Docs

Author: jessegrabowski

pytensor/tensor/extra_ops.py

@@ -2267,17 +2267,113 @@ def pack(
     Parameters
     ----------
     tensors: TensorVariable
-        Tensors to be packed into a single vector.
+        Tensors to be packed. Tensors can have varying shapes and dimensions, but must have the same size along each
+        of the dimensions specified in the `axes` parameter.
     axes: int or sequence of int, optional
-        Axes to be concatenated. All other axes will be raveled (packed) and joined. If None, all axes will be raveled
-        and joined.
+        Axes to be preserved. All other axes will be raveled (packed), and the output is the result of concatenating
+        on the new raveled dimension. If None, all axes will be raveled and joined. Axes can be either positive or
+        negative, but must be striclty increasing in both the positive and  negative parts of the list. Negative axes
+        must come after positive axes.
 
     Returns
     -------
     flat_tensor: TensorVariable
         A new symbolic variable representing the concatenated 1d vector of all tensor inputs
     packed_shapes: list of tuples of TensorVariable
         A list of tuples, where each tuple contains the symbolic shape of the original tensors.
+
+    Notes
+    -----
+    This function is a helper for joining tensors of varying shapes into a single tenor. This is done by choosing a
+    list of axes to concatenate, and raveling all other axes. The resulting tensor are then concatenated along the
+    raveled axis. The original shapes of the tensors are also returned, so that they can be unpacked later.
+
+    The `axes` parameter determines which dimensions are *not* raveled. The requested axes must exist in all input
+    tensors, but there are otherwwise no restrictions on the shapes or dimensions of the input tensors. For example, if
+    `axes=[0]`, then the first dimension of each tensor is preserved, and all other dimensions are raveled:
+
+    .. code-block:: python
+
+        import pytensor.tensor as pt
+
+        x = pt.tensor("x", shape=(2, 3, 4))
+        y = pt.tensor("y", shape=(2, 5))
+        packed_output, shapes = pack(x, y, axes=0)
+        # packed_output will have shape (2, 3 * 4 + 5) = (2, 17)
+
+    Since axes = 0, the first dimension of both `x` and `y` is preserved. This first example is equivalent to a simple
+    reshape and concat operation:
+
+    .. code-block:: python
+
+        x_reshaped = x.reshape(2, -1)  # shape (2, 12)
+        y_reshaped = y.reshape(2, -1)  # shape (2, 5)
+        packed_output = pt.concatenate(
+            [x_reshaped, y_reshaped], axis=1
+        )  # shape (2, 17)
+
+    `axes` can also be negative, in which case the axes are counted from the end of the tensor shape. For example,
+    if `axes=[-1]`, then the last dimension of each tensor is preserved, and all other dimensions are raveled:
+
+    .. code-block:: python
+
+        import pytensor.tensor as pt
+
+        x = pt.tensor("x", shape=(3, 4, 7))
+        y = pt.tensor("y", shape=(6, 2, 1, 7))
+        packed_output, shapes = pack(x, y, axes=-1)
+        # packed_output will have shape (3 * 4 + 6 * 2 * 1, 7) = (24, 7)
+
+    The most important restriction of `axes` is that there can be at most one "hole" in the axes list. A hole is
+    defined as a missing axis in the sequence of axes. The easiest way to define a hole is by using both positive
+    and negative axes together. For example, `axes=[0, -1]` has a hole between the first and last axes. In this case,
+    the first and last dimensions of each tensor are preserved, and all other dimensions are raveled:
+
+    .. code-block:: python
+
+        import pytensor.tensor as pt
+
+        x = pt.tensor("x", shape=(2, 3, 2, 3, 7))
+        y = pt.tensor("y", shape=(2, 6, 7))
+        packed_output, shapes = pack(x, y, axes=[0, -1])
+        # packed_output will have shape (2, 3 * 2 * 3 + 6, 7) = (2, 24, 7)
+
+    Multiple explicit holes are not allowed. For example, `axes = [0, 2, -1]` is illegal because there are two holes,
+    one between axes 0 and 2, and another between axes 2 and -1.
+
+    Implicit holes are also possible when using only positive or only negative axes. `axes = [0]` already has an
+    implicit hole to the right of axis 0. `axes = [2, 3]` has two implicit holes, one to the left of axis 2, and another
+    to the right. This is illegal, since there are two holes. However, `axes = [2, 3]` can be made legal if we interpret
+    axis 3 as the last axis (-1), which closes the right implicit hole. The interpretation requires that at least one
+    input tensor has exactly 4 dimensions:
+
+    .. code-block:: python
+
+        import pytensor.tensor as pt
+
+        x = pt.tensor("x", shape=(5, 2, 3, 4))
+        y = pt.tensor("y", shape=(2, 3, 4))
+        packed_output, shapes = pack(x, y, axes=[2, 3])
+        # packed_output will have shape (5 * 2 + 2, 3, 4) = (12, 3, 4)
+
+    Note here that `y` has only 3 dimensions, so axis 3 is interpreted as -1, the last axis. If no input has 4
+    dimensions, or if any input has more than 4 dimensions, an error is raised in this case.
+
+    Negative axes have similar rules regarding implicit holes. `axes = [-1]` has an implicit hole to the left of
+    axis -1. `axes = [-3, -2]` has two implicit holes. To arrive at a valid interpretation, we take -3 to be axis 0,
+    which closes the left implicit hole. This requires that at least one input tensor has exactly 3 dimensions:
+
+    .. code-block:: python
+
+        import pytensor.tensor as pt
+
+        x = pt.tensor("x", shape=(2, 3, 4))
+        y = pt.tensor("y", shape=(6, 4))
+        packed_output, shapes = pack(x, y, axes=[-3, -2])
+        # packed_output will have shape (2 + 6, 3, 4) = (8, 3, 4)
+
+    Similarly to the previous example, if no input has 3 dimensions, or if any input has more than 3 dimensions, an
+    error would be raised in this example.
     """
     if not tensors:
         raise ValueError("Cannot pack an empty list of tensors.")
@@ -2316,6 +2412,7 @@ def pack(
         inputs=tensors,
         outputs=[packed_output_tensor, *packed_output_shapes],
         name="Pack{axes=" + str(axes) + "}",
+        inline=True,
     )
 
     outputs = pack_op(*tensors)