Transform to remove Minibatch from model

pymc/model/transform/basic.py

@@ -13,9 +13,11 @@
 #   limitations under the License.
 from collections.abc import Sequence
 
-from pytensor import Variable
+from pytensor import Variable, clone_replace
 from pytensor.graph import ancestors
+from pytensor.graph.fg import FunctionGraph
 
+from pymc.data import MinibatchOp
 from pymc.model.core import Model
 from pymc.model.fgraph import (
     ModelObservedRV,
@@ -58,3 +60,25 @@ def parse_vars(model: Model, vars: ModelVariable | Sequence[ModelVariable]) -> l
     else:
         vars_seq = (vars,)
     return [model[var] if isinstance(var, str) else var for var in vars_seq]
+
+
+def remove_minibatched_nodes(model: Model) -> Model:
+    """Remove all uses of pm.Minibatch in the Model."""
+    fgraph, _ = fgraph_from_model(model)
+
+    replacements = {}
+    for var in fgraph.apply_nodes:
+        if isinstance(var.op, MinibatchOp):
+            for inp, out in zip(var.inputs, var.outputs):
+                replacements[out] = inp
+
+    old_outs, old_coords, old_dim_lengths = fgraph.outputs, fgraph._coords, fgraph._dim_lengths  # type: ignore[attr-defined]
+    # Using `rebuild_strict=False` means all coords, names, and dim information is lost
+    # So we need to restore it from the old fgraph
+    new_outs = clone_replace(old_outs, replacements, rebuild_strict=False)  # type: ignore[arg-type]
+    for old_out, new_out in zip(old_outs, new_outs):
+        new_out.name = old_out.name
+    fgraph = FunctionGraph(outputs=new_outs, clone=False)
+    fgraph._coords = old_coords  # type: ignore[attr-defined]
+    fgraph._dim_lengths = old_dim_lengths  # type: ignore[attr-defined]
+    return model_from_fgraph(fgraph, mutate_fgraph=True)

tests/model/transform/test_basic.py

@@ -11,9 +11,11 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
+import numpy as np
+
 import pymc as pm
 
-from pymc.model.transform.basic import prune_vars_detached_from_observed
+from pymc.model.transform.basic import prune_vars_detached_from_observed, remove_minibatched_nodes
 
 
 def test_prune_vars_detached_from_observed():
@@ -30,3 +32,17 @@ def test_prune_vars_detached_from_observed():
     assert set(m.named_vars.keys()) == {"obs_data", "a0", "a1", "a2", "obs", "d0", "d1"}
     pruned_m = prune_vars_detached_from_observed(m)
     assert set(pruned_m.named_vars.keys()) == {"obs_data", "a0", "a1", "a2", "obs"}
+
+
+def test_remove_minibatches():
+    data_size = 100
+    data = np.zeros((data_size,))
+    batch_size = 10
+    with pm.Model() as m1:
+        mb = pm.Minibatch(data, batch_size=batch_size)
+        x = pm.Normal("x")
+        y = pm.Normal("y", x, observed=mb, total_size=100)
+
+    m2 = remove_minibatched_nodes(m1)
+    assert m1.y.shape[0].eval() == batch_size
+    assert m2.y.shape[0].eval() == data_size