GitLab

* Refactor operator classes to inherit from TileOperator and update layout inference methods

- Changed base class of several operator classes (AtomicAdd, Copy, Gemm, etc.) from Operator to TileOperator for better alignment with tile operations.
- Updated InferLayout and Lower methods to use 'override' specifier for clarity and consistency.
- Adjusted header inclusions to replace "op.h" with "operator.h" across multiple files for improved organization.
- Added missing layout inference implementations for Fill and Conv2DIm2ColOp.
- Removed deprecated op.cc and op.h files to streamline the codebase.

* lint fix

* Refactor operator classes to use Node pattern and improve memory management

- Updated several operator classes (AtomicAdd, Copy, Gemm, etc.) to utilize the Node pattern for better memory management and encapsulation.
- Changed constructors to initialize member variables through a node object, enhancing clarity and reducing direct member access.
- Updated Clone methods to return TileOperator instances instead of unique pointers, aligning with the new design.
- Refactored InferLayout and Lower methods to ensure consistency across operator implementations.
- Adjusted header files to reflect the new class structure and removed deprecated code for a cleaner codebase.

* Enhance Clone methods in AtomicAdd and Copy classes to support parallel operation cloning

- Updated the Clone methods in AtomicAddNode and CopyNode to ensure that the parallel operation (par_op_) is properly cloned when defined, improving the integrity of cloned objects.
- Refactored the FillNode class to use ParallelOp directly instead of std::make_unique, streamlining the creation of parallel operations.
- Made minor adjustments in layout inference and other related methods for consistency and clarity.

* Refactor FillNode::Lower method to remove unused global function call

- Eliminated the call to the global function "tl.fill.lower" in the FillNode::Lower method, streamlining the code and improving clarity.
- Retained the core functionality of the method while enhancing maintainability by reducing unnecessary dependencies.

* Implement new free stage layout inference.

* Fix bug

* Make replication upcasting and unnormalizable iterators safe.

* Better handling of updating with more replica

* Remove unnecessary check.

* Fix compilation.

* Fix setup.py.

* Simplify development mode.

* Allow ParallelOp layout when there's already a compatible layout specified

* lint fix

* Add ProveFragmentContains function to validate thread access between small and large fragments

This function checks if the threads accessing elements of a smaller fragment are a subset of those accessing a larger fragment, ensuring valid access during updates. The implementation includes deriving thread indices, computing logical indices, and verifying thread mappings.

* Update dependencies in requirements files

* Remove 'thefuzz' from requirements-dev.txt
* Specify exact versions for 'torch' and add 'flash_attn' in requirements-test.txt

* Update CI workflow to use SHA256 hash for requirements file

* Update requirements and CI workflow for flash attention

* Removed specific version for 'torch' in requirements-test.txt
* Added installation of 'flash_attn==2.5.8' in CI workflow to ensure compatibility

* Refactor flash attention import handling in examples

* Removed availability checks for 'flash_attn' in multiple example scripts.
* Simplified import statements for 'flash_attn' to ensure consistent usage across examples.

---------
Co-authored-by: Huanqi Cao <caohuanqi@deepseek.com>

**Summarize part of the rebase pr:**

1. **Support T.thread_return() → CUDA return syntax**  
   Added support for translating `T.thread_return()` to CUDA's native `return` statement.

2. **Dynamic type support for function inputs**  
   Functions now accept dynamically typed parameters using `typing`:
   ```python
   dyn_type = T.int32 or T.float
   @T.prim_func
   def main(
       a: dyn_type,
   )
   ```

3. **Device Function Codegen**  
   Added support for generating `__device__` functions in CUDA:
   ```python
   @I.ir_module
   class Module:
       @T.prim_func(private=True)
       def add(a: T.int32, b: T.int32) -> T.int32:
           return a + b

       @T.prim_func
       def main(
           A: T.Buffer((128, 128), "int32"),
           B: T.Buffer((128, 128), "int32"),
           C: T.Buffer((128, 128), "int32"),
       ):
           T.func_attr({"global_symbol": "main"})
           length: T.int32 = Module.add(64, 64)  # Host call
           for bx in T.thread_binding(length, "blockIdx.x"):
               for tx in T.thread_binding(length, "threadIdx.x"):
                   C[bx, tx] = Module.add(A[bx, tx], B[bx, tx])  # Device call
   ```
   After compilation, `add` becomes a CUDA `__device__` function.

4. **Cython-based Python/C++ interop**  
   Replaced ctypes with Cython for all Python/C++ interactions:
   - Python → C++ calls
   - C++ → Cython calls  
   This improves performance by around 100x and reduces CPU overhead during compile/runtime.

5. **FP8 data type standardization**  
   Migrated `e5m2_float8` and similar types to Torch-standardized variants`float8_e5m2` and etc.



* Refactor CMakeLists.txt to set default build type and manage dependencies for tvm_cython modules

* Update default value of `check_well_formed` parameter in `prim_func` to False for improved flexibility in TIR function parsing.

* Add StorageRewrite function to transform module

Introduced the StorageRewrite function in the tilelang.transform module, which returns a TVM transform pass. This addition enhances the functionality of the module by providing a new transformation option for users.

* Refactor null option handling in IR and layout inference

- Updated instances of `NullOpt` to `std::nullopt` in `ir.cc` and `parallel.cc` for consistency with modern C++ practices.
- Enhanced layout inference logic in `layout_inference.cc` to improve type safety by replacing `as<Fragment>().get()` with `as<FragmentNode>()`.
- Adjusted error handling in `multi_version_buffer_rewriter.cc` and `persist_threadblock.cc` to use more concise null checks.
- Cleaned up test files by commenting out `tilelang.testing.main()` and replacing it with specific test function calls for better clarity.
- Removed unused test file `test_tilelang_kernel_deepseek_nsa.py` to streamline the testing suite.

* Update TVM subproject and refactor cluster planning and tile operation handling

- Updated the TVM subproject to a dirty commit state.
- Refactored copyright headers in `cluster_planning.cc` to reflect the new licensing.
- Enhanced error handling in `lower_tile_op.cc` to check for missing padding map annotations.
- Modified test files to improve clarity and functionality, including adjustments to kernel compilation and test assertions.
- Updated various test cases to ensure proper handling of annotations and configurations in the TileLang testing framework.

* Update annotation type in warp specialized test for consistency

- Changed the annotation type in the `test_warp_specialized` function from a literal integer to `T.int32(3)` for improved type safety and consistency with the TileLang framework.

* Refactor test execution in warp specialized test

- Replaced the direct call to `test_warp_specialized()` with `tilelang.testing.main()` in the test file to standardize test execution and improve integration with the TileLang testing framework.

* refactor

* [Enhancement] Add strict layout map for improved buffer layout inference (#594)

- Introduced a `strict_layout_map` to enhance layout inference by ensuring that buffers with strict layout requirements are properly accounted for during the inference process.
- Updated the inference logic to check for the presence of buffers in the `strict_layout_map` before applying layout changes, improving the accuracy of layout assignments.
- Refactored the layout inference steps to include the copying of layouts into the new strict map, ensuring a clear separation of layout handling based on inference levels.

* [Example] Update examples to use @tilelang.jit (#597)

* [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>

* [Enhancement] Refine error messaging in LowerBulkCopy for global and shared range checks (#599)

* [Enhancement] Improve error messaging for global and shared range legality checks in LowerBulkCopy

- Updated error messages in the LowerBulkCopy function to provide clearer context when global and shared ranges are illegal.
- Enhanced the readability of the error output by including tensor names, improving debugging and validation processes during bulk copy operations.

* [Enhancement] Refine error messaging in LowerBulkCopy for global and shared range checks

- Improved the clarity of error messages in the LowerBulkCopy function by enhancing the output format.
- Included additional context in error messages to aid debugging when global and shared ranges are found to be illegal, ensuring better traceability during bulk copy operations.

* [Enhancement] Introduce PassConfig `TL_ENABLE_AGGRESSIVE_SHARED_MEMORY_MERGE` to enable aggressive shared memory reuse (#602)

* [Enhancement] Add aggressive shared memory merge option in memory allocation

- Introduced a new configuration option `tl.enable_aggressive_shared_memory_merge` to enable aggressive merging of shared memory allocations.
- Updated the `SharedMemLinearAccessPatternFinder` class to support an aggressive merge strategy, allowing for improved memory reuse.
- Modified the `MergeSharedMemoryAllocations` function to incorporate the new merging strategy based on the configuration.
- Enhanced the `PassConfigKey` enumeration to include the new aggressive merge option, ensuring it can be configured appropriately.

* lint fix

* [Enhancement] Add aggressive shared memory merge configuration option

- Introduced a new configuration option `kEnableAggressiveSharedMemoryMerge` to enable aggressive merging of shared memory allocations, enhancing memory management capabilities.

* [Enhancement] Update MergeSharedMemoryAllocations to support aggressive merge option

- Modified the `MergeSharedMemoryAllocations` function to accept an `enable_aggressive_merge` parameter, allowing for more flexible memory management.
- Introduced a new helper function `should_enable_aggressive_merge` to determine the aggressive merge configuration based on the pass context and target.
- Updated the relevant calls in the `phase.py` and `__init__.py` files to utilize the new aggressive merge functionality, enhancing the overall memory allocation strategy.

* [Refactor] Update accumulation handling in gemm_sm90.h (#603)

- Replaced the use of `tiled_mma.accumulate_ = GMMA::ScaleOut::Zero` with a call to `clear(acc)` for better clarity and maintainability in the accumulation logic.
- This change enhances the readability of the code by standardizing the approach to clearing accumulation values across multiple sections of the file.

* [Enhancement] Add tma bulk copy. (#600)

* [Bugfix] Fixed mha_bwd shape inconsistency error (#604)

* lint fix

* Update requirements-lint.txt to maintain clang-format version consistency

* [Bugfix] Avoid duplicate data access when cross thread buffer meet replicate register (#606)

* [Enhancement] Improve debug output formatting in layout and fragment nodes

- Updated the `DebugOutput` methods in `LayoutNode` and `FragmentNode` to provide more structured and informative output, including transformation details and thread range information.
- Enhanced layout inference logic in `ParallelOp` to add predicates for cross-thread shared memory access, improving layout handling in parallel operations.
- Minor adjustment in `layout_inference.cc` to ensure clarity in parallel loop handling.

* lint fix

* [Enhancement] Support tf32 gemm_rs (#607)

- 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.

* [Enhancement] Introduce option `TL_DISABLE_FAST_MATH` and `TL_ENABLE_PTXAS_VERBOSE_OUTPUT` (#609)

* [Enhancement] Introduce new PassConfig options for fast math and PTXAS verbosity

- Added `kDisableFastMath` and `kEnablePTXASVerboseOutput` configuration options to enhance control over compilation settings.
- Updated `LibraryGenerator` to utilize these new pass configurations, allowing for more flexible compilation behavior based on user preferences.
- Enhanced `PassConfigKey` enumeration to include the new options, ensuring they can be configured appropriately in the pass context.

* [Refactor] Update PTXAS verbosity configuration key in LibraryGenerator

- Changed the configuration key for PTXAS verbosity from `TL_VERBOSE_PTXAS_OUTPUT` to `TL_ENABLE_PTXAS_VERBOSE_OUTPUT` to align with the new naming convention introduced in recent enhancements.
- This update ensures consistency in the configuration options used within the `LibraryGenerator` class, improving clarity and maintainability of the code.

* lint fix

* fix build

* [Experimental][Language] add `T.GEMM_SP` for sm90 sparse tensor core (#526)

* [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>

* [Doc] Phaseout Legacy documentations (#610)

- Added a new entry in the README for the introduction of `T.gemm_sp` supporting 2:4 sparse tensor core.
- Removed several outdated documentation files related to convolution, flash attention, and other tutorials to streamline the documentation structure.

* [Refactor] Phaseout Pass ParallelLoopTransformer (#611)

* Refactor layout inference by removing the ParallelLoopTransformer class. Updated layout inference logic to streamline buffer access collection and condition handling in parallel loops. This change simplifies the code structure and enhances maintainability.

* Update MHA backward test cases to use reduced dimensions for batch size and context length

* fix build

* [Enhancement] Update ReduceOp initialization values for integer types (#614)

* [Enhancement] Update ReduceOp initialization values for integer types

- Modified the `MakeInitValue` method in `ReduceOp` to handle integer data types correctly by returning appropriate minimum and maximum values based on the bit width.
- Added checks for integer types to ensure correct initialization for `kMax` and `kMin` reduction types, enhancing the robustness of the reduction operations.

* [Enhancement] Update ReduceOp to handle unsigned integer initialization values

- Enhanced the `MakeInitValue` method in `ReduceOp` to include support for unsigned integer data types.
- Added conditions to return appropriate initialization values for `kMax` and `kMin` reduction types based on the data type, improving the robustness of reduction operations.

* Bump transformers from 4.50.0 to 4.51.0 in /examples/bitnet-1.58b (#615)

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>

* [Refactor] refactor autotune examples (#617)

* [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.

* lint fix

* Bump transformers from 4.51.0 to 4.52.1 in /examples/bitnet-1.58b (#619)

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>

* Fix PTXAS options flag in LibraryGenerator for consistency (#620)

* Refactor FP8 type handling across multiple files to standardize usage of "float8_e4m3" and "float8_e5m2" instead of "e4m3_float8" and "e5m2_float8". This includes updates in benchmarks, examples, tests, and internal utilities.

* [Refactor] Add parallel loop transform pass for condition extraction (#618)

* [Refactor] Add parallel loop transform

* done format check

* pull 3rdparty repo

* Refactor loop variable handling in transformation utilities

- Updated the logic in `loop_parallel_transform_utils.h` to simplify the handling of related loop variables.
- Removed the check that enforced a single related loop variable, replacing it with a return statement when multiple variables are detected, enhancing clarity and maintainability of the transformation process.

* Update loop_parallel_transform_utils.h

* Refactor loop variable handling in transformation utilities

- Enhanced the logic in `loop_parallel_transform_utils.h` to improve clarity and maintainability by simplifying the handling of related loop variables.
- Replaced the previous enforcement of a single related loop variable with a return statement for multiple variables detected.

* remove disable cache flag as commit id has been key component

* lint fix

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

* [Dev] Update linear attention examples to enhance performance on Hopper GPUs (#621)

* Tune linear attention examples on H100

* Add retnet fwd kernel

* fix lint

* [Enhancement] Add ahead of time cython compilation in setup.py (#622)

* [Enhancement] Add Cython support and compiler detection in setup.py

- Introduced a new `CythonExtension` class for building Cython-based extensions, enhancing the build process for Cython projects.
- Implemented functions to detect the Cython compiler and C++ compiler, improving compatibility and user experience.
- Updated the build process to handle Cython extensions alongside CMake extensions, ensuring a seamless integration for users.
- Added caching mechanisms for Cython compilation to optimize build times and reduce unnecessary recompilation.

* [Enhancement] Add Cython dependency and enable CMake extension building

- Added Cython as a required dependency in `pyproject.toml` to support Cython-based extensions.
- Updated `setup.py` to enable building CMake extensions, improving the build process for projects utilizing both Cython and CMake.
- Modified the Cython compiler detection logic to streamline installation instructions for users.

* [Enhancement] Support more flexible layout host pythonic expr (#623)

* [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

* [Enhancement] support composable expression for shape with symbolic vars (#624)

* [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

* minor update

* 🐍

Fix the file name "test_exmaple_tilelang_nsa" (#629)

* [Enhancement] Add CPU utilization and count settings for Auto-Tuning (#630)

* [Enhancement] Add CPU utilization and count settings for Auto-Tuning

- Introduced environment variables for CPU utilization, counts, and maximum CPU count for auto-tuning.
- Updated the AutoTuner class to utilize these new settings, improving flexibility and performance in multi-threaded environments.
- Enhanced logging to provide better insights into the auto-tuning process based on the configured CPU settings.

* typo fix

* [AutoTune] Support `with set_autotune_inputs` to set auto tuning input tensors (#632)

* [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

* [Pass] Introduce flag to diable cp async lowering (#633)

* [Enhancement] Update PipelinePlanner to support async copy configuration

- Modified the `Substitute` method in `PipelinePlanner` to accept a `use_async_copy` parameter, allowing for more flexible pipeline planning based on async copy requirements.
- Updated the constructor of `PipelinePlanner` to initialize the `use_async_copy_` member variable.
- Adjusted the logic in the pipeline planning process to conditionally apply async copy annotations based on the new parameter.
- Commented out the `LoopVectorizeDynamic` call in `LowerAndLegalize` to prevent unintended modifications during the legalizing phase.

* Refactor PipelinePlanning function for improved readability

- Adjusted the formatting of the `use_async_copy` variable assignment in the `PipelinePlanning` function to enhance code clarity and maintainability.

* fix typo (#635)

* [Pass][Simplify] Introduce symbolic level simplify for condition expression (#634)

* [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

* Enhance test coverage by adding LLVM requirement decorator to multiple function call tests. This ensures that tests for argument count, type code, null data pointer, and dimensionality checks are only executed when LLVM is available, improving test reliability and clarity.

* lint fix

* Fix software pipeline stage annotation and update optional config handling in StmtSimplifier

* Add Python executable detection in CMake configuration and update TVM submodule reference. Remove unused vectorization tests for improved clarity.

* Update TVM submodule reference and refactor FFI registration to use static initialization blocks for improved organization and clarity.

* Refactor attribute handling in layout and IR nodes to use reflection registration. This change replaces the VisitAttrs method with a RegisterReflection method for improved clarity and organization across multiple classes, including KernelLaunchFrameNode, WarpSpecializeFrameNode, LayoutNode, FragmentNode, and SwizzledLayoutNode.

* finish rebase

* tvm update

* Refactor FFI registration across tilelang modules to use the updated `tvm.ffi` namespace. This includes changes in various files to replace `tvm._ffi` with `tvm.ffi`, enhancing consistency and clarity in the codebase.

* lint fix

* Update TVM submodule reference and modify CUDA runtime argument handling to use the new runtime constants for improved clarity and consistency.

* lint fix

* Refactor tensor data type references from "e4m3_float8" and "e5m2_float8" to "float8_e4m3" and "float8_e5m2" across multiple files for consistency and clarity.

* lint fix

* Refactor forward_index initialization in Fragment class to default to an empty array instead of None, ensuring consistent handling of optional outputs.

* test fix

* lint fix

* bugfix

* lint fix

* reduce fix

* lint fix

* carver fix

* cast fix

* Update submodule and enhance kernel launch functionality with optional block size parameter; add device kernel launch transformation.

* lint fix

* bugfix

* Refactor test execution in test_tilelang_cpu_gemm.py and enhance device call checks in lower.py to exclude C packed functions from kernel launch conditions.

* lint fix

* Update runtime.cc

* phase out lisence

* Update subproject commit for TVM to 555cc71

* Update subproject commit for TVM to d39953fa

* Update subproject commit for TVM to 9574805f

* Update subproject commit for TVM to a08b7c3

* fix ci

* ci fix

---------
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>
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Co-authored-by: Yuxi Chi <cherichy@outlook.com>
Co-authored-by: Nathan Chen <120630832+Nathancgy@users.noreply.github.com>
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* [Enhancement] Update AllReduce operation to include thread offset in kernel generation

- Modified the `ReduceOp::Lower` method to incorporate the thread offset in the AllReduce kernel generation for the sm_90 architecture.
- This change improves the accuracy of thread management during reduction operations, enhancing performance on specific GPU architectures.

* [Enhancement] Refactor thread offset handling in AllReduce kernel generation

- Updated the `ReduceOp::Lower` method to streamline the handling of thread offset for AllReduce operations, ensuring consistent usage across different architectures.
- This change enhances code clarity and maintains performance improvements for the sm_90 architecture by reducing redundancy in thread offset calculations.

* [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

* [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

* [Enhancement] Update ReduceOp initialization values for integer types

- Modified the `MakeInitValue` method in `ReduceOp` to handle integer data types correctly by returning appropriate minimum and maximum values based on the bit width.
- Added checks for integer types to ensure correct initialization for `kMax` and `kMin` reduction types, enhancing the robustness of the reduction operations.

* [Enhancement] Update ReduceOp to handle unsigned integer initialization values

- Enhanced the `MakeInitValue` method in `ReduceOp` to include support for unsigned integer data types.
- Added conditions to return appropriate initialization values for `kMax` and `kMin` reduction types based on the data type, improving the robustness of reduction operations.

[Enhancement] Enhance ReduceOp and JITKernel for improved dimension handling and initialization (#507)

* [Refactor] Update reduce functions to support default dimension values and improve dimension handling

* Added a helper function `_legalize_dim` to handle negative dimension values in reduction operations.
* Updated `reduce_max`, `reduce_min`, `reduce_sum`, `reduce_abssum`, and `reduce_absmax` functions to accept a default dimension value of -1, enhancing usability and flexibility in buffer reduction operations.
* Ensured consistent dimension handling across all reduction functions for improved clarity and correctness.

* Update submodule `tvm` to latest commit c2921fd, ensuring compatibility with recent changes.

* [Refactor] Enhance ReduceOp and JITKernel for improved dimension handling and initialization

* Updated ReduceOp to handle 1D reduction cases and ensure correct dimension checks, improving robustness in reduction operations.
* Initialized prim_func in JITKernel to enhance clarity and prevent potential null reference issues.
* Added whitespace for better code readability in reduce.py.

* [Refactor] Simplify buffer_region_to_tile_region function in copy.py

* Removed redundant logic for handling region extents in the buffer_region_to_tile_region function, streamlining the code for better readability and maintainability.
* Enhanced error handling by focusing on essential checks while eliminating unnecessary complexity related to variable extents.

* [Refactor] Improve layout equality checks and error messaging

* Updated the `IsEqual` method in `FragmentNode` to ensure consistent evaluation of thread ranges.
* Enhanced error messaging in `ParallelOp::InferLayout` to include source buffer information for better debugging.
* Adjusted `ReduceOp::InferLayout` to set thread range during layout condensation, improving layout inference accuracy.

* lintfix

* [Refactor] Rename SetThreadRange to BindThreadRange for clarity

* Updated the `SetThreadRange` method in `FragmentNode` and related classes to `BindThreadRange`, improving method naming consistency and clarity.
* Adjusted all references to the renamed method across the codebase, ensuring proper functionality and maintaining existing behavior.
* Enhanced layout equality checks to handle thread ranges more robustly in `IsEqual` method.
* Updated layout inference methods in `Gemm`, `ParallelOp`, and `ReduceOp` to utilize the new method name, ensuring seamless integration with the updated API.

* [Refactor] Update BindThreadRange usage across layout inference methods

* Modified the implementation of `BindThreadRange` in `FragmentNode` to create a new object instance, enhancing thread range binding functionality.
* Updated all references to `BindThreadRange` in layout inference methods across `Gemm`, `ParallelOp`, and `ReduceOp` to ensure consistency with the new implementation.
* Adjusted the return statements in various layout inference functions to utilize the updated method, maintaining existing behavior while improving clarity.

* lint 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.

* [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

* [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.

* [Dev] Add FP8 Quantization Examples and Absolute Maximum Reduction Operation Support

* Added `example_per_token_cast_to_fp8.py` in examples/cast, providing token-wise FP8 quantization implementation.
* Added `example_triton_cast_to_fp8.py` in examples/cast, providing Triton-based FP8 quantization implementation.
* Added support for absolute maximum (absmax) reduction operation in reduce.cc and reduce.h.
* Implemented `reduce_absmax` function in reduce.py, allowing absolute maximum reduction on input buffers.
* Updated tilelang.language module to include the new `reduce_absmax` function.

These changes enhance FP8 quantization capabilities and extend reduction operation support.

* [Enhancement] Update per_token_cast_to_fp8 for improved FP8 quantization

* Modified the `per_token_cast_to_fp8` function to support variable block sizes and improved memory layout annotations.
* Adjusted the handling of absolute maximum values and scaling factors for better performance and accuracy.
* Updated the main execution block to allow for larger matrix dimensions and refined the profiler setup for benchmarking.

These changes enhance the flexibility and efficiency of the FP8 quantization process.

* lint

* [Dev] Update per_token_cast_fp8.py

* [Bugfix] Fixed the handling logic of IfThenElseNode in if_stmt_binding

* [Bugfix] Fix logic error in ReduceOp when handling CUDA architecture

- Added a check for the existence of the target attribute "arch" to ensure that there is no undefined behavior when handling the specific architecture "sm_90". This change improves the robustness and compatibility of the code.

* remove llvm build

* [Refactor] Update kernel compilation and profiling in examples

- Replaced `tilelang.lower` with `tilelang.compile` in multiple example scripts to streamline kernel compilation.
- Updated profiling calls to utilize the new `get_profiler` method, enhancing performance measurement consistency.
- Adjusted assertions and benchmarking methods to align with the new profiling structure across various examples, ensuring correctness and clarity in performance evaluations.

* lint fix

* License Update

* [Refactor] Improve code formatting and documentation in CUDA header and HIP runtime files

- Adjusted formatting in `cuda.h` for better readability, including alignment of comments and struct fields.
- Cleaned up whitespace and improved comment clarity in `rt_mod_hip.cc` to enhance code maintainability.

* [Refactor] Enhance formatting and clarity in CUDA header and HIP runtime files

- Improved comment alignment and readability in `cuda.h`.
- Cleaned up whitespace and formatting in `rt_mod_hip.cc` to enhance maintainability.

* lint fix

* lint fix

* lint fix

* lint fix

* fix

* License update

* [Enhancement] Update JITKernel to use artifact for kernel source

- Assigned the generated artifact to `self.artifact` for better management.
- Updated kernel source references to use `artifact.kernel_source` for consistency in execution backend handling.

* lint fix

* Add @tilelang.testing.requires_llvm decorator to vectorization tests

* Enhance setup.py and env.py for library management

- Added functionality to remove original files after copying in CMakeBuild.
- Updated TVM_LIBRARY_PATH in env.py to include the PyPI build library path for better integration.

* Refactor TVM_LIBRARY_PATH assignment for improved readability in env.py

* Refactor CMakeBuild file handling in setup.py

- Added a check to ensure the target library directory exists before copying .so files.
- Improved the logic for creating the target directory and copying files to enhance robustness.

* bugfix

* Rename BuildTLDebug to BuildTileLangCUDAWithoutCompile and update registration. Add @tilelang.testing.requires_llvm decorator to multiple tests for LLVM requirement.

* lint fix

* Enhance TileLang code generation by adding support for device code generation without compilation. Updated `host_codegen` and `device_codegen` functions to include new transformations and registration for `tilelang_hip_without_compile`. Refactored JIT kernel adapters to accommodate host and device modules, improving overall integration and flexibility.

* lint fix

* Add support for C target in device code generation

- Updated `device_codegen_without_compile` to include handling for the C target by registering the `tilelang_cpp` function.

* [Enhancement] Implement auto-clear cache feature based on environment variable

* Added TILELANG_CLEAR_CACHE environment variable to control cache clearing.
* Updated CI workflow to set TILELANG_CLEAR_CACHE during testing.
* Modified cache initialization to clear cache if TILELANG_CLEAR_CACHE is set to true.

* [Refactor] Update kernel invocation and import paths in tests and cache

* Changed kernel invocation in `test_tilelang_kernel_dequantize_gemm.py` to return the result.
* Updated import statements in `test_tilelang_kernel_int4_gemm_mma.py` to use `bitblas` instead of `tilelang`.
* Refactored paths for artifact and parameters in `kernel_cache.py` for better maintainability.

* [Refactor] Clean up whitespace and improve code formatting in kernel_cache.py

* Removed unnecessary blank lines and adjusted spacing for better readability in the KernelCache class.
* Enhanced overall code formatting to align with project standards.

* [Enhancement] Add bfloat16 test case and improve kernel caching logic

* Introduced a new test case for bfloat16 matrix multiplication in `test_tilelang_kernel_gemm_mma_intrinsic.py`.
* Updated `KernelCache` to handle multiple kernel source files and improve error handling during saving and loading.
* Refactored `JITKernel` to support instantiation from a database, enhancing flexibility in kernel management.
* Adjusted `CtypesKernelAdapter` and `CythonKernelAdapter` to utilize the new kernel loading mechanism from the database.
* Improved code formatting and readability across several files.

* lint fix

* Update bfloat16 matrix multiplication test case to use larger dimensions for improved coverage

[Dev] Adjust computation logic to avoid precision loss when casting acc_s from float to float16 (#141)

- Remove redundant `acc_s_0` fragment in flash attention kernel
- Simplify memory copy and reduction operations
- Reorder memory copy and scaling steps for improved performance
- Add Hopper-specific synchronization method in CUDA reduce template
- Update reduce operation to use architecture-specific synchronization

* README.md fixed

* update test ci

* Lint and Typo Fix

* Clang Format Lint Fix

* Add format.sh script for code formatting and linting

* docs update

* center align the title

* lint fix

* add ignore

* Add .gitignore for 3rdparty directory

* Add requirements-dev.txt, requirements-test.txt, and requirements.txt

* 3rdparty

* Add gemm.h, CMakeLists.txt, _ffi_api.py, __init__.py, runtime.h, reduce.h, loop_partition.h, utils.h, and loop_vectorize.h

* Refactor CMakeLists.txt and include statements

- Update CMakeLists.txt to use a newer version of CMake and add project name
- Remove unnecessary include directories

Fix include paths in layout.cc, codegen.cc, codegen.h, rt_mod.cc, frontend_legalize.cc, inject_pipeline.cc, layout_inference.cc, loop_vectorize.cc, and lower_tile_op.cc

- Update include paths to use relative paths instead of absolute paths

* Update submodule for 3rdparty/tvm

* update

* load dll first

* Refactor CMakeLists.txt and include statements

* Refactor CMakeLists.txt and include statements

* git keep update

* Refactor CMakeLists.txt and include statements

* Refactor CMakeLists.txt and include statements

* refactor code structure

* Update Readme

* CMakeLists Customized

* update readme

* update README

* update readme

* update usage

* with TVM_IMPORT_PYTHON_PATH to handle own tvm build python import

* annotate lower transform global func with `transform` prefix

* Migrate Simplify Pass from tilelang tvm branch

* enhance system environment handling with __init__ and CMake

* Initial commit

* CODE_OF_CONDUCT.md committed

* LICENSE committed

* README.md committed

* SECURITY.md committed

* SUPPORT.md committed

* CODE_OF_CONDUCT Commit

* LICENSE Commit

* SECURITY Commit

* SUPPORT Commit

* Modify Support

* Update README.md

* security ci update

* remove examples

* Update and implement clang-format

* add composable kernel components

* Migrate from latest update

* submodule update

* Test update

* Update License

* Spell check

* lint fix

* add clang-tidy to apply static analysis for c source

* update tilelang examples

* Update Install Docs

* Refactor filetree

* Enhance Install

* conflict resloved

* annotate_version

* Initial Update

* test fix

* install

* Implement setup.py

* lint fix

* Separate Init

* Separate test

* docker file commit

* add logo

* Update Readme and Examples

* update readme

* update logo

* Implement AMD Installation

* Add License

* Update AMD MI300x Benchmark

* update README

* update mi300 benchmark scripts

* update ignore

* enhance build scirpt

* update image

* enhance setup.py to remove duplicated libraries

* remove debug files

* update readme

* update image

* update gemm examples

* update flashattention README

* readme update

* add cmake into requirements

* libinfo fix

* auto update submodule

* lint fix

* Fix AMD Build and Test

* Update check for transpose attribute for CDNA Arch

* typo fix for amd

* Implement Matmul Benchmark

* Refactor Code

* [TypoFix] Fix GEMM Example

* [Docs] Init Linear Attention README

* [TYPO] Typo fix

* [Lint] Lint Fix

* enhance example with intrinsics

* [Enhancement] Improve Buffer Collection during IR Parser

* [Dev] Introduce Current classmethod to get current frame

* submodule update

* fake test pass update

* support thread_extent_api

* code optimize

* Add GEMM function implementation for matrix multiplication

* Update logging format to reflect TileLang in logger messages

* Refactor CMakeLists.txt for improved readability and set default build type to Release

* Support Gemm SS Primitives Implementation

* [README] Upload Tile Language Logo (#5)

* update logo

* Update README.md to enhance formatting and center the title

---------
Co-authored-by: microsoft-github-operations[bot] <55726097+microsoft-github-operations[bot]@users.noreply.github.com>
Co-authored-by: Microsoft Open Source <microsoftopensource@users.noreply.github.com>
Co-authored-by: Yu Cheng <yu.cheng@pku.edu.cn>