GitLab

* Improve loop vectorize

* Improve loop vectorize

* Improve loop vectorize

* Improve loop vectorize

* Improve loop vectorize

* Add some vectorize tests and comments

* [Enhancement] Add __ldg intrinsic for CUDA read-only cache loads

* Introduced the __ldg intrinsic to enable explicit read-only cached loads from global memory in CUDA.
* Updated the corresponding documentation and added support in both CUDA and HIP code generation.
* Enhanced the Python interface for __ldg to accept BufferLoad and Buffer types, improving usability.

* [Enhancement] Update formatting and linting rules in pyproject.toml; minor test adjustment

* Added new formatting rules in pyproject.toml to enforce consistent code style, including hanging indents and argument splitting.
* Updated test_tilelang_language_intrinsics_codegen.py to improve readability by adding a blank line before the main execution block.
* Refactored error messages in builtin.py for better clarity and consistency, ensuring proper formatting in function definitions and raising ValueErrors.

* lint fix

* initial step

* modify builder

* scratch version of new frontend

* write some tests

* add many tests

* add typing stub for tir.ir

* remove idents

* minor update

* minor update

* First version of jitv2 (renamed to LazyJIT)

* fix pre-commit error

* minor fix

* fix lint error

* fix lint error

* Fix conditional check for PrimFunc instance

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

* [Enhancement] Implement dynamic unroll factor in CUDA code generation

This commit introduces support for specifying a dynamic unroll factor in the CUDA code generation. The `unroll_factor` map is added to store unroll factors for loop variables, allowing for more flexible and optimized loop unrolling. Additionally, the `unroll` function is integrated into the loop language, enabling users to define unroll factors directly in their code. This enhancement improves performance by allowing tailored unrolling strategies based on specific loop characteristics.

* lint fix

* [Bugfix] Correct initialization of non-zero counters in custom compress kernel and update TIR registration for gemm_sp_py to use the correct tile operation

* Add test for T.assume

* Add unit test for T.assume

* Add unit test for T.assume

* Add unit tests for T.assume

* Remove debug print for kernel source

Remove print statement for kernel source in tests.

* Update test_tilelang_language_assume.py

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

* [BugFix] Use BufferRegion in tl.cumsum to infer buffer shape

* remove debug lines

* remove rubbish

* Fix decorator syntax for atomic_different_memory_orders_program

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

* [Feat] Support warp reduce

* lint

* add test

* lint

* [Fix] Fix #1307 by adding macro inside function

* fix lint error

* add comments and fix lint error

* Remove debug print from enter_frame method

Removed debug print statement from enter_frame method.

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

* [Feat] add missing support of uint32x2

* [Feat] Add `T.Ref` annotation and tests

* fix lint error

* minor update for error message on twice decl

* Remove unused let_bindings_ in CodeGenC to fix #1300

* bugfix

* lint fix

* test

* lint fix

* increate procs

* recover

* [Feature] Add support for A: T.Tensor(n + 1) and A: T.Tensor(2*n)

* issue fix

* fix

* fix

* decreate nproc for debugging

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

* [Language][UX] Nested loop checker in pre-lowering stage

* rename

* comment

* address comments

* add typing stub for tir.ir

* remove idents

* minor update

* [Refactor] add numpy conversion for dtype

* fix lint error

* remove unused np.float_ in dtype conversion

* fix type in np.int_

* fix typo

* minor fix

* remove debug files

* fix memory leak bug

* fix lint error

* add comments

* fix lint error

* remove duplicated, because tilelang doesn't dependent deprecated

* [Refactor] Update FFI type handling and simplify argument management

* Refactored FFI type definitions in runtime and code generation files to use `TVMFFIAny` instead of `TVMValue`, enhancing type clarity.
* Updated function registration in `runtime.cc` to utilize canonical names for better consistency.
* Simplified argument handling in the `simplify` transformation, ensuring unused buffer parameters are removed only when simplification is enabled.
* Adjusted autotuner and profiler parameters to standardize the execution backend to `tvm_ffi`, improving clarity in backend selection.
* Removed obsolete `adapt_torch2tvm` function from tensor utilities to streamline the codebase and reduce complexity.

* [Update] Sync TVM submodule and enhance kernel source handling

* Updated the TVM submodule to commit cdc2aced, ensuring compatibility with recent changes.
* Added functionality to print kernel source in `example_blocksparse_gemm.py` for better debugging.
* Commented out the main execution call in test files to prevent unintended execution during testing.
* Introduced `tilelang.disable_cache()` in various test files to streamline testing and avoid cache-related issues.
* Refactored kernel source retrieval methods to improve clarity and consistency across different execution backends.

* [Refactor] Clean up imports and improve code formatting

* Removed unused import of `tilelang.testing` in `test_example_blocksparse_gemm.py` to streamline the code.
* Reformatted several lines in `arg_binder.cc`, `make_packed_api.cc`, `tvm_ffi.py`, and `adapter.py` for improved readability and consistency.
* Updated comments and spacing in `tvm_ffi.py` to enhance clarity without altering functionality.

* Update execution backend options and improve resolution logic

- Changed default execution backend from "cython" to "auto" in multiple locations to allow automatic selection based on the target.
- Expanded the list of supported execution backends to include "torch" and "nvrtc" across various classes and functions.
- Enhanced backend resolution logic in `KernelCache` and `AutoTuner` to ensure appropriate backend selection based on the target.
- Updated documentation to reflect changes in execution backend options and their defaults.

* lint fix

* fix

* Enhance argument handling in CUDA and HIP runtime modules

- Updated `ExtractFuncInfo` in `rt_mod_cuda.cc` and `rt_mod_hip.cc` to map boolean argument types to int32, ensuring compatibility with device runtime.
- Refactored `BindDLTensor` in `arg_binder.cc` to improve null handling and validation checks for DLTensor parameters, utilizing expression-level guards to prevent dereferencing null pointers.
- Enhanced error checking for buffer shape, strides, and data fields, ensuring robust handling of optional inputs and maintaining consistency across various checks.

* lint fix

* lint fix

* lint fix

* lint fix

* minor fix

* fix

* recover check

* Refactor argument binding and validation in `arg_binder.cc`

- Improved null handling and validation checks in `BindDLTensor`, ensuring safe dereferencing of pointers.
- Enhanced consistency checks for buffer shape, strides, and data fields, utilizing expression-level guards.
- Updated `MakePackedAPI` to maintain code clarity and consistency in argument handling.
- Minor adjustments in test files to streamline kernel execution and improve readability.

* lint fix

* stride fix

* minor fix

* fix

* lint fix

* lint fix

* Add CUDA stream access policy window helpers and integrate with L2 persistent cache management

- Introduced functions to set and reset the CUDA stream access policy window, allowing for better control over L2 cache usage.
- Updated runtime files to include new FFI packed functions for managing stream attributes.
- Modified lower_hopper_intrin to incorporate prologue and epilogue statements for L2 cache setup and teardown.
- Enhanced tests to verify the inclusion of new FFI calls in the generated kernel source.

* check with symbolic

* support null ptr

* Update CMakeLists and lower.py for code generation and subproject status

- Added `codegen_c_host.cc` to the list of source files in CMakeLists.txt for improved code generation support.
- Updated the function call in `lower.py` to use `target.build.tilelang_c` for C target host code generation, enhancing compatibility.
- Marked the TVM subproject as dirty to indicate local modifications.

* lint fix

* Update comments for clarity in quickstart.py

* [Language] Add shape check in T.view/reshape

* address comments

* Fix various issues under int64_t static and dynamic shape.

* Resolve reviewed issues.

* Add unit test.

* fix

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

* add typing stub for tir.ir

* remove idents

* minor update

* [Refactor] add numpy conversion for dtype

* fix lint error

* remove unused np.float_ in dtype conversion

* fix type in np.int_

* fix typo

* minor fix

* remove debug files

* fix nsa bwd and atomic

* [Lint]

* [BugFix]
- New implementation for atomicMax and atomicMin using atomicCAS
- PTX version atomicAdd for single 16-byte data
- Modify the test cases

* [Lint]

---------
Co-authored-by: tzj-fxz <tzjfxz@gmail.com>

* add typing stub for tir.ir

* remove idents

* minor update

* [Language] Add missing while statement

* add test

* Update layout handling and introduce reshape functionality

- Updated the `LayoutNode` class to include a new `Reshape` method, allowing for dynamic reshaping of layouts based on input shapes.
- Enhanced the `OutputShape` method to provide better handling of cases where the analyzer cannot form an `IntervalSet`, implementing fallback mechanisms to ensure safe extents.
- Refactored the `ReduceOpNode` to utilize `BufferRegion` for improved memory handling during reduction operations.
- Added tests for reshaping functionality and layout transformations to ensure correctness and performance in various scenarios.

* lint fix

* Revert tvm submodule pointer to 1815c3e0b6ec4ead36370bbd1562025d8529017c; keep src unchanged

* Update tvm submodule to commit f0bbd3bf741413c35c389ba5dedd5be206000ad1

* Update tvm submodule to commit f0bbd3bf741413c35c389ba5dedd5be206000ad1

* remove useless prove

* remove comment

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Co-authored-by: tilelang-bot <bot@tilelang>

* [Enhancement] Add thread count validation for ReduceOp fragment layout inference

* Introduced a check to ensure that the thread count is divisible by the replicate extent during layout inference in ReduceOpNode. This validation prevents layout inference failures and provides detailed error messages to guide users in resolving issues related to thread block sizes and fragment layouts.
* Updated tests to remove unsupported configurations that could lead to layout inference errors, ensuring more robust testing scenarios.

* lint fix

* [Feature] Support serial for with step

* add more tests

* fix

* Enhance trip count validation in SerialForWithStep to ensure non-zero step values and prevent undefined behavior. Added error handling for zero step values and improved logging for non-constant steps.

* Update builder.py

* fix lint error

---------
Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk>
Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>

* Update dependency version for apache-tvm-ffi in pyproject.toml to fix CI

* [Math] Add `tl.infinity` operation and update Python interface for infinity handling

- Implemented `infinity_op` in C++ to return infinity values for supported data types.
- Registered new operation `tl.infinity` with appropriate attributes.
- Updated Python interface to call the new `tl.infinity` operation instead of the previous method.

* Add unit tests for `tl.infinity` operation in TileLang

- Introduced a new test file `test_tilelang_language_infinity.py` to validate the behavior of the `tl.infinity` operation across multiple data types (float16, bfloat16, float32, float64).
- Implemented a kernel to fill a tensor with infinity values and asserted the correctness of the output against PyTorch's `torch.inf`.

* lint

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

* [Feat] add 2 phase binding to allow swap two var

* Minor update tvm dtype constructor

* fix lint error

* tilelang frontend v2

* syntax sugar: defining a local var by annotation

* [Refactor] fix type linting warning like `T.float32`

* Add tl.local_var_init for new tl.float32

* allow passing default argument as function annotation

* allow default arguments as annotation

* fix lint error

* minor fix

* [Refactor] refactor tilelang.jit and tilelang.autotune

* minor fix

* minor fix

* minor fix

* fix metal get function name

* add par_compile impl and tests

* Type consistency on tvm datatype
1. isinstance(tl.float32, tvm.DataType) == True
2. Allow `tl.float32` as function annotations
3. Allow `tl.float32` as argument to be passed to `tl.alloc` or other functions

* fix lint error

* add more warning in frontend

* update tvm version

* Minor fix on tvm_ffi annotations

* add document and examples

* fix lint error

* Simplify index calculations in example_chunk_o_bwd.py

Refactor index calculations for dg_last_fragment assignment.

* minor fix

* lint fix

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

* 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>

* [Testing] Move TMA 1D and test for its functionality

* [Lint]

* [Refactor] Enhance TLVectorizer with loop vectorization convenience method and improve let variable handling

* lint fix

* let test fix

* lint fix

* Enhance Cast vectorized

* Add Parallel vectorized cast test

* code lint

* merge newest commit

* [Fix] init var with complex expression

* fix lint error

* [Feature] Add vectorized float16 and float32 conversion support in CUDA codegen

* Implemented handling for conversions between float16 and float32 types, specifically for vectorized operations using __half22float2 and __float22half2_rn.
* Enhanced the existing code to support both directions of conversion based on the lane count.
* Improved overall type handling in the VisitExpr_ method for better compatibility with TileLang.

* [Feature] Add float32 to float8 conversion support in CUDA codegen

* Implemented handling for conversion from float32 to float8 (E4M3/E5M2) in the VisitExpr_ method.
* Added vectorized conversion support using __nv_cvt_float2_to_fp8x2 for float2 to fp8x2 transformations.
* Enhanced type handling for better compatibility with TileLang, particularly for float8 types.

* lint

* fix a bug

* [Enhancement] Support lanes=4 cases and add unit test for vectorized cast

* lint

* [Feature] Refactor bf16 convertion operations and remove legacy compile flags

* lint

* - carry existing local-var initializer map into OpaqueBlockLower, reattach it to
    generated Allocates and the PrimFunc attrs
  - thread the map through FlattenBuffer and StorageRewrite so flattened/merged
    allocations keep their tl.local_var_init annotations
  - teach annotation handling to accept scalar initializers, resolve buffers, and merge
    with existing stat

* lint fix

* enhance

* lint fix

* lint fix