Tests: `python test/inductor/test_aot_inductor.py -vvv -k device_tma`Device-side TMA in Triton allows the kernel author to construct the TMA descriptor on the device (which composes with things like autotuning much better). However, it also requires a scratch space to be provided into which the TMA descriptor will be constructed. In the new TMA API (tl.make_tensor_descriptor), this is implemented using a "global scratch space" - a tensor which is allocated beforehand and then passed in as an argument for the kernel.To support this in AOTI, this PR:* records the global scratch space needed (triton_heuristics.py), so that it can be used during AOTI codegen* allocates global scratch, if needed (cuda/device_op_overrides.py)* plumbs `device_idx_` into the triton caller function, so that global scratch can be allocated on the right device)* updates tests to verify this works for dynamically shaped inputsThis PR should support both inductor-generated device-side TMA (e.g. persistent TMA mm) and user-defined triton kernels that contain device-side TMA (which is the test I ran to verify this works)Note: this overrides any user-provided allocator function (typically with eager triton code, the user must provide their own custom allocator function that is used to allocate scratch space).For Meta reviewers, here is a tlparse from running `python test/inductor/test_aot_inductor.py -vvv -k test_triton_kernel_on_device_tma_dynamic_True_tma_version_new_cuda`https://manifold.edge.x2p.facebook.net/v0/read/tree/logs/.tmpFg13g1/index.html?bucketName=tlparse_reports&apiKey=tlparse_reports-key&withPayload=1&timeoutMsec=10000Differential Revision: [D77352139](https://our.internmc.facebook.com/intern/diff/D77352139)Pull Requestresolved:#155896Approved by:https://github.com/desertfire(cherry picked from commitb6c00df)