GitLab

* [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.

* [Feature] Add reshape and view functionalities to tilelang language module

- Introduced new test files for reshape and view operations in the tilelang language.
- Implemented reshape and view functions in the customize module, enhancing buffer manipulation capabilities.
- Updated the language initialization to include the new functionalities.
- Removed unnecessary import from test_tilelang_language_clamp.py for cleaner code.

* Update copyright to Tile-AI Corporation

* [Refactor] Clean up whitespace in test files for reshape and view functionalities

- Removed unnecessary blank lines in `test_tilelang_language_reshape.py` and `test_tilelang_language_view.py` for improved readability.
- Ensured consistent formatting across test files to enhance code clarity.

* 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>

- Introduce TMAStoreArrive and TMAStoreWait operations for CUDA TMA store synchronization
- Add new builtin operations in op/builtin.cc and op/builtin.h
- Implement TMAStoreSyncInjector to automatically inject TMA store synchronization calls
- Update CUDA codegen to support new TMA store synchronization intrinsics
- Add Python language bindings for new TMA store synchronization operations

[Refactor] Add SetMaxNRegCollector to Improve Register Hint Handling in Warp Specialized Rewriter (#194)

* [Refactor] Add SetMaxNRegCollector to Improve Register Hint Handling in Warp Specialized Rewriter

- Introduce `SetMaxNRegCollector` to collect register hints from SetMaxNReg calls
- Modify `WarpSpecializedRewriter` to use collected register hints for producer and consumer code
- Add validation checks for register hint values in the collector
- Remove SetMaxNReg calls during code transformation
- Enhance flexibility of register allocation in warp specialized rewriting

* temporary remove check in lower_hopper_intrin

* [Dev] Support clamp in language.

* [Bugfix]: Fix clamp

* [Refactor]

* 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

* Add kernel caching mechanism to TileLang

- Implement a new `cached` function in `tilelang/cache/__init__.py` to cache and reuse compiled kernels
- Expose the `cached` function in the main `tilelang/__init__.py`
- Add a test case for cached matrix multiplication in `testing/python/cache/test_tilelang_cache_matmul.py`
- Provide a `clear_cache()` function to reset the kernel cache when needed

* Refactor kernel caching test and implementation

- Simplify the `cached` function in `tilelang/cache/__init__.py`
- Update test script `test_tilelang_cache_matmul.py` to use `tilelang.testing.main()`
- Remove unnecessary whitespace and improve code formatting

* Update import for `cached` function in MHA examples

- Modify import statement in `example_mha_bwd.py` and `test_tilelang_kernel_mha_bwd.py`
- Change import from `tilelang.profiler import cached` to `tilelang import cached`
- Align with recent refactoring of kernel caching mechanism

* Refactor `cached` function signature in kernel caching

- Update function signature to use keyword-only arguments for `target` and `target_host`
- Improve parameter order and readability of the `cached` decorator
- Maintain existing functionality while enhancing function definition

* [Refactor] Update BitBLAS Benchmark with TileLang Carver Imports and Roller Hints Generation

- Replace BitBLAS imports with TileLang Carver imports in benchmark_matmul.py
- Modify roller hints generation using new TileLang Carver template and utility functions
- Update get_roller_hints_from_func to handle None cases and improve return logic
- Adjust DefaultPolicy to handle different codegen dictionary formats

* [Refactor] Update Thread Binding and Import Statements in TileLang Kernels

- Replace T.thread_binding() with T.get_thread_binding() across multiple kernel test files
- Update import statements for MMA layout and macro generator in dequantize GEMM and FP8 examples
- Move map_torch_type utility function to tilelang.utils.tensor
- Remove unnecessary imports and improve code organization

* Refactor Native Sparse Attention Example with Enhanced Triton Kernel

- Update parallel_nsa_fwd_kernel to support more flexible sparse attention computation
- Add support for block counts and offsets in the Triton kernel
- Modify kernel grid and computation logic for improved performance
- Update example script to use naive_nsa_simple reference implementation
- Improve type hints and kernel configuration

* Add Native Sparse Attention Examples with Tilelang and Triton Implementations

- Introduce new example scripts for native sparse attention:
  * example_tilelang_nsa_fwd.py: Forward pass implementation using TileLang
  * example_tilelang_nsa_decode.py: Decoding-specific sparse attention implementation
  * example_triton_nsa_fwd.py: Triton-based sparse attention forward pass
- Update reference.py with naive implementations for sparse attention
- Support different sparse attention scenarios including forward pass and inference
- Add comprehensive testing and validation against reference implementations

* lint fix

* Add Variable-Length Native Sparse Attention Examples for TileLang and Triton

- Introduce new example scripts for variable-length native sparse attention:
  * example_tilelang_nsa_fwd_varlen.py: TileLang implementation with variable sequence lengths
  * example_triton_nsa_fwd_varlen.py: Triton implementation with variable sequence lengths
- Update reference.py to support variable-length sparse attention scenarios
- Enhance existing sparse attention implementations to handle variable-length inputs
- Add comprehensive testing and validation for variable-length sparse attention

* Refactor Native Sparse Attention Examples: Code Style and Formatting Improvements

- Standardize function and parameter formatting across NSA example files
- Improve code readability by adjusting indentation and line breaks
- Enhance type hints and parameter alignment
- Remove unnecessary whitespaces and optimize imports
- Maintain consistent code style across TileLang and Triton implementations

* [Refactor] Update BitBLAS Benchmark with TileLang Carver Imports and Roller Hints Generation

- Replace BitBLAS imports with TileLang Carver imports in benchmark_matmul.py
- Modify roller hints generation using new TileLang Carver template and utility functions
- Update get_roller_hints_from_func to handle None cases and improve return logic
- Adjust DefaultPolicy to handle different codegen dictionary formats

* [Refactor] Update Thread Binding and Import Statements in TileLang Kernels

- Replace T.thread_binding() with T.get_thread_binding() across multiple kernel test files
- Update import statements for MMA layout and macro generator in dequantize GEMM and FP8 examples
- Move map_torch_type utility function to tilelang.utils.tensor
- Remove unnecessary imports and improve code organization

* Add cpu jit with backend ctypes

* Resolve some lint issues

* Apply PR feedback on head file and kernel example

* Add test cases

* Resolve formatting issues

* Resolve formatting issues

---------
Co-authored-by: xxw <1990389406@qq.con>

* [Dev] Adjust computation logic to avoid precision loss when casting acc_s from float to float16

- 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

* [Dev] Add DeepSeek MLA Decoding (Paged+Varlen) kernel and Performance Benchmark Script

- Implement comprehensive MLA (Multi-Head Latent Attention) decoding benchmark script
- Add support for multiple implementations: Torch, TileLang, FlashMLA, FlashInfer, and Triton
- Create flexible configuration for benchmarking different batch sizes, sequence lengths, and head configurations
- Implement performance comparison and CSV output for detailed performance analysis
- Add command-line argument support for targeted benchmarking and comparison

* [Dev] Refactor MLA Paged Decoding Kernel with Improved Block Handling and Precision

- Replace `d` parameter with `dv` to clarify value dimension in MLA decoding
- Enhance block distribution logic for split KV processing
- Improve handling of remaining blocks in split KV computation
- Add initialization of `lse_max_local` to prevent potential precision issues
- Optimize block start and range calculations for more accurate sequence processing

* lint

* Fix debug print buffer template for unsigned char type

- Update debug_print_buffer_value template specialization for unsigned char
- Modify test_tilelang_debug_print.py to include additional dtype tests
- Add test case for uint8 dtype in debug print buffer function

* Refactor debug print buffer template formatting for unsigned char

- Improve code formatting for debug_print_buffer_value template specialization
- Adjust line breaks and indentation for better readability
- Maintain consistent code style with other template specializations

* Extract map_torch_type utility function to tilelang.utils.tensor

- Move map_torch_type function from multiple test files to a centralized location
- Import map_torch_type from tilelang.utils.tensor in kernel test files
- Improve code reusability by creating a shared utility function for type mapping

* Add buffer dtype mapping for Cython kernel adapter

- Introduce buffer_dtype_map in CythonKernelAdapter to track buffer variable dtypes
- Add _process_buffer_dtype method to extract dtype information from TIR function
- Update CythonKernelWrapper to support setting and validating buffer dtypes
- Enhance type checking during kernel execution with dtype verification
- Improve logging message for Cython JIT adapter compilation

* Add static shape mapping for Cython kernel adapter

- Introduce static_shape_map in CythonKernelAdapter to track buffer variable static shapes
- Add _process_static_shape method to extract static shape information from TIR function
- Update CythonKernelWrapper to support setting and validating static shapes
- Enhance type checking during kernel execution with static shape verification

* Add Multi-Head Attention (MHA) Backward Pass Test for TileLang Kernel

- Implement comprehensive test for Multi-Head Attention backward pass
- Support both causal and non-causal attention scenarios
- Add reference implementation for comparing kernel outputs
- Test different batch sizes, head counts, sequence lengths, and head dimensions
- Verify forward and backward pass correctness using torch.testing.assert_close

* Set random seed for MHA backward pass test

- Add random seed initialization for consistent test reproducibility
- Use tilelang.testing.set_random_seed(42) to ensure deterministic test results

* Fix debug print buffer template for unsigned char type

- Update debug_print_buffer_value template specialization for unsigned char
- Modify test_tilelang_debug_print.py to include additional dtype tests
- Add test case for uint8 dtype in debug print buffer function

* Refactor debug print buffer template formatting for unsigned char

- Improve code formatting for debug_print_buffer_value template specialization
- Adjust line breaks and indentation for better readability
- Maintain consistent code style with other template specializations

* Change default log level from WARNING to INFO in TileLang initialization

* Refactor Flash Attention Variable-Length MHA Example with Cython Backend Support

- Update `example_mha_fwd_varlen.py` to use Cython backend for kernel compilation
- Remove unused imports and simplify function signature
- Modify `flashattn` function to handle max sequence length as a separate argument
- Update kernel call to include max sequence length parameter
- Improve code readability and remove commented-out code
- Add print statement to confirm successful assertion

* Refactor code formatting in TileLang lowering and example files

- Improve line breaks and code formatting in `lower.py`, `wrapper.py`, and `tensor.py`
- Simplify line breaks and reduce unnecessary whitespace
- Enhance code readability by adjusting indentation and line breaks
- Update example MHA forward pass script with cleaner tensor initialization

* Update TileLang kernel test with import path changes for MMA layout and macro generator

- Modify import statements in test_tilelang_kernel_dequantize_gemm.py
- Replace bitblas imports with tilelang.intrinsics imports for MMA-related utilities
- Update main function to use tilelang.testing.main()

* Add Block Sparse Attention Examples for TileLang and Triton

- Implement block sparse attention kernels for both TileLang and Triton
- Add utility functions for generating sparse attention masks using top-k and threshold methods
- Support causal and variable-length attention scenarios
- Include test cases for different sequence length configurations
- Demonstrate block-level sparse attention with configurable parameters

* Refactor Block Sparse Attention Examples with Code Style Improvements

- Improve code formatting in block_sparse_attn_tilelang.py and block_sparse_attn_triton.py
- Enhance readability by adjusting line breaks and indentation
- Simplify kernel and function calls with better formatting
- Add whitespace and line break improvements for better code clarity

* Enhance Layout Plotting with Multi-Replication and Dynamic Visualization

- Update plot_layout function to support multiple replications in thread and value mapping
- Improve thread and value mapping to handle replicated layouts
- Dynamically adjust figure size and legend positioning
- Add print statements for saved plot file paths
- Modify example fragment_mma_load_a.py to uncomment and enable warp and block layout plotting

* Refactor AtomicAdd functions in CUDA common header

- Implement a generic template for AtomicAdd function
- Specialize templates for half_t, bfloat16_t, and pointer types
- Reorganize and clean up existing AtomicAdd implementations
- Improve type handling and conversion in atomic operations

* Remove unused import in MHA backward test file

- Remove unnecessary argparse import from test_tilelang_kenrel_mha_bwd.py
- Add blank line for improved code formatting
- Minor code cleanup in test file

* Change default log level from WARNING to INFO in TileLang initialization

* Refactor Flash Attention Variable-Length MHA Example with Cython Backend Support

- Update `example_mha_fwd_varlen.py` to use Cython backend for kernel compilation
- Remove unused imports and simplify function signature
- Modify `flashattn` function to handle max sequence length as a separate argument
- Update kernel call to include max sequence length parameter
- Improve code readability and remove commented-out code
- Add print statement to confirm successful assertion

* Refactor code formatting in TileLang lowering and example files

- Improve line breaks and code formatting in `lower.py`, `wrapper.py`, and `tensor.py`
- Simplify line breaks and reduce unnecessary whitespace
- Enhance code readability by adjusting indentation and line breaks
- Update example MHA forward pass script with cleaner tensor initialization

* Update TileLang kernel test with import path changes for MMA layout and macro generator

- Modify import statements in test_tilelang_kernel_dequantize_gemm.py
- Replace bitblas imports with tilelang.intrinsics imports for MMA-related utilities
- Update main function to use tilelang.testing.main()

* Add Block Sparse Attention Examples for TileLang and Triton

- Implement block sparse attention kernels for both TileLang and Triton
- Add utility functions for generating sparse attention masks using top-k and threshold methods
- Support causal and variable-length attention scenarios
- Include test cases for different sequence length configurations
- Demonstrate block-level sparse attention with configurable parameters

* Refactor Block Sparse Attention Examples with Code Style Improvements

- Improve code formatting in block_sparse_attn_tilelang.py and block_sparse_attn_triton.py
- Enhance readability by adjusting line breaks and indentation
- Simplify kernel and function calls with better formatting
- Add whitespace and line break improvements for better code clarity

[Dev][Bugfix] Fix bug in ThreadTagChecker; Add WgmmaSync rewriter and add MHA WGMMA pipelined example (#128)

* [Dev] Add RetNet Linear Attention example

* [Dev] Add WgmmaSync rewriter for pipelined WGMMA operations and add MHA WGMMA pipelined example (FA3-like scheduling)

This commit introduces a new transformation pass `RewriteWgmmaSync` to optimize warp group matrix multiply accumulate (WGMMA) operations in the TileLang compiler:

- Implemented `WgmmaSyncRewriter` in `src/transform/wgmma_sync_rewriter.cc`
- Added pass registration for `RewriteWgmmaSync`
- Updated `tilelang/engine/phase.py` to include the new transformation pass
- Updated `tilelang/transform/__init__.py` to expose the new pass

The rewriter intelligently manages synchronization and dependencies between WGMMA operations, improving pipeline efficiency for complex matrix multiplication kernels.

* [Bugfix] Fix bug in ThreadTagChecker for warp specialization

Improve thread tag validation in warp specialized rewriter to prevent unintended transformations:
- Add more precise checks for threadIdx.y and threadIdx.z
- Validate thread extent to ensure only single-extent thread bindings are allowed
- Prevent warp specialization for multi-extent thread bindings in y and z dimensions

* lint

* [CI] Add TMA descriptor attribute to transformed module in test case

* refactor code

* enhance tutorial

* Enhance error handling and code generation in CUDA and TileLang components

This commit introduces several improvements across multiple files:
- Added more informative error messages in GEMM layout checks
- Updated CUDA codegen to support more flexible function signature generation
- Improved TMA descriptor initialization and kernel dispatch logic
- Refined library generation and source code parsing utilities
- Enhanced error handling in various adapter and wrapper classes

* Add thread tag validation for warp specialization

Introduce a ThreadTagChecker to validate that a PrimFunc only uses threadIdx.x before applying warp specialization. This prevents unintended transformations on kernels with complex thread binding and provides a clear warning to users about potential issues with warp specialization.

* Update TileLang Profiling and Compilation in Flash Decoding Examples

Refactor the profiling and compilation workflow in two flash decoding example scripts:
- Replace `tilelang.lower()` and `tilelang.Profiler()` with `tilelang.compile()`
- Simplify profiler initialization using `get_profiler()`
- Update method calls to use the new profiler and compiled kernel objects
- Maintain existing performance benchmarking and validation logic

* Refactor and clean up code formatting in TileLang testing and adapter modules

This commit includes several code style and formatting improvements:
- Adjust whitespace and line breaks in test files
- Improve code formatting in CUDA source wrapper and adapter utilities
- Enhance readability of function calls and argument handling
- Remove unnecessary whitespace and standardize indentation
- Simplify function signatures and argument parsing

* Refactor CUDA codegen and improve code formatting

This commit includes several improvements to CUDA code generation and formatting:
- Enhance function signature generation in CodeGenTileLangCUDA
- Improve code formatting and readability in CUDA-related files
- Simplify parameter handling and type annotations
- Clean up whitespace and line breaks in codegen and layout files

---------
Co-authored-by: Ubuntu <dlisuser@h100testl730RPS.xu5snccwrbtejcqqalluoku5hb.xx.internal.cloudapp.net>

* [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

* 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

* 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

* [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

* Add BF16 support to matrix multiplication and introduce corresponding test cases

* Add a blank line for improved readability in BF16 GEMM test

* Update acknowledgements in README to include supervision by Zhi Yang at Peking University

* enhance acknowledgement

* Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives

* Update subproject commit for TVM dependency

* Update subproject commit for TVM dependency

* Add int4_t type and functions for packing char values in CUDA common header

* Add plot_layout example and implement GetForwardVars method in layout classes

* Refactor code for improved readability by adjusting line breaks and formatting in layout and test files

* Fix formatting by removing unnecessary line break in layout.h

* Refactor make_int4 function for improved readability by adjusting parameter formatting

* Add legend to plot_layout for improved clarity of thread and local IDs

* Remove unnecessary dependencies from requirements files for cleaner setup

* Remove flash_mha.py and add .gitkeep to deepseek_mla directory

* Add build requirements and update installation scripts for improved setup

* Introduce carver

* Refactor imports and improve code formatting for consistency

* Add unit tests for carver recommendation hints

* lint fix

* Enhance ElementwiseTemplate and BaseTemplate with detailed docstrings for improved code documentation and clarity

* Refactor import statements and clean up whitespace in template files for improved readability

* Add README.md for Carver framework with usage examples and architecture support

* Refactor import statement in matmul_analysis.py for consistency

* Refactor TileDict and TensorCorePolicy methods for improved clarity and functionality

* Add tests for general matrix multiplication emit configurations

* Refactor formatting in test_tilelang_carver_generate_hints.py for improved readability

* Add FlashAttentionTemplate and related functionality for hint recommendations

* Refactor whitespace in FlashAttentionTemplate and test_tilelang_carver_recommend_hints for improved readability

* [CI][Test] Add test cases for tilelang transform MultiVersionBuffer and WarpSpecialized

* Relax the mismatch ratio restrictions in the flash_linear_attention and mha tests

* [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

* Add BF16 support to matrix multiplication and introduce corresponding test cases

* Add a blank line for improved readability in BF16 GEMM test

* Update acknowledgements in README to include supervision by Zhi Yang at Peking University

* enhance acknowledgement

* Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives

* Update subproject commit for TVM dependency

* Update subproject commit for TVM dependency

* Add int4_t type and functions for packing char values in CUDA common header

* Add plot_layout example and implement GetForwardVars method in layout classes

* Refactor code for improved readability by adjusting line breaks and formatting in layout and test files

* Fix formatting by removing unnecessary line break in layout.h

* Refactor make_int4 function for improved readability by adjusting parameter formatting

* Add legend to plot_layout for improved clarity of thread and local IDs

* Remove unnecessary dependencies from requirements files for cleaner setup

* Remove flash_mha.py and add .gitkeep to deepseek_mla directory

* Add build requirements and update installation scripts for improved setup

* Introduce carver

* Refactor imports and improve code formatting for consistency

* Add unit tests for carver recommendation hints

* lint fix

* Enhance ElementwiseTemplate and BaseTemplate with detailed docstrings for improved code documentation and clarity

* Refactor import statements and clean up whitespace in template files for improved readability

* Add README.md for Carver framework with usage examples and architecture support

* [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

* Add BF16 support to matrix multiplication and introduce corresponding test cases

* Add a blank line for improved readability in BF16 GEMM test

* Update acknowledgements in README to include supervision by Zhi Yang at Peking University

* enhance acknowledgement

* Replace tutorial on memory layout optimization with new tutorial on writing high-performance kernels with thread primitives

* Update subproject commit for TVM dependency

* Update subproject commit for TVM dependency

* Add int4_t type and functions for packing char values in CUDA common header

* Add plot_layout example and implement GetForwardVars method in layout classes

* Refactor code for improved readability by adjusting line breaks and formatting in layout and test files

* Fix formatting by removing unnecessary line break in layout.h

* Refactor make_int4 function for improved readability by adjusting parameter formatting

* [Dev] Add FlashDecoding example

* [CI][Test] Add test cases for tilelang kernel convolution

* [CI][Test] Add test cases for tilelang kernel FlashAttention

* Reduce the number of stages to ensure the shared memory allocation is valid

* Temporarily remove the dim128 case

* lint

* update einops in requirements-dev.txt

* update einops in requirements-test.txt

* remove einops in requirements-dev.txt

* [CI][Test] Add test cases for tilelang transform ClusterPlanning

* [CI][Test] Add test cases for tilelang transform LowerHopperIntrin

* [CI][Test] Add test cases for tilelang transform InjectFenceProxy

* [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

* Add BF16 support to matrix multiplication and introduce corresponding test cases

* Add a blank line for improved readability in BF16 GEMM test

* Update acknowledgements in README to include supervision by Zhi Yang at Peking University

* [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

* [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

* [Dev] Add FlashDecoding example

* [CI][Test] Add test cases for tilelang kernel convolution

* [CI][Test] Add test cases for tilelang kernel FlashAttention

* Reduce the number of stages to ensure the shared memory allocation is valid

* Temporarily remove the dim128 case

* lint

* update einops in requirements-dev.txt

* update einops in requirements-test.txt

* remove einops in requirements-dev.txt

* [CI][Test] Add test cases for tilelang transform ClusterPlanning

* [CI][Test] Add test cases for tilelang transform LowerHopperIntrin

* 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

* [Dev] Add FlashDecoding example

* [CI][Test] Add test cases for tilelang kernel convolution

* [CI][Test] Add test cases for tilelang kernel FlashAttention

* Reduce the number of stages to ensure the shared memory allocation is valid

* Temporarily remove the dim128 case

* lint

* update einops in requirements-dev.txt

* update einops in requirements-test.txt

* remove einops in requirements-dev.txt

* [CI][Test] Add test cases for tilelang transform ClusterPlanning

* [CI][Test] Add test cases for element_add

* [Doc] fix typo

* Parallelization

* format

* remove useless condition

* format

* [Dev] Add FlashDecoding example

* [CI][Test] Add test cases for tilelang kernel convolution

* [CI][Test] Add test cases for tilelang kernel FlashAttention

* Reduce the number of stages to ensure the shared memory allocation is valid

* Temporarily remove the dim128 case

* lint

* update einops in requirements-dev.txt

* update einops in requirements-test.txt

* remove einops in requirements-dev.txt

* implement jit test case

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

* Implement test for legalize memory access and vectorized loop

* lint fix