mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-24 07:04:00 +08:00
Lei WangandChenggang Zhao
1b308baf9a
* Update submodule 'tvm' to commit e11521e6936a827efa334588d29571fbb4620107 * Support strided tensors * Refactor target attribute helper functions for improved clarity * No code changes made in proxy.py and setup.py * lint fix * lint fix via gemini * lint fix * test fix * test fix * lint fix * Update wrapper.py * test fix * Enhance test for InjectSoftwarePipeline by adding LowerOpaqueBlock transformation and updating expected function signature to use match_buffer for better clarity. * lint fix --------- Co-authored-by: Chenggang Zhao <chenggangz@deepseek.com>
91 lines
3.0 KiB
Python
91 lines
3.0 KiB
Python
import tilelang
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import tilelang.language as T
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import torch
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import tilelang.testing
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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"):
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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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for i, j in T.Parallel(block_M, block_N):
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B[by * block_M + i, bx * block_N + j] = A[by * block_M + i, bx * block_N + 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"):
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program = tilelang_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_WARP_SPECIALIZED: True,
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tilelang.PassConfigKey.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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torch.testing.assert_close(b, a, 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)
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run_tilelang_copy(M=1024, N=576, block_M=32, block_N=576)
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run_tilelang_copy(M=1024, N=576, block_M=32, block_N=576, dtype="float")
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def tilelang_copy_with_stride(M, N, NN, 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.StridedTensor((M, N), (NN, 1), 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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for i, j in T.Parallel(block_M, block_N):
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B[by * block_M + i, bx * block_N + j] = A[by * block_M + i, bx * block_N + j]
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return main
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def run_tilelang_copy_with_stride(M=1024,
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N=1024,
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NN=2048,
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block_M=128,
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block_N=128,
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dtype="float16"):
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if isinstance(NN, int):
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assert NN > N, "NN must be greater than N"
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program = tilelang_copy_with_stride(M, N, NN, 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_WARP_SPECIALIZED: True,
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tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
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})
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if isinstance(NN, T.Var):
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NN = N * 2
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a = torch.randn(M, NN, device="cuda", dtype=getattr(torch, dtype))
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b = kernel(a[:, :N])
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torch.testing.assert_close(b, a[:, :N], rtol=1e-2, atol=1e-2)
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def test_tilelang_copy_with_stride():
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run_tilelang_copy_with_stride(M=1024, N=1024, NN=2048, block_M=128, block_N=128)
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run_tilelang_copy_with_stride(M=1024, N=1024, NN=T.symbolic("NN"), block_M=128, block_N=128)
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if __name__ == "__main__":
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tilelang.testing.main()
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