GitLab

* [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

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