feat(mge/utils): add array method for varnode

GitOrigin-RevId: 6e4d05b475667cbffe4573ede4f2f581672ae3c9

imperative/python/megengine/core/ops/special.py

@@ -8,6 +8,9 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import numpy as np
 
+from .._imperative_rt import make_const
+from .._imperative_rt.core2 import SymbolVar, Tensor
+
 
 class Const:
     def __init__(self, value=None, *, dtype=None, device=None):
@@ -19,7 +22,19 @@ class Const:
         from ...tensor import Tensor
 
         device = self.device
-        if device is None:
-            device = reference[0].device
+
+        if len(reference) != 0:
+            reference = reference[0]
+            assert isinstance(
+                reference, (SymbolVar, Tensor)
+            ), "Reference should be Tensor or VarNode"
+
+            if device is None:
+                device = reference.device
+
+            if isinstance(reference, SymbolVar):
+                cls = type(reference)
+                rst = cls(make_const(reference.graph, self.value, device, self.dtype))
+                return (rst,)
 
         return (Tensor(self.value, self.dtype, self.device, True),)

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

@@ -13,7 +13,7 @@ from typing import Union
 import numpy as np
 
 from .._imperative_rt.common import CompNode
-from .._imperative_rt.core2 import Tensor, apply
+from .._imperative_rt.core2 import SymbolVar, Tensor, apply
 from ..ops import builtin
 from ..ops.builtin import Elemwise, GetVarShape
 from . import utils
@@ -230,7 +230,9 @@ def _todo(*_):
 
 def _expand_args(args):
     if len(args) == 1:
-        if isinstance(args[0], (collections.abc.Sequence, Tensor, np.ndarray),):
+        if isinstance(
+            args[0], (collections.abc.Sequence, Tensor, SymbolVar, np.ndarray),
+        ):
             args = args[0]
     return args
 

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

@@ -10,7 +10,7 @@ from typing import Iterable
 
 import numpy as np
 
-from .._imperative_rt.core2 import Tensor, apply
+from .._imperative_rt.core2 import SymbolVar, Tensor, apply
 from .._trace_option import use_symbolic_shape
 from ..ops import builtin
 from ..ops.special import Const
@@ -149,13 +149,13 @@ def unpack_getitem(inp, tuple_val, *, allow_newaxis=True):
             return True
 
         def get_index(i):
-            if not isinstance(i, (Tensor)):
+            if not isinstance(i, (Tensor, SymbolVar)):
                 if is_bool_list(i) or isinstance(i, np.ndarray) and i.dtype == np.bool_:
-                    (i,) = Const(i, dtype=np.bool_, device=inp.device)()
+                    (i,) = Const(i, dtype=np.bool_, device=inp.device)(inp)
                 else:
-                    (i,) = Const(i, dtype=np.int32, device=inp.device)()
+                    (i,) = Const(i, dtype=np.int32, device=inp.device)(inp)
                     return i
-            assert isinstance(i, Tensor)
+            assert isinstance(i, (Tensor, SymbolVar))
             if i.dtype != np.bool_:
                 return i
             _, ind = apply(builtin.CondTake(), i, i)
@@ -197,9 +197,9 @@ def try_condtake(tensor, index):
     ):
         return []
     if isinstance(index, np.ndarray):
-        (index,) = Const(index, dtype=np.bool_, device=tensor.device)()
-    assert isinstance(index, Tensor)
-    if not isinstance(tensor, Tensor):
+        (index,) = Const(index, dtype=np.bool_, device=tensor.device)(tensor)
+    assert isinstance(index, (Tensor, SymbolVar))
+    if not isinstance(tensor, (Tensor, SymbolVar)):
         raise TypeError("input must be a tensor")
     if tensor.device != index.device:
         raise ValueError(
@@ -214,11 +214,16 @@ def getitem(tensor, index):
         return try_result[0]
     tensor, tensors, items, use_subtensor, ret_scalar = unpack_getitem(tensor, index)
     for v in tensors:
+        if v.shape is None:
+            break
         if isinstance(v.shape, v.__class__):
             break
         if len(v.shape) > 0 and v.shape[0] == 0:
-            (empty_tensor,) = Const([], dtype=tensor.dtype, device=tensor.device)()
+            (empty_tensor,) = Const([], dtype=tensor.dtype, device=tensor.device)(
+                tensor
+            )
             return empty_tensor
+
     if use_subtensor:
         op = builtin.Subtensor(items=items)
     else:
@@ -235,8 +240,8 @@ def setitem(tensor, index, value):
     if len(try_result) == 2:
         index = try_result[1]
         tensor = tensor.reshape(-1)
-    if not isinstance(value, Tensor):
-        (value,) = Const(value, dtype=tensor.dtype, device=tensor.device)()
+    if not isinstance(value, (Tensor, SymbolVar)):
+        (value,) = Const(value, dtype=tensor.dtype, device=tensor.device)(tensor)
     tensor, tensors, items, use_subtensor, _ = unpack_getitem(tensor, index)
     if use_subtensor:
         op = builtin.Subtensor(items=items)

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

@@ -11,8 +11,9 @@ from typing import Iterable, Union
 
 import numpy as np
 
-from .._imperative_rt import VarNode
-from .._imperative_rt.core2 import Tensor, apply, dtype_promotion, get_device
+from .._imperative_rt import make_const
+from .._imperative_rt.core2 import SymbolVar, Tensor, apply, dtype_promotion, get_device
+from .._wrap import device as as_device
 from ..ops import builtin
 from ..ops.special import Const
 from .dtype import is_dtype_equal, is_quantize
@@ -38,13 +39,9 @@ def set_convert_inputs(flag):
 
 
 def concatenate(inputs, axis=0, *, device=None):
-    dtype = dtype_promotion(inputs)
-    device = get_device(inputs)
-
-    def convert(x):
-        return convert_single_value(x, dtype=dtype, device=device)
-
-    inputs = tuple(map(convert, inputs))
+    inputs = convert_inputs(*inputs)
+    if device is None:
+        device = get_device(inputs)
     (result,) = apply(builtin.Concat(axis=axis, comp_node=device), *inputs)
     return result
 
@@ -60,7 +57,7 @@ def astype(x, dtype):
 
 
 def convert_single_value(v, *, dtype=None, device=None):
-    if isinstance(v, (Tensor, VarNode)):
+    if isinstance(v, (Tensor, SymbolVar)):
         if not is_quantize(v.dtype):
             v = astype(v, dtype)
     else:
@@ -68,17 +65,35 @@ def convert_single_value(v, *, dtype=None, device=None):
     return v
 
 
-def convert_inputs(*args: Tensor):
+def convert_inputs(*args, device=None):
     if not _enable_convert_inputs:
         return args
 
     dtype = dtype_promotion(args)
-    device = get_device(args)
+    if device is None:
+        device = get_device(args)
+    device = as_device(device)
+
+    graph = None
+    sym_type = None
+    for a in args:
+        if isinstance(a, SymbolVar):
+            if graph is None:
+                graph = a.var.graph
+                sym_type = type(a)
+            else:
+                assert graph == a.var.graph
+    args = list(args)
+    if graph is not None:
+        for i in range(len(args)):
+            if not isinstance(args[i], SymbolVar):
+                rst = make_const(graph, np.array(args[i]), device.to_c(), dtype)
+                args[i] = sym_type(rst)
 
     def convert(value):
         if value is None:
             return value
-        return convert_single_value(value, dtype=dtype, device=device)
+        return convert_single_value(value, dtype=dtype, device=device.to_c())
 
     return tuple(map(convert, args))
 
@@ -98,14 +113,14 @@ def result_type(*args):
 
 def isscalar(x):
 
-    if isinstance(x, Tensor):
+    if isinstance(x, (Tensor, SymbolVar)):
         return x._isscalar()
 
     return np.isscalar(x)
 
 
 def setscalar(x):
-    if isinstance(x, Tensor):
+    if isinstance(x, (Tensor, SymbolVar)):
         x._setscalar()
     else:
         raise NotImplementedError("Unsupport type {}".format(type(x)))
@@ -132,7 +147,7 @@ def astensor1d(x, *reference, dtype=None, device=None):
     if not isinstance(x, collections.abc.Sequence):
         raise TypeError
 
-    if any(isinstance(i, Tensor) for i in x):
+    if any(isinstance(i, (Tensor, SymbolVar)) for i in x):
         x = concatenate(x, device=device)
         if dtype is not None:
             x = astype(x, dtype)
@@ -142,7 +157,7 @@ def astensor1d(x, *reference, dtype=None, device=None):
 
 
 def _expand_int(s, i):
-    if isinstance(i, Tensor):
+    if isinstance(i, (Tensor, SymbolVar)):
         i_np = i.numpy()
         if i_np.ndim == 0:
             s.append(int(i_np))

imperative/python/megengine/functional/elemwise.py

@@ -9,8 +9,7 @@
 # pylint: disable=unused-argument,invalid-name,redefined-builtin,arguments-out-of-order
 import numpy as np
 
-from ..core._imperative_rt.core2 import apply
-from ..core._imperative_rt.graph import VarNode
+from ..core._imperative_rt.core2 import SymbolVar, apply
 from ..core.ops import builtin
 from ..core.ops.builtin import Elemwise
 from ..core.tensor import utils
@@ -72,7 +71,7 @@ __all__ = [
 
 
 def _elwise(*args, mode):
-    tensor_args = list(filter(lambda x: isinstance(x, (Tensor, VarNode)), args))
+    tensor_args = list(filter(lambda x: isinstance(x, (Tensor, SymbolVar)), args))
     if len(tensor_args) == 0:
         dtype = utils.dtype_promotion(args)
         first_arg = Tensor(args[0], dtype=dtype, device=get_default_device())

imperative/python/megengine/functional/tensor.py

@@ -12,7 +12,7 @@ from typing import Iterable, Optional, Sequence, Union
 import numpy as np
 
 from ..core._imperative_rt import CompNode
-from ..core._imperative_rt.core2 import apply
+from ..core._imperative_rt.core2 import SymbolVar, apply
 from ..core._wrap import device as as_device
 from ..core.ops import builtin
 from ..core.ops.builtin import Copy, Identity
@@ -101,7 +101,7 @@ def eye(N, M=None, *, dtype="float32", device: Optional[CompNode] = None) -> Ten
     return result
 
 
-def full(shape, value, dtype="float32", device=None):
+def full(shape, value, dtype="float32", device=None) -> Tensor:
     """
     Returns a tensor with given shape and value.
     """
@@ -115,7 +115,7 @@ def full(shape, value, dtype="float32", device=None):
     return broadcast_to(x, shape)
 
 
-def ones(shape, dtype="float32", device=None):
+def ones(shape, dtype="float32", device=None) -> Tensor:
     """
     Returns a ones tensor with given shape.
 
@@ -142,14 +142,14 @@ def ones(shape, dtype="float32", device=None):
     return full(shape, 1.0, dtype=dtype, device=device)
 
 
-def zeros(shape, dtype="float32", device=None):
+def zeros(shape, dtype="float32", device=None) -> Tensor:
     """
     Returns a zero tensor with given shape.
     """
     return full(shape, 0.0, dtype=dtype, device=device)
 
 
-def zeros_like(inp: Tensor) -> Tensor:
+def zeros_like(inp: Union[Tensor, SymbolVar]) -> Union[Tensor, SymbolVar]:
     """
     Returns a zero tensor with the same shape as input tensor.
 
@@ -176,21 +176,26 @@ def zeros_like(inp: Tensor) -> Tensor:
          [0 0 0]]
 
     """
-    return zeros(inp.shape, dtype=inp.dtype, device=inp.device)
+    return full_like(inp, 0.0)
 
 
-def ones_like(inp: Tensor) -> Tensor:
+def ones_like(inp: Union[Tensor, SymbolVar]) -> Union[Tensor, SymbolVar]:
     """
     Returns a ones tensor with the same shape as input tensor.
     """
-    return ones(inp.shape, dtype=inp.dtype, device=inp.device)
+    return full_like(inp, 1.0)
 
 
-def full_like(inp: Tensor, value: Union[int, float]) -> Tensor:
+def full_like(
+    inp: Union[Tensor, SymbolVar], value: Union[int, float]
+) -> Union[Tensor, SymbolVar]:
     """
     Returns a tensor filled with given value with the same shape as input tensor.
     """
-    return full(inp.shape, value, dtype=inp.dtype, device=inp.device)
+    (x,) = Const(value, dtype=inp.dtype, device=inp.device)(inp)
+    if inp.shape is ():
+        return x
+    return broadcast_to(x, inp.shape)
 
 
 def broadcast_to(inp: Tensor, shape: Union[int, Iterable[int]]) -> Tensor:
@@ -259,15 +264,10 @@ def concat(inps: Iterable[Tensor], axis: int = 0, device=None) -> Tensor:
     if len(inps) == 1:
         return inps[0]
 
-    dtype = dtype_promotion(inps)
+    inps = convert_inputs(*inps, device=device)
     if device is None:
         device = get_device(inps)
     device = as_device(device)
-
-    def convert(x):
-        return convert_single_value(x, dtype=dtype, device=device)
-
-    inps = tuple(map(convert, inps))
     (result,) = apply(builtin.Concat(axis=axis, comp_node=device.to_c()), *inps)
     return result
 
@@ -379,8 +379,14 @@ def split(inp, nsplits_or_sections, axis=0):
                     Ntotal, axis, Nsections
                 )
             )
+
+        func = (
+            floor_div
+            if isinstance(Nsections, (SymbolVar, Tensor))
+            else lambda x, y: x // y
+        )
         div_points = [0] + [
-            floor_div(Ntotal + Nsections - i - 1, Nsections) for i in range(Nsections)
+            func(Ntotal + Nsections - i - 1, Nsections) for i in range(Nsections)
         ]
         for i in range(2, Nsections + 1):
             div_points[i] = div_points[i - 1] + div_points[i]
@@ -925,11 +931,15 @@ def linspace(
             if not (cur_device is None or device == cur_device):
                 raise ("ambiguous device for linspace opr")
 
-    if not isinstance(start, Tensor):
+    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)):
         start = Tensor(start, device=device)
-    if not isinstance(stop, Tensor):
+    if not isinstance(stop, (Tensor, SymbolVar)):
         stop = Tensor(stop, device=device)
-    if not isinstance(num, Tensor):
+    if not isinstance(num, (Tensor, SymbolVar)):
         num = Tensor(num, device=device)
 
     op = builtin.Linspace(comp_node=device)
@@ -983,7 +993,7 @@ def arange(
         stop = stop.astype("float32")
     if isinstance(step, Tensor):
         step = step.astype("float32")
-    num = ceil(Tensor((stop - start) / step, device=device))
+    num = ceil((stop - start) / step)
     stop = start + step * (num - 1)
     result = linspace(start, stop, num, device=device)
     if np.dtype(dtype) == np.int32:

imperative/python/megengine/utils/network.py

@@ -16,6 +16,7 @@ from typing import Dict, List
 import numpy as np
 
 from ..core._imperative_rt import ComputingGraph
+from ..core._imperative_rt.core2 import SymbolVar
 from ..core.tensor import megbrain_graph as G
 from .comp_graph_tools import get_dep_vars, get_opr_type, get_oprs_seq
 from .network_node import (
@@ -60,12 +61,12 @@ class Network:
             )
             outputs = [new_outputs[i] for i in outspec]
         self._orig_outputs = outputs
-        self.add_dep_oprs(*outputs)
+        for x in self._orig_outputs:
+            self.output_vars.append(self._get_var(x))
+        self.add_dep_oprs()
         for x in self._orig_inputs:
             self.input_vars.append(self._get_var(x))
 
-        for x in self._orig_outputs:
-            self.output_vars.append(self._get_var(x))
         self.graph = self._orig_outputs[0].graph
         return self
 
@@ -197,6 +198,8 @@ class Network:
     def add_output(self, *vars: VarNode):
         """Adds vars into the network output node list
         """
+        if not all([var.owner for var in vars]):
+            self.add_dep_oprs(*vars)
         for var in vars:
             if var not in self.output_vars:
                 self.output_vars.append(var)
@@ -209,21 +212,25 @@ class Network:
                 self.output_vars.remove(var)
 
     def add_dep_oprs(self, *vars):
-        """Adds dependent opnodes and varnodes of vars into network
-        """
-        oprs = get_oprs_seq(vars, False, False)
-        for mge_opr in oprs:
+        if len(vars) == 0:
+            vars = self.output_vars
+        q = list(vars)
+        while len(q) > 0:
+            cur = q.pop(0)
+            if cur.owner is not None:
+                continue
+            if cur.name is None:
+                cur.name = cur.var.name
+            self.all_vars_map[cur.var.id] = cur
+            mge_opr = cur.var.owner
             if get_opr_type(mge_opr) == "Host2DeviceCopy":
                 self._orig_inputs.extend(mge_opr.outputs)
-            opr = self._add_opr(mge_opr)
-            if opr is not None:
-                for x in mge_opr.inputs:
-                    opr.add_inp_var(self._get_var(x))
-                # set out var
-                for x in mge_opr.outputs:
-                    opr.add_out_var(self._get_var(x))
-
-        return [self.all_vars_map[var.id] for var in vars]
+            cur.owner = self._add_opr(mge_opr)
+            if cur.owner is None:
+                cur.owner = self.all_oprs_map[mge_opr.id]
+                continue
+            q.extend(cur.owner.inputs)
+        return list(vars)
 
     def modify_opr_names(self, modifier):
         """Modifies names of operators **inplace**; useful for merging loaded
@@ -275,6 +282,9 @@ class Network:
         Replaces vars in the graph.
         :param repl_dict: the map {old_var: new_var} that specifies how to replace the vars.
         """
+        if not all([var.owner for var in repl_dict.values()]):
+            print(repl_dict.values())
+            self.add_dep_oprs(*list(repl_dict.values()))
         for var in self.all_vars:
             if var in repl_dict:
                 repl_var = repl_dict[var]
@@ -282,6 +292,7 @@ class Network:
                 idx = owner.outputs.index(repl_var)
                 owner.outputs[idx] = var
                 var.__dict__.update(repl_var.__dict__)
+                var.var = repl_var.var
 
     def replace_oprs(self, repl_dict: Dict[OpNode, OpNode]):
         """
@@ -297,6 +308,7 @@ 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
 
     def get_opr_by_type(self, oprcls, unique=True):
         assert issubclass(oprcls, OpNode)
@@ -381,11 +393,16 @@ class Network:
         return self.opr_filter.as_dict()
 
     # used for loading and building graph
-    def _add_opr(self, x):
+    def _add_opr(self, opr):
         # TODO: use megbrain C++ RTTI to replace type string
-        if x.id not in self.all_oprs_map:
-            self.all_oprs_map[x.id] = str_to_mge_class(get_opr_type(x)).load(x)
-            return self.all_oprs_map[x.id]
+        if opr.id not in self.all_oprs_map:
+            opnode = str_to_mge_class(get_opr_type(opr)).load(opr)
+            self.all_oprs_map[opr.id] = opnode
+            for var in opr.inputs:
+                opnode.add_inp_var(self._get_var(var))
+            for var in opr.outputs:
+                opnode.add_out_var(self._get_var(var))
+            return opnode
         else:
             return None
 
@@ -397,7 +414,7 @@ class Network:
 
     def _get_var(self, x):
         # auto convert to VarNode of Network
-        if x.id not in self.all_vars_map:
+        if x.id not in self.all_vars_map or self.all_vars_map[x.id].var != x:
             self.all_vars_map[x.id] = VarNode.load(x, self._get_opr(x.owner))
         return self.all_vars_map[x.id]
 
@@ -652,7 +669,7 @@ class NodeFilterHasInput(NodeFilter):
             assert isinstance(
                 i, OpNode
             ), "has_input() must be used with OpNode; " "got {!r}".format(i)
-            if self.var in i.inputs:
+            if any(self.var is _ for _ in i.inputs):
                 yield i
 
 

imperative/python/megengine/utils/network_node.py

@@ -6,16 +6,21 @@
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import abc
 import json
 import sys
-from typing import Callable
+from typing import Callable, Sequence
 
 import numpy as np
 
 from ..core import _imperative_rt as rt
+from ..core._imperative_rt.core2 import SymbolVar
 from ..core._wrap import Device
 from ..core.ops import builtin
-from ..core.tensor.megbrain_graph import InputNode
+from ..core.tensor.array_method import ArrayMethodMixin
+from ..core.tensor.indexing import getitem as _getitem
+from ..core.tensor.indexing import setitem as _setitem
+from ..core.tensor.megbrain_graph import InputNode, OutputNode
 from ..tensor import Tensor
 from .comp_graph_tools import replace_vars
 from .module_stats import (
@@ -29,9 +34,13 @@ class NetworkNode:
     pass
 
 
-class VarNode(NetworkNode):
-    def __init__(self, owner_opr=None, name=None):
-        self.var = None
+class VarNodeMeta(type(SymbolVar), type(ArrayMethodMixin)):
+    pass
+
+
+class VarNode(NetworkNode, SymbolVar, ArrayMethodMixin, metaclass=VarNodeMeta):
+    def __init__(self, var=None, *, owner_opr=None, name=None):
+        SymbolVar.__init__(self, var)
         self.owner = owner_opr
         self.name = name
         self.id = id(self)
@@ -58,6 +67,40 @@ class VarNode(NetworkNode):
     def dtype(self):
         return self.var.dtype if self.var else None
 
+    def __bool__(self):
+        return False
+
+    __index__ = None
+    __int__ = None
+    __float__ = None
+    __complex__ = None
+
+    def __hash__(self):
+        return id(self)
+
+    @property
+    def _tuple_shape(self):
+        return self.var.shape
+
+    def numpy(self):
+        o = OutputNode(self.var)
+        self.graph.compile(o.outputs).execute()
+        return o.get_value().numpy()
+
+    def __getitem__(self, index):
+        return _getitem(self, index)
+
+    def __setitem__(self, index, value):
+        if index is not Ellipsis:
+            value = _setitem(self, index, value)
+        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 = value.var
+        self.owner = None
+
     def set_owner_opr(self, owner_opr):
         self.owner = owner_opr
 
@@ -138,7 +181,7 @@ 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(self, self.name))
+            self.outputs.append(VarNode(owner_opr=self, name=self.name))
         self.outputs[0].var = outputs
         assert self.outputs[0].owner is self
 
@@ -176,8 +219,8 @@ class ImmutableTensor(OpNode):
     def set_value(self, data, device=None):
         assert self.graph is not None
         cn = device if device else self.device
-        assert isinstance(data, (int, float, np.ndarray))
-        if isinstance(data, (int, float)):
+        assert isinstance(data, (int, float, Sequence, np.ndarray))
+        if not isinstance(data, np.ndarray):
             data = np.array(data)
         if data.dtype == np.float64:
             data = data.astype(np.float32)
@@ -185,7 +228,7 @@ class ImmutableTensor(OpNode):
             data = data.astype(np.int32)
         varnode = rt.make_const(self.graph, data, cn, data.dtype, self.name)
         if len(self.outputs) == 0:
-            self.outputs.append(VarNode(self, self.name))
+            self.outputs.append(VarNode(owner_opr=self, name=self.name))
         self.outputs[0].var = varnode
         self._opr = varnode.owner
 

imperative/python/src/tensor.cpp

@@ -160,16 +160,21 @@ PyObject* py_apply(PyObject* self, PyObject*const* args, size_t nargs/* , PyObje
         if (ctx.op->same_type<BackwardGraph>()) {
             ctx.backward = true;
         }
-        
-        
-       if (py::isinstance<cg::VarNode>(py::handle(args[0]))){
-           SmallVector<cg::VarNode*> vinputs(nargs);
-           for (size_t i = 0; i < nargs; ++i) {
-               vinputs[i] = py::handle(args[i]).cast<cg::VarNode *>();
-           }
-           auto op = ctx.op.get();
-           return to_tuple(OpDef::apply_on_var_node(*op, vinputs)).release().ptr();
-       }
+
+        if (py::isinstance<PySymbolVar>(py::handle(args[0]))){
+            SmallVector<cg::VarNode*> vinputs(nargs);
+            for (size_t i = 0; i < nargs; ++i) {
+                    vinputs[i] = py::handle(args[i]).cast<PySymbolVar*>()->m_node;   
+            }
+            auto op = ctx.op.get();
+            auto rst = OpDef::apply_on_var_node(*op, vinputs);
+            auto ret = pybind11::tuple(rst.size());
+            auto typeobj = py::handle(args[0]).get_type();
+            for (size_t i = 0; i<rst.size(); ++i) {
+                ret[i] = typeobj(pybind11::cast(rst[i], pybind11::return_value_policy::automatic));
+            }
+            return ret.release().ptr();
+        }
 
         for (size_t i = 0; i < nargs; ++i) {
             if (TensorWrapper* tw = TensorWrapper::try_cast(args[i])) {
@@ -686,9 +691,9 @@ PyArray_Descr* _dtype_promotion(PyObject*const* args, size_t nargs) {
                 continue;
             }
 
-            if (py::isinstance<cg::VarNode>(py::handle(handle))){
-                auto var = py::handle(handle).cast<cg::VarNode *>();
-                mgb::DType type = var->dtype();
+            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());
@@ -737,19 +742,26 @@ CompNode _get_device(PyObject*const* args, size_t nargs) {
     bool valid = false;
     CompNode cn;
     for (size_t i = 0; i < nargs; ++i) {
-        PyObject* handle = is_tuple ? PyTuple_GetItem(tuple, i): args[i];
+        PyObject* handle = is_tuple ? PyTuple_GetItem(tuple, i) : args[i];
         TensorWrapper* tw = TensorWrapper::try_cast(handle);
 
-        bool is_var = py::isinstance<cg::VarNode>(py::handle(handle));
-        if (tw || is_var) {
+        bool is_symvar = py::isinstance<PySymbolVar>(py::handle(handle));
+        if (tw || is_symvar) {
             if (!valid) {
-                    cn = tw ? tw->m_tensor->comp_node() : py::handle(handle).cast<cg::VarNode *>()->comp_node();
+                cn = tw ? tw->m_tensor->comp_node()
+                        : py::handle(handle)
+                                     .cast<PySymbolVar*>()
+                                     ->m_node->comp_node();
                 valid = true;
             } else {
-                CompNode cn1 = tw ? tw->m_tensor->comp_node() : py::handle(handle).cast<cg::VarNode *>()->comp_node();
+                CompNode cn1 = tw ? tw->m_tensor->comp_node()
+                                  : py::handle(handle)
+                                               .cast<PySymbolVar*>()
+                                               ->m_node->comp_node();
                 if (cn1 != cn) {
                     throw py::value_error(ssprintf("ambiguous device: %s vs %s",
-                        cn.to_string().c_str(), cn1.to_string().c_str()));
+                                                   cn.to_string().c_str(),
+                                                   cn1.to_string().c_str()));
                 }
             }
         }
@@ -849,6 +861,32 @@ void init_tensor(py::module m) {
         .def("__call__", &TensorWeakRef::operator())
         .def("_use_cnt", &TensorWeakRef::_use_cnt);
 
+    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("_isscalar", [](PySymbolVar* v) { return v->is_scalar; })
+            .def("_setscalar",
+                 [](PySymbolVar* v) { return v->is_scalar = true; })
+            .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),

imperative/python/src/tensor.h

@@ -181,6 +181,12 @@ struct TensorWrapper {
     PyObject* _use_cnt() { return PyLong_FromSize_t(m_tensor.use_count()); };
 };
 
+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/test/helpers/utils.py

@@ -2,9 +2,11 @@ import io
 
 import numpy as np
 
+import megengine.core.tensor.megbrain_graph as G
 import megengine.utils.comp_graph_tools as cgtools
 from megengine import tensor
 from megengine.jit import trace
+from megengine.utils.network_node import VarNode
 
 
 def _default_compare_fn(x, y):
@@ -14,8 +16,23 @@ def _default_compare_fn(x, y):
         np.testing.assert_allclose(x.numpy(), y, rtol=1e-6)
 
 
+def make_tensor(x, network=None, device=None):
+    if network is not None:
+        if isinstance(x, VarNode):
+            return VarNode(x.var)
+        return network.make_const(x, device=device)
+    else:
+        return tensor(x, device=device)
+
+
 def opr_test(
-    cases, func, compare_fn=_default_compare_fn, ref_fn=None, test_trace=True, **kwargs
+    cases,
+    func,
+    compare_fn=_default_compare_fn,
+    ref_fn=None,
+    test_trace=True,
+    network=None,
+    **kwargs
 ):
     """
     :param cases: the list which have dict element, the list length should be 2 for dynamic shape test.
@@ -44,7 +61,7 @@ def opr_test(
         if not isinstance(results, (tuple, list)):
             results = (results,)
         for r, e in zip(results, expected):
-            if not isinstance(r, tensor):
+            if not isinstance(r, (tensor, VarNode)):
                 r = tensor(r)
             compare_fn(r, e)
 
@@ -72,9 +89,9 @@ def opr_test(
         raise ValueError("the input func should be callable")
 
     inp, outp = get_param(cases, 0)
-    inp_tensor = [tensor(inpi) for inpi in inp]
+    inp_tensor = [make_tensor(inpi, network) for inpi in inp]
 
-    if test_trace:
+    if test_trace and not network:
         copied_inp = inp_tensor.copy()
         for symbolic in [False, True]:
             traced_func = trace(symbolic=symbolic)(func)

imperative/python/test/unit/core/test_indexing_op.py

@@ -10,12 +10,17 @@ import collections
 
 import numpy as np
 import pytest
+from utils import make_tensor
 
 import megengine
+import megengine.core.tensor.megbrain_graph as G
+import megengine.functional as F
 from megengine.core._imperative_rt.core2 import apply
 from megengine.core._trace_option import use_symbolic_shape
 from megengine.core.ops import builtin
 from megengine.tensor import Tensor
+from megengine.utils.network import Network
+from megengine.utils.network_node import VarNode
 
 
 def cvt_to_shape_desc(val, inpvar, config=None):
@@ -387,108 +392,130 @@ def test_batched_mesh_indexing():
 
 
 # high level
+def get_value(x):
+    if isinstance(x, VarNode):
+        var = x.var
+        o = G.OutputNode(var)
+        graph = x.graph
+        graph.compile(o.outputs).execute()
+        return o.get_value().numpy()
+    else:
+        return x.numpy()
+
+
+@pytest.mark.parametrize("test_varnode", [True, False])
+def test_advance_indexing_high_level(test_varnode):
+    if test_varnode:
+        network = Network()
+    else:
+        network = None
 
-
-def test_advance_indexing_high_level():
     x = np.arange(25).reshape(5, 5).astype("int32")
     d = np.arange(15).reshape(3, 5).astype("int32")
-    xx = Tensor(x)
+    xx = make_tensor(x, network)
 
-    np.testing.assert_equal(x[1, :], xx[1, :].numpy())
-    np.testing.assert_equal(x[:, 1], xx[:, 1].numpy())
-    np.testing.assert_equal(x[1:3, :], xx[1:3, :].numpy())
+    np.testing.assert_equal(x[1, :], get_value(xx[1, :]))
+    np.testing.assert_equal(x[:, 1], get_value(xx[:, 1]))
+    np.testing.assert_equal(x[1:3, :], get_value(xx[1:3, :]))
 
-    np.testing.assert_equal(x[:, :], xx[:, :].numpy())
-    np.testing.assert_equal(x[1, 1], xx[1, 1].numpy())
+    np.testing.assert_equal(x[:, :], get_value(xx[:, :]))
+    np.testing.assert_equal(x[1, 1], get_value(xx[1, 1]))
     yy = xx[(0, 4, 2), :]
-    np.testing.assert_equal(x[(0, 4, 2), :], yy.numpy())
+    np.testing.assert_equal(x[(0, 4, 2), :], get_value(yy))
 
     x_ = x.copy()
     x_[(0, 4, 2), :] = d
-    xx_ = Tensor(xx)
+    xx_ = make_tensor(xx, network)
     xx_[(0, 4, 2), :] = d
-    np.testing.assert_equal(x_, xx_.numpy())
+    np.testing.assert_equal(x_, get_value(xx_))
 
     x = np.arange(27).reshape(3, 3, 3).astype("int32")
-    xx = Tensor(x)
+    xx = make_tensor(x, network)
 
-    np.testing.assert_equal(x[1, :, :], xx[1, :, :].numpy())
-    np.testing.assert_equal(x[1, :, 1], xx[1, :, 1].numpy())
-    np.testing.assert_equal(x[1, 0:1, :], xx[1, 0:1, :].numpy())
-    np.testing.assert_equal(x[0:1, 1, 1], xx[0:1, 1, 1].numpy())
-    np.testing.assert_equal(x[:, 1, 1], xx[:, 1, 1].numpy())
-    np.testing.assert_equal(x[:, 1], xx[:, 1].numpy())
-    np.testing.assert_equal(x[1, 1:2], xx[1, 1:2].numpy())
+    np.testing.assert_equal(x[1, :, :], get_value(xx[1, :, :]))
+    np.testing.assert_equal(x[1, :, 1], get_value(xx[1, :, 1]))
+    np.testing.assert_equal(x[1, 0:1, :], get_value(xx[1, 0:1, :]))
+    np.testing.assert_equal(x[0:1, 1, 1], get_value(xx[0:1, 1, 1]))
+    np.testing.assert_equal(x[:, 1, 1], get_value(xx[:, 1, 1]))
+    np.testing.assert_equal(x[:, 1], get_value(xx[:, 1]))
+    np.testing.assert_equal(x[1, 1:2], get_value(xx[1, 1:2]))
 
     x_ = x.copy()
     x_[1, 1, 1] = -1
     xx[1, 1, 1] = -1
-    np.testing.assert_equal(x_, xx.numpy())
+    np.testing.assert_equal(x_, get_value(xx))
 
     x_[:, 1, 1] = -2
     xx[:, 1, 1] = x_[:, 1, 1]
-    np.testing.assert_equal(x_, xx.numpy())
+    np.testing.assert_equal(x_, get_value(xx))
 
     x_[0:1, :, 1] = -3
     xx[0:1, :, 1] = x_[0:1, :, 1]
-    np.testing.assert_equal(x_, xx.numpy())
+    np.testing.assert_equal(x_, get_value(xx))
 
     x_[0:1, :, 1] = -4
-    y = Tensor(x_)
+    y = make_tensor(x_, network)
     xx[0:1, :, 1] = y[0:1, :, 1]
-    np.testing.assert_equal(y.numpy(), xx.numpy())
+    np.testing.assert_equal(get_value(y), get_value(xx))
 
     x[:] = 1
     xx[:] = 1
-    np.testing.assert_equal(x, xx.numpy())
+    np.testing.assert_equal(x, get_value(xx))
 
     x = np.arange(9).reshape(3, 3).astype("int32")
-    xx = Tensor(x)
+    xx = make_tensor(x, network)
     y = np.array([1, 2])
-    yy = Tensor(y)
-    np.testing.assert_equal(x[:, y[0]], xx[:, y[0]].numpy())
-    np.testing.assert_equal(x[:, y[0]], xx[:, yy[0]].numpy())
-    np.testing.assert_equal(x[:, y], xx[:, y].numpy())
-    np.testing.assert_equal(x[:, y], xx[:, yy].numpy())
+    yy = make_tensor(y, network)
+    np.testing.assert_equal(x[:, y[0]], get_value(xx[:, y[0]]))
+    np.testing.assert_equal(x[:, y[0]], get_value(xx[:, yy[0]]))
+    np.testing.assert_equal(x[:, y], get_value(xx[:, y]))
+    np.testing.assert_equal(x[:, y], get_value(xx[:, yy]))
 
     x_ = x.copy()
     x_[:, y[0]] = -1
-    xx_ = Tensor(x_)
+    xx_ = make_tensor(x_, network)
     xx[:, yy[0]] = xx_[:, yy[0]]
-    np.testing.assert_equal(x_, xx.numpy())
+    np.testing.assert_equal(x_, get_value(xx))
 
     x_[:, y] = -1
-    xx_ = Tensor(x_)
+    xx_ = make_tensor(x_, network)
     xx[:, yy] = xx_[:, yy]
-    np.testing.assert_equal(x_, xx.numpy())
+    np.testing.assert_equal(x_, get_value(xx))
 
     x = np.arange(9).reshape(3, 3).astype("int32")
-    xx = Tensor(x)
+    xx = make_tensor(x, network)
     y = np.array([1])
-    yy = Tensor(y)
-    np.testing.assert_equal(x[:, y[0]], xx[:, y[0]].numpy())
-    np.testing.assert_equal(x[:, y[0]], xx[:, yy[0]].numpy())
-    np.testing.assert_equal(x[:, y], xx[:, y].numpy())
+    yy = make_tensor(y, network)
+    np.testing.assert_equal(x[:, y[0]], get_value(xx[:, y[0]]))
+    np.testing.assert_equal(x[:, y[0]], get_value(xx[:, yy[0]]))
+    np.testing.assert_equal(x[:, y], get_value(xx[:, y]))
 
-    np.testing.assert_equal(x[:, y], xx[:, yy].numpy())
+    np.testing.assert_equal(x[:, y], get_value(xx[:, yy]))
 
     x = np.arange(9).reshape(3, 3).astype("int32")
-    xx = Tensor(x)
-    np.testing.assert_equal(x[[0, 1], 0], xx[[0, 1], 0].numpy())
-    np.testing.assert_equal(x[0:2, 0], xx[0:2, 0].numpy())
-
-
-def test_advance_indexing_with_bool():
+    xx = make_tensor(x, network)
+    np.testing.assert_equal(x[[0, 1], 0], get_value(xx[[0, 1], 0]))
+    np.testing.assert_equal(x[0:2, 0], get_value(xx[0:2, 0]))
+
+
+@pytest.mark.parametrize(
+    "test_varnode", [True, False],
+)
+def test_advance_indexing_with_bool(test_varnode):
+    if test_varnode:
+        network = Network()
+    else:
+        network = None
     a = np.arange(9).reshape(3, 3).astype(np.float32)
     b = np.array([1, 2, 3])
     c = np.array([1, 2, 3])
-    aa = Tensor(a)
-    bb = Tensor(b)
-    cc = Tensor(c)
-    np.testing.assert_equal(a[b == 1, c == 2], aa[bb == 1, cc == 2].numpy())
+    aa = make_tensor(a, network)
+    bb = make_tensor(b, network)
+    cc = make_tensor(c, network)
+    np.testing.assert_equal(a[b == 1, c == 2], get_value(aa[bb == 1, cc == 2]))
     a[b == 1, c == 2] = -1.0
     aa[bb == 1, cc == 2] = -1.0
-    np.testing.assert_equal(a, aa.numpy())
+    np.testing.assert_equal(a, get_value(aa))
 
     a = np.arange(9).reshape(3, 3).astype(np.float32)
     b = np.array([False, True, True])

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

@@ -11,13 +11,16 @@ import platform
 
 import numpy as np
 import pytest
-from utils import opr_test
+from utils import make_tensor, opr_test
 
 import megengine.functional as F
 from megengine import tensor
 from megengine.core._trace_option import use_symbolic_shape
+from megengine.core.tensor import megbrain_graph as G
 from megengine.core.tensor.utils import astensor1d
 from megengine.distributed.helper import get_device_count_by_fork
+from megengine.utils.network import Network
+from megengine.utils.network_node import VarNode
 
 
 def test_eye():
@@ -38,7 +41,13 @@ def test_eye():
         )
 
 
-def test_concat():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_concat(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     def get_data_shape(length: int):
         return (length, 2, 3)
 
@@ -50,18 +59,30 @@ def test_concat():
         return F.concat([data1, data2])
 
     cases = [{"input": [data1, data2]}, {"input": [data1, data3]}]
-    opr_test(cases, run, ref_fn=lambda x, y: np.concatenate([x, y]))
+    opr_test(cases, run, ref_fn=lambda x, y: np.concatenate([x, y]), network=network)
 
 
-def test_concat_device():
-    data1 = tensor(np.random.random((3, 2, 2)).astype("float32"), device="cpu0")
-    data2 = tensor(np.random.random((2, 2, 2)).astype("float32"), device="cpu1")
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_concat_device(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    data1 = make_tensor(np.random.random((3, 2, 2)).astype("float32"), network, "cpu0")
+    data2 = make_tensor(np.random.random((2, 2, 2)).astype("float32"), network, "cpu1")
 
     out = F.concat([data1, data2], device="cpu0")
     assert str(out.device).split(":")[0] == "cpu0"
 
 
-def test_stack():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_stack(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     data1 = np.random.random((3, 2, 2)).astype("float32")
     data2 = np.random.random((3, 2, 2)).astype("float32")
     data3 = np.random.random((3, 2, 2)).astype("float32")
@@ -72,12 +93,20 @@ def test_stack():
         def run(data1, data2):
             return F.stack([data1, data2], axis=ai)
 
-        opr_test(cases, run, ref_fn=lambda x, y: np.stack([x, y], axis=ai))
+        opr_test(
+            cases, run, ref_fn=lambda x, y: np.stack([x, y], axis=ai), network=network
+        )
+
 
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_split(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
 
-def test_split():
     data = np.random.random((2, 3, 4, 5)).astype(np.float32)
-    inp = tensor(data)
+    inp = make_tensor(data, network)
 
     mge_out0 = F.split(inp, 2, axis=3)
     mge_out1 = F.split(inp, [3], axis=3)
@@ -106,26 +135,42 @@ def test_split():
         assert str(e) == "Invalid nsplits_or_secions: [3, 3, 5]"
 
 
-def test_reshape():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_reshape(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     x = np.arange(6, dtype="float32")
-    xx = tensor(x)
+    xx = make_tensor(x, network)
     y = x.reshape(1, 2, 3)
 
     for shape in [
         (1, 2, 3),
         (1, -1, 3),
-        (1, tensor(-1), 3),
+        (1, make_tensor(-1, network), 3),
         np.array([1, -1, 3], dtype="int32"),
-        tensor([1, -1, 3]),
+        make_tensor([1, -1, 3], network),
     ]:
         yy = F.reshape(xx, shape)
         np.testing.assert_equal(yy.numpy(), y)
 
 
-def test_reshape_shape_inference():
-    x_shape_known = tensor([1, 2, 3, 4], dtype="float32")
-    x_shape_unknown = F.broadcast_to(tensor([1.0]), shape=tensor([1, 1, 1, 1]).sum())
-    tshp_unknown = astensor1d((tensor([2]), tensor([2])), x_shape_known)
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_reshape_shape_inference(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    x_shape_known = make_tensor([1, 2, 3, 4], network)
+    x_shape_unknown = F.broadcast_to(
+        make_tensor([1.0], network), shape=make_tensor([1, 1, 1, 1], network).sum()
+    )
+    tshp_unknown = astensor1d(
+        (make_tensor([2], network), make_tensor([2], network)), x_shape_known
+    )
     tshp_known = astensor1d((2, 2), x_shape_known)
     tshp_known_unspec = astensor1d((2, -1), x_shape_known)
 
@@ -146,12 +191,18 @@ def test_reshape_shape_inference():
         {"input": [x_shape_unknown, tshp_known], "output": [(2, 2),]},
         {"input": [x_shape_unknown, tshp_known_unspec], "output": [(2, 2),]},
     ]
-    opr_test(cases, func, compare_fn=check_shape, test_trace=True)
+    opr_test(cases, func, compare_fn=check_shape, test_trace=True, network=network)
+
 
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_squeeze(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
 
-def test_squeeze():
     x = np.arange(6, dtype="float32").reshape(1, 2, 3, 1)
-    xx = tensor(x)
+    xx = make_tensor(x, network)
 
     for axis in [None, 3, -4, (3, -4)]:
         y = np.squeeze(x, axis)
@@ -159,9 +210,15 @@ def test_squeeze():
         np.testing.assert_equal(y, yy.numpy())
 
 
-def test_expand_dims():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_expand_dims(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     x = np.arange(6, dtype="float32").reshape(2, 3)
-    xx = tensor(x)
+    xx = make_tensor(x, network)
 
     for axis in [2, -3, (3, -4), (1, -4)]:
         y = np.expand_dims(x, axis)
@@ -169,11 +226,17 @@ def test_expand_dims():
         np.testing.assert_equal(y, yy.numpy())
 
 
-def test_elemwise_dtype_promotion():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_elemwise_dtype_promotion(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     x = np.random.rand(2, 3).astype("float32")
     y = np.random.rand(1, 3).astype("float16")
-    xx = tensor(x)
-    yy = tensor(y)
+    xx = make_tensor(x, network)
+    yy = make_tensor(y, network)
     z = xx * yy
     np.testing.assert_equal(z.numpy(), x * y)
 
@@ -184,7 +247,13 @@ def test_elemwise_dtype_promotion():
     np.testing.assert_equal(z.numpy(), x - y)
 
 
-def test_linspace():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_linspace(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     cases = [
         {"input": [1, 9, 9]},
         {"input": [3, 10, 8]},
@@ -193,6 +262,7 @@ def test_linspace():
         cases,
         F.linspace,
         ref_fn=lambda start, end, step: np.linspace(start, end, step, dtype=np.float32),
+        network=network,
     )
 
     cases = [
@@ -203,20 +273,28 @@ def test_linspace():
         cases,
         F.linspace,
         ref_fn=lambda start, end, step: np.linspace(start, end, step, dtype=np.float32),
+        network=network,
     )
 
     cases = [
-        {"input": [1, tensor(9), 9]},
-        {"input": [tensor(1), 9, tensor(9)]},
+        {"input": [1, make_tensor(9, network), 9]},
+        {"input": [make_tensor(1, network), 9, make_tensor(9, network)]},
     ]
     opr_test(
         cases,
         F.linspace,
         ref_fn=lambda start, end, step: np.linspace(1, 9, 9, dtype=np.float32),
+        network=network,
     )
 
 
-def test_arange():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_arange(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     cases = [
         {"input": [1, 9, 1]},
         {"input": [2, 10, 2]},
@@ -225,6 +303,7 @@ def test_arange():
         cases,
         F.arange,
         ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
+        network=network,
     )
 
     cases = [
@@ -235,6 +314,7 @@ def test_arange():
         cases,
         F.arange,
         ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
+        network=network,
     )
 
     cases = [
@@ -245,20 +325,33 @@ def test_arange():
         cases,
         F.arange,
         ref_fn=lambda start, end, step: np.arange(start, end, step, dtype=np.float32),
+        network=network,
     )
 
 
-def test_round():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_round(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     data1_shape = (15,)
     data2_shape = (25,)
     data1 = np.random.random(data1_shape).astype(np.float32)
     data2 = np.random.random(data2_shape).astype(np.float32)
 
     cases = [{"input": data1}, {"input": data2}]
-    opr_test(cases, F.round, ref_fn=np.round)
+    opr_test(cases, F.round, ref_fn=np.round, network=network)
 
 
-def test_flatten():
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_flatten(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     data0_shape = (2, 3, 4, 5)
     data1_shape = (4, 5, 6, 7)
     data0 = np.random.random(data0_shape).astype(np.float32)
@@ -273,7 +366,7 @@ def test_flatten():
         {"input": data0, "output": output0},
         {"input": data1, "output": output1},
     ]
-    opr_test(cases, F.flatten, compare_fn=compare_fn)
+    opr_test(cases, F.flatten, compare_fn=compare_fn, network=network)
 
     output0 = (2, 3 * 4 * 5)
     output1 = (4, 5 * 6 * 7)
@@ -281,7 +374,7 @@ def test_flatten():
         {"input": data0, "output": output0},
         {"input": data1, "output": output1},
     ]
-    opr_test(cases, F.flatten, compare_fn=compare_fn, start_axis=1)
+    opr_test(cases, F.flatten, compare_fn=compare_fn, start_axis=1, network=network)
 
     output0 = (2, 3, 4 * 5)
     output1 = (4, 5, 6 * 7)
@@ -289,7 +382,7 @@ def test_flatten():
         {"input": data0, "output": output0},
         {"input": data1, "output": output1},
     ]
-    opr_test(cases, F.flatten, compare_fn=compare_fn, start_axis=2)
+    opr_test(cases, F.flatten, compare_fn=compare_fn, start_axis=2, network=network)
 
     output0 = (2, 3 * 4, 5)
     output1 = (4, 5 * 6, 7)
@@ -297,10 +390,23 @@ def test_flatten():
         {"input": data0, "output": output0},
         {"input": data1, "output": output1},
     ]
-    opr_test(cases, F.flatten, compare_fn=compare_fn, start_axis=1, end_axis=2)
+    opr_test(
+        cases,
+        F.flatten,
+        compare_fn=compare_fn,
+        start_axis=1,
+        end_axis=2,
+        network=network,
+    )
+
 
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_broadcast(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
 
-def test_broadcast():
     input1_shape = (20, 30)
     output1_shape = (30, 20, 30)
     data1 = np.random.random(input1_shape).astype(np.float32)
@@ -321,7 +427,7 @@ def test_broadcast():
         {"input": [data2, output2_shape], "output": output2_shape},
         {"input": [data3, output3_shape], "output": output3_shape},
     ]
-    opr_test(cases, F.broadcast_to, compare_fn=compare_fn)
+    opr_test(cases, F.broadcast_to, compare_fn=compare_fn, network=network)
 
     x = F.ones((2, 1, 3))
     with pytest.raises(RuntimeError):
@@ -334,35 +440,41 @@ def test_broadcast():
         F.broadcast_to(x, (1, 3))
 
 
-def test_utils_astensor1d():
-    reference = tensor(0)
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_utils_astensor1d(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    reference = make_tensor(0, network)
 
     # literal
     x = [1, 2, 3]
     for dtype in [None, "float32"]:
         xx = astensor1d(x, reference, dtype=dtype)
-        assert type(xx) is tensor
+        assert isinstance(xx, type(reference))
         np.testing.assert_equal(xx.numpy(), x)
 
     # numpy array
     x = np.asarray([1, 2, 3], dtype="int32")
     for dtype in [None, "float32"]:
         xx = astensor1d(x, reference, dtype=dtype)
-        assert type(xx) is tensor
+        assert isinstance(xx, type(reference))
         np.testing.assert_equal(xx.numpy(), x.astype(dtype) if dtype else x)
 
     # tensor
-    x = tensor([1, 2, 3], dtype="int32")
+    x = make_tensor([1, 2, 3], network)
     for dtype in [None, "float32"]:
         xx = astensor1d(x, reference, dtype=dtype)
-        assert type(xx) is tensor
+        assert isinstance(xx, type(reference))
         np.testing.assert_equal(xx.numpy(), x.numpy())
 
     # mixed
-    x = [1, tensor(2), 3]
+    x = [1, make_tensor(2, network), 3]
     for dtype in [None, "float32"]:
         xx = astensor1d(x, reference, dtype=dtype)
-        assert type(xx) is tensor
+        assert isinstance(xx, type(reference))
         np.testing.assert_equal(xx.numpy(), [1, 2, 3])
 
 
@@ -382,35 +494,60 @@ def test_device():
     np.testing.assert_almost_equal(y5.numpy(), y6.numpy())
 
 
-def test_identity():
-    x = tensor(np.random.random((5, 10)).astype(np.float32))
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_identity(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    x = make_tensor(np.random.random((5, 10)).astype(np.float32), network)
     y = F.copy(x)
     np.testing.assert_equal(y.numpy(), x)
 
 
-def copy_test(dst, src):
+def copy_test(dst, src, network):
     data = np.random.random((2, 3)).astype(np.float32)
-    x = tensor(data, device=src)
+    x = make_tensor(data, device=src, network=network)
     y = F.copy(x, dst)
     assert np.allclose(data, y.numpy())
-    z = x.to(dst)
-    assert np.allclose(data, z.numpy())
+    if network is None:
+        z = x.to(dst)
+        assert np.allclose(data, z.numpy())
 
 
 @pytest.mark.require_ngpu(1)
-def test_copy_h2d():
-    copy_test("cpu0", "gpu0")
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_copy_h2d(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    copy_test("cpu0", "gpu0", network=network)
 
 
 @pytest.mark.require_ngpu(1)
-def test_copy_d2h():
-    copy_test("gpu0", "cpu0")
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_copy_d2h(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    copy_test("gpu0", "cpu0", network=network)
 
 
 @pytest.mark.require_ngpu(2)
-def test_copy_d2d():
-    copy_test("gpu0", "gpu1")
-    copy_test("gpu0:0", "gpu0:1")
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_copy_d2d(is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    copy_test("gpu0", "gpu1", network=network)
+    copy_test("gpu0:0", "gpu0:1", network=network)
 
 
 @pytest.mark.parametrize(
@@ -425,7 +562,13 @@ def test_copy_d2d():
         ((), 10, None),
     ],
 )
-def test_repeat(shape, repeats, axis):
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_repeat(shape, repeats, axis, is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     def repeat_func(inp):
         return F.repeat(inp=inp, repeats=repeats, axis=axis)
 
@@ -437,7 +580,10 @@ def test_repeat(shape, repeats, axis):
         cases = [{"input": np.array(1.23)}]
 
     opr_test(
-        cases, repeat_func, ref_fn=lambda inp: np.repeat(inp, repeats, axis),
+        cases,
+        repeat_func,
+        ref_fn=lambda inp: np.repeat(inp, repeats, axis),
+        network=network,
     )
 
 
@@ -450,14 +596,16 @@ def test_repeat(shape, repeats, axis):
         ((2, 3, 4, 5), (2, 2, 2, 2, 2, 2, 2)),
     ],
 )
-def test_tile(shape, reps):
+@pytest.mark.parametrize("is_varnode", [True])
+def test_tile(shape, reps, is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
     def tile_func(inp):
         return F.tile(inp=inp, reps=reps)
 
-    cases = [
-        {"input": np.random.randn(*shape).astype("float32")},
-    ]
+    cases = [{"input": np.random.randn(*shape).astype("float32")}]
 
-    opr_test(
-        cases, tile_func, ref_fn=lambda inp: np.tile(inp, reps),
-    )
+    opr_test(cases, tile_func, ref_fn=lambda inp: np.tile(inp, reps), network=network)

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

@@ -34,13 +34,11 @@ def test_replace_var():
     vara = graph.var_filter.name("a").as_unique()
     varb = graph.var_filter.name("b").as_unique()
 
-    out = F.mul(vara.var, varb.var)
+    out = F.mul(vara, varb)
     out = F.relu(out)
 
-    var_list = graph.add_dep_oprs(out)
-
     opnode = list(graph.opr_filter.has_input(vara))
-    repl_dict = {opnode[0].outputs[0]: var_list[0]}
+    repl_dict = {opnode[0].outputs[0]: out}
     graph.replace_vars(repl_dict)
 
     modified_model = io.BytesIO()
@@ -72,14 +70,12 @@ def test_replace_opr():
     vara = graph.var_filter.name("a").as_unique()
     varb = graph.var_filter.name("b").as_unique()
 
-    out1 = F.sub(vara.var, varb.var)
+    out1 = F.sub(vara, varb)
     out1 = F.relu(out1)
-
-    var_list = graph.add_dep_oprs(out1)
-    repl_opr = as_oprnode(var_list)
+    out1 = graph.add_dep_oprs(out1)
     orig_opr = graph.opr_filter.has_input(vara).as_unique()
 
-    repl_dict = {orig_opr: repl_opr}
+    repl_dict = {orig_opr: out1[0].owner}
     graph.replace_oprs(repl_dict)
     modified_model1 = io.BytesIO()
     graph.dump(modified_model1)
@@ -171,8 +167,7 @@ def test_add_input():
     inp_c = graph.make_input_node((2,), np.int32, name="c")
     varo = graph.var_filter.name("o").as_unique()
 
-    out = F.add(varo.var, inp_c.var)
-    out = graph.add_dep_oprs(out)[0]
+    out = F.add(varo, inp_c)
     out.name = "o1"
     graph.remove_output(varo)
     graph.add_output(out)
@@ -206,12 +201,11 @@ def test_add_output():
     var_a = net.var_filter.name("a").as_unique()
     var_b = net.var_filter.name("b").as_unique()
 
-    y = F.add(var_a.var, var_b.var)
+    y = F.add(var_a, var_b)
     y = F.sigmoid(y)
 
-    new_vars = net.add_dep_oprs(y)[0]
-    new_vars.name = "o1"
-    net.add_output(new_vars)
+    y.name = "o1"
+    net.add_output(y)
 
     modified_model = io.BytesIO()
     net.dump(modified_model)