Allow freezing only subset of data and dims

*Also fix dim_lengths not being returned

Author: ricardoV94

pymc/model/core.py

@@ -1050,7 +1050,14 @@ def set_dim(self, name: str, new_length: int, coord_values: Sequence | None = No
                     expected=new_length,
                 )
             self._coords[name] = tuple(coord_values)
-        self.dim_lengths[name].set_value(new_length)
+        dim_length = self.dim_lengths[name]
+        if not isinstance(dim_length, SharedVariable):
+            raise TypeError(
+                f"The dim_length of `{name}` must be a `SharedVariable` "
+                "(created through `coords` to allow updating). "
+                f"The current type is: {type(dim_length)}"
+            )
+        dim_length.set_value(new_length)
         return
 
     def initial_point(self, random_seed: SeedSequenceSeed = None) -> dict[str, np.ndarray]:
@@ -1102,8 +1109,8 @@ def set_data(
         shared_object = self[name]
         if not isinstance(shared_object, SharedVariable):
             raise TypeError(
-                f"The variable `{name}` must be a `SharedVariable`"
-                " (created through `pm.Data()` or `pm.Data(mutable=True)`) to allow updating. "
+                f"The variable `{name}` must be a `SharedVariable` "
+                "(created through `pm.Data()` to allow updating.) "
                 f"The current type is: {type(shared_object)}"
             )
 

pymc/model/transform/optimization.py

@@ -11,16 +11,27 @@
 #   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.
+from collections.abc import Sequence
+
 from pytensor import clone_replace
 from pytensor.compile import SharedVariable
 from pytensor.graph import FunctionGraph
 from pytensor.tensor import constant
+from pytensor.tensor.sharedvar import TensorSharedVariable
+from pytensor.tensor.variable import TensorConstant
 
 from pymc import Model
 from pymc.model.fgraph import ModelFreeRV, fgraph_from_model, model_from_fgraph
 
 
-def freeze_dims_and_data(model: Model) -> Model:
+def _constant_from_shared(shared: SharedVariable) -> TensorConstant:
+    assert isinstance(shared, TensorSharedVariable)
+    return constant(shared.get_value(), name=shared.name, dtype=shared.type.dtype)
+
+
+def freeze_dims_and_data(
+    model: Model, dims: Sequence[str] | None = None, data: Sequence[str] | None = None
+) -> Model:
     """Recreate a Model with fixed RV dimensions and Data values.
 
     The dimensions of the pre-existing RVs will no longer follow changes to the coordinates.
@@ -30,41 +41,60 @@ def freeze_dims_and_data(model: Model) -> Model:
 
     This transformation may allow more performant sampling, or compiling model functions to backends that
     are more restrictive about dynamic shapes such as JAX.
+
+    Parameters
+    ----------
+    model : Model
+        The model where to freeze dims and data.
+    dims : Sequence of str, optional
+        The dimensions to freeze.
+        If None, all dimensions are frozen. Pass an empty list to avoid freezing any dimension.
+    data : Sequence of str, optional
+        The data to freeze.
+        If None, all data are frozen. Pass an empty list to avoid freezing any data.
+
+    Returns
+    -------
+    Model
+        A new model with the specified dimensions and data frozen.
     """
     fg, memo = fgraph_from_model(model)
 
+    if dims is None:
+        dims = tuple(model.dim_lengths.keys())
+    if data is None:
+        data = tuple(model.named_vars.keys())
+
     # Replace mutable dim lengths and data by constants
-    frozen_vars = {
-        memo[dim_length]: constant(
-            dim_length.get_value(), name=dim_length.name, dtype=dim_length.type.dtype
-        )
-        for dim_length in model.dim_lengths.values()
+    frozen_replacements = {
+        memo[dim_length]: _constant_from_shared(dim_length)
+        for dim_length in (model.dim_lengths[dim_name] for dim_name in dims)
         if isinstance(dim_length, SharedVariable)
     }
-    frozen_vars |= {
-        memo[data_var].owner.inputs[0]: constant(
-            data_var.get_value(), name=data_var.name, dtype=data_var.type.dtype
-        )
-        for data_var in model.named_vars.values()
-        if isinstance(data_var, SharedVariable)
+    frozen_replacements |= {
+        memo[datum].owner.inputs[0]: _constant_from_shared(datum)
+        for datum in (model.named_vars[datum_name] for datum_name in data)
+        if isinstance(datum, SharedVariable)
     }
 
-    old_outs, coords = fg.outputs, fg._coords  # type: ignore
+    old_outs, old_coords, old_dim_lenghts = fg.outputs, fg._coords, fg._dim_lengths  # type: ignore
     # Rebuild strict will force the recreation of RV nodes with updated static types
-    new_outs = clone_replace(old_outs, replace=frozen_vars, rebuild_strict=False)  # type: ignore
+    new_outs = clone_replace(old_outs, replace=frozen_replacements, rebuild_strict=False)  # type: ignore
     for old_out, new_out in zip(old_outs, new_outs):
         new_out.name = old_out.name
     fg = FunctionGraph(outputs=new_outs, clone=False)
-    fg._coords = coords  # type: ignore
+    fg._coords = old_coords  # type: ignore
+    fg._dim_lengths = {  # type: ignore
+        dim: frozen_replacements.get(dim_length, dim_length)
+        for dim, dim_length in old_dim_lenghts.items()
+    }
 
     # Recreate value variables from new RVs to propagate static types to logp graphs
     replacements = {}
     for node in fg.apply_nodes:
         if not isinstance(node.op, ModelFreeRV):
             continue
-        rv, old_value, *dims = node.inputs
-        if dims is None:
-            continue
+        rv, old_value, *_ = node.inputs
         transform = node.op.transform
         if transform is None:
             new_value = rv.type()

tests/model/transform/test_optimization.py

@@ -11,17 +11,22 @@
 #   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 pytest
+
+from pytensor.compile import SharedVariable
 from pytensor.graph import Constant
 
+from pymc import Deterministic
 from pymc.data import Data
 from pymc.distributions import HalfNormal, Normal
 from pymc.model import Model
 from pymc.model.transform.optimization import freeze_dims_and_data
 
 
-def test_freeze_existing_rv_dims_and_data():
+def test_freeze_dims_and_data():
     with Model(coords={"test_dim": range(5)}) as m:
-        std = Data("std", [1])
+        std = Data("test_data", [1])
         x = HalfNormal("x", std, dims=("test_dim",))
         y = Normal("y", shape=x.shape[0] + 1)
 
@@ -34,18 +39,96 @@ def test_freeze_existing_rv_dims_and_data():
     assert y_logp.type.shape == (None,)
 
     frozen_m = freeze_dims_and_data(m)
-    std, x, y = frozen_m["std"], frozen_m["x"], frozen_m["y"]
+    data, x, y = frozen_m["test_data"], frozen_m["x"], frozen_m["y"]
     x_logp, y_logp = frozen_m.logp(sum=False)
-    assert isinstance(std, Constant)
+    assert isinstance(data, Constant)
     assert x.type.shape == (5,)
     assert y.type.shape == (6,)
     assert x_logp.type.shape == (5,)
     assert y_logp.type.shape == (6,)
 
+    # Test trying to update a frozen data or dim raises an informative error
+    with frozen_m:
+        with pytest.raises(TypeError, match="The variable `test_data` must be a `SharedVariable`"):
+            frozen_m.set_data("test_data", values=[2])
+        with pytest.raises(
+            TypeError, match="The dim_length of `test_dim` must be a `SharedVariable`"
+        ):
+            frozen_m.set_dim("test_dim", new_length=6, coord_values=range(6))
+
+    # Test we can still update original model
+    with m:
+        m.set_data("test_data", values=[2])
+        m.set_dim("test_dim", new_length=6, coord_values=range(6))
+    assert m["test_data"].get_value() == [2]
+    assert m.dim_lengths["test_dim"].get_value() == 6
 
-def test_freeze_rv_dims_nothing_to_change():
+
+def test_freeze_dims_nothing_to_change():
     with Model(coords={"test_dim": range(5)}) as m:
         x = HalfNormal("x", shape=(5,))
         y = Normal("y", shape=x.shape[0] + 1)
 
     assert m.point_logps() == freeze_dims_and_data(m).point_logps()
+
+
+def test_freeze_dims_and_data_subset():
+    with Model(coords={"dim1": range(3), "dim2": range(5)}) as m:
+        data1 = Data("data1", [1, 2, 3], dims="dim1")
+        data2 = Data("data2", [1, 2, 3, 4, 5], dims="dim2")
+        var1 = Normal("var1", dims="dim1")
+        var2 = Normal("var2", dims="dim2")
+        x = data1 * var1
+        y = data2 * var2
+        det = Deterministic("det", x[:, None] + y[None, :])
+
+    assert det.type.shape == (None, None)
+
+    new_m = freeze_dims_and_data(m, dims=["dim1"], data=[])
+    assert new_m["det"].type.shape == (3, None)
+    assert isinstance(new_m.dim_lengths["dim1"], Constant) and new_m.dim_lengths["dim1"].data == 3
+    assert isinstance(new_m.dim_lengths["dim2"], SharedVariable)
+    assert isinstance(new_m["data1"], SharedVariable)
+    assert isinstance(new_m["data2"], SharedVariable)
+
+    new_m = freeze_dims_and_data(m, dims=["dim2"], data=[])
+    assert new_m["det"].type.shape == (None, 5)
+    assert isinstance(new_m.dim_lengths["dim1"], SharedVariable)
+    assert isinstance(new_m.dim_lengths["dim2"], Constant) and new_m.dim_lengths["dim2"].data == 5
+    assert isinstance(new_m["data1"], SharedVariable)
+    assert isinstance(new_m["data2"], SharedVariable)
+
+    new_m = freeze_dims_and_data(m, dims=["dim1", "dim2"], data=[])
+    assert new_m["det"].type.shape == (3, 5)
+    assert isinstance(new_m.dim_lengths["dim1"], Constant) and new_m.dim_lengths["dim1"].data == 3
+    assert isinstance(new_m.dim_lengths["dim2"], Constant) and new_m.dim_lengths["dim2"].data == 5
+    assert isinstance(new_m["data1"], SharedVariable)
+    assert isinstance(new_m["data2"], SharedVariable)
+
+    new_m = freeze_dims_and_data(m, dims=[], data=["data1"])
+    assert new_m["det"].type.shape == (3, None)
+    assert isinstance(new_m.dim_lengths["dim1"], SharedVariable)
+    assert isinstance(new_m.dim_lengths["dim2"], SharedVariable)
+    assert isinstance(new_m["data1"], Constant) and np.all(new_m["data1"].data == [1, 2, 3])
+    assert isinstance(new_m["data2"], SharedVariable)
+
+    new_m = freeze_dims_and_data(m, dims=[], data=["data2"])
+    assert new_m["det"].type.shape == (None, 5)
+    assert isinstance(new_m.dim_lengths["dim1"], SharedVariable)
+    assert isinstance(new_m.dim_lengths["dim2"], SharedVariable)
+    assert isinstance(new_m["data1"], SharedVariable)
+    assert isinstance(new_m["data2"], Constant) and np.all(new_m["data2"].data == [1, 2, 3, 4, 5])
+
+    new_m = freeze_dims_and_data(m, dims=[], data=["data1", "data2"])
+    assert new_m["det"].type.shape == (3, 5)
+    assert isinstance(new_m.dim_lengths["dim1"], SharedVariable)
+    assert isinstance(new_m.dim_lengths["dim2"], SharedVariable)
+    assert isinstance(new_m["data1"], Constant) and np.all(new_m["data1"].data == [1, 2, 3])
+    assert isinstance(new_m["data2"], Constant) and np.all(new_m["data2"].data == [1, 2, 3, 4, 5])
+
+    new_m = freeze_dims_and_data(m, dims=["dim1"], data=["data2"])
+    assert new_m["det"].type.shape == (3, 5)
+    assert isinstance(new_m.dim_lengths["dim1"], Constant) and new_m.dim_lengths["dim1"].data == 3
+    assert isinstance(new_m.dim_lengths["dim2"], SharedVariable)
+    assert isinstance(new_m["data1"], SharedVariable)
+    assert isinstance(new_m["data2"], Constant) and np.all(new_m["data2"].data == [1, 2, 3, 4, 5])