This is similar to what I did in https://github.com/pytorch/pytorch/pull/94790/files#diff-39e82af71afdadbc56a4ecf552ec668ddcda794f8ea3ec41edaef23456fd56e9
but I have to do this everywhere we have a broadcasting implementation.

If you want me to spend some BE time deduping these, please holler.

Signed-off-by: Edward Z. Yang <ezyangmeta.com>

ghstack-source-id: 13eef87
Pull Request resolved: #95217

This PR gets `reflect @ R @ reflect` working, where R has unbacked batch size. This pattern occurred in CrystalDPR. The billing of changes:

* torch.broadcast_shapes avoids guarding on unbacked SymInts when testing for broadcastable dims. I extracted this to #95217 for separate review; it's repeated in this PR as it is necessary for the E2E test
* I disable matrix multiply folding when there is an unbacked SymInt on any input. Folding is strictly a performance optimization and can be omitted. Also, I believe export would prefer to get matmul (rather than bmm/etc), so we should eventually actually get #91081 going
* I switch `reshape` to use the Python implementation, which is easier to debug than the C++ one. Previously we couldn't easily do this as it was composite, but now we can with Python dispatcher.
* I hand-write a meta function for expand, rather than using the PrimTorch decomposition. I couldn't really figure out how to make the PrimTorch decomposition guard free, but with the hand-written meta it is clear where the divergence lies: we cannot easily choose the correct stride for the unbacked dim, as we need to know whether or not the size is one (in which case we give the predicted stride) versus non-one (in which case we MUST give zero.) In composability sync, we agreed that changes to striding behavior are fair game with unbacked SymInts, so I just unconditionally give these zero stride.

Signed-off-by: Edward Z. Yang <ezyangmeta.com>

[ghstack-poisoned]

This PR gets `reflect @ R @ reflect` working, where R has unbacked batch size. This pattern occurred in CrystalDPR. The billing of changes:

* torch.broadcast_shapes avoids guarding on unbacked SymInts when testing for broadcastable dims. I extracted this to #95217 for separate review; it's repeated in this PR as it is necessary for the E2E test
* I disable matrix multiply folding when there is an unbacked SymInt on any input. Folding is strictly a performance optimization and can be omitted. Also, I believe export would prefer to get matmul (rather than bmm/etc), so we should eventually actually get #91081 going
* I switch `reshape` to use the Python implementation, which is easier to debug than the C++ one. Previously we couldn't easily do this as it was composite, but now we can with Python dispatcher.
* I hand-write a meta function for expand, rather than using the PrimTorch decomposition. I couldn't really figure out how to make the PrimTorch decomposition guard free, but with the hand-written meta it is clear where the divergence lies: we cannot easily choose the correct stride for the unbacked dim, as we need to know whether or not the size is one (in which case we give the predicted stride) versus non-one (in which case we MUST give zero.) In composability sync, we agreed that changes to striding behavior are fair game with unbacked SymInts, so I just unconditionally give these zero stride.

Signed-off-by: Edward Z. Yang <ezyangmeta.com>

[ghstack-poisoned]

This is similar to what I did in https://github.com/pytorch/pytorch/pull/94790/files#diff-39e82af71afdadbc56a4ecf552ec668ddcda794f8ea3ec41edaef23456fd56e9
but I have to do this everywhere we have a broadcasting implementation.

If you want me to spend some BE time deduping these, please holler.

Signed-off-by: Edward Z. Yang <ezyangmeta.com>

ghstack-source-id: 7a1119e
Pull Request resolved: #95217

This PR gets `reflect @ R @ reflect` working, where R has unbacked batch size. This pattern occurred in CrystalDPR. The billing of changes:

* torch.broadcast_shapes avoids guarding on unbacked SymInts when testing for broadcastable dims. I extracted this to #95217 for separate review; it's repeated in this PR as it is necessary for the E2E test
* I disable matrix multiply folding when there is an unbacked SymInt on any input. Folding is strictly a performance optimization and can be omitted. Also, I believe export would prefer to get matmul (rather than bmm/etc), so we should eventually actually get #91081 going
* I add a direct Python transcription of the reshape composite adapted from #84584 . I cannot use the PrimTorch composite as it has problems when I register it pre-autograd. It has the same implementation as regular reshape, but at the beginning there is one more test for trivial reshapes, which is sufficient for the matmul example.
* I hand-write a meta function for expand, rather than using the PrimTorch decomposition. I couldn't really figure out how to make the PrimTorch decomposition guard free, but with the hand-written meta it is clear where the divergence lies: we cannot easily choose the correct stride for the unbacked dim, as we need to know whether or not the size is one (in which case we give the predicted stride) versus non-one (in which case we MUST give zero.) In composability sync, we agreed that changes to striding behavior are fair game with unbacked SymInts, so I just unconditionally give these zero stride.

Signed-off-by: Edward Z. Yang <ezyangmeta.com>

[ghstack-poisoned]