Add Tile Distribution Encoding Register Mapping debug utility
for MFMA / WMMA unification work. (#4804)
## Motivation
This PR adds a small utility that allows you to use Tile Distribution
Encodings to directly map matrix elements to register locations and vice
versa. It can also print forward and backward layout mappings similar to
the Matrix Calculator utility. The utility is not meant for index
calculations in actual kernels, but rather as a debugging tool and
probably for automated verification of the policy structs in the new
WMMA / MFMA unification design.
## Technical Details
Tile Distribution Encodings are a core part of CK Tile which can define
the relationship between register and intrinsic matrix fragment
elements. They allow for any mapping based on unmerge and merge
transformations. Also, they allow for a special "Repeat" dimensions
which acts like an additional matrix dimension and allows for
replication of certain matrix elements. The new mapping utility can deal
with all aspects.
## Test Plan
Since this is a debug utility there is nothing to directly test, but
there is an example file that defines four different Tile Distribution
Encodings and prints their forward and backward mappings, along with
some extra parameters.
## Test Result
## Submission Checklist
- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
* Addition of streamk fp8 example for CK Tile
* Adding in bf8 streamk example in CK Tile
* Refactoring fp8/bf8 unit tests
Refactored the unit tests for fp8/bf8 to utilize the test harness.
Implemented smoke tests with layouts: CCR, CRR, RCR, RRR for fp8/bf8.
The tests are using 128x128x32 for the tile configuration, as other
configurations revealed implementation gaps that are currently being
documented.
* Initial commit. create batched_contraction_kernel file
* initial problem definition
* implement initial example to launch kernel
* add universal gemm to contraction. initial phase
* complete implementation for special case all Dims are 1 and no Ds
* clean code
* initial changes to support multi dimensional G
* more progress in implementing multiple G
* tmp commit
* manage dynamic NumDimG in kernel
* improving example for multi M,N,K,G handling. start generalizing kernel. it is a temporary commit
* implement the example for general Multi dimension G M N K and test different reference calculation algorithms
* 2 functions for reference using multi dimensional and flat indexing
* clean the code for muti dimentional G, M, N, K contraction and add some logs
* Add Make descriptor function in kernel for merging Ms, Ns, Ks for A, B, E
* some cleaning on kernel
* clean the code for calculating the offsets from flatten batch number
* Start adding MultiD support to kernel and example
* more changes to manage multi D in kernel and example
* manage passing multi d to kernel and testing.
* complete multi D support in kernel. modify example code to support it
* Correct algorithm to calc the correct offset values for D tensor batches and some code cleaning
* Minor fix
* Generalize example code for variable NumD tensors and apply cleanup based on review feedback
* Refactored code and addressed review feedback
* refactoring, cleaning, add documents, in kernel side and example codes
* Optimize batch offset calculation in kernel
* Inline CalculateBatchOffset in batched contraction kernel, update CHANGELOG.md
---------
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
* Pooling 2D/3D with refernce
* Tests & cleanup
- added test for ppoling
- cleanup
- removed 2d example
* Comment resolution
- README added
- example target name rectified
- appropriate arg description and comments added
* clang-format
* appropriate blocksize calc
* modifications for future indexing addition
- instead of transforming views we now transform the descriptors, so
that the same descriptor can be re-used for index tensor in the future
* some basic fixes
* comment resolutions
* comment resolutions
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Addition of initial CK Tile Stream-K example for bf16 and fp16. These
examples are minimal. As more functionality and gtests are added for
Stream-K (coming in future PRs), these examples will be expanded.
* unify pipeline signature with existing example
* iwyu
* move stuff around in load-tile-transpose
* cleanups in batched transpose pipeline
* comments
* use same inputs size
* cleaner printf
* print host args
* use 64 block sides in the 37_transpose example
* roll back grid dimension size adjustment for 37_transpose example
* transpose grid for 37_transpose to unify with 35_batched_transpose
* unify grid computation logic
* make policy methods device only (since they are used only on device from the pipeline)
* more host/device attribute cleanups
* copy over problem
* move over pipeline and policy
* add switch to batched transpose api
* make the lds problem more similar to original problem
* factor out logic into traits
* factor out conditional compilation into trait parameter
* propagate pipeline to args
* unhardcode pipeline dispatch parameter
* refactor vector size
* put warp tile out of dispatch
* rename template parameter for trait
* rewrite vector size in terms of problem
* mark policy-internal struct variable as device
* factor out input distribution and thread access pattern from policies
* reword vector size
* use datatype across batched transpose pipelines, problems and kernel
* remove transpose traits from lds pipeline
* add padding to the lds pipeline *interface*
* add comment
* remove ck_tile example #37
* update cmakelists
* add test for new pipeline
* update batched transpose test
* roll back load_tile_transpose changes
* remove comments
* pack dispatch parameters into a config
* padM can be enabled
* adjust lds vector size to enable padding along N
* update test
* clean up logic
* swap m/n input vector size
* adjust perf test script
* sweep over C/W in perf test
* count both read and written bytes into bandwidth (x2 the number)
* clang-format
* widen size range for perf test
* remove 64k x 64k case; it's too large for index
* remove thread tile from dispatch
* Solve merge conflict
* fix compile
* modify the transpose
* solve the test error and clang format
* Add v3 support for Groupd fwd conv+bias+clamp & ckProfiler (#2463)
* Add logging to IsSupported.
* Less casting in AddClamp
* Conv+bias+clamp instances & profiler BF16
* Fix 3D instances & run just 1x for verification.
* :Run just once for verification conv fwd.
* ckProfiler conv fwd clampwq
* Remove exec bit & formatting
* Add support for MultiD for grouped conv fwd v3.
* Enable 2Lds.
* clean
* align instances
* align instances
* profiler fixes
* Fixes
* fix
* fix
---------
Co-authored-by: Adam Osewski <root@quanta-ccs-aus-f01-19.cs-aus.dcgpu>
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
* Fixing 0ms and inf GB/s issue in img2col (#2565)
issue :
====
``` sh
$ bin/tile_example_img2col
Perf: 0 ms, inf GB/s
```
solution :
======
Problem occured because config.time_kernel is false by default.
if false, then no need to calculate perf, just print proper message
`image_to_coloumn: pass, No Perf generated due to config.time_kernel=0`
* merge with develop
* solve clang format
---------
Co-authored-by: ThomasNing <thomas.ning@amd.com>
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
Co-authored-by: Adam Osewski <root@quanta-ccs-aus-f01-19.cs-aus.dcgpu>
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
Co-authored-by: rahjain-amd <Rahul.Jain@amd.com>
* Elementwise kernel implementation
Co-authored-by: Sami Aario <samaario@amd.com>
Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com>
Co-authored-by: yashagar <yashagar@amd.com>
* Elementwise with generalized nDims
* Adding the n-ary input tensor feature
* Generalize dimensions on top of inputs
* Add TFLOPS + remove std usage for tuples
* 1D basecase optimization
* Cleanup code + refactoring to a common interface
* Generalize to unary and add an example
* Cleanup, refactoring and commenting
* Suggestions for LWPCK-3170: elementwise kernel improvements
* Clang-format: remod.py
* Replace InputTensorType with XDataType as the type of input_tensors
* Add Tuple::apply and use it in ElementWiseKernel::operator to call operation with the exact number of arguments in xs
* Move examples to folder 19_elementwise
* Add missing copyright headers and fix some existing ones
* Replace an assert with throw std::runtime_error in elementwise example
* Avoid reading the output by using make_static_distributed_tensor for y_tile
* Removed two unused includes
* No need to move windows to the next block when each workgroup processes a single tile
* Only copy input tensors to the device
* Use get_warp_size to obtain warp size, and use ceiling division for grid size also for the unary example
* Adding output strides to the kernel, transposition example and update the other examples
* Changes made by remod.py
* Use default template parameter values for memory operation and coherence in a call to make_naive_tensor_view
* Move binary operations to include/ck_tile/ops/elementwise/binary_elementwise_operation.hpp
* Reuse generic reference binary/unary operation in examples + refactoring the transpose reference
* Fix comments in elementwise_example.cpp
- Refer to AMD terminology except when suggesting NVIDIA alternatives in parentheses
- ElementWiseTraits was renamed to ElementWiseShape
- Adopt suggestions made by Copilot when prompted to check for factual or typographical errors
* Simplify CMakeLists.txt and remove the unused variables this uncovers
* Rename a file and fix some copyright statements
* Changes made by script/clang-format-overwrite.sh
* Add basic unit test for ElementWiseKernel
* Remove left-over uninformative comment in apply unit test
* Changes made by clang-format-overwrite.sh
* fixup! Use default template parameter values for memory operation and coherence in a call to make_naive_tensor_view
* Clean up test_tuple_apply.cpp and test_elementwise_1d.cpp
* Use make_uniform_array_with_factory to define h_xs and d_xs_mems_owner as type std::array
* Use a DeviceMem constructor that calls get_element_space_size_in_bytes internally
* Move examples to folder 20_elementwise
* Reduced register pressure on the CK tile elementwise kernel + add 4d input example to be able benchmark against old CK
* Fix CLang formating
* Bump up the elementwise example folder number
* Elementwise: add padding + minor cleanup
* Add Vector Size inference + fix issue with wrong vectorization due to missing GuaranteedLastDimensionVectorStride setting in make_naive_tensor_view
* Add isSupportedArg to Elementwise kernel + addapt example and unit tests
* Fix clang-format on the unit test file
---------
Co-authored-by: Damien Lejeune <damien.lejeune@amd.com>
Co-authored-by: Sami Aario <samaario@amd.com>
Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com>
Co-authored-by: Aviral Goel <aviral.goel@amd.com>
* fix async copytest bug
* Add block_sync_lds_direct_load utility
* fix the s_waitcnt_imm calculation
* Improve s_waitcnt_imm calculation
* fix vmcnt shift
* add input validation and bug fix
* remove unnecessary output
* move test_copy into test
* change bit width check
* refactor macros into constexpr functions
which still get inlined
* wrap s_waitcnt api
* parameterize test
* cleanup
* cleanup fp8 stub
* add fp8 test cases; todo which input parameters are valid?
* replace n for fp8 in test cases
* add large shapes; fp8 fails again
* change input init
* test sync/async
* time the test
* clang-format test
* use float instead of bfloat to cover a 4-byte type
* fix logic - arg sections should be 'or'd
* make block_sync_lds_direct_load interface similar to old ck
* fix a few comment typos
* name common shapes
* revert the example to original logic of not waiting lds
* clang-format
---------
Co-authored-by: Max Podkorytov <4273004+tenpercent@users.noreply.github.com>
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
* ck_tile kernel for gemm with groupwise quantized A or B tensor.
This change introduces new pipelines with Intrawave scheduler and block gemm primitives that loads the scale tensor to registers to perform dequantization post MFMA on C tensor in registers.
Scale tensor data, AQ/BQ is spliced across threads in registers and not stored in LDS.
Current support is for the following combinations, but it should be fairly straightforward to extend support to more formats.
1. fp8, fp8 -> f32
2. bf8, bf8 -> f32
3. i4, fp8 -> f32
4. i4, bf8 -> f32
Group size can go down to as low as K length of underlying WarpGemm primitive.
For Gemm problems with quantized B tensor, this change also introduces preliminary support for flatmm pipeline which loads B tensor directly into registers.
* [Block Scale Gemm] Only run gemm quant examples on __gfx94__
- Only run gemm quant examples on __gfx94__ for usage of
`v_cvt_pk_fp8_f32`
- Format the code
* [Block Scale Gemm] Remove Bquant Gemm BlockScale
This cleanup is in preparation for future development of bquant. By
isolating Aquant-related code, we can streamline the codebase and make
it easier to add and maintain bquant functionality in subsequent
updates.
* [Block Scale Gemm] Format code with clang-format-12
The latest clang-format (v19) in ROCm 7.0 generate different result than
clang-format-12 which is used in CK CI.
Format code with clang-format-12 for consistency.
* [Block Scale Gemm] Split the k direction loop
- Split the k direction loop in block_universal_gemm_as_quant_bs_cr.hpp
to make the logic clearer.
- Disable C transposition.
* [Block Scale Gemm] Move block scale gemm example to 38_block_scale_gemm
* [Block Scale Gemm] Update copyright
* test
* Add TailHandler
* Move TileDistributionEncodingPatternAQ
* Refactor
* refactor
* fix bug
* fix bug
* help solve the PR comment
* Format the code
* [Block Scale Gemm] Add unit tests
* [Block Scale Gemm] Add support to 16x16x32 MFMA
- Add support to 16x16x32 MFMA
- Fix a bug when exchange data crossing lanes
---------
Co-authored-by: Vijay Krishnamoorthy <vjkrish@meta.com>
Co-authored-by: Cong MA <congma13@ctr2-alola-ctrl-01.amd.com>
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* add transpose load; no real logic
* fix some compile errors
* fix some issues
* update transpose load logic
* add some fixes
* fix a distribution issue
* update some codes
* add some fix
* can pass; but no logic
* transpose load enable
* update tile transpose
* miss output tile distribution mapping
* hack for transpose 16x16
* update output tensor distribution
* delete unused variables
* fix transpose related codes
* update transpose load example
* exchange the iteration order
* fix 16x16 related dimension transpose
* fix a transpose index issue
* fix a transpose index issue
* fix clang format check
* update load tile transpose related codes
* fix compile errors and pass 16x16 tests
* fix a typo
* update logic
* check other data types
* add transpose load api
* update transpose load api
* fix clang format check
* change file name
* refactor codes
* update code name
* delete some unused codes
* delete the unused oob flag for transpose load
* update tensor view api for transpose load
* update for testing
* fix a typo error
* move transpose ops to example directory
* update transpose api
* update include file
* fix for pr review
* fix compile errors
* add transpose load; no real logic
* fix some compile errors
* fix some issues
* update transpose load logic
* add some fixes
* fix a distribution issue
* update some codes
* add some fix
* can pass; but no logic
* transpose load enable
* update tile transpose
* miss output tile distribution mapping
* hack for transpose 16x16
* update output tensor distribution
* delete unused variables
* fix transpose related codes
* update transpose load example
* exchange the iteration order
* fix 16x16 related dimension transpose
* fix a transpose index issue
* fix a transpose index issue
* fix clang format check
* update load tile transpose related codes
* fix compile errors and pass 16x16 tests
* fix a typo
* update logic
* check other data types
* add transpose load api
* update transpose load api
* fix clang format check
* change file name
* refactor codes
* update code name
* delete some unused codes
* delete the unused oob flag for transpose load
* update tensor view api for transpose load
* update for testing
* fix a typo error
* move transpose ops to example directory
* update transpose api
* update include file
* fix for pr review
* fix compile errors
* change directory name
* delete the duplicated directory
* update cmakelists file
* delete the unused codes
* update function names
* update transpose policy
* update code after remod.py
* update codes
* add some comment
* Polish the instr infrastructure
* build up the fixed instr
* redesign the transpose api, currently it has numerical error
* add the bf16 transpose
* fix some issues
* add some comments
* update document
* Finished the refactor of API and pass through the verification
* fix the merging issue
---------
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* Multiple d, initial commit
* Check Ds Layout
* Readme and clang format
* Update branch & conflicts
* Multiple D - fix clang-formatter
* Rename elemetwise_op
* Fix CI
* Code review part1
* Remove printf
* Remove unnecessary comment
* Add new tests with Col layout
* Review part 2
* Added support for Multiple D GEMM
* Update comment
* Remove maybe_unused
* Clang-format
* Review part 3
* Add comment to function
* Add comment to function: another
* Take number of params for a refrence function
* Remove additional d param for 0 tensor
* Change name of function
* Fix CI fails
* Test Copy kernel code for testing tile distribution logic
* Fix the error
* Solved the problem
* Updated comments and document formatting
* Removed unused tile distribution and code cleanup
* Added README.md and formatting for CI/CD.
---------
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* add ck tile examples to package
* Update jenkinsfile
* fix for jenkinsfile
* fix for building ck tile code on non gfx9
* compile ck tile examples only for gfx94
* include ck tile examples in all target
* fix for basic gemm UseStructuredSparsity
* Update CMakeLists.txt
* Update gemm_pipeline_problem.hpp
* add targets to rocm install
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
* Ck-tile, impl. grouped gemm
* Workspace is allocated by user, and is passed to the function
* Prepare test to new api design
* Unify GemTransKernelArgs, removing N0 param
* Add 1 to dim3 in paritioner
* Typo: gem - > gemm
---------
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
* Add reduce2d new api
* Prevent user use cross warp reduction
* Fix bug of std caculation
* Add rmsnorm2d
* Add rmsnorm small example
* Remove static assert to prevent compile fail
* Add script to test performance and correctness
* Add missing cmake change
* refine naming
* refine example of rmsnorm
* Fix bug of rmsnorm
* Refine naming
* Fix cmake
* clang format
* Refine pipeline name
* Add add_rmsnorm2d_rdquant kernel
* Add reduce op
* host verification
* Fix bug of one pass pipeline
* Refine tile size
* Add two pass pipeline
* Rename two pass to three pass
* Fix bug of kSaveX == false
* Add instance library
* Add test script
* Fix bug of x verification
* Add save_x to trait
* Add README
* Move reduce2d into reduce folder
* Fix bug of welford when number of m warp > 1
* remove reduncant comment
* 1. move 06_rmsnorm2d to 10_rmsnorm2d
2. move 07_add_rmsnorm2d_rdquant to 11_add_rmsnorm2d_rdquant
* clang format and add missing header
* Add host validation of add + layernorm2d + rsquant
* Revert "Add host validation of add + layernorm2d + rsquant"
This reverts commit 936cb45797.
* Remove deprecated flag
* port layernorm
* change warp_welford.hpp
* Update warpshuffle
* 1. Add save mean and save std back
2. Move construction of tensor_view and tile_window to operator()
* refine welford max count calculation
* unify layernorm api
* Rename file
* Remove save mean and inv std
* Revert "refine welford max count calculation"
This reverts commit 022365802b.
* Fix order of parameter
* refine welford max count calculation again
* Remove fp32 instances
* Fix bug of padding
* refactor api
* Support bf16
* Extract common function
* Refine arg of operator()
* Add kMThreadPerBlock to template parameter
* clang format
* Refine variable name
* Refine file name
* remove redundant line
* refactor layernorm2d pipeline and add block-per-block utility
* fix name
* rename more
* add more block-per-tile instance
* remove duplicated define
* update instance for 2048, 1024 case
* support up to 2048 now
* opt loading
* add n1536
* Add two pass pipeline
* format
* Fix incorrect type
* parallel compilation
* Use smaller N
* fix 2p pass
* Support Repeat_M in distribution
* Refine nameing
* Add reduce example
---------
Co-authored-by: letaoqin <letaoqin@amd.com>
Co-authored-by: aska-0096 <haocwang@amd.com>
Co-authored-by: rocking <ChunYu.Lai@amd.com>
Co-authored-by: carlushuang <carlus.huang@amd.com>
* Checkpoint: Finished with the tile example & kernel verification, working on the different matrix layout
* Finished the Matrix Layout feature set up. Note: Need to modify the inner block to solve the shuffle problem in the future.
* Fix: Clang Format, API fixed from fmha
* fix with better naming convention
* revert back the pipeline code of fmha
* Fixed: Addressed the comments and merge the GEMM shape of GEMM Operator and FMHA Operator to one.
* clang format with the reference_gemm file
* convert the clang format with the remod.py
* Changed the format and variable name of the kernel gemm_shape and partitioner
---------
Co-authored-by: thomasning <thomasning@banff-cyxtera-s70-4.ctr.dcgpu>
* Add layernorm2d forward
* Refind file path
* clang format
* Exclude ck_tile op from all
* use add_executable instead
* refactor layernorm2d_fwd example
---------
Co-authored-by: carlushuang <carlus.huang@amd.com>
* enable gfx940
* switch between intrinsic mfma routines on mi100/200 and mi300
* fix mfma_int8 on MI300
* disable 2 int8 examples on MI300
* Update cmake-ck-dev.sh
* restore gitignore file
* modify Jenkinsfile to the internal repo
* Bump rocm-docs-core from 0.24.0 to 0.29.0 in /docs/sphinx
Bumps [rocm-docs-core](https://github.com/RadeonOpenCompute/rocm-docs-core) from 0.24.0 to 0.29.0.
- [Release notes](https://github.com/RadeonOpenCompute/rocm-docs-core/releases)
- [Changelog](https://github.com/RadeonOpenCompute/rocm-docs-core/blob/develop/CHANGELOG.md)
- [Commits](https://github.com/RadeonOpenCompute/rocm-docs-core/compare/v0.24.0...v0.29.0)
---
updated-dependencies:
- dependency-name: rocm-docs-core
dependency-type: direct:production
update-type: version-update:semver-minor
...
Signed-off-by: dependabot[bot] <support@github.com>
* initial enablement of gfx950
* fix clang format
* disable examples 31 and 41 int8 on gfx950
* add code
* fix build wip
* fix xx
* now can build
* naming
* minor fix
* wip fix
* fix macro for exp2; fix warpgemm a/b in transposedC
* unify as tuple_array
* Update the required Python version to 3.9
* Update executable name in test scripts
* re-structure tuple/array to avoid spill
* Merge function templates
* Fix format
* Add constraint to array<> ctor
* Re-use function
* Some minor changes
* remove wrong code in store_raw()
* fix compile issue in transpose
* Rename enum
Rename 'cood_transform_enum' to 'coord_transform_enum'
* let more integral_constant->constant, and formating
* make sure thread_buffer can be tuple/array
* temp fix buffer_store spill
* not using custom data type by default, now we can have ISA-level same code as opt_padding
* fix compile error, fp8 not ready now
* fix fp8 duplicated move/shift/and/or problem
* Default use CK_TILE_FLOAT_TO_FP8_STOCHASTIC rounding mode
* fix scratch in fp8 kernel
* update some readme
* fix merge from upstream
* sync with upstream
* sync upstream again
* sync 22
* remove unused
* fix clang-format
* update README of ck_tile example
* fix several issue
* let python version to be 3.8 as minimal
* remove ck_tile example from default cmake target like all/install/check
* remove mistake
* 1).support receipe in generate.py 2).use simplified mask type 3).change left/right to pass into karg
* fix some bug in group-mode masking and codegen. update README
* F8 quantization for FMHA forward (#1224)
* Add SAccElementFunction, PComputeElementFunction, OAccElementFunction in pipeline
* Add element function to fmha api
* Adjust P elementwise function
* Fix bug of elementwise op, our elementwise op is not inout
* Add some elementwise op, prepare to quantization
* Let generate.py can generate different elementwise function
* To prevent compiler issue, remove the elementwise function we have not used.
* Remove f8 pipeline, we should share the same pipeline even in f8
* Remove remove_cvref_t
* Avoid warning
* Fix wrong fp8 QK/KV block gemm setting
* Check fp8 rounding error in check_err()
* Set fp8 rounding error for check_err()
* Use CK_TILE_FLOAT_TO_FP8_STANDARD as default fp8 rounding mode
* 1. codgen the f8 api and kernel
2. f8 host code
* prevent warning in filter mode
* Remove not-in-use elementwise function kargs
* Remove more not-in-use elementwise function kargs
* Small refinements in C++ source files
* Use conditional_t<> to simplify code
* Support heterogeneous argument for binary function types
* Re-use already-existing scales<> functor template
* Fix wrong value produced by saturating
* Generalize the composes<> template
* Unify saturates<> implementation
* Fix type errors in composes<>
* Extend less_equal<>
* Reuse the existing template less_equal<> in check_err()
* Add equal<float> & equal<double>
* Rename check_err() parameter
* Rename check_err() parameter
* Add FIXME comment for adding new macro in future
* Remove unnecessary cast to void
* Eliminate duplicated code
* Avoid dividing api pool into more than 2 groups
* Use more clear variable names
* Use affirmative condition in if stmt
* Remove blank lines
* Donot perfect forwarding in composes<>
* To fix compile error, revert generate.py back to 4439cc107d
* Fix bug of p element function
* Add compute element op to host softmax
* Remove element function in api interface
* Extract user parameter
* Rename pscale and oscale variable
* rename f8 to fp8
* rename more f8 to fp8
* Add pipeline::operator() without element_functor
* 1. Remove deprecated pipeline enum
2. Refine host code parameter
* Use quantization range as input
* 1. Rename max_dtype to dtype_max.
2. Rename scale to scale_s
3.Add init description
* Refine description
* prevent early return
* unify _squant kernel name in cpp, update README
* Adjust the default range.
* Refine error message and bias range
* Add fp8 benchmark and smoke test
* fix fp8 swizzle_factor=4 case
---------
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
Co-authored-by: carlushuang <carlus.huang@amd.com>
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Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: illsilin <Illia.Silin@amd.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: Jing Zhang <jizha@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Po-Yen, Chen <PoYen.Chen@amd.com>
Co-authored-by: rocking <ChunYu.Lai@amd.com>
