mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-21 06:40:23 +08:00
Lei Wang
c750fb8a98
* [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.
120 lines
3.4 KiB
Python
120 lines
3.4 KiB
Python
# type: ignore
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import tilelang
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import tilelang.testing
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import tilelang.language as T
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def debug_print_buffer(M=16, N=16, dtype=T.float16):
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@T.prim_func
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def program(Q: T.Tensor((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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shared_buf = T.alloc_shared([M, N], dtype)
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T.print(shared_buf)
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jit_kernel = tilelang.compile(program, target="cuda", execution_backend="tvm_ffi")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_buffer():
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debug_print_buffer(dtype=T.bool)
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debug_print_buffer(dtype=T.int8)
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debug_print_buffer(dtype=T.int16)
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debug_print_buffer(dtype=T.int32)
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debug_print_buffer(dtype=T.int64)
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debug_print_buffer(dtype=T.uint8)
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debug_print_buffer(dtype=T.uint16)
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debug_print_buffer(dtype=T.uint32)
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debug_print_buffer(dtype=T.uint64)
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debug_print_buffer(dtype=T.float16)
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debug_print_buffer(dtype=T.float32)
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debug_print_buffer(dtype=T.float64)
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debug_print_buffer(dtype=T.bfloat16)
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debug_print_buffer(dtype=T.float8_e4m3fn)
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debug_print_buffer(dtype=T.float8_e4m3fn)
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debug_print_buffer(dtype=T.float8_e4m3fnuz)
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debug_print_buffer(dtype=T.float8_e5m2)
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debug_print_buffer(dtype=T.float8_e5m2fnuz)
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def debug_print_buffer_conditional(M=16, N=16):
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dtype = T.float16
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@T.prim_func
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def program(Q: T.Tensor((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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shared_buf = T.alloc_shared([M, N], dtype)
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if bx == 0 and by == 0 and bz == 0:
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T.print(shared_buf)
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_buffer_conditional():
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debug_print_buffer_conditional(16, 16)
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def debug_print_value_conditional(M=16, N=16):
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dtype = T.float16
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@T.prim_func
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def program(Q: T.Tensor((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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tid = T.get_thread_binding()
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if tid == 0:
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T.print(bx + by + bz)
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_value_conditional():
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debug_print_value_conditional(16, 16)
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def debug_print_register_files(M=16, N=16):
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dtype = T.float16
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@T.prim_func
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def program(Q: T.Tensor((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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register_buf = T.alloc_fragment([M, N], dtype)
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for i, j in T.Parallel(M, N):
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T.print(register_buf[i, j])
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_register_files():
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debug_print_register_files(16, 16)
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def debug_print_msg(M=16, N=16):
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dtype = T.float16
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@T.prim_func
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def program(Q: T.Tensor((M, N), dtype)):
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with T.Kernel(4, 4, 2, threads=128 * 2) as (bx, by, bz):
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tid = T.get_thread_binding()
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if tid == 0:
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T.print(bx + by + bz, msg="hello world")
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jit_kernel = tilelang.compile(program, target="cuda")
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profiler = jit_kernel.get_profiler()
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profiler.run_once()
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def test_debug_print_msg():
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debug_print_msg(16, 16)
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if __name__ == "__main__":
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tilelang.testing.main()
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