Implement pack/unpack helpers

[jessegrabowski]

Description

Adds pt.pack and pt.unpack helpers, roughly conforming to the einops functions of the same name.

These helps are for situations where we have a ragged list of inputs that need to be raveled into a single flat list for some intermediate step. This occurs in places like optimization.

Example usage:

x = pt.tensor("x", shape=shapes[0])
y = pt.tensor("y", shape=shapes[1])
z = pt.tensor("z", shape=shapes[2])

flat_params, packed_shapes = pt.pack(x, y, z)

Unpack simply undoes the computation, although there's norewrite to ensure pt.unpack(*pt.pack(*inputs)) is the identity function:

x, y, z = pt.unpack(flat_params, packed_shapes)

The use-case I forsee is creating replacement for a function of the inputs we're packing, for example:

loss = (x + y.sum() + z.sum()) ** 2

flat_packed, packed_shapes = pack(x, y, z)
new_input = flat_packed.type()
new_outputs = unpack(new_input, packed_shapes)

loss = pytensor.graph.graph_replace(loss, dict(zip([x, y, z], new_outputs)))
fn = pytensor.function([new_input], loss)

Note that the final compiled function depends only on new_input, only because the shapes of the 3 packed variables were statically known. This leads to my design choices section:

1. I decided to work with the static shapes directly if they are available. This means that pack will eagerly return a list of integer shapes as packed_shapes if possible. If not possible, they will be symbolic shapes. This is maybe an anti-pattern -- we might prefer a rewrite to handle this later, but it seemed easy enough to do eagerly.
2. I didn't add support for batch dims. This is left to the user to do himself using pt.vectorize.
3. The einops API has arguments to support packing/unpacking on arbitrary subsets of dimensions. I didn't do this, because I couldn't think of a use-case that a user couldn't get himself using DimShuffle and vectorize.

Related Issue

• Closes Implement pack/unpack Ops #1553
• Related to Gradient of MinimizeOp fails with certain parameter shapes #1550

Checklist

• Checked that the pre-commit linting/style checks pass
• Included tests that prove the fix is effective or that the new feature works
• Added necessary documentation (docstrings and/or example notebooks)
• If you are a pro: each commit corresponds to a relevant logical change

Type of change

• New feature / enhancement
• Bug fix
• Documentation
• Maintenance
• Other (please specify):

---

📚 Documentation preview 📚: https://pytensor--1578.org.readthedocs.build/en/1578/

[jessegrabowski]

The pack -> type -> unpack -> replace pattern might be common enough to merit it's own helper. PyMC has tools for doing this, for example, in RaveledArray and DictToArrayBijector, that could be replaced with appropriate symbolic operations.

One other thing I forgot to mention is that this will all fail on inputs with shape 0, since that will ruin the prod(shape) used to get the shape of the flat output. Not sure what to do in that case.

[codecov]

Codecov Report

❌ Patch coverage is 73.33333% with 4 lines in your changes missing coverage. Please review.
✅ Project coverage is 81.63%. Comparing base (c932ffb) to head (fcbd0af).

Files with missing lines	Patch %	Lines
pytensor/tensor/extra_ops.py	73.33%	2 Missing and 2 partials ⚠️

❌ Your patch check has failed because the patch coverage (73.33%) is below the target coverage (100.00%). You can increase the patch coverage or adjust the target coverage.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1578      +/-   ##
==========================================
- Coverage   81.63%   81.63%   -0.01%     
==========================================
  Files         231      231              
  Lines       52993    53006      +13     
  Branches     9395     9397       +2     
==========================================
+ Hits        43263    43272       +9     
- Misses       7281     7283       +2     
- Partials     2449     2451       +2     

Files with missing lines	Coverage Δ
pytensor/tensor/extra_ops.py	88.21% <73.33%> (-0.35%)	⬇️

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[ricardoV94]

1. Better to have the same types as return, static shape to constant is introduced during rewrites already

2 and 3. I would really like to have these, it's what I needed for the batched_dot_to_core rewrites.This isn't a simple case of vectorize because the dims I want to pack are both on the left and right of other dims

[ricardoV94]

Would be nice to have a docstring (doctested) example

[jessegrabowski]

yeah i will of course add docstrings. This was just to get a PR on the board and see your reaction to the 3 issues I raised. I didn't want to document everything before we decided on the final API

[ricardoV94]

I am inclined to making this a core op and not just a helper. It obliviates most uses of reshape and it's much easier to reason about, not having to worry about pesky -1 or whether the reshape shape comes from the original input shapes or not.

That would pretty much address #883

We could use OFG and/or specialize to reshape/split later. It need also not be done in this PR. It's an implementation detail as far as the user is concerned.