* support bf16*mxfp4 gemm
* rebase bf16*fp4 example to develop branch
* Clean up commented debug code in GEMM kernel
* rename example folder
* support bf16*mxfp4 gemm
* rebase bf16*fp4 example to develop branch
* Clean up commented debug code in GEMM kernel
* rename example folder
* rebase to new develop
* fix clang format
* update code according to reviewer's comment
* Update README.md
* update code according to reviewer's comment
* update code according to reviewer's comment
* Update CMakeLists.txt
* Update README.md
* Update CMakeLists.txt
* Delete files
* Delete files
* Add unit tests
* Update test_gemm_quant_base.hpp
* merge bf16*fp4 example to develop branch
* fix clang format
* fix clang format
* Update CMakeLists.txt
* fix ci test
* fix clang format
* resolve conflicts
---------
Co-authored-by: eliotwang <charyang@smci355-ccs-aus-m10-29.cs-aus.dcgpu>
Co-authored-by: ShaoChunLee <Shao-Chun.Lee@amd.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
* First version of split-K autodeduction.
* Fix circular dependency and kernel construction.
* Fix tolerance calculation for bwd weight example.
* Simplify kernel construction.
* Fix kernel launching bug for split-K autodeduce.
* Add split-K autodeduction support for the two stage example.
* Fix a corner case.
* Fix clang-format.
* Fix clang-format for inc files.
* Add missing header.
* Prevent too large split-K values.
* Fix formatting.
* Add unit tests for IsSupportedArgument in grouped bwd conv.
* clang-format.
* Fix merge conflicts.
* Address feedback from code review.
* clang-format
* Fix new tests after merge.
---------
Co-authored-by: Ville Pietilä <>
* Add help for example
* Refactore the compute reference batched contraction to manage stride-aware calculation and some code cleanings
* Add stride-aware reference for batched contraction with independent D tensor layouts
* Add -num_d argument for runtime D tensor count selection in batched contraction
* Add stride vector arguments in example code for testing non-contiguous batched contraction inputs
* Add descriptor-based architecture for batched contraction multi-dimensional stride support
* Add multi-dimensional non-contiguous stride support to batched contraction, num_d = 0
* Add complete multi-dimensional stride support via descriptors
* Enable vectorization in descriptor-based batched contraction. Add pad_tensor_view to local RunGemm
* Clean up batched contraction: remove old UniversalGemmKernel path
* Clean up batched contraction: remove legacy paths and finalize docs
* Optimize batched contraction example: pass dimension sizes not vectors
* correct the reference calculation, unsigned int to int
* Fix batched_contraction C++17 build errors for gfx90a CI
* Introduces the new partitioner to implement the reduction StreamK kernel
* Add more doc text to functions
* Add persistent-dp option to streamk example
* Update example/ck_tile/40_streamk_gemm/README.md
* Refactor quant group size to be configurable for M/N/K, not just K
* add some asserts for configurations not implemented
* start setting of group size for N dimension
* enable 2d for reference quant gemm
* WIP: trying to figure out tile dstr and/or indexing for scale matrix
* WIP
* Fix handling of n dim blocks in tile windows etc
* remove commented code and enable all tests again
* fix formatting
* Add more specialized tile distributions
* Enable NWarps replication for bquant tile dstr
* fix formatting
* fix format
* Fix some issues from the merge
* fix formatting
* one more fix to tile dstr, and revert debug initialization
* Remove commented code
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* simplify conditions that are needed for tile distributions
* only enable the working group sizes in tests
* fix formatting
* Update tile distribution for 2D bquant
* add some documentation and 2d block scale example
* fix formatting
* Add in Changlog and restructure the quant 2d example
* fix CMake
* support the change for blockscale 2d
* fix the test file
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Cong Ma <congma13@amd.com>
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* Summary:
- Refactor epilogue (with CShuffle) to support fused operations:
- EpilogueCShuffleBase holds common parts
- EpilogueCShuffle: runs CShuffle and write out
- EpilogueWelfordCShuffle: holds Welford specific arguments, runs CShuffle, write out, Welford first part and Welford write out
- Extend thread transfer v7r3:
- Support for intermediate data type different from src and dst type
- New functionality to write to dst buffer and keep data (to be able to use them for additional operations)
* Adress review comments
* Pass hdim to tile_example_fmha_fwd in fp8 tests
* Add WMMA support to fwd FMHA pipelines
* Tune tile sizes a bit for less spilling
fp16 256 is still quite slow
* Fix Q grad tile distribution for warp size = 32 and hdim >= 256
With AccDataType = float and warp size = 32, K0 becomes 0, K repeat is required to correcty distribute the tile.
* Use code based on BlockDropout in BlockDropoutBwd
* Fix split KV combine kernel for gfx12 (warp size 32) and make it more universal
* Fix LSE LDS tensor descriptors: kMaxSplits and kM0 were swapped, it worked on gfx9
because they both equal to 8 while on gfx12 they are 8 and 4;
* Fix Oacc LDS tensor descriptor: it was transposed even though its shape=[4 * kM0, kN1],
it worked on gfx9 because 4 * kM == kN1 == 32;
* Removing these hidden dependecies allows to support:
* any number of warps (power-of-2), not only 4;
* kN1 = 16, not only 32;
* any number of splits;
* Rename ids like o_acc_4 and Oacc4 to eliminate confusion: kNumWarps doesn't have to be 4 now
* Replace hard-coded kN1 in dispatch code with the requested tile size
* Add gfx12-specific tile sizes for split KV
* Pass GPU architecture to kernel generation scripts
This is still a temporary solution.
* Build and run FMHA CI tests for gfx12
* Fix issue after merging
* Fix bwd tile sizes
The current pipelines always read only one tile K and V tile, this
requires bk0 == bhdq and bk2 == bhdv (kK0 == kQKHeaddim and
kK2 == kVHeaddim).
* Use hardware f32->f8 on gfx12, remove v_perm
__builtin_amdgcn_perm is not needed because
__builtin_amdgcn_cvt_pk_fp8_f32 allows to specify which word (16 bit of
32-bit dword) is used to store results (two f8 values).
* Update changelog
* Add WMMA support to pagedkv
* Fix scripts after rebasing
* Support 16x16 (MFMA, WMMA) and 32x32 (MFMA) tiles in fwd and bwd BlockDropout
Add comments with dropout implementation details
Fix performance regression of fwd+dropout
* Remove some usage of type punning (reinterpret_cast with ref or ptr) in Philox;
* "scalarize" seed and offset, they may come either from kernel args or from device memory
(presumably loaded with vector loads).
These changes help the compiler to procude more optimal code and reduce register spilling.
Use WarpGemmDispatcher instead of explicit WarpGemmMfma... to get CWarpDstrEncoding
Use code based on BlockDropout in BlockDropoutBwd
Refactor BlockDropout (fwd)
Implement BlockDropout (fwd) for WMMA
Originally BlockDropout only supported 32x32 tiles (IsWG32 = true),
this version supports 16x16 tiles.
If MPerBlock > MWarp * 16, it can generate numbers for two 16x16 tiles, similarly
to BlockDropoutBwd.
Implement BlockDropoutBwd for WMMA
Remove MakeRandValLds* functions unused in BlockDropoutBwd
Remove unused Run overload from BlockDropoutBwd
* Fix regression with philox seed and offset when they exceed 32-bit int
__builtin_amdgcn_readfirstlane works with 32-bit values, seed and offset
are 64-bit so they get truncated.
* Fix names after cherry-picking
* Fix selection of a fallback tile based on bm0
The assumption that the largest bm0 == 128 is not always true for
current fp32 tiles.
* Do not use filters related to qr_async_trload
They disable tiles/pipelines which are valid for gfx12.
* Use different dstr encoding when C is transposed
* Do not call GetQKBlockGemm (and hence WarpGemmDispatcher) in host code
Some WarpGemmDispatcher instantiations are defined only
for specific archs and undefined on host.
Calculations related to sched barriers are moved from Pipeline's public
fields into pipeline's operator().
* Fix incorrect name WarpGemmMfmaFp8Fp8F32M32N32K16SwizzleBTransposedCDistribution
Correct name is WarpGemmMfmaFp8Fp8F32M32N32K32SwizzleBTransposedCDistribution
because it's 32x32x16 with IterateK = 2 so K = 32, also all tiles used
in codegen scripts are 32, 32, 32.
* Generalize usages of WarpGemmDispatcher for MFMA and WMMA
WarpGemmMfmaFp8Fp8F32M32N32K32SwizzleBTransposedCDistribution is still
used explicitly becaus of swizzle factor = 4.
* Mark has_load_tr as maybe_unused
There are no transpose loading for RDNA.
* Remove CK_TILE_USE_MFMA/WMMA from fmha-related code
* Detect BlockSize on host based on warp size of the current device
If kBlockSize == kNumWarps * get_warp_size(), the kernel is launched with
kBlockSize / 2 because on host get_warp_size() == 64 always.
* Fix calculation of grid size for combine kernel with warp size = 32
* Add missing includes and header
* Support multiple archs in one binary for fwd
* Support multiple archs in one binary for fwd_splitkv, fwd_appendkv, pagedkv_prefill
* Support multiple archs in one binary for bwd
* trload kernels are compiled only for gfx950;
* instances with padding are checked after instances without padding so
they can be used as fallbacks (similarly to fwd);
* Extract common code from register_traits
* Revert "Fix regression with philox seed and offset when they exceed 32-bit int"
To simplify merging , the proper fix is in develop already.
* Support new numerical d paddings in trait ordering checks
* Build fp32 tests only on gfx9
* Do not use hardcoded M0 = 64 for dot bwd kernel
* Use textwrap.indent from standard library
* Make fp8 pipelines on gfx12 consistent with gfx9
* Update tests for current pipelines
* Make ninja check more responsive in CI
ninja buffers output so this job looks hanging.
* Support fp8fp32 by limiting O vector size
The fp32 output type requires storing 8 * sizeof(float) = 32 bytes,
which is not implemented (here 8 is the number of C values per lane for
v_wmma_f32_16x16x16...).
* Remove unused cmake options
* Unify including amd_buffer_addressing.hpp/_builtins.hpp
* Temporarily use amd_buffer_addressing.hpp on >=gfx10
amd_buffer_addressing_builtins.hpp uses inline asm for loads/stores
which is not compatible with >=gfx10:
* 1 scalar for exec masks instead of 2,
* gfx12 uses different instruction names etc.
* Update asm in bf16 conversions to work with warp 32
* Do not generate splitkv/appendkv with vlayout=col for consistency with fwd
* Add arch tags to kernels/host funcs, compile for each arch separately
* Add kM0 to fmha_bwd_dot_do_o kernel name to match filename
* Add workaround for miscompilation of bwd with padded hdim
SWDEV-559729: v_wmma instructions can be incorrectly placed in divergent
branches used to store padded tensors (when some lanes are inactive due
to padding). Inline asm with dummy dependencies on VGPRs of the tensors
prevents the compiler doing this.
* Fix add_gtest_executable for absolute paths
Some tests (like gemm_tile_engine) pass absolute paths to source files.
In CI the branch name is a part of the root dir, and if the branch name
contains "wmma", "xdl" etc., files can be incorrectly excluded.
* Run only hdim 128 smoke tests for fp8fp32
There are no instances for hdim 64 and 256.
* Format py with ruff to simplify merging develop
* Fix incorrect var name
* Codegen for gfx9,gfx950 when --targets is not specified
Aiter and Pytorch require changes for passing their targets to the codegen scripts.
With this temporary solution the files are generated but not all of them
have to be really built (depending on the used --offload-arch=).
* Combine arch-related values into ArchTrait
This more centralized approach removes duplication of various formatting templates.
* Try a workaround for Jenkins error "groovyjarjarasm.asm.MethodTooLargeException: Method too large"
Some code is extracted into a function.
* Fix compilation of the grouped conv examples.
* Fix grouped conv bwd weight example output in CK Tile.
* Add number of groups to merge to ck tile grouped gemm example.
* Initial set of tests for TransformConvBwdWeightToGemm.
* Added unit tests for TransformConvBwdWeightToGemm conv groups are merged.
* WIP: Tensor transformations.
* Add unit tests for coordinate transforms.
* Fully working conv group merging for TransformConvBwdWeightToGemm.
* WIP: Merged conv groups offset calculation.
* Adde unit tests for tensor view.
* WIP: Merged conv groups epilogue.
* Enable running multiple conv groups per batch.
* Add tests for tile_distribution_encoding.
* Change example to match optimally depthwise convolution with merged groups.
* Add more tests for tensor view.
* Integration test for reading diagonal blocks from grouped distributed tensor.
* Improved integration test.
* Improve test for accessing diagonal blocks.
* Added integration test for cshuffle epilogue LDS tile distribution.
* Add more logging.
* Increase the max number of reported errors.
* WIP: merged conv groups GEMM epilogue changes.
* LDS to global memory copy.
* Fix tile window size for c block.
* Integration test for CShuffle epilogue.
* Improved CShuffle test.
* WIP: Separate epilogue for merged conv groups.
* Tile example parameters changes to match depthwise conv.
* Offset fixes.
* Epilogue fixes.
* Working baseline for depthwise covolution with merged conv groups.
* Fix build.
* Initial unit tests for tensor descriptor.
* Add one more unit test for tensor view.
* WIP: LDS to global mem transfer using CK tile tensor descriptor and tile distribution encoding.
* Fully functional LDS to global mem transfer using tensor descriptor and tile distribution encoding.
* Add more comments, disable debug code.
* Remove debug and other dead code.
* Code clean-up for bwd tensor transformations.
* Enable running multiple GEMM batches of merged conv groups.
* Add compile check for assumed row-mjor layout.
* Fix strides in 1D conv to gemm transformation.
* WIP: Simplify conv to gemm transformations and handle K > 1 and C > 1 cases.
* Fix case k > 1 and c=1.
* Remove debug code.
* Make MPerGroup and NPerGroup template parameters.
* Add additional check for non-supported c > 1 case.
* WIP: Put back the generic tensor descriptors for convolutions.
* Fix tensor descriptors.
* Remove the obsolete template parameters.
* Add more instances.
* Fix bugs in merged conv groups tensor descriptors.
* Fix tensor descriptors for merged conv groups when K > 1.
* Remove debug output.
* Remove dead code.
* Fix merge conflicts.
* Code clean-up.
* Remove unused code.
* Run clang-formatting.
* Remove debug prints and obsolete tests.
* Check that number of convolution groups is multiple of merged groups.
* Fix build after removing obsolete functionality.
* Remove obsolete enumeration.
* Fix new unit projects.
* Remove unnecessary includes.
* Fix passing the number of merged groups.
* Remove unrelated tests.
* Fix IsSupportedArgument for bwd weight conv kernel.
* Fix clang formatting.
* Fix the bwd weight conv to gemm mapping for num merged groups > 1.
* GEMM config for conv group merging.
* Fix clang-formatting.
* Remove obsolete comment.
* Fix typos in comment strings.
* Increase the max number of reported errors when testing against reference implementation.
* Rename gemm_config to conv_config.
* Rename GemmConfig to ConvConfig and move NumGroupsToMerge into ConvConfig.
* Change num_groups_to_merge to a boolean flag in the ck tile grouped conv example.
* Run clang-format.
* Add number of merged groups into kernel name string.
* Remove group merging flag from CK Tile grouped conv example.
* Add indexing support to pooling operator
- Add IndexDataType template parameter to pooling problem and kernel
definitions
- Enable pooling kernel to output indices of selected elements during
max/absmax pooling
- Add overloaded operators for Max and AbsMax that track when values
change using bool changed parameter
- Support optional index buffer allocation and management in device
memory
- Modify BlockReduce2d classes to handle index tensors alongside value
tensors
- Add separate shared memory allocation for index data in cross-warp
reductions
- Create validate_pool_indices function to verify index correctness
- Modify pool3d.cpp example to demonstrate index output functionality
- Add tests for index output
* fixes
* Refactor BlockReduce2D functions to get rid auxiliary private types.
* comment resolutions and some changes to block_reduce2d
- index reference implementation improved
- reduce_operator.hpp cleanedup
- updated the block_reduce2d.hpp to have index calculation for
BlockReduce2dLinearCrossWarpSync as well
* conditionally used variable declaration improvement
- the conditionally used vairbales are used only when indexing is
enabled. To inform the compiler that they may be unused and declare them
with least size possible. This may allow it to be optimized compared to
the previous declarations
* comment resolutions
* lexical ordering of the indicies
- introduced accumulate methods that handle the intermediate steps if
needed to order the indexes
* add reduce_operator_accumulate.hpp to core.hpp
---------
Co-authored-by: Adam Osewski <Adam.Osewski@amd.com>
* Implement argument passing to element-wise functions for fwd convolution
* Add files for fwd + bias + clamp example
* Implement Bias
* Implement Clamp
* Elementwise function composition
* Composition unit test
* Implement fwd + bias + clamp example
* Simplify argument passing and composition
* elfunc -> bias_and_clamp
* Rename function to specify example
* Move element-wise function instantiation to kernel
* Make bias a runtime tensor
* No ugly namespace aliasing
* Initialize element-wise function on host
* Remove function initialization helper, simplify Compose initialization
* Remove unintended LSP compatibility patch
* Clean up includes and unused code
* Switch names in cshuffle epilogue
* Move CDElementwise to conv traits
* Re-add required include
* Initialize bias in same way as other tensors
* Better type specification for ds pointer
* Disable 1D convolution
* Add warning for non-group-constant bias
* [CK_TILE] Correct BlockWarps calculation and fix smoke-test in rmsnorm
* Update rmsnorm host reference
* Update tree reduction of rmsnorm for reference host
* Fix cross warp for m > 1 cases
* Add RMSNorm model selectable option for host reference
* Fix save_unquant cases
* Update reference rmsnorm forward function to use enum for model sensitivity
* Update reference rmsnorm calculation for model sensitivity
* Fix m warp for layernorm
* Adjust parameter of reference for twoPass
* Fix clang format
* Run clang-format-overwrite.sh to fix formating issue
* fix clang format
---------
Co-authored-by: MHYang <mengyang@amd.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* 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>
* Support 16x16 (MFMA, WMMA) and 32x32 (MFMA) tiles in fwd and bwd BlockDropout
Add comments with dropout implementation details
Fix performance regression of fwd+dropout
* Remove some usage of type punning (reinterpret_cast with ref or ptr) in Philox;
* "scalarize" seed and offset, they may come either from kernel args or from device memory
(presumably loaded with vector loads).
These changes help the compiler to procude more optimal code and reduce register spilling.
Use WarpGemmDispatcher instead of explicit WarpGemmMfma... to get CWarpDstrEncoding
Use code based on BlockDropout in BlockDropoutBwd
Refactor BlockDropout (fwd)
Implement BlockDropout (fwd) for WMMA
Originally BlockDropout only supported 32x32 tiles (IsWG32 = true),
this version supports 16x16 tiles.
If MPerBlock > MWarp * 16, it can generate numbers for two 16x16 tiles, similarly
to BlockDropoutBwd.
Implement BlockDropoutBwd for WMMA
Remove MakeRandValLds* functions unused in BlockDropoutBwd
Remove unused Run overload from BlockDropoutBwd
* Fix regression with philox seed and offset when they exceed 32-bit int
__builtin_amdgcn_readfirstlane works with 32-bit values, seed and offset
are 64-bit so they get truncated.
* Add F32 MFMA warp gemms
* Support f32 in fwd FMHA
* Implement transpose_vectors for 4-byte types (float)
* Fix unexpected implicit f32->uint32 cast in buffer_store<4>
__builtin_amdgcn_raw_buffer_store_b32 expects unsigned int but float was passed (implicitly casted to uint).
mbuf_t types in other buffer_store<> are changed for consistency.
* Support F32 in bwd FMHA
hdim = 256 is disabled for now because it uses too much memory on gfx90a
* Support Headdim = 48 (divisible by 16) in fwd
* Add fp32-specific receipts (800 and 801)
* Tune fwd tiles
* Tune bwd tiles
* Use small tiles only for small seqlen_q
* Fix after rebasing
* Fix selection of a fallback tile based on bm0
The assumption that the largest bm0 == 128 is not always true for
current fp32 tiles.
* Remove constraints and adjust filtering for fp32
Custom constraints are no longer needed because now the smallest tile
is selected automtically based on seqlen_q.
Filters related to qr_async_trload disabled valid fp32 tiles.
* Add fp32 tests
* Make splitkv and appendkv compile for fp32 only
There are no instances yet, but API still must compile when only fp32 is
requested.
* Remove unimportant f32 instances
* Add test_ck_tile_fmha_*_fp32 to REGRESSION_TESTS
* Replace magic numbers with a constant, improve comments for dropout
* Update changelog
* Fix condition that dq_acc must be set to zero when mask is used
The change was introduced in #2799
* Replace warp_uniform with recently added amd_wave_read_first_lane
* Add hdim = 96 and 192 to fwd
* rename gemm_group_quant to gemm_quant
* Add TensorWise quant mode
* Cshuffle epilogue tests with tensor scaling
* Add tensor quant to example
* Don't use readfirstlane for reading scales - doesn't work for some reason
* Add to changelog
* revert include - from a merge problem?
* revert common.hpp include
* revert host.hpp include
* remove unused utility function
* rename quant pipeline problem
* refactor quant tests
* remove aquant utils
* use TEST_F
* fix all tests by changing gemm config
* Use typed tests
* fix copyright
* Improve random number generation
* use different seed for each input (Q, K, V...);
* use deterministic generation of:
* seqstart_q/k (for group mode);
* block_table (for paged-kvcahe);
* cache_batch_idx (for kvcache);
* Extract arg_parser-related code from run functions to use them as tests
* Split examples into main programs and fmha runners, build instances separately
* Add dummy tests that use instances and runners
* Fix a missed corner case of f32->f8 conversion
When value if < min f8 denormal but > min f8 denormal / 2, it must be
rounded to min f8 denormal (i.e. 0b1), not to 0.
* Fix incorrect fp8 scales for P and O in validation code
DataTypeConfig was incorrectly compared with fp8_t.
* Add host generation of dropout random values and use it for validation
Previously host validation (reference_batched_dropout) used random
numbers generated by BlockDropout of the kernel, meaning that incorrect
generation on device (bad distribution, repeated numbers, too many zeros,
etc.) would not trigger any validation errors.
* Implement tests from smoke_test_bwd.sh
* Return result as enum to distinguish failure and missing instance
* Add tests for bwd features: bias, alibi, dropout
* Implement tests from smoke_test_fwd.sh
* Pass seqlen_q/k as vectors to fwd and bwd runners
* Add tests for fwd features: bias, alibi, dropout
* Add tests for pagedkv and splitkv
* Fix conditions when to use splitkv and pagedkv kernels
splitkv was executed only when use_kvcache which == (need_append_kvcache || use_cache_batch_idx || 0 < page_block_size).
In the SplitKV tests: the regular fwd kernel was executed if use_cache_batch_idx was not requested even when num_splitkv > 1.
In the AppendKV tests: the pagedkv kernel was executed but it often failed to find an instance.
* Add tests for appendkv
* Use is_v_rowmajor = true because there are no instances with column layout anymore
* Split public and private compile options for instances
Tests and examples need to know only about CK_TILE_FMHA_FWD_*_API.
* Improve parsing validation in bias and mask
* Pass bias as string for consistency with mask
* Catch parsing and other exceptions
* Add bwd test for deterministic flag
* Initialize fp8 tensors (-init=ufq) similarly to uf
* Fix splitkv/pagedkv invocation: use padded sk when seqlen_k_ptr is not null
seqlen_k cannot be used to determine padding when seqlen_k_ptr is
provided. The actual seqlen_k is taken from seqlen_k_ptr[b].
Even seqlen_k values (% bn0 == 0) use padded seqlen_k while seqlen_k_ptr
may contain arbitrary values.
In the example or tests this produces incorrect results with appendkv
(for example, -d=32 -s=1 -s_k=64 -s_knew=7 -vlayout=c -b=8).
* Fix use_pagedkv value when kvcache = true but page_block_size = 0
In this case block_table_ptr is nullptr which is accessed in the kernel.
* Clean up bwd tests
* Unify fwd tests for f16/bf16 and fp8
* Use better explicit instantiation declaration for fmha_bwd<2>
* Use the same seed for all tests, allow to override it with env variable
* Undo clang-format of one irrelevant file
For some reason my local clang-format-18 and the one in CI work differently.
* Do not build instances and tests on unsupported archs
* Build instance libraries as OBJECT library
* CI: Enable sccache for HIP
There are source files with LANGUAGE HIP, they need
-DCMAKE_HIP_COMPILER_LAUNCHER=sccache
* Add tests to REGRESSION_TESTS
* Fix OOB accesses in deterministic bwd due to incorrectly assumed kN0
The runner assumes kN0 = (hdim_q <= 128) ? 128 : 64 but there are
smaller tiles (for tr_load or fp32). This can create too small dq_acc_buf.
* Pass CK_TILE_FMHA_FWD_*_API as INTERFACE compile options
The instances don't actually depend on them, only examples and tests do.
Passing these definitions as INTERFACE allows to change FMHA_FWD_ENABLE_APIS
without recompiling instances that are already in ccache.
* Fix formatting and names
- Add support for tensor A/B in both fp16+pk_int4_t and fp8+pk_int4_t formats
- Implement A(bf8) B(i4) support in universal GEMM
- Use new implementation for i4 to fp8 conversion in Block Scale
* Add cshuffle epilogue test
* add the poc implementation to the epilogue and tests
* refactor cshuffle epilogue
* WIP: adding tensor/tile usage to scale_tile
* fix usage of tile_elementwise_inout
* add gemm_quant_kernel for generalizing gemm quant kernel
* Add problem specific to different quants, add QuantType to Traits
* Add quant_type to quant_kernel template parameters
* Create aq/bq_block_windows and views depending on QuantType
* Use tile windows as inputs in cshuffle epilogue
* Fix some issues in epilogue
* initial new example code for new general gemm quant kernel test
* Fix issues in kernel
* Add verification check for rowcol Quantmode
* use AccDataType instead of AQ in pipeline
* fix aquant preshuffle
* fix formatting
* some cleanup
* remove gemm_aquant_basic.cpp
* remove gemm_aquant_kernel.hpp
* fix tests for the renamed quant kernel
* fix formatting
* clean example files
* fix some merge conflicts
* fix preshufflequant rename issue
* fix some templates after merging with develop
* fix test preshuffle parameter
* fix formatting
* Unify bquant kernel to the common quant kernel
* remove bquant kernel also from common header
* fix formatting
* clean up commented code
* fix formatting config hpp
* fix merge mistake
* Non-const for movable windows
* fix formatting
* Fix grammar in README
Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>
* Remove #include<bit> and clean up example
* fix strides
* Add some descriptions for move_windows
---------
Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com>
Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>
* feat(check_err): add a variable to adjust number of incorrect values to print
* feat(host_tensor): add printing capability for fp8 bf8 int8 int4
* fix(gemm_utils): update acceptable data type
* fix(host_tensor): print both 4 bit ints in pk_int4_t
* refactor(HostTensor): define pk_int4_t_to_int8x2_t and fix typo in vector_type.hpp
* feat(host_tensor): add print first n elements functions
* base working version for single groupped conv bwd data
* Fix 2d descriptor
* fix groups
* Add 3d support
* fixes
* fixes
* fixes
---------
Co-authored-by: Jakub Piasecki <jakpia21@gmail.com>
* General 2D Reduction Kernel
* Move the reduction kernel from the example
* Split the code and add the necessary policy, problem, shape files as
per ck_tile convention
* Add/modify the headers
* Modified the example to work with the 'new' kernel
* Added tests for the kernel
* N-D refernce reduce
* Added support for N-D input with transform to 2D
* Added padding to support various input sized tensors
* Bug fix in the thread buffer constructor
* Some comments to explain the reduce2d block kernel
* comments resolution
* clang-format
* comments resolution
* clang-format
* clang-format
* comments resolution
* clang-format
Resolves R_X86_64_32 relocation out of range errors in grouped conv2d instances
by splitting debug information into separate .dwo files.
Add explicit cast to avoid signed/unsigned comparison warning.
