Add more specialized static output shape to Eye

Importantly, it now provides broadcastability information which is needed elsewhere

Author: ricardoV94

pytensor/tensor/basic.py

@@ -1273,6 +1273,7 @@ def triu_indices_from(
 
 
 class Eye(Op):
+    _output_type_depends_on_input_value = True
     __props__ = ("dtype",)
 
     def __init__(self, dtype=None):
@@ -1287,10 +1288,13 @@ def make_node(self, n, m, k):
         assert n.ndim == 0
         assert m.ndim == 0
         assert k.ndim == 0
+
+        _, static_shape = infer_static_shape((n, m))
+
         return Apply(
             self,
             [n, m, k],
-            [TensorType(dtype=self.dtype, shape=(None, None))()],
+            [TensorType(dtype=self.dtype, shape=static_shape)()],
         )
 
     def perform(self, node, inp, out_):

tests/tensor/test_basic.py

@@ -937,38 +937,46 @@ def test_infer_static_shape():
     assert static_shape == (1,)
 
 
-# This is slow for the ('int8', 3) version.
-def test_eye():
-    def check(dtype, N, M_=None, k=0):
-        # PyTensor does not accept None as a tensor.
-        # So we must use a real value.
-        M = M_
-        # Currently DebugMode does not support None as inputs even if this is
-        # allowed.
-        if M is None and config.mode in ["DebugMode", "DEBUG_MODE"]:
-            M = N
-        N_symb = iscalar()
-        M_symb = iscalar()
-        k_symb = iscalar()
-        f = function([N_symb, M_symb, k_symb], eye(N_symb, M_symb, k_symb, dtype=dtype))
-        result = f(N, M, k)
-        assert np.allclose(result, np.eye(N, M_, k, dtype=dtype))
-        assert result.dtype == np.dtype(dtype)
+class TestEye:
+    # This is slow for the ('int8', 3) version.
+    def test_basic(self):
+        def check(dtype, N, M_=None, k=0):
+            # PyTensor does not accept None as a tensor.
+            # So we must use a real value.
+            M = M_
+            # Currently DebugMode does not support None as inputs even if this is
+            # allowed.
+            if M is None and config.mode in ["DebugMode", "DEBUG_MODE"]:
+                M = N
+            N_symb = iscalar()
+            M_symb = iscalar()
+            k_symb = iscalar()
+            f = function(
+                [N_symb, M_symb, k_symb], eye(N_symb, M_symb, k_symb, dtype=dtype)
+            )
+            result = f(N, M, k)
+            assert np.allclose(result, np.eye(N, M_, k, dtype=dtype))
+            assert result.dtype == np.dtype(dtype)
 
-    for dtype in ALL_DTYPES:
-        check(dtype, 3)
-        # M != N, k = 0
-        check(dtype, 3, 5)
-        check(dtype, 5, 3)
-        # N == M, k != 0
-        check(dtype, 3, 3, 1)
-        check(dtype, 3, 3, -1)
-        # N < M, k != 0
-        check(dtype, 3, 5, 1)
-        check(dtype, 3, 5, -1)
-        # N > M, k != 0
-        check(dtype, 5, 3, 1)
-        check(dtype, 5, 3, -1)
+        for dtype in ALL_DTYPES:
+            check(dtype, 3)
+            # M != N, k = 0
+            check(dtype, 3, 5)
+            check(dtype, 5, 3)
+            # N == M, k != 0
+            check(dtype, 3, 3, 1)
+            check(dtype, 3, 3, -1)
+            # N < M, k != 0
+            check(dtype, 3, 5, 1)
+            check(dtype, 3, 5, -1)
+            # N > M, k != 0
+            check(dtype, 5, 3, 1)
+            check(dtype, 5, 3, -1)
+
+    def test_static_output_type(self):
+        l = lscalar("l")
+        assert eye(5, 3, l).type.shape == (5, 3)
+        assert eye(1, l, 3).type.shape == (1, None)
 
 
 class TestTriangle: