GitLab

* [BugFix] Correct direct copy from bf16 to fp8

* fix lint

* implement overloaded cast codegen for type conversion

* fix lint

* remove test

* fix lint

* trigger CI

* Overload fp8 for implicit conversion

* format

* new format

* fix: Reinterpret types to cute types in GEMM

* new format

* fix lint

* new format

* fix lint

* format

* trigger ci

---------
Co-authored-by: nicunxiao <nicunxiao@bytedance.com>

* Remove an incorrect check

* add fp8 pack function

* code lint

* minor fix

* minor fix

* minor fix

* Minor fix

* Minor fix

* update sm100 related utcmma, tmem, ld/st256 in src
* update sm100 related utcmma, tmem, ld/st256 in tilelang
* Remove deprecated GEMM examples and related README documentation for SM100 architecture support
* Update GEMM implementation to replace UTCMMA with TCGEN5MMA across relevant files
* Remove gemm_umma.py example and update README to reflect TCGEN5MMA terminology changes
* Update README.md for gemm_sm100 example by removing outdated API sections and streamlining documentation
* Update README and source files to reflect TCGEN5.MMA terminology changes
* Refactor CUDA GEMM header for improved readability

* [Refactor] Enhance GEMM Warp Partitioning Logic and Introduce Buffer Remapping (#516)

* Improved the warp partitioning logic in `Gemm::ComputeWarpPartition` to better accommodate various GEMM policies, including FullRow, FullCol, and Square, ensuring optimal performance based on matrix dimensions.
* Introduced a new `RemapBufferRewriter` class to handle buffer reference updates and padding annotations during statement transformations, enhancing memory access safety and clarity.
* Updated the `OptimizeForTarget` function to include a new step for configuring index bitwidth, improving the overall optimization process.
* Refactored existing code to utilize constants for warp sizes, enhancing maintainability and readability.
* Added checks to ensure correct warp allocation and padding map handling, improving robustness in memory management strategies.

* [Refactor] Update ConfigIndexBitwidthRewriter to Support Auto-Check Feature

* Modified the constructor of `ConfigIndexBitwidthRewriter` to include an `auto_check` parameter, allowing for dynamic bitwidth adjustments based on input conditions.
* Enhanced the `VisitExpr_` methods to apply the new auto-check logic, ensuring that integer types are upgraded to 64 bits when necessary, or to a specified index bitwidth otherwise.
* Updated the `ConfigIndexBitwidth` pass to determine the index bitwidth based on the presence of configuration, improving flexibility in handling different scenarios.

* Add dynamic matrix multiplication example and corresponding test

* Introduced `example_dynamic.py` to demonstrate dynamic matrix multiplication using TileLang and PyTorch, including a main function for execution and performance profiling.
* Added `test_example_dynamic.py` to validate the functionality of the dynamic matrix multiplication example.
* The example includes detailed parameter configurations and checks against PyTorch's implementation for correctness.

* lint fix

* Add get_num_sms function to retrieve the number of streaming multiprocessors on the CUDA device

* Implemented the `get_num_sms` function in `cuda_driver.py` to return the count of streaming multiprocessors for a specified CUDA device.
* Updated the `__init__.py` file to include the new function in the module exports.

* lint fix

* Add global barrier state and expectation handling in CUDA code generation

* Introduced `vid_global_barrier_state_` and `vid_global_barrier_expect_` to manage global barrier synchronization in the CUDA code generator.
* Updated `Finish` method to declare the global barrier state if needed.
* Implemented handling for `EvaluateNode` to initialize the barrier expectation.
* Removed unnecessary extern declaration for the global barrier state in `PrintStorageSync` method.
* Enhanced CUDA FP8 type definitions for better alignment and structure.

* Enhance CUDA FP8 type handling and debug printing

* Updated `cuda_fp8.h` to replace NVidia's FP8 types with Cute's FP8 types for better compatibility and structure.
* Added specializations for `debug_print_var` and `debug_print_buffer_value` functions to support the new FP8 types, improving debugging capabilities for these data types.
* Updated `debug.h` to include the new `cuda_fp8.h` header for access to the FP8 type definitions.

* Refactor CUDA code generation to remove unnecessary managed qualifier for global barrier state

* Updated the `Finish` method in `codegen_cuda.cc` to declare the global barrier state without the `__managed__` qualifier, simplifying the declaration.
* Added a new `sync_global` function in `builtin.py` to synchronize all threads in a block, enhancing synchronization capabilities in the TileLang framework.

* Remove deprecated CUDA kernel and Python script for FP8 E4M3 casting

* Deleted the `cast_to_fp8_e4m3_kernel` CUDA kernel implementation and its corresponding Python script, streamlining the codebase by removing unused components related to FP8 E4M3 type casting.
* This cleanup enhances maintainability and reduces potential confusion regarding obsolete code.

* lint fix

* [Refactor] Enhance GEMM Warp Partitioning Logic and Introduce Buffer Remapping (#516)

* Improved the warp partitioning logic in `Gemm::ComputeWarpPartition` to better accommodate various GEMM policies, including FullRow, FullCol, and Square, ensuring optimal performance based on matrix dimensions.
* Introduced a new `RemapBufferRewriter` class to handle buffer reference updates and padding annotations during statement transformations, enhancing memory access safety and clarity.
* Updated the `OptimizeForTarget` function to include a new step for configuring index bitwidth, improving the overall optimization process.
* Refactored existing code to utilize constants for warp sizes, enhancing maintainability and readability.
* Added checks to ensure correct warp allocation and padding map handling, improving robustness in memory management strategies.

* [Refactor] Update ConfigIndexBitwidthRewriter to Support Auto-Check Feature

* Modified the constructor of `ConfigIndexBitwidthRewriter` to include an `auto_check` parameter, allowing for dynamic bitwidth adjustments based on input conditions.
* Enhanced the `VisitExpr_` methods to apply the new auto-check logic, ensuring that integer types are upgraded to 64 bits when necessary, or to a specified index bitwidth otherwise.
* Updated the `ConfigIndexBitwidth` pass to determine the index bitwidth based on the presence of configuration, improving flexibility in handling different scenarios.

* Add dynamic matrix multiplication example and corresponding test

* Introduced `example_dynamic.py` to demonstrate dynamic matrix multiplication using TileLang and PyTorch, including a main function for execution and performance profiling.
* Added `test_example_dynamic.py` to validate the functionality of the dynamic matrix multiplication example.
* The example includes detailed parameter configurations and checks against PyTorch's implementation for correctness.

* lint fix

* Add get_num_sms function to retrieve the number of streaming multiprocessors on the CUDA device

* Implemented the `get_num_sms` function in `cuda_driver.py` to return the count of streaming multiprocessors for a specified CUDA device.
* Updated the `__init__.py` file to include the new function in the module exports.

* lint fix

* Add global barrier state and expectation handling in CUDA code generation

* Introduced `vid_global_barrier_state_` and `vid_global_barrier_expect_` to manage global barrier synchronization in the CUDA code generator.
* Updated `Finish` method to declare the global barrier state if needed.
* Implemented handling for `EvaluateNode` to initialize the barrier expectation.
* Removed unnecessary extern declaration for the global barrier state in `PrintStorageSync` method.
* Enhanced CUDA FP8 type definitions for better alignment and structure.

* 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

* upgrade cutlass to upstream v3.8.0

* Implement fp8 gemm and add example script

* Fix dtype retrieval with map_torch_type for fp8 inputs

* Disable vectorization of fp8 values

* Make MMA declaration compatible with cutlass 3.4.0+

* Add test for fp8 T.gemm

* fix indent

* fix indent

* Add copyright and license header

* Add copyright and license header

* lint fix

* Refactor matmul_nt and assert_matmul_correctness functions for improved readability by consolidating parameter definitions and adjusting formatting.

* clang format lint

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

* [Enhancement] Add VectorizeLoop function and update imports for compatibility

* [CI][Test] Improve test cases for vectorization and fix typos in parser comments

* lint fix

* Fix incorrect module reference for VectorizeLoop transformation

* Refactor vectorize_loop transformation by removing unused extent mutation logic

* [Enhancement] Add support for FP8 data types and global barriers in CUDA codegen

* Fix formatting in CUDA FP8 header file for consistency

* Refactor CI workflow to use 'tilelang_ci' virtual environment and update CUDA type printing for better clarity

* Update submodule 'tvm' to latest commit for improved functionality

* Refactor execution backend references from 'dl_pack' to 'dlpack' for consistency and clarity; add apply_simplify function to simplify PrimFunc or IRModule.

* Refactor CUDA code for improved readability; clean up formatting and remove unnecessary whitespace in multiple files.

* Refactor import statement in test_tilelang_kernel_dequantize_gemm.py to use 'tilelang.language' for consistency

* Add CUDA requirements to FP8 test cases and update references for clarity

* Add a blank line for improved readability in test_tilelang_kernel_fp8_gemm_mma.py

* Fix data type in reference result calculation for consistency in test_tilelang_kernel_gemm_mma_intrinsic.py

* Add CUDA requirements and FP8 test cases for matmul and gemv simulations

* Remove debug print statements and use tilelang's testing assertion for result validation in test_tilelang_kernel_gemm_mma_intrinsic.py

* Remove outdated comment regarding FP8 tests in test_tilelang_kernel_gemv_simt.py