GitLab

[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

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

[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.

* [Test] Add cp async to avoid register spill

* [BugFix] GQA fwd and bwd
- Fix the undefined behavior of -inf in acc_s
- Fix the causal loop range in varlen scenario

* [TMA] Move on to TMA and locate the register spill issue

* [Debug] Not the reason of zero-assignment. Probably the combination of Parallel op & conditional qkT

* [Debug] The SIMT copy in producer occupies too many registers

* [BugFix] Use 3D lse and delta to avoid illegal instruction

* [Perf] Relaxed order for dQ and SIMT store for dKdV

* [Feat] For atomic add version

* [Lint]

* [Bugfix] Enable code lowering with producer‑copy‑only program (#1168)

* bugfix

* lint fix

* Enhance warp group register allocation to handle missing consumer bodies gracefully. Updated logic to annotate producer side when consumer is absent, ensuring robustness in degenerate warp-specialized patterns.

* Refactor VisitExpr_ method in inject_tma_barrier.cc for improved readability. Adjusted formatting and spacing for clarity in barrier handling logic.

* Update barrier handling in inject_tma_barrier.cc to accommodate newly appended entries. Adjusted the size of the replace vector to ensure it covers the full needed length, and modified the logic for appending barriers based on the updated replace conditions.

* [Bugfix] Support 16bits shfl_sync (#1169)

* Add type-safe warp shuffle helpers for 16-bit float types in common.h

- Introduced generic passthrough functions for warp shuffle operations: `shfl_xor_sync`, `shfl_down_sync`, `shfl_up_sync`, and `shfl_sync`.
- Added specializations for `cutlass::half_t` and `cutlass::bfloat16_t` to ensure type safety during shuffle operations.
- Updated `reduce.h` to utilize the new shuffle functions, enhancing code clarity and maintainability.

* lint fix

* [Testing] Move TMA 1D and test for its functionality (#1167)

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

* [Lint]

* [Refactor]: Change the params in pytest to avoid oom error during ci (#1170)

* [Refactor]: Change the params in pytest to avoid oom error during ci

* format

* fix

* Update test_example_cast.py

* Update parameters in test_example_cast

* Update test_example_flash_attention.py

* update

* format

* fix

* fix

* format

* [Bugfix] Fix tvm import path for editable build (#1172)

* [Language] Expose `T.warpgroup_fence_operand` for nvcc code motion (#986)

* 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

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

* [Language] Add Correctness and performance check scripts for V2 (#1174)

* fix

* lint fix

* fix

* lint fix

* fix

* upd

* [Bugfix] Legalize Datatype for mma intrinisc codegen  (#1179)

* fix

* lint fix

* Enhance CUDA code generation by updating register type handling for float data types. Introduced a workaround for TF32 type compatibility and improved the registration of MMA register types for A and B operands.

* [Perf] Add layout and use_tma to boost performance

* [Lint]

* [Note]

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Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>
Co-authored-by: Yuqi Dong <134183314+yyttt6@users.noreply.github.com>
Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk>

* [Feature] Support Reduce operators for bitwise and/or/xor

* [Lint]

* [Feature][Example] Support TMA reduce operation and update GQA bwd example

* move GQA bwd with TMA reduce to new example

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

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

* revert split+sum template for MHA backward

* lint

* Update example_mha_bwd.py

* Update example_mha_bwd_wgmma_pipelined.py

* Refactor attention sink examples to support bf16 and user-defined softmax scale

* fix typos

* Adding compile flags for fast math optimizations and enabling BF16 support in both GQA and MHA backward implementations.

* Update backward configuration for GQA and MHA examples to align with flash attention

* Refactor GQA backward implementation to improve atomic add performance

* Allow for slightly larger numerical error for bf16

* upd readme to show bf16 benchmark results

* lint

* fix ci and lint

* fix comments and lint

* refactor atomic add

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

[Example] Introduce split+sum template, and optimize `atomic_add` performance for bwd examples (#940)

* example fix

* lint fix

* bug fix

* reduce test size.

* [Enhancement] Enhance the GQA backward kernel by calculating `dq` and `dv` via copy&sum

* [Example] Implement GQA backward example for Hopper with customized tiling and pipeline

* [Example] Add relevant tests

* Fix all typos of wrong shape of `V_shared` in macros

* [Enhancement] Enable fast math optimization in tilelang JIT configurations

- Updated multiple examples and kernel functions to include `pass_configs` for enabling fast math optimization.
- Added support for the `TL_ENABLE_FAST_MATH` configuration option in the built-in operations.
- Enhanced the `LibraryGenerator` to handle the new fast math configuration, ensuring compatibility with existing settings.
- Updated documentation to reflect the changes in fast math handling and deprecation of the `TL_DISABLE_FAST_MATH` option.

* lint fix

* [Refactor] Introduce deprecated_warning utility for improved deprecation handling

- Added a new `deprecated_warning` function to streamline deprecation messages.
- Updated the `LibraryGenerator` to utilize the new function for warning about the deprecated `TL_DISABLE_FAST_MATH` configuration.
- Enhanced the `deprecated` decorator to support phaseout version messaging, improving clarity for users.

* [Refactor] Merge bulk copy into copy and refactor layout inference for bulk copy

* Deleted the `bulk_copy` operator implementation and its header file as it is no longer needed.
* Introduced a new function `cuTensorMapType()` to return the data type for CUDA tensor mapping.
* Updated related files to reflect these changes, ensuring that the codebase remains clean and maintainable.

* lint fix

* Fix typos in intrinsic names and remove unused print statement in block_sparse_attn_tilelang.py. Updated references from `ptx_ldmatirx` to `ptx_ldmatrix` across multiple files for consistency.

* remove bulk copy

* Refactor copy and atomic add operations to support TMA lower configuration

- Updated `GetCopyInst` to accept a `disable_tma_lower` parameter, allowing for conditional usage of TMA in bulk load/store operations.
- Modified `Lower` method in `Copy` to incorporate the new TMA configuration.
- Refactored `AtomicAdd::Lower` to streamline layout inference and vectorization logic.
- Removed unused `disable_tma_lower` field from `LowerArgs` structure for clarity.
- Enhanced atomic add vectorization by replacing the buggy implementation with a more robust loop vectorization approach.

* Enhance TMA bulk copy logic in `LowerBulkCopy` method

- Added a condition to set `desc.swizzle` to `CU_TENSOR_MAP_SWIZZLE_NONE` when `shared_layout` matches `linear_layout`, improving clarity in layout handling.
- Updated warning log to provide more detailed information about fallback scenarios, including source and destination buffer names and shapes, enhancing debugging capabilities.

* lint fix

* Remove fallback logging for non-swizzled global layout in `LowerBulkCopy` method to streamline the bulk copy logic. This change enhances code clarity by eliminating unnecessary warning messages related to inner box dimensions.

* Enhance reshape kernel compilation in `run_reshape` and `run_reshape_smem_1d_2_2d` functions

- Updated the `tl.compile` method to include `pass_configs` that disable TMA lower and warp specialization, addressing shared memory layout transformation limitations.
- Added TODO comments to indicate the need for further improvements in shared memory handling.

* Update `native_sparse_attention` function to include TMA configuration options

- Added `pass_configs` to the JIT decorator to disable TMA lower and warp specialization, addressing potential issues with shared memory layout transformations.
- Updated comments to clarify modifications in tensor shapes for inference, specifically setting `q` sequence length to 1.

* Refactor JIT decorator formatting in `native_sparse_attention` function

- Improved readability by reformatting the JIT decorator parameters for `native_sparse_attention`, ensuring consistent style across the codebase.
- No functional changes were made; this update focuses on code clarity and maintainability.

* Enhance thread management and logging in TileLang compilation

- Added a method to check if printing is enabled during compilation, improving control over logging behavior.
- Updated the JIT kernel class to utilize the new method for logging compilation status, ensuring consistent and clear output.
- Added comments to clarify the purpose of changes and improve code readability.

* Add warp specialization scope and refactor register management in TileLang

- Introduced a new constant `kWarpSpecializationScope` in `builtin.h` for better attribute management.
- Removed the `SetMaxNRegCollector` class and its related logic from `warp_specialized_rewriter.cc`, streamlining the warp specialization process.
- Added functions `annotate_producer_reg_dealloc` and `annotate_consumer_reg_alloc` in `builtin.py` to facilitate register management.
- Implemented `AnnotateWarpGroupRegAlloc` in `__init__.py` to inject register allocation calls into warp-specialized functions, enhancing the overall register handling in the compilation process.

* Refactor test for InjectSetMaxNReg pass in TileLang

- Improved readability by restructuring conditional checks and assertions in the test cases.
- Enhanced clarity in the collection of `set_max_nreg` calls by simplifying the logic.
- Ensured consistent formatting and spacing throughout the test functions for better maintainability.

* Enhance bulk copy and store checks in `Copy` class

- Updated scope validation for source and destination tensors in `CheckBulkLoad` and `CheckBulkStore` methods to include both `shared.dyn` and `shared` as valid options.
- Modified `CheckLDSMCopy` and `CheckSTSMCopy` methods to accommodate the new scope validation, ensuring compatibility with shared memory configurations.
- Improved logging in `LowerBulkCopy` to provide clearer warnings regarding unsupported swizzle layouts, including source and destination names for better debugging.

* lint fix

[BugFix] Do not modify strict layout in common or relax level of layout inference. More conditions on layout checking (#653)

* [BugFix] Do not modify strict layout in common or relax level of layout inference. More conditions on layout checking

* Lint

* test fix

* Update CI workflow to install dependencies without user site packages

- Modified the installation commands in the CI workflow to include the `--no-user` flag for both `requirements-dev.txt` and `requirements-test.txt`, ensuring that packages are installed in the virtual environment rather than the user site directory.

* Update CI workflow to install pip without user site packages

- Added the `--no-user` flag to the pip installation command in the CI workflow for both development and testing dependencies, ensuring that packages are installed within the virtual environment.

* Update requirements-test.txt

* reduce ci problem size,

* Refactor example_mla_decode.py for consistent formatting and remove unused imports in test_example_mla_decode.py

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

Fix assertion in GQA backward example to ensure correct tensor comparison for gradient validation (#568)

* [CI] Add CI test for flash_attention examples

* Update example_gqa_fwd_bshd.py

* Update example_mha_fwd_bshd_wgmma_pipelined.py

* [CI] Added conditional annotations for tests in flash_attention

* [CI] Added conditional annotations for tests in flash_attention

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

* [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.

* remove llvm build

* [Refactor] Update kernel compilation and profiling in examples

- Replaced `tilelang.lower` with `tilelang.compile` in multiple example scripts to streamline kernel compilation.
- Updated profiling calls to utilize the new `get_profiler` method, enhancing performance measurement consistency.
- Adjusted assertions and benchmarking methods to align with the new profiling structure across various examples, ensuring correctness and clarity in performance evaluations.

* lint fix

* License Update

* [Refactor] Improve code formatting and documentation in CUDA header and HIP runtime files

- Adjusted formatting in `cuda.h` for better readability, including alignment of comments and struct fields.
- Cleaned up whitespace and improved comment clarity in `rt_mod_hip.cc` to enhance code maintainability.

* [Refactor] Enhance formatting and clarity in CUDA header and HIP runtime files

- Improved comment alignment and readability in `cuda.h`.
- Cleaned up whitespace and formatting in `rt_mod_hip.cc` to enhance maintainability.

* lint fix

* lint fix

* lint fix

* lint fix

* fix

* License update

* [Enhancement] Update JITKernel to use artifact for kernel source

- Assigned the generated artifact to `self.artifact` for better management.
- Updated kernel source references to use `artifact.kernel_source` for consistency in execution backend handling.

* lint fix

* Add @tilelang.testing.requires_llvm decorator to vectorization tests

* Enhance setup.py and env.py for library management

- Added functionality to remove original files after copying in CMakeBuild.
- Updated TVM_LIBRARY_PATH in env.py to include the PyPI build library path for better integration.

* Refactor TVM_LIBRARY_PATH assignment for improved readability in env.py

* Refactor CMakeBuild file handling in setup.py

- Added a check to ensure the target library directory exists before copying .so files.
- Improved the logic for creating the target directory and copying files to enhance robustness.

* bugfix

* Rename BuildTLDebug to BuildTileLangCUDAWithoutCompile and update registration. Add @tilelang.testing.requires_llvm decorator to multiple tests for LLVM requirement.

* lint fix

* Enhance TileLang code generation by adding support for device code generation without compilation. Updated `host_codegen` and `device_codegen` functions to include new transformations and registration for `tilelang_hip_without_compile`. Refactored JIT kernel adapters to accommodate host and device modules, improving overall integration and flexibility.

* lint fix

* Add support for C target in device code generation

- Updated `device_codegen_without_compile` to include handling for the C target by registering the `tilelang_cpp` function.

* [Enhancement] Implement auto-clear cache feature based on environment variable

* Added TILELANG_CLEAR_CACHE environment variable to control cache clearing.
* Updated CI workflow to set TILELANG_CLEAR_CACHE during testing.
* Modified cache initialization to clear cache if TILELANG_CLEAR_CACHE is set to true.

* [Refactor] Update kernel invocation and import paths in tests and cache

* Changed kernel invocation in `test_tilelang_kernel_dequantize_gemm.py` to return the result.
* Updated import statements in `test_tilelang_kernel_int4_gemm_mma.py` to use `bitblas` instead of `tilelang`.
* Refactored paths for artifact and parameters in `kernel_cache.py` for better maintainability.

* [Refactor] Clean up whitespace and improve code formatting in kernel_cache.py

* Removed unnecessary blank lines and adjusted spacing for better readability in the KernelCache class.
* Enhanced overall code formatting to align with project standards.

* [Enhancement] Add bfloat16 test case and improve kernel caching logic

* Introduced a new test case for bfloat16 matrix multiplication in `test_tilelang_kernel_gemm_mma_intrinsic.py`.
* Updated `KernelCache` to handle multiple kernel source files and improve error handling during saving and loading.
* Refactored `JITKernel` to support instantiation from a database, enhancing flexibility in kernel management.
* Adjusted `CtypesKernelAdapter` and `CythonKernelAdapter` to utilize the new kernel loading mechanism from the database.
* Improved code formatting and readability across several files.

* lint fix

* Update bfloat16 matrix multiplication test case to use larger dimensions for improved coverage

- Introduce `example_gqa_bwd.py` demonstrating the backward pass of FlashAttention with pipelined execution.
- Implement forward and backward functions for FlashAttention, including preprocessing and postprocessing steps.
- Enhance argument parsing for batch size, heads, context size, and dimensions.
- Include a reference implementation for validation and performance benchmarking.

* 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

* [CI][Test] Add test cases for tilelang transform MultiVersionBuffer and WarpSpecialized

* Relax the mismatch ratio restrictions in the flash_linear_attention and mha tests

* [Dev] Add mha backward example