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https://github.com/tile-ai/tilelang.git
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* [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.
224 lines
6.0 KiB
Python
224 lines
6.0 KiB
Python
from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang as tl
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import tilelang.language as T
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tilelang.testing.set_random_seed()
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def _make_shared_reduce(M, N, dtype, reduce_cb):
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@T.prim_func
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def main(
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A: T.Tensor((M, N), dtype),
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B: T.Tensor((M,), dtype),
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):
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with T.Kernel(1) as _:
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A_shared = T.alloc_shared((M, N), dtype)
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B_shared = T.alloc_shared((M,), dtype)
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T.copy(A, A_shared)
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reduce_cb(T, A_shared, B_shared)
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T.copy(B_shared, B)
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return main
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def _run_program(program, ref_program, atol=1e-2, rtol=1e-2):
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jit_kernel = tl.compile(program, out_idx=-1)
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profiler = jit_kernel.get_profiler()
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profiler.assert_allclose(ref_program, atol=atol, rtol=rtol)
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def reduce_max_test(M, N, dtype=T.float16):
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor((M, N), dtype),
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B: T.Tensor((M,), dtype),
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):
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with T.Kernel(1) as _:
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A_local = T.alloc_fragment((M, N), dtype)
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B_local = T.alloc_fragment((M,), dtype)
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T.copy(A, A_local)
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T.reduce_max(A_local, B_local, dim=1)
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T.copy(B_local, B)
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return main
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def reduce_sum_test(M, N, dtype=T.float32):
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor((M, N), dtype),
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B: T.Tensor((M,), dtype),
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):
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with T.Kernel(1) as _:
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A_local = T.alloc_fragment((M, N), dtype)
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B_local = T.alloc_fragment((M,), dtype)
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T.copy(A, A_local)
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T.reduce_sum(A_local, B_local, dim=1)
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T.copy(B_local, B)
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return main
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def reduce_sum_ss(M, N, dtype=T.float32):
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return _make_shared_reduce(M, N, dtype, lambda T, src, dst: T.reduce_sum(src, dst, dim=1))
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def reduce_max_ss(M, N, dtype=T.float32):
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return _make_shared_reduce(M, N, dtype, lambda T, src, dst: T.reduce_max(src, dst, dim=1))
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def reduce_min_ss(M, N, dtype=T.float32):
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return _make_shared_reduce(M, N, dtype, lambda T, src, dst: T.reduce_min(src, dst, dim=1))
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def reduce_abssum_ss(M, N, dtype=T.float32):
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return _make_shared_reduce(M, N, dtype, lambda T, src, dst: T.reduce_abssum(src, dst, dim=1))
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def reduce_absmax_ss(M, N, dtype=T.float32):
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return _make_shared_reduce(M, N, dtype, lambda T, src, dst: T.reduce_absmax(src, dst, dim=1))
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def run_reduce_sum(M, N, dtype=T.float32, mode="rr"):
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if mode == "rr":
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program = reduce_sum_test(M, N, dtype)
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elif mode == "ss":
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program = reduce_sum_ss(M, N, dtype)
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else:
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raise NotImplementedError("run_reduce_sum only supports rr and ss")
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_run_program(program, lambda A: A.sum(dim=1))
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def run_shared_reduce(program_builder, ref_program, M, N, dtype=T.float32):
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program = program_builder(M, N, dtype)
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_run_program(program, ref_program)
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def run_reduce_max(M, N, dtype=T.float16):
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program = reduce_max_test(M, N, dtype)
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_run_program(program, lambda A: A.max(dim=1).values, atol=1e-2, rtol=1e-2)
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def test_reduce_sum():
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run_reduce_sum(256, 256)
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run_reduce_sum(512, 128)
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run_reduce_sum(128, 512)
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def test_reduce_sum_shared():
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run_reduce_sum(64, 64, mode="ss")
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def test_reduce_max():
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run_reduce_max(256, 256, T.float16)
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run_reduce_max(512, 128, T.float16)
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run_reduce_max(256, 256, T.float32)
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def test_reduce_max_shared():
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run_shared_reduce(reduce_max_ss, lambda A: A.max(dim=1).values, 64, 64, T.float32)
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def test_reduce_min_shared():
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run_shared_reduce(reduce_min_ss, lambda A: A.min(dim=1).values, 64, 64, T.float32)
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def test_reduce_abssum_shared():
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run_shared_reduce(reduce_abssum_ss, lambda A: A.abs().sum(dim=1), 64, 64, T.float32)
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def test_reduce_absmax_shared():
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run_shared_reduce(reduce_absmax_ss, lambda A: A.abs().max(dim=1).values, 64, 64, T.float32)
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def reduce_sum_test_clear(M, N, dtype=T.float32):
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor((M, N), dtype),
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B: T.Tensor((M,), dtype),
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):
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with T.Kernel(1, threads=32) as _:
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A_local = T.alloc_fragment((M, N), dtype)
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B_local = T.alloc_fragment((M,), dtype)
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T.copy(A, A_local)
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T.fill(B_local, 1)
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T.reduce_sum(A_local, B_local, dim=1, clear=False)
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T.copy(B_local, B)
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return main
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def run_reduce_sum_clear(M, N, dtype=T.float32):
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program = reduce_sum_test_clear(M, N, dtype)
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jit_kernel = tl.compile(program, out_idx=-1)
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def ref_program(A):
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return A.sum(dim=1) + 1
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import torch
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dummy_A = torch.randn((M, N), dtype=getattr(torch, dtype)).cuda()
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ref_out = ref_program(dummy_A)
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tl_out = jit_kernel(dummy_A)
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torch.testing.assert_close(tl_out, ref_out, atol=1e-2, rtol=1e-2)
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def test_reduce_sum_clear():
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run_reduce_sum_clear(256, 256, T.float32)
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run_reduce_sum_clear(512, 128, T.float32)
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run_reduce_sum_clear(128, 512, T.float32)
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def reduce_max_test_clear(M, N, dtype=T.float16):
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import tilelang.language as T
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@T.prim_func
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def main(
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A: T.Tensor((M, N), dtype),
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B: T.Tensor((M,), dtype),
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):
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with T.Kernel(1, threads=32) as _:
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A_local = T.alloc_fragment((M, N), dtype)
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B_local = T.alloc_fragment((M,), dtype)
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T.copy(A, A_local)
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T.fill(B_local, -T.infinity(dtype))
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T.reduce_max(A_local, B_local, dim=1, clear=False)
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T.copy(B_local, B)
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return main
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def run_reduce_max_clear(M, N, dtype=T.float16):
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program = reduce_max_test_clear(M, N, dtype)
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jit_kernel = tl.compile(program, out_idx=-1)
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def ref_program(A):
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return A.max(dim=1).values
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import torch
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dummy_A = torch.randn((M, N), dtype=getattr(torch, dtype)).cuda()
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ref_out = ref_program(dummy_A)
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tl_out = jit_kernel(dummy_A)
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torch.testing.assert_close(tl_out, ref_out, atol=1e-2, rtol=1e-2)
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def test_reduce_max_clear():
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run_reduce_max_clear(256, 256, T.float16)
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if __name__ == "__main__":
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tilelang.testing.main()
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