GitLab

* Fix CopyNode Lower method to include disable_tma flag in GetCopyInst call

* Refactor flash attention implementation to disable TMA for specific copy and allow TMA for other operations

* attempt to fix lint

* update sm100 related utcmma, tmem, ld/st256 in src
* update sm100 related utcmma, tmem, ld/st256 in tilelang
* Remove deprecated GEMM examples and related README documentation for SM100 architecture support
* Update GEMM implementation to replace UTCMMA with TCGEN5MMA across relevant files
* Remove gemm_umma.py example and update README to reflect TCGEN5MMA terminology changes
* Update README.md for gemm_sm100 example by removing outdated API sections and streamlining documentation
* Update README and source files to reflect TCGEN5.MMA terminology changes
* Refactor CUDA GEMM header for improved readability

[Layout] Introduce Flexible Parallel to Support T.serial and local buffers inside T.Parallel loop (#844)

* Support T.serial and local buffers inside T.Parallel loop.

* Fix reducer layout in T.Parallel nested inside other loops

* Debug output with LOG(INFO)

* Add disable option for WGMMA.

* fix

* Use DLOG; fix missing registration for new pass config

* bug fix

* lint fix

* Enhance GEMM instruction set with UTCMMA and improve local buffer handling in casting example

* Update format.sh shebang, improve logging in layout inference, and enhance buffer store wrapper with detailed comments

* Enhance GEMM instantiation logic and improve layout inference for local buffer detection

- Updated the GEMM instantiation logic to include a check for WGMMA compatibility, ensuring that the conditions for using WGMMA are more robust.
- Refined the layout inference process to better identify when loops manipulate only local buffers, improving the accuracy of thread binding decisions in parallel loops.

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Co-authored-by: Huanqi Cao <caohuanqi@deepseek.com>

* Enhance attention sink examples with swizzled layout and performance metrics

- Added `make_swizzled_layout` annotations for shared tensors in the `flashattn` function across MHA and GQA examples to optimize memory access patterns.
- Updated benchmark outputs to include speedup calculations comparing Triton and TileLang implementations.

* Add README for Attention Sink example with algorithm details and benchmark results

- Introduced a new README.md file for the Attention Sink example, outlining the forward and backward algorithms, including the computation of `dsinks`.
- Provided benchmark results comparing performance metrics of the optimized implementation against Triton, highlighting speedup across various configurations.

* Update README.md for Attention Sink example to include link to Triton implementation

* Update examples/attention_sink/README.md
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* Update examples/attention_sink/example_gqa_sink_fwd_bhsd_wgmma_pipelined.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* typo

---------
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* Update MANIFEST.in and setup.py to include commit ID in versioning and adjust included files

- Modified MANIFEST.in to include shared library files `libtvm.so` and `libtvm_runtime.so`.
- Updated setup.py to conditionally include the commit ID in the package version based on the `WITH_COMMITID` environment variable.
- Enhanced versioning logic in version.py to use a truncated commit ID for better compatibility.

* Update setup.py and related scripts to enable commit ID inclusion in package metadata

- Changed the default value of the `WITH_COMMITID` environment variable in setup.py to "True".
- Updated tox.ini to set `WITH_COMMITID` to "TRUE" for the testing environment and "FALSE" for the build environment.
- Modified pypi_distribution.sh to pass `WITH_COMMITID=FALSE` during the wheel build process.

* Update MANIFEST.in to include additional files and directories for packaging

- Added VERSION, CMakeLists.txt, and various requirements files to the package.
- Included recursive inclusion of source files and third-party libraries, while excluding specific clang and llvm directories.

* Add fast math operations for CUDA: exp, exp10, log, log2, log10, tan, cos, and sin (#865)

* Refactor fast math operation definitions for consistency and readability in CUDA code. Consolidated multiple definitions into single lines and improved formatting in related test files for better clarity.

* Remove unnecessary pass configurations for warp specialization and TMA lowering in fast math operation tests for CUDA. This simplifies the test setup while maintaining the focus on fast math functionality.

* Update fastmath tests to reflect that tl.* intrinsics generate no fastmath versions and disable cache in main execution.

* Fix formatting in fastmath test comments for clarity on tl.* intrinsics behavior.

* Add precision comparison tool for CUDA operations

This commit introduces a new Python script and CUDA source file for a precision comparison tool that evaluates the accuracy of various CUDA operations (including division, reciprocal, exponential, logarithmic, and trigonometric functions) across different implementations: CUDA Precise, CUDA Fast, Triton, Triton LibDevice, and TileLang. The tool generates test data, executes the operations, and summarizes the error statistics for each implementation against a double precision reference. Additionally, a README file is added to document the results of the comparisons for various operations.

* Add precision comparison tool for CUDA operations

This commit introduces a new precision comparison tool implemented in Python and CUDA, designed to evaluate the accuracy of various mathematical operations (division, reciprocal, exponential, logarithmic, trigonometric, square root, etc.) across different frameworks including CUDA Precise/Fast, Triton, Triton LibDevice, PyTorch, and TileLang. The tool includes functionality for generating test data, executing operations, and summarizing error statistics for each implementation. Additionally, it provides a comprehensive README with error metrics for each operation tested.

* Add IEEE-compliant mathematical operations and refactor fast math module

This commit introduces new high precision mathematical operations including ieee_add, ieee_sub, ieee_mul, ieee_fmaf, ieee_frcp, ieee_fsqrt, ieee_frsqrt, and ieee_fdiv to the TileLang framework. The fast math module has been refactored to remove the deprecated fastmath.py file and update the import paths accordingly. Additionally, the CUDA code generation has been enhanced to support these new operations, ensuring compatibility with IEEE standards for floating-point arithmetic.

* debug removed

* Refactor IEEE math tests for improved readability and consistency

This commit enhances the formatting of the `test_ieee_math.py` and `test_mathops_fastmath.py` files by adjusting line breaks for better clarity. It also removes unnecessary comments and ensures that the main execution of tests is streamlined. These changes aim to improve the overall maintainability of the test code.

* Update README.md to enhance formatting of precision comparison results

This commit reformats the precision comparison results in the README.md file, converting the error statistics tables into a more structured markdown format. This change improves readability and accessibility of the data for various mathematical operations across different implementations, including FP32 Precise, Triton, TileLang, and CUDA.

* [Example] Add a new example to support attention sink for MHA

- Introduced a new example script for multi-head attention (MHA) with sliding window attention and sink tokens.
- Added a reference attention function to validate the implementation against PyTorch.
- Included argument parsing for command-line execution of the example.

* [Example] Replace MHA sink forward example with updated implementation

- Removed the old example script for multi-head attention (MHA) with sliding window attention and sink tokens.
- Introduced a new example script that modifies the attention mechanism to enhance performance and maintainability.
- Updated argument parsing and reference functions to align with the new implementation.

* Enhance MHA sink example with sliding window support

- Added a `window_size` parameter to the `flashattn` function to enable sliding window attention.
- Implemented assertions to ensure `window_size` is compatible with `block_N`.
- Updated the main function to include a `tune` option for performance tuning.
- Introduced a new test file to validate both full attention and sliding window scenarios.
- Adjusted FLOPS calculation to account for the sliding window configuration.

* lint

* [Fix] Add checkinf process to fix the bug of swa

* Migrate to BSHD layout to align with triton baselines

* lint

* fix typo

* Refactor MHA sink example to use seq_q and seq_kv parameters to accommodate the new sequence length parameters.

* Add GQA sink example for optimized attention mechanism & lint fix

* fix several typos and bugs

* lint

* fix speed issues of swa

* Add flash attention example with backward pass for BHSD layout and corresponding test cases

* Add backward pass implementation for flash attention with sinks and corresponding test case

* fix lint and typo

* Optimze the calculation of `dsinks`

* Add support for swa backward and update examples

* fix previous typos

* Add example for GQA sink backward pass and update tests for both MHA and GQA sinks

* fix lint

* fix previous typos

* typo

[FastMath] Disable default TVM fastmath intrinsic dispatch and add explicit fastmath op to invoke (#875)

* Add fast math operations for CUDA: exp, exp10, log, log2, log10, tan, cos, and sin (#865)

* Refactor fast math operation definitions for consistency and readability in CUDA code. Consolidated multiple definitions into single lines and improved formatting in related test files for better clarity.

* Remove unnecessary pass configurations for warp specialization and TMA lowering in fast math operation tests for CUDA. This simplifies the test setup while maintaining the focus on fast math functionality.

* Update fastmath tests to reflect that tl.* intrinsics generate no fastmath versions and disable cache in main execution.

* Fix formatting in fastmath test comments for clarity on tl.* intrinsics behavior.

* Add precision comparison tool for CUDA operations

This commit introduces a new Python script and CUDA source file for a precision comparison tool that evaluates the accuracy of various CUDA operations (including division, reciprocal, exponential, logarithmic, and trigonometric functions) across different implementations: CUDA Precise, CUDA Fast, Triton, Triton LibDevice, and TileLang. The tool generates test data, executes the operations, and summarizes the error statistics for each implementation against a double precision reference. Additionally, a README file is added to document the results of the comparisons for various operations.

* Add precision comparison tool for CUDA operations

This commit introduces a new precision comparison tool implemented in Python and CUDA, designed to evaluate the accuracy of various mathematical operations (division, reciprocal, exponential, logarithmic, trigonometric, square root, etc.) across different frameworks including CUDA Precise/Fast, Triton, Triton LibDevice, PyTorch, and TileLang. The tool includes functionality for generating test data, executing operations, and summarizing error statistics for each implementation. Additionally, it provides a comprehensive README with error metrics for each operation tested.

* Add atomic operations for CUDA templates in new atomic.h file

- Introduced atomic functions including AtomicMax, AtomicMin, AtomicAdd, and their return variants for various data types.
- Implemented support for half, bfloat16, and float types with appropriate memory ordering.
- Moved atomic-related utilities from common.h to the new atomic.h file for better organization.
- Added Python bindings for atomic operations in tilelang, including atomic_max, atomic_min, atomic_add, and their vectorized counterparts.
- Updated customize.py to utilize the new atomic functions, enhancing modularity and maintainability.

* Refactor atomic operations in CUDA templates for improved readability

- Reformatted atomic operation implementations in atomic.h for better code clarity.
- Adjusted function signatures in tilelang's atomic.py to enhance readability by aligning parameters.
- Cleaned up unnecessary whitespace and comments in customize.py to streamline the codebase.

* Add thread storage synchronization configuration option

- Introduced a new configuration option `tl.disable_thread_storage_sync` to control the automatic insertion of thread synchronization barriers in shared memory access.
- Updated the `ThreadSync` pass to check this configuration and bypass synchronization if disabled.
- Enhanced documentation in `builtin.h` and `pass_config.py` to clarify the purpose and usage of the new option.

* Refactor thread storage sync configuration retrieval

- Simplified the retrieval of the thread storage sync configuration in the `ThreadSync` pass by removing unnecessary intermediate variables.
- Ensured that the inclusion of `builtin.h` is consistent by moving it to the appropriate location in the file.

* test fix

* Update atomic operations and tests for improved functionality

- Updated atomic operations in CUDA templates to remove unnecessary address_of calls, enhancing performance and readability.
- Refactored atomic operation signatures in tilelang's atomic.py to accept references instead of pointers.
- Added new atomic operations and corresponding test cases for atomic add, max, min, and load/store functionalities in the testing suite.
- Updated the TVM subproject to the latest commit for better compatibility.

* Update attention sink examples to use 32 heads

- Modified the `heads` parameter in both `example_gqa_sink_fwd_bhsd_wgmma_pipelined.py` and `example_mha_sink_fwd_bhsd_wgmma_pipelined.py` from 1 to 32 to enhance performance in attention mechanisms.
- Ensured consistency across example scripts for improved usability and testing.

* Refactor atomic add handling in vectorization

- Simplified the extraction of buffer loads for atomic add operations by removing unnecessary address_of calls, improving code clarity and performance.
- Updated the data type retrieval for vectorization size calculation to directly access the buffer load node, enhancing efficiency.

* Add loop break functionality and enhance thread synchronization

- Introduced a new `loop_break` function in `customize.py` to allow breaking out of loops, returning a call to the `tl.loop_break` intrinsic.
- Updated the `sync_threads` function in `builtin.py` to accept optional parameters for `barrier_id` and `arrive_count`, improving its flexibility for thread synchronization.
- Added necessary imports in `__init__.py` to include the new `loop_break` function for broader accessibility.

* test fix

* Update submodule TVM to latest commit and fix condition comparison in merge_if_stmt.cc

* Update submodule TVM to latest commit 0524f760

* lint fix

* Update submodule 'tvm' to latest commit 7a71ee34

* lint fix

* Fix bug 0905: vectorize with broadcasted value

* fix lint error

* [Refactor] Use `tvm::tir::UseVar` and use Vectorizer

* Add loop size check in vectorize planner

* fix lint error

* [Example] Add a new example to support attention sink for MHA

- Introduced a new example script for multi-head attention (MHA) with sliding window attention and sink tokens.
- Added a reference attention function to validate the implementation against PyTorch.
- Included argument parsing for command-line execution of the example.

* [Example] Replace MHA sink forward example with updated implementation

- Removed the old example script for multi-head attention (MHA) with sliding window attention and sink tokens.
- Introduced a new example script that modifies the attention mechanism to enhance performance and maintainability.
- Updated argument parsing and reference functions to align with the new implementation.

* Enhance MHA sink example with sliding window support

- Added a `window_size` parameter to the `flashattn` function to enable sliding window attention.
- Implemented assertions to ensure `window_size` is compatible with `block_N`.
- Updated the main function to include a `tune` option for performance tuning.
- Introduced a new test file to validate both full attention and sliding window scenarios.
- Adjusted FLOPS calculation to account for the sliding window configuration.

* lint

* [Fix] Add checkinf process to fix the bug of swa

* Migrate to BSHD layout to align with triton baselines

* lint

* fix typo

* Refactor MHA sink example to use seq_q and seq_kv parameters to accommodate the new sequence length parameters.

* Add GQA sink example for optimized attention mechanism & lint fix

* fix several typos and bugs

* lint

* fix speed issues of swa

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

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

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

* Enhance LayoutNode::Forward method to handle variable transformations more robustly

- Updated the method to check for a minimum number of input dimensions.
- Introduced a mechanism to transform the last InputDim() elements of the input variables.
- Concatenated transformed variables with the remaining input variables for a comprehensive output.

* Refactor LayoutNode::Forward method for improved readability

- Removed unnecessary whitespace to enhance code clarity.
- Maintained existing functionality while streamlining the transformation process of input variables.

* fix flash attention examples  for `seqlen_q<seqlen_kv` cases

* lint

* Refactor matmul example to include ReLU activation and update batch size in benchmark script

* lint fix

* Enhance autotuning capabilities in benchmark script and update argument defaults

- Introduced a new `get_configs` function to generate autotuning configurations for the benchmark.
- Updated the default batch size and kv context length in the argument parser for improved performance.
- Renamed the `--auto_tune` argument to `--autotune` for consistency.
- Modified the kernel invocation logic to support autotuning based on the new configurations.

* lint fix

- Updated `init_desc_arg_map` to use `Var` as the key instead of `String` in `lower_hopper_intrin.cc`.
- Enhanced `func_call_args` method in `TLCUDASourceWrapper` to accept additional parameters for better argument mapping.
- Added assertions to ensure consistency between function parameters and arguments during kernel launches.
- Modified `generate_tma_descriptor_args` to utilize a mapping of variable names for TMA descriptor initialization.

* Refactor matmul example to include ReLU activation and update batch size in benchmark script

* lint fix

* bump version to 0.1.6

* phaseout py38

* py39

* Update submodule 'tvm' to latest commit adc0e48

* [Build] Update CMake and Python environment settings

- Added static linking flags for GCC and libstdc++ in CMakeLists.txt to enhance library linking.
- Removed the cmake version requirement from pyproject.toml to allow for broader compatibility.
- Updated the tox command in the Docker distribution script to include Python 3.8 for testing environments.

* [Build] Update Python version requirements in scripts and documentation

- Changed Python version requirement in README.md from 3.9+ to 3.8+.
- Updated installation and testing scripts to use Python 3.8 instead of 3.9, ensuring compatibility with the new minimum version.
- Adjusted tox commands in local and PyPI distribution scripts to include Python 3.8 in the testing environments.

* [Build] Update Python and CMake requirements in Dockerfile and pyproject.toml

- Added CMake version requirement (>=3.26) to pyproject.toml for build compatibility.
- Created a Python 3.8 environment in the Dockerfile and added a symlink for easier access to the Python 3.8 executable.

* [Build] Update CMake and Dockerfile for improved compatibility

- Removed static linking flags from CMakeLists.txt to simplify build configuration.
- Updated Dockerfile to use Ubuntu 20.04 and streamlined the installation of dependencies, removing gcc-9 and g++-9.
- Adjusted symlink creation for Python environments to use the `-sf` option for safer linking.

* [Build] Bump version to 0.1.6.post1 for post-release updates

* [Build] Remove static linking flags from CMakeLists.txt

- Eliminated static linking flags for GCC and libstdc++ to simplify build configuration and avoid potential conflicts with Python extensions.

* [Build] Update Docker distribution scripts for manylinux compatibility

- Changed base image from `tilelang-builder:18.04` to `tilelang-builder:manylinux` in both local and PyPI distribution scripts.
- Updated Dockerfile references to use `pypi.manylinux.Dockerfile`.
- Added `--gpus all` flag to the Docker run command to enable GPU support during execution.

* lint fix

* add cmake

* bump version to 0.1.6

* phaseout py38

* py39

* Update submodule 'tvm' to latest commit adc0e48

* [Build] Update CMake and Python environment settings

- Added static linking flags for GCC and libstdc++ in CMakeLists.txt to enhance library linking.
- Removed the cmake version requirement from pyproject.toml to allow for broader compatibility.
- Updated the tox command in the Docker distribution script to include Python 3.8 for testing environments.

* [Build] Update Python version requirements in scripts and documentation

- Changed Python version requirement in README.md from 3.9+ to 3.8+.
- Updated installation and testing scripts to use Python 3.8 instead of 3.9, ensuring compatibility with the new minimum version.
- Adjusted tox commands in local and PyPI distribution scripts to include Python 3.8 in the testing environments.

* [Build] Update Python and CMake requirements in Dockerfile and pyproject.toml

- Added CMake version requirement (>=3.26) to pyproject.toml for build compatibility.
- Created a Python 3.8 environment in the Dockerfile and added a symlink for easier access to the Python 3.8 executable.

- Updated the `AddWrapperForSingleBufStore` function to improve the handling of buffer stores by adding detailed checks for fragment buffer accesses and ensuring only index 0 is used.
- Introduced new helper functions for collecting buffer accesses and indices, enhancing code readability and maintainability.
- Refined the logic for determining tile operations and thread bindings to ensure accurate transformations without affecting existing parallel structures.

* Update submodule TVM to commit 872e32c1 and adjust type hints in nvcc.py and utils.py for compatibility with Python typing standards.

* Update requirements.txt to specify ml_dtypes without a version constraint, indicating that versions greater than 0.5.1 are needed for fp4 support.

* [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.

* bugfix

* fix

* test fix

* bugfix

* [Build] Update build dependencies and Dockerfile configuration

- Updated `pyproject.toml` and `requirements-build.txt` to specify Cython version as `Cython>=3.0.0`.
- Removed unnecessary dependencies from the build system.
- Enhanced `pypi.Dockerfile` to install gcc-9 and g++-9, and added ninja-build for improved build performance.
- Updated conda environment creation to include Python 3.9 to 3.12, while removing the Python 3.8 environment.

* cmake fix

* fix

* fix

* [Enhancement] Enhance dequantization examples and utilities

- Added a new example for grouped matrix multiplication with experts in `example_dequant_groupgemm_bf16_mxfp4_hopper.py`.
- Improved dequantization logic in existing examples by replacing nested loops with vectorized operations for better performance.
- Updated `torch_convert_bit_twiddling` function in `utils.py` to utilize parallel processing, enhancing efficiency and clarity in the conversion process.
Co-authored-by: Zhengju Tang <97930865+tzj-fxz@users.noreply.github.com>

* fix typos in docstrings

* remove redundant code

* [Format] Unreproducible debug with T.print

* [BugFix] Correct dtype in ref dequantize; larger data distribution

* [Format]

* [Refactor] Clean up and optimize example_dequant_groupgemm_bf16_mxfp4_hopper.py and utils.py

- Removed unnecessary cache disabling and manual seed setting in the example.
- Simplified nested loops into parallelized operations for better readability and performance.
- Updated the assertion function in utils.py to print detailed error messages.
- Adjusted tensor sizes in examples

* [Refactor] Update import path in example_dequant_gemm_fine_grained.py

- Changed the import statement for `_tir_packed_to_unsigned_convert` from `bitblas.quantization` to `tilelang.quantize` to reflect the new module structure.

* lint

* rename and add test

* lint

* [Feature] Enhance autotuning and configuration generation in example_dequant_groupedgemm_bf16_mxfp4_hopper.py

- Added a new function `get_configs()` to generate hyperparameter configurations for tuning.
- Updated the `matmul` function to utilize autotuning with the new configurations.
- Improve kernel performance via vectorization and threadblock swizzle.
- Enhanced the main function to support the new autotuning inputs and updated parameters for better performance.

* lint

* fix typo

* fix typo and lint

* make ci format check happy

* fix ci

---------
Co-authored-by: Zhengju Tang <97930865+tzj-fxz@users.noreply.github.com>
Co-authored-by: tzj-fxz <tzjfxz@gmail.com>

* bug fix when git is not installed

* ml_dtypes_fix