**Introduces symbolic shape guards into dynamo.**
In this PR, we take the existing fake tensor infra and plumbing in dynamo and we start passing a shape_env around. This shape_env does not get plumbed down to middle layers / backend yet - it only collects expressions from frontend invocations at the moment. We then translate these expressions into guards at the point where we take other guards installed throughout dynamo - and add them to check_fn.
Part 1 of https://docs.google.com/document/d/1QJ-M4zfMkD-fjHIqW089RptjLl9EgozZGCceUbvmgfY/edit#
cc @jansel @lezcano @fdrocha @mlazos @soumith @yanboliang @penguinwu @anijain2305
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87570
Approved by: https://github.com/ezyang
Right now, example_value is doing two jobs:
- We use it to propagate metadata (e.g. return type, shapes, etc.)
throughout the graph
- We use it to satisfy queries for the actual value (e.g. torch.cond,
`assume_constant_result`)
This is further complicated by the fact that we have two modes, one
where `example_value` is a fake tensor, and one where it is a real
tensor (this is the `fake_tensor_propagation` config flag).
This leads to scenarios where we don't support every combination of
job + mode,
e.g. if `fake_tensor_propagation=False`, `assume_constant_result` is
broken.
This is made worse by the fact that "fake tensor mode" is the default
and is required if you want dynamic shapes to work.
So, this PR introduces a `get_real_value` API that just runs the graph
up to `node` in order to get a concrete value. This API is orthogonal
to
`example_value`, so it doesn't care about `fake_tensor_propagation`.
When `fake_tensor_propagation=True`: `example_value` is a fake tensor,
you must use the `get_real_value` API to get a concrete value. This
will
be the only configuration in the future.
When `fake_tensor_propagation=False`: `example_value` and
`get_real_value` will produce the same value. This is redundant but we
will be removing this config soon.
To support this, I introduce a cache for computed real values, to
memoize the work involved if we're asking for real values a lot.
I attached this state to `OutputGraph` because it seems to be what
historically managed `example_value` lifetimes, but idk.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87091
Approved by: https://github.com/wconstab
