diff --git a/imperative/python/megengine/optimizer/optimizer.py b/imperative/python/megengine/optimizer/optimizer.py
index 412579da..0a09496b 100644
--- a/imperative/python/megengine/optimizer/optimizer.py
+++ b/imperative/python/megengine/optimizer/optimizer.py
@@ -97,7 +97,7 @@ class Optimizer(metaclass=ABCMeta):
                     "optimizer can only optimize Parameters, but one of the params is "
                     + str(type(param))
                 )
-            param._reset(Tensor(param.numpy(), no_cache=True, format=param.format))
+            param[...] = Tensor(param.numpy(), no_cache=True)
 
         for name, default in self._defaults.items():
             if default is required and name not in param_group:
diff --git a/imperative/python/src/tensor.cpp b/imperative/python/src/tensor.cpp
index 8f8bf036..e76ca5a3 100644
--- a/imperative/python/src/tensor.cpp
+++ b/imperative/python/src/tensor.cpp
@@ -525,7 +525,34 @@ TensorWrapper::TensorWrapper(PyObject* args, PyObject* kwargs) {
     }
     mgb_assert(tup.size() == 7);
     if (auto* t = try_cast(tup[0].ptr())) {
-        m_tensor = t->m_tensor->copy();
+        m_tensor = t->m_tensor;
+        // TODO: merge two path in arg parse
+        if (!tup[1].is_none()) {
+            auto dtype = tup[1].cast<DType>();
+            mgb_assert(
+                    dtype == m_tensor->dtype(), "dtype mismatch: %s vs %s",
+                    dtype.name(), m_tensor->dtype().name());
+        }
+        if (!tup[2].is_none()) {
+            auto device = as_comp_node(tup[2]);
+            mgb_assert(
+                    device == m_tensor->comp_node(), "device mismatch: %s vs %s",
+                    device.to_string().c_str(),
+                    m_tensor->comp_node().to_string().c_str());
+        }
+        mgb_assert(!tup[3].cast<bool>(), "expect is_const == False, got True");
+        bool no_cache = tup[4].cast<bool>();
+        if (no_cache) {
+            // always copy because it's hard to tell whether this tensor is cached
+            m_tensor = m_tensor->copy();
+        }
+        // ignore name
+        if (!tup[6].is_none()) {
+            Format format = tup[6].cast<std::string>();
+            mgb_assert(
+                    format == m_tensor->format(), "format mismatch: %s vs %s",
+                    format.to_string().c_str(), m_tensor->format().to_string().c_str());
+        }
     } else {
         auto data = tup[0];
         DType dtype = tup[1].cast<DType>();
@@ -1030,7 +1057,7 @@ void init_tensor(py::module m) {
                 try {
                     self.compiled->compile();
                 } catch (const std::exception& e) {
-                    mgb_log_error(e.what());
+                    mgb_log_error("error in trace: %s", e.what());
                 }
             }
             // register transformations
diff --git a/imperative/python/src/tensor.h b/imperative/python/src/tensor.h
index 1f849f2b..47783234 100644
--- a/imperative/python/src/tensor.h
+++ b/imperative/python/src/tensor.h
@@ -47,7 +47,7 @@ public:
 
     ~Tensor() = default;
 
-    inline Tensor copy() { return *this; }
+    inline Tensor copy() { return Tensor(imperative::apply(DupTensor(), data())[0]); }
 
     inline DType dtype() { return *data().dtype(); }
     inline CompNode comp_node() { return *data().device(); }
diff --git a/imperative/python/test/unit/core/test_tensor_wrapper.py b/imperative/python/test/unit/core/test_tensor_wrapper.py
index 154f8945..fce911cf 100644
--- a/imperative/python/test/unit/core/test_tensor_wrapper.py
+++ b/imperative/python/test/unit/core/test_tensor_wrapper.py
@@ -5,7 +5,9 @@ import numpy as np
 import pytest
 from utils import get_var_value, make_tensor
 
+from megengine import _full_sync
 from megengine.core.tensor.dtype import get_scale, get_zero_point, qint8, quint8
+from megengine.device import get_default_device
 from megengine.tensor import Parameter, Tensor
 from megengine.utils.network import Network
 
@@ -220,3 +222,16 @@ def test_tensor_from_bool():
     assert x.dtype == np.bool_
     x = Tensor([True, False])
     assert x.dtype == np.bool_
+
+
+def test_tensor_construct_tensor():
+    x = Tensor(0, dtype=np.float32, device="xpu0:1", name="MyName")
+    assert Tensor(x.astype(np.int32)).dtype == np.int32
+    with pytest.raises(RuntimeError):
+        Tensor(x.astype(np.int32), dtype=np.float32)
+    assert Tensor(x).name == ""
+    assert Tensor(x, name="MyName2").name == "MyName2"
+    with pytest.raises(RuntimeError):
+        assert Tensor(x.to("xpu0:2"), device="xpu0:1").device == "xpu0:1"
+    assert Tensor(x.to("xpu0:2")).device == x.to("xpu0:2").device
+    _full_sync()
diff --git a/imperative/src/impl/transformations/eval.cpp b/imperative/src/impl/transformations/eval.cpp
index 314c78e9..ae9604be 100644
--- a/imperative/src/impl/transformations/eval.cpp
+++ b/imperative/src/impl/transformations/eval.cpp
@@ -126,6 +126,11 @@ ValueRefList InterpreterTransformation::apply_transformation(
         } else {
             return {ValueRef()};
         }
+    } else if (op.is<DupTensor>()) {
+        auto& input = inputs[0].cast(m_value_type);
+        DeviceTensorND dev_tensor;
+        dev_tensor.copy_from(m_channel->get_dev_tensor(input.handle()->handle()));
+        return m_value_type.make(share_handle(m_channel->put(dev_tensor, {})));
     } else {
         return op.fallback(inputs);
     }
diff --git a/imperative/src/include/megbrain/imperative/basic_operators.h b/imperative/src/include/megbrain/imperative/basic_operators.h
index 5d785857..f12527e0 100644
--- a/imperative/src/include/megbrain/imperative/basic_operators.h
+++ b/imperative/src/include/megbrain/imperative/basic_operators.h
@@ -196,5 +196,10 @@ public:
     std::string to_string() const override;
 };
 
+class DupTensor final : public OperatorImpl<DupTensor, Operator::IdentityLike> {
+public:
+    std::string to_string() const override { return "DupTensor"; }
+};
+
 }  // namespace imperative
 }  // namespace mgb