feat(mge/imperative): add graph load and cgtools for imperative

GitOrigin-RevId: ba251f452a

imperative/python/megengine/__init__.py

@@ -76,6 +76,7 @@ from .logger import enable_debug_log, get_logger, set_log_file, set_log_level
 from .serialization import load, save
 from .tensor import Parameter, Tensor, tensor
 from .version import __version__
+from .core import cgtools
 
 _set_fork_exec_path_for_timed_func(
     sys.executable,

imperative/python/megengine/core/__init__.py

@@ -10,3 +10,5 @@ import os
 import sys
 
 from .tensor import Tensor
+from .tensor.megbrain_graph import Graph
+from .utils import comp_graph_tools as cgtools

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

@@ -7,6 +7,7 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import collections
+import json
 import threading
 import weakref
 from concurrent.futures import Future, ThreadPoolExecutor
@@ -162,14 +163,42 @@ def optimize_for_inference(dest_vars, **kwargs):
     return [VarNode(i) for i in res_vars]
 
 
-def dump(*args):
+def dump_graph(*args):
     return _imperative_rt.dump_graph([i._node for i in args])
 
 
+CompGraphLoadResult = collections.namedtuple(
+    "CompGraphLoadResult", ["graph", "output_vars_dict", "output_vars_list"]
+)
+
+
+def load_graph(fpath):
+    """Load a serialized computing graph from file.
+
+    :parma fpath: Path or Handle for the output file
+    :return: An instance of namedtuple :class:`CompGraphLoadResult`,
+        whose fields are:
+
+            * ``graph`` loaded CompGraph
+            * ``output_vars_dict`` A Python dict, mapping name to output SymbolVar
+            * ``output_vars_list`` A Python list, containing output vars in the
+                                   order passed to serialize_comp_graph_to_file
+    """
+    output_vars_map = []
+    output_vars_list = []
+    if isinstance(fpath, str):
+        buf = open(fpath, "rb").read()
+    else:
+        buf = fpath.read()
+    cg = _imperative_rt.load_graph(buf, output_vars_map, output_vars_list)
+    return CompGraphLoadResult(cg, dict(output_vars_map), output_vars_list)
+
+
 class VarNode(TensorBase):
     def __init__(self, node: _imperative_rt.VarNode):
         self._node = node
-        self.graph._var_cache[node] = self
+        if hasattr(self.graph, "_var_cache"):
+            self.graph._var_cache[node] = self
 
     @property
     def graph(self) -> Graph:
@@ -177,12 +206,19 @@ class VarNode(TensorBase):
 
     @property
     def op(self):
-        return self.graph._wrap(self._node.owner)
+        if hasattr(self.graph, "_wrap"):
+            return self.graph._wrap(self._node.owner)
+        else:
+            return self._node.owner
 
     @property
     def name(self):
         return self._node.name
 
+    @property
+    def id(self):
+        return self._node.id
+
     @name.setter
     def name(self, name):
         self._node.name = name
@@ -207,7 +243,8 @@ class VarNode(TensorBase):
 class OpNode:
     def __init__(self, node: _imperative_rt.OperatorNode):
         self._node = node
-        self.graph._op_cache[node] = self
+        if hasattr(self.graph, "_op_cache"):
+            self.graph._op_cache[node] = self
 
     @property
     def graph(self) -> Graph:
@@ -217,29 +254,53 @@ class OpNode:
     def name(self):
         return self._node.name
 
+    @property
+    def id(self):
+        return self._node.id
+
     @name.setter
     def name(self, name):
         self._node.name = name
 
     @property
     def inputs(self):
-        return tuple(map(self.graph._wrap, self._node.inputs))
+        if hasattr(self.graph, "_wrap"):
+            return tuple(map(self.graph._wrap, self._node.inputs))
+        else:
+            return self._node.inputs
 
     @property
     def outputs(self):
-        return tuple(map(self.graph._wrap, self._node.outputs))
+        if hasattr(self.graph, "_wrap"):
+            return tuple(map(self.graph._wrap, self._node.outputs))
+        else:
+            return self._node.outputs
+
+    @property
+    def params(self):
+        return json.loads(self._node.params)
+
+    @property
+    def type(self):
+        return self._node.type
 
 
 def _wrap(x):
     if isinstance(x, collections.abc.Sequence):
         return type(x)(map(_wrap, x))
-    return x.graph._wrap(x)
+    if hasattr(x.graph, "_wrap"):
+        return x.graph._wrap(x)
+    else:
+        return x
 
 
 def _unwrap(x):
     if isinstance(x, collections.abc.Sequence):
         return type(x)(map(_unwrap, x))
-    return x._node
+    if isinstance(x, VarNode):
+        return x._node
+    else:
+        return x
 
 
 @apply.register()

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

@@ -0,0 +1,253 @@
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# 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 collections
+from typing import Dict, List
+
+from .. import _imperative_rt
+from .._imperative_rt import OperatorNode, VarNode
+
+
+def get_dep_vars(var: VarNode, var_type: str = None) -> List[VarNode]:
+    """return :class:`.tensor.core.megbrain_graph.VarNode` of type ``var_type`` that input ``var``
+    depands on. If ``var_type`` is None, return all types.
+    """
+    outputs = []
+    memo = set()
+
+    if isinstance(var, VarNode):
+        var = [var]
+
+    if isinstance(var_type, str):
+        var_type = [var_type]
+
+    q = list(var)
+    while q:
+        v = q.pop()
+        if v in memo:
+            continue
+        memo.add(v)
+        q.extend(get_owner_opr_inputs(v))
+        if var_type is not None:
+            if get_owner_opr_type(v) in var_type:
+                outputs.append(v)
+        else:
+            outputs.append(v)
+
+    return outputs
+
+
+def get_owner_opr_inputs(var: VarNode) -> List[VarNode]:
+    """get the inputs of owner opr of a variable
+    """
+    assert isinstance(var, VarNode)
+    return var.owner.inputs
+
+
+def get_owner_opr_type(var: VarNode) -> str:
+    """get the type of owner opr of a variable
+
+    """
+    assert isinstance(var, VarNode)
+    return var.owner.type
+
+
+def get_opr_type(opr: OperatorNode) -> str:
+    """get the type of a opr
+    """
+    assert isinstance(opr, OperatorNode)
+    return opr.type
+
+
+def graph_traversal(outputs: VarNode):
+    """helper function to traverse the computing graph and return enough useful information
+
+    :param outputs: model outputs
+    :return:  tuple (map_oprs, map_vars, var2oprs, opr2receivers, indegree2opr, opr2indegree)
+        WHERE
+        map_oprs is dict from opr_id to actual opr
+        map_vars is dict from var_id to actual var
+        var2oprs is dict from var to dest oprs along with index
+        opr2receivers is dict from current opr to next opr
+        indegree2opr is dict from in_degree to opr in computing graph
+        opr2indegree is dict from opr in computing graph to in_degree
+
+        (indegree2opr, opr2indegree) are only used in topological sort in get_oprs_seq function
+    """
+    # meta information for comp graph
+    map_oprs = collections.defaultdict(set)
+    map_vars = collections.defaultdict(set)
+
+    var2oprs = collections.defaultdict(list)
+    opr2receivers = collections.defaultdict(list)
+
+    queue = list(map(lambda x: x.owner, outputs))
+    visited = set(map(lambda x: x.id, queue))
+
+    # iterate through whole comp_graph, fill in meta information
+    indegree2opr = collections.defaultdict(set)
+    opr2indegree = {}
+
+    idx = 0
+    while idx < len(queue):
+        cur_opr = queue[idx]
+        map_oprs[cur_opr.id] = cur_opr
+
+        idx += 1
+
+        indegree = 0
+        for var_idx, var in enumerate(cur_opr.inputs):
+            map_vars[var.id] = var
+            var2oprs[var.id].append((cur_opr.id, var_idx))
+
+            pre_opr = var.owner
+
+            if pre_opr.id not in visited:
+                visited.add(pre_opr.id)
+                queue.append(pre_opr)
+
+            indegree += 1
+            opr2receivers[pre_opr.id].append(cur_opr.id)
+
+        indegree2opr[indegree].add(cur_opr.id)
+        opr2indegree[cur_opr.id] = indegree
+
+    return map_oprs, map_vars, var2oprs, opr2receivers, indegree2opr, opr2indegree
+
+
+def get_oprs_seq(outputs: List[VarNode], prune_reshape=False) -> List[OperatorNode]:
+    """get oprs in some topological order for a dumped model
+
+    :param outputs: model outputs
+    :param prune_reshape: whether to prune the operators useless during inference
+    :return: opr list with some correct execution order
+    """
+
+    def topological_sort(map_oprs, opr2receivers, indegree2opr, opr2indegree):
+        # generate an execution order with topological sort algorithm
+        oprs_seq = []
+        nr_remain = len(map_oprs)
+        while indegree2opr[0]:
+            opr_id = indegree2opr[0].pop()
+            opr = map_oprs[opr_id]
+            nr_remain -= 1
+
+            # skip const value generation operator
+            if get_opr_type(opr) != "ImmutableTensor":
+                oprs_seq.append(opr)
+
+            for post_id in opr2receivers[opr_id]:
+                indegree = opr2indegree[post_id]
+                indegree2opr[indegree].remove(post_id)
+
+                indegree -= 1
+                indegree2opr[indegree].add(post_id)
+                opr2indegree[post_id] = indegree
+
+        assert nr_remain == 0, "there are {} remaining nodes; cyclic graph?".format(
+            nr_remain
+        )
+        return oprs_seq
+
+    # reshape op definition: reshape(input_tensor, dest_shape) -> output_tensor
+    # when inferencing, shape of output_tensor is already known, so one can prune some operators related to dest_shape in the loaded graph
+    def prune_reshape_oprs(outputs, oprs_seq, var2oprs):
+        def iterative_pruning(cur_opr, post_opr, marked_opr_ids):
+            useless = True
+            for oup in cur_opr.outputs:
+                if "workspace" not in oup.name:
+                    var_idx = post_opr.inputs.index(oup)
+                    var2oprs[oup.id].remove((post_opr.id, var_idx))
+                    useless = useless and (len(var2oprs[oup.id]) == 0)
+
+            if useless:
+                marked_opr_ids.append(cur_opr.id)
+
+                for inp in cur_opr.inputs:
+                    iterative_pruning(inp.owner, cur_opr, marked_opr_ids)
+
+        reshape_vars = get_dep_vars(outputs, "Reshape")
+        reshape_oprs = [var.owner for var in reshape_vars]
+
+        marked_opr_ids = []
+        for reshape_opr in reshape_oprs:
+            iterative_pruning(reshape_opr.inputs[1].owner, reshape_opr, marked_opr_ids)
+
+        # filter out all marked oprs
+        return list(filter(lambda x: x.id not in marked_opr_ids, oprs_seq))
+
+    map_oprs, _, var2oprs, opr2receivers, indegree2opr, opr2indegree = graph_traversal(
+        outputs
+    )
+    oprs_seq = topological_sort(map_oprs, opr2receivers, indegree2opr, opr2indegree)
+    if prune_reshape is True:
+        oprs_seq = prune_reshape_oprs(outputs, oprs_seq, var2oprs.copy())
+    return oprs_seq
+
+
+def replace_vars(dst: VarNode, varmap: Dict[VarNode, VarNode]) -> List[VarNode]:
+    """replace vars in the graph
+
+    :param dst: target vars representing the graph
+    :param varmap: the map that specifies how to replace the vars
+
+    :return: new vars that correspond to ``dst`` with all the dependencies
+        replaced
+    """
+    dst_vec = []
+    repl_src_vec = []
+    repl_dst_vec = []
+    for i in dst:
+        assert isinstance(i, VarNode)
+        dst_vec.append(i)
+
+    for i, j in getattr(varmap, "items", lambda: varmap)():
+        assert isinstance(i, VarNode)
+        assert isinstance(j, VarNode)
+        repl_src_vec.append(i)
+        repl_dst_vec.append(j)
+
+    return _imperative_rt.graph._replace_vars(repl_src_vec, repl_dst_vec, dst_vec)
+
+
+def replace_oprs(
+    dst: List[VarNode], oprmap: Dict[OperatorNode, OperatorNode]
+) -> List[VarNode]:
+    """Replace operators in the graph.
+
+    :param dst: target vars representing the graph
+    :param oprmap: the map that specifies how to replace the operators
+
+    :return: new vars that correspond to ``dst`` with all the dependencies
+        replaced
+    """
+    dst_vec = []
+    repl_src_vec = []
+    repl_dst_vec = []
+    for i in dst:
+        assert isinstance(i, VarNode)
+        dst_vec.append(i)
+
+    for i, j in getattr(oprmap, "items", lambda: oprmap)():
+        assert isinstance(i, OperatorNode)
+        assert isinstance(j, OperatorNode)
+        repl_src_vec.append(i)
+        repl_dst_vec.append(j)
+
+    return _imperative_rt.graph._replace_oprs(repl_src_vec, repl_dst_vec, dst_vec)
+
+
+def set_priority_to_id(dest_vars):
+    """For all oprs in the subgraph constructed by dest_vars
+       set its priority to id if its original priority is zero
+    :param dest_vars: target vars representing the graph
+    """
+    dest_vec = []
+    for i in dest_vars:
+        assert isinstance(i, VarNode)
+        dest_vec.append(i)
+    _imperative_rt.graph._set_priority_to_id(dest_vec)

imperative/python/megengine/jit/tracing.py

@@ -569,7 +569,7 @@ class trace:
         if isinstance(file, str):
             permission = "wb" if append == False else "ab"
             file = open(file, permission)
-        file.write(G.dump(*dest_vars))
+        file.write(G.dump_graph(*dest_vars))
 
     def _process_inputs(self, *args, **kwargs):
         if self._untraced:

imperative/python/src/graph_rt.cpp

@@ -64,7 +64,60 @@ auto def_rendezvous(py::object m, const char* name) {
 using TensorAttr = LogicalTensorDesc;
 using HostNDWithEvent = std::pair<HostTensorND, std::shared_ptr<CompNode::Event>>;
 
+std::vector<mgb::cg::VarNode*>  _replace_vars(const std::vector<mgb::cg::VarNode*>& repl_src,
+                                 const std::vector<mgb::cg::VarNode*>& repl_dst,
+                                 const std::vector<mgb::cg::VarNode*>& vars) {
+        mgb::ThinHashMap<SymbolVar, SymbolVar> varmap;
+        for (size_t i = 0; i < repl_src.size(); ++i) {
+            varmap[SymbolVar(repl_src[i])] = SymbolVar(repl_dst[i]);
+        }
+        SymbolVarArray symvars(vars.begin(), vars.end());
+        auto sym_result = mgb::cg::replace_vars(symvars, varmap);
+        std::vector<mgb::cg::VarNode*> result;
+        for (auto symvar : sym_result){
+            result.push_back(symvar.node());
+        }
+        return result;
+    }
+
+typedef std::vector<mgb::cg::OperatorNodeBase*> OperatorArray;
+std::vector<mgb::cg::VarNode*> _replace_oprs(const OperatorArray& repl_src,
+                                 const OperatorArray& repl_dst,
+                                 const std::vector<mgb::cg::VarNode*>& vars) {
+        mgb::ThinHashMap<mgb::cg::OperatorNodeBase*, mgb::cg::OperatorNodeBase*>
+                oprmap;
+        for (size_t i = 0; i < repl_src.size(); ++i) {
+            oprmap[repl_src[i]] = repl_dst[i];
+        }
+        const SymbolVarArray symvars(vars.begin(), vars.end());
+        auto sym_result = mgb::cg::replace_oprs(symvars, oprmap);
+        std::vector<mgb::cg::VarNode*> result;
+        for (auto symvar : sym_result){
+            result.push_back(symvar.node());
+        }
+        return result;
+    }
+
+
+
+void _set_priority_to_id(const std::vector<mgb::cg::VarNode*>& dest_vars) {
+        auto on_opr = [](mgb::cg::OperatorNodeBase* opr) {
+            if (opr->node_prop().attribute().priority == 0) {
+                opr->node_prop().attribute().priority = opr->id();
+            }
+        };
+        mgb::cg::DepOprIter dep_iter{on_opr};
+        for (const auto& var : dest_vars) {
+            dep_iter.add(SymbolVar(var));
+        }
+}
+
+
+
 void init_graph_rt(py::module m) {
+
+   static const std::unique_ptr<mgb::OprFootprint> _imperative_sm_opr_footprint_ptr{std::make_unique<mgb::OprFootprint>()};
+
     def_rendezvous<DeviceTensorND>(m, "DeviceTensorNDRendezvous");
 
     def_rendezvous<HostNDWithEvent>(m, "HostTensorNDRendezvous");
@@ -99,7 +152,10 @@ void init_graph_rt(py::module m) {
                     return py::none();
                 }
                 return py::cast(*val).attr("numpy")();
-            });
+            })
+        .def_property_readonly("id",[](cg::VarNode* v){
+            return (v->id());
+        });
 
     py::class_<cg::OperatorNodeBase, GraphNodePtr<cg::OperatorNodeBase>>(m, "OperatorNode")
         .def_property_readonly("graph", [](cg::OperatorNodeBase* opr) {return opr->owner_graph();})
@@ -110,7 +166,17 @@ void init_graph_rt(py::module m) {
             })
         .def_property_readonly("outputs", [](cg::OperatorNodeBase* opr) {
                 return to_tuple(opr->usable_output());
-            });
+            })
+        .def_property_readonly("id",[](cg::OperatorNodeBase* opr){
+            return opr->id();
+        })
+        .def_property_readonly("params",[](cg::OperatorNodeBase* opr){
+            return _imperative_sm_opr_footprint_ptr->calc_footprint(opr).param->to_string();
+        })
+        .def_property_readonly("type",[](cg::OperatorNodeBase* opr){
+            return opr->dyn_typeinfo()->name;
+        });
+
 
     py::class_<cg::AsyncExecutable>(m, "AsyncExecutable")
         .def("execute", &cg::AsyncExecutable::execute, py::call_guard<py::gil_scoped_release>())
@@ -174,6 +240,44 @@ void init_graph_rt(py::module m) {
     });
 
 
+    m.def("load_graph", [](std::string& buf, py::list& _output_var_map, py::list& _output_var_list) {
+        using namespace mgb::serialization;
+        auto file = InputFile::make_mem_proxy(buf.c_str(), buf.length());
+        auto format = GraphLoader::identify_graph_dump_format(*file);
+        auto loader = GraphLoader::make(std::move(file), format.val());
+        GraphLoader::LoadConfig config;
+        auto rst = loader->load(config);
+        std::vector<std::pair<std::string, SymbolVar>> output_var_map;
+        SymbolVarArray output_var_list;
+        output_var_map = {rst.output_var_map.begin(), rst.output_var_map.end()};
+        output_var_list = std::move(rst.output_var_list);
+        for (auto i : output_var_list){
+            _output_var_list.append(i.node());
+        }
+        for (auto i : output_var_map){
+            _output_var_map.append(py::make_tuple(i.first,i.second.node()));
+        }
+        std::unordered_map<HostTensorND*, const std::string*> tensor2name;
+        for (const auto& pair : rst.tensor_map) {
+            tensor2name[pair.second.get()] = &pair.first;
+        }
+        auto cb = [&tensor2name, graph=rst.graph](cg::OperatorNodeBase* opr) {
+            if (!opr->same_type<opr::Host2DeviceCopy>())
+                return;
+            auto& h2d = opr->cast_final_safe<opr::Host2DeviceCopy>();
+            auto it = tensor2name.find(h2d.host_data().get());
+            mgb_throw_if(it == tensor2name.end(), GraphError,
+                        "unbound Host2DeviceCopy in loaded graph");
+            h2d.output(0)->name(*it->second);
+        };
+        cg::DepOprIter iter{cb};
+        for (const auto& var : output_var_list) {
+            iter.add(var.node()->owner_opr());
+        }
+        return rst.graph;
+
+    });
+
 #define CURRENT_CLASS cg::ComputingGraph::Options
 
     auto PyComputingGraphOptions = py::class_<cg::ComputingGraph::Options>(PyComputingGraph, "Options")
@@ -287,6 +391,10 @@ void init_graph_rt(py::module m) {
             return opr::Host2DeviceCopy::make(graph, std::make_shared<HostTensorND>(cn, shape, dtype), config).node();
         }, py::arg(), py::arg(), py::arg(), py::arg() = py::none(), py::arg() = py::none());
 
+    m.def("_replace_vars", &_replace_vars,py::arg(),py::arg(),py::arg());
+    m.def("_replace_oprs", &_replace_oprs,py::arg(),py::arg(),py::arg());
+    m.def("_set_priority_to_id",&_set_priority_to_id,py::arg());
+
     m.def("input_callback", [input_callback](std::function<DeviceTensorND(void)> callback,
                                              const CompNode& comp_node,
                                              const DType& dtype,

imperative/python/src/graph_rt.h

@@ -16,7 +16,7 @@
 #include <memory>
 #include <mutex>
 #include <future>
-
+#include "megbrain/plugin/opr_footprint.h"
 #include "megbrain/graph.h"
 
 template<typename T>

imperative/python/test/unit/test_cgtools.py

@@ -0,0 +1,90 @@
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# 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 io
+
+import numpy as np
+
+import megengine
+import megengine.functional as F
+import megengine.module as M
+from megengine import cgtools
+from megengine.core.tensor import megbrain_graph as mgb_graph
+from megengine.core.tensor.raw_tensor import as_raw_tensor
+from megengine.jit import trace
+
+
+def make_dev_tensor(value, dtype=None, device=None):
+    return as_raw_tensor(value, dtype=dtype, device=device)._dev_tensor()
+
+
+def test_replace_vars():
+    g = mgb_graph.Graph()
+    g.options.async_exec_level = 0b100
+    device = "xpux"
+    dtype = np.float32
+    a = mgb_graph.InputNode(device=device, dtype=dtype, graph=g)
+    const = g.make_const(1.234)
+    a_plus_a = F.add(a.outputs[0], a.outputs[0])
+    a_plus_a_mul_const = F.mul(a_plus_a, const)
+    rst = F.add(a_plus_a_mul_const, a.outputs[0])
+    (new,) = cgtools.replace_vars([rst._node], {const._node: a_plus_a._node})
+    out = mgb_graph.OutputNode(mgb_graph.VarNode(new))
+    func = g.compile(out.outputs[0])
+    func.execute()
+    x = make_dev_tensor(5.0, device=device)
+    a.set_value(x)
+    res = out.get_value().numpy()
+    np.testing.assert_equal(res, np.array([105.0]))
+
+
+def test_replace_oprs():
+    g = mgb_graph.Graph()
+    g.options.async_exec_level = 0b100
+    device = "xpux"
+    dtype = np.float32
+    a = mgb_graph.InputNode(device=device, dtype=dtype, graph=g)
+    const = g.make_const(1.25)
+    a_plus_a = F.add(a.outputs[0], a.outputs[0])
+    old_opr = a_plus_a.op
+    a_plus_a_mul_const = F.mul(a_plus_a, const)
+    a_mul_a = F.mul(a.outputs[0], a.outputs[0])
+    new_opr = a_mul_a.op
+    (new,) = cgtools.replace_oprs(
+        [a_plus_a_mul_const._node], {old_opr._node: new_opr._node}
+    )
+    out = mgb_graph.OutputNode(mgb_graph.VarNode(new))
+    func = g.compile(out.outputs[0])
+    func.execute()
+    x = make_dev_tensor(5.0, device=device)
+    a.set_value(x)
+    res = out.get_value().numpy()
+    np.testing.assert_equal(res, np.array([5.0 * 5.0 * 1.25]))
+
+
+def test_graph_traversal():
+    net = M.Conv2d(3, 32, 3)
+
+    @trace(symbolic=True, capture_as_const=True)
+    def fun(data):
+        x = net(data)
+        return x
+
+    data = np.random.random([1, 3, 224, 224]).astype(np.float32)
+    for i in range(3):
+        fun(megengine.tensor(data))
+
+    file = io.BytesIO()
+    fun.dump(file)
+    file.seek(0)
+    cg, _, outputs = mgb_graph.load_graph(file)
+
+    _, map_vars, var2oprs, *_ = cgtools.graph_traversal(outputs)
+    input_var = map_vars[1]
+    _, var_idx = var2oprs[input_var.id][0]
+
+    assert var_idx == 0

imperative/python/test/unit/test_tracing.py

@@ -13,6 +13,10 @@ import numpy as np
 import pytest
 
 from megengine import tensor
+import megengine
+import megengine.core.tensor.megbrain_graph as mgb_graph
+import megengine.module as M
+from megengine import cgtools
 from megengine.core.ops import builtin as ops
 from megengine.core.tensor import megbrain_graph as G
 from megengine.core.tensor.core import apply
@@ -21,6 +25,29 @@ from megengine.functional import exp, log
 from megengine.jit import exclude_from_trace, trace
 
 
+def load_and_inference(file, inp_data):
+    cg, _, out_list = mgb_graph.load_graph(file)
+    inputs = cgtools.get_dep_vars(out_list, "Host2DeviceCopy")
+    replace_dict = {}
+    inp_node_list = []
+    for i in inputs:
+        inp_node = mgb_graph.InputNode(
+            device="xpux", dtype=inputs[0].dtype, graph=inputs[0].graph
+        )
+        replace_dict[i] = inp_node.outputs[0]
+        inp_node_list.append(inp_node)
+    new_out = cgtools.replace_vars(out_list, replace_dict)
+    out_node_list = [mgb_graph.OutputNode(i) for i in new_out]
+    new_out_list = [i.outputs[0] for i in out_node_list]
+    new_cg = new_out_list[0].graph
+    func = new_cg.compile(new_out_list)
+    for node, value in zip(inp_node_list, inp_data):
+        node.set_value(as_raw_tensor(value)._dev_tensor())
+    func.execute()
+    out_data_list = [o.get_value().numpy() for o in out_node_list]
+    return out_data_list
+
+
 def test_trace():
     for symbolic in [False, True]:
 
@@ -82,12 +109,57 @@ def test_print_in_trace():
 
 def test_dump():
     @trace(symbolic=True, capture_as_const=True)
+    def f(a, b):
+        op = ops.Elemwise(mode="add")
+        (y,) = apply(op, a, b)
+        return y
+
+    a = as_raw_tensor([2]).numpy()
+    b = as_raw_tensor([4]).numpy()
+    y = f.__wrapped__(as_raw_tensor(a), as_raw_tensor(b)).numpy()
+
+    for i in range(3):
+        np.testing.assert_equal(f(as_raw_tensor(a), as_raw_tensor(b)).numpy(), y)
+
+    file = io.BytesIO()
+    f.dump(file)
+    file.seek(0)
+    result = load_and_inference(file, [a, b])
+    np.testing.assert_equal(result[0], y)
+
+
+def test_capture_dump():
+    a = as_raw_tensor([2])
+
+    @trace(symbolic=True, capture_as_const=True)
+    def f(x):
+        op = ops.Elemwise(mode="mul")
+        (y,) = apply(op, x, a)
+        return y
+
+    x = as_raw_tensor([3]).numpy()
+    y = f.__wrapped__(as_raw_tensor(x)).numpy()
+
+    for i in range(3):
+        np.testing.assert_equal(f(as_raw_tensor(x)).numpy(), y)
+
+    file = io.BytesIO()
+    f.dump(file)
+    file.seek(0)
+    result = load_and_inference(file, [x])
+    np.testing.assert_equal(result[0], y)
+
+
+def test_dump_volatile():
+    p = as_raw_tensor([2])
+
+    @trace(symbolic=True, capture_as_const=True)
     def f(x):
-        op = ops.Elemwise(mode="negate")
-        (y,) = apply(op, x)
+        op = ops.Elemwise(mode="mul")
+        (y,) = apply(op, x, p)
         return y
 
-    x = as_raw_tensor([1]).numpy()
+    x = as_raw_tensor([3]).numpy()
     y = f.__wrapped__(as_raw_tensor(x)).numpy()
 
     for i in range(3):
@@ -95,6 +167,13 @@ def test_dump():
 
     file = io.BytesIO()
     f.dump(file)
+    file.seek(0)
+    cg, _, outputs = mgb_graph.load_graph(file)
+    (out,) = outputs
+    assert (
+        cgtools.get_owner_opr_type(cgtools.get_owner_opr_inputs(out)[1])
+        == "SharedDeviceTensor"
+    )
 
 
 def test_trace_profiler():

sdk/load-and-run/dump_with_testcase_mge.py

@@ -471,11 +471,9 @@ def main():
         assert not testcase, 'extra inputs provided in testcase: {}'.format(
             testcase.keys()
         )
-        mgb.serialize_comp_graph_to_file(
-            args.output,
-            output_mgbvars,
-            append=True,
-            output_strip_info=args.output_strip_info)
+        with open(args.output, "ab") as fout:
+            fout.write(G.dump_graph(*output_mgbvars))
+
 
 if __name__ == '__main__':
     main()