[AOTInductor] ProxyExecutor support ReinterpretView inputs

[SherlockNoMad]

Summary:
See wrapper.codegen_reinterpret_view(), it return a temporary handle for tensor, which has following problem.

            # NB, the return handle here represents a temporary tensor, which will be automatically
            # released.
            # Here's a sample usage in the cpp wrapper code:
            # ```
            # aoti_torch_addmm_out(
            #     buf1,
            #     arg1_1,
            #     RAIIAtenTensorHandle(tmp_tensor_handle_0),
            #     buf0,
            #     1L,
            #     1L));
            # ```
            # RAIIAtenTensorHandle(tmp_tensor_handle_0) will be released after the call to addmm_out.
            # This could be problematic when it's used in a different pattern, for example:
            # ````
            # AtenTensorHandle tensor_args[] = {RAIIAtenTensorHandle(tmp_tensor_handle_2), buf5, buf6};
            # aoti_torch_proxy_executor_call_function(..., tensor_args);
            # ````
            # RAIIAtenTensorHandle(tmp_tensor_handle_2) will be invalid when it's used in the latter
            # kernel call.
            return f"RAIIAtenTensorHandle({tmp_name})"

As a result, ProxyExecutor would generate following code, which cause invalid memory access.

Before:

    // Source Nodes: [fn_with_tuple_output], Original ATen: [fb.fn_with_tuple_output]
    AtenTensorHandle tmp_tensor_handle_2;
    AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch__reinterpret_tensor(buf3, 2, int_array_0, int_array_1, 0L, &tmp_tensor_handle_2));
    ...
    AtenTensorHandle tensor_args[] = {RAIIAtenTensorHandle(tmp_tensor_handle_2), buf5, buf6};
    int64_t int_args[] = {1};
    aoti_torch_proxy_executor_call_function(proxy_executor, 1, 1, int_args, 3, tensor_args);
    buf3.reset();

With fix in this diff, ProxyExecutor generates following code

After:

    // Source Nodes: [fn_with_tuple_output], Original ATen: [fb.fn_with_tuple_output]
    AtenTensorHandle tmp_tensor_handle_2;
    AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch__reinterpret_tensor(buf3, 2, int_array_0, int_array_1, 0L, &tmp_tensor_handle_2));
    ...
    aoti_torch_proxy_executor_call_function(proxy_executor, 1, 1, std::vector<int64_t>{1}.data(), 3, std::vector<AtenTensorHandle>{RAIIAtenTensorHandle(tmp_tensor_handle_2), buf5, buf6}.data());
    buf3.reset();

I am not exactly a big fan of such std::vector{...}.data() for creating a temp array, but I can't think of another fix.

Test Plan: buck2 run mode/dev-nosan deeplearning/aot_inductor/test:test_custom_ops

Reviewed By: desertfire

Differential Revision: D49758764

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov @ColinPeppler @avikchaudhuri @gmagogsfm

[pytorch-bot]

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✅ You can merge normally! (1 Unrelated Failure)

As of commit 7f0e57b with merge base a8a31bc ():

UNSTABLE - The following job failed but was likely due to flakiness present on trunk and has been marked as unstable:

• trunk / linux-focal-rocm5.6-py3.8 / test (default, 3, 3, linux.rocm.gpu, unstable) (gh)

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This pull request was exported from Phabricator. Differential Revision: D49758764

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D49758764

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D49758764

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