GitLab

* [experimental] add a draft gemm_sp

* [3rdparty] bump cutlass to v3.9.3

* [lint] run format.sh

* [chore] rebase

* [chore] use abs path

* [gemm_sp] add metadata layout

* [ci] add more example

* [lint] run format.sh

* [chore] polish

* [chore] move gemm_sp to experimental

* [chore] polish

* [lint] run format.sh

* [Enhancement] Improve bulk copy handling and update GEMM sparse tensor test

* Added a warning log for unsupported non-swizzled global layouts in the bulk copy operation, ensuring fallback to normal copy.
* Refactored the GEMM sparse tensor test by removing unnecessary imports and simplifying the kernel compilation process.
* Updated the test to directly call the `run_gemm_sp` function, enhancing clarity and functionality.

* Implement Test

* [Enhancement] Update GEMM SP and SM89 templates for improved functionality

* Refactored GEMM SP computation to enhance warp partitioning logic, ensuring compatibility with Hopper architecture.
* Updated layout inference to support new WGMMA conditions and improved error messaging for unsupported targets.
* Modified SM89 templates to utilize new MMA atom structures, enhancing performance and compatibility with fp8 types.
* Added conditional inclusion for GEMM SP header based on CUDA architecture version.

* lint fix

* [gemm_sp] support more layout and data types

* Enhancement: sync T.gemm_sp's layout inference with T.gemm

* Enhancement: support more block_k in compress util

* [Enhancement] enable block_k=64

* [Lint] run format.sh

* [Enhancement] compressor support more dtype

* Enhancement: enable block_K=32

* [Lint] format.sh

* [Fixbug] fix shape

* Refactor: sync gemm

* [Enhancement] enable transpose

* [Enhancement] enable fp8_e4m3

* [Enhancement] enable int8

* [Lint] run format.sh

* [Benchmark] add gemm_sp benchmark

* [Example] fix 256 threads hang

* [CI] fix ci

* [Chore] resolve gemini feedback

* [Benchmark] increase search space

* [Lint] format

* [CI] skip sparse tensor core related tests as only sm90 is supported

* [CI] pass local run

* Update gemm_sm89.h

* lint fix

* lint fix

* [Enhancement] Add support for sparse GEMM and initialize CUDA architecture flags

- Introduced a new boolean flag `enable_sparse_gemm_` to control the inclusion of sparse GEMM functionality in CUDA code generation.
- Updated the `Finish` method to conditionally include the sparse GEMM header based on the new flag.
- Implemented logic in `VisitStmt_` to enable sparse GEMM when the corresponding external call is detected.
- Added a function to initialize the `TORCH_CUDA_ARCH_LIST` environment variable based on the target compute version, enhancing compatibility with PyTorch.
- Refactored the initialization function into the appropriate module and ensured it is called in the sparse utilities module.

* Update test_compress_utils.py

---------
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

* [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.

* 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

* [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.

* [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

* [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>

* [Enhancement] Add commit ID to versioning and improve logging initialization

* Updated `get_tilelang_version` to include an optional commit ID in the version string.
* Enhanced the `TileLangBuilPydCommand` to write the version with commit ID to the VERSION file during the build process.
* Introduced a new function `get_git_commit_id` in `version.py` to retrieve the current git commit hash.
* Refactored logger initialization in `autotuner/__init__.py` to ensure handlers are set up only once, improving performance and clarity.
* Minor fixes in `flatten_buffer.cc` and `kernel_cache.py` for better handling of versioning and logging.

* [Refactor] Enhance AutoTuner and JITKernel for improved performance and caching

* Refactored the AutoTuner class to include new methods for setting compilation and profiling arguments, enhancing configurability.
* Introduced caching mechanisms for tuning results, allowing for faster retrieval of previously computed configurations.
* Updated JITKernel to store tuning results, including latency and configuration details, improving the kernel's performance tracking.
* Added new methods for generating cache keys and saving/loading results to/from disk, streamlining the tuning process.
* Enhanced the overall structure and readability of the autotuning logic, ensuring better maintainability and clarity.
* Minor adjustments in related modules to support the new caching and profiling features.

* [Refactor] Clean up code formatting and improve readability in AutoTuner and related modules

* Consolidated import statements and removed unnecessary line breaks for better readability.
* Standardized function argument formatting across the AutoTuner and CompileArgs classes.
* Enhanced consistency in the use of whitespace and indentation throughout the codebase.
* Minor adjustments in the Profiler and JITKernel classes to improve clarity and maintainability.
* Ensured that all changes adhere to the project's coding style guidelines.

* [Refactor] Remove redundant type hints in AutoTuner modules

* Simplified import statements in `__init__.py` and `param.py` by removing unnecessary duplicate type hints for `Any`.
* Improved code readability and maintainability by streamlining type imports across the AutoTuner module.

* [Refactor] Update AutoTuner configuration for improved profiling and target detection

* Enhanced the AutoTuner configuration across multiple examples by adding `set_profile_args` to better manage profiling settings.
* Standardized the use of `target="auto"` in compile arguments to ensure automatic target detection.
* Removed redundant target specifications in certain instances to streamline the configuration process.
* Improved overall clarity and maintainability of the autotuning logic in various example scripts.

* [Refactor] Simplify code formatting and improve readability in example scripts

* Consolidated function argument formatting in `benchmark_mla_decode_amd_tilelang.py`, `example_elementwise_add.py`, and `performance.py` for better clarity.
* Removed unnecessary line breaks and standardized argument placement across multiple files.
* Enhanced overall code readability and maintainability in autotuning examples and performance scripts.

* [Refactor] Update JIT decorator usage across multiple files

* Removed redundant parameters from the JIT decorator in various benchmark and example scripts, simplifying the code.
* Standardized the import of the JIT decorator from `tilelang`, enhancing consistency across the codebase.
* Improved overall readability and maintainability by consolidating import statements and cleaning up function definitions.

* [Refactor] Standardize JIT decorator formatting across benchmark and example scripts

* Simplified the formatting of the JIT decorator in multiple files by removing unnecessary line breaks.
* Enhanced code readability and consistency in the usage of the JIT decorator across benchmark and example scripts.
* Improved overall maintainability by ensuring uniformity in function definitions and decorator usage.

* Added a new attribute `kPaddingMap` in `builtin.h` for managing padding annotations.
* Enhanced `SafeMemorysRewriter` to utilize an annotated padding map for buffer stores, improving memory access safety.
* Implemented checks in `layout_inference.cc` to ensure buffers are correctly referenced during layout mapping.
* Introduced a new test file for validating the padding annotation functionality in TileLang.

* [Refactor] Rename `jit` class to `_JitImplementation` and improve debug path handling

* Refactored the `jit` class to `_JitImplementation` for clarity and encapsulation.
* Enhanced handling of `debug_root_path` to ensure it is correctly set as an absolute path when provided.
* Updated the public `jit` function to serve as a decorator interface, allowing for both default and configured usage.
* Added validation to ensure input tensors are contiguous in the Cython wrapper, improving error handling.

* [Refactor] Improve formatting and handling in `_JitImplementation` and `jit` function

* Refactored the `_JitImplementation` class to enhance readability by adjusting comment formatting and consolidating conditions for setting `debug_root_path`.
* Updated the `jit` function signature for better alignment and clarity in parameter definitions.
* Ensured consistent spacing and comments throughout the code for improved maintainability.

* [Refactor] Update GEMM test parameters for performance optimization

* Set num_stages to 0 and adjusted matrix dimensions in the GEMM test function to enhance performance and consistency across tests in test_tilelang_jit_gemm.py.
* Reduced the number of threads used in the test to align with the updated configuration, improving overall test efficiency.

* [Refactor] Enhance buffer error logging in layout inference

* Updated the warning message in layout inference to provide clearer context when a buffer cannot be inferred due to its absence in the use list. This change improves the clarity of error reporting during layout inference operations.
* Refactored tensor handling in the Cython wrapper to ensure input tensors are checked for contiguity before processing, enhancing error handling and robustness in tensor management.

* bugfix

* [Refactor] Update JIT kernel functions and streamline GEMM tests

* Renamed and refactored matmul and run_gemm functions to matmul_kernel_jit and run_gemm_kernel_jit for clarity.
* Removed redundant JIT decorator from the matmul function, ensuring it is applied only to the kernel function.
* Updated test function names to reflect changes in the kernel functions, enhancing consistency and readability.
* Cleaned up commented-out code and unnecessary imports to improve overall code quality.

* Update main function call in GEMM test to use tilelang testing framework

* Update README and example scripts to include JIT decorator comments

* Added comments in README.md and various example scripts to indicate the use of the @tilelang.jit decorator for returning torch functions.
* Removed redundant comments that previously instructed to add the decorator, streamlining the documentation and improving clarity.

* Update GEMM test parameters for improved performance

* Set num_stages to 0 and adjusted matrix dimensions in test functions to enhance performance and consistency across GEMM tests in test_tilelang_kernel_gemm.py.

* Remove debug print statement from block_sparse_attn_triton.py and implement a timeout handler in autotuner for function execution. This enhances the robustness of the autotuner by allowing it to handle timeouts gracefully.

* Enhance the autotuner module by adding a timeout handler for function execution, improving robustness in handling long-running tasks. This change includes the introduction of a custom TimeoutException and updates to the run_with_timeout function for better signal management.

* Add merge shared memory allocations pass and related configurations

- Introduced a new pass for merging shared memory allocations in GPU kernels, allowing for more efficient memory usage.
- Registered configuration options for debugging and controlling the merging behavior.
- Updated relevant files to integrate the new pass into the TileLang engine and transform modules.
- Adjusted import paths and added documentation for the new functionality.

* Reduce num_stages parameter in GEMM functions from 3 to 1 for improved performance in test_tilelang_kernel_gemm.py

* [Refactor] Enhance makeGemmFragmentB to support transposition

* Updated the `makeGemmFragmentB` function to include a `transposed` parameter, allowing for flexible layout generation based on matrix transposition.
* Adjusted layout calculations for both transposed and non-transposed cases to ensure correct fragment generation.
* Modified the function signature in `layout.h` and updated all relevant calls in `gemm.cc` to accommodate the new parameter.
* Added a new `matmul_sr` function in the test suite to validate the behavior of the updated fragment generation with transposition support.

* [Refactor] Enhance makeGemmFragmentA and makeGemmFragmentB for transposition support

* Updated the `makeGemmFragmentA` and `makeGemmFragmentB` functions to include a `transposed` parameter, allowing for flexible layout generation based on matrix transposition.
* Adjusted layout calculations for both transposed and non-transposed cases to ensure correct fragment generation.
* Modified function signatures in `layout.h` and updated all relevant calls in `gemm.cc` to accommodate the new parameter.
* Added a new `matmul_rs` function in the test suite to validate the behavior of the updated fragment generation with transposition support.
*

* Improve error messaging in layout equality checks

* Enhanced the error output in layout equality checks to provide clearer context by adding line breaks for better readability in the debug output.
* This change ensures that when layouts are structurally unequal, the current and previous layouts are displayed more distinctly, aiding in debugging.

* Add example for warp specialization with flash attention

* Introduced a new example script `example_warp_specialize_flashmla.py` demonstrating flash attention using warp specialization in TileLang.
* Implemented the `flashattn` function with shared memory allocation and memory barrier synchronization for improved performance.
* Added a reference program for validation against PyTorch's implementation, including profiling for latency and performance metrics.
* Removed the outdated `example_warp_specialize_mla.py` to streamline examples and focus on the new implementation.

* Add memory barrier functions to builtin.py

* Introduced `barrier_wait` and `barrier_arrive` functions for memory barrier synchronization.
* Enhanced documentation with detailed docstrings for both functions, clarifying their usage and parameters.
* The `barrier_wait` function serves as a wrapper for `mbarrier_wait_parity`, supporting parity values 0 and 1.
* Improved code organization and readability by adding blank lines for better separation of logical sections.

* Enhance code readability by adding blank lines in example_warp_specialize_flashmla.py and builtin.py

* Added blank lines to improve code organization and separation of logical sections in `example_warp_specialize_flashmla.py`.
* Included blank lines in `builtin.py` around the `wait_wgmma` and `barrier_wait` functions for better readability.

* [Refactor] Update barrier functions and add new example for GEMM with warp specialization

* Refactored memory barrier functions in `example_warp_specialize_flashmla.py` to use the new `barrier_wait` and `barrier_arrive` methods for improved clarity and consistency.
* Introduced a new example script `example_warp_specialize_gemm_copy_gemm_0_1.py` demonstrating matrix multiplication with warp specialization and shared memory allocation.
* Enhanced the `layout.cc` and `elem.cc` files to improve structural equality checks and error handling in copy operations.
* Updated `warpgroup.py` to refine thread ID calculations for better performance in warp specialization scenarios.
* Added new shuffle operations in `builtin.py` for enhanced functionality in parallel computations.

* lint fix

* Update loop variable checks in SIMT loop and buffer region validation

* Modified checks in `elem.cc` to ensure loop variable sizes are less than or equal to source and destination range sizes for better error handling.
* Adjusted assertions in `copy.py` to reflect the updated logic, allowing for more flexible region extent comparisons and improved error messaging.

* lint fix

* test fix

* [Refactor] Update KernelLaunch to clarify CPU and GPU kernel launch logic

* Added comments to distinguish between CPU and GPU kernel launch sections for better code readability.
* Changed the creation of empty blocks to use a consistent "root" identifier, enhancing clarity in frame management.

* [Refactor] Rename operations for consistency in lower_hopper_intrin and related files

* Updated function names from CamelCase to snake_case for better consistency across the codebase.
* Refactored calls to `CreateTMADescriptorOp`, `CreateListofMBarrierOp`, and similar functions to their new names: `create_tma_descriptor`, `create_list_of_mbarrier`, etc.
* Adjusted corresponding test cases to reflect these changes, ensuring compatibility with the new naming conventions.

* [Refactor] Rename operations to snake_case for consistency

* Updated function names from CamelCase to snake_case across various files, including `CreateTMADescriptorOp` to `create_tma_descriptor`, `GetMBarrierOp` to `get_mbarrier`, and others.
* Adjusted corresponding calls and definitions in the codebase to reflect these naming changes, ensuring uniformity and improved readability.
* Enhanced layout inference and loop partitioning logic to accommodate the new naming conventions.

* [Feature] Introduce Warp Specialization and Eliminate Storage Sync for MBarrier

* Added a new example `gemm_ws.py` demonstrating matrix multiplication with warp specialization using TileLang.
* Implemented `WarpSpecializeFrame` and `WarpSpecialize` functionality to manage warp group indices in TIR frames.
* Introduced `EliminateStorageSyncForMBarrier` transformation to optimize storage synchronization in mbarrier regions.
* Enhanced the TileLang API with new methods for retrieving block and thread extents.
* Updated the `LowerAndLegalize` and `OptimizeForTarget` functions to incorporate the new transformation.
* Improved layout inference and kernel launch logic for better performance and clarity.

* [Refactor] Clean up code formatting and improve readability

* Added blank lines for better separation of code blocks in `gemm_ws.py`, `phase.py`, `kernel.py`, and `warpgroup.py`.
* Reformatted the `tilelang.compile` call in `gemm_ws.py` for improved clarity.
* Updated comments in `warpgroup.py` to clarify the availability of the `WarpSpecialize` function for NVIDIA GPUs.
* Ensured consistent spacing and formatting across multiple files to enhance overall code readability.

* lint fix

* [Refactor] Update mbarrier functions for improved clarity and consistency

* Refactored `mbarrier_wait_parity` and `mbarrier_arrive` functions in `builtin.py` to accept explicit parameters for better readability.
* Updated calls in `gemm_ws.py` to use the new function signatures, enhancing code clarity.
* Adjusted `warpgroup.py` to remove unused thread extent variable, streamlining the code.
* Added detailed docstrings to clarify usage examples for memory barrier functions.

* Added blank lines in `mbarrier_wait_parity` and `mbarrier_arrive` functions in `builtin.py` for improved code readability and separation of logical sections.

* Fix typo

* bugfix

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

[Enhancement] Simplify vectorization process in loop_vectorize.cc and add atomic add test (#436) (#439)

* Removed redundant simplification step in vectorization logic to streamline performance.
* Introduced a new test for atomic addition in TileLang, validating functionality with a reference implementation using PyTorch.

* [Enhancement] Update reduce operations to support clear option in sum and abs sum (#436)

* Modified reduce_sum and reduce_absmax functions to include a clear parameter, allowing for accumulation on existing values.
* Updated ReduceOp::Lower method to handle initialization and buffer duplication based on the clear flag for sum and abs sum operations.
* Added new tests for reduce_sum and reduce_max with clear functionality to ensure correctness in various scenarios.
* Enhanced documentation for reduce functions to clarify the behavior of the clear parameter.

* lint fix

* Update tensor type annotations in test_tilelang_transform_annotate_device_regions.py from Buffer to Tensor

* Update tensor type in reduce sum tests from float16 to float32 for improved precision

* [Refactor] Adjust layout inference calculations in Gemm and ParallelOp

* Updated block size calculation in Gemm to account for the range of thread bounds, improving accuracy in layout inference.
* Simplified layout conflict error messages in ParallelOp for better clarity, enhancing debugging experience.
* Removed redundant buffer checks in ParallelOp layout inference logic, streamlining the code.

* [Refactor] Clean up layout inference logic in Gemm and ParallelOp

* Removed unnecessary warning log in Gemm related to WGMMA conditions, streamlining the layout inference process.
* Commented out redundant checks in ParallelOp's layout inference, improving code clarity while maintaining functionality.
* Enhanced error messages in ParallelOp to provide clearer context for layout conflicts, aiding in debugging efforts.

* lint fix

* [Enhancement] Improve cumulative sum functionality and annotations handling

* Updated the `cumsum` function to include detailed documentation and error handling for dimension bounds.
* Modified the `run_cumsum` test to utilize a random tensor supply type for profiling, enhancing test robustness.
* Added annotations to the fused loop in `loop_fusion_utils.h`, ensuring proper metadata is preserved during loop fusion.

* lint fix

* Update submodule 'tvm' to latest commit f4a8f9b

* lint fix

* [Feature] Implement CumSum operation in TileLang

* Added CumSumOp class for cumulative sum operations, including argument validation and lowering logic.
* Introduced CumSum2D template for CUDA, supporting both forward and reverse cumulative sums.
* Created tests for CumSum functionality in shared memory and fragment contexts.
* Updated language interface to include cumsum operation, enhancing the reduction capabilities of TileLang.
* Refactored reduce.py to support cumsum functionality with appropriate memory allocation and copying mechanisms.

* lint fix

* [Enhancement] Introduce thread range management in layout and operation handling

* Added `SetThreadRange` method to `FragmentNode` for managing thread ranges.
* Updated `LayoutNode::Inverse` to provide more informative error messages.
* Refactored layout inference and operation lowering to utilize `thread_bounds` instead of `block_size`, enhancing flexibility for thread management.
* Introduced new tests for tilelang operations to validate thread range functionality and ensure correctness in parallel execution scenarios.

* lint fix

* [Refactor] Improve thread variable handling in layout inference and operation lowering

* Removed workaround for undefined thread_var in layout inference, ensuring proper handling of thread bounds.
* Updated logic to define thread bounds based on the presence of thread_var, enhancing robustness in thread management.
* Refactored thread_var initialization in lower_tile_op to maintain consistency across the codebase.

* [Refactor] Update thread variable handling in layout inference and operation lowering

* Refactored thread variable checks to ensure bounds are only accessed when defined, improving safety and clarity.
* Initialized thread_var with a default range to prevent undefined behavior.
* Updated logic in lower_tile_op to align with new thread variable handling, enhancing consistency across the codebase.

* Add bf16 support for AMD in quickstart example

* Reduced git diff

* Move bf16 vector definition into common.h

* Added unit tests for basic AMD bf16 matmul

* lint fix

---------
Co-authored-by: OscarSavNS <oscar.savolainen@nscale.com>
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

* [BugFix] Address should aligned with access size in tail split

* Lint

* Lint

[Dynamic Symbolic] Add pass_config to customize vectorization and tail split
[Pytest Fix] Wrap tests in dynamic benchmark

* [Dynamic Symbolic] Add pass_config to customize vectorization and tail split

* Lint

* Only check for vectorized dimension. Add docs.

* Lint

* Update comment for cache directory in .gitignore

* Use CUTLASS convention to represent dynamic alignment. Fix bugs

* Add benchmark examples

* Add more benchmarks. Fix accumulate type bug.

* Lint

* Lint

* Test Lint

* Lint

* Test Lint

* Lint

* Fix typo

* Lint

* Lint

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

* Update legalize_safe_memory_access.cc

* Add cache path handling and file locking in Cython adapter

- Introduced a new cache path based on the code hash for the Cython JIT adapter, enhancing cache management.
- Added a lock file mechanism to ensure safe access during cache operations, improving concurrency handling.
- These changes aim to optimize the compilation process and prevent race conditions during library loading.

* lint fix

* refactor

* refactor

* Add GlobalCopyPatternDetector to identify global memory copy patterns

- Introduced a new class, GlobalCopyPatternDetector, to detect specific memory copy patterns in statements.
- Enhanced the PipelinePlanner to utilize this detector for determining copy stages based on global and local memory scopes.
- Improved code clarity and maintainability by encapsulating detection logic within the new class.

* Refactor copy stage detection logic in pipeline planning

- Simplified the determination of copy stages by directly assigning the result of GlobalCopyPatternDetector to pinfo.copy_stage.
- Removed redundant checks for read and write scopes, enhancing code clarity and maintainability.

* lint fix

* [Enhancement] Introduce logical operations `any_of` and `all_of` for buffer checks

- Added new logical operations `any_of` and `all_of` to the TileLang language interface, allowing users to check conditions across buffer elements.
- Implemented corresponding intrinsic calls for CUDA, enhancing the functionality of the TileLang framework.
- Updated the `allocate.py` to handle boolean types correctly in shared memory allocations.
- Introduced tests for the new logical operations to ensure correctness and performance.
Co-authored-by: Zhiwen Mo <zhiwen.mo25@ic.ac.uk>

* lint fix

---------
Co-authored-by: Zhiwen Mo <zhiwen.mo25@ic.ac.uk>

[Enhancement] Support pass config `disable_warp_specialize` to disable auto specialization on hopper (#357)

* [Enhancement] Add warp specialization configuration option and update related functionality

* [Add] Introduced a new pass configuration option `kDisableWarpSpecialized` to control warp specialization behavior.
* [Refactor] Updated `WarpSpecializedRewriter` and `WSCodeEmitter` to utilize the new configuration option, allowing for more flexible optimization strategies.
* [Update] Modified the optimization pipeline in `phase.py` to include pipeline planning when warp specialization is disabled, enhancing performance with async copy.
* [Documentation] Updated JIT compilation parameters to reflect the new configuration option for better clarity.

* lint fix

* [Add] Implement test for GEMM with warp specialization configuration

* Introduced a new test file `test_tilelang_pass_config_disable_warp_specialized.py` to validate the functionality of the warp specialization configuration option.
* Added a `run_gemm` function to execute matrix multiplication tests with and without warp specialization, ensuring correctness through profiling against reference results.
* Included a specific test case for GEMM with float16 data types, enhancing test coverage for the new configuration feature.

* [Refactor] Improve formatting in test_tilelang_pass_config_disable_warp_specialized.py

* Reformatted the `tilelang.compile` call in the `run_gemm` function for better readability by breaking it into multiple lines.
* Added a blank line for improved code structure and clarity in the `test_gemm_f16f16f16_nn` function.

* [Enhancement] Update GEMM examples and autotuner for improved performance

- Modified `example_gemm_intrinsics.py` to enhance matrix multiplication configurations, increasing warp sizes and adjusting data types for better performance.
- Updated the kernel compilation process to utilize the new `tilelang.compile` method and improved latency measurement with the profiler.
- Refactored `example_gemm.py` to include a new autotuning configuration and ensure consistency in latency checks against reference results.
- Adjusted tensor supply generation in `tilelang/utils/tensor.py` to use `torch.randn` for better randomness in tensor initialization.
- Enhanced the `JITContext` in `tilelang/autotuner/__init__.py` to replace the profiler with a kernel instance for performance measurement, improving the overall structure of the autotuner.

* bug fix

* fix

* [Enhancement] Update convolution tests and profiling assertions

- Added a random seed setting for reproducibility in convolution tests.
- Removed several redundant convolution test cases to streamline the testing process.
- Updated the assertion in the matrix multiplication profiling to include a maximum mismatched ratio for improved accuracy in results.
- Enabled the main testing function for better test execution.

* lint fix

* [Refactor] Update GEMM Fragment Layout and Improve Matrix Multiplication Functionality

- Adjusted the layout configuration in `gemm_layouts.cc` to correct the repetition parameters for warp and block layouts, enhancing the efficiency of the GEMM fragment generation.
- Refactored the `matmul_rs` function in `test_tilelang_test_amd.py` to improve readability by restructuring the function signature and ensuring consistent formatting.
- Updated the test execution call to run the new `test_gemm_rs_f16f32f32_nt` function, enhancing test coverage for the GEMM functionality.

* lint fix

* bugfix

* [Refactor] Clean up whitespace in CUDA-related files

- Removed unnecessary blank lines in `cuda.py`, `__init__.py`, and `cuda_driver.py` to improve code readability and maintainability.
- This change enhances the overall organization of the codebase without altering functionality.

* [Benchmark] Add FP8 Matrix Multiplication Benchmark Script

- Introduced a new benchmark script for FP8 matrix multiplication in `benchmark/matmul_fp8/benchmark_matmul.py`.
- The script includes functions for reference matrix multiplication, configuration generation for autotuning, and an autotuned kernel for performance measurement.
- Added command-line argument parsing for matrix dimensions and the option to enable BitBLAS roller for search space exploration.
- The benchmark computes and prints the best latency and performance metrics, enhancing the benchmarking capabilities for FP8 operations.

* lint fix

* Enhance variable creation by associating data types in IR and layout files, and introduce ExpandIndexDataType transformation

- Updated variable creation in `ir.cc`, `gemm_layouts.cc`, and `elem.cc` to include data types for better type safety.
- Added a new transformation `ExpandIndexDataType` to promote integer types to int64 where necessary, improving compatibility and performance.
- Integrated the new transformation into the optimization pipeline in `phase.py`.
- Documented the new transformation in `__init__.py` for clarity.

* lint fix

* Add configuration option for index bitwidth and remove ExpandIndexDataType transformation

- Introduced a new pass configuration option `kConfigIndexBitwidth` to allow customization of index bitwidth.
- Updated the optimization pipeline in `phase.py` to utilize the new configuration option instead of the removed `ExpandIndexDataType` transformation.
- Documented the new configuration option in the JIT compilation function's parameters for clarity.
- Removed the `ExpandIndexDataType` transformation implementation from the codebase to streamline the transformation process.

* lint fix

* Refactor index bitwidth configuration handling

- Updated the `ConfigIndexBitwidth` pass to only apply the bitwidth transformation if the configuration option is defined, preventing potential errors with undefined values.
- Changed the default value of `tl.config_index_bitwidth` in the JIT compilation function's parameters from 32 to None for better clarity and flexibility.

* lint fix

* lint fix

---------
Co-authored-by: LeiWang1999 <wyatuestc@gmail.com>

[Enhancement] Add new matrix multiplication functions and tests for GEMM with transpose options (#331)

- Introduced `matmul_rs` function for flexible matrix multiplication with optional transposition.
- Added `run_gemm_rs` function to facilitate testing of the new matrix multiplication implementation.
- Expanded test coverage for GEMM with additional cases for transposition configurations.
- Corrected index usage in `gemm.h` to ensure proper matrix layout handling.

These changes enhance the GEMM functionality and improve testing capabilities for various matrix configurations.

* [Enhancement] Update GEMM and ROCm Integration

- Removed the restriction on transposing matrix B for CDNA in `gemm.cc`, allowing for more flexible matrix operations.
- Added a new debug header file `debug.h` for enhanced debugging capabilities in ROCm kernels.
- Updated `codegen_hip.cc` to include the new debug header and improved handling of float16 and bfloat16 types in vector element stores.
- Refactored `rt_mod_hip.cc` to return a ROCM module directly from `BuildTileLangHIPWithoutCompile`, enhancing the module creation process.
- Introduced a new ROCm utility in `rocm.py` for linking and managing ROCm paths, improving the build process for ROCm applications.
- Updated tests to reflect changes in GEMM configurations and ensure compatibility with the new features.

These changes enhance the flexibility and debugging capabilities of the GEMM operations and improve the integration with the ROCm backend.

* [Fix] Corrected syntax error in pyproject.toml and improved error message formatting in rocm.py

- Added missing quotation mark for "HSA" in the `select` section of `pyproject.toml`.
- Simplified the error message formatting in `get_rocm_arch` function of `rocm.py` for better readability and consistency.

* lint fix

* Update tilelang/jit/adapter/wrapper.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* lint fix

---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* [Feature] Implement ParallelLoopTransformer for enhanced loop analysis

- Introduced the ParallelLoopTransformer class to improve the handling of parallel loops in layout inference.
- Enhanced the analysis of loop variables and their extents, allowing for more accurate index range calculations.
- Added a BufferAccessCollector to gather buffer access information, ensuring correct index mapping and condition handling.
- Updated the LayoutInference pass to utilize the new transformer, improving overall performance and accuracy in loop transformations.

* test fix

* Fix typo in buffer variable documentation and enhance loop variable handling in layout inference. Added checks for related loop variables and improved condition handling for index mapping.

* Refactor loop variable handling in layout inference. Updated loop index variable from `i` to `j` for clarity and improved condition handling for index mapping by replacing `indices[i]` with `index` in predicate construction.

* [Refactor] Improve flash attention example and layout comparison logic

- Removed unnecessary annotation for `lse_local_split` in the flash attention example to streamline the code.
- Updated the handling of `lse_local_split` to utilize parallel processing for better performance.
- Refactored kernel compilation and profiling logic to enhance clarity and maintainability in the flash attention example.
- Added a condition in `FragmentNode::IsEqual` to handle broadcast cases, improving the robustness of layout comparisons.

* lint fix

* [Enhancement] Add support for shared memory scope in Fill operation

- Introduced handling for `shared.dyn` and `shared` memory scopes in the Fill operation.
- Implemented parallel operation and layout inference for improved performance in shared memory scenarios.
- Updated thread loop partitioning and vectorization logic to accommodate new memory scope handling.

* [Refactor] Remove deprecated decorator and enhance Cython kernel handling

- Removed the deprecated decorator from the main module and added a new implementation in the utils module for better organization.
- Introduced a pointer map in the Cython kernel adapter to manage pointer arguments, improving runtime shape resolution.
- Updated the Cython kernel wrapper to utilize the new pointer map for handling kernel arguments.
- Enhanced error checking in the tensor utility functions to ensure static shapes are enforced.
- Added a new proxy module for buffer and tensor handling, streamlining the interface for TIR programs.

* [Feature] Add matrix multiplication test and kernel implementation

- Introduced a new test file `test_tilelang_language_ptr.py` that implements a matrix multiplication function using TileLang's primitives.
- The `matmul_test` function defines a kernel for performing tile-level GEMM operations with customizable block sizes and data types.
- Added a `run_matmul` function to compile and execute the kernel, along with a test function to validate the implementation.
- Updated the `proxy.py` file to enhance type handling for buffer and tensor proxies, ensuring compatibility with TIR programs.
- Minor formatting improvements in `deprecated.py` for better readability.

* lint fix

* [Refactor] Update tensor creation in matrix multiplication test

- Replaced `T.Tensor.from_ptr` with `T.make_tensor` in `matmul_test` for improved clarity and consistency.
- Updated imports in `__init__.py` to include `make_tensor`.
- Added `make_tensor` function in `proxy.py` to streamline tensor creation from pointers.

* [Refactor] Update tensor definitions across multiple files

- Replaced instances of `T.Tensor` with updated tensor definitions in various benchmark and example files to enhance consistency and clarity.
- Adjusted tensor shapes and types in functions related to matrix multiplication, attention mechanisms, and other operations.
- Improved documentation in README and example files to reflect changes in tensor usage.

* lint fix

* [Refactor] Update tensor types in attention and matrix multiplication examples

- Replaced instances of `T.Tensor` with `T.SharedTensor` and `T.FragmentTensor` in various attention and matrix multiplication functions to improve consistency and clarity.
- Adjusted tensor definitions in benchmark and example files to align with the new tensor types.
- Enhanced the overall structure and readability of the code by standardizing tensor usage across multiple files.

* lint fix

* [Refactor] Update tensor types in GEMM example and test files

- Replaced instances of `T.Tensor` with `T.LocalTensor` and `T.Buffer` in the GEMM example and related test functions to improve consistency and clarity.
- Enhanced the overall structure of the code by standardizing tensor usage across multiple files, aligning with recent updates in tensor definitions.

* [Refactor] Update tensor usage in customize.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `reshape` and `view` functions to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the file.

* [Refactor] Update tensor types in test_tilelang_transform_annotate_device_regions.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `before` and `expected` methods of the `TestAnnotateThreadExtent` and `TestAnnotateDeviceScope` classes to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the test file.

* [Refactor] Update tensor types to SharedBuffer and FragmentBuffer

- Replaced instances of `T.SharedTensor` and `T.FragmentTensor` with `T.SharedBuffer` and `T.FragmentBuffer` across multiple benchmark, example, and test files to enhance consistency with recent tensor definitions.
- Improved code clarity and structure by standardizing buffer usage in attention and matrix multiplication functions.

* [Refactor] Introduce Tensor alias for Buffer in proxy.py

- Added a new alias `Tensor` for `Buffer` in `proxy.py` to facilitate JIT compilation, ensuring that inputs and outputs are mapped with `torch.Tensor`.
- This change enhances clarity and consistency in tensor usage across the codebase.

* [Refactor] Revamp cache management and enhance documentation in env.py and proxy.py

- Replaced global cache functions with a CacheState class to improve encapsulation and management of kernel caching.
- Updated the `from_ptr` method in BufferProxy and BaseTensorProxy classes to include detailed docstrings for better clarity on parameters and return values.
- Enhanced class docstrings across various proxy classes to provide clearer descriptions of their purpose and functionality, improving overall code documentation.

* [Refactor] Update imports in __init__.py for tir compatibility

- Added imports for `prim_func` and `tir.op` to enhance compatibility with the upstream tir script.
- Marked imports with `# noqa: F401` to suppress linting warnings for unused imports, indicating future removal once compatibility is achieved.

* lint fix

* [Refactor] Update imports in tir.ir.py for improved compatibility

- Removed unused import of `PrimExpr` from `tvm.script.ir_builder.tir` and replaced it with the correct import from `tvm.tir`.
- Added import for `tir.ir` in `__init__.py` to enhance module accessibility and maintain compatibility with upstream changes.

* [Refactor] Update function calls in tir.ir.py to return values

- Modified the `serial`, `parallel`, `vectorized`, `unroll`, `thread_binding`, and `grid` functions to return the results of their respective calls to `_ir` methods, enhancing clarity and ensuring proper value propagation.

* bugfix

* [Enhancement] Add support for uint16 data type in TLCUDASourceWrapper

- Introduced the "uint16" mapping to the type dictionary in the TLCUDASourceWrapper class, expanding the range of supported data types for CUDA operations.

* bugfix

* [Update] Sync subproject commit and modify CUDA atomic add functions

- Updated the subproject commit for TVM to edd35139a0481e9359aa269e3e50450b95ba2f5a.
- Commented out the CUDA capability check in the example convolution script to prevent execution errors.
- Refactored atomic add functions for BFLOAT16 in common.h to include a conditional compilation directive for improved compatibility with CUDA architectures.

* [Refactor] Improve flash attention example and layout comparison logic

- Removed unnecessary annotation for `lse_local_split` in the flash attention example to streamline the code.
- Updated the handling of `lse_local_split` to utilize parallel processing for better performance.
- Refactored kernel compilation and profiling logic to enhance clarity and maintainability in the flash attention example.
- Added a condition in `FragmentNode::IsEqual` to handle broadcast cases, improving the robustness of layout comparisons.

* lint fix

* [Enhancement] Add support for shared memory scope in Fill operation

- Introduced handling for `shared.dyn` and `shared` memory scopes in the Fill operation.
- Implemented parallel operation and layout inference for improved performance in shared memory scenarios.
- Updated thread loop partitioning and vectorization logic to accommodate new memory scope handling.

* [Refactor] Remove deprecated decorator and enhance Cython kernel handling

- Removed the deprecated decorator from the main module and added a new implementation in the utils module for better organization.
- Introduced a pointer map in the Cython kernel adapter to manage pointer arguments, improving runtime shape resolution.
- Updated the Cython kernel wrapper to utilize the new pointer map for handling kernel arguments.
- Enhanced error checking in the tensor utility functions to ensure static shapes are enforced.
- Added a new proxy module for buffer and tensor handling, streamlining the interface for TIR programs.

* [Feature] Add matrix multiplication test and kernel implementation

- Introduced a new test file `test_tilelang_language_ptr.py` that implements a matrix multiplication function using TileLang's primitives.
- The `matmul_test` function defines a kernel for performing tile-level GEMM operations with customizable block sizes and data types.
- Added a `run_matmul` function to compile and execute the kernel, along with a test function to validate the implementation.
- Updated the `proxy.py` file to enhance type handling for buffer and tensor proxies, ensuring compatibility with TIR programs.
- Minor formatting improvements in `deprecated.py` for better readability.

* lint fix

* [Refactor] Update tensor creation in matrix multiplication test

- Replaced `T.Tensor.from_ptr` with `T.make_tensor` in `matmul_test` for improved clarity and consistency.
- Updated imports in `__init__.py` to include `make_tensor`.
- Added `make_tensor` function in `proxy.py` to streamline tensor creation from pointers.

* [Refactor] Update tensor definitions across multiple files

- Replaced instances of `T.Tensor` with updated tensor definitions in various benchmark and example files to enhance consistency and clarity.
- Adjusted tensor shapes and types in functions related to matrix multiplication, attention mechanisms, and other operations.
- Improved documentation in README and example files to reflect changes in tensor usage.

* lint fix

* [Refactor] Update tensor types in attention and matrix multiplication examples

- Replaced instances of `T.Tensor` with `T.SharedTensor` and `T.FragmentTensor` in various attention and matrix multiplication functions to improve consistency and clarity.
- Adjusted tensor definitions in benchmark and example files to align with the new tensor types.
- Enhanced the overall structure and readability of the code by standardizing tensor usage across multiple files.

* lint fix

* [Refactor] Update tensor types in GEMM example and test files

- Replaced instances of `T.Tensor` with `T.LocalTensor` and `T.Buffer` in the GEMM example and related test functions to improve consistency and clarity.
- Enhanced the overall structure of the code by standardizing tensor usage across multiple files, aligning with recent updates in tensor definitions.

* [Refactor] Update tensor usage in customize.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `reshape` and `view` functions to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the file.

* [Refactor] Update tensor types in test_tilelang_transform_annotate_device_regions.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `before` and `expected` methods of the `TestAnnotateThreadExtent` and `TestAnnotateDeviceScope` classes to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the test file.

* [Refactor] Update tensor types to SharedBuffer and FragmentBuffer

- Replaced instances of `T.SharedTensor` and `T.FragmentTensor` with `T.SharedBuffer` and `T.FragmentBuffer` across multiple benchmark, example, and test files to enhance consistency with recent tensor definitions.
- Improved code clarity and structure by standardizing buffer usage in attention and matrix multiplication functions.

* [Refactor] Introduce Tensor alias for Buffer in proxy.py

- Added a new alias `Tensor` for `Buffer` in `proxy.py` to facilitate JIT compilation, ensuring that inputs and outputs are mapped with `torch.Tensor`.
- This change enhances clarity and consistency in tensor usage across the codebase.

* add autotune to example_gemm.py

* format init.py

* [Refactor] Improve flash attention example and layout comparison logic

- Removed unnecessary annotation for `lse_local_split` in the flash attention example to streamline the code.
- Updated the handling of `lse_local_split` to utilize parallel processing for better performance.
- Refactored kernel compilation and profiling logic to enhance clarity and maintainability in the flash attention example.
- Added a condition in `FragmentNode::IsEqual` to handle broadcast cases, improving the robustness of layout comparisons.

* lint fix

* [Enhancement] Add support for shared memory scope in Fill operation

- Introduced handling for `shared.dyn` and `shared` memory scopes in the Fill operation.
- Implemented parallel operation and layout inference for improved performance in shared memory scenarios.
- Updated thread loop partitioning and vectorization logic to accommodate new memory scope handling.

* [Refactor] Remove deprecated decorator and enhance Cython kernel handling

- Removed the deprecated decorator from the main module and added a new implementation in the utils module for better organization.
- Introduced a pointer map in the Cython kernel adapter to manage pointer arguments, improving runtime shape resolution.
- Updated the Cython kernel wrapper to utilize the new pointer map for handling kernel arguments.
- Enhanced error checking in the tensor utility functions to ensure static shapes are enforced.
- Added a new proxy module for buffer and tensor handling, streamlining the interface for TIR programs.

* [Feature] Add matrix multiplication test and kernel implementation

- Introduced a new test file `test_tilelang_language_ptr.py` that implements a matrix multiplication function using TileLang's primitives.
- The `matmul_test` function defines a kernel for performing tile-level GEMM operations with customizable block sizes and data types.
- Added a `run_matmul` function to compile and execute the kernel, along with a test function to validate the implementation.
- Updated the `proxy.py` file to enhance type handling for buffer and tensor proxies, ensuring compatibility with TIR programs.
- Minor formatting improvements in `deprecated.py` for better readability.

* lint fix