recipes_source/recipes/benchmark.py [574:593]:
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    sub_label=f"{params['k0']:<6} x {params['k1']:<4} {'' if tensor_params['x']['is_contiguous'] else '(discontiguous)'}"
    results.append(benchmark.Timer(
        stmt='batched_dot_mul_sum(x, x)',
        setup='from __main__ import batched_dot_mul_sum',
        globals=tensors,
        label='Batched dot',
        sub_label=sub_label,
        description='mul/sum',
    ).blocked_autorange(min_run_time=1))
    results.append(benchmark.Timer(
        stmt='batched_dot_bmm(x, x)',
        setup='from __main__ import batched_dot_bmm',
        globals=tensors,
        label='Batched dot',
        sub_label=sub_label,
        description='bmm',
    ).blocked_autorange(min_run_time=1))

compare = benchmark.Compare(results)
compare.trim_significant_figures()
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recipes_source/recipes/benchmark.py [629:648]:
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    sub_label=f"{params['k0']:<6} x {params['k1']:<4} {'' if tensor_params['x']['is_contiguous'] else '(discontiguous)'}"
    results.append(benchmark.Timer(
        stmt='batched_dot_mul_sum(x, x)',
        setup='from __main__ import batched_dot_mul_sum',
        globals=tensors,
        label='Batched dot',
        sub_label=sub_label,
        description='mul/sum',
    ).blocked_autorange(min_run_time=1))
    results.append(benchmark.Timer(
        stmt='batched_dot_bmm(x, x)',
        setup='from __main__ import batched_dot_bmm',
        globals=tensors,
        label='Batched dot',
        sub_label=sub_label,
        description='bmm',
    ).blocked_autorange(min_run_time=1))

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