* universal streamk with atomics with ckprofiler support. grid_size and streamk strategy are tunable. grid_size of -1 leads to #WGs = maximum occupancy X num_CUs. implementation supports many different streamk policies: 1-tile, 2-tile, 3-tile and 4-tile. streamk strategy of -1 leads to default streamk policy (4-tile).
* Update README.md
* fixing clang-format issues
* removed conflicts in struct members between streamk and universal streamk
* corrected arg parsing for streamk and universal streamk
* added stream-k policies for 3 tile and 4 tile
* fixed argument type issue with parsing cmd args
* changes suggested in PR review are made- removing comments and correcting copyright
* file permissions updated
* added default value support for grid_size and streamk-policy selection set to -1
* print messages for arguments
* print messages for arguments
* print messages for arguments1
* Format
* Format
* Format
* Remove const
* Use the right template
* Format
* Format
* add row/col instances
* Add missing file
* fixed
* fixing block to etile error
* Format
* Updates
* Format
* fixed rrr layout
* generating a sample JSON file: currently contains includes, prologue/epilogue and instances
* version where the json is passed into the instances to generate a key
* updated run function to just launch kernel
* updated run function: only contains kernel object, json file is updated but still needs to be cleaned up, added front-end API to parse JSON into character buffer
* adding in testing files
* cleaned up comments, still need to work on including header files
* removed unneeded files
* removed/commented out JSON implementation
* added fusion(prologue/epilogue) into instance generation
* working on instance selection
* added instance selection, need to fix instance validation
* removed block2etile map validity check for testing purposes
* test running: failing due to incorrect files/input
* all grid descs/ptrs completed, but device file not found
* Update test and embed modules
* Restore older version
* added convolution operation, written test, debugging generated code for compilation
* attempting to include CK in host directory: _Float16 error
* CK header file issues
* slight fix
* don't crash when hip can't report total memory
* dump generated code to a file
* changing sizes
* creating tensor descriptors using CK methods: set up grid desc manually, also trying to set up an argument pointer - this needs to be fixed
* some fixes to call the device code
* separating test files for conv and gemm
* completed arg ptr, now have linking errors
* clang format fix
* resolved linker issues in conv test
* remove dependency on libutility from ck
* resolved num dim error
* properly passing arg ptr, errors with passing typenames: redefinition/redeclaration
* undo the commenting of device function
* hand created kernel code to find rtc issues
* dump the full src to file
* resolved redeclaration errors, cleaned up errors for Amber's kernel code
* debugging purposes: redeclaration error
* config files
* resolved errors for NumTensor and redeclaration, formatted version.h
* resolved most errors in manually added kernel and my own. error with calling kernel object: overloaded function type
* WIP: close to getting kernel compiled
* WIP: fixing rtc errors
* fixed sequence errors, formatting, still one error with run fcn
* yay: kernel compiles and runs
* updated templated/generated version to run and compile
* minor fixes
* working generated example, resolved memory access error due to padding
* adding in reference kernel, validation failing against reference
* debugging: printing kernel argsz
* reduced error in results
* debugged reference kernel and output errors, added to generated version, currently debugging prologue function issues
* working validation (using reference convolution) with prologue function for both hard-coded and generated version
* WIP: create an alt version that creates Argument on the device
* wip: added new duplicate files, fixed fusion templating errors from working example, setting up kernel arguments
* wip: making necessary methods device code
* added grid descs, working on grid pointers, errors with stl numerics
* wip: updating kernel args - issue, replacing some std functions
* replaced std::accumulate call with temp hardcoded version
* wip: args causing memory issue
* Construct Argument object inside the kernel and use it to call convolution device function. Code runs and verification passes
* adding object file dump
* temporary hardcoding of grid size, can remove device op inst + arg ptr
* minor fix for grid size
* added modified example where arg ptr is created on the device for generated version as well
* removed device op instance and arg ptr from modified examples
* moving device op file for testing purposes and to properly build CK
* commenting out print-outs
* adjust compiler args to produce a valid ELF file
* temporary removal of validation
* reverting compiler args back for working example
* retrieve necessary arguments from generated template parameters in correct format
* calculating grid size on host-side, still need to clean up process, pass parameters to host functions properly
* scaled up factory functions/wrapper structs to implement host-side launch parameter calculations using CK host side functions - in hard-coded example
* temporary change to generate ELF format binary object file
* removed unecessary code, added comments
* formatting fix
* cleaned up code, added new tests, restructured library: move helper into CK
* refactored launch parameter calculation to be more concise
* renamed files and variables for more clarity/uniformity
* more code cleaning, removed debug statements
* moved majority of my files into codegen directory, running properly
* updated Embed.cmake(string_view) in codegen directory
* updated host directory to match Embed.cmake as well
* added old tests in
* updated instance generation methods to be more concise
* removed layout from launch parameter calculation
* working test
* fixed issue with verification, all instances working
* updated verification in other tests
* removed duplicate matrix padder file, removed code dumps
* removed old hard-coded tests
* removed old host directory, all files in codegen directory now
* fixed copyright in files
* commenting out validation
* renamed files
* made changes for review: fixed copyright, renamed files for clarity, removed comments, refactored code
* updated headers
* removing duplicate file for fwd conv to gemm, merging with original file
* fix building codegen with clang++ directly
* resolving build error from conv_fwd_to_gemm
* fix for previous error
* renaming tests
* created common test file
* cleaned up code, added comments
* renamed device op
* fixed typos in comments
* removed extra space
* code cleanup: resolving Amber's comments
* removed wrapper struct for matrix padder, fixed template
* cleaned up if statements for better readability
---------
Co-authored-by: Paul <pfultz2@yahoo.com>
Co-authored-by: Jing Zhang <jizha@amd.com>
Co-authored-by: M. Amber Hassaan <amber_474@yahoo.com>
Co-authored-by: illsilin <Illia.Silin@amd.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
* 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>
We are adding more instances of grouped convolution 3d forward with a ConvScale element-wise operation.
This commit handles bf8@bf8->fp8 data types combination.
* Included an example.
* Added instances.
* Added a client example.
---------
Co-authored-by: Rostyslav Geyyer <rosty.geyyer@amd.com>
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
* Add NullBlockDropout to be used when kHasDropout is false
* Change to BlockDropout::Run() for forward to reduce conditional checkings
* Re-format files
---------
Co-authored-by: PoYen, Chen <PoYen.Chen@amd.com>
* Update the element op
* Add an example
* Add instances
* Add a client example
* make sure new instances only build on gfx9
* Update element op and its handling
* Format
* Update instances to take element op as an argument
* Update examples to use random scale values
* Format
* Update client example with random scales
* Format
---------
Co-authored-by: illsilin <Illia.Silin@amd.com>
* Add a scale op
* Update the element op
* Add instances
* Add an example
* Add a client example
* Add a flag check
* Revert flag check addition
* Fix flag check
* Update d strides in example
* Update d strides in client example
* Apply suggestions from code review
Update copyright header
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
* Move the example
* Move the client example
* Update element op
* Update example with the new element op
* Add scalar layout
* Update example
* Update kernel for scalar Ds
* Revert kernel changes
* Update element op
* Update example to use scales' pointers
* Format
* Update instances
* Update client example
* Move element op to unary elements
* Update element op to work with values instead of pointers
* Update instances to take element op as an argument
* Update examples to use random scale values
---------
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
* add f8 gemm with multiD for both row/col wise
* change compute_type to fp8
* changed tuning parameters in the example
* add rcr example
* post-merge fix
* fix
* reduce init range
Also bundle the CK library and include files with the pip package.
The package is pip-installable with
`pip install
git+https://github.com/tenpercent/composable_kernel@enable-pip`
(substitute the repo path and branch if necessary)
Testing:
`myenv/bin/python3 -m ck4inductor.universal_gemm.gen_instances`
(prints a list of instances)
`tree myenv/lib/python3.12/site-packages/ck4inductor`
(observe the list of sources along the installed package)
