def bench_dropout()

in xformers/benchmarks/benchmark_triton_dropout.py [0:0]
62 lines of code
13 McCabe index (conditional complexity)

def bench_dropout(bias: bool, backward: bool, activation: Optional[Activation]):
    device = torch.device("cuda")

    for dtype in [
        torch.float16,
        torch.float32,
    ]:
        results: Dict[str, Any] = {}

        for B, M, K in SHAPES:
            a = torch.rand(
                (B, M, K), device=device, dtype=dtype, requires_grad=backward
            )
            b = torch.rand(K, device=device, dtype=dtype, requires_grad=backward)
            torch_act = build_activation(activation)
            triton_dropout = FusedDropoutBias(
                P, bias_shape=K if bias else None, activation=activation
            )

            def torch_step(x):
                x_ = x + b if bias else x
                y = torch.nn.functional.dropout(x_, P)
                if activation:
                    y = torch_act(y)

                if backward:
                    y.grad = None
                    torch.norm(y).backward()
                return y

            def triton_step(x):
                y = triton_dropout(x)
                if backward:
                    y.grad = None
                    torch.norm(y).backward()
                return y

            for testcase in [
                TestCase(
                    torch_step,
                    "pytorch - bias: {} - fw{} - act: {}".format(
                        bias, "+bw" if backward else "", activation
                    ),
                ),
                TestCase(
                    triton_step,
                    "triton - bias: {} - fw{} - act: {}".format(
                        bias, "+bw" if backward else "", activation
                    ),
                ),
            ]:
                time = triton.testing.do_bench(
                    lambda: testcase.function(a), grad_to_none=[a, b]
                )[0]
                key = f"B={B}, M={M}, K={K}"
                if key not in results:
                    results[key] = {}

                # Record BW
                bandwidth = to_gbs_fw(a, time, bias)
                results[key][testcase.name] = f"{bandwidth:.1f}"

        pretty_print(results, title="\n --- Type: {} --- ".format(dtype), units="GB/s")
        pretty_plot(
            results,
            title="Dropout-Bias-{}-FW{}-{}-Act: {}".format(
                bias, "+BW" if backward else "", dtype, activation
            ),
            units="GB/s",
            dash_key="pytorch",
        )