fix(models): Fix Perceiver interpolate_pos_encoding interpolating to the source size

[harshaljanjani]

What does this PR do?

The following failing Perceiver use case was identified and fixed in this PR:

→ c6d2848 (🚨 Fix torch.jit.trace for interpolate_pos_encoding in all vision models) refactored all vision models' interpolate_pos_encoding methods for torch.jit.trace; the canonical pattern used across other vision models (e.g. modeling_vit.py, modeling_deit.py) is that they passes the target (height, width) to nn.functional.interpolate; but the Perceiver diff passed the source grid dims practically making the interpolation a no-op; this should fix that!
→ I also checked if other models have the exact same issue; and they don't, they compute new_height = height // self.patch_size (target patch grid) and pass that.

Fixes #44898

Before the fix (feel free to cross-check; these errors are reproducible):

After the fix (feel free to cross-check):

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[zucchini-nlp]

Let's add a test if we don't yet have

[zucchini-nlp]

sounds reasonable. I am not super familiar with perceiver, do we not need to divide the height/width by patch size, so it matches with patched image features?

[harshaljanjani]

Sorry I think I could've made this clearer as well in the PR description! I think this traces back to the reason it was missed as well, which was the prevalent ViT-family fix which worked for most models was applied here, only here there's no patch_size to divide by so it's incorrect (Perceiver uses conv1x1 with spatial_downsample=1).
→ ViT/DeiT: new_height = height // self.patch_size makes the value in new_height the target.
→ Perceiver: new_height = torch_int(num_positions**0.5) makes it the source grid size, so the same size=(new_height, new_width) pattern that's correct everywhere else becomes a no-op here.

[harshaljanjani]

Let's add a test if we don't yet have

Thank you for your time @zucchini-nlp! Checked the test coverage, this behavior is covered by test_modeling_perceiver.py::PerceiverModelIntegrationTest::test_inference_interpolate_pos_encoding which currently fails. I just verified that this change fixes that as well!
If I may, I was wondering if I might ask if there's an update on this PR https://github.com/huggingface/transformers/pull/44695 as well at your convenience :))

[zucchini-nlp]

@harshaljanjani I see, thaks a lot for explaining! Do you mind adding a fast test, PerceiverModelTest with dummy weights?

[harshaljanjani]

Added the test!

Before the fix:

After the fix:

[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: perceiver