diff --git a/imperative/python/test/unit/test_tracing.py b/imperative/python/test/unit/test_tracing.py
index ce4c1e91c8530f8174513d97cb35bd9ea2909886..722b582b3e9ea2cdd2d361653aeefdd929cc9773 100644
--- a/imperative/python/test/unit/test_tracing.py
+++ b/imperative/python/test/unit/test_tracing.py
@@ -12,11 +12,10 @@ from tempfile import mkstemp
 import numpy as np
 import pytest
 
-from megengine import tensor
 import megengine
-import megengine.core.tensor.megbrain_graph as mgb_graph
+import megengine.core.tensor.megbrain_graph as G
 import megengine.module as M
-from megengine import cgtools
+from megengine import cgtools, tensor
 from megengine.core.ops import builtin as ops
 from megengine.core.tensor import megbrain_graph as G
 from megengine.core.tensor.core import apply
diff --git a/sdk/load-and-run/dump_with_testcase_imperative.py b/sdk/load-and-run/dump_with_testcase_imperative.py
new file mode 100755
index 0000000000000000000000000000000000000000..8ebd12747547feb882631cd8db142a72e3691150
--- /dev/null
+++ b/sdk/load-and-run/dump_with_testcase_imperative.py
@@ -0,0 +1,516 @@
+# -*- coding: utf-8 -*-
+# 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 argparse
+import os
+import re
+import struct
+
+import cv2
+import numpy as np
+
+import megengine as mge
+import megengine.core._imperative_rt as rt
+import megengine.core.tensor.megbrain_graph as G
+from megengine import cgtools
+from megengine.core.ops import builtin
+from megengine.core.tensor.core import apply
+from megengine.core.tensor.raw_tensor import as_raw_tensor
+
+logger = mge.get_logger(__name__)
+
+
+def auto_reformat_image(args, path, data, dst_shape):
+    """reformat image to target shape
+
+    :param data: image data as numpy array
+    :param dst_shape: target shape
+    """
+    dim3_format = False  # required input format does not contain batch
+    hwc_format = False  # required input format is NHWC
+
+    if not dst_shape:  # input tensor shape is not predefined
+        if len(data.shape) == 2:
+            chl = 1
+            h = data.shape[0]
+            w = data.shape[1]
+        else:
+            assert len(data.shape) == 3, "Input image must be of dimension 2 or 3"
+            h, w, chl = data.shape
+        dst_shape = (1, chl, h, w)
+
+    if len(dst_shape) == 3:
+        dst_shape = (1,) + dst_shape
+        dim3_format = True
+
+    assert len(dst_shape) == 4, "bad dst_shape: {}".format(dst_shape)
+    chl = dst_shape[1]
+    if chl in [1, 3]:
+        n, c, h, w = dst_shape
+        dst_shape = (n, h, w, c)
+    else:
+        chl = dst_shape[3]
+        assert chl in [1, 3], "can not infer input format from shape: {}".format(
+            dst_shape
+        )
+        hwc_format = True
+
+    # dst_shape has now been normalized to NHWC format
+
+    if args.resize_input:
+        h, w = dst_shape[1:3]
+        data = cv2.resize(data, (w, h))
+        logger.info("input {} resized to {}".format(path, data.shape))
+
+    if chl == 1:
+        data = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
+        data = data[:, :, np.newaxis]
+
+    assert data.ndim == 3
+    data = data[np.newaxis]
+    # data normalized to NHWC format
+
+    if not hwc_format:
+        data = np.transpose(data, (0, 3, 1, 2))
+
+    if dim3_format:
+        data = np.squeeze(data, 0)
+
+    return data
+
+
+def read_input_data(args, dst_shape, dtype, path, repeat):
+    def check_shape_equal(dst_shape, data_shape):
+        if len(dst_shape):
+            assert len(data_shape) == len(
+                dst_shape
+            ), "input/data shapes mismatch: {} vs {}".format(dst_shape, data_shape)
+
+            if data_shape[1:] != dst_shape[1:]:
+                logger.warning(
+                    "dst_shape is {}; data_shape is {}".format(dst_shape, data_shape)
+                )
+
+    if path.startswith("#"):
+        assert not args.resize_input
+        assert not args.input_transform
+        spec = path
+        m = re.match(r"^#rand\(([-0-9.]*)\s*,\s*([-0-9.]*)\s*(,[^\)]+)?\)$", spec)
+        assert m, "bad spec {}".format(spec)
+
+        rng_min = float(m.group(1))
+        rng_max = float(m.group(2))
+        if m.group(3):
+            shape_str = m.group(3)
+            try:
+                shape = shape_str[1:].split(",")
+                if shape[-1].strip() == "...":
+                    shape = shape[:-1]
+                    shape.extend(list(dst_shape[len(shape) :]))
+                data_shape = tuple(map(int, shape))
+            except ValueError as e:
+                raise ValueError("bad spec {}: {}".format(spec, e.args))
+        else:
+            data_shape = dst_shape
+
+        check_shape_equal(dst_shape, data_shape)
+        return np.random.uniform(rng_min, rng_max, data_shape).astype(dtype)
+
+    # try to load image
+    data = cv2.imread(path, cv2.IMREAD_COLOR)
+    if data is None:
+        assert not args.resize_input
+        data = np.load(path)
+        assert isinstance(data, np.ndarray)
+    else:
+        # load image succeeds, so we expect input format is image format
+        data = auto_reformat_image(args, path, data, dst_shape)
+
+    data = np.repeat(data, repeat, axis=0)
+    if repeat > 1:
+        logger.info(
+            "repeat input for {} times, data shape is {}".format(repeat, data.shape)
+        )
+
+    check_shape_equal(dst_shape, data.shape)
+
+    if args.input_transform:
+        data = eval(args.input_transform, {"data": data, "np": np})
+
+    return data
+
+
+def gen_one_testcase(args, inputs, spec):
+    paths = spec.split(";")
+    if len(paths) != len(inputs):
+        if len(paths) == 1 and paths[0].startswith("#"):
+            paths = ["{}:{}".format(name, paths[0]) for name in inputs.keys()]
+    assert len(paths) == len(inputs), "required inputs: {}; data paths: {}".format(
+        inputs.keys(), paths
+    )
+    if len(paths) == 1 and ":" not in paths[0]:
+        paths[0] = next(iter(inputs.keys())) + ":" + paths[0]
+
+    ret = {}
+    for path in paths:
+        var, path = path.split(":")
+        if args.repeat:
+            repeat = args.repeat
+        else:
+            repeat = 1
+        ret[var] = read_input_data(
+            args, inputs[var].shape, inputs[var].dtype, path, repeat
+        )
+    return ret
+
+
+def make_feeds(args):
+    cg_rt, _, outputs = G.load_graph(args.input)
+    inputs = cgtools.get_dep_vars(outputs, "Host2DeviceCopy")
+
+    inputs = {i.name: i for i in inputs}
+    if not args.no_assert:
+
+        replace_varmap = {}
+        inp_map = {}
+        # replace var use InputNode
+        for name, var in inputs.items():
+            inp = G.InputNode(
+                device="xpux", dtype=var.dtype, shape=var.shape, graph=cg_rt
+            )
+            replace_varmap[var] = inp.outputs[0]
+            inp_map[name] = inp
+
+        new = cgtools.replace_vars(outputs, replace_varmap)
+        if isinstance(new, rt.VarNode):
+            new = list(new)
+
+        output_nodes = [G.OutputNode(var) for var in new]
+        func = cg_rt.compile([node.outputs[0] for node in output_nodes])
+
+        def make_dev_tensor(value, dtype=None, device=None):
+            return as_raw_tensor(value, dtype=dtype, device=device)._dev_tensor()
+
+        def calculate(*args, **kwargs):
+            output_val = []
+            # set inputs value
+            for name, var in inputs.items():
+                val = kwargs.pop(name, None)
+                assert val is not None, "miss input name{}".format(name)
+                dev_tensor = make_dev_tensor(val, dtype=var.dtype, device="xpux")
+                inp_map[name].set_value(dev_tensor)
+
+            func.execute()
+
+            for res in output_nodes:
+                output_val.append(res.get_value().numpy())
+            return output_val
+
+        def expect_name(var):
+            return "{}:expect".format(var.name)
+
+    testcases = []
+
+    np.set_printoptions(precision=2, threshold=4, suppress=True)
+
+    data_list = []
+    for item in args.data:
+        if item.startswith("@"):
+            with open(item[1:], "r") as f:
+                data_list.extend([line.rstrip() for line in f if line.rstrip() != ""])
+        else:
+            data_list.append(item)
+
+    for inp_spec in data_list:
+        cur_testcase = gen_one_testcase(args, inputs, inp_spec)
+        assert len(cur_testcase) == len(
+            inputs
+        ), "required inputs: {}; given data: {}".format(
+            inputs.keys(), cur_testcase.keys()
+        )
+
+        if not args.no_assert:
+            outputs_get = calculate(**cur_testcase)
+            for var, val in zip(outputs, outputs_get):
+                cur_testcase[expect_name(var)] = val
+                logger.info(
+                    "generate test groundtruth: var={} shape={} range=({}, {})"
+                    " mean={} var={}".format(
+                        var, val.shape, val.min(), val.max(), np.mean(val), np.var(val)
+                    )
+                )
+        testcases.append(cur_testcase)
+        logger.info(
+            "add testcase: \n {}".format(
+                "\n ".join(
+                    "{}: shape={} dtype={} range=({:.2f},{:.2f}) "
+                    "mean={:.2f} sd={:.2f}".format(
+                        k, v.shape, v.dtype, v.min(), v.max(), np.mean(v), np.std(v)
+                    )
+                    for k, v in sorted(cur_testcase.items())
+                )
+            )
+        )
+
+    if not args.no_assert:
+
+        def expect_shp(var):
+            ret = var.shape
+            if ret:
+                return ret
+            return testcases[0][expect_name(var)].shape
+
+        def assert_equal(expect, real, **kwargs):
+            op = builtin.AssertEqual(**kwargs)
+            (res,) = apply(op, expect, real)
+            return res
+
+        verbose = not args.silent
+
+        outputs_new = []
+        for i in outputs:
+            device = rt.CompNode("xpux")
+            dtype = i.dtype
+            name = expect_name(i)
+            shape = expect_shp(i)
+            # make expect output as one input of model.
+            expect_get = rt.make_h2d(cg_rt, device, dtype, shape, name)
+            # insert assert opr to check expect and real.
+            outputs_new.append(
+                assert_equal(
+                    G.VarNode(expect_get),
+                    G.VarNode(i),
+                    verbose=verbose,
+                    maxerr=args.maxerr,
+                )
+            )
+            inputs[expect_name(i)] = expect_get
+        outputs = outputs_new
+
+    return cg_rt, {"outputs": outputs, "testcases": testcases}
+
+
+def optimize_for_inference(args, outputs):
+    args_map = {
+        "enable_io16xc32": "f16_io_f32_comp",
+        "enable_ioc16": "f16_io_comp",
+        "enable_hwcd4": "use_nhwcd4",
+        "enable_nchw4": "use_nchw4",
+        "enable_nchw88": "use_nchw88",
+        "enable_nchw44": "use_nchw44",
+        "enable_nchw44_dot": "use_nchw44_dot",
+        "enable_nchw32": "use_nchw32",
+        "enable_chwn4": "use_chwn4",
+        "enable_fuse_conv_bias_nonlinearity": "fuse_conv_bias_nonlinearity",
+        "enable_fuse_conv_bias_with_z": "fuse_conv_bias_with_z",
+    }
+    kwargs = {}
+    for k, v in args_map.items():
+        if getattr(args, k):
+            assert (
+                args.optimize_for_inference
+            ), "optimize_for_inference should be set when {} is given".format(k)
+            kwargs[v] = True
+
+    # TODO: add optimize for inference
+    # if args.optimize_for_inference:
+    #     return mgb.optimize_for_inference(outputs, **kwargs)
+
+    return outputs
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Pack computing graph, input values and expected output "
+        "values into one file for checking correctness. README.md gives more "
+        "details on the usage",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("input", help="MegEngine dumped model file")
+    parser.add_argument("-o", "--output", help="output file", required=True)
+    parser.add_argument(
+        "-d",
+        "--data",
+        default=[],
+        action="append",
+        required=True,
+        help="Given input test data when input file is a network, "
+        "and current network output would be used as groundtruth. "
+        "The format is var0:file0;var1:file1... to specify data files for "
+        "input vars. It can also be #rand(min,max,shape...) for generating "
+        "random input data, for example, #rand(0,255), "
+        "#rand(0,255,1,3,224,224) or #rand(0, 255, 1, ...) where `...` means "
+        "the remaining part of the original shape. "
+        "If the shape is not specified, the shape of "
+        "corresponding input tensors in the network will be used. "
+        "If there is only one input var, its name can be omitted. "
+        "Each data file can either be an image which can be loaded by opencv, "
+        "or a pickled numpy.ndarray. "
+        "This option can be given multiple times to add multiple testcases. "
+        " *NOTE* "
+        "If you start the data with the letter @, the rest should be a "
+        "filename, and each line in the file should be a single datum in "
+        "the format described above. ",
+    )
+    parser.add_argument(
+        "--repeat",
+        type=int,
+        default=1,
+        help="Specify how many times the input image is repeated. "
+        "Useful when running benchmark for batch size other than one. "
+        "Have no effect on randomly generated input data.",
+    )
+    parser.add_argument(
+        "--silent",
+        action="store_true",
+        help="set verbose to False in asserti_equal opr",
+    )
+    parser.add_argument(
+        "--optimize-for-inference",
+        action="store_true",
+        help="enbale optimization for inference",
+    )
+    parser.add_argument(
+        "--no-assert",
+        action="store_true",
+        help="do not insert assert_equal opr to check result; "
+        "this option is useful for benchmarking",
+    )
+    parser.add_argument(
+        "--maxerr",
+        type=float,
+        default=1e-4,
+        help="max error for assert_equal check during runtime",
+    )
+    parser.add_argument(
+        "--resize-input",
+        action="store_true",
+        help="resize input image to fit input var shape",
+    )
+    parser.add_argument(
+        "--input-transform",
+        help="a python expression to transform the input data. "
+        "Example: data / np.std(data)",
+    )
+    parser.add_argument(
+        "--discard-var-name",
+        action="store_true",
+        help="discard variable and param names in the " "generated output",
+    )
+    parser.add_argument(
+        "--output-strip-info", action="store_true", help="output code strip information"
+    )
+    parser.add_argument(
+        "--enable-io16xc32",
+        action="store_true",
+        help="transform the mode to float16 io float32 compute",
+    )
+    parser.add_argument(
+        "--enable-ioc16",
+        action="store_true",
+        help="transform the dtype of the model to float16 io " "and compute",
+    )
+    parser.add_argument(
+        "--enable-fuse-conv-bias-nonlinearity",
+        action="store_true",
+        help="fuse convolution bias and nonlinearity opr to a "
+        "conv_bias opr and compute",
+    )
+    parser.add_argument(
+        "--enable-hwcd4",
+        action="store_true",
+        help="transform the model format from NCHW to NHWCD4 "
+        "for inference; you may need to disable CUDA and set "
+        "MGB_USE_MEGDNN_DBG=2",
+    )
+    parser.add_argument(
+        "--enable-nchw4",
+        action="store_true",
+        help="transform the model format from NCHW to NCHW4 " "for inference",
+    )
+    parser.add_argument(
+        "--enable-nchw88",
+        action="store_true",
+        help="transform the model format from NCHW to NCHW88 " "for inference",
+    )
+    parser.add_argument(
+        "--enable-nchw44",
+        action="store_true",
+        help="transform the model format from NCHW to NCHW44 " "for inference",
+    )
+    parser.add_argument(
+        "--enable-nchw44-dot",
+        action="store_true",
+        help="transform the model format from NCHW to NCHW44_DOT "
+        "for optimizing armv8.2 dot in inference",
+    )
+    parser.add_argument(
+        "--enable-nchw32",
+        action="store_true",
+        help="transform the model format from NCHW4 to NCHW32 "
+        "for inference on nvidia TensoCore",
+    )
+    parser.add_argument(
+        "--enable-chwn4",
+        action="store_true",
+        help="transform the model format to CHWN4 "
+        "for inference, mainly used for nvidia tensorcore",
+    )
+    parser.add_argument(
+        "--enable-fuse-conv-bias-with-z",
+        action="store_true",
+        help="fuse conv_bias with z input for inference on "
+        "nvidia GPU (this optimization pass will result in mismatch "
+        "of the precision of output of training and inference)",
+    )
+    args = parser.parse_args()
+
+    _, feeds = make_feeds(args)
+
+    assert isinstance(feeds, dict) and feeds["testcases"], "testcases can not be empty"
+
+    output_mgbvars = feeds["outputs"]
+    output_mgbvars = optimize_for_inference(args, output_mgbvars)
+
+    inputs = cgtools.get_dep_vars(output_mgbvars, "Host2DeviceCopy")
+    inputs = sorted((i.name, i.dtype) for i in inputs)
+
+    if args.discard_var_name:
+        sereg_kwargs = dict(keep_var_name=0, keep_param_name=False)
+    else:
+        sereg_kwargs = dict(keep_var_name=2, keep_param_name=True)
+
+    with open(args.output, "wb") as fout:
+        fout.write(b"mgbtest0")
+        fout.write(struct.pack("I", len(feeds["testcases"])))
+        fout.write(rt.dump_graph(output_mgbvars))
+
+    def make_dev_tensor(value, dtype=None, device=None):
+        return as_raw_tensor(value, dtype=dtype, device=device)._dev_tensor()
+
+    for testcase in feeds["testcases"]:
+        assert isinstance(testcase, dict)
+        cg = G.Graph()
+        output_mgbvars = []
+        for name, dtype in inputs:
+            output_mgbvars.append(
+                cg.make_const(
+                    make_dev_tensor(testcase.pop(name), dtype=dtype, device="cpux")
+                )
+            )
+        assert not testcase, "extra inputs provided in testcase: {}".format(
+            testcase.keys()
+        )
+        with open(args.output, "ab") as fout:
+            fout.write(G.dump_graph(*output_mgbvars))
+
+
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sdk/load-and-run/dump_with_testcase_mge.py b/sdk/load-and-run/dump_with_testcase_mge.py
index 4cbbba89e173b6ca00efdb4b8867513b8570027a..57b933b4bb9a6b5801d68fa69528f1f8b838ef49 100755
--- a/sdk/load-and-run/dump_with_testcase_mge.py
+++ b/sdk/load-and-run/dump_with_testcase_mge.py
@@ -471,9 +471,11 @@ def main():
         assert not testcase, 'extra inputs provided in testcase: {}'.format(
             testcase.keys()
         )
-        with open(args.output, "ab") as fout:
-            fout.write(G.dump_graph(*output_mgbvars))
-
+        mgb.serialize_comp_graph_to_file(
+            args.output,
+            output_mgbvars,
+            append=True,
+            output_strip_info=args.output_strip_info)
 
 if __name__ == '__main__':
     main()