GitLab

* add autotune to example_gemm.py

* add autotune to example_gemm.py

* add autotune to example_gemm.py

* add autotune to example_gemm.py

* [Enhancement] Add matrix multiplication functions for integer and float variables in Cython JIT

- Introduced `matmul_int_variable` and `matmul_float_variable` functions to support matrix multiplication with dynamic shapes and additional parameters.
- Implemented corresponding `run_matmul_int_variable` and `run_matmul_float_variable` functions for testing.
- Updated test cases to validate the new matrix multiplication implementations.
- Enhanced error handling in library initialization and compilation processes across various modules.
- Improved dynamic memory handling in CUDA kernel initialization to provide better error reporting.

* lint fix

* optimize

* 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

* [Typo] Fix formatting in installation instructions in README.md

* [Enhancement] Improve CUDA path detection and update configuration handling

* fix typo

* remove IS_WINDOWS constant

* lint fix

* Improve error messages for CUDA detection failure

* lint fix

* lint fix

* Fix .gitignore to correctly include venv directory

* [Doc] Add instructions for installing nightly version of TileLang

* update installation instructions

* update install instruction

* fix bug of mismatching dtype in testing and set the default value of check_dtype in torch_assert_close to true

* lint fix

* fix bug

* use map_torch_type

* [Refactor] Update KernelParam integration across modules

- Replaced instances of TensorType with KernelParam in various modules to standardize parameter handling.
- Updated JITKernel, BaseKernelAdapter, and CythonKernelAdapter to utilize KernelParam for improved type consistency.
- Enhanced Profiler class to include KernelParam in its parameters, ensuring better integration with the new parameter structure.
- Adjusted tensor handling in utility functions to accommodate the new KernelParam type, improving overall code clarity and maintainability.
- Updated copyright headers to reflect the correct organization.

* [Refactor] Clean up whitespace in kernel, profiler, and tensor modules

- Added blank lines for improved readability in kernel.py, __init__.py, and tensor.py.
- Enhanced code clarity by ensuring consistent formatting across these modules.

* [Enhancement] Add detailed docstrings to KernelParam and Profiler classes

- Enhanced KernelParam class with comprehensive docstrings for better understanding of its purpose and methods.
- Updated Profiler class to include detailed docstrings for its attributes and methods, improving code documentation and usability.
- Removed unused do_bench function to streamline the profiler module and improve clarity.

* [Refactor] Update type hints in do_bench function and clean up whitespace in profiler module

- Changed type hints for grad_to_none and quantiles parameters in do_bench function to use Optional for better clarity.
- Added a blank line in __init__.py for improved readability and consistency in the profiler module.

* [Refactor] Update type hint in do_bench function for consistency

- Changed the return type hint in the do_bench function from a union type to a more explicit List type for better clarity and consistency in type annotations.

* [Refactor] Update return type hint in do_bench function for clarity

- Changed the return type hint in the do_bench function from a union type to Union[float, List[float]] for improved clarity and consistency in type annotations.

* [Enhancement] Add func property to Profiler class for adapter access

- Introduced a new property `func` in the Profiler class to provide access to the adapter, ensuring that the adapter is set before retrieval. This enhancement improves the usability of the Profiler class by allowing easier access to the adapter functionality.

* [Refactor] Update kernel compilation and profiling in tests

- Replaced instances of `TL.lower` and `TL.Profiler` with `tilelang.compile` and the new profiler interface across multiple test files.
- Enhanced the kernel compilation process to utilize the updated API, improving consistency and maintainability in the testing framework.
- Updated assertions to use the new profiler methods for better clarity and functionality in performance testing.

* [Refactor] Simplify kernel invocation and remove unused parameters in tests

- Updated the kernel invocation in `test_tilelang_dynamic_symbolic.py` to directly assign the result to `C`, improving clarity.
- Removed the `execution_backend` parameter from `tilelang.compile` calls in `test_tilelang_jit_callback.py` and `test_tilelang_jit_gemm.py` for consistency with the updated API.
- Commented out the call to `tilelang.testing.main()` in `test_tilelang_jit_callback.py` and replaced it with a direct call to `test_gemm_jit_kernel()` to streamline test execution.
- Adjusted the dtype mapping in `TorchDLPackKernelAdapter` to use the parameter's dtype directly, enhancing code simplicity.

* [Refactor] Remove unused imports in test files for cleaner code

- Eliminated unnecessary imports of `tilelang` as `TL` in various test files to enhance code clarity and maintainability.
- Updated multiple test files to streamline the codebase and reduce potential confusion from unused references.

* [Refactor] Simplify kernel invocation in tilelang kernel test

- Updated the kernel invocation in `test_tilelang_kernel_bf16_gemm_mma.py` to directly assign the result to `C`, enhancing code clarity and consistency with recent changes in the API.

* [Refactor] Simplify kernel invocation in tilelang kernel tests

- Updated kernel invocations in multiple test files to directly assign the result to `C`, improving code clarity and consistency with the updated API.
- Removed unnecessary initialization of `C` as a zero tensor, streamlining the code further.

* [Refactor] Update kernel invocation in tilelang transform tests

- Replaced the use of `TL.Profiler` with `tilelang.compile` in `test_tilelang_transform_simplify.py`, enhancing code clarity and consistency with the updated API.
- Streamlined the kernel invocation process by directly assigning the result to `C`, improving readability and maintainability of the test code.

* Optimize CMake build process with dynamic job count calculation

- Modify build_csrc function to use 90% of available CPU cores
- Ensure at least one job is used during compilation
- Improve build performance by dynamically adjusting parallel job count

* Optimize build_csrc function with multiprocessing module

- Replace os.cpu_count() with multiprocessing.cpu_count()
- Maintain existing 90% CPU utilization logic
- Improve CPU core count calculation for build process

* Add dynamic shape support with out_idx in Cython JIT kernel compilation

- Implement `run_cython_dynamic_shape_with_out_idx` function in test_tilelang_jit_gemm_cython.py
- Update Cython wrapper to handle dynamic symbolic shapes during tensor allocation
- Add support for resolving dynamic shape dimensions using input tensor references
- Enhance flexibility of JIT kernel compilation with symbolic shape handling

* Enhance error reporting for dynamic symbolic shape resolution in Cython JIT kernel

- Add detailed error message when a dynamic symbolic dimension is not found in dynamic_symbolic_map
- Improve debugging by providing context about missing symbolic dimensions
- Maintain existing dynamic shape resolution logic

* [Feature] Add CTypes JIT kernel support for dynamic shapes and multi-stream execution

- Enhance CtypesKernelAdapter to handle dynamic symbolic shapes
- Add support for multi-stream kernel execution in CTypes backend
- Implement dynamic shape handling in test_tilelang_jit_gemm_ctypes.py
- Add symbolic shape utility function in tilelang.language
- Update profiler to improve flexibility in benchmark selection

* Remove redundant thread binding in GEMM kernel implementations

- Remove unnecessary `thread_binding` line in GEMM kernel functions
- Clean up code in `examples/gemm/README.md` and `testing/python/kernel/test_tilelang_kernel_int4_gemm_mma.py`
- Enhance code readability by removing redundant thread binding annotation

* Fix indentation in int4 GEMM kernel test file

- Correct indentation for function calls in `test_tilelang_kernel_int4_gemm_mma.py`
- Remove extra indentation in `mma_emitter.ldmatrix_a()` and `mma_emitter.ldmatrix_b()` calls
- Improve code formatting for better readability

* [Feature] Add Cython JIT kernel support for dynamic shapes and multi-stream execution

- Implement CythonKernelAdapter to handle dynamic symbolic shapes
- Add support for multi-stream kernel execution in Cython backend
- Create comprehensive test suite for Cython GEMM kernel in test_tilelang_jit_gemm_cython.py
- Update JITKernel to include "cython" as a valid execution backend
- Add Cython-specific wrapper and library generation modules
- Update .gitignore to exclude Cython cache directory
- Modify setup.py to include Cython source files in package data

* lint fix

* [Refactor] Replace JITKernel with compile() function for kernel compilation

- Add new `compile()` function in tilelang/jit/__init__.py as a wrapper for JITKernel
- Update multiple test files and examples to use `tilelang.compile()` instead of `tilelang.JITKernel()`
- Modify kernel adapters to support optional kernel-only source retrieval
- Update `__init__.py` to import the new `compile()` function
- Improve kernel source retrieval for different execution backends

* lint fix

* remove debug print

* Add C/C++ compiler utility module and update Cython JIT kernel support

- Introduce new `tilelang/contrib/cc.py` module with cross-platform C/C++ compiler utilities
- Add functions to detect and retrieve system C/C++ compilers
- Implement cross-compilation and shared library creation support
- Update Cython JIT kernel to validate C++ compiler availability
- Modify Cython adapter to use detected C++ compiler for library generation

* Refactor float8 dtype mapping in tensor utility module

- Move float8_dtype_map inside adapt_torch2tvm function
- Simplify global scope by localizing the dtype mapping
- Maintain existing functionality for converting torch float8 tensors to TVM ndarray

* Refactor float8 dtype mapping in tensor utility module

- Move float8_dtype_map inside adapt_torch2tvm function
- Simplify global scope by localizing the dtype mapping
- Maintain existing functionality for converting torch float8 tensors to TVM ndarray

* revert

* Enhance Cython JIT adapter with Cython compiler detection

- Add `get_cython_compiler()` function to dynamically locate Cython executable
- Update Cython adapter to use detected Cython compiler instead of hardcoded command
- Raise an exception if no Cython compiler is found
- Update requirements.txt to specify minimum PyTorch version (>=2.2.0)

* Fix Cython kernel wrapper stream handling and type annotations

- Update stream parameter type to int64_t for better compatibility
- Directly use torch.cuda.current_stream().cuda_stream instead of casting
- Improve type safety and precision in Cython kernel wrapper

* [Feature] Add CTypes JIT kernel support for dynamic shapes and multi-stream execution

- Enhance CtypesKernelAdapter to handle dynamic symbolic shapes
- Add support for multi-stream kernel execution in CTypes backend
- Implement dynamic shape handling in test_tilelang_jit_gemm_ctypes.py
- Add symbolic shape utility function in tilelang.language
- Update profiler to improve flexibility in benchmark selection

* Remove redundant thread binding in GEMM kernel implementations

- Remove unnecessary `thread_binding` line in GEMM kernel functions
- Clean up code in `examples/gemm/README.md` and `testing/python/kernel/test_tilelang_kernel_int4_gemm_mma.py`
- Enhance code readability by removing redundant thread binding annotation

* Fix indentation in int4 GEMM kernel test file

- Correct indentation for function calls in `test_tilelang_kernel_int4_gemm_mma.py`
- Remove extra indentation in `mma_emitter.ldmatrix_a()` and `mma_emitter.ldmatrix_b()` calls
- Improve code formatting for better readability

* bump version into v0.1.0

* [Enhancement] Add custom develop command for editable installs and update .gitignore

* [Documentation] Update README to include system dependencies installation instructions

* [Build] Update setup.py to support library file copying for both release and develop modes

* [Build] Refactor library file copying logic in setup.py

* [Documentation] Remove unnecessary install section header in Installation.md

* [Build] Add tox configuration and local distribution script for multi-Python version support

* [Build] Improve git submodule update function with better error handling

* [Build] Update LLVM configuration path in ROCm installation script

* [Build] Add .tox/ to .gitignore for tox testing environment

* [Build] Add support for TVM prebuild path configuration in CMakeLists.txt

* [Cleanup] Remove unused TVM runtime error codes header

* [Cleanup] Fix TVM grid constant type reference in CUDA module

* [Cleanup] Remove unused customized_code function from IR module

* [Feature] Add TileLang thread synchronization and storage access analysis passes

* [Build] Reorder DLL search path directories for more flexible library loading

* [Refactor] Improve thread synchronization and library path handling

- Rename ThreadSync and TileLangThreadSync functions in C++ code
- Update Python docstring for ThreadSync with more detailed description
- Reorder library path detection in tilelang environment setup
- Minor comment and code cleanup in CUDA and warp specialization modules

* [Refactor] Improve thread synchronization code style and formatting

- Standardize pointer type spacing in storage_access.h and storage_access.cc
- Update whitespace and indentation in thread_storage_sync.cc
- Reorder include statements in thread_partial_sync.cc
- Minor code formatting improvements across thread synchronization files

* [Refactor] Fix global function registration for ThreadSync

- Correct global function registration to use ThreadSync instead of TileLangThreadSync
- Update TVM global registration to match recent refactoring efforts

* [Refactor] Simplify ThreadSync global function registration

- Remove unnecessary whitespace in global function registration
- Compact the TVM global registration line for ThreadSync

* [Feature] Add WebGPU code generation support in TileLang

- Implement WebGPU code generator (codegen_webgpu.cc and codegen_webgpu.h)
- Add WebGPU target support in lower.py and target.py
- Update CMakeLists.txt to include WebGPU codegen source files
- Introduce WebGPU-specific code generation for WGSL shader language

* [Refactor] Improve WebGPU code generation formatting and readability

- Enhance code formatting in codegen_webgpu.cc and codegen_webgpu.h
- Standardize pointer type spacing and indentation
- Improve line breaks and reduce line length for better readability
- Minor code style improvements in WebGPU code generation

* [Test] Add WebGPU matrix multiplication code generation test

- Implement test_webgpu_codegen.py for WebGPU matrix multiplication
- Add assert_gemm_codegen function to validate WebGPU code generation
- Include basic matrix multiplication kernel test case

* Update README with WebGPU codegen support announcement

* Support multi version pypi package build via tox

* Add support for CPU device backend with C code generation

- Introduce `is_cpu_device_backend` function to detect CPU backend with C code generation
- Modify `lower` function to handle special case of CPU device backend
- Update host and device call filtering for CPU backend
- Add conditional source code generation for C host target
- Extend JITKernel to support optional target_host parameter

* lint fix

* Enhance JIT kernel adapters with CTypes and Torch C++ backends

- Add CtypesKernelAdapter with dynamic library generation and kernel wrapping
- Implement TorchCPPKernelAdapter for CUDA kernel compilation
- Refactor BaseKernelAdapter to support more flexible initialization
- Improve error handling and argument processing in kernel adapters
- Update adapter initialization to support various execution backends

* Refactor and clean up code style in JIT CTypes adapter modules

- Apply consistent code formatting and whitespace in CTypes adapter files
- Remove unused imports and improve import organization
- Enhance readability of code in adapter, libgen, and wrapper modules
- Add missing whitespace and improve line breaks
- Minor linting and code style improvements across CTypes adapter files

* Add test for TileLang JIT GEMM with CTypes backend

- Implement comprehensive test for matrix multiplication using CTypes execution backend
- Create test functions for GEMM with float16 data type
- Add kernel source verification with custom callback
- Implement reference implementation using PyTorch for result validation
- Support various matrix multiplication configurations (transposition, block sizes)

* test fix

* Update TileLang JIT callback registration with override parameter

- Modify tilelang_callback_cuda_postproc to use @tvm.register_func(override=True)
- Ensure proper function registration with ability to replace existing implementations

* Reorder TileLang lowering passes for Hopper intrinsics and PTX async copy

- Adjust the order of LowerHopperIntrin and InjectPTXAsyncCopy passes
- Move these passes to ensure correct synchronization and device preparation

* Rebase main

* shared.dyn

* lint fix

* test fix

* Add environment variable handling for TileLang template and CUTLASS paths

- Introduce fallback logic for TL_TEMPLATE_PATH environment variable
- Add support for optional TL_CUTLASS_PATH configuration
- Include TODO comment for future environment variable renaming

* bump version into v0.1.0

* [Enhancement] Add custom develop command for editable installs and update .gitignore

* [Documentation] Update README to include system dependencies installation instructions

* [Build] Update setup.py to support library file copying for both release and develop modes

* [Build] Refactor library file copying logic in setup.py

* [Documentation] Remove unnecessary install section header in Installation.md

* [Build] Add tox configuration and local distribution script for multi-Python version support

* [Build] Improve git submodule update function with better error handling

* [Build] Update LLVM configuration path in ROCm installation script

* [Build] Add .tox/ to .gitignore for tox testing environment

* [Build] Add support for TVM prebuild path configuration in CMakeLists.txt

* [Cleanup] Remove unused TVM runtime error codes header

* [Cleanup] Fix TVM grid constant type reference in CUDA module

* [Cleanup] Remove unused customized_code function from IR module

* [Feature] Add TileLang thread synchronization and storage access analysis passes

* [Build] Reorder DLL search path directories for more flexible library loading

* [Refactor] Improve thread synchronization and library path handling

- Rename ThreadSync and TileLangThreadSync functions in C++ code
- Update Python docstring for ThreadSync with more detailed description
- Reorder library path detection in tilelang environment setup
- Minor comment and code cleanup in CUDA and warp specialization modules

* [Refactor] Improve thread synchronization code style and formatting

- Standardize pointer type spacing in storage_access.h and storage_access.cc
- Update whitespace and indentation in thread_storage_sync.cc
- Reorder include statements in thread_partial_sync.cc
- Minor code formatting improvements across thread synchronization files

* [Refactor] Fix global function registration for ThreadSync

- Correct global function registration to use ThreadSync instead of TileLangThreadSync
- Update TVM global registration to match recent refactoring efforts

* [Refactor] Simplify ThreadSync global function registration

- Remove unnecessary whitespace in global function registration
- Compact the TVM global registration line for ThreadSync

* [Feature] Add WebGPU code generation support in TileLang

- Implement WebGPU code generator (codegen_webgpu.cc and codegen_webgpu.h)
- Add WebGPU target support in lower.py and target.py
- Update CMakeLists.txt to include WebGPU codegen source files
- Introduce WebGPU-specific code generation for WGSL shader language

* [Refactor] Improve WebGPU code generation formatting and readability

- Enhance code formatting in codegen_webgpu.cc and codegen_webgpu.h
- Standardize pointer type spacing and indentation
- Improve line breaks and reduce line length for better readability
- Minor code style improvements in WebGPU code generation

* [Test] Add WebGPU matrix multiplication code generation test

- Implement test_webgpu_codegen.py for WebGPU matrix multiplication
- Add assert_gemm_codegen function to validate WebGPU code generation
- Include basic matrix multiplication kernel test case

* Update README with WebGPU codegen support announcement

* Support multi version pypi package build via tox

* Add support for CPU device backend with C code generation

- Introduce `is_cpu_device_backend` function to detect CPU backend with C code generation
- Modify `lower` function to handle special case of CPU device backend
- Update host and device call filtering for CPU backend
- Add conditional source code generation for C host target
- Extend JITKernel to support optional target_host parameter

* lint fix

* Enhance JIT kernel adapters with CTypes and Torch C++ backends

- Add CtypesKernelAdapter with dynamic library generation and kernel wrapping
- Implement TorchCPPKernelAdapter for CUDA kernel compilation
- Refactor BaseKernelAdapter to support more flexible initialization
- Improve error handling and argument processing in kernel adapters
- Update adapter initialization to support various execution backends

* Refactor and clean up code style in JIT CTypes adapter modules

- Apply consistent code formatting and whitespace in CTypes adapter files
- Remove unused imports and improve import organization
- Enhance readability of code in adapter, libgen, and wrapper modules
- Add missing whitespace and improve line breaks
- Minor linting and code style improvements across CTypes adapter files

* Add test for TileLang JIT GEMM with CTypes backend

- Implement comprehensive test for matrix multiplication using CTypes execution backend
- Create test functions for GEMM with float16 data type
- Add kernel source verification with custom callback
- Implement reference implementation using PyTorch for result validation
- Support various matrix multiplication configurations (transposition, block sizes)

* test fix

* Update TileLang JIT callback registration with override parameter

- Modify tilelang_callback_cuda_postproc to use @tvm.register_func(override=True)
- Ensure proper function registration with ability to replace existing implementations

* [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

* implement jit test case

* [Dev] implement auto tune test case for matrix multiplication

* Implement test for legalize memory access and vectorized loop

* lint fix

* introduce run_once

* Refactor callback function names for consistency and improve code readability

* enhance documentations

* lint fix

* lint fix

* lint fix

* lint fix

* fix formatting issues in rt_mod_hip.cc

* add random seed initialization for deterministic testing

* implement jit test case

* [Dev] implement auto tune test case for matrix multiplication

* Implement test for legalize memory access and vectorized loop

* lint fix

* instruction update

* replace link with TileLang/tile-lang

* [Dev][Adapter] Implement Torch DLPack Kernel Adapter and related utilities

* lint fix

* Implement JIT Compiler Components

* Documents update

* lint fix

* update logo

* install script fix