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67b81609dc
* [Refactor] Add parallel loop transform * done format check * pull 3rdparty repo * Refactor loop variable handling in transformation utilities - Updated the logic in `loop_parallel_transform_utils.h` to simplify the handling of related loop variables. - Removed the check that enforced a single related loop variable, replacing it with a return statement when multiple variables are detected, enhancing clarity and maintainability of the transformation process. * Update loop_parallel_transform_utils.h * Refactor loop variable handling in transformation utilities - Enhanced the logic in `loop_parallel_transform_utils.h` to improve clarity and maintainability by simplifying the handling of related loop variables. - Replaced the previous enforcement of a single related loop variable with a return statement for multiple variables detected. * remove disable cache flag as commit id has been key component * lint fix --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com> Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
import tilelang
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import tilelang.testing
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import tilelang.language as T
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import torch
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# add decorator @tilelang.jit if you want to return a torch function
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# @tilelang.jit
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def tilelang_composable_copy(M, N, block_M, block_N, dtype="float16"):
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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 * N), dtype),
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):
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# Initialize Kernel Context
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
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A_local = T.alloc_fragment([block_M, block_N], dtype)
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B_local = T.alloc_fragment([block_M * block_N], dtype)
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T.copy(A[by * block_M, bx * block_N], A_local)
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for i, j in T.Parallel(block_M, block_N):
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B_local[i * block_N + j] = A_local[i, j]
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for i in T.Parallel(block_M * block_N):
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B[by * block_M * N + bx * block_N + i // block_N * N + i % block_N] = B_local[i]
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return main
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def run_tilelang_composable_copy(M=1024, N=1024, block_M=128, block_N=128, dtype="float16"):
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program = tilelang_composable_copy(M, N, block_M, block_N, dtype)
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kernel = tilelang.compile(
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program,
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out_idx=[1],
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target="cuda",
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pass_configs={
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tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
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tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
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})
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a = torch.randn(M, N, device="cuda", dtype=getattr(torch, dtype))
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b = kernel(a)
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torch.testing.assert_close(b.flatten(), a.flatten(), rtol=1e-2, atol=1e-2)
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def test_tilelang_copy():
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run_tilelang_composable_copy(M=1024, N=1024, block_M=128, block_N=128)
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run_tilelang_composable_copy(M=1024, N=576, block_M=32, block_N=576)
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run_tilelang_composable_copy(M=1024, N=576, block_M=32, block_N=576, dtype="float")
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if __name__ == "__main__":
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tilelang.testing.main()
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