Adding expand_dims for xtensor

pytensor/xtensor/rewriting/shape.py

@@ -1,6 +1,7 @@
 from pytensor.graph import node_rewriter
 from pytensor.tensor import (
     broadcast_to,
+    expand_dims,
     join,
     moveaxis,
     specify_shape,
@@ -10,6 +11,7 @@
 from pytensor.xtensor.rewriting.basic import register_lower_xtensor
 from pytensor.xtensor.shape import (
     Concat,
+    ExpandDims,
     Squeeze,
     Stack,
     Transpose,
@@ -121,7 +123,7 @@ def lower_transpose(fgraph, node):
 
 @register_lower_xtensor
 @node_rewriter([Squeeze])
-def local_squeeze_reshape(fgraph, node):
+def lower_squeeze(fgraph, node):
     """Rewrite Squeeze to tensor.squeeze."""
     [x] = node.inputs
     x_tensor = tensor_from_xtensor(x)
@@ -132,3 +134,33 @@ def local_squeeze_reshape(fgraph, node):
 
     new_out = xtensor_from_tensor(x_tensor_squeezed, dims=node.outputs[0].type.dims)
     return [new_out]
+
+
+@register_lower_xtensor
+@node_rewriter([ExpandDims])
+def lower_expand_dims(fgraph, node):
+    """Rewrite ExpandDims using tensor operations."""
+    x, size = node.inputs
+    out = node.outputs[0]
+
+    # Convert inputs to tensors
+    x_tensor = tensor_from_xtensor(x)
+    size_tensor = tensor_from_xtensor(size)
+
+    # Get the new dimension name and position
+    new_axis = 0  # Always insert at front
+
+    # Use tensor operations
+    if out.type.shape[0] == 1:
+        # Simple case: just expand with size 1
+        result_tensor = expand_dims(x_tensor, new_axis)
+    else:
+        # Otherwise broadcast to the requested size
+        result_tensor = broadcast_to(x_tensor, (size_tensor, *x_tensor.shape))
+
+    # Preserve static shape information
+    result_tensor = specify_shape(result_tensor, out.type.shape)
+
+    # Convert result back to xtensor
+    result = xtensor_from_tensor(result_tensor, dims=out.type.dims)
+    return [result]

pytensor/xtensor/shape.py

@@ -1,12 +1,15 @@
 import warnings
-from collections.abc import Sequence
+from collections.abc import Hashable, Sequence
 from types import EllipsisType
 from typing import Literal
 
+import numpy as np
+
 from pytensor.graph import Apply
 from pytensor.scalar import discrete_dtypes, upcast
 from pytensor.tensor import as_tensor, get_scalar_constant_value
 from pytensor.tensor.exceptions import NotScalarConstantError
+from pytensor.tensor.type import integer_dtypes
 from pytensor.xtensor.basic import XOp
 from pytensor.xtensor.type import as_xtensor, xtensor
 
@@ -380,3 +383,121 @@ def squeeze(x, dim=None):
         return x  # no-op if nothing to squeeze
 
     return Squeeze(dims=dims)(x)
+
+
+class ExpandDims(XOp):
+    """Add a new dimension to an XTensorVariable."""
+
+    __props__ = ("dim",)
+
+    def __init__(self, dim):
+        if not isinstance(dim, str):
+            raise TypeError(f"`dim` must be a string, got: {type(self.dim)}")
+
+        self.dim = dim
+
+    def make_node(self, x, size):
+        x = as_xtensor(x)
+
+        if self.dim in x.type.dims:
+            raise ValueError(f"Dimension {self.dim} already exists in {x.type.dims}")
+
+        size = as_xtensor(size, dims=())
+        if not (size.dtype in integer_dtypes and size.ndim == 0):
+            raise ValueError(f"size should be an integer scalar, got {size.type}")
+        try:
+            static_size = int(get_scalar_constant_value(size))
+        except NotScalarConstantError:
+            static_size = None
+
+        # If size is a constant, validate it
+        if static_size is not None and static_size < 0:
+            raise ValueError(f"size must be 0 or positive, got: {static_size}")
+        new_shape = (static_size, *x.type.shape)
+
+        # Insert new dim at front
+        new_dims = (self.dim, *x.type.dims)
+
+        out = xtensor(
+            dtype=x.type.dtype,
+            shape=new_shape,
+            dims=new_dims,
+        )
+        return Apply(self, [x, size], [out])
+
+
+def expand_dims(x, dim=None, create_index_for_new_dim=None, axis=None, **dim_kwargs):
+    """Add one or more new dimensions to an XTensorVariable."""
+    x = as_xtensor(x)
+
+    # Store original dimensions for axis handling
+    original_dims = x.type.dims
+
+    # Warn if create_index_for_new_dim is used (not supported)
+    if create_index_for_new_dim is not None:
+        warnings.warn(
+            "create_index_for_new_dim=False has no effect in pytensor.xtensor",
+            UserWarning,
+            stacklevel=2,
+        )
+
+    if dim is None:
+        dim = dim_kwargs
+    elif dim_kwargs:
+        raise ValueError("Cannot specify both `dim` and `**dim_kwargs`")
+
+    # Check that dim is Hashable or a sequence of Hashable or dict
+    if not isinstance(dim, Hashable):
+        if not isinstance(dim, Sequence | dict):
+            raise TypeError(f"unhashable type: {type(dim).__name__}")
+        if not all(isinstance(d, Hashable) for d in dim):
+            raise TypeError(f"unhashable type in {type(dim).__name__}")
+
+    # Normalize to a dimension-size mapping
+    if isinstance(dim, str):
+        dims_dict = {dim: 1}
+    elif isinstance(dim, Sequence) and not isinstance(dim, dict):
+        dims_dict = {d: 1 for d in dim}
+    elif isinstance(dim, dict):
+        dims_dict = {}
+        for name, val in dim.items():
+            if isinstance(val, str):
+                raise TypeError(f"Dimension size cannot be a string: {val}")
+            if isinstance(val, Sequence | np.ndarray):
+                warnings.warn(
+                    "When a sequence is provided as a dimension size, only its length is used. "
+                    "The actual values (which would be coordinates in xarray) are ignored.",
+                    UserWarning,
+                    stacklevel=2,
+                )
+                dims_dict[name] = len(val)
+            else:
+                # should be int or symbolic scalar
+                dims_dict[name] = val
+    else:
+        raise TypeError(f"Invalid type for `dim`: {type(dim)}")
+
+    # Insert each new dim at the front (reverse order preserves user intent)
+    for name, size in reversed(dims_dict.items()):
+        x = ExpandDims(dim=name)(x, size)
+
+    # If axis is specified, transpose to put new dimensions in the right place
+    if axis is not None:
+        # Wrap non-sequence axis in a list
+        if not isinstance(axis, Sequence):
+            axis = [axis]
+
+        # require len(axis) == len(dims_dict)
+        if len(axis) != len(dims_dict):
+            raise ValueError("lengths of dim and axis should be identical.")
+
+        # Insert new dimensions at their specified positions
+        target_dims = list(original_dims)
+        for name, pos in zip(dims_dict, axis):
+            # Convert negative axis to positive position relative to current dims
+            if pos < 0:
+                pos = len(target_dims) + pos + 1
+            target_dims.insert(pos, name)
+        x = Transpose(dims=tuple(target_dims))(x)
+
+    return x

pytensor/xtensor/type.py

@@ -481,6 +481,47 @@ def squeeze(
             raise NotImplementedError("Squeeze with axis not Implemented")
         return px.shape.squeeze(self, dim)
 
+    def expand_dims(
+        self,
+        dim: str | Sequence[str] | dict[str, int | Sequence] | None = None,
+        create_index_for_new_dim: bool = True,
+        axis: int | Sequence[int] | None = None,
+        **dim_kwargs,
+    ):
+        """Add one or more new dimensions to the tensor.
+
+        Parameters
+        ----------
+        dim : str | Sequence[str] | dict[str, int | Sequence] | None
+            If str or sequence of str, new dimensions with size 1.
+            If dict, keys are dimension names and values are either:
+                - int: the new size
+                - sequence: coordinates (length determines size)
+        create_index_for_new_dim : bool, default: True
+            Currently ignored. Reserved for future coordinate support.
+            In xarray, when True (default), creates a coordinate index for the new dimension
+            with values from 0 to size-1. When False, no coordinate index is created.
+        axis : int | Sequence[int] | None, default: None
+            Not implemented yet. In xarray, specifies where to insert the new dimension(s).
+            By default (None), new dimensions are inserted at the beginning (axis=0).
+            Symbolic axis is not supported yet.
+            Negative values count from the end.
+        **dim_kwargs : int | Sequence
+            Alternative to `dim` dict. Only used if `dim` is None.
+
+        Returns
+        -------
+        XTensorVariable
+            A tensor with additional dimensions inserted at the front.
+        """
+        return px.shape.expand_dims(
+            self,
+            dim,
+            create_index_for_new_dim=create_index_for_new_dim,
+            axis=axis,
+            **dim_kwargs,
+        )
+
     # ndarray methods
     # https://docs.xarray.dev/en/latest/api.html#id7
     def clip(self, min, max):

tests/xtensor/test_shape.py

@@ -8,10 +8,10 @@
 from itertools import chain, combinations
 
 import numpy as np
-import pytest
 from xarray import DataArray
 from xarray import concat as xr_concat
 
+from pytensor.tensor import scalar
 from pytensor.xtensor.shape import (
     concat,
     squeeze,
@@ -369,3 +369,113 @@ def test_squeeze_errors():
     fn2 = xr_function([x2], y2)
     with pytest.raises(Exception):
         fn2(x2_test)
+
+
+def test_expand_dims():
+    """Test expand_dims."""
+    x = xtensor("x", dims=("city", "year"), shape=(2, 2))
+    x_test = xr_arange_like(x)
+
+    # Implicit size 1
+    y = x.expand_dims("country")
+    fn = xr_function([x], y)
+    xr_assert_allclose(fn(x_test), x_test.expand_dims("country"))
+
+    # Test with multiple dimensions
+    y = x.expand_dims(["country", "state"])
+    fn = xr_function([x], y)
+    xr_assert_allclose(fn(x_test), x_test.expand_dims(["country", "state"]))
+
+    # Test with a dict of name-size pairs
+    y = x.expand_dims({"country": 2, "state": 3})
+    fn = xr_function([x], y)
+    xr_assert_allclose(fn(x_test), x_test.expand_dims({"country": 2, "state": 3}))
+
+    # Test with kwargs (equivalent to dict)
+    y = x.expand_dims(country=2, state=3)
+    fn = xr_function([x], y)
+    xr_assert_allclose(fn(x_test), x_test.expand_dims(country=2, state=3))
+
+    # Test with a dict of name-coord array pairs
+    y = x.expand_dims({"country": np.array([1, 2]), "state": np.array([3, 4, 5])})
+    fn = xr_function([x], y)
+    xr_assert_allclose(
+        fn(x_test),
+        x_test.expand_dims({"country": np.array([1, 2]), "state": np.array([3, 4, 5])}),
+    )
+
+    # Symbolic size 1
+    size_sym_1 = scalar("size_sym_1", dtype="int64")
+    y = x.expand_dims({"country": size_sym_1})
+    fn = xr_function([x, size_sym_1], y)
+    xr_assert_allclose(fn(x_test, 1), x_test.expand_dims({"country": 1}))
+
+    # Test with symbolic sizes in dict
+    size_sym_2 = scalar("size_sym_2", dtype="int64")
+    y = x.expand_dims({"country": size_sym_1, "state": size_sym_2})
+    fn = xr_function([x, size_sym_1, size_sym_2], y)
+    xr_assert_allclose(fn(x_test, 2, 3), x_test.expand_dims({"country": 2, "state": 3}))
+
+    # Test with symbolic sizes in kwargs
+    y = x.expand_dims(country=size_sym_1, state=size_sym_2)
+    fn = xr_function([x, size_sym_1, size_sym_2], y)
+    xr_assert_allclose(fn(x_test, 2, 3), x_test.expand_dims({"country": 2, "state": 3}))
+
+    # Test with axis parameter
+    y = x.expand_dims("country", axis=1)
+    fn = xr_function([x], y)
+    xr_assert_allclose(fn(x_test), x_test.expand_dims("country", axis=1))
+
+    # Test with negative axis parameter
+    y = x.expand_dims("country", axis=-1)
+    fn = xr_function([x], y)
+    xr_assert_allclose(fn(x_test), x_test.expand_dims("country", axis=-1))
+
+    # Add two new dims with axis parameters
+    y = x.expand_dims(["country", "state"], axis=[1, 2])
+    fn = xr_function([x], y)
+    xr_assert_allclose(
+        fn(x_test), x_test.expand_dims(["country", "state"], axis=[1, 2])
+    )
+
+    # Add two dims with negative axis parameters
+    y = x.expand_dims(["country", "state"], axis=[-1, -2])
+    fn = xr_function([x], y)
+    xr_assert_allclose(
+        fn(x_test), x_test.expand_dims(["country", "state"], axis=[-1, -2])
+    )
+
+    # Add two dims with positive and negative axis parameters
+    y = x.expand_dims(["country", "state"], axis=[-2, 1])
+    fn = xr_function([x], y)
+    xr_assert_allclose(
+        fn(x_test), x_test.expand_dims(["country", "state"], axis=[-2, 1])
+    )
+
+
+def test_expand_dims_errors():
+    """Test error handling in expand_dims."""
+
+    # Expanding existing dim
+    x = xtensor("x", dims=("city",), shape=(3,))
+    y = x.expand_dims("country")
+    with pytest.raises(ValueError, match="already exists"):
+        y.expand_dims("city")
+
+    # Invalid dim type
+    with pytest.raises(TypeError, match="Invalid type for `dim`"):
+        x.expand_dims(123)
+
+    # Duplicate dimension creation
+    y = x.expand_dims("new")
+    with pytest.raises(ValueError, match="already exists"):
+        y.expand_dims("new")
+
+    # Find out what xarray does with a numpy array as dim
+    # x_test = xr_arange_like(x)
+    # x_test.expand_dims(np.array([1, 2]))
+    # TypeError: unhashable type: 'numpy.ndarray'
+
+    # Test with a numpy array as dim (not supported)
+    with pytest.raises(TypeError, match="unhashable type"):
+        y.expand_dims(np.array([1, 2]))