* allow building CK for specific data types
* add CI build and test stage on Naiv3x without some int8 instances
* add missing gemm fp16 instances
* add the changes to the missed cmake file
* add empty lines at end of source files
* Do not build quantization client example on navi3 in CI
* disable batched_gemm_multi_d_int8 instances with DTYPES
* disable device_conv2d_bwd_data_instance with DTYPES
* fix ckprofiler for conv_bwd_data for int8
* properly isolate the conv_bwd_data int8 instances
* remove empty line
* Add basic fp8 definitions and prn-generator
* Format
* Add fp8<->fp32 type_convert
* Format
* Split type_convert and cast_to/from_f8
* Format
* Minor fix
* Minor fix
* Move fp8 utils to a separate header
* Add elementwise ops
* Add fp8_convert_sr
* Format
* Add element op
* Eliminate magic numbers
* Split f8_convert_sr in host and device
* Format
* Add some constexpr
* Add a datatype test
* Format
* Another format
* Add fp8<->fp16 tests
* Update type_converts
* Format
* Add fp16 casting functions
* Format
* Use seed as a runtime arg
* Use element location for PRNG
* Format
* Add fp8<->fp16 to PassThrough element op
* Clean up
* Merge host and device implementations
* Add comments on rounding modes
* Remove leftover code
* Put type_converts into a separate header
* Put random number gen to a separate header
* Rearrange f8_utils' namespaces
* Refactor type_convert.hpp
* Move f8_t definition
* Add maxpool f32 kernel and example
* Revise copyright
* Add device pool bwd device op
* Support f16 and bf16
* Add compute datatype for reference code.
Prevent error in bf16
* Fix type error
* Remove layout
* Fix bf16 error
* Add f16 and bf16 example
* Add more operations
* Implement IsSupportedArgument
* Add changelog
* Add comment
* Add comment
* Remove useless header
* Move initialize of workspace to the run
* Move set din zero to the device operator
* Save din_length_raw
* Remove useless header
* Calculate gridsize according to the number of CU
* Calculate gridSize according to the number of CU.
Remove useless header
* Add put example
* Remove useless header
* Fix CI fail
* Add NumReduceDim template parameter to DeviceSoftmax and Softmax client API to simplify instances collecting
* Move the generic kernel instance to be the first of the instance list for elementwise op of normalization
* Add GetGenericInstance() interface for DeviceOperationInstanceFactory class of DeviceSoftmax
* Add testing of GetGenericInstance() in client_example of Softmax
* Revert "Add testing of GetGenericInstance() in client_example of Softmax"
This reverts commit f629cd9a93.
* Revert "Add GetGenericInstance() interface for DeviceOperationInstanceFactory class of DeviceSoftmax"
This reverts commit a9f0d000eb.
* Support generic kernel instance to be the first instance returned by GetInstances() for GroupNorm
* Move generic kernel instance to separate tuple for elementwise op of normalization
* Remove un-used files for softmax instance
* Store generic kernel instance to separate tuple for softmax
* Add IsSupported checking for generic instance to client example of softmax
* Replace the get_device_normalize_from_mean_meansquare_instances() by the DeviceOperationInstanceFactory class for elementwise-normalization
* clang-format fix
* Remove int8 from softmax instances
---------
Co-authored-by: zjing14 <zhangjing14@gmail.com>
* Expand the base class of pool2d, prepare to share base class with pool3d
* Add pool3d device op
* Add pool3d f16 example
* Refactor the base class. implement generic pooling in the future
* clang format
* get original index in max pooling
* Add outputindex to base class
* Fix dimension
* Add pooling instance
* Use indexType instead
* Remove useless header
* Extract IndexDataType to template
* Extract pooling reference code
* clang format
* clang format
* Fix typo
* Add tensor stride
* Add missing header
* Add index stride and output stride
* Refine naming
* Add type to base class
* Rename file
* Use proper size
* Fix typo
* Refine naming
* Modify the argument into vector.
* Add max pool profiler
* Refine naming
* Support f32 pool
* Fix typo
* Add avg pool2d fwd in profiler
* clang format
* Rename AccDatatype to ComputeDatatype
* Fix init
* test pool
* Extract variable
* Add client example
* Check the pooling dim
* clang format
* Connect argv and arg_parser
* Add found check
* Remove useless header
* Refine naming
* Adjust the order of device_pool_fwd
* Add contraction profiler and tests
* Build and style fixes
* Allow to use any elementwise operator for ref_contraction
* Introduce profile_contraction_scale and profile_contraction_bilinear
* Make ref_contraction generic and extend interface tests
* Stylistic minor fixes
* Extend test_contraction_interface
* Add TypeConvert class and start refactoring
* Refactor TypeConvert as a struct
* Get back to template functions type_convert
* Add a type_convert_bf16_rtn, set rtz as default
* Clean up
* Add UnaryConvertPrecision struct for high-precision workloads
* Format
* Update type_convert to UnaryConvert on threadwise level
* Update UnaryConvertPrecision
* Format
* Fix chmod
* Add a flag to pick converion method
* Format
* Remove the added flag
* Merge elementwise op with type conversion
* Move type_convert to elemwise op, update the op
* Update type_convert_precision -> bf16_convert_rtn
* Clean up
* Update comments
* Update the CK_WORKAROUND_DENORM_FIX flag handling
* Update the unneeded op to work but warn user
* Remove the message
* Use a PassThrough instead of ConvertBF16RTN to calcaulate reference
* Format
* Add missing include
* [What] Remove pure conv int8 instance
[Why] We will never use pure int8 conv in AI, use int8 quantization instead
* Change layout
* Share the kernel parameter
* Support more type of NHWGC for group conv
* Revise client example of conv 2d, use NHWGC layout
* Add instance to cmake
* Revise layout of group conv quantization instance
* Revise layout of external api of group conv quantization
* Revise layout of group conv quantization client example
* Fix clang format
* Add comment to describe meaning of each parameter
* Rename to proper naming
* Add example of groupnorm + swish
* Extract duplicate code in example
* Add groupnorm + swish instances
* Ractor instance generation, split into multiple cpp file
* Add external api and client example
* Refine profiler message
* Use ck math version of exp
* Refine problem size in example
* Add host version of exp
* Add conv perlayer quantization
* Add gemm_dlops quantization
* Support int8 for innerproduct
* Refine gemm dlops int8 kernel parameter
* Support gfx908(MI100) and gfx90a(MI200)
* clang-format
* Rename example number
* Support different layout for d tensor
* Add conv dlops perchannel quantization example
* Move to example 40
* Extract the common code for different platform (dlops and xdlops)
* Move ot subfolder. Prepare to add other op of quantization
* Refine the quantization instance library
* Add conv dl instances and client example
* Remove unnecessary type
* Add gemm quantization instance
* Add external api and client example
* Refine num_bytes
* Separete different layout to different cpp
* Add more xdl instances
* Revert "Remove unnecessary type"
This reverts commit 820869182f.
* Remove CShuffleDataType in dlops
Let acc and CShuffleDataType be the same in xdlops
---------
Co-authored-by: zjing14 <zhangjing14@gmail.com>
* Grouped gemm + Gelu instances.
* Device Instance Factory for GroupedGemm+Gelu
* Client example
* Rangify fill helper functions.
* Fix name clash.
* Profiler for grouped_gemm+gelu
* No need to use full namespace name.
* Add check for MRaw divisible by vector load.
* Ugly fix for big errors.
* Add grouped_gemm+gelu to profiler CMakelists.
* Store in argument additional info.
* Information about Mraw, Nraw, Kraw values.
* Use FastGelu instead of Gelu.
* Change client ex to use FastGelu
* Remove relaxed error precision.
* Remove duplicate output elementwise-op
---------
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>
* Modify Doxygen config to pick up include directories recursively
* Add DeviceMem struct to API Reference guide
* Add classes that are used in Flash Attention kernel
* Add a reference and config for generating bibliography
Co-authored-by: Philip Maybank <Philip.Maybank@amd.com>
* Add gemm + layernorm instance
* Add ckProfiler
* Add test
* Add client example
* Detect if user forger to set the workrspace
* Use literal in the example
* [What] use builtin function for sqrt
[Why] compiler will not use v_sqrt_f64_e64 if we use ::sqrt()
* check gemm vaildity in IsSupportedArgument
* Add more testcases
* Merge duplicated folder in client example
* Print more infomation
* Use better kernel parameter for MS problem size
* clang format
* Add constexpr for if condition and remove redundant include
* Remove cstdlib and add constexpr
* 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
