Fix: #125387

This PR helps keep track of whether an instantiated `ViewMeta` has symbolic values as
input or not. This is used for checking whether we use the AOTAutograd `ViewMeta`-replay
execution path, e.g. it doesn't support tensors that have `ViewMeta` with symbolic inputs.

In summary, the changes are:

- Add the field `ViewMeta::has_symbolic_inputs` and make it a required constructor
parameter
- Add the field `FunctionalTensorWrapper::is_symbolic_` and the method
`FunctionalTensorWrapper::maybe_mark_symbolic`
    - Marks a `FunctionalTensorWrapper` as symbolic iff any of its `ViewMeta` have
    symbolic inputs
- Add the plumbing of `FunctionalTensorWrapper::is_symbolic` to the Python API
- Codegen the computation of `ViewMeta::has_symbolic_inputs` for each view operation
- Use the AOTAutograd `ViewMeta`-replay path if:
    - `target_functional_tensor` is not `None`; and
    - `target_functional_tensor` is not symbolic (instead of using a functorch config)

[ghstack-poisoned]

Fix: #125387

This PR helps keep track of whether an instantiated `ViewMeta` has symbolic values as
input or not. This is used for checking whether we use the AOTAutograd `ViewMeta`-replay
execution path, e.g. it doesn't support tensors that have `ViewMeta` with symbolic inputs.

In summary, the changes are:

- Add the field `ViewMeta::has_symbolic_inputs` and make it a required constructor
parameter
- Add the field `FunctionalTensorWrapper::is_symbolic_` and the method
`FunctionalTensorWrapper::maybe_mark_symbolic`
    - Marks a `FunctionalTensorWrapper` as symbolic iff any of its `ViewMeta` have
    symbolic inputs
- Add the plumbing of `FunctionalTensorWrapper::is_symbolic` to the Python API
- Codegen the computation of `ViewMeta::has_symbolic_inputs` for each view operation
- Use the AOTAutograd `ViewMeta`-replay path if:
    - `target_functional_tensor` is not `None`; and
    - `target_functional_tensor` is not symbolic (instead of using a functorch config)

cc bdhirsh miladm lezcano 

[ghstack-poisoned]

Fix: #125387

This PR helps keep track of whether an instantiated `ViewMeta` has symbolic values as
input or not. This is used for checking whether we use the AOTAutograd `ViewMeta`-replay
execution path, e.g. it doesn't support tensors that have `ViewMeta` with symbolic inputs.

In summary, the changes are:

- Add the field `ViewMeta::has_symbolic_inputs` and make it a required constructor
parameter
- Add the field `FunctionalTensorWrapper::is_symbolic_` and the method
`FunctionalTensorWrapper::maybe_mark_symbolic`
    - Marks a `FunctionalTensorWrapper` as symbolic iff any of its `ViewMeta` have
    symbolic inputs
- Add the plumbing of `FunctionalTensorWrapper::is_symbolic` to the Python API
- Codegen the computation of `ViewMeta::has_symbolic_inputs` for each view operation
- Use the AOTAutograd `ViewMeta`-replay path if:
    - `target_functional_tensor` is not `None`; and
    - `target_functional_tensor` is not symbolic (instead of using a functorch config)

cc bdhirsh miladm lezcano 

[ghstack-poisoned]

Fix: #125387

This PR helps keep track of whether an instantiated `ViewMeta` has symbolic values as
input or not. This is used for checking whether we use the AOTAutograd `ViewMeta`-replay
execution path, e.g. it doesn't support tensors that have `ViewMeta` with symbolic inputs.

In summary, the changes are:

- Add the field `ViewMeta::has_symbolic_inputs` and make it a required constructor
parameter
- Add the field `FunctionalTensorWrapper::is_symbolic_` and the method
`FunctionalTensorWrapper::maybe_mark_symbolic`
    - Marks a `FunctionalTensorWrapper` as symbolic iff any of its `ViewMeta` have
    symbolic inputs
- Add the plumbing of `FunctionalTensorWrapper::is_symbolic` to the Python API
- Codegen the computation of `ViewMeta::has_symbolic_inputs` for each view operation
- Use the AOTAutograd `ViewMeta`-replay path if:
    - `target_functional_tensor` is not `None`; and
    - `target_functional_tensor` is not symbolic (instead of using a functorch config)

cc bdhirsh miladm lezcano 

[ghstack-poisoned]

Fix: #125387

This PR helps keep track of whether an instantiated `ViewMeta` has symbolic values as
input or not. This is used for checking whether we use the AOTAutograd `ViewMeta`-replay
execution path, e.g. it doesn't support tensors that have `ViewMeta` with symbolic inputs.

In summary, the changes are:

- Add the field `ViewMeta::has_symbolic_inputs` and make it a required constructor
parameter
- Add the field `FunctionalTensorWrapper::is_symbolic_` and the method
`FunctionalTensorWrapper::maybe_mark_symbolic`
    - Marks a `FunctionalTensorWrapper` as symbolic iff any of its `ViewMeta` have
    symbolic inputs
- Add the plumbing of `FunctionalTensorWrapper::is_symbolic` to the Python API
- Codegen the computation of `ViewMeta::has_symbolic_inputs` for each view operation
- Use the AOTAutograd `ViewMeta`-replay path if:
    - `target_functional_tensor` is not `None`; and
    - `target_functional_tensor` is not symbolic (instead of using a functorch config)

cc bdhirsh miladm lezcano 

[ghstack-poisoned]

Fix: #125387

This PR helps keep track of whether an instantiated `ViewMeta` has symbolic values as
input or not. This is used for checking whether we use the AOTAutograd `ViewMeta`-replay
execution path, e.g. it doesn't support tensors that have `ViewMeta` with symbolic inputs.

In summary, the changes are:

- Add the field `ViewMeta::has_symbolic_inputs` and make it a required constructor
parameter
- Add the field `FunctionalTensorWrapper::is_symbolic_` and the method
`FunctionalTensorWrapper::maybe_mark_symbolic`
    - Marks a `FunctionalTensorWrapper` as symbolic iff any of its `ViewMeta` have
    symbolic inputs
- Add the plumbing of `FunctionalTensorWrapper::is_symbolic` to the Python API
- Codegen the computation of `ViewMeta::has_symbolic_inputs` for each view operation
- Use the AOTAutograd `ViewMeta`-replay path if:
    - `target_functional_tensor` is not `None`; and
    - `target_functional_tensor` is not symbolic (instead of using a functorch config)

cc bdhirsh miladm lezcano 

[ghstack-poisoned]