GitLab

* Provide layout visualization tool

Adds a layout visualization tool to TileLang, which helps users understand and debug the layout transformations applied during compilation.

This tool visualizes the memory layout of tensors at different stages of the compilation process, allowing developers to identify potential inefficiencies and optimize their code for better performance.

The visualization can be enabled via a pass config option.

* format

* add layout visual example

* Adds vis extra with matplotlib dependency

* rafactor pass config name

* fix lint

* Enables configurable layout visualization formats

Allows users to specify the output formats (png, pdf, svg) for layout visualization through a pass config option.

This change provides more flexibility in how layout visualizations are generated, allowing users to choose the formats that best suit their needs.

It also fixes a bug where layout visualization was not correctly disabled when the config option was set to "false".

* Adds visual layout inference tool docs

* fix lint

* fix lint

* Rafactor configurable layout visualization formats

* fix lint

* fix typo

* add some comments

* fix lints

* add some warnings for user

* Moves layout visualization

* Refactors layout visualization pass configuration

Updates the layout visualization pass configuration to use boolean flag for enabling and a string for specifying formats.

* Enables multiple layout visualization formats

* Updates layout visualization docs

* Moves layout visualization to analysis

* [misc] add a cpp side wrapper for gemm_sp_py

* [misc] typing

* [IR] bind GemmSPWarpPolicy

* [chore] add wrapper code

* [IR] fix GemmSPWarpPolicy

* [codegen] apply ptxas instructions

* [intrinsic] add typical (unused) mma layout

* [template] add uint16 debug func

* [intrinsic] add b matrix layout

* [gemm_sp] enable fp16/bf16 on sm8x

* [layout] refactor fp16/bf16 layout

* [gemm_sp] enable int8

* [chore] update test case dtype

* [gemm_sp] enable fp32

* [layout] refactor layouts

* [intrinsic] enable ldmatrix for mat A

* [layout] enable ldsm for matrix b

* [layout] add ldmatrix for fp32 and fp8

* [chore] refine

* [chore] refactor

* [chore] add fp8 efactor

* [chore] refactor

* [chore] add remove negative zero util

* [example] add a custom compress kernel

* [chore] minor update

* [test] refactor gemm_sp test

* [refactor] make metadata layout func

* [example] add option for using cutlass layout

* [doc] add a gemm_sp doc

* [doc] minor polish

* [chore] remove unused

* [bugfix] fix non replicate b case

* [test] refactor

* [chore] add a check

* [bugfix] fix util bug

* [wip] init a new test case for v2

* [chore] minor refactor

* [chore] minor update

* [bugfix] enable 16bit rs

* [language] enable rs

* [language] enable gemm_sp_sr

* [language] enable gemm_sp_rr

* [test] enable more tests

* [tvm] update ffi binding

* [chore] remove print

* [chore] fix benchmark script

* [lint] precommit lint

* [chore] apply feedback

* [test] use arch 8.0

* [chore] rollback ::ordered_metadata for backward compatibility

* [bugfix] fix captialized

* [example] keep gemm_sp on hopper

* [test] fix no fp8 normal kernel

* [test] reduce matmul size to satisfy accum error

* [test] use cal_diff for assertion

* [bugfix] expand float8 type

* [lib] add make_int4 for short type

* [language] add transpose E

* [bugfix] fix wrong var

* [format] format

* [chore] refactor binding

* [chore] fix wrong passing var

* [Refactor] Improve assertion handling in CodeGenCHost and ArgBinder

This commit refines the assertion message generation in CodeGenCHost by optimizing the handling of equality checks and reducing buffer size for error messages. Additionally, it enhances the ArgBinder by introducing a nullable guard mechanism for assertions, allowing for more precise error handling when binding arguments. The changes improve the clarity and efficiency of assertion handling across the codebase.

* [Enhancement] Update matmul kernel and optimize argument binding

This commit enhances the matmul kernel by introducing additional tensor parameters and refining the pipeline stages for improved performance. It also updates the argument binding mechanism to include a flag indicating whether buffers are used, enhancing the efficiency of buffer management. Furthermore, the optimization phase in the engine is improved by adding a simplification step, ensuring better performance and clarity in the generated code.

* lint fix

* [Enhancement] Add tensor checks documentation and improve argument binding assertions

This commit introduces a new documentation page for host-side tensor checks, detailing the automatic validations performed by TileLang on kernel arguments. It enhances the ArgBinder by adding assertions for non-null pointers when arguments are used, improving error handling. Additionally, the optimization phase in the engine is updated to include a simplification step, ensuring better performance and clarity in the generated code.

* [Enhancement] Update .gitignore and refine matmul kernel for improved performance

This commit adds host checks logs to the .gitignore file to prevent unnecessary log files from being tracked. Additionally, it refines the matmul kernel by adjusting pipeline stages, updating tensor parameters, and enhancing argument handling for better performance. The changes also include improved error messages in the argument binding process, ensuring clearer diagnostics for users.

* lint fix

* lint fix

* [Refactor] Simplify tensor_null_test function and remove ptr_null_test

This commit refactors the tensor_null_test function by adding a with_bias parameter and removing the ptr_null_test function, which was previously unused. The run_test function is updated to reflect these changes, streamlining the testing process for tensor operations.

* lint fix

* fix

* [Docs] Improve installation guide

* address comments

* remove debug print

* pipeline fix

* use the correct buffer access scope

* rs support

* warp warpgroup_fence_operand

* fix

* fp8 dtype ptx enhance

* mma fix

* TCGEN05 Interface

* tcgen05 support

* rebase

* update

* Enhance TCGEN05 support by adding new intrinsic operations and descriptors. Introduced `ptx_tcgen05_mma_ts` for tensor-memory to shared-memory instructions and `tcgen05_mma_arrive` for signaling barrier completion. Updated existing descriptors and code generation logic to accommodate these changes, ensuring compatibility with new instruction sets. Refactored related allocation functions and improved handling of shared memory descriptors.

* lint fix

* Refactor buffer reference handling in CUDA code generation and update test execution in tilelang. Ensure default annotations for unrolling are set correctly in TIR IR module.

* wgmma fix

---------
Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk>

* update rules

* ruff check

* other fixes

* fmt

* do not touch examples

* fmt

* Improve target docs and helper messaging

  Commit Message:

  - add SUPPORTED_TARGETS metadata and expose describe_supported_targets()
  - relax target validation to accept option suffixes and upgrade error messages
  - document target usage and compute capability mapping in docs/get_started/targets.md
  - note preference for string targets when caching and link the new guide in docs/index.md

* remove american english spelling

* recommend using T.dynamic instead of T.symbolic

* lint fix

* lint fix

* Load libs from build dir, if present, to support faster rebuild.

* typo

* upd

* refine check

* md lint

Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>

* cleanup

* init

* build first wheel that may not work

* build cython ext

* fix tvm build

* use sabi

* update rpath to support auditwheel

* pass editible build

* update ci

* fix warnings

* do not use ccache in self host runner

* test local uv cache

* test pip index

* update lib search to respect new lib location

* fix

* update ci

* enable cuda by default

* update src map

* fix

* fix

* fix

* Generate version with backend and git information at build time

* copy tvm_cython to wheels

* fix tvm lib search

* fmt

* remove unused

* auto detect ccache

* add back backend-related files

* remove jit cython adaptor to simplify code

* fmt

* fix ci

* ci fix 2

* ci fix 3

* workaround metal

* ci fix 4

* fmt

* fmt

* Revert "ci fix 4"

This reverts commit d1de8291c3e40927955f3ad3cf87a75c78813676.

* tmp

* fix metal

* trivial cleanup

* add detailed build-time version for cuda

* add back mlc

* Restore wheel info and other trivial updates

* update

* fix cuda

* upd

* fix metal ci

* test for ga build

* test for nvidia/cuda

* test ubuntu 20

* fix

* fix

* Do not use `uv build`

* fix

* fix

* log toolchain version

* merge wheel

* update

* debug

* fix

* update

* skip rocm

* update artifacts each

* fix

* fix

* add mac

* fix cache

* fix cache

* fix cache

* reset and add comment

* upd

* fix git version

* update deps

* trivial update

* use in-tree build dir and install to src to speedup editable build

* Revert "use in-tree build dir and install to src to speedup editable build"

This reverts commit 6ab87b05c5eed811210136b8dca4fc3677dd51f2.

* add build-dir

* update docs

* remove old scrips

* [1/n] cleanup scripts

* [Lint]: [pre-commit.ci] auto fixes [...]

* fix and update

* wait for tvm fix

* revert some tmp fix

* fix

* fix

* spell

* doc update

* test cibuildwheel

* fix and test macos on ci

* Update .github/workflows/dist.yml
Co-authored-by: Xuehai Pan <XuehaiPan@outlook.com>

* fix

* test ga event

* cleanup

* bump tvm to support api3

* test final version

* add cron

* Update .github/workflows/dist.yml
Co-authored-by: Xuehai Pan <XuehaiPan@outlook.com>

* fix

* test ccache for metal cibuildwheel

* test newer macos

* finish

---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Xuehai Pan <XuehaiPan@outlook.com>

[Refactor] Refactor Pass `InjectFenceProxy` and expose some warp group primitives in frontend (#977)

* • InjectFenceProxy docs and tests

  - annotate proxy fence injector with context comments for async/generic detection
  - add compiler internals doc covering the pass mechanics and link it in docs index
  - repair fence proxy test by fixing descriptor init usage and fence counter logic

* do not consider call_extern as async.

* doc update.

* reduce test size for sparse mla

* chore: misc cleanup

* feat: add pre-commit config

* chore: update lint dependencies

* style: fix lint issues

* feat: add pre-commit hooks

* fix: fix typos

* chore: update .gitattributes

* [Lint]: [pre-commit.ci] auto fixes [...]

* docs: update CONTRIBUTING.md

* chore: update default venv name

* chore: revert and exclude CUDA files

---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* remove debug print

* Remove inline let expressions from the LowerAndLegalize function in phase.py

* add test

* Update sparse MLA examples to support SKV adjustment and correctness checks

- Changed SKV parameter from 32768 to 8192 in sparse MLA backward and forward tests.
- Added check_correctness parameter to test functions for validation of outputs.
- Updated test cases to reflect new SKV values and correctness checks.

* reduce test shape

* Update documentation structure and refactor main function parameters in example_fusedmoe_tilelang.py

- Added a new section for compiler internals in the documentation.
- Refactored the main function in example_fusedmoe_tilelang.py to accept parameters for hidden dimensions, expert configurations, and batch/sequence sizes, improving flexibility and readability.

* Update buffer access checks in merge_shared_memory_allocations.cc

- Changed the condition for buffer access from less than (<) to less than or equal to (<=) to allow access at the same scope level.
- Adjusted the logic for determining the access level when touching buffers to ensure correct handling of scope levels.

* lint fix

* Support pipeline with LetStmt

* lint fix

* • Fix LowerTileOp let handling to avoid LetInline dependency

  - inline let-bound BufferLoad nodes via resolver helpers and structured return
  - remap layouts/buffers using original data vars and only rewrite when needed
  - update pipeline planner to understand let-bound address_of buffers
  - document the new inline behaviour in docs/let_inline_fix.md

* fix for wgmma pipeline with let binding

* lint fix

* test fix

* reduce smem usage.

* let binding enhancement

* fix for dpgm

* fix simplify

* lint fix

* use tilelang.Simplify instead of tir.Simplify

* • Add TL_FORCE_LET_INLINE pass config and gate eager LetInline usage

  - register the new config in builtin headers/registration
  - add helper to pipeline enabling LetInline based on pass context
  - document LetStmt inlining controls and usage

* chore: fix typos

* chore: fix ruff

* chore: fix clang-format

* [Bugfix][Docs] Update documentation build process and configurations for autoapi support

* lint fix

* [Refactor] Update tilelang kernel functions and remove unused imports

- Refactored the `flashattn_fwd`, `flashattn_bwd_preprocess`, and `flashattn_bwd_postprocess` functions to utilize direct kernel calls instead of cached versions, improving clarity and performance.
- Added `@tilelang.jit` decorators with specified output indices to enhance kernel compilation.
- Removed unused import of `cached` from `tilelang`, streamlining the code.
- Commented out the main testing function call in `test_tilelang_kernel_mha_bwd.py` for potential future use.

* [Refactor] Simplify configuration generation in benchmark and example scripts

- Refactored the `get_configs` functions in multiple benchmark and example scripts to utilize a dictionary-based approach for parameter configuration, improving readability and maintainability.
- Updated the `flashattn` and `chunk_scan_fwd` functions to directly accept configuration parameters, enhancing flexibility in kernel tuning.
- Removed redundant code and streamlined the configuration generation process across various files, ensuring consistency in how configurations are defined and utilized.

* [Refactor] Update configuration handling in benchmark scripts

- Refactored the `get_configs` functions in benchmark scripts to accept a variable argument list, improving flexibility in configuration management.
- Enhanced the `matmul` and `flashattn` functions to utilize the updated configuration approach, streamlining parameter handling for kernel tuning.
- Added `@autotune` decorators to relevant functions, ensuring consistent autotuning behavior across benchmarks.
- Cleaned up redundant code and improved overall readability in the affected files.

* [Refactor] Clean up formatting and update subproject commit

- Updated the subproject commit reference in the TVM directory to indicate a dirty state.
- Removed unnecessary blank lines and improved formatting in the `benchmark_matmul` and `benchmark_matmul_fp8` scripts for better readability.
- Streamlined the function definitions in the `flashattn` example script to enhance clarity and maintainability.

* [Refactor] Update AutoTuner configuration handling

- Modified the AutoTuner class to check if kernel parameters are set before processing tunable arguments, improving robustness in configuration handling.
- Enhanced the logic for skipping compilation when tunable parameters are already provided, ensuring efficient use of resources.
- Updated comments for clarity and maintainability.

* lint fix

* Update TVM subproject commit to indicate dirty state and modify MHA backward test cases

- Updated the subproject commit reference in the TVM directory to reflect a dirty state.
- Adjusted the `test_mha_bwd` function to use a new configuration for the MHA backward tests, changing the context size from 128 to 256.
- Uncommented the main testing function call for potential execution.

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

* Update Installation.md

* Update installation prerequisites in documentation

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

* Add deepseek_mla to documentation index (#380)

* lint fix

* [Add] Introduce deepseek_mla documentation for high-performance FlashMLA with TileLang

- Added a comprehensive guide on writing high-performance kernels using TileLang, focusing on the Multi-Head Latent Attention (MLA) mechanism.
- Included benchmark results comparing FlashMLA, TileLang, Torch, Triton, and FlashInfer, highlighting TileLang's efficiency and ease of use.
- Detailed implementation strategies, including layout inference, threadblock swizzling, shared memory swizzling, and warp specialization.
- Provided examples and explanations of optimization techniques to enhance performance in GPU kernel programming.

* doc update

* [Add] Enhance AMD FlashMLA implementation and documentation

- Refactored variable names in `benchmark_mla_decode_amd_tilelang.py` for clarity, changing `Q_shared` and `Q_pe_shared` to `Q_local` and `Q_pe_local` to reflect their usage in register allocation.
- Added a new `README.md` detailing the high-performance FlashMLA implementation on AMD MI300X accelerators, including architectural considerations, optimization strategies, and performance evaluation.
- Introduced a performance comparison figure to illustrate the efficiency of the TileLang implementation against other frameworks.

* lint fix

* [Add] Expand deepseek_mla documentation for AMD MI300X optimization strategies

- Introduced a new section detailing architectural differences and optimization strategies for implementing FlashMLA on AMD MI300X accelerators.
- Highlighted key considerations such as instruction set variations, shared memory constraints, tile size flexibility, and memory bank conflict swizzling.
- Included performance evaluation results demonstrating TileLang's efficiency compared to other frameworks.
- Discussed future optimization opportunities for memory bank conflict mitigation and dimension parallelization.

This pull request includes a change to the `gemv.md` file. The changes
add heading level to title in the document to make the heading level
right.

* [doc/example] init gemv doc and examples

* [example] add vectorized read

* [example] use local register instead of smem

* [example] add bench

* [doc] update doc

* [doc] refine doc

* [lint] format code

* [doc] add tips

* [doc/example] fix typo

* [example] use tmv_all_reduce

* [doc] update doc accordingly

* [doc] add benchmark table

* [lint] format code

* fix bug

* update performance.py

* update python api docs

* test workflow

* fix dependency

* fix bug

* fix

* update correct git config

* test workflow

* clear cache

* lint fix

* fix exclude path

* [Refactor] Improve flash attention example and layout comparison logic

- Removed unnecessary annotation for `lse_local_split` in the flash attention example to streamline the code.
- Updated the handling of `lse_local_split` to utilize parallel processing for better performance.
- Refactored kernel compilation and profiling logic to enhance clarity and maintainability in the flash attention example.
- Added a condition in `FragmentNode::IsEqual` to handle broadcast cases, improving the robustness of layout comparisons.

* lint fix

* [Enhancement] Add support for shared memory scope in Fill operation

- Introduced handling for `shared.dyn` and `shared` memory scopes in the Fill operation.
- Implemented parallel operation and layout inference for improved performance in shared memory scenarios.
- Updated thread loop partitioning and vectorization logic to accommodate new memory scope handling.

* [Refactor] Remove deprecated decorator and enhance Cython kernel handling

- Removed the deprecated decorator from the main module and added a new implementation in the utils module for better organization.
- Introduced a pointer map in the Cython kernel adapter to manage pointer arguments, improving runtime shape resolution.
- Updated the Cython kernel wrapper to utilize the new pointer map for handling kernel arguments.
- Enhanced error checking in the tensor utility functions to ensure static shapes are enforced.
- Added a new proxy module for buffer and tensor handling, streamlining the interface for TIR programs.

* [Feature] Add matrix multiplication test and kernel implementation

- Introduced a new test file `test_tilelang_language_ptr.py` that implements a matrix multiplication function using TileLang's primitives.
- The `matmul_test` function defines a kernel for performing tile-level GEMM operations with customizable block sizes and data types.
- Added a `run_matmul` function to compile and execute the kernel, along with a test function to validate the implementation.
- Updated the `proxy.py` file to enhance type handling for buffer and tensor proxies, ensuring compatibility with TIR programs.
- Minor formatting improvements in `deprecated.py` for better readability.

* lint fix

* [Refactor] Update tensor creation in matrix multiplication test

- Replaced `T.Tensor.from_ptr` with `T.make_tensor` in `matmul_test` for improved clarity and consistency.
- Updated imports in `__init__.py` to include `make_tensor`.
- Added `make_tensor` function in `proxy.py` to streamline tensor creation from pointers.

* [Refactor] Update tensor definitions across multiple files

- Replaced instances of `T.Tensor` with updated tensor definitions in various benchmark and example files to enhance consistency and clarity.
- Adjusted tensor shapes and types in functions related to matrix multiplication, attention mechanisms, and other operations.
- Improved documentation in README and example files to reflect changes in tensor usage.

* lint fix

* [Refactor] Update tensor types in attention and matrix multiplication examples

- Replaced instances of `T.Tensor` with `T.SharedTensor` and `T.FragmentTensor` in various attention and matrix multiplication functions to improve consistency and clarity.
- Adjusted tensor definitions in benchmark and example files to align with the new tensor types.
- Enhanced the overall structure and readability of the code by standardizing tensor usage across multiple files.

* lint fix

* [Refactor] Update tensor types in GEMM example and test files

- Replaced instances of `T.Tensor` with `T.LocalTensor` and `T.Buffer` in the GEMM example and related test functions to improve consistency and clarity.
- Enhanced the overall structure of the code by standardizing tensor usage across multiple files, aligning with recent updates in tensor definitions.

* [Refactor] Update tensor usage in customize.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `reshape` and `view` functions to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the file.

* [Refactor] Update tensor types in test_tilelang_transform_annotate_device_regions.py

- Replaced instances of `T.Tensor` with `T.Buffer` in the `before` and `expected` methods of the `TestAnnotateThreadExtent` and `TestAnnotateDeviceScope` classes to enhance consistency with recent tensor definitions.
- Improved code clarity by standardizing buffer usage across the test file.

* [Refactor] Update tensor types to SharedBuffer and FragmentBuffer

- Replaced instances of `T.SharedTensor` and `T.FragmentTensor` with `T.SharedBuffer` and `T.FragmentBuffer` across multiple benchmark, example, and test files to enhance consistency with recent tensor definitions.
- Improved code clarity and structure by standardizing buffer usage in attention and matrix multiplication functions.

* [Refactor] Introduce Tensor alias for Buffer in proxy.py

- Added a new alias `Tensor` for `Buffer` in `proxy.py` to facilitate JIT compilation, ensuring that inputs and outputs are mapped with `torch.Tensor`.
- This change enhances clarity and consistency in tensor usage across the codebase.

* Add GPU kernel for 2D continuous cumulative sum in TileLang example

- Introduced a new example script `example_tilelang_cumsum.py` that generates a GPU kernel for 2D continuous cumulative sum.
- Implemented functions to handle kernel configuration, memory allocation, and inclusive scan operations.
- Added a main execution block to demonstrate the kernel's functionality using PyTorch for tensor operations.
- Enhanced the example with error handling for power-of-two configurations and validation of results against PyTorch's built-in cumulative sum function.

* Refactor TileLang examples and enhance kernel compilation

- Updated `example_tilelang_cumsum.py` to improve GPU kernel generation for 2D continuous cumulative sum, including better parameter handling and error checking.
- Refactored `example_mha_bwd.py` to enhance kernel compilation readability and maintainability.
- Modified `kernel_cache.py` to prevent saving kernels to disk when using the DLPack backend, ensuring proper cache management.
- Added `get_block_bindings` function to `kernel.py` for improved access to block bindings in kernel launch frames.
- Cleaned up import statements in `__init__.py` for better organization and clarity.

* Enhance GPU kernel for 2D continuous cumulative sum in TileLang example

- Added additional spacing for improved readability in `example_tilelang_cumsum.py`.
- Refined kernel structure to enhance clarity and maintainability during GPU kernel generation for cumulative sum operations.

* Refactor CUDA post-processing callback registration in TileLang

- Introduced a new decorator `register_cuda_postproc_callback` for registering CUDA post-processing functions, enhancing usability and flexibility.
- Updated existing callback implementations to utilize the new decorator, improving code clarity and maintainability.
- Added debug prints to the CUDA code generation process for better traceability during development.
- Refactored the `OptimizeForTarget` function to streamline conditional statement handling in the pipeline transformation.
- Cleaned up the `inject_pipeline.cc` file by removing redundant code related to statement grouping and condition handling.

* lint fix

* Enhance BlockSparse GEMM Example with Autotuning and Configurable Parameters

- Added argument parsing to allow dynamic configuration of matrix dimensions and sparsity ratio.
- Implemented a function to generate various kernel configurations for autotuning.
- Refactored the main execution block to support both autotuned and default configurations.
- Improved the block mask generation to accommodate specified sparsity levels.
- Updated the kernel compilation process to utilize the new configurations and ensure accurate results verification.

* [Example] Modify tuning configurations for FlashAttention example

* [Examples] formatting example_gqa_fwd_bshd.py

* [Examples] Implement elementwise add kernel

* [Doc] Update ElementWise Operators document

* [Examples] Replace the example of elementwise add.

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

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

* fix typo

* remove IS_WINDOWS constant

* lint fix

* Improve error messages for CUDA detection failure

* lint fix

* lint fix

* Fix .gitignore to correctly include venv directory

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

* update installation instructions

* update install instruction

* [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