Foward compile_kwargs to ADVI when init = "advi+..."

pymc/pytensorf.py

@@ -22,7 +22,6 @@
 import pytensor.tensor as pt
 import scipy.sparse as sps
 
-from pytensor import scalar
 from pytensor.compile import Function, Mode, get_mode
 from pytensor.compile.builders import OpFromGraph
 from pytensor.gradient import grad
@@ -415,31 +414,6 @@ def hessian_diag(f, vars=None, negate_output=True):
         return empty_gradient
 
 
-class IdentityOp(scalar.UnaryScalarOp):
-    @staticmethod
-    def st_impl(x):
-        return x
-
-    def impl(self, x):
-        return x
-
-    def grad(self, inp, grads):
-        return grads
-
-    def c_code(self, node, name, inp, out, sub):
-        return f"{out[0]} = {inp[0]};"
-
-    def __eq__(self, other):
-        return isinstance(self, type(other))
-
-    def __hash__(self):
-        return hash(type(self))
-
-
-scalar_identity = IdentityOp(scalar.upgrade_to_float, name="scalar_identity")
-identity = Elemwise(scalar_identity, name="identity")
-
-
 def make_shared_replacements(point, vars, model):
     """
     Make shared replacements for all *other* variables than the ones passed.

pymc/sampling/mcmc.py

@@ -1553,6 +1553,7 @@ def init_nuts(
             callbacks=cb,
             progressbar=progressbar,
             obj_optimizer=pm.adagrad_window,
+            compile_kwargs=compile_kwargs,
         )
         approx_sample = approx.sample(
             draws=chains, random_seed=random_seed_list[0], return_inferencedata=False
@@ -1566,6 +1567,7 @@ def init_nuts(
         potential = quadpotential.QuadPotentialDiagAdapt(
             n, mean, cov, weight, rng=random_seed_list[0]
         )
+
     elif init == "advi":
         approx = pm.fit(
             random_seed=random_seed_list[0],
@@ -1575,6 +1577,7 @@ def init_nuts(
             callbacks=cb,
             progressbar=progressbar,
             obj_optimizer=pm.adagrad_window,
+            compile_kwargs=compile_kwargs,
         )
         approx_sample = approx.sample(
             draws=chains, random_seed=random_seed_list[0], return_inferencedata=False
@@ -1592,6 +1595,7 @@ def init_nuts(
             callbacks=cb,
             progressbar=progressbar,
             obj_optimizer=pm.adagrad_window,
+            compile_kwargs=compile_kwargs,
         )
         approx_sample = approx.sample(
             draws=chains, random_seed=random_seed_list[0], return_inferencedata=False

pymc/variational/inference.py

@@ -82,9 +82,18 @@
 
     def run_profiling(self, n=1000, score=None, **kwargs):
         score = self._maybe_score(score)
-        fn_kwargs = kwargs.pop("fn_kwargs", {})
-        fn_kwargs["profile"] = True
-        step_func = self.objective.step_function(score=score, fn_kwargs=fn_kwargs, **kwargs)
+        if "fn_kwargs" in kwargs:
+            warnings.warn(
+                "fn_kwargs is deprecated, please use compile_kwargs instead", DeprecationWarning
+            )
+            compile_kwargs = kwargs.pop("fn_kwargs")
+        else:
+            compile_kwargs = kwargs.pop("compile_kwargs", {})
+
+        compile_kwargs["profile"] = True
+        step_func = self.objective.step_function(
+            score=score, compile_kwargs=compile_kwargs, **kwargs
+        )
         try:
             for _ in track(range(n)):
                 step_func()
@@ -134,7 +143,7 @@
             Add custom updates to resulting updates
         total_grad_norm_constraint: `float`
             Bounds gradient norm, prevents exploding gradient problem
-        fn_kwargs: `dict`
+        compile_kwargs: `dict`
             Add kwargs to pytensor.function (e.g. `{'profile': True}`)
         more_replacements: `dict`
             Apply custom replacements before calculating gradients
@@ -729,7 +738,7 @@
         Add custom updates to resulting updates
     total_grad_norm_constraint: `float`
         Bounds gradient norm, prevents exploding gradient problem
-    fn_kwargs: `dict`
+    compile_kwargs: `dict`
         Add kwargs to pytensor.function (e.g. `{'profile': True}`)
     more_replacements: `dict`
         Apply custom replacements before calculating gradients

pymc/variational/opvi.py

@@ -61,6 +61,8 @@
 
 from pytensor.graph.basic import Variable
 from pytensor.graph.replace import graph_replace
+from pytensor.scalar.basic import identity as scalar_identity
+from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.shape import unbroadcast
 
 import pymc as pm
@@ -74,7 +76,6 @@
     SeedSequenceSeed,
     compile,
     find_rng_nodes,
-    identity,
     reseed_rngs,
 )
 from pymc.util import (
@@ -332,6 +333,7 @@
         more_replacements=None,
         total_grad_norm_constraint=None,
         score=False,
+        compile_kwargs=None,
         fn_kwargs=None,
     ):
         R"""Step function that should be called on each optimization step.
@@ -362,17 +364,30 @@
             Bounds gradient norm, prevents exploding gradient problem
         score: `bool`
             calculate loss on each step? Defaults to False for speed
-        fn_kwargs: `dict`
+        compile_kwargs: `dict`
             Add kwargs to pytensor.function (e.g. `{'profile': True}`)
+        fn_kwargs: dict
+            arbitrary kwargs passed to `pytensor.function`
+
+            .. warning:: `fn_kwargs` is deprecated and will be removed in future versions
+
         more_replacements: `dict`
             Apply custom replacements before calculating gradients
 
         Returns
         -------
         `pytensor.function`
         """
-        if fn_kwargs is None:
-            fn_kwargs = {}
+        if fn_kwargs is not None:
+            warnings.warn(
+                "`fn_kwargs` is deprecated and will be removed in future versions. Use "
+                "`compile_kwargs` instead.",
+                DeprecationWarning,
+            )
+            compile_kwargs = fn_kwargs
+
+        if compile_kwargs is None:
+            compile_kwargs = {}
         if score and not self.op.returns_loss:
             raise NotImplementedError(f"{self.op} does not have loss")
         updates = self.updates(
@@ -388,14 +403,14 @@
         )
         seed = self.approx.rng.randint(2**30, dtype=np.int64)
         if score:
-            step_fn = compile([], updates.loss, updates=updates, random_seed=seed, **fn_kwargs)
+            step_fn = compile([], updates.loss, updates=updates, random_seed=seed, **compile_kwargs)
         else:
-            step_fn = compile([], [], updates=updates, random_seed=seed, **fn_kwargs)
+            step_fn = compile([], [], updates=updates, random_seed=seed, **compile_kwargs)
         return step_fn
 
     @pytensor.config.change_flags(compute_test_value="off")
     def score_function(
-        self, sc_n_mc=None, more_replacements=None, fn_kwargs=None
+        self, sc_n_mc=None, more_replacements=None, compile_kwargs=None, fn_kwargs=None
     ):  # pragma: no cover
         R"""Compile scoring function that operates which takes no inputs and returns Loss.
 
@@ -405,22 +420,34 @@
             number of scoring MC samples
         more_replacements:
             Apply custom replacements before compiling a function
+        compile_kwargs: `dict`
+            arbitrary kwargs passed to `pytensor.function`
         fn_kwargs: `dict`
             arbitrary kwargs passed to `pytensor.function`
 
+            .. warning:: `fn_kwargs` is deprecated and will be removed in future versions
+
         Returns
         -------
         pytensor.function
         """
-        if fn_kwargs is None:
-            fn_kwargs = {}
+        if fn_kwargs is not None:
+            warnings.warn(
+                "`fn_kwargs` is deprecated and will be removed in future versions. Use "
+                "`compile_kwargs` instead",
+                DeprecationWarning,
+            )
+            compile_kwargs = fn_kwargs
+
+        if compile_kwargs is None:
+            compile_kwargs = {}
         if not self.op.returns_loss:
             raise NotImplementedError(f"{self.op} does not have loss")
         if more_replacements is None:
             more_replacements = {}
         loss = self(sc_n_mc, more_replacements=more_replacements)
         seed = self.approx.rng.randint(2**30, dtype=np.int64)
-        return compile([], loss, random_seed=seed, **fn_kwargs)
+        return compile([], loss, random_seed=seed, **compile_kwargs)
 
     @pytensor.config.change_flags(compute_test_value="off")
     def __call__(self, nmc, **kwargs):
@@ -451,7 +478,7 @@
     require_logq = True
     objective_class = ObjectiveFunction
     supports_aevb = property(lambda self: not self.approx.any_histograms)
-    T = identity
+    T = Elemwise(scalar_identity)
 
     def __init__(self, approx):
         self.approx = approx