GitLab

* [Enhancement] Improve buffer conflict detection in thread storage synchronization

- Added a new boolean variable `range_is_overlap` to accurately determine if buffer indices overlap, enhancing the conflict detection logic in `thread_storage_sync.cc`.
- Updated the return logic to reflect the overlap status, ensuring correct conflict resolution based on buffer index comparisons.
- Removed an unnecessary comment in `OptimizeForTarget` to streamline the code and improve clarity.

* example fix

* enhancement

* improve ci

[BugFix] Do not modify strict layout in common or relax level of layout inference. More conditions on layout checking (#653)

* [BugFix] Do not modify strict layout in common or relax level of layout inference. More conditions on layout checking

* Lint

* test fix

* Update CI workflow to install dependencies without user site packages

- Modified the installation commands in the CI workflow to include the `--no-user` flag for both `requirements-dev.txt` and `requirements-test.txt`, ensuring that packages are installed in the virtual environment rather than the user site directory.

* Update CI workflow to install pip without user site packages

- Added the `--no-user` flag to the pip installation command in the CI workflow for both development and testing dependencies, ensuring that packages are installed within the virtual environment.

* Update requirements-test.txt

* reduce ci problem size,

* Refactor example_mla_decode.py for consistent formatting and remove unused imports in test_example_mla_decode.py

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

Co-authored-by: zhangnju <ningzhan@SMC-SC-DI08-33.dh144.dcgpu>

[Enhancement] Add compile_flags parameter to JIT kernel and adapter classes for improved compilation control (#656)

* [Enhancement] Add compile_flags parameter to JIT kernel and adapter classes for improved compilation control

* lint fix

* upd

* lint fix

* fix typo

* update typing

* update the use case of compile flags

* ci fix

* fix

* Fix CI workflow to correctly activate virtual environment from shared cache directory

* use local cache

* fix

* fix

* fix

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

* fix CI bugs in hopper

* lint fix

* Update bulk_copy.cc

* Refactor bulk copy logic in LowerBulkCopy function

- Removed unnecessary blank lines for improved code readability.
- Enhanced stride validation by checking for null pointers in global stride calculations, ensuring robustness against symbolic strides.
- Updated pass configuration handling in dynamic tile language tests to streamline dynamic alignment and TMA lower pass settings.

* test fix

* ci fix

* Update flash-attention dependencies and clean up example code

- Downgraded `flash-attn` dependency version in `requirements-test.txt` to `<=2.2.0`.
- Removed unused imports and commented-out code in various example files to enhance readability and maintainability.
- Updated the `flashattn` function signature to include default parameters for `block_M`, `block_N`, `num_stages`, and `threads`.
- Cleaned up the `example_mha_fwd_varlen.py` and `example_mha_bwd_wgmma_pipelined.py` files by removing unnecessary comments and improving code clarity.
- Deleted the `example_mha_inference.py` file as it is no longer needed.

* Update CI workflow to remove `--user` flag from pip install commands

- Removed the `--user` flag from the pip install commands in both the development and testing sections of the CI workflow to ensure proper installation of dependencies in the virtual environment.

* Update CI workflow to include `--no-user` flag in pip install commands

- Added the `--no-user` flag to the pip install commands in both the development and testing sections of the CI workflow to ensure dependencies are installed correctly within the virtual environment.

* Update CI workflow to include `--no-user` flag in pip install command for wheel mode

- Added the `--no-user` flag to the pip install command in the wheel mode section of the CI workflow to ensure dependencies are installed correctly within the virtual environment.

* test fix

* avoid conflict with system environments

* test fix

* add commnets

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

* Add paged block-sparse flash-decoding kernel

* Update example_tilelang_sparse_gqa_decode_paged.py

* lint fix

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

* [Enhancement] Improve memory access condition checks in GlobalMemChecker

- Updated the condition checks in the GlobalMemChecker to utilize symbolic bounds in the CanProve method, enhancing the accuracy of memory access validations.
- This change ensures that both upper and lower bound conditions are evaluated with improved proof strength, contributing to more robust memory access analysis.

* lintfix

* [Enhancement] Add legality checks for shared memory and global range in LowerBulkCopy

- Implemented checks to ensure that the shared memory range and global range are legal during the bulk copy operation.
- Added assertions to validate that the extents of global and shared ranges match, improving the robustness of memory access validation in the LowerBulkCopy function.

* [Refactor] Update barrier and clear operations in warp specialization examples

- Replaced `mbarrier_wait_parity` and `mbarrier_arrive` with `barrier_wait` and `barrier_arrive` for improved clarity and consistency in synchronization.
- Adjusted the order of `clear` operations for local fragments in `example_warp_specialize_gemm_copy_1_gemm_0` to enhance parallel execution efficiency.

* [Enhancement] Implement thread partial synchronization and improve shared memory allocation handling

- Added support for thread partial barrier synchronization in CUDA, allowing for more flexible thread management.
- Enhanced the `MergeSharedMemoryAllocations` function to accept alignment bytes, improving memory allocation efficiency based on target requirements.
- Updated the `Lower` methods in `Copy` and `Fill` classes to include conditional predicates for thread execution, ensuring better control over thread behavior.
- Refactored the `print` function to include warp group and warp IDs for more detailed debugging output.
- Improved the handling of dynamic shared memory allocations in the `LowerAndLegalize` function to align with target-specific requirements.

* [Enhancement] Add support for disabling TMA in Copy operations

- Introduced a new `disable_tma` parameter in the `Copy` class to control thread memory access behavior.
- Updated the `Lower` method to conditionally execute bulk copy operations based on the `disable_tma` flag.
- Enhanced the `copy` function to accept the `disable_tma` argument, allowing for more flexible memory copy operations.
- Improved handling of `coalesced_width` to ensure it defaults to -1 when not provided, enhancing robustness in memory operations.

* [Refactor] Clean up whitespace and formatting in multiple files

- Removed unnecessary blank lines and adjusted line breaks for improved code readability in `example_mla_decode.py`, `example_warp_specialize_gemm_copy_gemm_0_1.py`, `phase.py`, and `copy.py`.
- Ensured consistent formatting across functions to enhance maintainability and clarity of the codebase.

* [Enhancement] Refactor flash attention implementation for improved performance and configurability

- Split the shared memory allocations for query and key-value pairs to optimize memory usage.
- Introduced command-line arguments for batch size, number of heads, and dimensions, enhancing flexibility in running the example.
- Updated kernel execution parameters to improve thread management and synchronization.
- Enhanced the overall structure of the flash attention function for better readability and maintainability.

* fix

* Update layout inference in ParallelOp to account for thread bounds; remove debug print in OptimizeForTarget

* Refactor barrier handling and update example configurations

- Replaced commented-out barrier creation with new barrier allocation in GEMM example.
- Updated kernel configuration in warp specialization example to include async copy settings.
- Enhanced barrier management in the phase optimization process to improve synchronization handling.
- Introduced new barrier allocation function for better memory management in shared contexts.

* Refactor barrier handling in LowerAndLegalize and OptimizeForTarget

- Reintroduced barrier lowering in OptimizeForTarget to enhance synchronization.
- Removed commented-out barrier lowering in LowerAndLegalize for cleaner code.
- Added exit() call in OptimizeForTarget to halt execution after barrier lowering.

* Enhance CMake configuration and clean up example scripts

- Enabled compile command export in CMakeLists.txt for better build integration.
- Removed unnecessary print statement in the warp specialization example.
- Cleaned up commented-out code in GEMM example for improved readability.
- Updated barrier handling in shared memory allocation transformations for better synchronization.

* Refactor barrier handling in warp specialization examples

- Replaced commented-out mbarrier code with new barrier allocation using T.alloc_barrier for improved synchronization.
- Updated barrier wait and arrive calls to align with the new allocation method across multiple example scripts.
- Enhanced code readability by removing unnecessary comments and ensuring consistent barrier management.

* Update lower_shared_barrier.cc

* Update phase.py

* Update warp specialization example and Cython wrapper

- Removed commented-out pass configuration options in the warp specialization example for clarity.
- Added functionality to write the generated kernel source to a file named "kernel.cu".
- Enhanced Cython wrapper to support boolean type conversion for improved type handling.

* Add storage synchronization call in shared barrier transformation

- Introduced a new evaluation statement to call the TVM storage sync function with "shared" as an argument, enhancing synchronization in the shared barrier handling process.

* remove debug files

* Remove kernel source output to file in warp specialization example

* remove comments

* Refactor tensor handling and update test execution in TileLang

- Changed `Buffer` to `Tensor` in `customize.py` for better type consistency.
- Updated `mbarrier_wait_parity` and `mbarrier_arrive` functions in `builtin.py` to use `tir.BufferLoad` instead of `BufferLoad`.
- Commented out the main testing function in `test_tilelang_language_reshape.py` and replaced it with a direct call to `run_reshape_smem` for streamlined testing.
- Removed unnecessary NVCC compiler flags in `libgen.py` to reduce verbosity.

* Update test_tilelang_language_reshape.py

[Dev] Update benchmark and decoding scripts to refine condition checks and optimize tensor operations (#637)

- Enhanced the condition in `compare_ab` to ensure baseline checks align with target exclusions.
- Removed unnecessary tensor allocation in `mla_decode_tilelang`, optimizing memory usage and improving performance by directly using shared tensors in GEMM operations.

* [Enhancement] Add argument simplification option to StmtSimplifier

- Introduced a new `simplify_arguments` flag in the `StmtSimplifier::Apply` method to control argument simplification behavior.
- Updated the `Simplify` function to accept the new flag, allowing for enhanced flexibility in the simplification process.
- Adjusted the `LowerAndLegalize` and `_Simplify` functions to utilize the new argument, ensuring consistent behavior across the codebase.
- Added comments to clarify the purpose of the new flag and its impact on simplification logic.

* lint fix

* [Enhancement] Improve layout inference and reduce operation handling

- Updated `ParallelOp::InferLayout` to check for pure buffer stores, enhancing layout inference logic.
- Modified `ReduceOp::Lower` to include all threads in the AllReduce operation, improving performance on specific architectures.
- Added a TODO comment in `AllReduce` to consider merging synchronization barriers for optimization.

* lint fix

* [Enhancement] Add input validation for GEMM parameters

- Introduced checks to ensure that the dimensions M and N are divisible by their respective warp sizes (kMPerWarp and kNPerWarp) in the Gemm::ComputeWarpPartition method.
- Added informative error messages to assist in debugging when the input parameters do not meet the required conditions.

* bug fix

* [Refactor] Simplify and modularize autotuner implementation

- Removed unused imports and extensive code sections from the autotuner module to enhance readability and maintainability.
- Modularized the code by introducing new imports for autotuning and capturing functionalities, streamlining the overall structure.
- Improved logging setup and removed redundant timeout handling functions, focusing on core autotuning logic.
- Updated the AutoTuner class to better utilize the new modular structure, ensuring efficient performance during auto-tuning processes.

* [Refactor] Clean up and enhance capture and tuner modules

- Improved code readability by removing unnecessary blank lines and organizing imports in `capture.py` and `tuner.py`.
- Enhanced logging in the `AutoTuner` class to provide clearer warnings regarding the usage of `supply_prog` in the context of auto-tuning.
- Streamlined the `CaptureStack` class for better thread-local context management.

* lint fix

* [Refactor] Simplify configuration and autotuning logic in blocksparse GEMM example

- Updated `get_configs` function to reduce the number of configurations, enhancing performance and clarity.
- Removed the `get_best_config` function, integrating its logic directly into the `blocksparse_matmul` function with the `@autotune` decorator for streamlined autotuning.
- Adjusted the main function to directly utilize the autotuned kernel, simplifying the overall structure and improving readability.
- Deleted obsolete test file for autotuning decorator, cleaning up the codebase.

* [Refactor] Improve code formatting and readability in autotune test file

- Reformatted the `matmul` function and `get_configs` function for better readability by adjusting line breaks and indentation.
- Fixed a typo in the `enable_rasteration` parameter name to ensure consistency.
- Cleaned up unnecessary blank lines to enhance overall code clarity.

* Update example_blocksparse_gemm.py

* Update capture.py

* [Refactor] Enhance expression handling in utils.py and update wrapper to use pythonic_expr

- Added support for additional TIR expressions (FloorDiv, Min, Max, Add, Sub, FloorMod) in the pythonic_expr function to improve string representation.
- Replaced the deprecated legalize_c function calls in TLCUDASourceWrapper and TLCPUSourceWrapper with pythonic_expr for better expression handling in kernel launch code.

* [Refactor] Simplify expression handling in pythonic_expr function

- Consolidated binary and min/max operation handling in the pythonic_expr function to improve readability and maintainability.
- Replaced individual checks for binary operations with a mapping approach, streamlining the code and enhancing performance in expression representation.

* [Enhancement] Improve expression representation in pythonic_expr function

- Added operator precedence handling to the pythonic_expr function, enhancing the conversion of TVM PrimExpr to Python-style strings.
- Updated the visitor logic to intelligently add parentheses based on operator precedence, improving the accuracy of expression representation.
- Included a docstring for better clarity on the function's purpose and usage.

* test fix

* Tune linear attention examples on H100

* Add retnet fwd kernel

* fix lint

Bumps [transformers](https://github.com/huggingface/transformers) from 4.51.0 to 4.52.1.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](https://github.com/huggingface/transformers/compare/v4.51.0...v4.52.1

)

---
updated-dependencies:
- dependency-name: transformers
  dependency-version: 4.52.1
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* [Refactor] Update tilelang kernel functions and remove unused imports

- Refactored the `flashattn_fwd`, `flashattn_bwd_preprocess`, and `flashattn_bwd_postprocess` functions to utilize direct kernel calls instead of cached versions, improving clarity and performance.
- Added `@tilelang.jit` decorators with specified output indices to enhance kernel compilation.
- Removed unused import of `cached` from `tilelang`, streamlining the code.
- Commented out the main testing function call in `test_tilelang_kernel_mha_bwd.py` for potential future use.

* [Refactor] Simplify configuration generation in benchmark and example scripts

- Refactored the `get_configs` functions in multiple benchmark and example scripts to utilize a dictionary-based approach for parameter configuration, improving readability and maintainability.
- Updated the `flashattn` and `chunk_scan_fwd` functions to directly accept configuration parameters, enhancing flexibility in kernel tuning.
- Removed redundant code and streamlined the configuration generation process across various files, ensuring consistency in how configurations are defined and utilized.

* [Refactor] Update configuration handling in benchmark scripts

- Refactored the `get_configs` functions in benchmark scripts to accept a variable argument list, improving flexibility in configuration management.
- Enhanced the `matmul` and `flashattn` functions to utilize the updated configuration approach, streamlining parameter handling for kernel tuning.
- Added `@autotune` decorators to relevant functions, ensuring consistent autotuning behavior across benchmarks.
- Cleaned up redundant code and improved overall readability in the affected files.

* [Refactor] Clean up formatting and update subproject commit

- Updated the subproject commit reference in the TVM directory to indicate a dirty state.
- Removed unnecessary blank lines and improved formatting in the `benchmark_matmul` and `benchmark_matmul_fp8` scripts for better readability.
- Streamlined the function definitions in the `flashattn` example script to enhance clarity and maintainability.

* [Refactor] Update AutoTuner configuration handling

- Modified the AutoTuner class to check if kernel parameters are set before processing tunable arguments, improving robustness in configuration handling.
- Enhanced the logic for skipping compilation when tunable parameters are already provided, ensuring efficient use of resources.
- Updated comments for clarity and maintainability.

* lint fix

* Update TVM subproject commit to indicate dirty state and modify MHA backward test cases

- Updated the subproject commit reference in the TVM directory to reflect a dirty state.
- Adjusted the `test_mha_bwd` function to use a new configuration for the MHA backward tests, changing the context size from 128 to 256.
- Uncommented the main testing function call for potential execution.

Bumps [transformers](https://github.com/huggingface/transformers) from 4.50.0 to 4.51.0.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](https://github.com/huggingface/transformers/compare/v4.50.0...v4.51.0

)

---
updated-dependencies:
- dependency-name: transformers
  dependency-version: 4.51.0
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* [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>

- Added a line break in `quickstart.py` for better readability.
- Simplified the JIT kernel compilation in `quickstart.py` by removing the unused execution backend option.
- Modified `example_elementwise_add.py` to disable cache for `tilelang` and optimized the element-wise addition kernel by utilizing shared memory for input tensors, improving performance.
- Updated default values for matrix dimensions and block sizes in the argument parser to enhance usability.

* [Example] Update kernel compilation in examples to use @tilelang.jit

- Refactored multiple examples to eliminate the use of `tilelang.compile` for kernel creation, directly invoking the functions instead.
- Added `@tilelang.jit` decorators with appropriate output indices to enhance performance and maintainability.
- Improved code clarity by simplifying the kernel invocation process across various examples, ensuring consistency in how kernels are defined and executed.

* format

* Update example_tilelang_sparse_gqa_decode_varlen_indice.py

* Update example_dequant_gemm_fine_grained.py

* Update example_gemm_autotune.py

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

* feat: add a easy version for online softmax

* fix: set x & y to fragment memory to load data from global memory

* feat: apply format check

* Add License

---------
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.

* [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

* Update FLA import path for `prepare_token_indices`

* Update FLA import path for `prepare_token_indices`

* Compare versions via packaging.version.parse

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

* [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] 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

* [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>

* [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>

* [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

* 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

* [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.

* Refactor convolution example to streamline configuration and remove unused code

* Updated the `check_hopper` function to properly check for CUDA availability and compute capability.
* Removed the `get_configs` and `get_best_config` functions, simplifying the example by eliminating unused autotuning logic.
* Adjusted argument parsing in the `main` function to directly compile the convolution kernel without autotuning options.
* Cleaned up the code for better readability and maintainability.

* Update examples/convolution/example_convolution.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>

* [CI] Add gemm and gemm_fp8 example to CI

* Fix lint via format.sh

* Resolved the issues with profiler API usage and parse_args