Add Tensordot op (#344)

python/src/ops.cpp

@@ -3194,4 +3194,44 @@ void init_ops(py::module_& m) {
         Returns:
           result (array): The dequantized version of ``w``
       )pbdoc");
+  m.def(
+      "tensordot",
+      [](const array& a,
+         const array& b,
+         const std::variant<int, std::vector<std::vector<int>>>& dims,
+         StreamOrDevice s) {
+        if (auto pv = std::get_if<int>(&dims); pv) {
+          return tensordot(a, b, *pv, s);
+        } else {
+          auto x = std::get<std::vector<std::vector<int>>>(dims);
+          if (x.size() != 2) {
+            throw std::invalid_argument(
+                "[tensordot] dims must be a list of two lists.");
+          }
+          return tensordot(a, b, {x[0], x[1]}, s);
+        }
+      },
+      "a"_a,
+      "b"_a,
+      py::pos_only(),
+      "dims"_a = 2,
+      py::kw_only(),
+      "stream"_a = none,
+      R"pbdoc(
+        tensordot(a: array, b: array, /, dims: Union[int, List[List[int]]] = 2, *, stream: Union[None, Stream, Device] = None) -> array
+
+        Compute the tensor dot product along the specified axes.
+
+        Args:
+          a (array): Input array
+          b (array): Input array
+          dims (int or list(list(int)), optional): The number of dimensions to
+            sum over. If an integer is provided, then sum over the last
+            ``dims`` dimensions of ``a`` and the first ``dims`` dimensions of
+            ``b``. If a list of lists is provided, then sum over the
+            corresponding dimensions of ``a`` and ``b``. (default: 2)
+        
+        Returns:
+          result (array): The tensor dot product.
+      )pbdoc");
 }

python/tests/test_ops.py

@@ -1547,6 +1547,22 @@ class TestOps(mlx_tests.MLXTestCase):
         expected_3 = np.repeat(data_3, 2, axis=0)
         self.assertEqualArray(repeat_3, mx.array(expected_3))
 
+    def test_tensordot(self):
+        x = mx.arange(60.0).reshape(3, 4, 5)
+        y = mx.arange(24.0).reshape(4, 3, 2)
+        z = mx.tensordot(x, y, dims=([1, 0], [0, 1]))
+        self.assertEqualArray(z, mx.array(np.tensordot(x, y, axes=([1, 0], [0, 1]))))
+        x = mx.random.normal((3, 4, 5))
+        y = mx.random.normal((4, 5, 6))
+        z = mx.tensordot(x, y, dims=2)
+        self.assertEqualArray(z, mx.array(np.tensordot(x, y, axes=2)))
+        x = mx.random.normal((3, 5, 4, 6))
+        y = mx.random.normal((6, 4, 5, 3))
+        z = mx.tensordot(x, y, dims=([2, 1, 3], [1, 2, 0]))
+        self.assertEqualArray(
+            z, mx.array(np.tensordot(x, y, axes=([2, 1, 3], [1, 2, 0])))
+        )
+
 
 if __name__ == "__main__":
     unittest.main()