Implement as_jax_op #1614

aseyboldt · 2025-09-16T11:28:17Z

revisit #1120, which seems abandoned.

@jdehning I hope it is ok if I continue this PR?

📚 Documentation preview 📚: https://pytensor--1614.org.readthedocs.build/en/1614/

…f as_jax_op

…he previous approach for testing purposes

…be used without the decorator @as_jax_op

Copilot

Pull Request Overview

This PR implements the as_jax_op decorator which allows JAX functions to be used within PyTensor graphs. The decorator wraps JAX functions to make them compatible with PyTensor's variable system while preserving gradient computation capabilities.

Implements JAXOp class for wrapping JAX functions as PyTensor operations
Creates as_jax_op decorator for easy conversion of JAX functions to PyTensor-compatible operations
Adds comprehensive test coverage for various input/output patterns and data types

Reviewed Changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
pytensor/link/jax/ops.py	Core implementation of JAXOp class and as_jax_op decorator
tests/link/jax/test_as_jax_op.py	Comprehensive test suite covering various use cases and data types
pytensor/init.py	Exports as_jax_op function with fallback for missing dependencies
pytensor/link/jax/dispatch/basic.py	JAX dispatch registration for the new JAXOp
doc/library/index.rst	Documentation entry for the new functionality
doc/environment.yml	Updates documentation environment to include JAX dependencies
doc/conf.py	Adds Equinox to intersphinx mapping
.github/workflows/test.yml	Updates CI to install Equinox dependency

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pytensor/link/jax/ops.py

tests/link/jax/test_as_jax_op.py

codecov · 2025-09-16T15:57:08Z

Codecov Report

❌ Patch coverage is 90.84507% with 13 lines in your changes missing coverage. Please review.
✅ Project coverage is 81.72%. Comparing base (1dc982c) to head (f832126).

Files with missing lines	Patch %	Lines
pytensor/link/jax/ops.py	90.57%	11 Missing and 2 partials ⚠️

❌ Your patch check has failed because the patch coverage (90.84%) 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    #1614      +/-   ##
==========================================
+ Coverage   81.69%   81.72%   +0.02%     
==========================================
  Files         230      231       +1     
  Lines       52950    53092     +142     
  Branches     9404     9419      +15     
==========================================
+ Hits        43260    43389     +129     
- Misses       7256     7267      +11     
- Partials     2434     2436       +2

Files with missing lines	Coverage Δ
pytensor/link/jax/dispatch/basic.py	`83.52% <100.00%> (+0.81%)`	⬆️
pytensor/link/jax/ops.py	`90.57% <90.57%> (ø)`

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ricardoV94 · 2025-09-16T15:59:25Z

pytensor/link/jax/ops.py

+            if any(s is None for s in shape):
+                _, shape = pt.basic.infer_static_shape(var.shape)
+                if any(s is None for s in shape):
+                    raise ValueError(


Can we use this instead? https://docs.jax.dev/en/latest/export/shape_poly.html#shape-polymorphism

PyTensor only needs to know the dtype and rank of the outputs

I don't think that would be reliable.
I think jax will throw an error if the code tries to broadcast arrays when it cannot prove that they have compatible shapes.
If we have dims, we could use those to generate jax symbolic shapes. (But only with object dims, not with string ones I think?).

The best we could do right now is create a new shape variable for every input dimension that is not statically known.
But then it would fail as soon as you even add two of those together.

Does it fail or does it infer they must match?

It fails:

u, v = export.symbolic_shape("u, v") x1 = jax.ShapeDtypeStruct((u,), dtype=np.int32) x2 = jax.ShapeDtypeStruct((v,), dtype=np.int32) export.export(jax.jit(lambda x, y: x + y))(x1, x2) # add got incompatible shapes for broadcasting: (u,), (v,).

aseyboldt · 2025-09-16T16:59:58Z

I kept most of what was in the original PR, but made a few changes:

There is no longer a different op for the gradient. That is just again a JaxOp
I kept support for jax tree inputs and outputs, I think those are quite valuable. For instance when we have a neural network in a model, or if we want to solve an ODE, it is much nicer if we don't have to take apart all jax trees everywhere by hand. I did remove wrapping of returned functions though. That lead to some trouble if the jax trees contain callables that should not be wrapped, and seems overall a bit hackish to me. I also can't think of a use-case where we would really need that? If that does come along, maybe we can revisit this idea.

I think the biggest problem right now is that the as_jax_op wrapper needs pytensor inputs with static shapes.

If we want to avoid having the user specify the output types, we need to call the function at least once. We can do that with jax.infer_shape, but that still needs static shape info.

I'm not sure right now what the best way to handle this is.

jdehning · 2025-09-16T17:53:26Z

revisit #1120, which seems abandoned.

@jdehning I hope it is ok if I continue this PR?

📚 Documentation preview 📚: https://pytensor--1614.org.readthedocs.build/en/1614/

Yes, sure. Sorry that I dropped the ball.

jdehning · 2025-09-16T18:13:04Z

I think the biggest problem right now is that the as_jax_op wrapper needs pytensor inputs with static shapes.

I did use pytensor.compile.builders.infer_shape to get static shapes in the original PR. It did work for me for pymc models, if initial static shapes are lost because of a pt.cumsum. However, if I remember well, I didn't test whether it works with pm.Data, i.e. shared variables in the graph, and what happens when the shape of shared variables is changed between runs by setting new values

jdehning · 2025-09-16T18:22:41Z

I did remove wrapping of returned functions though. That lead to some trouble if the jax trees contain callables that should not be wrapped, and seems overall a bit hackish to me. I also can't think of a use-case where we would really need that? If that does come along, maybe we can revisit this idea.

I wrote it for ODEs that depend on time-dependent parameters; we need a function that takes a time point and returns some time-changing variables that interpolate between parameters. Wrapping the callable was the most user-friendly way to achieve it, as it allows defining the interpolation function and ODE solver separately. However, I agree it was somewhat hackish and not easily parsable. And its usage can be reasonably well avoided if both the interpolation function and the ODE solver are defined in a single function.

aseyboldt · 2025-09-16T18:43:26Z

Yes, sure. Sorry that I dropped the ball.

No worries :-)

I did use pytensor.compile.builders.infer_shape to get static shapes in the original PR.

That works for cases where the shape is in principle known, but pytensor missed it during the static shape inference.
It does not help if the shape really is dynamic, which is the case if you use dimensions in a pymc model.

In the case where the input shapes only depend on random variables (which will usually be the case in pymc models), we Ricardo realized that we can just eval the shapes once. It should be also be fine if we find just some valid input shapes, if the shapes change later that shouldn't be a problem. We only have to set the output shapes to None in that case, because we don't actually know what the output shapes should be.

I wrote it for ODEs that depend on time-dependent parameters; we need a function that takes a time point and returns some time-changing variables that interpolate between parameters. Wrapping the callable was the most user-friendly way to achieve it, as it allows defining the interpolation function and ODE solver separately. However, I agree it was somewhat hackish and not easily parsable. And its usage can be reasonably well avoided if both the interpolation function and the ODE solver are defined in a single function.

I thought it might have been something like that. I think that makes sense, but for now I'd rather get this in as-is. We can always revisit this later if we think we need it.

aseyboldt · 2025-09-16T18:44:26Z

We also should decide on a name: I think wrap_jax is maybe better than as_jax_op?
Or possibly jax_to_pytensor or jax_bridge?

ricardoV94 · 2025-09-16T18:36:08Z

pytensor/__init__.py

@@ -167,6 +167,18 @@ def get_underlying_scalar_constant(v):
 from pytensor.scan.views import foldl, foldr, map, reduce
 from pytensor.compile.builders import OpFromGraph

+try:


why this eager import? would be great if we didn't need to slowdown PyTensor import by default

I removed the try/except, that was already in the original function.
as_jax_op shouldn't try to import jax now, the ops.py file delays importing jax.

ricardoV94 · 2025-09-16T18:39:49Z

pytensor/link/jax/ops.py

+    [array([1., 1.], dtype=float32)]
+    """
+
+    __props__ = ("input_types", "output_types", "jax_func", "name")


why name in props, only stuff that affects the behavior of the Op should be

ricardoV94 · 2025-09-16T18:42:12Z

pytensor/link/jax/ops.py

+            )
+
+        # Create VJP operation
+        vjp_op = JAXOp(


PyTensor won't be able to fuse duplicate applications of the grad (if they somehow exist) as it will create a new function under the hood and that's used for the Op equality (whatever is on __props__ is used)

Not a blocker, just laying it out. Alternatively if the grad was an Op parametrized by the original Op/func and the connected gradients it could be merged.

However, if running on jax backed, JAX itself may be able to avoid duplication.

I would err on keeping it simple for now like you did

ricardoV94 · 2025-09-16T18:47:52Z

pytensor/link/jax/ops.py

+                [],
+                resolved_input_shapes,
+                on_unused_input="ignore",
+                mode="FAST_COMPILE",


Suggested change

mode="FAST_COMPILE",

mode=Mode(linker="py", optimizer="fast_compile"),

No reason for C code I guess

ricardoV94 · 2025-09-16T18:56:15Z

I like as_jax_op in line with as_op?

They do the same except in this case we also know how to generate gradients and dispatch to jax?

ricardoV94 · 2025-09-16T18:57:38Z

Alternatively, jax_as_op?

aseyboldt · 2025-09-16T21:53:28Z

as_jax_op sounds like it should do the opposite of what it does, export something to jax. jax_as_op solves that problem...

I don't think Op is a good name to put into the public interface that much. Most people who use pymc won't even know what we mean by it. The function also doesn't return an op, it just happens to create one internally, and doesn't even show that to the user.

I see the argument of it being like as_op, but I also don't quite like that name for the same reasons, and I don't think many people are using it in the first place.

Other options: pytensorize_jax or jax_to_pytensor?

I think something that describes what it does, instead of how it does it would be much friendlier?

jdehning · 2025-09-17T08:54:10Z

+1 for jax_to_pytensor. It is for me the most easily understandable

jdehning and others added 15 commits September 16, 2025 13:19

first commit for a decorator that transforms JAX to pytensor

9cb5e82

Add more tests

d3a277e

Define JAXOp outside of the decorator

5914162

Added comment regarding flattening of inputs

b810e99

Add as_jax_op to pytensor.__init__.py and to documentation

df76e73

Add [jax] requirement to readthedocs in order to read the docstring o…

c2338fb

…f as_jax_op

Added an example to the docstring of as_jax_op

89474ae

Use infer_static_shape, currently still with the possibility to use t…

4e2e005

…he previous approach for testing purposes

Remove sol in variable names

e6b52d6

Rename tests and make static variables test more meaningfull

abf99f1

More test renaming, forgot a few

21d252d

Refactoring of ops.py: code is in general cleaner, and JAXOp can now …

7291696

…be used without the decorator @as_jax_op

Clean up tests

3e2949d

Add to some tests a direct call to JAXOp

38b17b5

temporary as_jax_op fix

bb75938

aseyboldt force-pushed the as-jax-opt2 branch 3 times, most recently from 10dfa2e to ead4ac7 Compare September 16, 2025 15:08

Simplify as_jax_op

d04f41d

aseyboldt force-pushed the as-jax-opt2 branch from ead4ac7 to d04f41d Compare September 16, 2025 15:29

aseyboldt requested a review from Copilot September 16, 2025 15:33

Copilot AI reviewed Sep 16, 2025

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ricardoV94 reviewed Sep 16, 2025

View reviewed changes

optionally eval shapes in as_jax_op

8ecf45c

minor coding style changes in as_jax_op

abd668b

set output shape to None if not statically known in as_jax_op

fcff09b

aseyboldt marked this pull request as ready for review September 16, 2025 18:47

ricardoV94 reviewed Sep 16, 2025

View reviewed changes

aseyboldt added 4 commits September 17, 2025 00:14

clean up global import

866b4ba

remove name from JaxOp.__props__

0ae53a0

don't compile in shape eval of as_jax_op

07a2c43

more tests for as_jax_op

f832126

	mode="FAST_COMPILE",
	mode=Mode(linker="py", optimizer="fast_compile"),

Implement as_jax_op #1614

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