feat(imperative): remove symbolvar of imperative

GitOrigin-RevId: 16da6d1491

imperative/python/megengine/core/tensor/array_method.py

@@ -7,9 +7,7 @@ from typing import Union
 import numpy as np
 
 from .. import _config
-from .._imperative_rt.common import CompNode
 from .._imperative_rt.core2 import (
-    SymbolVar,
     Tensor,
     apply,
     astype_cpp,
@@ -17,9 +15,11 @@ from .._imperative_rt.core2 import (
     broadcast_cpp,
     getitem_cpp,
     matmul_cpp,
+    reshape_cpp,
+    setitem_cpp,
+    squeeze_cpp,
+    transpose_cpp,
 )
-from .._imperative_rt.core2 import reduce_to_scalar as _reduce_to_scalar
-from .._imperative_rt.core2 import reshape_cpp, setitem_cpp, squeeze_cpp, transpose_cpp
 from ..ops import builtin
 from . import amp
 from .utils import _normalize_axis, astensor1d, cast_tensors, make_shape_tuple, subgraph
@@ -189,9 +189,7 @@ def _todo(*_):
 
 def _expand_args(args):
     if len(args) == 1:
-        if isinstance(
-            args[0], (collections.abc.Sequence, Tensor, SymbolVar, np.ndarray),
-        ):
+        if isinstance(args[0], (collections.abc.Sequence, Tensor, np.ndarray),):
             args = args[0]
     return args
 

imperative/python/megengine/core/tensor/utils.py

@@ -8,7 +8,6 @@ import numpy as np
 from .._imperative_rt import make_const
 from .._imperative_rt.core2 import (
     Const,
-    SymbolVar,
     Tensor,
     _get_convert_inputs,
     _set_convert_inputs,
@@ -77,7 +76,7 @@ def result_type(*args):
 
 def isscalar(x):
 
-    if isinstance(x, (Tensor, SymbolVar)):
+    if isinstance(x, Tensor):
         return x._isscalar()
 
     return np.isscalar(x)
@@ -283,7 +282,7 @@ def interpret_subgraph(func, dtype, device):
                 return results
 
         def apply_const(value, dtype=dtype, device=device):
-            return Const(value, dtype, device, None)
+            return Const(value, dtype, device)
 
         outputs, outputs_has_grad = func(args, apply_expr, apply_const)
         outputs = [

imperative/python/megengine/functional/elemwise.py

@@ -2,7 +2,7 @@
 # pylint: disable=unused-argument,invalid-name,redefined-builtin,arguments-out-of-order
 import numpy as np
 
-from ..core._imperative_rt.core2 import SymbolVar, apply
+from ..core._imperative_rt.core2 import apply
 from ..core.ops import builtin
 from ..core.ops.builtin import Elemwise
 from ..core.tensor.array_method import _elwise

imperative/python/megengine/functional/math.py

@@ -538,7 +538,7 @@ def topk(
     op = builtin.TopK(mode=mode)
 
     if not isinstance(k, Tensor):
-        k = Const(k, "int32", inp.device, None)
+        k = Const(k, "int32", inp.device)
 
     if len(inp.shape) == 1:
         if kth_only:

imperative/python/megengine/functional/nn.py

@@ -1222,7 +1222,7 @@ def batch_norm(
             raise ValueError("Invalid param_dim {}".format(param_dim))
 
         if x is None:
-            x = Const(value, inp.dtype, inp.device, None)
+            x = Const(value, inp.dtype, inp.device)
             shape = astensor1d(pshape, inp, dtype="int32", device=inp.device)
             (result,) = apply(builtin.Broadcast(), x, shape)
             return result
@@ -1446,7 +1446,7 @@ def sync_batch_norm(
 
     def _make_full_if_none(x, value):
         if x is None:
-            x = Const(value, inp.dtype, _device, None)
+            x = Const(value, inp.dtype, _device)
             (result,) = apply(builtin.Broadcast(), x, reduce_shape)
             return result
         elif x.ndim == 1:

imperative/python/megengine/functional/tensor.py

@@ -7,7 +7,6 @@ import numpy as np
 from ..core._imperative_rt import CompNode
 from ..core._imperative_rt.core2 import (
     Const,
-    SymbolVar,
     apply,
     broadcast_cpp,
     dtype_promotion,
@@ -151,7 +150,7 @@ def full(
         shape = (shape,)
     if device is None:
         device = get_default_device()
-    x = Const(value, dtype, device, None)
+    x = Const(value, dtype, device)
     if type(shape) in (list, tuple) and len(shape) == 0:
         return x
     return broadcast_to(x, shape)
@@ -216,7 +215,7 @@ def zeros(
     return full(shape, 0.0, dtype=dtype, device=device)
 
 
-def zeros_like(inp: Union[Tensor, SymbolVar]) -> Union[Tensor, SymbolVar]:
+def zeros_like(inp: Tensor) -> Tensor:
     r"""Returns a tensor filled with zeros with the same shape and data type as input tensor.
 
     Args:
@@ -235,7 +234,7 @@ def zeros_like(inp: Union[Tensor, SymbolVar]) -> Union[Tensor, SymbolVar]:
     return full_like(inp, 0.0)
 
 
-def ones_like(inp: Union[Tensor, SymbolVar]) -> Union[Tensor, SymbolVar]:
+def ones_like(inp: Tensor) -> Tensor:
     r"""Returns a tensor filled with ones with the same shape and data type as input tensor.
 
     Args:
@@ -253,9 +252,7 @@ def ones_like(inp: Union[Tensor, SymbolVar]) -> Union[Tensor, SymbolVar]:
     return full_like(inp, 1.0)
 
 
-def full_like(
-    inp: Union[Tensor, SymbolVar], value: Union[int, float]
-) -> Union[Tensor, SymbolVar]:
+def full_like(inp: Tensor, value: Union[int, float]) -> Tensor:
     r"""Returns a tensor filled with given value with the same shape as input tensor.
 
     Args:
@@ -272,7 +269,7 @@ def full_like(
         Tensor([[2 2 2]
          [2 2 2]], dtype=int32, device=xpux:0)
     """
-    x = Const(value, inp.dtype, inp.device, inp)
+    x = Const(value, inp.dtype, inp.device)
     if inp.ndim == 0:
         return x
     return broadcast_to(x, inp.shape)
@@ -668,9 +665,9 @@ def cond_take(mask: Tensor, x: Tensor) -> Tensor:
         >>> print(v.numpy(), index.numpy())
         [1. 4.] [0 3]
     """
-    if not isinstance(x, (Tensor, SymbolVar)):
+    if not isinstance(x, Tensor):
         raise TypeError("input must be a tensor")
-    if not isinstance(mask, (Tensor, SymbolVar)):
+    if not isinstance(mask, Tensor):
         raise TypeError("mask must be a tensor")
     if mask.dtype != np.bool_:
         raise ValueError("mask must be bool")
@@ -843,15 +840,11 @@ def linspace(
             if not (cur_device is None or device == cur_device):
                 raise ("ambiguous device for linspace opr")
 
-    is_symbolvar = list(isinstance(x, SymbolVar) for x in [start, stop, num])
-    if any(is_symbolvar) and not all(is_symbolvar):
-        raise TypeError("start, stop and num should all be VarNode or none of them")
-
-    if not isinstance(start, (Tensor, SymbolVar)):
+    if not isinstance(start, Tensor):
         start = Tensor(start, device=device)
-    if not isinstance(stop, (Tensor, SymbolVar)):
+    if not isinstance(stop, Tensor):
         stop = Tensor(stop, device=device)
-    if not isinstance(num, (Tensor, SymbolVar)):
+    if not isinstance(num, Tensor):
         num = Tensor(num, device=device)
 
     op = builtin.Linspace(comp_node=device)
@@ -901,9 +894,12 @@ def arange(
     if stop is None:
         start, stop = 0, start
 
-    start = Tensor(start, dtype="float32")
-    stop = Tensor(stop, dtype="float32")
-    step = Tensor(step, dtype="float32")
+    if not isinstance(start, Tensor):
+        start = Tensor(start, dtype="float32")
+    if not isinstance(stop, Tensor):
+        stop = Tensor(stop, dtype="float32")
+    if not isinstance(step, Tensor):
+        step = Tensor(step, dtype="float32")
 
     num = ceil((stop - start) / step)
     stop = start + step * (num - 1)

imperative/python/megengine/functional/tensor_cache.py

@@ -7,11 +7,11 @@ small_tensor_cache = {}
 def _get_scalar_tensor_with_value(value, dtype=None, device=None):
     global small_tensor_cache
     if is_tracing():
-        ret = Const(value, dtype, device, None)
+        ret = Const(value, dtype, device)
     else:
         cache_key = (value, dtype, device)
         if cache_key not in small_tensor_cache:
-            ret = Const(value, dtype, device, None)
+            ret = Const(value, dtype, device)
             small_tensor_cache[cache_key] = ret
         else:
             ret = small_tensor_cache[cache_key]

imperative/python/megengine/tensor.py

@@ -154,6 +154,8 @@ class Tensor(_Tensor, ArrayMethodMixin):
     @name.setter
     def name(self, name):
         self._custom_name = name
+        if name == None:
+            name = ""
         self._name = self._prefix + "." + name if self._prefix else name
         self._set_name(self._name)
 

imperative/python/megengine/traced_module/expr.py

@@ -756,7 +756,7 @@ class Constant(Expr):
 
     def interpret(self, *inputs):
         if isinstance(self.value, RawTensor):
-            return (Const(self.value.numpy(), None, None, None),)
+            return (Const(self.value.numpy(), None, None),)
         return (self.value,)
 
     def __repr__(self):

imperative/python/megengine/utils/network.py

@@ -395,7 +395,7 @@ class Network:
                 for ind, var in enumerate(opr.outputs):
                     var.owner = repl_dict[opr]
                     var.__dict__.update(repl_dict[opr].outputs[ind].__dict__)
-                    var.var = repl_dict[opr].outputs[ind].var
+                    var._reset_var(repl_dict[opr].outputs[ind].var)
                 repl_dict[opr].outputs = opr.outputs
         self._compile()
 

imperative/python/megengine/utils/network_node.py

@@ -6,11 +6,11 @@ from typing import Sequence
 import numpy as np
 
 from ..core import _imperative_rt as rt
-from ..core._imperative_rt.core2 import SymbolVar, apply
+from ..core._imperative_rt.core2 import apply, set_py_varnode_type
 from ..core._trace_option import use_symbolic_shape
 from ..core._wrap import Device
 from ..core.ops import builtin
-from ..core.tensor.array_method import ArrayMethodMixin
+from ..tensor import Tensor
 from .comp_graph_tools import replace_vars
 from .module_stats import (
     preprocess_receptive_field,
@@ -23,26 +23,72 @@ class NetworkNode:
     pass
 
 
-class VarNodeMeta(type(SymbolVar), type(ArrayMethodMixin)):
-    pass
+class VarNode(NetworkNode, Tensor):
+    _users = None
+    _owner = None
+    _name = None
+    _id = None
 
+    def __new__(cls, var, *, owner_opr=None, name=None):
+        obj = Tensor.__new__(cls, var)
+        return obj
 
-class VarNode(NetworkNode, SymbolVar, ArrayMethodMixin, metaclass=VarNodeMeta):
-    def __init__(self, var=None, *, owner_opr=None, name=None):
-        SymbolVar.__init__(self, var)
-        self.users = []  # List[OpNode]
-        self.owner = owner_opr
+    def __init__(self, var, *, owner_opr=None, name=None):
+        self._owner = owner_opr
         self.name = name
-        self.id = id(self)
 
     @classmethod
     def load(cls, sym_var, owner_opr):
-        obj = cls()
+        obj = cls(sym_var)
         obj.var = sym_var  # mgb varnode
         obj.name = sym_var.name
         obj.owner = owner_opr
         return obj
 
+    @property
+    def users(self):
+        if self._users is None:
+            self._users = []
+        return self._users
+
+    @property
+    def owner(self):
+        return self._owner
+
+    @owner.setter
+    def owner(self, owner):
+        self._owner = owner
+
+    @property
+    def id(self):
+        if self._id is None:
+            self._id = id(self)
+        return self._id
+
+    @property
+    def var(self):
+        return super().var()
+
+    @var.setter
+    def var(self, var):
+        self._reset(var)
+
+    def _reset(self, other):
+        if not isinstance(other, Tensor):
+            other = VarNode(other)
+        super()._reset(other)
+        self.owner = None
+
+    def _reset_var(self, var):
+        origin_owner = self.owner
+        self.var = var
+        self.var.name = self.name
+        self.owner = origin_owner
+
+    @property
+    def graph(self):
+        return super().graph()
+
     def _get_var_shape(self, axis=None):
         opdef = (
             builtin.GetVarShape() if axis is None else builtin.GetVarShape(axis=axis)
@@ -77,14 +123,6 @@ class VarNode(NetworkNode, SymbolVar, ArrayMethodMixin, metaclass=VarNodeMeta):
             return rst
         return self._get_var_shape() if self.var else None
 
-    @property
-    def dtype(self):
-        return self.var.dtype if self.var else None
-
-    @property
-    def ndim(self):
-        return super().ndim
-
     def __bool__(self):
         return False
 
@@ -92,27 +130,11 @@ class VarNode(NetworkNode, SymbolVar, ArrayMethodMixin, metaclass=VarNodeMeta):
     __int__ = None
     __float__ = None
     __complex__ = None
+    __repr__ = lambda self: "VarNode:" + self.name
 
     def __hash__(self):
         return id(self)
 
-    def numpy(self):
-        return super().numpy()
-
-    def _reset(self, other):
-        if not isinstance(other, VarNode):
-            assert self.graph, "VarNode _reset must have graph"
-            node = ImmutableTensor(other, graph=self.graph)
-            node.compile(self.graph)
-            other = node.outputs[0]
-        if self.owner is not None:
-            idx = self.owner.outputs.index(self)
-            self.owner.outputs[idx] = VarNode(
-                self.var, owner_opr=self.owner, name=self.var.name
-            )
-        self.var = other.var
-        self.owner = None
-
     def set_owner_opr(self, owner_opr):
         self.owner = owner_opr
 
@@ -158,8 +180,7 @@ class OpNode(NetworkNode):
             assert len(outputs) == len(self.outputs)
             self._opr = outputs[0].owner
             for i in range(len(self.outputs)):
-                self.outputs[i].var = outputs[i]
-                self.outputs[i].var.name = self.outputs[i].name
+                self.outputs[i]._reset_var(outputs[i])
                 assert self.outputs[i].owner is self
 
     def add_inp_var(self, x):
@@ -214,8 +235,9 @@ class Host2DeviceCopy(OpNode):
             outputs = rt.make_h2d(graph, self.device, self.dtype, self.shape, self.name)
             self._opr = outputs.owner
             if len(self.outputs) == 0:
-                self.outputs.append(VarNode(owner_opr=self, name=self.name))
-            self.outputs[0].var = outputs
+                self.outputs.append(VarNode(outputs, owner_opr=self, name=self.name))
+            else:
+                self.outputs[0]._reset_var(outputs)
         assert self.outputs[0].owner is self
 
 
@@ -262,8 +284,9 @@ class ConstOpBase(OpNode):
             data = data.astype(np.int32)
         varnode = type(self).rt_fun(self.graph, data, cn, data.dtype, self.name)
         if len(self.outputs) == 0:
-            self.outputs.append(VarNode(owner_opr=self, name=self.name))
-        self.outputs[0].var = varnode
+            self.outputs.append(VarNode(varnode, owner_opr=self, name=self.name))
+        else:
+            self.outputs[0]._reset_var(varnode)
         self._opr = varnode.owner
 
     @classmethod
@@ -313,7 +336,7 @@ class ReadOnlyOpNode(OpNode):
         if bool(repl_dict):
             out_vars = replace_vars(self._opr.outputs, repl_dict)
             for ind, o in enumerate(self.outputs):
-                o.var = out_vars[ind]
+                o._reset_var(out_vars[ind])
 
 
 class Elemwise(OpNode):
@@ -785,3 +808,6 @@ class AssertEqual(OpNode):
 class CvtColorForward(OpNode):
     type = "CvtColor"
     opdef = builtin.CvtColor
+
+
+set_py_varnode_type(VarNode)

imperative/python/src/graph_rt.cpp

@@ -114,6 +114,8 @@ void _set_priority_to_id(const std::vector<mgb::cg::VarNode*>& dest_vars) {
     }
 }
 
+py::object Py_Varnode = py::none();
+
 void init_graph_rt(py::module m) {
     static const std::unique_ptr<mgb::OprFootprint> _imperative_sm_opr_footprint_ptr{
             std::make_unique<mgb::OprFootprint>()};
@@ -124,40 +126,44 @@ void init_graph_rt(py::module m) {
 
     def_rendezvous<TensorAttr>(m, "TensorAttrRendezvous");
 
-    py::class_<cg::VarNode, GraphNodePtr<cg::VarNode>>(m, "VarNode")
-            .def_property_readonly(
-                    "owner", [](cg::VarNode* v) { return v->owner_opr(); })
-            .def_property_readonly(
-                    "graph", [](cg::VarNode* v) { return v->owner_graph(); })
-            .def_property(
-                    "name", py::overload_cast<>(&VarNode::name, py::const_),
-                    py::overload_cast<std::string>(&VarNode::name))
-            .def_property_readonly("dtype", [](cg::VarNode* v) { return v->dtype(); })
-            .def_property_readonly(
-                    "comp_node", [](cg::VarNode* v) { return v->comp_node(); })
-            .def_property_readonly(
-                    "shape",
-                    [](cg::VarNode* v) -> const TensorShape* {
-                        auto&& mgr = v->owner_graph()->static_infer_manager();
-                        return mgr.infer_shape_fallible(v);
-                    })
-            .def_property_readonly(
-                    "value",
-                    [](cg::VarNode* v) -> py::object {
-                        auto&& mgr = v->owner_graph()->static_infer_manager();
-                        auto&& type = mgr.get_infer_type(v);
-                        using InferType = cg::static_infer::InferType;
-                        if (!(type.value & (InferType::CONST | InferType::RT_STATIC))) {
-                            return py::none();
-                        }
-                        auto* val = mgr.infer_value_fallible(v);
-                        if (!val) {
-                            return py::none();
-                        }
-                        return py::cast(*val).attr("numpy")();
-                    })
-            .def_property_readonly("id", [](cg::VarNode* v) { return (v->id()); })
-            .def("__repr__", [](cg::VarNode* v) { return "Var:" + v->name(); });
+    Py_Varnode =
+            py::class_<cg::VarNode, GraphNodePtr<cg::VarNode>>(m, "VarNode")
+                    .def_property_readonly(
+                            "owner", [](cg::VarNode* v) { return v->owner_opr(); })
+                    .def_property_readonly(
+                            "graph", [](cg::VarNode* v) { return v->owner_graph(); })
+                    .def_property(
+                            "name", py::overload_cast<>(&VarNode::name, py::const_),
+                            py::overload_cast<std::string>(&VarNode::name))
+                    .def_property_readonly(
+                            "dtype", [](cg::VarNode* v) { return v->dtype(); })
+                    .def_property_readonly(
+                            "comp_node", [](cg::VarNode* v) { return v->comp_node(); })
+                    .def_property_readonly(
+                            "shape",
+                            [](cg::VarNode* v) -> const TensorShape* {
+                                auto&& mgr = v->owner_graph()->static_infer_manager();
+                                return mgr.infer_shape_fallible(v);
+                            })
+                    .def_property_readonly(
+                            "value",
+                            [](cg::VarNode* v) -> py::object {
+                                auto&& mgr = v->owner_graph()->static_infer_manager();
+                                auto&& type = mgr.get_infer_type(v);
+                                using InferType = cg::static_infer::InferType;
+                                if (!(type.value &
+                                      (InferType::CONST | InferType::RT_STATIC))) {
+                                    return py::none();
+                                }
+                                auto* val = mgr.infer_value_fallible(v);
+                                if (!val) {
+                                    return py::none();
+                                }
+                                return py::cast(*val).attr("numpy")();
+                            })
+                    .def_property_readonly(
+                            "id", [](cg::VarNode* v) { return (v->id()); })
+                    .def("__repr__", [](cg::VarNode* v) { return "Var:" + v->name(); });
 
     py::class_<cg::OperatorNodeBase, GraphNodePtr<cg::OperatorNodeBase>>(
             m, "OperatorNode")

imperative/python/src/graph_rt.h

@@ -8,6 +8,9 @@
 #include "megbrain/graph.h"
 #include "megbrain/plugin/opr_footprint.h"
 
+namespace py = pybind11;
+extern py::object Py_Varnode;
+
 template <typename T>
 class GraphNodePtr {
     std::shared_ptr<mgb::cg::ComputingGraph> m_graph;

imperative/python/src/tensor.cpp

@@ -48,58 +48,11 @@ namespace mgb::imperative::python {
 
 namespace {
 WeakKeyMap<ValueWeakRef, py::object> module_trace_info_map;
-
-struct SymbolVarContext {
-    TransformationContext context;
-    std::shared_ptr<SymbolTransformation> symbol_tsf;
-    std::shared_ptr<ScalarTransformation> scalar_tsf;
-    std::shared_ptr<DTypePromoteTransformation> dtype_promote_tsf;
-    std::shared_ptr<DimExpansionTransformation> dim_expansion_tsf;
-
-    SymbolVarContext(cg::ComputingGraph* graph) {
-        symbol_tsf = std::make_shared<SymbolTransformation>(graph);
-        scalar_tsf = std::make_shared<ScalarTransformation>();
-        dtype_promote_tsf = std::make_shared<DTypePromoteTransformation>();
-        dim_expansion_tsf = std::make_shared<DimExpansionTransformation>();
-        Transformation::swap_context(context);
-    }
-
-    void init() {
-        symbol_tsf->register_at(Transformation::top());
-        scalar_tsf->register_at(Transformation::top());
-        dtype_promote_tsf->register_at(Transformation::top());
-        dim_expansion_tsf->register_at(Transformation::top());
-    }
-
-    ValueRef symvar2val(py::handle py_symbol_var) {
-        auto* symbol_var = py_symbol_var.cast<PySymbolVar*>();
-        ValueRef value = symbol_tsf->value_type().make(symbol_var->m_node);
-        if (symbol_var->is_scalar) {
-            value = scalar_tsf->value_type().make(value);
-        }
-        return value;
-    }
-
-    py::object val2symvar(py::handle typeobj, ValueRef value) {
-        bool is_scalar = false;
-        if (auto* scalar_value = value.as(scalar_tsf->value_type())) {
-            value = scalar_value->value();
-            is_scalar = true;
-        }
-        auto* node = value.cast(symbol_tsf->value_type()).node();
-        auto py_symbol_var =
-                typeobj(pybind11::cast(node, pybind11::return_value_policy::automatic));
-        py_symbol_var.cast<PySymbolVar*>()->is_scalar = is_scalar;
-        return py_symbol_var;
-    }
-
-    ~SymbolVarContext() { Transformation::swap_context(context); }
-};
-
 }  // namespace
 
 interpreter::Interpreter::Channel* interpreter_for_py = nullptr;
 PyTypeObject* py_tensor_type = nullptr;
+PyTypeObject* py_varnode_type = nullptr;
 pybind11::handle py_device_type = nullptr;
 PyObject* cpp_use_symbolic_shape;
 
@@ -136,22 +89,6 @@ PyObject* py_apply(
         auto op = py::handle(py_op).cast<std::shared_ptr<OpDef>>();
         SmallVector<ValueRef, 8> tensors(nargs);
 
-        SmallVector<bool, 8> is_symbol_var(nargs, false);
-        ComputingGraph* cg = nullptr;
-        for (size_t i = 0; i < nargs; ++i) {
-            if ((!TensorWrapper::try_cast(args[i])) &&
-                py::isinstance<PySymbolVar>(py::handle(args[i]))) {
-                is_symbol_var[i] = true;
-                ComputingGraph* cur_cg =
-                        py::handle(args[i]).cast<PySymbolVar*>()->m_node->owner_graph();
-                if (cg == nullptr) {
-                    cg = cur_cg;
-                } else {
-                    mgb_assert(cg == cur_cg);
-                }
-            }
-        }
-
         mgb::CompNode target_cn;
         mgb::DType target_dtype;
 
@@ -174,35 +111,11 @@ PyObject* py_apply(
             }
         };
 
-        if (cg != nullptr) {
-            // swap to a special context to reuse scalar handle
-            size_t symbol_var_idx = 8;
-            SymbolVarContext context(cg);
-            context.init();
-            for (size_t i = 0; i < nargs; ++i) {
-                if (is_symbol_var[i]) {
-                    symbol_var_idx = i;
-                    tensors[i] = context.symvar2val(args[i]);
-                } else if (
-                        DTypePromoteCfg::convert_input_enabled &&
-                        op->same_type<Elemwise>()) {
-                    tensors[i] = convert_pyinput_to_tensor(i);
-                } else {
-                    PyErr_SetString(
-                            PyExc_TypeError, "py_apply expects tensor as inputs");
-                    return nullptr;
-                }
-            }
-            auto outputs = imperative::apply(*op, tensors);
-            auto ret = pybind11::tuple(outputs.size());
-            auto typeobj = py::handle(args[symbol_var_idx]).get_type();
-            for (size_t i = 0; i < outputs.size(); ++i) {
-                ret[i] = context.val2symvar(typeobj, outputs[i]);
-            }
-            return ret.release().ptr();
-        }
-
+        bool is_varnode_apply = false;
         for (size_t i = 0; i < nargs; ++i) {
+            if (PyObject_TypeCheck(args[i], py_varnode_type)) {
+                is_varnode_apply = true;
+            }
             if (TensorWrapper* tw = TensorWrapper::try_cast(args[i])) {
                 tensors[i] = tw->m_tensor->data();
             } else if (
@@ -218,8 +131,9 @@ PyObject* py_apply(
         auto outputs = [&] { return imperative::apply(*op, tensors); }();
         size_t nout = outputs.size();
         auto ret = py::tuple(nout);
+        PyTypeObject* py_type = is_varnode_apply ? py_varnode_type : py_tensor_type;
         for (size_t i = 0; i < nout; ++i) {
-            ret[i] = TensorWrapper::make(py_tensor_type, std::move(outputs[i]));
+            ret[i] = TensorWrapper::make(py_type, std::move(outputs[i]));
         }
         return ret.release().ptr();
     }
@@ -622,9 +536,17 @@ TensorWrapper::TensorWrapper(PyObject* args, PyObject* kwargs) {
             CreateTensor::Kind kind = is_const ? CreateTensor::Const
                                     : no_cache ? CreateTensor::Unique
                                                : CreateTensor::Common;
-            auto&& hval = pyobj2hval(data, cn, dtype);
-            auto val = imperative::apply(
-                    CreateTensor(kind, cn, hval.dtype, hval.shape), hval.storage)[0];
+            ValueRef val;
+            if (py::isinstance(data, Py_Varnode)) {
+                cg::VarNode* m_node = py::handle(data).cast<cg::VarNode*>();
+                val = imperative::apply(
+                        CreateNode(m_node), Span<ValueRef>(nullptr, nullptr))[0];
+            } else {
+                auto&& hval = pyobj2hval(data, cn, dtype);
+                val = imperative::apply(
+                        CreateTensor(kind, cn, hval.dtype, hval.shape),
+                        hval.storage)[0];
+            }
             m_tensor.emplace(val);
         }
 
@@ -734,6 +656,20 @@ PyObject* TensorWrapper::isscalar() {
     }
 }
 
+PyObject* TensorWrapper::_var() {
+    TypedValueRef<NodeValue> value =
+            imperative::apply(GetVarVal(), m_tensor->data())[0].as_ref<NodeValue>();
+    auto* node = value->node();
+    return py::cast(node).release().ptr();
+}
+
+PyObject* TensorWrapper::_graph() {
+    TypedValueRef<NodeValue> value =
+            imperative::apply(GetVarVal(), m_tensor->data())[0].as_ref<NodeValue>();
+    auto* graph = value->graph();
+    return py::cast(graph).release().ptr();
+}
+
 struct TensorWeakRef {
     ValueWeakRef data;
 
@@ -808,6 +744,10 @@ void init_tensor(py::module m) {
                                       std::make_shared<ScalarTransformation>())
                               .release());
     MGB_MARK_USED_VAR(transformations
+                              .register_at<Segment::Symbol>(
+                                      std::make_shared<SymbolTransformation>())
+                              .release());
+    MGB_MARK_USED_VAR(transformations
                               .register_at<Segment::DTypePromote>(
                                       std::make_shared<DTypePromoteTransformation>())
                               .release());
@@ -863,6 +803,8 @@ void init_tensor(py::module m) {
                     .def<&TensorWrapper::_detail>("_detail")
                     .def<&TensorWrapper::_set_name>("_set_name")
                     .def<&TensorWrapper::_watch>("_watch")
+                    .def<&TensorWrapper::_var>("var")
+                    .def<&TensorWrapper::_graph>("graph")
                     .def_getset<
                             &TensorWrapper::module_trace_info,
                             &TensorWrapper::set_module_trace_info>("_NodeMixin__node")
@@ -875,43 +817,6 @@ void init_tensor(py::module m) {
             .def(py::init<const TensorWrapper&>())
             .def("__call__", &TensorWeakRef::operator());
 
-    py::class_<PySymbolVar, std::shared_ptr<PySymbolVar>>(m, "SymbolVar")
-            .def_property_readonly(
-                    "dtype", [](PySymbolVar* v) { return v->m_node->dtype(); })
-            .def_property(
-                    "var", [](PySymbolVar* v) { return v->m_node; },
-                    [](PySymbolVar* s, cg::VarNode* v) { s->m_node = v; })
-            .def_property_readonly(
-                    "device", [](PySymbolVar* v) { return v->m_node->comp_node(); })
-            .def_property_readonly(
-                    "graph", [](PySymbolVar* v) { return v->m_node->owner_graph(); })
-            .def_property_readonly(
-                    "shape",
-                    [](PySymbolVar* v) -> const TensorShape* {
-                        auto&& mgr = v->m_node->owner_graph()->static_infer_manager();
-                        return mgr.infer_shape_fallible(v->m_node);
-                    })
-            .def("numpy",
-                 [](PySymbolVar* v) {
-                     auto&& mgr = v->m_node->owner_graph()->static_infer_manager();
-                     auto&& type = mgr.get_infer_type(v->m_node);
-                     using InferType = cg::static_infer::InferType;
-                     if (!(type.value & (InferType::CONST | InferType::RT_STATIC))) {
-                         throw py::value_error("value invalid!");
-                     }
-                     auto* val = mgr.infer_value_fallible(v->m_node);
-                     if (!val) {
-                         throw py::value_error("value invalid!");
-                     }
-                     auto np_val = py::cast(*val).attr("numpy")();
-                     return np_val;
-                 })
-            .def("_isscalar", [](PySymbolVar* v) { return v->is_scalar; })
-            .def(py::init([](cg::VarNode* node) {
-                     return std::make_shared<PySymbolVar>(node);
-                 }),
-                 py::arg() = nullptr);
-
     static PyMethodDef method_defs[] = {
             MGE_PY_INTERFACE(apply, py_apply),
             MGE_PY_INTERFACE(dtype_promotion, dtype_promotion),
@@ -1027,6 +932,10 @@ void init_tensor(py::module m) {
         py_tensor_type = reinterpret_cast<PyTypeObject*>(type_obj.inc_ref().ptr());
     });
 
+    m.def("set_py_varnode_type", [](py::object type_obj) {
+        py_varnode_type = reinterpret_cast<PyTypeObject*>(type_obj.inc_ref().ptr());
+    });
+
     m.def("set_py_device_type",
           [](py::object type_obj) { py_device_type = type_obj.inc_ref(); });
 
@@ -1217,31 +1126,6 @@ void init_tensor(py::module m) {
                 }
             });
 
-    m.def("reduce_to_scalar", [](py::object op, py::object tensor) -> py::object {
-        auto reduce_to_scalar = [](const OpDef& op, const ValueRef& input) {
-            auto make_scalar_shape = [&](CompNode device) {
-                return imperative::apply(
-                        CreateTensor(CreateTensor::Const, device, dtype::Int32(), {0}),
-                        HostStorage::make(device))[0];
-            };
-            return imperative::apply(op, input, make_scalar_shape(*input.device()))[0];
-        };
-        if (py::isinstance<PySymbolVar>(tensor)) {
-            auto* graph = tensor.cast<PySymbolVar*>()->m_node->owner_graph();
-            SymbolVarContext context(graph);
-            context.init();
-            auto output = reduce_to_scalar(
-                    *op.cast<std::shared_ptr<OpDef>>(), context.symvar2val(tensor));
-            auto typeobj = tensor.get_type();
-            return context.val2symvar(typeobj, output);
-        } else {
-            auto* tw = TensorWrapper::try_cast(tensor.ptr());
-            auto output = reduce_to_scalar(
-                    *op.cast<std::shared_ptr<OpDef>>(), tw->m_tensor->data());
-            return TensorWrapper::make(py_tensor_type, output);
-        }
-    });
-
     m.def("name_tensor", [](std::string name, py::object tensor) {
         auto* tw = TensorWrapper::try_cast(tensor.ptr());
         auto output = imperative::apply(TraceMarkVar(name), tw->m_tensor->data())[0];

imperative/python/src/tensor.h

@@ -10,6 +10,8 @@
 
 #include "./pyext17.h"
 #include "megbrain/imperative/dispatch.h"
+#include "megbrain/imperative/transformations/scalar.h"
+#include "megbrain/imperative/transformations/symbol.h"
 #include "megbrain/imperative/utils/span.h"
 
 namespace mgb::imperative::python {
@@ -27,6 +29,7 @@ namespace mgb::imperative::python {
 
 extern interpreter::Interpreter::Channel* interpreter_for_py;
 extern PyTypeObject* py_tensor_type;
+extern PyTypeObject* py_varnode_type;
 extern pybind11::handle py_device_type;
 extern PyObject* cpp_use_symbolic_shape;
 extern PyObject* cpp_astensor1d;
@@ -126,16 +129,11 @@ public:
     void set_module_trace_info(PyObject*);
     void _set_name(PyObject*);
     PyObject* _detail();
+    PyObject* _var();
+    PyObject* _graph();
     void _watch();
 };
 
-struct PySymbolVar {
-    cg::VarNode* m_node = nullptr;
-    bool is_scalar = false;
-    PySymbolVar() = default;
-    PySymbolVar(VarNode* m) : m_node(m) {}
-};
-
 PyObject* py_apply(
         PyObject* self, PyObject* const* args, size_t nargs /* , PyObject* kwnames */);
 

imperative/python/src/tensor_utils.cpp

@@ -146,15 +146,6 @@ PyArray_Descr* _dtype_promotion(PyObject* const* args, size_t nargs) {
                 continue;
             }
 
-            if (py::isinstance<PySymbolVar>(py::handle(handle))) {
-                auto var = py::handle(handle).cast<PySymbolVar*>();
-                mgb::DType type = var->m_node->dtype();
-                auto&& descr = npy::dtype_mgb2np_descr(type);
-                Py_INCREF(descr.get());
-                tensors.emplace_back(descr.get());
-                continue;
-            }
-
             PyArray_Descr* descr = scalar2dtype(handle);
             if (descr) {
                 scalars.emplace_back(descr);
@@ -204,17 +195,12 @@ CompNode _get_device(PyObject* const* args, size_t nargs) {
         PyObject* handle = is_tuple ? PyTuple_GetItem(tuple, i) : args[i];
         TensorWrapper* tw = TensorWrapper::try_cast(handle);
 
-        bool is_symvar = py::isinstance<PySymbolVar>(py::handle(handle));
-        if (tw || is_symvar) {
+        if (tw) {
             if (!valid) {
-                cn = tw ? tw->m_tensor->comp_node()
-                        : py::handle(handle).cast<PySymbolVar*>()->m_node->comp_node();
+                cn = tw->m_tensor->comp_node();
                 valid = true;
             } else {
-                CompNode cn1 = tw ? tw->m_tensor->comp_node()
-                                  : py::handle(handle)
-                                               .cast<PySymbolVar*>()
-                                               ->m_node->comp_node();
+                CompNode cn1 = tw->m_tensor->comp_node();
                 if (cn1 != cn) {
                     throw py::value_error(ssprintf(
                             "ambiguous device: %s (from %s) vs %s (from %s)",
@@ -258,10 +244,6 @@ PyObject* get_device(PyObject* self, PyObject* const* args, size_t nargs) {
 }
 
 bool is_scalar(PyObject* tensor) {
-    if (py::isinstance<PySymbolVar>(py::handle(tensor))) {
-        auto var = py::handle(tensor).cast<PySymbolVar*>();
-        return var->is_scalar;
-    }
     auto* tw = TensorWrapper::try_cast(tensor);
     if (tw) {
         return tw->m_tensor->is_scalar();
@@ -319,8 +301,7 @@ py::object device2obj(py::handle device, bool mapping = false) {
     }
 }
 
-py::object _Const(
-        py::handle value, py::handle dtype, py::handle device, py::handle ref_hdl) {
+py::object _Const(py::handle value, py::handle dtype, py::handle device) {
     py::object val = py::reinterpret_borrow<py::object>(value);
     if (PyArray_Check(value.ptr())) {
         py::tuple strides =
@@ -338,32 +319,6 @@ py::object _Const(
             val = val.attr("reshape")(orig_shp);
         }
     }
-    py::object ref;
-    if (py::isinstance<py::tuple>(ref_hdl)) {
-        py::tuple tup = py::reinterpret_borrow<py::tuple>(ref_hdl);
-        if (tup.size()) {
-            ref = tup[0];
-        } else {
-            ref = py::none();
-        }
-    } else {
-        ref = py::reinterpret_borrow<py::object>(ref_hdl);
-    }
-    if (py::isinstance<PySymbolVar>(ref)) {
-        auto ref_var = ref.cast<PySymbolVar*>();
-        auto* graph = ref_var->m_node->owner_graph();
-        CompNode cn;
-        if (device.ptr() == Py_None) {
-            cn = ref_var->m_node->comp_node();
-        } else {
-            cn = device2obj(device).cast<CompNode>();
-        }
-        OperatorNodeConfig config(cn);
-        auto hv = npy::np2tensor(
-                val.ptr(), npy::Meth::borrow(cn), dtype.cast<mgb::DType>());
-        auto typeobj = ref.get_type();
-        return typeobj(opr::ImmutableTensor::make(*graph, hv, config).node());
-    }
     py::object device_obj = device2obj(device, true);
     py::tuple tup = py::make_tuple(val, dtype, device_obj, true, false, py::none());
     return TensorWrapper::make(py_tensor_type, tup.ptr(), nullptr);
@@ -373,7 +328,7 @@ py::tuple _make_shape_tuple(py::handle shape) {
     py::list orig;
     py::list ret(0);
     auto solve_one = [&](py::handle val) {
-        if (TensorWrapper::try_cast(val.ptr()) || py::isinstance<PySymbolVar>(val)) {
+        if (TensorWrapper::try_cast(val.ptr())) {
             py::object np = getattr(val, "numpy")();
             PyArrayObject* arr = (PyArrayObject*)np.ptr();
             PyObject* maybe_list = PyArray_ToList(arr);
@@ -415,25 +370,53 @@ py::tuple _make_shape_tuple(py::handle shape) {
     return py::reinterpret_steal<py::tuple>(PyList_AsTuple(ret.ptr()));
 }
 
-bool is_tensor_or_symbolvar(py::handle arg) {
-    return bool(TensorWrapper::try_cast(arg.ptr())) || py::isinstance<PySymbolVar>(arg);
+bool is_tensor(py::handle arg) {
+    return bool(TensorWrapper::try_cast(arg.ptr()));
 }
 
 bool is_py_sequence(py::handle arg) {
-    if (PyArray_Check(arg.ptr()) || TensorWrapper::try_cast(arg.ptr()) ||
-        py::isinstance<PySymbolVar>(arg)) {
+    if (PyArray_Check(arg.ptr()) || TensorWrapper::try_cast(arg.ptr())) {
         return false;
     }
     return PySequence_Check(arg.ptr());
 }
 
-mgb::DType _get_dtype(py::handle tensor) {
-    if (auto tw = TensorWrapper::try_cast(tensor.ptr())) {
-        return tw->m_tensor->dtype();
+py::object get_res_by_refhdl(
+        py::handle value, py::handle dtype, py::handle device, py::handle ref_hdl) {
+    py::object res = _Const(value, dtype, device);
+    py::object ref;
+    if (py::isinstance<py::tuple>(ref_hdl)) {
+        py::tuple tup = py::reinterpret_borrow<py::tuple>(ref_hdl);
+        if (tup.size()) {
+            ref = tup[0];
+        } else {
+            ref = py::none();
+        }
     } else {
-        auto var = tensor.cast<PySymbolVar*>();
-        return var->m_node->dtype();
+        ref = py::reinterpret_borrow<py::object>(ref_hdl);
+    }
+    if (PyObject_TypeCheck(ref.ptr(), py_varnode_type)) {
+        auto temp = dtype.cast<mgb::DType>();
+        ComputingGraph* graph = getattr(ref, "graph").cast<ComputingGraph*>();
+        cg::VarNode* node = getattr(ref, "var").cast<cg::VarNode*>();
+        CompNode cn;
+        if (device.ptr() == Py_None) {
+            cn = node->comp_node();
+        } else {
+            cn = device2obj(device).cast<CompNode>();
+        }
+        OperatorNodeConfig config(cn);
+        auto hv = npy::np2tensor(
+                value.ptr(), npy::Meth::borrow(cn), dtype.cast<mgb::DType>());
+        auto typeobj = ref.get_type();
+        return typeobj(opr::ImmutableTensor::make(*graph, hv, config).node());
     }
+    return res;
+}
+
+mgb::DType _get_dtype(py::handle tensor) {
+    auto tw = TensorWrapper::try_cast(tensor.ptr());
+    return tw->m_tensor->dtype();
 }
 
 py::object _astype_cpp(py::handle tensor, py::handle dtype_hdl) {
@@ -457,12 +440,12 @@ py::object _astype_cpp(py::handle tensor, py::handle dtype_hdl) {
 
 py::object _convert_single_value_cpp(
         py::handle value, py::handle dtype, py::handle device) {
-    if (is_tensor_or_symbolvar(value)) {
+    if (is_tensor(value)) {
         if (_get_dtype(value).category() != DTypeCategory::QUANTIZED) {
             return _astype_cpp(value, dtype);
         }
     } else {
-        return _Const(value, dtype, device, py::none());
+        return _Const(value, dtype, device);
     }
     return py::reinterpret_borrow<py::object>(value);
 }
@@ -475,28 +458,8 @@ py::object _convert_inputs_cpp(
     for (size_t i = 0; i < nargs; ++i) {
         py::handle h = py::handle(args[i]);
         lis.append(h);
-        if (py::isinstance<PySymbolVar>(h)) {
-            auto var = h.cast<PySymbolVar*>();
-            auto g = var->m_node->owner_graph();
-            if (!graph) {
-                graph = g;
-                typeobj = h.get_type();
-            } else {
-                mgb_assert(graph == g);
-            }
-        }
-    }
-    if (graph) {
-        CompNode cn = device2obj(device).cast<CompNode>();
-        for (size_t i = 0; i < nargs; ++i) {
-            OperatorNodeConfig config(cn);
-            auto hv = npy::np2tensor(
-                    lis[i].ptr(), npy::Meth::borrow(cn), dtype.cast<mgb::DType>());
-            if (!py::isinstance<PySymbolVar>(lis[i])) {
-                lis[i] = typeobj(opr::ImmutableTensor::make(*graph, hv, config).node());
-            }
-        }
     }
+
     auto convert = [&](py::object value) {
         if (value.is_none()) {
             return value;
@@ -517,7 +480,8 @@ py::object _astensor1d_cpp(
     if (device.ptr() != Py_None) {
         device_obj = device2obj(device);
     }
-    if (py::isinstance<PySymbolVar>(value)) {
+
+    if (PyObject_TypeCheck(value.ptr(), py_varnode_type)) {
         try {
             getattr(value, "ndim");
         } catch (py::error_already_set& err) {
@@ -537,14 +501,15 @@ py::object _astensor1d_cpp(
             return ret;
         }
     }
+
     size_t ndim = 999;
     if (hasattr(value, "ndim")) {
         ndim = getattr(value, "ndim").cast<size_t>();
         if (ndim != 0 && ndim != 1) {
             throw py::value_error("ndim != 1 or 0, get : " + std::to_string(ndim));
         }
-        if (!is_tensor_or_symbolvar(value)) {
-            return _Const(value, dtype, device, ref);
+        if (!is_tensor(value)) {
+            return get_res_by_refhdl(value, dtype, device, ref);
         } else {
             return py::reinterpret_borrow<py::object>(value);
         }
@@ -555,13 +520,13 @@ py::object _astensor1d_cpp(
     py::list lis = py::reinterpret_steal<py::list>(PySequence_List(value.ptr()));
     bool need_concat = false;
     for (size_t i = 0; i < lis.size(); ++i) {
-        if (is_tensor_or_symbolvar(lis[i])) {
+        if (is_tensor(lis[i])) {
             need_concat = true;
             break;
         }
     }
     if (!need_concat) {
-        return _Const(value, dtype, device, ref);
+        return get_res_by_refhdl(value, dtype, device, ref);
     }
     if (lis.size() > 1) {
         std::vector<PyObject*> c_args(lis.size() + 1);
@@ -600,10 +565,9 @@ py::object _astensor1d_cpp(
 }
 
 py::object _get_index(py::object tensor, py::object src) {
-    if (!TensorWrapper::try_cast(tensor.ptr()) &&
-        !py::isinstance<PySymbolVar>(tensor)) {
+    if (!TensorWrapper::try_cast(tensor.ptr())) {
         auto get_const = [&](mgb::DType dtype) -> py::object {
-            return _Const(tensor, py::cast(dtype), src.attr("device"), src);
+            return _Const(tensor, py::cast(dtype), src.attr("device"));
         };
         if (is_bool_list(tensor.ptr()) || is_bool_dtype(tensor.ptr())) {
             tensor = get_const(dtype::Bool());
@@ -636,9 +600,8 @@ py::tuple _try_cond_take(py::handle tensor, py::handle index) {
     }
     py::object iobj;
     if (PyArray_Check(index.ptr())) {
-        iobj =
-                _Const(index, py::cast((mgb::DType)dtype::Bool()),
-                       getattr(tensor, "device"), tensor);
+        iobj = _Const(
+                index, py::cast((mgb::DType)dtype::Bool()), getattr(tensor, "device"));
     } else {
         iobj = py::reinterpret_borrow<py::object>(index);
     }
@@ -920,8 +883,8 @@ py::object _expand_args(py::handle args) {
         return py::reinterpret_borrow<py::object>(args);
     }
     py::tuple args_tup = py::reinterpret_borrow<py::tuple>(args.ptr());
-    if (args_tup.size() == 1 && (PySequence_Check(args_tup[0].ptr()) ||
-                                 is_tensor_or_symbolvar(args_tup[0].ptr()))) {
+    if (args_tup.size() == 1 &&
+        (PySequence_Check(args_tup[0].ptr()) || is_tensor(args_tup[0].ptr()))) {
         return py::reinterpret_borrow<py::object>(args_tup[0]);
     } else {
         return py::reinterpret_steal<py::list>(PySequence_List(args_tup.ptr()));
@@ -948,7 +911,8 @@ std::tuple<std::vector<int32_t>, bool> tuple2vector(py::object shape) {
 bool enable_fastpath(py::handle inp) {
     auto&& tm_tr = TransformationManager::get_instance()
                            .segments[TransformationManager::Segment::ModuleTrace];
-    if (!TensorWrapper::try_cast(inp.ptr()) ||
+    bool is_varnode = PyObject_TypeCheck(inp.ptr(), py_varnode_type);
+    if (is_varnode ||
         TransformationManager::get_instance()
                         .segments[TransformationManager::Segment::Trace]
                         .size() > 0 ||
@@ -1181,10 +1145,8 @@ py::object _getitem_cpp(py::handle inp_hdl, py::handle idx_hdl) {
 py::object _setitem_cpp(py::handle inp_hdl, py::handle idx_hdl, py::handle val_hdl) {
     py::object org_shape = getattr(inp_hdl, "shape");
     py::object val = py::reinterpret_borrow<py::object>(val_hdl);
-    if (!TensorWrapper::try_cast(val.ptr()) && !py::isinstance<PySymbolVar>(val)) {
-        val =
-                _Const(val_hdl, getattr(inp_hdl, "dtype"), getattr(inp_hdl, "device"),
-                       inp_hdl);
+    if (!TensorWrapper::try_cast(val.ptr())) {
+        val = _Const(val_hdl, getattr(inp_hdl, "dtype"), getattr(inp_hdl, "device"));
     }
 
     py::tuple up = _unpack_indexes(inp_hdl, idx_hdl);
@@ -1308,12 +1270,12 @@ py::object _split_cpp(
                         repr(nsplits_or_sections_hdl).cast<std::string>());
             }
             py::object pos = div_points[i] - div_points[i - 1];
-            if (is_tensor_or_symbolvar(pos)) {
+            if (is_tensor(pos)) {
                 partitions.append(pos);
             } else {
                 partitions.append(
                         _Const(pos, py::cast((mgb::DType)dtype::Int32()),
-                               getattr(inp_hdl, "device"), inp_hdl));
+                               getattr(inp_hdl, "device")));
             }
         }
         op = Split::make(axis, 0);
@@ -1438,7 +1400,7 @@ py::object _squeeze_cpp(py::handle inp_hdl, py::handle axis_hdl) {
 py::object _transpose_cpp(py::handle inp_hdl, py::handle args) {
     py::object obj = _expand_args(args);
     py::list lis;
-    if (!is_tensor_or_symbolvar(obj.ptr()) && PySequence_Check(obj.ptr())) {
+    if (!is_tensor(obj.ptr()) && PySequence_Check(obj.ptr())) {
         lis = py::reinterpret_steal<py::list>(PySequence_List(obj.ptr()));
     } else {
         py::object np = getattr(obj, "numpy")();
@@ -1631,7 +1593,7 @@ PyObject* pixel_shuffle_cpp(PyObject* self, PyObject* const* args, size_t nargs)
 
 PyObject* Const(PyObject* self, PyObject* const* args, size_t nargs) {
     try {
-        return _Const(args[0], args[1], args[2], args[3]).release().ptr();
+        return _Const(args[0], args[1], args[2]).release().ptr();
     }
     PYEXT17_TRANSLATE_EXC_RET(nullptr)
 }
@@ -1696,4 +1658,4 @@ PyObject* astensor1d_cpp(PyObject* self, PyObject* const* args, size_t nargs) {
     PYEXT17_TRANSLATE_EXC_RET(nullptr)
 }
 
-}  // namespace mgb::imperative::python
+}  // namespace mgb::imperative::python

imperative/python/src/transformation.h

@@ -20,11 +20,12 @@ public:
         DimExpansion,
         Grad,
         Scalar,
+        Symbol,
         Trace,
         Eval,
     };
 
-    std::array<std::vector<std::shared_ptr<Transformation>>, 7> segments;
+    std::array<std::vector<std::shared_ptr<Transformation>>, 8> segments;
 
 private:
     template <Segment segment>

imperative/python/test/helpers/utils.py

@@ -11,7 +11,7 @@ from megengine.utils.network_node import VarNode
 
 
 def _default_compare_fn(x, y):
-    if isinstance(x, tensor):
+    if isinstance(x, tensor) and not isinstance(x, VarNode):
         x = x.numpy()
     elif not isinstance(x, np.ndarray):
         x = get_var_value(x)

imperative/python/test/unit/functional/test_tensor.py

@@ -679,6 +679,18 @@ def test_utils_astensor1d(is_varnode):
         assert isinstance(xx, type(reference))
         np.testing.assert_equal(xx.numpy(), [1, 2, 3])
 
+    # varnode
+    if is_varnode:
+        a = np.array([[1, 2, 3], [4, 5, 6]]).astype("float32")
+        b = np.array([[True, False, True], [False, True, True]])
+        aa = make_tensor(a, network)
+        bb = make_tensor(b, network)
+        x, y = F.cond_take(bb, aa)
+        for dtype in [None, "float32"]:
+            xx = astensor1d(x, reference, dtype=dtype)
+            assert isinstance(xx, type(reference))
+            np.testing.assert_equal(get_var_value(xx), get_var_value(x))
+
 
 def test_device():
     x = tensor([1, 2, 3], dtype="float32")

imperative/python/test/unit/utils/test_network.py

@@ -114,8 +114,10 @@ def test_replace_opr():
     vara = graph.var_filter.name("a").as_unique()
     varb = graph.var_filter.name("b").as_unique()
 
-    out1 = F.sub(vara, varb)
+    out1 = F.mul(vara, varb)
     out1 = F.relu(out1)
+    out1 += 2
+    out1 *= 3
     out1 = graph.add_dep_oprs(out1)
     orig_opr = graph.opr_filter.has_input(vara).as_unique()
 
@@ -135,7 +137,7 @@ def test_replace_opr():
 
     load_graph = GraphInference(modified_model1)
     out = load_graph.run(a, b)
-    np.testing.assert_equal(out["o"], [0, 0])
+    np.testing.assert_equal(out["o"], [30, 60])
 
 
 def test_splice_network():

imperative/src/impl/basic_operators.cpp

@@ -82,6 +82,10 @@ std::string DTRCommand::to_string() const {
     return ssprintf("DTRCommandValue{kind=%d}", (int)m_kind);
 }
 
+std::string CreateNode::to_string() const {
+    return "CreateNode";
+}
+
 std::string GetName::to_string() const {
     return "GetName{}";
 }
@@ -94,5 +98,9 @@ std::string IsScalar::to_string() const {
     return "IsScalar";
 }
 
+std::string GetVarVal::to_string() const {
+    return "GetVarVal";
+}
+
 }  // namespace imperative
 }  // namespace mgb

imperative/src/include/megbrain/imperative/basic_operators.h

@@ -157,5 +157,22 @@ public:
     std::string to_string() const override;
 };
 
+class GetVarVal final : public OperatorImpl<GetVarVal, Operator::GetAttrLike> {
+public:
+    std::string to_string() const override;
+};
+
+class CreateNode final : public OperatorImpl<CreateNode> {
+private:
+    cg::VarNode* m_node;
+
+public:
+    CreateNode(cg::VarNode* node) : m_node(node) {}
+
+    cg::VarNode* node() const { return m_node; }
+
+    std::string to_string() const override;
+};
+
 }  // namespace imperative
 }  // namespace mgb

imperative/src/include/megbrain/imperative/basic_values.h

@@ -173,5 +173,24 @@ public:
     std::string to_string() const override;
 };
 
+class NodeStorage {
+private:
+    cg::VarNode* m_node;
+
+public:
+    NodeStorage() = default;
+    NodeStorage(VarNode* node) : m_node(node) {}
+    VarNode* node() const { return m_node; }
+    ComputingGraph* graph() const { return m_node->owner_graph(); }
+    std::string to_string() const { return m_node->name(); }
+};
+
+class NodeValue final : public PrimitiveValue<NodeValue, NodeStorage> {
+public:
+    using PrimitiveValue::PrimitiveValue;
+
+    std::string to_string() const override { return NodeStorage::to_string(); }
+};
+
 }  // namespace imperative
 }  // namespace mgb

imperative/src/include/megbrain/imperative/transformations/symbol.h

@@ -39,13 +39,39 @@ private:
     ObjectType<SymbolValue> m_value_type{"SymbolValue"};
 
 public:
-    SymbolTransformation(ComputingGraph* graph) : m_graph(graph) {}
+    SymbolTransformation() {}
     ValueRefList apply_transformation(
             const Operator& op, Span<ValueRef> inputs) override {
+        ComputingGraph* cg = nullptr;
+        if (auto* node_value = op.as<CreateNode>()) {
+            return {m_value_type.make(node_value->node())};
+        }
+        for (auto&& input : inputs) {
+            if (auto* val = input.as(m_value_type)) {
+                auto* node = val->node();
+                ComputingGraph* cur_cg = node->owner_graph();
+                if (cg == nullptr) {
+                    cg = cur_cg;
+                } else {
+                    mgb_assert(cg == cur_cg, "input varnode gragh should be the same");
+                }
+            }
+        }
+        if (!cg) {
+            return imperative::apply(op, inputs);
+        }
+
         if (auto* apply_op = op.as<ApplyOp>()) {
             SmallVector<VarNode*> input_nodes;
             for (auto&& input : inputs) {
-                input_nodes.push_back(input.cast(m_value_type).node());
+                if (!input.is(m_value_type)) {
+                    auto* node = opr::ImmutableTensor::make(
+                                         *cg, input.numpy()->as_nd(true), {})
+                                         .node();
+                    input_nodes.push_back(node);
+                } else {
+                    input_nodes.push_back(input.cast(m_value_type).node());
+                }
             }
             auto output_nodes = OpDef::apply_on_var_node(apply_op->op(), input_nodes);
             ValueRefList outputs(output_nodes.size());
@@ -53,15 +79,9 @@ public:
                 outputs[i] = m_value_type.make(output_nodes[i]);
             }
             return outputs;
-        } else if (auto* create_tensor = op.as<CreateTensor>()) {
-            auto&& args = create_tensor->parse(inputs);
-            mgb_assert(
-                    args.kind == CreateTensor::Const,
-                    "only const value is allowed here");
-            auto* node = opr::ImmutableTensor::make(*m_graph, *args.host, {}).node();
-            return {m_value_type.make(node)};
         } else if (auto* get_attr = op.as<GetAttr>()) {
             auto* node = inputs.item().cast(m_value_type).node();
+            auto* m_graph = node->owner_graph();
             switch (get_attr->attr()) {
                 case GetAttr::DType:
                     return {DTypeValue::make(node->dtype())};
@@ -105,6 +125,10 @@ public:
                             MegBrainError, "Symbol: malformed GetAttr: %s",
                             op.to_string().c_str());
             }
+        } else if (auto* get_attr = op.as<GetVarVal>()) {
+            cg::VarNode* node = inputs.item().cast(m_value_type).node();
+            NodeStorage inp_var = NodeStorage(node);
+            return {NodeValue::make(inp_var)};
         } else {
             return op.fallback(inputs);
         }

imperative/src/include/megbrain/imperative/value.h

@@ -33,6 +33,7 @@ class ShapeValue;
 class DTypeValue;
 class CompNodeValue;
 class StringValue;
+class NodeValue;
 
 class Operator;