Actually actionable list of batching rules to write

[zou3519]

For each of the items here, we should make sure all compositions (vmap, vmap x vjp) have a batching rule. All of these items should be actionable (in that it is possible to write a batching rule and we are not blocked on functionalization, which is coming soon).

Note: you may need to write an OpInfo for the operator if it doesn't exist already or wait for one to be added. A lot of folks are adding OpInfos right now, so if the OpInfo doesn't exist please ask first to see if someone is working on it.

Note: if any of the operations decompose into in-place operations, then we need functionalization to handle them. I think I've already filtered out all of those, but please check me on that.

Parcel 1: top nn.functional.* and top torch.* foo

• torch.nn.functional.interpolate (this involves adding batching rules for adaptive_avg_pool1d, but we might as well do adaptive_avg_pool{1, 2, 3}d as well as their backward variants while we're at it)
• nn.functional.unfold. Involves writing a batching rule for im2col im2col_backward (Added im2col batch rule and enabled vmap for nn.functional.unfold op #262)
• nn.functional.grid_sample. Involves writing a batching rule for the backward operator. @vfdev-5
• torch.pow. The backward needs batching rule for logical_and ; use this as an opportunity to write the batching rules for logical_{and, or, xor} if those don't exist yet. We may need to also add a change to PyTorch core to make the logical_* functions primitives w.r.t. autograd

Parcel 2: new_blah

• new_empty, new_full, new_ones, new_zeros, empty_like, zeros_like, ones_like, full_like
• adaptive_max_pool{1, 2, 3}d as well as the backward variants
• diagonal_scatter, select_scatter, slice_scatter @kshitij12345
• linalg.householder_product (forward pass only)
• pixel_shuffle, pixel_unshuffle (these might just be OP_DECOMPOSE)
• isinf, isfinite, isnan
• _cdist_forward, _cdist_backward (try to write a batching rule if possible. If not possible, we may need to write a decomposition) @vfdev-5

Parcel 3: linalg things

• _lu_with_info (forward pass only) @vfdev-5
• linalg.eig (forward pass only) @vfdev-5
• torch.addr @vfdev-5
• cholesky_solve (forward pass only) @vfdev-5

Parcel 4:

• index_select, index_copy, etc, all need a backward formula in pytorch/pytorch vmap over composite out-of-place ops whose in-place variant is non-composite #260

[vfdev-5]

@zou3519 nn.functional.pad with circular option requires to do a copy: out[..., out_d0:out_d1] = input[..., in_d0:in_d1] and thus there is the following error is raised:

>           out[..., out_d0:out_d1] = input[..., in_d0:in_d1]                                                                                                                               
E           RuntimeError: vmap: aten::copy_(self, *extra_args) is not possible because there exists a Tensor `other` in extra_args that has more elements than `self`. This happened due to `other` being vmapped over but `self` not being vmapped over at level 2. Please try to use out-of-place operators instead of aten::copy_. If said operator is being called inside the PyTorch framework, please file a bug report instead. 

Can we do something with that ?

[zou3519]

Aha. Nope, we can't do anything about that until functionalization is in, good catch.

[Padarn]

Hi @zou3519, the "forward pass only" ops above means that the vjp and related operators require the functionalization too?