* 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>
* [CK_TILE] fmha: Add query padding support to backward pass
Introduces support for query sequence padding (q_padding) in the FMHA backward pass kernels.
- Passing `seqlen_q_ptr` to the backward kernels to distinguish logical from physical sequence lengths.
- Updating `OGradDotO`, `ConvertQGrad`, and `DQDKDV` kernels to respect logical lengths and handle zero-length sequences.
- Aligning LSE indexing in the forward kernel with the padded layout for consistency.
- Adding a new GTest suite (`test_fmha_bwd_kernel_padding.cpp`) with comprehensive tests for various padding scenarios, including zero-length
sequences and deterministic mode.
* fix clang format
* Adapt fmha_bwd_runner.cpp to new q, kv sequence padding
Add backward q/kv sequence padding unit tests.
* [CK_TILE] fmha: Unify sequence length and padding handling
Refactor the handling of sequence lengths and padding in the
FMHA forward and backward kernels to provide a more unified and flexible
interface.
- Replaced `seqstart_padded_*_ptr` with a more robust system that uses
`seqstart_*_ptr` for physical sequence lengths and introduces
`seqlen_*_ptr` and `cu_seqlen_*_ptr` for logical (unpadded) lengths.
- Established a clear order of precedence for determining sequence
length: cumulative lengths (`cu_seqlen_*_ptr`) take priority,
followed by per-sequence lengths (`seqlen_*_ptr`), and finally
physical lengths derived from `seqstart_*_ptr`.
- Clarified the distinction between "group mode" and "batch mode" and
how sequence lengths are handled in each case.
- Renamed `cu_seqlen_kv_ptr` to `cu_seqlen_k_ptr` for consistency.
- Updated comments and documentation to reflect the new argument
structure and usage.
---------
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
* update test cases
* format codes
* use GTEST_FAIL
* add bquant to grouped_gemm
* fix a bug in test_grouped_gemm_util
* skip test when use wmma on grouped_quant kernel
* add tensorwise quant in grouped gemm
* fix example issue
* update test cases
* format codes
* fix a bug in test_grouped_gemm_util
* tests(quant_grouped_gemm): add unit tests to cover bquant in grouped_gemm
* Update test/ck_tile/grouped_gemm_quant/test_grouped_gemm_util_quant.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update example/ck_tile/17_grouped_gemm/quant_grouped_gemm.hpp
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* feat: add bf8 support
* chore: remove unnecessary decltype usage
* chore: add default quant_mode to function signature as fallback
* fix: pass correct runtime pipeline params in grouped_gemm bquant kernel
Calculate has_hot_loop, num_loop, and tail_number on device side for each
GEMM problem instead of using default values. This fixes incorrect results
when different problems in the group have different K dimensions.
* chore: set default quant mode in function signature
* test: add additional test cases to cover edge case of no hotloop
* chore: clang formatting
---------
Co-authored-by: kyle-256 <Kyle.Zhao@amd.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.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
* Persistent Stream-K Kernel Implementation
This change implements an operator() function in the
reboot::StreamKKernel class that is enabled when the Persistent flag is
set to true. In this case, the data-parallel portion and the Stream-K
portion of the kernel are fully persistent.
The changes were made in the reboot namespace. A future PR will remove
the old Stream-K kernel class and remove the reboot namespace.
* Unit Tests for Persistent Stream-K Kernel
This change contains the inital test suite for the Persitent Stream-K
Kernel. The files contain "reboot" in the name; a future PR will remove
tests for the old Stream-K Kernel and remove the "reboot" naming.
A future commit will add tests for the non-persistent kernel.
Also added estimate_num_wgs_per_tile to the StreamKTilePartitionerBase
class. This allows us to estimate the number of accumulations done per
macro tile in C to use during validation when computing relative and
absolute tolerance.
* Adding implementation for the Non-Persistent Stream-K kernel
This code is adding the operator() function for the Non-Persistent Stream-K
kernel. Persistency of the kernel is determined through a template argument.
The Non-Persistent kernel will allocate additional workgroups for the data
parallel section, leading to a different structure for processing the data
parallel and Stream-K sections.
There has been an addition to the TilePartitioner to get access to the whether
Persistent has been set to true or false in the StreamKKernel.
* Adding in the tests for the Non-Persistent Stream-K kernel
* Refactor Stream-K Reboot Unit Tests
This commit makes the following changes:
- Update test cases to determine M, N, and K based on the number of CUs.
This ensures that each test case is one of Edge Case, SK Only, DP
Only, or DP + 2 Tile SK regardless of the architecture.
- Since the DP + 2 Tile SK test case takes long to run, this change
moves this case into a separate .inc file and labels it as an extended
test.
- Since the extended test takes > 30 seconds to run, this test is added
to the list of regression tests.
* Fix spelling errors in comments for test cases
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Changes based on review
Removed const volatile for typenames
Set up alias for is_tuple_t
Naming changes for clarity: GemmCommon -> BaseGemm
Moved std::enable_if_t out of template parameters and changed to a return type for operator()
Added constructor for StreamKKernelArgs to clarify UniversalGemm inheritance
---------
Co-authored-by: Emily Martins <emily.martins@amd.com>
Co-authored-by: Christopher Millette <63608002+cgmillette@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Add name member to unary elementwise ops.
* Update elementwise_op_name to check for name attribute.
* Require that the layout is derived from BaseTensorLayout struct.
* add tensorwise quant in grouped gemm
* fix example issue
* update test cases
* format codes
* clang format
* use GTEST_FAIL
* fix a bug in test_grouped_gemm_util
* skip test when use wmma on grouped_quant kernel
* change cmake
* change code based on comments
---------
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* Introduce tree reduction for BlockReduce2dCrossWarpSync
* Rename original impl to BlockReduce2dLinearCrossWarpSync
* Replace warp_size with get_warp_size()
---------
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Prior to this change, the number of accumulations passed into
calculate_rtol_atol was 1. That said, in most cases, this is not correct
when there are multiple workgroups contributing to the same macro tile
in C.
This change ensures uses the function estimate_num_wgs_per_tile, which
was extracted into a common file and generalized, to estimate the number
of workgroups per macro tile. This estimate is passed into
calculate_rtol_atol to ensure we get a better relative and absolute
tolerance.
The following changes were made
- Renamed iter to iter_start
- Renamed tile_iter to tile_iter_start
- Moved documentation from member variables to getters
- Removed double underscore from extra_iters_before_me variable
- Defined parent header in impl file
- Removed unused inlcudes
There are 2 derived structs based on whether Stream-K is persistent or not.
If it's persistent that means that both the data parallel and Stream-K sections
are data parallel. If it's non-persistent that means that only the
Stream-K section is persistent, while the data parallel section will have
separate workgroups allocated for it. Both structs will have a template
argument for Persistent.
The 2 derived classes will inherit common variables and functions from the
Stream-K TilePartitioner base class. There are additional variables for the
differing data parallel sections that will be added to each derived class,
that are in charge of the indexing/bookkeeping for the data parallel sections.
The only additional function that will differ between the 2 structs is GridSize(),
as the non-persistent will allocate extra workgroups for data parallel.
Unit tests for the derived structs are included.
To better align with the original Stream-K paper, this change implements
a new Stream-K tile partitioner base class. This class will handle the
Stream-K setup that is common to both a persistent and non-persistent DP
section. A later change will implement derived classes to handle the
differences between persistent and non-persistent DP.
This change also includes unit tests for the base tile partitioner.
* [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>
* Enable the adapted LDS B layout for Row-Major
* fix formatting
* Implement specialized col-major A LDS block descriptor
* Fix formatting
* Use VecLoadSize for AK1/BK1
* Fix some thread access pattern values
* Use GetVectorSizeA for A
* Fix formatting
* Add extra condition to avoid division by zero
* disable layout for wave32
* remove extra else
* fix formatting
* Fix formatting
* Rename one remaining TileDistributionEncodingPattern2D
* Use integer ceil division
* revert remod.py changes
* also revert utility.hpp
* use getA/BTileAccessPattern everywhere
* use integer_divide_ceil for AK0 too
---------
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
Co-authored-by: Adam Osewski <Adam.Osewski@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>
* Add initial fp16_mem_128x128x32_2x2x1_32x32x16_NonPersistent test suite
* Account for stride when computing K offsets for A and B tensor
This change ensures that the correct stride is used when computing the K
offsets into the A and B tensors in the Stream-K Kernel's operator()
function. This ensures that the kernel executes correct regardless of
whether A and B are row or column major.
* Move helper code to test_gemm_streamk_util.hpp
* Separate tests into smoke/regression/extended. Add bf16 datatype
* Run clang-format
* Refactor combinatorial macro expansion and naming
* Adjust the initialization values to account for better tolerance on bf16
* Correct BF16 datatypes in comments
* Move the extended tests under the REGRESSION_TESTS label
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Emily Martins <emily.martins@amd.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* debugging
* debugging for prefill shapes
* comment unused code
* fix for prefill shapes
* clearing up the code
* add int4 to universal gemm example
* clang formatted
* adding test for prefill shapes in block scale gemm
* lil improv on the block pipeline
* Address Review Comment
---------
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* reuse local prefetch logic from compute v4 pipeline
add single-tile test
explicit lambda capture
reuse lds block descriptors from base policy for the transposed case
match the test case kernel configuration with compute v4
* add comments
* 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>
* WIP: add memory pipeline boiler plate code that compiles and works for one block
* WIP: tail handling works for memory pipeline
* WIP: numerical errors appears to have gone by adding block_sync_lds()
* fix: numerical error with memory pipeline by adding block_sync_lds() and new tail handler
* refactror: remove debug print statements and lints
* fix: remove redundant sync barriars
* chore: remove lint
* fix: remove unused code from tile handler and remove redundant block_sync_lds()
* fix: correct parent struct name for memory pipeline
* fix: remove static assert check from parent struct and add it to child struct because not all child structs needs to static assert
* fix: defer block sync lds to just before prefill
* [CK TILE GEMM] Support Aquant GEMM with transposeC and preshuffle
When TransposeC and QuantPreshuffle are both true, Aquant generates
correct result.
* [CK TILE GEMM] Support Aquant GEMM with transposeC and preshuffle
- Add unit tests
* Fix bug in is_quantpreshuffle_enabled
* clang format
---------
Co-authored-by: ThomasNing <thomas.ning@amd.com>
* check in pipeline and policy
for async load in mi350, need to make sure TileAccessPattern is warp_raked or block_raked
solve merge conflicts
* fix cmakelists
* make it build
* fix? buffer async fence
* relax fences; it appears it only is needed between pairs of ping-pongs
* remove fences
* remove fences
* cleanup and reformat
* add steps annotations
* comment all pipeline steps / remove unexplainable syncs
* clang-format
* add comment
* cleanup kernel types for test
* fix comment
* fix hardcoded warp size
* faithfully copy block gemm from compute v4 policy to async policy
* make async test gfx950 only
* fix cmake logic
* set separate compile options for async
* refine comment in policy
* try update hotloop scheduler
* cleanup comments
* test more K block sizes
* unhardcode Ks, sort of
* add large odd test case
* fix build for quant
* add comment to hot loop scheduler and rename enum
* reformat
* reword the pipeline description
* reformat
* address review / add static asserts / typo fix
* update changelog
* Use __builtin_amdgcn_readfirstlane for buffer resource in fused_moe
* also do the same for amd_buffer_addressing_builtins.hpp
* merge with develop
* fix clang format
---------
Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
Co-authored-by: ThomasNing <thomas.ning@amd.com>
Co-authored-by: illsilin_amdeng <Illia.Silin@amd.com>
* feat(grouped_gemm_multi_d): add new example that integrates grouped_gemm and multi_d_gemm feature
* refactor: grouped_gemm_multi_d relies on grouped_gemm_kernel
* tests(grouped_gemm): grouped_gemm test suite passes with minor adjustments
* fix: segfault fix by passing correct parameters for d tensors
* style: clang format
* WIP: host code for grouped_gemm_multi_d persistent kernel compiles but segfaults
* feat(grouped_gemm_multi_d): add functionality to run persistant kernel
* feat(grouped_gemm_multi_d): add new example that integrates grouped_gemm and multi_d_gemm feature
* refactor: grouped_gemm_multi_d relies on grouped_gemm_kernel
* tests(grouped_gemm): grouped_gemm test suite passes with minor adjustments
* fix: segfault fix by passing correct parameters for d tensors
* style: clang format
* fix: incorrect validation method and Dtensor layout in test suite
* docs: improved README text based on review comments
* fix: parameterize NumDTensor in GroupedGemmHostArgs and remove lint
* initial commit
* remove extra files
* fixing errors
* updated ReadMe file for mapping of diff quants with diff configs
* addressing review comments
* addressing review comments
* Resolved merge conflicts
* [CK TILE GEMM] Replace get_preshuffle_or with is_quantpreshuffle_enabled
The get_preshuffle_or was not working as expected, which led to incorrect behavior
in the quantization preshuffle process. This change replaces it with the more reliable
is_quantpreshuffle_enabled function to properly determine when preshuffle should be applied.
* initial commit
* debugging
* working fp8 for init constant
* fp8 working with all inits
* updated block level code with comments
* changing the loop iter
* debugging
* debugging
* debugging
* code fix
* code clean up
* clang formatted
* Add comment
* code cleanup
* clang formatted
* merge conflicts fixes
* applying the latest int4 changes to the piepline
* fixing test code for updated traits
* Adding gtest
* review comments addressed
* addressing review comments
* remove c++20 code
* added flush cache changes
---------
Co-authored-by: Cong Ma <congma13@amd.com>
Co-authored-by: root <root@banff-cyxtera-s73-2.ctr.dcgpu>
* Change the return type of run_gemm_combinations in the basic tests
* Change the return type of run_gemm_combinations in the universal tests
* Add universal GEMM tests for bf16 x pk_i4 and fp16 x pk_i4
* Add universal GEMM test for fp8 x pk_i4
* Add basic GEMM tests for bf16 x pk_i4, fp16 x pk_i4 and fp8 x pk_i4.
* Add missing GemmTypeConfig<ck_tile::fp8_t, ck_tile::pk_int4_t, ck_tile::half_t>
* Add missing GemmTypeConfig<ck_tile::bf16_t, ck_tile::pk_int4_t, ck_tile::bf16_t>
* No need for utility in test_ck_tile_elementwise_1d
* Fix conversion from pk_int4x4_t to bf16x8_t in PassThroughPack8
* Avoid union-based type punning in float_to_bf16_truc_raw to make it constexpr compliant
* For consistency also make float_to_bf16_truc_nan_raw constexpr compliant by removing the union
* Use a static_cast to bfloat16_t only when CK_TILE_USE_LLVM_BUILTIN_BF16 is enforced
* Convert from float to bf16 during compilation rather than using magic values
* Fix conversion from pk_int4x4_t to fp8x8_t in PassThroughPack8
* Comment out the basic test for fp16 x pk_i4 as it does not pass
* Add missing GemmTypeConfig<ck_tile::bf8_t, ck_tile::pk_int4_t, ck_tile::half_t>
* Fix conversion from pk_int4x4_t to bf8x8_t in PassThroughPack8
* Add basic and universal GEMM tests for bf8 x pk_i4
* Switch back to amd_assembly_i4_to_fp8x8 in PassThroughPack8 as it works now
* Switch back to amd_assembly_i4_to_bf8x8 in PassThroughPack8 as it works now
* Remove the inefficient fallbacks for fp8 and bf8 in elementwise/unary_element_wise_operation.hpp
* Use explicit macros for enabling and disabling the the constexpr lookup based converters
* Fix two failing tests
* Avoid union-based type punning in float_to_bf16_rtn_raw to make it constexpr compliant
* Use float_to_bf16_rtn_raw instead of float_to_bf16 to create the bf16 lookup table for use in conversions from pk_int4 to bf16
* On ROCm 7.0.1 we need an explicit cast to from uint16_t to bf16_t
