* 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<>
* 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>
* GEMM + Reduce max fp16+fp32
* GEmm + Max bf16 + int8
* Refactor common definitions.
* Refactor common func of mean meansquare example.
* More examples for mean meansquare.
* Update int8 examples and skip them cause of random errors.
* Int4 examples.
* Fix examples for max int4/8
* Tensor conversion for int4 input data for mean meansquare example.
* Remove int4 mean_meansquare example
* Fix int8 mean_meansquare example.
-All ReductionAccData and R<N>DataType have to be F32. The INT32 data
type is giving wrong results.
* Guard int4 with ifdef
* Change int8 example to add_addsquare due to div rounding err.
* Clang format
* Change the return type of common function.
* Get back int8 example with division.
* Remove int8 mean meansquare.
* Use proper cast for BF16 data type.
* Use ck::literals.
* Use proper data type for host tensors & reference.
- Use ReduceAccDataType for reference gemm output data type.
- Cast host reference output tensor to EDataType
- Fix ifdefs for int4.
Co-authored-by: Adam Osewski <aosewski@amd.com>
* Add GEMM examples for int4
Currently the source files are just copied from int8 examples
* Re-use pre-defined alias in int4 exmples
* Distinguish user-side type from kernel-side type
* Add int4_t support for check_err()
* Allow conversion between Tensor<> specializations
* Re-format source files
* Use different type for host tensors
* Re-use CopyAsType<>() to implement copy ctor
* Re-use element-wise operation type alias
* Fix typo in alias names
* Complete the int4 examples
* Add constraint to Tensor<> templated methods
* Add type traits 'is_signed_integral<>'
* Add type constraints for integer version check_err<>()
* Allow comparing different-sized integral types in check_err()
* Check converted Tensor<int4_t> with golden Tensor<int8_t>
* Remove constraint of Tensor<>::CopyAsType()
* Avoid compilation error while disabling ck::int4_t support
* Remove debug messages
* Add #error directive to prevent compile sources with wrong setting
* Simplify tensor usages in examples
* Add constraint to check_err() input reference type
* Align design with other PR
* Use ""_uz to simplify example code
* Avoid too much generalizing check_err()
* Re-format GEMM instance template arguments
* Extract int4 example common codes
* Sort include directives
* Move #include directives into new header
* Move common codes together
* Re-format template argument in example code
* Reuse same implementation code for most of GEMM examples
* Re-format common.hpp
* Unify structured comment in examples
* Use reinterpret_cast<>() for cross-type pointer conversion
* Revert "Add type traits 'is_signed_integral<>'"
This reverts commit f2c148efae.
* Allow unsigned integer arguments for check_err()
* Fix compilation error in check_err()
* Remove unnecessary copy ctor for Tensor<>
* Mark Tensor<> special member functions as 'default'
* Use more strict condition to add code in examples
* Fix wrong program return value of GEMM examples
* Handle the case while user specify all the strides
* Fix never-ran examples
* Exit successfully if GEMM instance does not support given problem
* Add missing 'else' keyword
* Re-format CMakeLists.txt
* Add wrapper function to hide value conversion while copying memory
* Add new DeviceMem API to copy memory
* Use new DeviceMem API to implement examples
* Revert "Add new DeviceMem API to copy memory"
This reverts commit 3f190b0779.
* Add conversion ctor for Tensor<>
* Write Tensor<> conversion logics explicitly in example code
* Convert Tensor<> values after transfer data to host
* Add custom target to bundle examples together
* Add int4 example conditionally (just copy from int8 example)
* Extract common code into common.hpp
* Move ref gemm type alias into data-type-specific sources
* Add #error directive to prevent compile with wrong setting
* Let AddAddFastGelu support int4 parameter type
* Let check_err() support int4 parameter type
* Add wrapper function to hide value conversion while copying memory
* Finish int4 example for GEMM + AddAddFastGelu
* Add new DeviceMem API to copy memory
* Use new DeviceMem API to implement examples
* Fix wrongly use of macro 'CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4'
* Revert "Add new DeviceMem API to copy memory"
This reverts commit e26e7af71e.
* Add conversion ctor for Tensor<>
* Add 'const' specifier to Tensor<>::CopyAsType()
* Convert Tensor<> values before/after transfer between host & device
* 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
* Change all device operations to use add_instance_library to avoid duplicated cmake configuration.
* update DeviceMem
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* initial stub for gemm_gemm_xdl_cshuffle
* set up example code
* compiles
* prevent integer overflow
* harmonize interface between ref_gemm and ref_batched_gemm
* batched_gemm_gemm
* fix example
* host tensor gen: diagonal pattern in lowest two-dimensions only
* make c descriptors containing only integral constants
* clean up
* add BlockwiseGemmXdlops_v2 while exploring an unified approach
* implement proper interface
* tidy up example
* fix compilation warnings
* coarsely controlled 2nd gemm padding
* remove rocm-cmake's hard requirement for certain revision
* clang-format
* resolve merge conflict
* fix compilation error on gfx10
* adds acc0 elementwise op to interface
* add gemm_gemm instances and tests
* avoid LDS data hazard
* fix build
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* initial stub for gemm_gemm_xdl_cshuffle
* set up example code
* compiles
* prevent integer overflow
* harmonize interface between ref_gemm and ref_batched_gemm
* batched_gemm_gemm
* fix example
* host tensor gen: diagonal pattern in lowest two-dimensions only
* make c descriptors containing only integral constants
* clean up
* add BlockwiseGemmXdlops_v2 while exploring an unified approach
* implement proper interface
* tidy up example
* fix compilation warnings
* coarsely controlled 2nd gemm padding
* remove rocm-cmake's hard requirement for certain revision
* clang-format
* resolve merge conflict
* fix compilation error on gfx10
* adds acc0 elementwise op to interface
* attention host validation
* add blockwsie softmax v1
* iteratively update softmax+gemm
* transpose both gemm0 and gemm1 xdl output so as to avoid broadcasting softmax max/sum
* add init method for easier debugging
* do away with manual thread cluster calculation
* generalize blockwise softmax interface
* row-wise softmax sum & max
* format
* rename to DeviceBatchedGemmSoftmaxGemm
* add gemm_softmax_gemm instances and tests
* comment
Co-authored-by: ltqin <letao.qin@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* Add always_false<> util to delay symbol resolution
* Use always_false<> to prevent trying instantiate unwanted method
* Add new specializations of AddAddFastGelu::operator() method
* Add GEMM + AddAddFastGelu examples for data types: int8, bf16, fp32
* Use floating point literal to simplify code
* Remove unnecessary capture in lambda expressions
* Extract fast GeLU calculation as standalone method
* Mark methods as 'constexpr'
* Add constraint for HostTensorDescriptor templated ctors
* Simplify HostTensorDescriptor ctor calls
* Add C++23 std::size_t literal suffix
* Use _uz suffix to shorten example code
* Remove unnecessary conversion to std::array<>
* Re-order include directives
* Remove C-style casting by literal suffix
* Remove unnecessary statements in main()
* Remove unused type parameter of always_false<>
* Remove unused include directive
* Exit main() by returning meaningful value
* Use 'if constexpr' to switch example flow
* Use std::is_same_v<> to shorten example code
* Add 'inline' specifier to literal functions
* Unify output methods in example
* Move common codes into .inc file
* Add type check in type_convert<>()
* Add type_convert<float>() before computation
* Merge AddAddFastGelu method specializations
* Remove always_false<>
* Add constraint to AddAddFastGelu::operator() parameter types
* Add int8 specialization for elementwise Add and Subtract.
* CGEMM examples bf16, fp32, int8
* Add convert reference output to CDataType.
* Skip BF16 data type during testing.
* Lower K value to get rid of accumulation error.
* Fix merge artifact.
* Fix changed function name: GetElementSpaceSize()
* Fix merge artifact.
Co-authored-by: Adam Osewski <aosewski@amd.com>
* add verify flag and update scripts
* replace old check_error function with the new check_err
* fix syntax
* remove blank spaces
* remove empty line
* add check_err for tensors
* fix syntax
* replace tensors with vectors in check_err calls
* fix syntax
* remove blank spaces
* fix syntax
* add new line at end of file
* disable conv2d_bwd_weight test, add gpu check
* set check_gpu using export
* check GPU using runShell
* add definition of runShell
* fix script syntax
* reduce the number of threads, add full qa option
* run processing scripts in bash
* fix the branch and host names in performance scripts, add chronos
* replace parameterizedCron with cron
* archive the perf log files
* try to fix git call
* pass branch and host names as arguments into scripts
* fix script arguments
* fix script arguments
* process results on master
* fix pipeline
* add definition of gpu_arch
* run processing scripts in docker
* fix the brackets
* add agent master for the processing stage
* get rid of show_node_info call on master
* try using mici label instead of master, disable MI100 tests for now
* fix syntax
* simplify container for results processing
* remove node(master) from the process_results stage
* put all stages in original order
* change the agent label from master to mici for gfx908
* 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>
* use 'sweep once' softmax kernel where applicable
* threadwise copy's dst buffer can specify invalid element value
* add int8 in/out float compute softmax support
give a bit of leeway for int absolute tolerance as there's a single data point of all test cases showing off-by-1 error
* format
* softmax inherits DeviceNormalization
* softmax profiler stub
* tighten up reference softmax interface
* example prints tensor dimension
* add fp32 to softmax profiler
* rename header
* hook with ckProfiler
* format
* resolve merge conflict
* resolve merge conflicts
* update normalization profiler help string
* resolve conflict
* typo
* remove residual
* softmax profiler: address feedback
* test for mixed precision input/output
* fully qualify ck::math::isnan
* add comment for device normalization interface
* revise wording
* constness for alpha/beta scaler pointer
* 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
* UniforFill with integer values.
* Log tested instance type string.
* Add UT for all convolution specializations.
* debugging conv
* Fix dangling reference bug.
* Small refinements.
* Fix call to error checking function.
* Small refinements to tests.
* Configure error tolerance
* Change problem size.
* Remove OddC case from types that do not support it.
* Add helper traits for AccumulatorDataType.
* Print first 5 errs in check_err for integral types.
* Rename FillUniform to FillUniformDistribution
* Refactor
* Do not use typed tests.
* Instead use plain fixture class with templatized member functions.
* Initialize tensors with integer values.
* Refine test instances.
* Properly set accumulator data type.
* Add another "big" instance.
* Refactor convolution tests.
* Revert "debugging conv"
This reverts commit b109516455.
* Add pragma once + format + small refinement.
* Fix some unwanted changes.
* Clang-format
* Fix profile_convnd to use renamed tensor initializer.
* Add instances for ConvFWDND kernel case 2D
* Helpers to get ConvNDFwd 2D instances.
* Refactoring.
* Remove "small block" instance as it was generating compiler errors.
* Remove default template parameters values.
* Refine and fix test.
* Fix problem with default template parameter types.
* Adjust error thresholds for floating point values test.
* Use integer values initialization for instances test.
* Add tests for ConvNDFwd 2D case.
* Remove AccumulatorDataType type trait.
* Update unit-tests.
* Remove operator<< overload.
* Unlock conv1d/3d nd fwd instances.
* Enable skipping calculating reference using flag.
* Fix number of channels for first ResNet50 layer.
* Clang-format.
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* initial stub for standalone softmax
* start device_softmax_mk_to_mk as a wrapper to device_reduce_mk_to_m
* host softmax validates
* compiles; to implement beta scaling
* use NaN trick to efficiently ignore OOB values during sum of exponentials
* freeload device_reduce's utility functions
* clean up interface
* adding prior value (beta scaling)
* remove restriction related to perf considerations
* apply clang-format
* clean; disable diagnostics
* resolve conflicts
* add exp wrapper
* honor HostTensorDesc interface; allow implicit cast from different vector<T> type
* test softmax for fp16/fp32
* update readme
* amend commit NaN trick
* remove redundant param added during development
* format
* replace ScalarDataType with AccDataType
* separate out test programs by precision type
* move softmax sample code to its own folder
* format
* keep up with recent changes in reduction API
* remove extra header
* 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()
* 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
* Reference CGEMM + test stub
* Format.
* Incomplete simple implementation
* Library instances
* Sketch of tests
* Test fixes.
* Example added
* Cosmetics
* Add elementwise operation kernel and example
* Add comment
* Add template argument of dim . Prepare to support multiple dimension
* Rename example
* Support 1 dimension
* Add static assert
* Add comment
* Second auxiliary buffer added
* Extract pad
* Remove redundant argument
* Support any dimension for elementwise operation
* Remove line
* Let it be the multiple number of CU
* Move thread per block to the parameter of constructor
* Consuming binary ops to do A+B / A-B
* Fix + cosmetics + bf16 test commented out temporarily
* Format
* Enabling bf16 test
* Revert "Enabling bf16 test"
This reverts commit f497e2ba44.
* Fix + test reenabled
* fix build
* Revert "fix build"
This reverts commit d73102384b.
* post PR #235 merge fix
* amend
* Single workspace for cgemm + helper
* Perf calc fix
* Review remarks: static_cast
* Review remarks: binary ops templated
* Cleaning
* Removal of instances and their tests
* Review remarks from aosew addressed
* Review remark: unnecessary attribute
* Post-merge fixes
* Restrict 4gemm to PassThrough + bug fix
* Review remarks
* update licence
* change cgemm example to fp16
Co-authored-by: rocking <chunylai@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Anthony Chang <ac.chang@outlook.com>
* add intrin_mfma_f64_16x16x4f64
* add example
* gemm reference add double data type
* chang init data
* fix M N PerXdlops
* fix ifdef
* add comparsion config
* add conv fwd example
* format log out
* change rc matrix egister layout
* reorganize example
* reorganize example 2
* format,because merge develop
* fix call impl adding acc data type
* lost ;
* add compiler warning
* change example tunning parameters
* add test for fp64
* add instance
* add test/gemm/gemm_fp64.cpp
* fix get name issue
* remove some tunning parameter
* fix conflict
* format
* use integer value for GEMM test
* add acc data type
* remove typeid because fp16
* fix streamconfig etc bug from merging develop
* format
* remove test_gemm_xdl_fp64
* add AccDataType
* AccDataType problem
Co-authored-by: qinletao <letaoqin@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* start adding navi21 GEMM
* navi_gemm_km_kn_mn_fp32 compiles and passes one test.
* rename variables and functions in gridwise_gemm_dlops_v1r3
* add other 3 layouts; format instance
* adding more tuning parameters
add tuning parameters for other 3 layouts
* add gemm_dlops_f16
* tmp
* add dependence of DeviceGemm::IsSupportedArg() on arch
* minor changes
* minor changes
* minor changes
* minor changes
* minor changes
* minor changes
* minor changes
* push gemm_dlops into profiler
* minor changes
* if using xdl or dlops is moved into profiler_gemm_impl
* minor changes
* minor changes
* remove is_xdl from profile_gemm_impl
* make IsSupportedArg dependent on arch for other device_gemm
* minor changes
* minor changes
* fix a bug in f_generate_tensor_value
* add 64x64x64 for gemm_dlops_int8
* add 64x64x64 for gemm_dlops_int8
* comment out 3 layouts in gemm_dlops_int8; add 32x32x32 for gemm_dlops_int8; init A values to 1
* fix
* start fixing tuning parameters
* monir
* minor changes
* minor changes
* minor changes
* fixing
* adding example
* adding example
* adding example
* add gemm fp32 example
* clean up
* use 128x128x16 as MNK tile in navi21 gemm example
* bug fix
* fix test
* use new block c tile
* clean
* fix build
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: shaojiewang <wsjmessi@163.com>
* Tiny fix in dynamic_buffer.hpp to support vectorized AtomicAdd for double type
* Update to host layer and host reduction
* Merge and remove reduction kernels
* Merge and remove reduction device interfaces and update pooling device interface
* Merge and remove useless reduction device instances
* Update to reduction profiler and reduction ctests
* Update to reduction and pooling examples and add one reduction example
* Change to reduction examples to let them testable by ctest
* Add explicit pass checking for reduction and pooling examples
* Explicit assignment of tensor shapes in example reduce_blockwise_two_call
* Use atomic_add to repace atomicAdd and add atomic_add for double type
* Add reduce ctest support for double data type
* Replace to_int_vector() by using c++ std::vector::assign()
* Keep DeviceReduceThreadWise separated from DeviceReduceBlockWise
* Merge DeviceReduceBlockWise and DeviceReduceMultiBlockAtomicAdd into DeviceReduceMultiBlock
* Add GetAtomicOperationZeroValue() support for AtomicMax
* Tiny change to reduce example README.md
* Fix some tiny issues due to branch merging
* Revoke previous change in dynamic_buffer.hpp and add atomic_add for double2_t
* Add reduce multiblock_atomic_add instances for fp64 to verify vectorized atomic_add on fp64
* Renaming
* Clean the header includings in device_reduce instances header files
* enable example of conv 1d/3d for bwd weight
* make bf16 kernel do not use atomic add
* using new gridwise gemm for bwd weight on convnd bwd weight
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* [What] Rename the example
[Why] Prepare to add unary reduction
* Add global oparation to the parameter
* Add atomicmax
* Fix compile error
* Support atomicMax (hip library)
* Rename the reduction example
* Fix target name
* use p_d1_grid as the indicator directly
* Prevent performance issue. Let passthrough handle it.
* Implement the function template the specialize the float2
* No need to separate into two lines
* Remove empty line
* add comment
* Fix compile error due to merge from develop
* make the implementation of atomic_max / atomic_add explicit for each datatype
* Refine typo
* For future CI test
* Fix compiler error in ckProfiler
* Merge commit 'de2769e3a6695b38a20529261273ddc5cdaab2fe'
* simply use remove_pointer
* Rename type and var
* Refine example
* Modify reducemax example
* Fix bug in reduction
* Change initialize range
* Implement F64 version of atomicMax
* Move reduction code together
* Add buffer atomic_max
* Fix coding style by clang-format
* Integrate new api of DeviceGemmReduce_Xdl_CShuffle
* Integrate Batch gemm reduction
* Fix example
* fix example
* clean up
* Fix batch gemm tensor operation
* Fix coding style
* Fix template augument
* Fix clang format
* Keep flexible of different stride for each D tensor
* Fix compile error for ckProfiler
* Fix typo
* [What] Fix naming
[Why] Prepare to add out elementop
* Add DoutElementOp
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: rocking <chunylai@amd.com>
* Add elementwise operation kernel and example
* Add comment
* Add template argument of dim . Prepare to support multiple dimension
* Rename example
* Support 1 dimension
* Add static assert
* Add comment
* Extract pad
* Remove redundant argument
* Support any dimension for elementwise operation
* Remove line
* Let it be the multiple number of CU
* Move thread per block to the parameter of constructor
* rename threadPerBlock with blockSize
* Support double
* rename kernel function name
* remove redundant include header
* Refine type
* Need to the final dimension
* Refine variable name
* Refine type
* Use index_t instead of int in API
Co-authored-by: rocking <chunylai@amd.com>
