def bench_matmul_with_mask()

in xformers/benchmarks/benchmark_core.py [0:0]

• 65 lines of code
• 4 McCabe index (conditional complexity)

def bench_matmul_with_mask():
    min_run_time = MIN_RUN_TIME
    prob = 0.9
    device = torch.device("cuda")
    results = []

    for B, M, K in zip(*SHAPES):
        a = torch.rand(B, M, K, device=device)
        b = torch.rand(B, K, M, device=device)
        mask = torch.rand(B, M, M, device=device) > prob

        results.extend(
            [
                benchmark.Timer(
                    stmt="_matmul_with_mask(a, b, mask)",
                    globals={
                        "a": a,
                        "b": b,
                        "mask": None,
                        "_matmul_with_mask": _matmul_with_mask,
                    },
                    label="matmul_with_mask",
                    sub_label="dense",
                    description=f"B={B}, M={M}, K={K}",
                ).blocked_autorange(min_run_time=min_run_time),
                benchmark.Timer(
                    stmt="_matmul_with_mask(a, b, mask)",
                    globals={
                        "a": a,
                        "b": b,
                        "mask": mask,
                        "_matmul_with_mask": _matmul_with_mask,
                    },
                    label="matmul_with_mask",
                    sub_label="dense with masking",
                    description=f"B={B}, M={M}, K={K}",
                ).blocked_autorange(min_run_time=min_run_time),
            ]
        )
        for sputnik, prob in itertools.product([False, True], SPARSITIES):
            mask = _create_random_sparsity(
                torch.ones(B, M, M, dtype=torch.bool, device=device), prob
            )
            aa = a
            bb = b
            if sputnik:
                mask = SparseCS(mask, device)
                aa = a
                bb = b.transpose(-2, -1).contiguous().transpose(-2, -1)
            else:
                mask = mask.to_sparse()
            results.append(
                benchmark.Timer(
                    stmt="_matmul_with_mask(a, b, mask)",
                    globals={
                        "a": aa,
                        "b": bb,
                        "mask": mask,
                        "_matmul_with_mask": _matmul_with_mask,
                    },
                    label="matmul_with_mask",
                    sub_label=f"sparsity {'sputnik' if sputnik else 'pytorch'}: {prob:0.2f}",
                    description=f"B={B}, M={M}, K={K}",
                ).blocked_autorange(min_run_time=min_run_time)
            )

    compare = benchmark.Compare(results)
    compare.print()