mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-24 07:04:00 +08:00
Lei Wang
556d411e99
* Update test parameters and remove debug print statement - Adjusted test cases in `test_tilelang_dynamic_symbolic_bench.py` to use smaller matrix sizes (1024x1024) for improved performance and quicker execution. - Removed a debug print statement from `phase.py` to clean up the code and enhance clarity. * Refactor loop stack management in warp_specialized_rewriter - Introduced a new `LoopInfo` struct to encapsulate loop variable details, including `loop_var`, `extent`, and `min`, enhancing clarity and maintainability. - Updated the `loop_stack_` to utilize `LoopInfo` instead of a pair, improving type safety and readability. - Adjusted linear index calculations to account for the new structure, ensuring correct behavior in loop transformations. * Remove unused `torch.backends` import and `tilelang.disable_cache()` calls from multiple test files to enhance code clarity and maintainability.
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
import tilelang
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import tilelang.language as T
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import tilelang.testing
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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_copy(M, N, block_M, block_N, dtype="float16", pad_value=0):
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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_shared = T.alloc_shared((block_M, block_N), dtype)
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T.annotate_padding({A_shared: pad_value})
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for i, j in T.Parallel(block_M, block_N):
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A_shared[i, j] = A[by * block_M + i - 10, bx * block_N + j]
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for i, j in T.Parallel(block_M, block_N):
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B[by * block_M + i, bx * block_N + j] = A_shared[i, j]
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return main
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def run_tilelang_copy(M=1024, N=1024, block_M=128, block_N=128, dtype="float16", pad_value=0):
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program = tilelang_copy(M, N, block_M, block_N, dtype, pad_value=pad_value)
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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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"tl.disable_warp_specialized": True,
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"tl.disable_tma_lower": 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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ref_b = torch.zeros_like(a)
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for i in range(M):
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if i >= 10:
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ref_b[i, :] = a[i - 10, :]
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else:
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ref_b[i, :] = pad_value
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torch.testing.assert_close(b, ref_b, rtol=1e-2, atol=1e-2)
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def test_tilelang_copy():
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run_tilelang_copy(M=1024, N=1024, block_M=128, block_N=128, pad_value=10)
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if __name__ == "__main__":
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tilelang.testing.main()
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