Add a non-one shape constraint to TensorType

aesara/link/jax/dispatch/shape.py

@@ -46,12 +46,21 @@ def shape_i(x):
 def jax_funcify_SpecifyShape(op, **kwargs):
     def specifyshape(x, *shape):
         assert x.ndim == len(shape)
-        assert jnp.all(x.shape == tuple(shape)), (
-            "got shape",
-            x.shape,
-            "expected",
-            shape,
-        )
+        for s_x, s in zip(x.shape, shape):
+            if s == -1:
+                assert s_x != 1, (
+                    "got shape",
+                    s_x,
+                    "expected",
+                    s,
+                )
+            elif s > -1:
+                assert s_x == s, (
+                    "got shape",
+                    s_x,
+                    "expected",
+                    s,
+                )
         return x
 
     return specifyshape

aesara/link/numba/dispatch/basic.py

@@ -2,7 +2,7 @@
 import warnings
 from contextlib import contextmanager
 from functools import singledispatch
-from textwrap import dedent
+from textwrap import dedent, indent
 from typing import Union
 
 import numba
@@ -650,17 +650,26 @@ def numba_funcify_SpecifyShape(op, node, **kwargs):
     shape_input_names = ["shape_" + str(i) for i in range(len(shape_inputs))]
 
     func_conditions = [
-        f"assert x.shape[{i}] == {shape_input_names}"
-        for i, (shape_input, shape_input_names) in enumerate(
+        dedent(
+            f"""
+        if {shape_name} == -1:
+            assert x.shape[{i}] != 1
+        if {shape_name} > -1:
+            assert x.shape[{i}] == {shape_name}
+        """
+        )
+        for i, (shape_input, shape_name) in enumerate(
             zip(shape_inputs, shape_input_names)
         )
         if shape_input is not NoneConst
     ]
 
+    conditions_block = "\n".join(func_conditions)
+
     func = dedent(
         f"""
         def specify_shape(x, {create_arg_string(shape_input_names)}):
-            {"; ".join(func_conditions)}
+            {indent(conditions_block, ' ' * 12)}
             return x
         """
     )

aesara/tensor/shape.py

@@ -1,7 +1,7 @@
 import warnings
 from numbers import Number
 from textwrap import dedent
-from typing import Dict, List, Tuple, Union
+from typing import Dict, List, Sequence, Tuple, Union
 
 import numpy as np
 
@@ -17,11 +17,65 @@
 from aesara.tensor import basic as at
 from aesara.tensor import get_vector_length
 from aesara.tensor.exceptions import NotScalarConstantError
-from aesara.tensor.type import DenseTensorType, TensorType, int_dtypes, tensor
+from aesara.tensor.type import (
+    DenseTensorType,
+    TensorType,
+    int_dtypes,
+    shape_encode,
+    tensor,
+)
 from aesara.tensor.type_other import NoneConst
 from aesara.tensor.var import TensorConstant, TensorVariable
 
 
+def filter_shape_vars(
+    ref_shape: Tuple[int, ...], shape: Sequence[Variable], shape_is_encoded: bool = True
+) -> Tuple[int, ...]:
+    r"""Compute the most \"informative\" shape based on a static reference.
+
+    Parameters
+    ----------
+    ref_shape
+        A static shape reference using static shape constraint encoding.
+    shape
+        A symbolic shape.
+    shape_is_encoded
+        If ``True``, `shape` is assumed to be static shape constraint encoded.
+
+    Returns
+    -------
+    The most specific, and compatible (with `ref_shape`), static shape
+    constraint encoded values.
+    """
+    shape_bottom = shape_encode(None)
+    type_shape = ()
+    for i, (xts, s) in enumerate(zip(ref_shape, shape)):
+
+        try:
+            # TODO FIXME: We shouldn't need to do this; let a rewrite
+            # do constant folding and update the `TensorType`s.
+            s_val = at.get_scalar_constant_value(s)
+
+            if isinstance(s_val, np.ndarray):
+                s_val = s_val.item()
+
+            if shape_is_encoded or s_val is not None and s_val > 0:
+                type_s = shape_encode(s_val)
+            else:
+                type_s = shape_bottom
+        except NotScalarConstantError:
+            type_s = shape_bottom
+
+        if not (xts <= -1 or type_s <= -1 or type_s == xts):
+            raise AssertionError(
+                f"SpecifyShape: Got shape {xts} at index {i}, expected {type_s}."
+            )
+
+        type_shape += (max(type_s, xts),)
+
+    return type_shape
+
+
 def register_shape_c_code(type, code, version=()):
     """
     Tell Shape Op how to generate C code for an Aesara Type.
@@ -394,7 +448,6 @@ class SpecifyShape(COp):
     _f16_ok = True
 
     def make_node(self, x, *shape):
-        from aesara.tensor.basic import get_scalar_constant_value
 
         x = at.as_tensor_variable(x)
 
@@ -417,18 +470,7 @@ def make_node(self, x, *shape):
                 f"Input `x` is {x.type.ndim}-dimensional and will never match a shape of length {len(shape)}."
             )
 
-        type_shape = [None] * x.ndim
-        for i, (xts, s) in enumerate(zip(x.type.shape, shape)):
-            if xts is not None:
-                type_shape[i] = xts
-            else:
-                try:
-                    type_s = get_scalar_constant_value(s)
-                    if type_s is not None:
-                        type_shape[i] = int(type_s)
-                except NotScalarConstantError:
-                    pass
-
+        type_shape = filter_shape_vars(x.type.shape_encoded, shape)
         out_var = x.type.clone(shape=type_shape)()
 
         return Apply(self, [x, *shape], [out_var])
@@ -441,10 +483,10 @@ def perform(self, node, inp, out_):
             raise AssertionError(
                 f"SpecifyShape: Got {x.ndim} dimensions (shape {x.shape}), expected {ndim} dimensions with shape {tuple(shape)}."
             )
-        if not all(xs == s for xs, s in zip(x.shape, shape) if s is not None):
-            raise AssertionError(
-                f"SpecifyShape: Got shape {x.shape}, expected {tuple(int(s) if s is not None else None for s in shape)}."
-            )
+        for xs, s in zip(x.shape, shape):
+            if (s == -1 and xs == 1) or (s is not None and s > -1 and not xs == s):
+                raise AssertionError(f"SpecifyShape: Got shape {xs}, expected {s}.")
+
         out[0] = x
 
     def infer_shape(self, fgraph, node, shapes):
@@ -454,11 +496,11 @@ def infer_shape(self, fgraph, node, shapes):
         for dim in range(node.inputs[0].type.ndim):
             s = shape[dim]
             try:
-                s = at.get_scalar_constant_value(s)
-                # We assume that `None` shapes are always retrieved by
+                s = shape_encode(at.get_scalar_constant_value(s))
+                # We assume that negative shapes are always retrieved by
                 # `get_scalar_constant_value`, and only in that case do we default to
                 # the shape of the input variable
-                if s is None:
+                if s < 0:
                     s = xshape[dim]
             except NotScalarConstantError:
                 pass
@@ -502,6 +544,9 @@ def c_code(self, node, name, i_names, o_names, sub):
                 );
                 {fail};
             }}
+
+            npy_intp shp;
+            npy_intp actual_shp;
             """
         )
 
@@ -510,9 +555,11 @@ def c_code(self, node, name, i_names, o_names, sub):
                 continue
             code += dedent(
                 f"""
-                if (py_{shp_name} != Py_None){{
-                    dtype_{shp_name} shp = ((dtype_{shp_name}*)PyArray_GETPTR1({shp_name}, 0))[0];
-                    if (PyArray_DIMS({x_name})[{i}] != shp) {{
+                shp = ((dtype_{shp_name}*)PyArray_GETPTR1({shp_name}, 0))[0];
+
+                if (shp > -2) {{
+                    actual_shp = PyArray_DIMS({x_name})[{i}];
+                    if (actual_shp == -1 && shp == 1 || actual_shp != shp) {{
                         PyErr_Format(PyExc_AssertionError,
                             "SpecifyShape: dim %d of input has shape %d, expected %d.",
                             {i}, PyArray_DIMS({x_name})[{i}], shp
@@ -533,7 +580,7 @@ def c_code(self, node, name, i_names, o_names, sub):
         return code
 
     def c_code_cache_version(self):
-        return (2,)
+        return (3,)
 
 
 _specify_shape = SpecifyShape()