Upgrade to PyTorch 2.3.

[stellaraccident]

As discussed on Discord, this is a significant upgrade because it is the first stable release that has a fully functional torch.export.export with the preferred dynamic shapes support. It is also just prior to nightlies that completely remove support for the old constraints based API, so is therefore a good point to stop for a moment and support both styles.

This patch makes a number of API changes:

• Issues deprecation warnings if the constraints= keyword for jittable is used, otherwise not passing it to PyTorch. This should make jittable not immediately incompatible with later nightlies unless if that feature is used.
• Adds the ability for a CompiledModule to directly have an attribute of a torch.export.ExportedProgram, allowing the user to pre-export with Torch and then construct a compiled module from that (vs the jittable approach where the CompiledModule API was directly invoking Torch internals to do so). This defaults to exporting as public if given a name not starting with an underscore and private otherwise. Private ExportedPrograms can be called from procedures just as with jittable.
• shark_turbine.aot.export() now accepts either an CompiledModule, nn.Module, a or a torch.export.ExportedProgram. For the last two, a new external_params= bool is available to control whether parameters are inlined or externalized. For an nn.Module arguments corresponding to torch.export.export are added. Internally, for an nn.Module, it simply calls torch.export.export. jittable is no longer used internally.

Some attempt has been made to be backwards compatible with Torch 2.1.0. New features will not work, but we should be able to support a short buffer window where older pinned systems are not completely broken. The repository prior to this patch will be branched to torch_2.1.

Breaking changes:

• ops.iree.trace_tensors (plural) had to be removed because the PyTorch auto functionalization thing has a TODO around lists of tensors. We can add a wrapper that takes a list and invokves trace_tensors multiple times and/or ass a functional_trace_tensors which works a bit better with the infra.
• stateless_llama_test.py::test_rerotated_torch_comparison marked as expectedFailure. Filed stateless_llama test_rerotated_torch_comparison test broken with PyTorch 2.3 #560

[dan-garvey]

Some small nits but wow this is amazing. 👏 👏