GitLab

* [Refactor] Remove cache existence check in kernel saving logic

- Eliminated redundant checks for existing cache paths in `AutotuneResult` and `AutoTunerCache` classes, simplifying the kernel saving process.
- Ensured that the cache directory is always created before saving kernel source code, improving reliability in kernel storage.

* [Enhancement] Improve input tensor compatibility checks in AutoTuner

- Enhanced the input tensor caching logic in the AutoTuner class to ensure compatibility between cached tensors and newly generated tensors during configuration trials.
- Added detailed logging to warn users about potential mismatches in tensor properties, including shape and dtype, when caching is enabled.
- Implemented a mechanism to regenerate input tensors if compatibility issues are detected, improving the robustness of the autotuning process.

* [Refactor] Update L2 persistent map initialization in CUDA wrapper

- Adjusted the L2 persistent map initialization function to use a consistent size parameter for cache limits and byte counts, improving clarity and reducing potential errors in memory management.
- Simplified the formatting of the initialization function to enhance readability and maintainability of the code.

* Update tilelang/autotuner/__init__.py
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* Enhancement: Update `pythonic_expr` to accept `tvm.tir.PrimExpr` and improve type handling

- Modified the `pythonic_expr` function to check for `tvm.tir.PrimExpr` type, ensuring proper handling of expressions.
- Refactored device and dtype checks in `CythonKernelWrapper` for better clarity and error messaging, enhancing robustness in tensor validation.

* Enhancement: Refine `pythonic_expr` function to support additional expression types

- Updated the `pythonic_expr` function to accept `tvm.tir.PrimExpr` and handle both integer and float immediate types, improving expression representation and type handling.

* [Enhancement] Update `pythonic_expr` to format type casts and improve tensor validation in Cython wrapper

- Enhanced `pythonic_expr` to represent type casts as `(type)value` for better clarity in expression representation.
- Modified tensor validation in `CythonKernelWrapper` to conditionally check for tensor contiguity based on a new `skip_tensor_validation` parameter.
- Improved type mapping in `map_torch_type` to include version checks for new float8 types, ensuring compatibility with specific PyTorch versions.

* [Feature] Implement dynamic shared memory allocation alignment

- Added a new transformation pass `AlignDynamicSharedMemoryAllocations` to align dynamic shared memory allocations to specified byte boundaries, enhancing memory access efficiency.
- Introduced a new utility class `TileLangAlignDynamicSharedMemoryAllocations` to handle the alignment logic for both allocation and buffer operations.
- Updated the `LowerAndLegalize` function to apply the alignment transformation based on the target device's capabilities, ensuring compatibility with different architectures.

* [Enhancement] Update dtype and argument defaults in GEMM autotuning example

- Changed data type from `float16` to `bfloat16` for improved precision in computations.
- Updated the default value of the `--with_roller` argument from `True` to `False` to modify the behavior of the autotuning process.

* [Enhancement] Improve thread range computation in storage access

- Added a new method `ComputeThreadRange` to calculate the range of threads for better access tracking.
- Updated `AccessEntry` structure to include `thread_range`.
- Modified various visitor methods to utilize `IRVisitorWithAnalyzer` for improved analysis during expression and statement visits.
- Ensured thread range is computed and stored during buffer load and store operations, enhancing memory access efficiency.

* [Refactor] Update comments for clarity in dynamic shared memory allocation alignment

- Translated comments in `align_dynamic_shared_memory_allocations.cc` from Chinese to English for better understanding.
- Removed an unnecessary call to `IRVisitorWithAnalyzer::VisitStmt_` in `storage_access.cc`.
- Added a blank line for improved readability in `thread_storage_sync.cc`.

* [Refactor] Enhance storage access analysis and thread range computation

- Introduced `ExtractRealCondition` to improve condition handling in `IfThenElseNode` visits.
- Updated `ComputeThreadRange` to use `Var` instead of `IterVar` for thread range mapping, enhancing clarity and consistency.
- Wrapped statement visits in `With<arith::ConstraintContext>` to ensure proper analysis context during condition evaluations.

* [Enhancement] Update default matrix dimensions in GEMM autotune example

- Changed default values for matrix dimensions M, N, and K from 16384 to 4096 in `example_gemm_autotune.py` to facilitate quicker testing and benchmarking.

* typo fix

* enhancement

* [Fix] Add conflict detection for buffer index size mismatch in thread storage sync

- Implemented a check to return true if the sizes of previous and current buffer indices do not match, indicating a conflict.

* [Enhancement] Update AutoTuner and Profiler for improved kernel handling and output validation

- Modified AutoTuner to store cache in a dedicated "autotuner" directory.
- Enhanced kernel source code saving logic in AutotuneResult and AutoTunerCache to check for None before writing.
- Updated Profiler to handle None outputs gracefully during tensor comparisons, improving robustness in output validation.

* lint fix

* [Enhancement] Improve error handling and documentation in AutoTuner

- Added traceback logging for exceptions during configuration testing to enhance debugging.
- Expanded the AutoTuner class docstring to include detailed descriptions of new parameters for input tensor generation and validation, improving clarity for users.

* Fix L2 cache size calculation to handle symbolic expressions and ensure float conversion of hit ratios in annotation

* [Enhancement] Update warp specialization check in phase.py

* lint fix

* [Enhancement] Add ContainsSeqStmt method to improve statement handling in merge_shared_memory_allocations.cc

* [Refactor] Simplify memory copy operations in GEMM kernel tests

- Updated memory copy operations in `test_tilelang_kernel_gemm.py` to use shared memory allocations for both A and B matrices, improving clarity and performance.
- Adjusted the main execution block to include a new `run_gemm_rs` function call for testing, enhancing the test structure.

* revert memory reuse pass.

* revert the memory resue and thread sync pass/

* Update test_tilelang_kernel_gemm.py

* Update test_tilelang_kernel_mha_bwd.py

- Modified `KernelParam.from_var` to map Torch data types to a more appropriate format.
- Enhanced `CythonKernelWrapper` to support additional tensor types and ensure proper conversion of tensor dtypes to C types, improving error handling for unsupported types.

[Enhancement] Update `pythonic_expr` to format type casts and improve tensor validation in Cython wrapper (#581)

- Enhanced `pythonic_expr` to represent type casts as `(type)value` for better clarity in expression representation.
- Modified tensor validation in `CythonKernelWrapper` to conditionally check for tensor contiguity based on a new `skip_tensor_validation` parameter.
- Improved type mapping in `map_torch_type` to include version checks for new float8 types, ensuring compatibility with specific PyTorch versions.

[Enhancement] Introduce wrapper util `pythonic_expr` to transform a PrimExpr into python string (#577)

* [Feature] Add Quarter Bank Swizzle Layout and Update GEMM Layout Logic

- Introduced a new `makeQuarterBankSwizzleLayout` function for layout swizzling of 32 bytes.
- Updated `makeGemmABLayout` to include an `enable_padding` parameter, allowing for conditional layout selection between padded and quarter bank swizzle layouts.
- Adjusted layout inference in GEMM operations to utilize the new quarter bank swizzle layout when appropriate.
- Enhanced bulk copy operations to recognize and handle the new layout type, improving memory access patterns.

* lint fix

* lint fix

* rebase

* rebase

* typo

* requirement fix

* revert flash atten requirenemts

* [CI] Add flash_decoding example to CI

* Add output of ref latency

* format example_gqa_decode.py

* [BugFix] Fix precision issue in GQA decode when block_N exceeds seqlen/num_split

* format example_gqa_decode.py

Fix L2 cache size calculation to handle symbolic expressions and ensure float conversion of hit ratios in annotation (#576)

* Update FLA import path for `prepare_token_indices`

* Update FLA import path for `prepare_token_indices`

* Compare versions via packaging.version.parse

* [CI] Modify test requirements and CI workflow

- Replaced `flash-attn` with `packaging` and `wheel` in `requirements-test.txt` to ensure proper package management.
- Updated the CI workflow to install `flash-attn` without build isolation, improving the installation process.

* [CI] remove redundant packages

* [CI] Update test requirements and CI workflow

- Added `flash-attn` to `requirements-test.txt` to ensure it is included in the testing environment.
- Modified the CI workflow to install packages from `requirements-test.txt` with `PIP_NO_BUILD_ISOLATION=1`, improving the installation process.

* [Feature] Add Quarter Bank Swizzle Layout and Update GEMM Layout Logic

- Introduced a new `makeQuarterBankSwizzleLayout` function for layout swizzling of 32 bytes.
- Updated `makeGemmABLayout` to include an `enable_padding` parameter, allowing for conditional layout selection between padded and quarter bank swizzle layouts.
- Adjusted layout inference in GEMM operations to utilize the new quarter bank swizzle layout when appropriate.
- Enhanced bulk copy operations to recognize and handle the new layout type, improving memory access patterns.

* lint fix

* [Refactor] Update GEMM Layout Functions and Inference Logic

- Removed the `enable_padding` parameter from `makeGemmABLayout` to simplify its signature.
- Introduced `makeGemmABLayoutHopper` for enhanced layout handling specific to Hopper architecture.
- Updated layout inference in GEMM operations to utilize the new `makeGemmABLayoutHopper` function, improving clarity and maintainability in layout selection.
- Adjusted related layout functions to ensure consistent behavior across different architectures.

* [Refactor] Remove tf32 Casting Logic from GEMM Templates

- Eliminated the `cast_float_to_tf32` function from `gemm_sm80`, `gemm_sm89`, and `gemm_sm90` templates to streamline the code.
- Removed conditional casting logic for float32 to tfloat32 conversion, enhancing clarity and maintainability.
- Updated relevant sections in GEMM operations to reflect the removal of casting, ensuring consistent behavior across templates.
- Adjusted tensor view handling to improve performance and accuracy in matrix operations.

* Update bulk_copy.cc

* Fix profiler initialization in GEMM test by removing TensorSupplyType argument for improved flexibility.

- Modified the serialization of function scripts in both KernelCache and AutoTunerCache to include metadata by setting `show_meta=True` in `cloudpickle.dumps()`. This change enhances the hash key generation for kernel configurations, improving cache accuracy and consistency.

Fix assertion in GQA backward example to ensure correct tensor comparison for gradient validation (#568)

* [Feature] Add Quarter Bank Swizzle Layout and Update GEMM Layout Logic

- Introduced a new `makeQuarterBankSwizzleLayout` function for layout swizzling of 32 bytes.
- Updated `makeGemmABLayout` to include an `enable_padding` parameter, allowing for conditional layout selection between padded and quarter bank swizzle layouts.
- Adjusted layout inference in GEMM operations to utilize the new quarter bank swizzle layout when appropriate.
- Enhanced bulk copy operations to recognize and handle the new layout type, improving memory access patterns.

* lint fix

* [Refactor] Update GEMM Layout Functions and Inference Logic

- Removed the `enable_padding` parameter from `makeGemmABLayout` to simplify its signature.
- Introduced `makeGemmABLayoutHopper` for enhanced layout handling specific to Hopper architecture.
- Updated layout inference in GEMM operations to utilize the new `makeGemmABLayoutHopper` function, improving clarity and maintainability in layout selection.
- Adjusted related layout functions to ensure consistent behavior across different architectures.

* Update bulk_copy.cc

* Update __init__.py

* [Enhancement] Update AutoTuner and Profiler for improved kernel handling and output validation

- Modified AutoTuner to store cache in a dedicated "autotuner" directory.
- Enhanced kernel source code saving logic in AutotuneResult and AutoTunerCache to check for None before writing.
- Updated Profiler to handle None outputs gracefully during tensor comparisons, improving robustness in output validation.

* lint fix

- Updated the dtype handling logic in the KernelParam class to enhance clarity and maintainability. The dtype string is now modified only if it starts with "torch.", simplifying the return statement for boolean type checks.

* [Feature] Added Support for Synchronizing Grids and Persistent Threadblock Transformation

- Defined the sync_grid operation in builtin.cc and builtin.h, allowing synchronization of all threads within a grid.
- Implemented support for sync_grid in codegen_cuda.cc, ensuring proper handling of this operation in the generated CUDA code.
- Added the PersistThreadblock transformation, enabling the conversion of thread blocks to persistent thread blocks, enhancing support for persistent kernels.
- Updated relevant documentation and comments to reflect the addition of new features and usage instructions.

* [Example] Add MLA Decode With Persistent Threadblock Example

* [Feature] Introduce Persistent Loop and Update GEMM Example

- Added a new persistent loop construct in the TIR framework, enabling more efficient kernel execution.
- Updated the GEMM example to utilize the new persistent primitive, enhancing performance for matrix multiplication.
- Introduced a `loop_break` intrinsic for better control flow within persistent loops.
- Updated relevant files to support the new features, including changes in code generation and language interface.

* lint fix

* [Enhancement] Update AutoTuner and JIT compilation arguments

* Added functionality to return compile arguments in the JIT implementation, enhancing the autotuner's caching capabilities.
* Modified `CompileArgs` and `AutotuneResult` classes to support optional `out_idx` parameter, improving flexibility in compile argument handling.
* Refactored the `_AutoTunerImplementation` to utilize the new compile arguments, ensuring better integration and performance during tuning processes.

* Update tilelang/autotuner/param.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* remove redundant comments

* Refactor kernel source retrieval and logging levels

* Updated `AutotuneResult` to use `kernel.get_kernel_source()` instead of `kernel.adapter.get_kernel_source()`.
* Changed logging level in `KernelCache` from `ERROR` to `DEBUG` for improved verbosity during kernel caching operations.
* Removed unnecessary verbose logging in JIT compilation process to streamline output.

* Merge branch 'main' of https://github.com/tile-ai/tilelang

 into bugfix_autotune_0604

* lint fix

---------
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* [Enhancement] Fix multi-version buffer index in nested-loop

* [Feature] Support persistent kernels and add persistent GEMM example

* lint fix

* lint fix

* [CI] Remove test_tilelang_transform_annotate_device_regions.py

* Add tf32 casting functionality to GEMM templates

- Introduced a `cast_float_to_tf32` function to convert float32 values to tfloat32 format across gemm_sm80, gemm_sm89, and gemm_sm90 templates.
- Implemented conditional casting in relevant sections of the GEMM operations to ensure compatibility with tfloat32 types.
- Enhanced the handling of tensor views to support the new casting logic, improving performance and accuracy in matrix operations.

* lint fix

* Refactor tfloat32 casting logic in GEMM templates

- Replaced the `is_tfloat32` boolean with `need_tfloat32_cast` to improve clarity and accuracy in determining when to cast float32 to tfloat32.
- Updated relevant sections in `gemm_sm80`, `gemm_sm89`, and `gemm_sm90` to utilize the new casting logic, enhancing compatibility with tfloat32 types.
- Ensured consistent application of casting across tensor views, improving performance and correctness in matrix operations.

* Refactor GEMM template functions for improved readability

- Simplified the function signature of `body_rs` in both `gemm_sm80` and `gemm_sm90` templates for better clarity.
- Adjusted the casting logic in `gemm_sm90` to ensure consistent application of `cast_float_to_tf32` across tensor views, enhancing performance and maintainability.

* Enhance tf32 casting logic in GEMM templates

- Updated the `cast_float_to_tf32` function in `gemm_sm80`, `gemm_sm89`, and `gemm_sm90` to conditionally apply the casting only if the input is finite, improving robustness.
- Simplified the `need_tfloat32_cast` logic to clarify the conditions under which tfloat32 casting is required, enhancing code readability and maintainability.

* Refactor GEMM template functions and layout inference logic

- Removed the `cast_float_to_tf32` function from `gemm_sm90` and updated the `body_sr` function to streamline the casting process for tensor views, enhancing code clarity and maintainability.
- Improved layout inference in `layout_inference.cc` by adding checks for the layout map's definition, ensuring robustness in handling layout annotations.
- Simplified the handling of layout maps in the `annotate_layout` function, allowing for more flexible layout definitions and error handling.

* [CI] Add CI test for flash_attention examples

* Update example_gqa_fwd_bshd.py

* Update example_mha_fwd_bshd_wgmma_pipelined.py

* [CI] Added conditional annotations for tests in flash_attention

* [CI] Added conditional annotations for tests in flash_attention

---------
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

* [wip] feat: add nvrtc backend

* [wip] fix: handle out_idx

* [wip] refactor: move lib logic to libgen

* feat: cache for nvrtc backend

* fmt: run format

* fix: handle cuda bindings import error

* fix: handle cuda bindings import error

* fix: handle cuda bindings import error

* fix: handle cuda bindings import error

* fix: get kernel source

* refactor: speedup pyimport

* Improve error handling for missing cuda-python dependency in nvrtc backend. Raise ImportError with detailed installation instructions instead of logging a warning.

* Enhance nvrtc backend error handling by introducing a flag to check for cuda-python availability. Raise ImportError with detailed installation instructions during initialization if the nvrtc backend is unavailable, improving user experience and clarity.

* Update README.md to include recent NVRTC Backend addition, highlighting reduced compilation time for CUDA templates.

* fix tl_templates

* ensure CUDA context

---------
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

* [CI] Add FusedMoE example

* Lint

* Fix import bug

* Fix comment bug

* Update example_fusedmoe_torch.py

---------
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

* Update VERSION to 0.1.5

* Add DEBUG_MODE support in setup.py and update CMake build type; enhance pypi.Dockerfile with git installation

* [CI]Add norm and layout_plot

* fix lint

* Remove obsolete test files for RMS normalization and plot layout, streamlining the testing suite.

* Add make_mma_load_base_layout function to create MMA result layouts

- Introduced a new function `make_mma_load_base_layout` for generating layout functions for storing MMA results in fragment buffers.
- Added detailed docstring explaining parameters, return values, and potential exceptions.
- Implemented logic for handling different data types and matrix configurations, including assertions for input validation.
- Defined internal functions for mapping fragment indices to threads and local indices, enhancing the layout functionality.

* Enhance MMA load test with additional imports and functionality

- Added imports for `tilelang.language`, `Literal`, `Callable`, `DataType`, `IndexMap`, and `get_mma_micro_size` to support extended functionality.
- Improved the `make_mma_load_base_layout` function by ensuring it can handle various data types and configurations.
- Updated the test function `test_mma_load_base_layout` to validate the layout for float16 matrix A.

* Fix formatting in test_fragment_mma_load_a.py by adding a blank line for improved readability.

* Add RMS normalization functions to test_rms_norm.py

- Introduced `rms_norm` and `rms_norm_splitk` functions for RMS normalization, enhancing the testing capabilities.
- Implemented kernel functions with shared memory allocation and parallel processing for improved performance.
- Updated the test function to validate the new RMS normalization implementations.

* Add reference program for RMS normalization in test_rms_norm.py

- Introduced `ref_program` function to provide a reference implementation for RMS normalization.
- This addition enhances the testing framework by allowing comparisons against a known reference output.

* Enhance RMS normalization tests with additional imports and formatting

- Added import for `tilelang.language` to support extended functionality in `test_rms_norm.py`.
- Improved code readability by adding blank lines for better separation of code sections.

* Update RMS normalization test parameters and enhance layout plotting

- Increased matrix dimensions in `test_rms_norm` to 8192 for improved performance testing.
- Removed obsolete test functions in `test_fragment_mma_load_a.py` to streamline the test suite.
- Enhanced layout plotting functionality by ensuring proper visualization of base, warp, and block layouts in `test_fragment_mma_load_a.py`.

* Refactor RMS normalization test parameters and improve layout plotting readability

- Simplified the parameters in `test_rms_norm` by removing `blk_k` for clarity.
- Enhanced code readability in `test_fragment_mma_load_a.py` by adjusting the formatting of the `block_layout` definition and removing the unused `warp_cols` variable.

* Enhance RMS normalization with split-k implementation and additional profiling

- Added a new function `test_rms_norm_splitk` to test the split-k variant of RMS normalization.
- Updated the main RMS normalization script to include profiling for the split-k implementation.
- Ensured all checks pass with appropriate latency measurements for both reference and tile-lang implementations.

* Remove obsolete test file `test_fragment_mma_load_a.py` to streamline the test suite.

* Refactor `rms_norm.py` to streamline benchmarking output and remove redundant code. Comment out the `plot_layout` call in `fragment_mma_load_a.py` for clarity.

* Refactor `test_rms_norm.py` by removing redundant test function `test_rms_norm_splitk` to streamline the test suite and improve clarity.

---------
Co-authored-by: Your Name <you@example.com>

* Add linear attention examples.

* Add license

* Remove comments

* Run yapf and ruff

* [Enhancement] Update AutoTuner and JIT compilation arguments

* Added functionality to return compile arguments in the JIT implementation, enhancing the autotuner's caching capabilities.
* Modified `CompileArgs` and `AutotuneResult` classes to support optional `out_idx` parameter, improving flexibility in compile argument handling.
* Refactored the `_AutoTunerImplementation` to utilize the new compile arguments, ensuring better integration and performance during tuning processes.

* Update tilelang/autotuner/param.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* remove redundant comments

* Update tilelang/jit/__init__.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

---------
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* Enhance Layout

* strict update

* lint fix

* Refactor layout inference by removing unnecessary logging statements in `parallel.cc` and `layout_inference.cc`. This cleanup enhances code readability and reduces log clutter during layout inference steps.

* lint fix

* Refactor file copying logic in setup.py to simplify directory creation and file copying process. Removed unnecessary existence check before copying source files to the target directory.

* [Enhancement] Add support for new FP8 types in HIP code generation

* Updated `PrintConst` function in `codegen_hip.cc` to handle `float8_e4m3fnuz` type.
* Introduced new functions in `hip_fp8.h` for creating FP8 types, including `make_fp8_e4_4_t` and `make_fp8_e4_8_t`, enhancing type handling for FP8 data structures.
* Improved overall compatibility and performance for FP8 data types in HIP.

* workaround for competition

* enhance autotune

* autotune cache fix

* Implement validation for unused keys in AutoTuner configuration

* Added a check in the AutoTuner class to raise a ValueError if there are unused keys in the configuration, enhancing error handling and ensuring configuration integrity.

* lint fix

* revert changes of threads

* Update pipelining in `example_mla_decode.py` to improve performance

* Changed the number of stages in the pipelined loop from 0 to 2, enhancing the efficiency of the attention mechanism in the decoding process.

* Enhance Cython kernel validation by adding tensor attribute checks

* Updated the `CythonKernelWrapper` to include dedicated methods for validating tensor device, dtype, and static shape.
* Modified the `forward` method to utilize these new validation methods, improving error handling and ensuring input integrity.
* Updated the `lambda_forward` function in `CythonKernelAdapter` to reflect changes in validation parameters.

* Remove unused 2D continuous cumulative sum example and related functions from the cumsum module.

* lint fix

* fix split k example

* Enable cache disabling in gemm_streamk example and add validation checks in if_stmt_binding transformation

* Update gemm_streamk example to use tilelang's cdiv function for block calculations and add copyright notice

* [CI] Add hadamard example to CI

* Run yapf and ruff

* Run yapf and ruff

* [Doc] Include DeepWiki badge in README

* include more badges

* [Enhancement] Add support for FP8 types in CUDA and HIP code generation

* Updated `GetFP8Type` function in `codegen_cuda.cc` and `codegen_hip.cc` to handle new FP8 types, including `kFloat8_e4m3fnuz`.
* Introduced a new header file `hip_fp8.h` for FP8 type definitions in HIP.
* Modified type mappings in `dlpack.py` and `mfma_macro_generator.py` to accommodate new FP8 types.
* Enhanced type handling in `TLHIPSourceWrapper` and `tensor.py` for better integration with FP8 types.
* Added necessary includes and logic to support FP8 in the code generation process, improving performance and compatibility with FP8 data types.

* lint fix

* Update src/target/codegen_hip.cc
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* Update tilelang/intrinsics/mfma_macro_generator.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* workaround

* fix

* Update submodule TVM to latest commit 587028ffebfff0ded520f8f90d62f0f6b165906c

* bug fix

* Refactor tilelang matrix multiplication to support transposition and packing options. Adjusted shared memory shapes and loading logic for A and B matrices. Updated test cases to validate new functionality.

* Refactor assertion function for tilelang matrix multiplication to improve readability by formatting parameters and aligning code. Cleaned up whitespace in intrinsic layout functions for consistency.

* Update bfloat16 type definitions in common.h and gemm.h for consistency. Changed __hip_bfloat16 to hip_bfloat16 and updated MfmaTraits specialization accordingly.

* lint fix

---------
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>