Implement pad (#748)

* Add `pt.pad`

* Refactor linspace, logspace, and geomspace to match numpy implementation

* Add `pt.flip`

* Move `flip` to `tensor/subtensor.py`, add docstring

* Move `slice_at_axis` to `tensor/subtensor` and expose it in `pytensor.tensor`

pytensor/link/jax/dispatch/__init__.py

@@ -6,6 +6,7 @@
 import pytensor.link.jax.dispatch.blockwise
 import pytensor.link.jax.dispatch.elemwise
 import pytensor.link.jax.dispatch.extra_ops
+import pytensor.link.jax.dispatch.pad
 import pytensor.link.jax.dispatch.math
 import pytensor.link.jax.dispatch.nlinalg
 import pytensor.link.jax.dispatch.random

pytensor/link/jax/dispatch/pad.py

@@ -0,0 +1,53 @@
+import jax.numpy as jnp
+import numpy as np
+
+from pytensor.link.jax.dispatch import jax_funcify
+from pytensor.tensor.pad import Pad
+
+
+@jax_funcify.register(Pad)
+def jax_funcify_pad(op, **kwargs):
+    pad_mode = op.pad_mode
+    reflect_type = op.reflect_type
+    has_stat_length = op.has_stat_length
+
+    if pad_mode == "constant":
+
+        def constant_pad(x, pad_width, constant_values):
+            return jnp.pad(x, pad_width, mode=pad_mode, constant_values=constant_values)
+
+        return constant_pad
+
+    elif pad_mode == "linear_ramp":
+
+        def lr_pad(x, pad_width, end_values):
+            # JAX does not allow a dynamic input if end_values is non-scalar
+            if not isinstance(end_values, int | float):
+                end_values = tuple(np.array(end_values))
+            return jnp.pad(x, pad_width, mode=pad_mode, end_values=end_values)
+
+        return lr_pad
+
+    elif pad_mode in ["maximum", "minimum", "mean"] and has_stat_length:
+
+        def stat_pad(x, pad_width, stat_length):
+            # JAX does not allow a dynamic input here, need to cast to tuple
+            return jnp.pad(
+                x, pad_width, mode=pad_mode, stat_length=tuple(np.array(stat_length))
+            )
+
+        return stat_pad
+
+    elif pad_mode in ["reflect", "symmetric"]:
+
+        def loop_pad(x, pad_width):
+            return jnp.pad(x, pad_width, mode=pad_mode, reflect_type=reflect_type)
+
+        return loop_pad
+
+    else:
+
+        def pad(x, pad_width):
+            return jnp.pad(x, pad_width, mode=pad_mode)
+
+        return pad

pytensor/tensor/__init__.py

@@ -130,6 +130,7 @@ def _get_vector_length_Constant(op: Op | Variable, var: Constant) -> int:
 from pytensor.tensor.extra_ops import *
 from pytensor.tensor.io import *
 from pytensor.tensor.math import *
+from pytensor.tensor.pad import pad
 from pytensor.tensor.shape import (
     reshape,
     shape,