Add complex tensor with subclassing

complex_tensor.py

@@ -0,0 +1,57 @@
+import torch
+
+
+class ComplexTensor(torch.Tensor):
+    def __new__(cls, re, im):
+        assert (
+            re.device == im.device
+            and re.layout == im.layout
+            and re.requires_grad == im.requires_grad
+            and re.dtype == im.dtype
+        )
+        res = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
+            cls,
+            size=re.size(),
+            strides=re.stride(),  # todo: contiguous only
+            storage_offset=0,
+            dtype=torch.complex64,  # todo: real to complex dtype
+            layout=re.layout,
+            device=re.device,
+            requires_grad=False,  # todo: autograd support
+        )
+        res.re = re
+        res.im = im
+        return res
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if func is torch.ops.aten.mm.default:
+            assert not kwargs
+            x, y = args
+            re = x.re * y.re - x.im * y.im
+            im = x.re * y.im + x.im * y.re
+            return ComplexTensor(re, im)
+        raise NotImplementedError(f"todo {func}")
+
+    def __tensor_flatten__(self):
+        return ["re", "im"], None
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, meta):
+        assert meta is None
+        re, im = inner_tensors["re"], inner_tensors["im"]
+        return ComplexTensor(re, im)
+
+    def __repr__(self):
+        return f"ComplexTensor(real={self.re}, imag={self.im})"
+
+
+if __name__ == "__main__":
+
+    @torch.compile()
+    def f(x, y):
+        return x @ y
+
+    x = ComplexTensor(torch.tensor([[1]]), torch.tensor([[2]]))
+    y = ComplexTensor(torch.tensor([[3]]), torch.tensor([[4]]))
+
+    print(f(x, y))  # (1 + 2i) * (3 + 4i) = -5 + 10i