GitLab

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

* issue fix

* fix

* fix

* decreate nproc for debugging

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

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

* rename

* comment

* address comments

Co-authored-by: cheeryBloosm <liu_yu_hao@126.com>

- Updated the `tilelang_callback_cuda_compile` function to accept a `pass_config` parameter, allowing for more flexible compilation options.
- Introduced handling for fast math and PTXAS options based on the provided pass configuration.
- Modified the CUDA build process in `rt_mod_cuda.cc` to utilize the current pass context, improving the integration of compilation settings.
- Refactored NVCC command construction to use a dedicated function for better clarity and maintainability.

* 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

* fix for bool dtype

* lint fix

* fix

* ci fix

* [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 argument order for fla chunk_gated_delta_rule_fwd_h

* explicit import assert_similar from utils

* rename utils module to avoid name clash

* set store_final_state and save_new_value to True

* fix

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

* Fix various issues under int64_t static and dynamic shape.

* Resolve reviewed issues.

* Add unit test.

* fix

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

* [BugFix] Adding extra parameters into autotune hashkey

* lint

* None check

* check serializable

[Enhancement] Keep max score attention across blocks in FlashAttention for better numerical stablity (#1269)

* Implement max score retention across blocks in FlashAttention for improved stability

* fix manual pipeline parameters

* Update examples/flash_attention/example_gqa_fwd_varlen.py
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

* fix typo

* more

* fix a previous typo

---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

* [Docs] Improve installation guide

* address comments

[EXAMPLE] In the flash attention example keep the max of all blocks seen in scores_max numerical stability (#1148)

* Keep the max of all blocks seen in scores_max for stability

* ruff formatting

* 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

* [Example] Add page table for gqa decode

* [Example] Page table for varlen decoding

* [Lint]

* [Refactor] Remove redundant code

* [Lint]

* [Lint]

* [Lint]

* 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>

* [fix] NVRTC execution backend

* [fmt] run pre-commit

* [fix] coderabbit reviews

* [test] add cuda-python to test dep

* [fix] coderabbit reviews

* [fix] CUDA 13 compatibility

* [fix] sm90

* [fix] CUDA 13 compatibility

* [fix] pre-commit

* [fix] always use cuda::std::__atomic_ref_impl

* [fix] restore to external API

* Revert "[fix] restore to external API"

This reverts commit 49bd875638fb631d270015f408991d38fd1e9a5d.

* [fmt] use space instead tabs for py codegen

* [fix] im2col API

* [fix] revert atomic.h

* [fix] dynamic shape

* [refactor] extract common utils

* [feat] support L2 persistent map

* [fix] l2 persistent map

* [fix] pre-commit

* [fix] restore _TYPE_MAP

* [fix] pre-commit

* [fix] avoid duplicate TMA descs

* [docs] add docstring

* [fix] coderabbit

* [fix] coderabbit

* [fix] coderabbit

* [fix] coderabbit

[BugFix] Refactor attention kernel to handle OOB positions by filling with `-inf` instead of clearing accumulators. (#1222)

* Refactor attention kernel to handle OOB positions by filling with `-inf` instead of clearing accumulators.

* lint

* pre-commit

* Update imports in flash attention test file to use new backward and forward examples for better clarity and consistency.

* [BugFix] Add autotune and exp2 for GDN kernel

* [Lint]

* [Lint]

* add typing stub for tir.ir

* remove idents

* minor update

* [Language] Add missing while statement

* add test

* [Refactor] Update buffer handling in copy and atomic operations

* Refactored the `copy` and `atomic_add` functions to use element-wise minimum for defining copy extents, ensuring correct handling of overlapping regions.
* Updated utility functions to create `BufferLoad` instances with explicit extents, improving memory management and clarity.
* Removed unused imports from `atomic.py` and `copy.py` to streamline the codebase.
* Adjusted logging in `copy.cc` to provide clearer warnings for fallback scenarios in bulk copy operations.

* Remove obsolete .git_commit.txt file

* Add unit test for dynamic copy extent handling in TileLang

* Introduced a new test file `test_tilelang_issue_1237.py` to verify that the `T.copy` function correctly manages dynamic extents during primitive function building.
* The test reproduces a specific issue related to dynamic slice lengths and static buffer sizes, ensuring robustness in the handling of such scenarios.
* The test does not require execution of the kernel, as building the primitive function is sufficient to validate the fix.

* lint fix

* fix

* Revert "fix"

This reverts commit 828b4c1e4de76a7d11e4d4092927303fbbe00097.

* Update TVM submodule and refactor atomic and copy functions

* Updated the TVM submodule to a dirty state.
* Refactored `atomic_add` and `copy` functions to pass extents explicitly to the `_to_region` helper, improving clarity and correctness in handling buffer regions.
* Commented out the main execution call in the test example for `cast` and added a new function call to better demonstrate the example usage.

* Enhance extent handling in atomic and copy functions

* Introduced `legalize_pairwise_extents` utility to align and broadcast extent lists for `atomic_add` and `copy` functions, ensuring compatibility and correctness in buffer operations.
* Updated both functions to utilize the new utility, improving clarity and robustness in handling dynamic and static extents.
* Added comments to clarify the extent handling logic.

* Enhance `legalize_pairwise_extents` function with early-exit rule

* Added an early-exit condition to the `legalize_pairwise_extents` function to return original extents if the number of non-1 dimensions in both source and destination extents is equal, improving performance by avoiding unnecessary adjustments.
* Updated the function's documentation to clarify the new behavior and maintain clarity in the extent handling logic.

* lint fix

* fix

* Refactor tensor reshaping in fp8_lighting_indexer.py

- Replaced the allocation of `s_reshaped` with a reshape operation to improve clarity and performance.
- Updated the logic in the computation of `s_reshaped` to utilize the reshaped tensor, enhancing the overall functionality of the attention mechanism.

* Refactor analyzer usage in Layout and Fragment reshaping

- Consolidated analyzer logic in the `Reshape` methods of `LayoutNode` and `FragmentNode` to utilize a fallback analyzer, improving code clarity and preventing potential null dereference issues.
- Updated variable binding and simplification calls to use the selected analyzer consistently, enhancing robustness in shape validation and index computation.

* [Enhancement] Add FP8 support and reproducibility in lighting indexer

* Introduced a manual seed in `test_fp8_lighting_indexer` to ensure reproducible performance.
* Added specializations for `cute::float_e4m3_t` and `cute::float_e5m2_t` in `gemm_mma.h` for enhanced FP8 support across multiple CUDA architectures, ensuring compatibility and improved functionality.ix

* Fix typos in `fp8_lighting_indexer.py` and improve formatting in `gemm_mma.h`

* Corrected a typo in the comment for `test_fp8_lighting_indexer` to enhance clarity.
* Reformatted lines in `gemm_mma.h` for better readability by aligning template specializations across multiple CUDA architectures.

* test fix

* bug fix

* Deleted the LoopVectorizeDynamic implementation from the transform module.
* Removed associated references in the phase and initialization files to streamline the codebase.
* This change simplifies the transformation pipeline by eliminating unused functionality.
Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk>

* Fix division by zero in RMS normalization

* Fix rsqrt calculation to avoid division by zero

* Add correctness evaluation script for GEMM v2

- Introduced a new Python script `correctness_evaluation_tcgen05.py` for testing the correctness of GEMM v2 implementations using pytest.
- Implemented matrix multiplication and compilation checks, along with parameterized tests for various input configurations.
- Enhanced the testing framework to validate GEMM operations with different data types and configurations, ensuring robustness in the implementation.
- Updated logging in `legalize_negative_index.cc` to reduce verbosity by changing from WARNING to DLOG.
- Adjusted assertions in `tcgen05_macro_generator.py` to accommodate new warp size requirements for improved performance.
- Removed unused variable in `gemm_tcgen05.py` to streamline the codebase.

* lint fix

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

* 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

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

* add typing stub for tir.ir

* remove idents

* minor update

* Add kernel selection option for GEMM v1 in environment settings

- Introduced `TILELANG_USE_GEMM_V1` environment variable to control the selection of GEMM version.
- Added `use_gemm_v1` method in the `Environment` class to determine if GEMM v1 should be used based on the environment variable.
- Updated GEMM function assignment to default to v2, allowing for v1 to be forced via the new environment variable.

* bug fix

* Add kernel selection option for GEMM in environment settings

- Introduced `TILELANG_USE_GEMM_V1` environment variable to allow users to select between GEMM v1 and v2 implementations.
- Updated `gemm` function to default to v2 but switch to v1 if the environment variable is set to a truthy value.
- Added a method `use_gemm_v1` in the `Environment` class to facilitate this selection based on the environment variable.

* Refactor GEMM macro generator to use BufferRegion instead of Buffer

- Updated `wgmma` and `wgmma_rs` methods in `TensorCoreIntrinEmitter` to accept `BufferRegion` parameters instead of `Buffer`.
- Adjusted related calls in `GemmWGMMA` to ensure compatibility with the new parameter types.
- Simplified buffer access logic for better clarity and maintainability.

* Refactor GEMM functions to utilize BufferRegion for improved memory handling

- Updated `run_gemm`, `run_gemm_rs`, `run_gemm_sr`, and `run_gemm_rr` functions to set `num_stages` based on block dimensions, enhancing performance for larger matrices.
- Simplified calls to GEMM functions by removing redundant parameters and ensuring compatibility with BufferRegion.
- Introduced utility functions for converting between Buffer, BufferLoad, and BufferRegion, improving code clarity and maintainability.
- Enhanced error handling for full region checks in GEMM operations to ensure correctness in memory access.

* Refactor GEMM code for improved readability and consistency

- Cleaned up formatting and spacing in GEMM-related files for better readability.
- Standardized comments and code structure across various GEMM functions and macros.
- Enhanced error messages for clarity in buffer region checks.
- Removed redundant lines and improved overall code maintainability.

* Update GEMM correctness evaluation and macro generator for improved functionality

- Modified `N_VALUES` in `correctness_evaluation_sm70.py` to include only relevant sizes for tests.
- Updated test function call in `correctness_evaluation.py` to use `test_gemm_false_true` for better accuracy in testing.
- Refactored buffer handling in `mma_sm70_macro_generator.py` to improve clarity and consistency in shared buffer access.
- Enhanced `gemm_mma_sm70.py` to ensure full region checks for input and output buffers, improving correctness in GEMM operations.

* Refactor GEMM and intrinsic files for improved clarity and functionality

- Removed unused variable `A_stride_last` in `mma_sm70_macro_generator.py` to streamline code.
- Adjusted function signature formatting in `swizzle.py` for better readability.
- Restored the return of `GemmWGMMA` in `__init__.py` for correct GEMM instantiation.
- Removed unused variable `B_buf` in `gemm_mma_sm70.py` to enhance code cleanliness.
- Improved function signature formatting in `language.py` for consistency.

* Enhance GEMM and MMA functionality for FP64 support

- Refactored `GemmNode` to streamline the decision-making process for GEMM instruction selection.
- Added support for FP64 inputs in the MMA dispatcher, enabling new tensor operations.
- Introduced a new layout function for FP64 in `mma_layout.py` to facilitate shared memory storage.
- Updated `TensorCoreIntrinEmitter` to handle FP64 data types, including adjustments for micro tile dimensions and loading mechanisms.
- Enhanced utility functions to accommodate FP64 index mapping for shared memory operations.

* lint fix

* Refactor GEMM correctness evaluation and shared memory alignment handling

- Reverted the GEMM function call in `correctness_evaluation.py` to the original implementation for consistency.
- Added a helper function in `merge_shared_memory_allocations.cc` to streamline the marking of shared variables under alignment scope.
- Enhanced the `VisitExpr_` methods to ensure proper handling of shared memory alignment for `BufferLoadNode` and `VarNode` types.
- Cleaned up commented-out test code in `correctness_evaluation.py` for better readability.

* Enhance GEMM and MMA implementations with region-based memory handling

- Updated GEMM and MMA classes to utilize BufferRegion for input and output buffers, improving memory management and supporting strided GEMM operations.
- Added checks to ensure full region compliance for input buffers, enhancing correctness in matrix multiplication.
- Implemented clear accumulation functionality to reset output buffers before accumulation, ensuring accurate results in GEMM operations.

* Refactor test_tilelang_example_deepseek_v32.py to improve import structure and function calls

- Updated import statements to directly reference modules instead of individual test functions, enhancing clarity.
- Modified function calls to use the new module structure for better organization and maintainability in testing examples.

* Enhance OnArrayDeclaration method to handle repeated buffer declarations

- Updated the OnArrayDeclaration method to merge metadata for buffers that may appear in multiple Allocate statements, improving robustness against upstream transformations.
- Added logic to prefer concrete element data types and record extents when previously unknown, enhancing the handling of buffer declarations.

* Add abbreviation for bfloat16 data type in mfma_macro_generator.py

- Introduced a new abbreviation "bf16" for the bfloat16 data type in the mfma_macro_generator.py file, enhancing clarity and consistency in data type representation.

* Refactor CodeGenTileLangHIP to enhance dtype handling and mfma call generation

- Introduced a mapping function to normalize input data types to their corresponding scalar types, improving compatibility with MfmaTraits.
- Updated the mfma call generation to utilize the new mapping, streamlining the code and enhancing clarity.
- Removed outdated dtype mapping and replaced it with a more flexible approach to support additional data types like FP8.

* lint fix

* Enhance backend configuration in CMakeLists.txt and improve dtype handling in CodeGenTileLangHIP

- Introduced a macro to define backend options for CUDA, ROCM, and Metal, allowing user overrides and caching of settings.
- Updated logic to track user-selected backends and conditionally enable defaults based on environment variables.
- Refactored dtype handling in CodeGenTileLangHIP to streamline mfma call generation and improve clarity.
- Added support for bfloat16 in the mfma_macro_generator.py, enhancing data type representation consistency.

* Update bfloat16 handling in CodeGenTileLangHIP and mfma_macro_generator.py

- Changed the representation of bfloat16 in CodeGenTileLangHIP from "bfloat16x4" to "bfloat16x4_vec" for improved clarity.
- Adjusted the mfma_suffix generation in mfma_macro_generator.py to remove the underscore before "bf16", aligning with HIP intrinsic requirements.

* Change logging level from WARNING to DLOG in LegalizeNegativeIndex for non-negative index checks to reduce log verbosity.

* Refactor attention sink examples to simplify index calculations

- Updated index handling in `example_gqa_sink_bwd_bhsd.py` and `example_mha_sink_bwd_bhsd.py` to eliminate unnecessary local allocations and streamline logic for determining start and end indices.
- Improved readability by using direct calculations instead of local variables for index bounds in pipelined loops.

* Refactor attention sink examples to streamline index calculations

- Simplified index handling in `example_gqa_sink_bwd_bhsd.py`, `example_gqa_sink_fwd_bhsd_wgmma_pipelined.py`, `example_mha_sink_bwd_bhsd.py`, `example_mha_sink_fwd_bhsd_wgmma_pipelined.py`, and `example_mha_sink_fwd_bhsd.py` by removing unnecessary local allocations for start and end indices.
- Enhanced readability by directly calculating index bounds for pipelined loops, improving overall code clarity.

* lint fix

* bugfix

* Refactor reduce operation handling in CUDA and Python

- Removed outdated shared memory reduction logic from `reduce.cc`.
- Introduced fragment allocation and improved buffer handling in `reduce.py` to support shared and fragment scopes.
- Updated CUDA header to define a wider accumulator type for better numerical accuracy.
- Enhanced error handling for buffer scope validation in the reduction process.

* Fix ReduceOpNode to correctly compute AbsMax by using absolute values of inputs

* Enhance unit loop handling by refining annotation checks

- Updated the condition for identifying effectively empty annotations in unit loops to include cases where only the `pragma_unroll_explicit` hint is present.
- Introduced a new method, `IsEffectivelyEmptyAnnotation`, to encapsulate this logic, improving code clarity and maintainability.

* clean clode