* (2/5) bilinear gemm pass, perf bug: skip a lds has lower performance than skip b lds
* (3/5) batched gemm pass, perf bug: skip a lds has lower performance than skip b lds
* (4/5) grouped conv pass
* (5/5) attention pass, todo: debug lds perf bug
* AIT Attention API refactor (#8)
* sanity pass
* sanity pass 2
* confirm significant performance regression.
* turn on all instances
* turn off instance format
* Fix bug & tunning & format
* DML meta, self_attn+cross_attn
* sanity pass
* remove useless flag
* update tile and problem size used in AIT attention
* bug fix in grouped conv supporting check
* deprecate inline asm wmma
* Bug fix: double lds skip
* clang-format
* Fix errors in
1. example, fmha
2. gridwise pipeline
3. deviceop, fmha, change some containers from vector to array
* part2 of previous commit
* clang format
* API fix of gridwisegemmpipeline
* separate array base and vector base attention tensor transformation
* fix gemm
* clang format
* add gemm fp16 instances
* Temp save
* fpAintB kernel compile pass
* Sanity pass.
* Temp save
* debug code enabled
* Fp16AInt8B_GEMM sanity
* MQA implementation
* GQA-4 example
* tempsave
* Compile pass
* New implementation of fp16Aint8B Gemm, Acheieve similar math throughput with native fp16 Gemm
* 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
* initial navi4x enablement
* remove extra endif
* enabled dl_gemm
* update s_barrier and s_waitcnt for gfx12
* fix the gfx12 assembly syntax
* fixed block_sync_lds
* add support for more dl kernels on navi4
* add wmma
* format
* Todo: fix gemm_bilinear_wmma instances compilation bug
* Solve a bug when K1=16
* remove unnecessary changes
* Remove tensor layout limitation to LDS usage in tesnor contraction
* fixed block_sync_lds
* merge navi3_ref
* update self-attention and cross-attention
* fix a typo of name
* fixed layout
* debugging
* Add arch limiter for fp8 gemm
* fixed wmma
* enable fp8 gemm_xdl for all gfx9 targets
* temporarily disable gemm_xdl_fp16_fp8 on MI100/200
* fix the cmake logic for gemm_xdl_fp16_fp8
* fixed c_output
* re-enable the gemm_xdl_fp16_fp8 on MI100/200
* fixed gfx12
* fixed
* fixed
* seperate gfx12 blockwise_gemm
* fixed
* enable fwd conv on navi4x
* enable gridwise
* enabled gemm
* fixed merge
* remove empty example fold
* fixed conflicts
* some small changes
* Update cmake-ck-dev.sh
* Update cmake-ck-dev.sh
* enabled other types
* fixed register loads
* test fa
* enable gfx12
* clean up
* enable some instances on gfx12
* add gfx1201 macro in amd_wmma header
* fix clang format
* enable batched_gemm_softmax_gemm_perm_wmma for gfx12
* disable instances with blocksize=256 in attention examples
* debuggging
* debug
* fixed lds_enabled
* debugging
* Fix and add limit to skiplds feature
* Enable skipLds feature and fix compilation bugs
* add ck_tile definitions for gfx12
* fix clang format and test/wmma_op
* updage instances cmake for gfx12
* disable the test_wmma_op on gfx12
* fix the builds for gfx950
* add gfx12 and gfx950 to default target list
* clean-up cmake file
* Initial introduction of OFP8 data types.
* Renamed FP8 and BF8 tests into FP8_FNUZ and BF8_FNUZ.
* Implementation of ConvertFP32Nearest in test_fp8_ocp.
* Remove dependence on possibly undeclared alias.
* Implement FP8OCP test for stochastic rounding mode.
* Implement FP8OCP tests for half_t type conversions.
* enable bf16 atomic add on gfx950
* Implement ConvertFP32Nearest test.
* Implement ConvertFP32Stochastic test.
* Implement ConvertFP16Nearest and ConvertFP16Stochastic tests.
* Refactoring. Move FP8 definitions into a separate header file.
* Enable easy switching between architectures.
* Fix compilation error for gfx942 architecture.
* only builf gfx950 branch for gfx950 target by default
* Enable OCP build of example_gemm_xdl_fp8.
* Fix formatting.
* fix the build logic for gfx950
* Improve GEMM example verbosity.
* Add constexpr where applicable.
* fix the logic of enabling XDL and WMMA instances
* Improve GEMM example verbosity.
* Enable build of example_gemm_xdl_fp8_bf8 test.
* Fix tests for gfx1101 architecture.
* Build DPP examples only on gfx103 and gfx11 architectures.
* Optionaly run either CPU or GPU verifications with GEMM examples.
* Extend GeneratorTensor_Sequential to produce values of prescribed data types.
* Add missing constructor.
* Improve infrastructure for OFP8 data type support.
* BUGFIX. Should not use FP8 as Compute/Accum data type.
* Add custom target for grouped_convnd_bwd_weight tests.
* Can build `tests` target on gfx950.
* Bugfixes on gfx1101 architecture.
* Fix dependencies.
* Provide single point of truth for FP8 INF and NAN checks
* Prevent instantiation of operators that are not supported by FP8 data types
* Add FP8 type selection into client_axample CMakeLists.txt
* Prevent sccache server from shutting down during build
* Fix test success reporting logic
* Change default verification method to CPU.
GPU verification takes too much time to complete on the emulator.
* Make sure all tests and examples are built for gfx950
* Facilitate testing of FP8 data types on the emulator
* Introduce two new tensor generators
* Enable instances built for gfx94 to be built on gfx950
* Verify 35_splitk_gemm on floating point numbers.
splitk gemm appears to be losing precision VS reference implementation when FP numbers are involved.
* Verify 04_gemm_add_add_fastgelu on floating point numbers
* Verify 20_grouped_conv_bwd_weight on floating point numbers
* Verify 38_grouped_conv_bwd_data_multiple_d on floating point numbers
* Verify more tests on floating point data
* Fix data types and improve testing verbocity.
* Upgrade to NPI 573 build docker.
* Skip on gemm_universal tests.
The tests take too long to complete on the emulator.
Need to see if it is possible to reduce the scope of the testing to just FP8 data types.
* Fix gfx1101 build
* Document test availability
* Re-enable fp8 gemms for gfx94/95
* Cherry-pick GEMM Universal tests for FP8 data types
* Cleanup
* CK_USE_GFX94 has already been set on this branch
* Address formatting issues and leftovers
* Make fail/pass logic consistent within 01_gemm folder
Removed multiple negations in fail/pass logic to propagate `true` as the success indicator.
* Fix GPU verification reporting logic.
* Update year in copyright notice.
* Cleanup
* Use `enum class` instead of `enum`
* Remove set_property for FP8 tests
* Narrowing the scope of PR to OCP FP8 enablement only
* Add tests for OCP FP8 vector_type storage
* Enable gemm kernel on all gfx9 architectures (#227)
* clean-up
* Implement `non_native_vector_base` with `ext_vector_type` array. (#232)
* Enable support of 1, 2, 4, and 8-byte custom types in CK.
* Fix pool tests for OCP FP8 data type
* fix jenkins file
* restore cron trigger
---------
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: aska-0096 <haocwang@amd.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Jing Zhang <jizhan@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>
Co-authored-by: Jun Liu <Liu.Jun@amd.com>
Co-authored-by: Andriy Roshchenko <andriy.roshchenko@amd.com>
Co-authored-by: Andriy Roshchenko <107577548+andriy-ca@users.noreply.github.com>
* parse examples inside the add_example_executable function
* fix the example 64 cmake file
* add xdl flag to the gemm_bias_softmax_gemm_permute example
* add filtering of tests based on architecture type
* enable test_grouped_gemm for gfx9 only
* enable test_transpose only for gfx9
* only linnk test_transpose if it gets built
* split the gemm instances by architectures
* split gemm_bilinear,grouped_conv_bwd_weight instances by targets
* split instances by architecture
* split grouped_conv instances by architecture
* fix clang format
* fix the if-else logic in group_conv headers
* small fix for grouped convolution instances
* fix the grouped conv bwd weight dl instances
* fix client examples
* only enable client examples 3 and 4 on gfx9
* set the gfx9 macro
* make sure the architecture macros are set by cmake
* use separate set of xdl/wmma flags for host code
* sinmplify the main cmake file
* add conv_fwd_bf8 instance declaration
* refactor cmake files for the tests
* refactor cmake files for examples
* fix cmake for gemm example
* fix the cmake file for all examples
* add splitting by data types in gemm_splitk instance header
* rename test to reflect only dl instances are used
* clean up CI workspace, update cmake for instances
* change the jenkinsfile syntax
* build all instances except DL on gfx11
* move workspace cleanup after stages
* clean up workspace after every stage
* isolate data types in grouped_conv_fwd header
* isolate dl instances for grouped_conv2d_fwd
* fix syntax
* fix cmake and batchnorm instances
* fix typo
* fix reduction instances
* fix grouped_conv headers
* fix syntax
* replace parsing logic for instances, replace bfp16 with bf16
* fix the client examples build
* clean up DTYPES from instances cmake files
* update the parsing logic in cmake files
* make an exception for reduction kernels
* update few remaining cmake files to handle DTYPES
* fix syntax
* fix cmake conflicts
* replace f8 with fp8 test name
* resolve conflicts for dpp instances
* properly split conv_nd_bwd_data instances
* split conv2d_fwd instance data types
* split the gemm, conv2d_fwd and batched_gemm_softamx_gemm
* split the tests by data types where possible
* filter examples by DTYPES
* split few remaining examples by DTYPES
* filter most instances by DTYPES
* add new lines at end of headers, fix grouped_gemm profiler
* fix syntax
* split the ckprofiler instances by DTYPES
* split the conv2d and quantization DL and XDL instances
* fix the splitting of conv2d DL instances
* split softmax and pool_fwd tests for fp16 and fp32 types
* fix syntax
* fix the dl_int8 quantization instances isolation
* 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>
* 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
* reopen masking att instance due to CI is upgraded
* re-enable instances previously failed on 9110
* enable ksize-kpadding pair validity test
* add non-masked attention+permute test; expose masking boolean to attention kernel handles
* disable bench
* fix test
* move files
* bulk rename batched_gemm_masking_scale_softmax_gemm_permute to batched_gemm_softmax_gemm_permute
* format
* amend rename
* disable bench in test
* add mask/no-mask test for non-permute attention kernels
* disable broken kernel instance
* example working
add non-permuted problem statement
evaluating whether overhead comes from permutation or the extra kernel arg
* interface for bias addition without implementing it
* test and profiler running
* tidy
* mask type determined by enum class
* unify example code
* move masking specialization to its own header
* align formats
* extract helper functions
* experiment merging dims for attn w/ permute; shows perf parity with attn wo/ permute
* add tensor specialization to template args
since tensor spec packed shows perf parity when permutation isn't needed
remove redundant template args
comment on 'packed' tensor specialization
* grouped attention with input/output permute example
* format
* clean up
* refactor acc0 tile visitor
Co-authored-by: shaojiewang <wsjmessi@163.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
* Move kernel implementation files under impl directory.
* Update examples paths.
* Update device kernel impl include paths.
* Update tensor operation instances include paths.
* Update profiler and tests include paths.
* Clang-format
* Update include paths for batched gemm reduce
* Refactor UnitTest ConvNDBwdWeight.
* Refactor fwd and bwd data convND UT.
* Fix used test macro.
* Fix include path.
* Fix include paths.
* Fix include paths in profiler and tests.
* Fix include paths.
Co-authored-by: Adam Osewski <aosewski@amd.com>
* modify comment
* trim unnecessary check
* add gemm spec in kernel name
* add TNTT gemm_gemm + atten kernel instances
* refactor attention padding to better fit in unit tests
This streamlines usage where "ResetNaNToMinusInf" is now hidden from user facing device op.
Also added compile-time conditionals that load OOB value as NaN only after padding is enabled
* add adhoc padding test for atten
* shrink input value range for attention kernel validation to avoid occasional error by 1e-3
Still unsure whether this kind of deterministic floating point accurary issue is expected
or not. May want to try exact same approach as the GPU kernel in the host reference
GEMM+Softmax+GEMM function to see if the accuracy discrepancy goes away. Until then,
shrink the input value range as it is less likely to produce errors of around ~1e-3.
* attention kernel proper granular padding for all 4 dims
* IsSupportedArgument checks
* test more padded cases
* block PadK specialization in attention kernels
* workaround clang crash for gfx908
(gfx908 only) workaround for compiler crash in fused kernels on mainline #9110; #10738 seems ok
error message was "fatal error: error in backend: Error while trying to spill VGPR0 from class
VGPR_32: Cannot scavenge register without an emergency spill slot!"
this fall back to less ideal way of handle NPadding in fused attention kernel
* comment out kernels giving wrong results on MI100; MI200 doesn't seem affected
* add padding algo for bmm+scale+softmax+bmm. Version for verification
* remove verification code
* remove comments
* add padded bmm scale softmax bmm example
* format
* refactor
* add comments for usages of padding bmm+scale+softmax+bmm
Co-authored-by: Chao Liu <lc.roy86@gmail.com>
* comment on specialization for TensorSpecialization::Packed
* gemm_softmax_gemm with output permutation
* scaling
* refactor MatrixPadder; rename to GemmPadder
* remove old sanity check
* restore original gemm_softmax_gemm
* revise comment in gemm_softmax_gemm example
* use GetElementSpaceSize()
* remove extra header
* typo
* remove archaic DeviceOpPtr
