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Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Lei Wang
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c750fb8a98 |
[Enhancement] Update examples and tests for improved type handling functionality (#1448)
* [Enhancement] Update examples and tests for improved type handling and functionality
- Enhanced various example scripts to support new data types and improve compatibility with PyTorch.
- Updated tests across multiple modules to ensure correct functionality with the latest changes in type handling.
- Refactored code in examples to streamline operations and improve clarity, particularly in tensor operations and memory management.
- Added comprehensive tests for new features and fixed existing issues related to type conversions and buffer handling.
* [Refactor] Update accumulation data type to float32 across examples
- Changed accumulation data type from "float" to T.float32 in multiple example scripts to ensure consistency and improve numerical stability.
- This update affects various modules including flash attention, GEMM analysis, convolution, and deepseek MLA examples, enhancing type handling across the board.
* [Refactor] Standardize data type usage across benchmark scripts
- Updated data type definitions in benchmark scripts to use T.float16 and T.float32 consistently, enhancing clarity and type handling.
- Adjusted dtype assignments in matmul functions and configuration setups to align with the new standard.
- Improved overall code consistency and maintainability by ensuring uniform data type usage across various modules.
* [Refactor] Standardize data type usage in templates and scripts
- Updated data type definitions in various templates and scripts to use string representations (e.g., "float16", "int32") instead of T.float16 and T.int32 for improved consistency and clarity.
- Enhanced overall code maintainability by ensuring uniform data type usage across multiple modules, including convolution, elementwise operations, and matrix multiplication templates.
- This change aims to streamline type handling and improve compatibility with existing workflows.
* [Refactor] Standardize data type usage in examples and benchmarks
- Updated data type definitions in various example and benchmark scripts to use T.float16 and T.int32 consistently, enhancing clarity and maintainability.
- Adjusted dtype assignments in kernel functions and configuration setups to align with the new standard.
- Improved overall code consistency by ensuring uniform data type usage across multiple modules, including attention mechanisms, matrix multiplication, and GEMM examples.
* [Refactor] Import dtypes from language.v2 module
- Added import statement for dtypes from the language.v2 module to enhance type handling and maintain consistency across the codebase.
- This change aims to streamline data type management and improve overall code clarity.
* fix
* [Refactor] Standardize data type usage across scripts
- Updated data type definitions in various scripts to use string representations (e.g., "float16", "int8") instead of T.float16 and T.int8 for improved consistency and clarity.
- Adjusted dtype assignments in functions and configuration setups to align with the new standard, enhancing overall code maintainability.
- This change affects multiple modules, including benchmark and attention mechanisms, ensuring uniform data type usage throughout the codebase.
* [Refactor] Update data type handling for consistency and clarity
- Changed string representations of data types in the Hint class to use T.float32 and T.int32 for improved consistency.
- Added new data types "int4" and "int16" to the dtypes module, enhancing type support across the codebase.
- Updated function signatures and assertions in the lop3 and mxfp modules to utilize the new data types, ensuring uniformity in type handling.
- This refactor aims to streamline data type management and improve overall code clarity and maintainability.
* [Enhancement] Improve data type handling and error messaging
- Introduced a mapping for canonical data types to their display strings, enhancing clarity in type representation.
- Updated the dtype creation logic to utilize the new mapping, ensuring more intuitive handling of string inputs.
- Refined error messages in the lop3 module to provide clearer feedback on invalid source formats, improving debugging and user experience.
* [Fix] Correct boolean flag in GEMM SP test case
- Updated the boolean flag in the test_gemm_sp_sm90 function to ensure proper functionality in the test case.
- This change enhances the accuracy of the test and aligns it with expected behavior for the GEMM SP implementation.
* [Refactor] Standardize data type usage across scripts
- Updated data type definitions in various scripts to use T.float16 and T.bfloat16 consistently, enhancing clarity and maintainability.
- Adjusted dtype assignments in function signatures and argument parsing to align with the new standard, ensuring uniform data type usage throughout the codebase.
- This change affects multiple modules, including benchmarks and examples, improving overall code consistency and readability.
* [Refactor] Standardize data type usage in various modules
- Updated data type assignments in multiple scripts to utilize T.float32, T.int8, and T.int32 consistently, enhancing clarity and maintainability.
- Adjusted function signatures and parameter types across benchmarks, examples, and tests to align with the new standard, ensuring uniform data type usage throughout the codebase.
- This change improves overall code consistency and readability, impacting modules related to matrix multiplication, GEMM, and tensor operations.
* [Refactor] Update argument parsing for data types in benchmarks
- Changed argument parsing for data types in benchmark_matmul_intrinsic.py and benchmark_matmul_sp.py to use string representations ("float16", "int8", "float") instead of T.float16 and T.float.
- This update enhances consistency in data type handling across benchmark scripts, improving clarity and maintainability.
* [Refactor] Update data type handling in benchmark and example scripts
- Changed data type arguments in benchmark and example scripts to use string representations ("float16") instead of T.float16 for improved consistency.
- Updated function signatures and argument parsing to align with the new standard, enhancing clarity and maintainability across the codebase.
- This change affects multiple modules related to attention mechanisms and tensor operations, ensuring uniform data type usage throughout the examples.
* [Refactor] Fix data type conversion in multiple scripts
- Corrected the usage of the data type conversion method from dtype..as_torch() to dtype.as_torch() across various benchmark and example scripts.
- This change enhances consistency in data type handling and improves code readability, impacting modules related to attention mechanisms and tensor operations.
* [Refactor] Update float8 data type usage across multiple scripts
- Changed instances of T.float8_e4m3 to T.float8_e4m3fn in various benchmark, example, and test scripts to ensure consistency in data type handling.
- This update enhances clarity and maintainability across the codebase, particularly in modules related to matrix multiplication and tensor operations.
* [Refactor] Enhance float8 data type handling in CUDA code generation
- Updated the handling of float8 data types in the CUDA code generation to include additional float8 variants, improving type conversion logic.
- Adjusted conditions to ensure proper type checks for float8 conversions, enhancing clarity and maintainability in the codebase.
- Modified layout inference to streamline float8 type checks, ensuring consistency across the implementation.
- This change impacts modules related to matrix operations and CUDA code generation, improving overall type handling and conversion accuracy.
* [Refactor] Streamline float8 data type handling in CUDA and related modules
- Enhanced float8 data type handling in CUDA code generation by refining type conversion logic and ensuring consistent type checks.
- Updated layout inference for float8 types to improve clarity and maintainability across the implementation.
- This change impacts modules related to matrix operations and CUDA code generation, improving overall type handling and conversion accuracy.
* [Refactor] Remove unnecessary cache disabling in float8 example script
- Eliminated the call to tilelang.disable_cache() in example_group_per_split_token_cast_to_fp8.py to streamline the code.
- This change enhances clarity and maintainability of the example script without affecting its functionality.
* [Refactor] Update data type usage in debug print tests
- Changed the argument for dtype in the test_debug_print_buffer function from a string representation to the corresponding T.bool type.
- This update enhances consistency in data type handling within the test suite, improving clarity and maintainability.
* lint fix
* Update function parameter types from `str` to `T.dtype` for improved type safety in attention sink and related examples
* Refactor `gemv_alloc_reducer` function signature for improved readability by formatting parameters across multiple lines.
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Lei Wang
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29051439db | [Lint] Phaseout Yapf format and embrace ruff format (#1417) | ||
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Chaofan LinandLei Wang
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9dda774aff |
[BugFix] Use BufferRegion in tl.cumsum to infer buffer shape (#1321)
* [BugFix] Use BufferRegion in tl.cumsum to infer buffer shape * remove debug lines * remove rubbish * Fix decorator syntax for atomic_different_memory_orders_program --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> |
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Lei Wang
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d4b6d0945e |
[Enhancement] Shared Memory Size Can be Dynamic (#1294)
* bugfix * lint fix * test * lint fix * increate procs * recover |
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Kevinzzandtzj-fxz
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2de566e798 |
[BugFix] Remove memory_order in atomic constexpr and fix NSA bwd (#1260)
* 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> |
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Lei Wang
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aa0b1090c9 |
[Language] Support atomic add with ret (#870)
* 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 |
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Lei Wang
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3c5190e019 |
[Enhancement] Simplify vectorization process in loop_vectorize.cc and add atomic add test (#436) (#439)
* Removed redundant simplification step in vectorization logic to streamline performance. * Introduced a new test for atomic addition in TileLang, validating functionality with a reference implementation using PyTorch. |