Implemented Eye Op in PyTorch

pytensor/link/pytorch/dispatch/basic.py

@@ -6,7 +6,7 @@
 from pytensor.graph.fg import FunctionGraph
 from pytensor.link.utils import fgraph_to_python
 from pytensor.raise_op import CheckAndRaise
-from pytensor.tensor.basic import Alloc, AllocEmpty, ARange, Join
+from pytensor.tensor.basic import Alloc, AllocEmpty, ARange, Eye, Join
 
 
 @singledispatch
@@ -100,3 +100,19 @@
         return torch.cat(tensors, dim=axis)
 
     return join
+
+
+@pytorch_funcify.register(Eye)
+def pytorch_funcify_eye(op, **kwargs):
+    torch_dtype = getattr(torch, op.dtype)
+
+    def eye(N, M, k):
+        major, minor = (M, N) if k > 0 else (N, M)
+        k_abs = torch.abs(k)
+        zeros = torch.zeros(N, M, dtype=torch_dtype)
+        if k_abs < major:
+            l_ones = torch.min(major - k_abs, minor)
+            return zeros.diagonal_scatter(torch.ones(l_ones, dtype=torch_dtype), k)
+        return zeros
+
+    return eye

tests/link/pytorch/test_basic.py

@@ -13,7 +13,7 @@
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.op import Op
 from pytensor.raise_op import CheckAndRaise
-from pytensor.tensor import alloc, arange, as_tensor, empty
+from pytensor.tensor import alloc, arange, as_tensor, empty, eye
 from pytensor.tensor.type import matrix, scalar, vector
 
 
@@ -275,3 +275,22 @@ def test_pytorch_Join():
             np.c_[[5.0, 6.0]].astype(config.floatX),
         ],
     )
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    ["int64", config.floatX],
+)
+def test_eye(dtype):
+    N = scalar("N", dtype="int64")
+    M = scalar("M", dtype="int64")
+    k = scalar("k", dtype="int64")
+
+    out = eye(N, M, k, dtype=dtype)
+
+    fn = function([N, M, k], out, mode=pytorch_mode)
+
+    for _N in range(1, 6):
+        for _M in range(1, 6):
+            for _k in list(range(_M + 2)) + [-x for x in range(1, _N + 2)]:
+                np.testing.assert_array_equal(fn(_N, _M, _k), np.eye(_N, _M, _k))