GitLab

* [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.

* 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>

* [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.

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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

* 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

* add typing stub for tir.ir

* remove idents

* minor update

* [Language] Add missing while statement

* add test

* [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

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

* [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

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