May want to try exact same approach as the GPU kernel in the host reference
GEMM+Softmax+GEMM function to see if the accuracy discrepancy goes away. Until then,
shrink the input value range as it is less likely to produce errors of around ~1e-3.
* attention kernel proper granular padding for all 4 dims
* IsSupportedArgument checks
* test more padded cases
* block PadK specialization in attention kernels
* workaround clang crash for gfx908
(gfx908 only) workaround for compiler crash in fused kernels on mainline #9110; #10738 seems ok
error message was "fatal error: error in backend: Error while trying to spill VGPR0 from class
VGPR_32: Cannot scavenge register without an emergency spill slot!"
this fall back to less ideal way of handle NPadding in fused attention kernel
* comment out kernels giving wrong results on MI100; MI200 doesn't seem affected
* Refactor the design of DeviceGemmMultipleDMultipleR_Xdl_CShuffle
* Add 'DeviceGroupedConvFwdMultipleDMultipleR' interface
* Add DeviceGroupedConvFwdMultipleDMultipleR_Xdl_CShuffle
* Remove 'GridwiseConvFwdMultipleDMultipleR_xdl_cshuffle'
* Add 'TransformConvFwdToGemm<>' utility class (from Chao)
* Use 'TransformConvFwdToGemm<>' to shorten code
* Fix ill-formed method declaration
* Re-implement MakeRGridDescriptor_M() function
* Change problem description
* Use macro to define layout types
* Define K-reduced output tensor layout types
* Let user to decide R output tensor layout
* Rename variables
* Add padding to the reduced output tensor if necessary
* Extract common code as helper method
* Remove debug message
* Add missing include directive
* Add partial fp16 Conv + Reduction example
* Add example verification code for 2D Conv problem
* Use type alias to simplify code
* Share code across different-dimension Conv problems
* Rename file/functions from run_conv_fwd* to run_convnd_fwd*
* Make example code more verbose
* Add code to support 1D & 3D Conv + Reduction on host
* Add more examples for data type: bf16, fp32
* Add example for int8
* Add custom target to group examples
* Use more general custom target name
* Change the description in error message
* Disable testing for example other than fp32
* Add examplel for int4 (just copy from int8)
* Fix wrong data type
* Use larger data type for intermediate tensors
* Finish int4 example
* Undefine macro PP_DEFINE_LAYOUT_TYPE() after use
* Use named variables to replace magic numbers
* Remove debug messages
* Use same A/B data type for host Conv in int4 example
* Add check for the 'RLayout' type argument
* Group same-dim-layouts together in 'LayoutSetting<>'
* Add 'final' specifier to utility classes
* Use different initialization method for examples
* Remove macro PP_DEFINE_LAYOUT_TYPE()
* Fix code-comment mismatch
* Use more reasonable initialization value for all data types
* Default use init_method=1 for all examples
* Remove never-used code
* Remove confusing out-of-date comments
* clean
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Chao Liu <lc.roy86@gmail.com>
