* Expand the bandwidth of direct_global_to_lds for gfx950
* clang-format
* fix the remod.py and script for clang format
---------
Co-authored-by: Max Podkorytov <4273004+tenpercent@users.noreply.github.com>
* 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>
* Use read_tr in universal gemm
* Enable all instances back
* Revert example37 changes
* Resolve comments
* resolve comments 2
* Fix assertion msg
* fix the gemm basic
* change index_t to bool for preshuffle variable
* Solve the comment
---------
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
Co-authored-by: Max Podkorytov <4273004+tenpercent@users.noreply.github.com>
Co-authored-by: AviralGoelAMD <aviral.goel@amd.com>
1. Port NCHW support from ConvFwd (#2375) to conv bwd data
2. Add new instance device_grouped_conv_bwd_data_xdl_f16_nchw_instances for nchw
Co-authored-by: azhuang <anzhong.huang@amd.com>
* fix bug in loops that need use local tokens to compute
* support extra chain local_token
* update
* update
* refine some main
* update
* support dispatch_policy
* fix 15 example
* [draft] Add pk_fp4 and test
* Add hw conversion for fp4
* Refine test code and pk_fp4 constructor.
* fix test indent
* modify according to comment.
* fix clang-format
* modify according comments.
---------
Co-authored-by: asleepzzz <hanwen.chang@amd.com>
* Initial commit
* Adding new tile partitioner to flatmm
* intermediate changes
* debugging kernels
* Updating flatmm example to universal gemm example
* updated flatmm kernel to run via gemmKernel
* update universal gemm to incorporate flatmm
* debug
* Fix flatmm call
* Fixing other kernels and tests for API changes
* clang formatted
* fixing gemm tests
* added test for flatmm and simplify kernel arguments
* adding flatmm test
* fix test for flatmm
* simplify gemm kernel with flatmm
* remove flatmm related files
* addressing review comments and code clean up
* resolving empty file
* resolving empty file
* clang formatted
* addressing review comments
* enable persistent kernel for flatmm
* reverted the removed files for flatmm
* reverted the removed files for flatmm
* changed flatmm to weightPReshuffle; removed the _1 added in teh faltmm example
* some more renames
* clang formatted
