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59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
import torch
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import tilelang
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import tilelang.language as T
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def ref_program(x, y):
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return x + y
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@tilelang.jit(out_idx=[-1])
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def elementwise_add(M, N, block_M, block_N, in_dtype, out_dtype, threads):
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@T.prim_func
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def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T.Tensor(
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(M, N), out_dtype)):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_N), in_dtype)
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B_shared = T.alloc_shared((block_M, block_N), in_dtype)
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C_local = T.alloc_fragment((block_M, block_N), out_dtype)
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C_shared = T.alloc_shared((block_M, block_N), out_dtype)
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T.copy(A[by * block_M, bx * block_N], A_shared)
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T.copy(B[by * block_M, bx * block_N], B_shared)
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for (local_y, local_x) in T.Parallel(block_M, block_N):
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C_local[local_y, local_x] = A_shared[local_y, local_x] + B_shared[local_y, local_x]
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T.copy(C_local, C_shared)
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T.copy(C_shared, C[by * block_M, bx * block_N])
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return elem_add
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def run_elementwise_add(M, N):
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a = torch.randn(M, N, dtype=torch.float32, device="cuda")
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b = torch.randn(M, N, dtype=torch.float32, device="cuda")
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# Default config
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block_M, block_N = 128, 128
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config = {"block_M": block_M, "block_N": block_N, "threads": 128}
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kernel = elementwise_add(M, N, **config, in_dtype="float32", out_dtype="float32")
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out = kernel(a, b)
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torch.testing.assert_close(out, ref_program(a, b), rtol=1e-2, atol=1e-2)
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code = kernel.get_kernel_source()
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if block_N == N:
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assert "tma_load" in code and "CUtensorMap" not in code
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else:
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assert "tma_load" in code and "CUtensorMap" in code
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def main():
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run_elementwise_add(128, 128)
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run_elementwise_add(256, 128)
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run_elementwise_add(256, 256)
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if __name__ == "__main__":
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main()
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