Repository: pytorch/functorch (language: python)

Issue:
Actually actionable list of batching rules to write
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
- [x] 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)
- [x] nn.functional.unfold. Involves writing a batching rule for im2col im2col_backward (https://github.com/pytorch/functorch/pull/262)
- [x] nn.functional.grid_sample. Involves writing a batching rule for the backward operator. @vfdev-5 
- [x] 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
- [x] new_empty, new_full, new_ones, new_zeros, empty_like, zeros_like, ones_like, full_like
- [x] `adaptive_max_pool{1, 2, 3}d` as well as the backward variants
- [x] diagonal_scatter, select_scatter, slice_scatter @kshitij12345 
- [ ] linalg.householder_product (forward pass only)
- [x] pixel_shuffle, pixel_unshuffle (these might just be OP_DECOMPOSE)
- [x] isinf, isfinite, isnan
- [x] _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
- [x] _lu_with_info (forward pass only) @vfdev-5 
- [x] linalg.eig (forward pass only) @vfdev-5 
- [x] torch.addr @vfdev-5 
- [x] cholesky_solve (forward pass only) @vfdev-5 

Parcel 4:
- [x] ~~index_select, index_copy, etc, all need a backward formula in pytorch/pytorch https://github.com/pytorch/functorch/issues/260~~


Discussion / hints:

Fix patch:
diff --git a/functorch/csrc/BatchRulesModules.cpp b/functorch/csrc/BatchRulesModules.cpp
index aa733fa1c..da746d675 100644
--- a/functorch/csrc/BatchRulesModules.cpp
+++ b/functorch/csrc/BatchRulesModules.cpp
@@ -401,7 +401,7 @@ TORCH_LIBRARY_IMPL(aten, FT_BATCHED_KEY, m) {
   VMAP_SUPPORT("cudnn_grid_sampler", GRID_SAMPLE_BATCH_RULE(cudnn_grid_sampler));
   VMAP_SUPPORT("cross", cross_batch_rule);
 
-  UNARY_POINTWISE(constant_pad_nd);
+  VARIADIC_BDIMS(constant_pad_nd);
   EXISTING_BDIM(reflection_pad1d);
   EXISTING_BDIM(reflection_pad2d);
   EXISTING_BDIM(reflection_pad3d);


Test patch:
diff --git a/test/test_ops.py b/test/test_ops.py
index d55603579..277447b4f 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -310,17 +310,12 @@ def vjp_of_vjp(*args_and_cotangents):
         xfail('diag_embed'),
         xfail('eig'),
         xfail('nn.functional.conv_transpose2d'),
-        xfail('nn.functional.pad', 'constant'),
         xfail('view_as_complex'),
-        xfail('fft.fft'),
-        xfail('fft.ifft'),
         xfail('fft.ihfft'),
         xfail('fft.ihfft'),
         xfail('fft.rfft'),
         xfail('fft.rfft'),
-        xfail('fft.fftn'),
         xfail('fft.rfftn'),
-        xfail('fft.ifftn'),
         xfail('cdist'),
         xfail('fmax'),
         xfail('fmin'),
@@ -358,8 +353,6 @@ def vjp_of_vjp(*args_and_cotangents):
         xfail('nanmean'),
         xfail('block_diag'),
         xfail('nn.functional.dropout'),
-        xfail('fft.fft2'),
-        xfail('fft.ifft2'),
         xfail('fft.ihfft2'),
         xfail('fft.ihfftn'),
         xfail('fft.rfft2'),
@@ -389,7 +382,6 @@ def test_vmapvjp(self, device, dtype, op):
 
     @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,))
     @skipOps('TestOperators', 'test_vmapvjp_has_batch_rule', vmapvjp_fail.union({
-        xfail('nn.functional.pad', 'constant'),
         xfail('view_as_complex'),
         xfail('__getitem__'),
         xfail('__rpow__'),
@@ -407,10 +399,6 @@ def test_vmapvjp(self, device, dtype, op):
         xfail('diag'),
         xfail('diag_embed'),
         xfail('eig'),
-        xfail('fft.fft'),
-        xfail('fft.fftn'),
-        xfail('fft.ifft'),
-        xfail('fft.ifftn'),
         xfail('fft.ihfft'),
         xfail('fft.rfft'),
         xfail('fft.rfftn'),
@@ -500,8 +488,6 @@ def test_vmapvjp(self, device, dtype, op):
         xfail('_masked.sum'),
         xfail('_masked.prod'),
         xfail('cholesky_solve'),
-        xfail('fft.fft2'),
-        xfail('fft.ifft2'),
         xfail('fft.ihfft2'),
         xfail('fft.ihfftn'),
         xfail('fft.rfft2'),
diff --git a/test/test_vmap.py b/test/test_vmap.py
index 8cffb0674..d4d5a42a8 100644
--- a/test/test_vmap.py
+++ b/test/test_vmap.py
@@ -3013,14 +3013,8 @@ class TestVmapOperatorsOpInfo(TestCase):
         xfail('linalg.svd', device_type='cuda'),
         xfail('index_put'),
         xfail('matrix_exp'),
-        xfail('fft.fft'),
-        xfail('fft.ifft'),
-        xfail('fft.ihfft'),
-        xfail('fft.rfft'),
-        xfail('fft.rfftn'),
         xfail('nn.functional.batch_norm'),
         xfail('lu_unpack'),
-        xfail('nn.functional.pad', 'constant'),
         xfail('empty_like'),
         xfail('histogramdd'),
         xfail('nn.functional.embedding'),