GitLab

* [CI] optimize CI time

* [CI] fix transpose && format

* [misc] apply coderabbit suggestions && fix typo

**Summarize part of the rebase pr:**

1. **Support T.thread_return() → CUDA return syntax**  
   Added support for translating `T.thread_return()` to CUDA's native `return` statement.

2. **Dynamic type support for function inputs**  
   Functions now accept dynamically typed parameters using `typing`:
   ```python
   dyn_type = T.int32 or T.float
   @T.prim_func
   def main(
       a: dyn_type,
   )
   ```

3. **Device Function Codegen**  
   Added support for generating `__device__` functions in CUDA:
   ```python
   @I.ir_module
   class Module:
       @T.prim_func(private=True)
       def add(a: T.int32, b: T.int32) -> T.int32:
           return a + b

       @T.prim_func
       def main(
           A: T.Buffer((128, 128), "int32"),
           B: T.Buffer((128, 128), "int32"),
           C: T.Buffer((128, 128), "int32"),
       ):
           T.func_attr({"global_symbol": "main"})
           length: T.int32 = Module.add(64, 64)  # Host call
           for bx in...

* fix CI bugs in hopper

* lint fix

* Update bulk_copy.cc

* Refactor bulk copy logic in LowerBulkCopy function

- Removed unnecessary blank lines for improved code readability.
- Enhanced stride validation by checking for null pointers in global stride calculations, ensuring robustness against symbolic strides.
- Updated pass configuration handling in dynamic tile language tests to streamline dynamic alignment and TMA lower pass settings.

* test fix

* ci fix

* Update flash-attention dependencies and clean up example code

- Downgraded `flash-attn` dependency version in `requirements-test.txt` to `<=2.2.0`.
- Removed unused imports and commented-out code in various example files to enhance readability and maintainability.
- Updated the `flashattn` function signature to include default parameters for `block_M`, `block_N`, `num_stages`, and `threads`.
- Cleaned up the `example_mha_fwd_varlen.py` and `example_mha_bwd_wgmma_pipelined.py` files by removing unnecessary comments and improving code clarity.
- Deleted the `example_mha_inference.py` file as it is no longer needed.

* Update CI workflow to remove `--user` flag from pip install commands

- Removed the `--user` flag from the pip install commands in both the development and testing sections of the CI workflow to ensure proper installation of dependencies in the virtual environment.

* Update CI workflow to include `--no-user` flag in pip install commands

- Added the `--no-user` flag to the pip install commands in both the development and testing sections of the CI workflow to ensure dependencies are installed correctly within the virtual environment.

* Update CI workflow to include `--no-user` flag in pip install command for wheel mode

- Added the `--no-user` flag to the pip install command in the wheel mode section of the CI workflow to ensure dependencies are installed correctly within the virtual environment.

* test fix

* avoid conflict with system environments

* test fix

* add commnets

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

* [experimental] add a draft gemm_sp

* [3rdparty] bump cutlass to v3.9.3

* [lint] run format.sh

* [chore] rebase

* [chore] use abs path

* [gemm_sp] add metadata layout

* [ci] add more example

* [lint] run format.sh

* [chore] polish

* [chore] move gemm_sp to experimental

* [chore] polish

* [lint] run format.sh

* [Enhancement] Improve bulk copy handling and update GEMM sparse tensor test

* Added a warning log for unsupported non-swizzled global layouts in the bulk copy operation, ensuring fallback to normal copy.
* Refactored the GEMM sparse tensor test by removing unnecessary imports and simplifying the kernel compilation process.
* Updated the test to directly call the `run_gemm_sp` function, enhancing clarity and functionality.

* Implement Test

* [Enhancement] Update GEMM SP and SM89 templates for improved functionality

* Refactored GEMM SP computation to enhance warp partitioning logic, ensuring compatibility with Hopper architecture.
* Updated layout inference to support new WGMMA conditions and improved error messaging for unsupported targets.
* Modified SM89 templates to utilize new MMA atom structures, enhancing performance and compatibility with fp8 types.
* Added conditional inclusion for GEMM SP header based on CUDA architecture version.

* lint fix

* [gemm_sp] support more layout and data types

* Enhancement: sync T.gemm_sp's layout inference with T.gemm

* Enhancement: support more block_k in compress util

* [Enhancement] enable block_k=64

* [Lint] run format.sh

* [Enhancement] compressor support more dtype

* Enhancement: enable block_K=32

* [Lint] format.sh

* [Fixbug] fix shape

* Refactor: sync gemm

* [Enhancement] enable transpose

* [Enhancement] enable fp8_e4m3

* [Enhancement] enable int8

* [Lint] run format.sh

* [Benchmark] add gemm_sp benchmark

* [Example] fix 256 threads hang

* [CI] fix ci

* [Chore] resolve gemini feedback

* [Benchmark] increase search space

* [Lint] format

* [CI] skip sparse tensor core related tests as only sm90 is supported

* [CI] pass local run

* Update gemm_sm89.h

* lint fix

* lint fix

* [Enhancement] Add support for sparse GEMM and initialize CUDA architecture flags

- Introduced a new boolean flag `enable_sparse_gemm_` to control the inclusion of sparse GEMM functionality in CUDA code generation.
- Updated the `Finish` method to conditionally include the sparse GEMM header based on the new flag.
- Implemented logic in `VisitStmt_` to enable sparse GEMM when the corresponding external call is detected.
- Added a function to initialize the `TORCH_CUDA_ARCH_LIST` environment variable based on the target compute version, enhancing compatibility with PyTorch.
- Refactored the initialization function into the appropriate module and ensured it is called in the sparse utilities module.

* Update test_compress_utils.py

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