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Higher Order Kernel - associative scan
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| import torch | |
| import torch.nn as nn | |
| from torch._higher_order_ops.associative_scan import associative_scan | |
| from triton.testing import do_bench | |
| torch.set_default_device('cuda') | |
| def combine_fn(i, j): | |
| ia, ib = i | |
| ja, jb = j | |
| return ia * ja, ib * ja + jb | |
| a = torch.randn(1024, 1024 * 10) | |
| b = torch.randn(1024, 1024 * 10) | |
| def baseline(v, u): | |
| A = [] | |
| A.append(b[:, 0]) | |
| for i in range(1, v.shape[1]): | |
| A.append(a[:, i] * A[i - 1] + b[:, i]) | |
| return torch.stack(A, dim=1) | |
| @torch.compile | |
| def compiled_scan(a, b): | |
| return associative_scan(combine_fn, (a, b), dim=-1)[1] | |
| out1 = baseline(a, b) | |
| out2 = compiled_scan(a, b) | |
| print((out1 - out2).abs().max()) | |
| print("eager", do_bench(lambda: baseline(a, b))) | |
| print("compiled", do_bench(lambda: compiled_scan(a, b))) | |
| print("two cumprods", do_bench(lambda: [torch.cumprod(a, dim=-1), torch.cumprod(b, dim=-1)])) |
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