feat(mge/dump): enable jit.dump to dump with testcase

GitOrigin-RevId: 5dce356452

imperative/python/megengine/jit/tracing.py

@@ -13,10 +13,17 @@ import itertools
 import json
 import os
 import pickle
+import re
+import struct
 from typing import Any
 
+import cv2
 import numpy as np
 
+from megengine.logger import get_logger
+
+from .. import tensor
+from ..core import _imperative_rt as rt
 from ..core._imperative_rt import GraphProfiler, GraphProfiler2, SerializationMetadata
 from ..core._imperative_rt.core2 import Tensor as RawTensor
 from ..core._imperative_rt.core2 import (
@@ -38,12 +45,15 @@ from ..core._wrap import as_device
 from ..core.ops.builtin import BatchNorm, OpDef
 from ..core.tensor import megbrain_graph as G
 from ..core.tensor.utils import setscalar
+from ..utils import comp_graph_tools as cgtools
 from ..utils.naming import AutoNaming
 from ..utils.profiler import is_profiling
 from .dtr_config import DTRConfig
 from .graph_opt_config import GraphOptimizationConfig
 from .sublinear_memory_config import SublinearMemoryConfig
 
+logger = get_logger(__name__)
+
 
 def _input_node_use_static_shape():
     return os.environ.get("MEGENGINE_INPUT_NODE_USE_STATIC_SHAPE") is not None
@@ -692,6 +702,289 @@ class trace:
                 self._process_outputs(outputs)
             return outputs
 
+    def _make_feed(
+        self,
+        graph,
+        outputs,
+        input_data,
+        repeat,
+        silent,
+        no_assert,
+        maxerr,
+        resize_input,
+        input_transform,
+    ):
+        def auto_reformat_image(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 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(dst_shape, dtype, path):
+            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 resize_input
+                assert not 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 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(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 input_transform:
+                data = eval(input_transform, {"data": data, "np": np})
+
+            return data
+
+        def gen_one_testcase(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(":")
+                ret[var] = read_input_data(inputs[var].shape, inputs[var].dtype, path)
+            return ret
+
+        inputs = cgtools.get_dep_vars(outputs, "Host2DeviceCopy")
+        inputs = {i.name: i for i in inputs}
+
+        if not 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=graph
+                )
+                replace_varmap[var] = inp.outputs[0]._node
+                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 = graph.compile(*[node.outputs[0]._node for node in output_nodes])
+
+            def make_dev_tensor(value, dtype=None, device=None):
+                return 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 input_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(inputs, inp_spec)
+            assert len(cur_testcase) == len(
+                inputs
+            ), "required inputs: {}; given data: {}".format(
+                inputs.keys(), cur_testcase.keys()
+            )
+
+            if not 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 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 = AssertEqual(**kwargs)
+                (res,) = G.apply_normal_varnode(op, expect, real)
+                return res._node
+
+            verbose = not 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(graph, device, dtype, shape, name)
+                # insert assert opr to check expect and real.
+                outputs_new.append(
+                    assert_equal(expect_get, i, verbose=verbose, maxerr=maxerr,)
+                )
+                inputs[expect_name(i)] = expect_get
+            outputs = outputs_new
+
+        return {"outputs": outputs, "testcases": testcases}
+
     def dump(
         self,
         file,
@@ -708,6 +1001,13 @@ class trace:
         optimize_for_inference=True,
         user_info: Any = None,
         enable_metadata: bool = True,
+        input_data=None,
+        repeat=1,
+        silent=False,
+        no_assert=False,
+        maxerr=1e-4,
+        resize_input=False,
+        input_transform=None,
         **kwargs
     ):
         r"""Serializes trace to file system.
@@ -738,6 +1038,27 @@ class trace:
                 will skip all optimize options if this is False. Default: True
             user_info: any type object, which will be pickled to bytes.
             enable_metadata: whether to save metadata into output file.
+            input_data: input test data 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. 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. *NOTE* If `input_data` is not None,
+                you can only use load-and-run to run the output file.
+            repeat: 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.
+            silent: whether set verbose to False in assert_equal opr.
+            no_assert: whether insert assert_equal opr to check result; this option is useful for
+                benchmarking.
+            maxerr: max error for assert_equal check during runtime.
+            resize_input: whether resize input image to fit input var shape.
+            input_transform: a python expression to transform the input data.
+                Example: data / np.std(data)
 
         Keyword Arguments:
 
@@ -778,6 +1099,8 @@ class trace:
           input for inference on nvidia backend(this optimization pass will
           result in mismatch of the precision of output of training and
           inference)
+        * enable_fuse_preprocess: whether to fuse astype\pad_channel\dimshuffle and
+          etc opr
         """
         if not self._capture_as_const:
             raise ValueError(
@@ -892,8 +1215,28 @@ class trace:
                 v.name = output_names[i]
             dest_vars.append(v)
 
+        dest_vars = [i._node for i in dest_vars]
+
+        if input_data is not None:
+            feeds = self._make_feed(
+                graph,
+                dest_vars,
+                input_data,
+                repeat,
+                silent,
+                no_assert,
+                maxerr,
+                resize_input,
+                input_transform,
+            )
+            assert (
+                isinstance(feeds, dict) and feeds["testcases"]
+            ), "testcases can not be empty"
+            dest_vars = feeds["outputs"]
+
         if optimize_for_inference:
             dest_vars, optimize_options = G.optimize_for_inference(dest_vars, **kwargs)
+            dest_vars = [i._node for i in dest_vars]
 
         metadata = SerializationMetadata()
         if enable_metadata:
@@ -910,6 +1253,9 @@ class trace:
         if keep_opr_priority:
             graph._set_priority_to_id(dest_vars)
 
+        if input_data is not None:
+            file.write(b"mgbtest0")
+            file.write(struct.pack("I", len(feeds["testcases"])))
         dump_content, dump_info = G.dump_graph(
             dest_vars,
             keep_var_name=keep_var_name,
@@ -921,6 +1267,34 @@ class trace:
             metadata=metadata,
         )
         file.write(dump_content)
+
+        if input_data is not None:
+            inputs = cgtools.get_dep_vars(dest_vars, "Host2DeviceCopy")
+            inputs = sorted((i.name, i.dtype) for i in inputs)
+
+            def make_dev_tensor(value, dtype=None, device=None):
+                return 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()
+                )
+                dump_content, _ = G.dump_graph(
+                    output_mgbvars, strip_info_file=strip_info_file, append_json=True,
+                )
+                file.write(dump_content)
+
         return dump_info
 
     def _process_inputs(self, *args, **kwargs):

imperative/python/test/unit/jit/test_tracing.py

@@ -287,6 +287,16 @@ def test_dump_backward_graph():
     np.testing.assert_equal(results[1], dx0)
 
 
+def test_dump_with_testcase():
+    @trace(symbolic=True, capture_as_const=True)
+    def f(x):
+        return exp(x)
+
+    f(tensor(1.0))
+    file = io.BytesIO()
+    f.dump(file, input_data=["#rand(0, 255, 1)"])
+
+
 @pytest.mark.parametrize("trace_mode", [False, True])
 def test_trace_profiler(trace_mode):
     @trace(symbolic=trace_mode, profiling=True)

sdk/load-and-run/dump_with_testcase_mge.py

@@ -1,535 +0,0 @@
-# -*- coding: utf-8 -*-
-# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
-#
-# Copyright (c) 2014-2021 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 tensor
-from megengine.core._imperative_rt.core2 import apply
-from megengine.core.ops import builtin
-from megengine.utils import comp_graph_tools as cgtools
-
-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):
-    ret = G.load_graph(args.input)
-    cg_rt, outputs = ret.graph, ret.output_vars_list
-    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 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(
-                    expect_get,
-                    i,
-                    verbose=verbose,
-                    maxerr=args.maxerr,
-                )
-            )
-            inputs[expect_name(i)] = expect_get
-        outputs = outputs_new
-
-    return {"outputs": outputs, "testcases": testcases}
-
-
-def optimize_for_inference(args, outputs):
-    args_list = [
-        "enable_io16xc32",
-        "enable_ioc16",
-        "enable_hwcd4",
-        "enable_nchw4",
-        "enable_nchw88",
-        "enable_nchw44",
-        "enable_nchw44_dot",
-        "enable_nchw32",
-        "enable_chwn4",
-        "enable_fuse_conv_bias_nonlinearity",
-        "enable_fuse_conv_bias_with_z",
-        "enable_fuse_preprocess",
-    ]
-    kwargs = {}
-    for k in args_list:
-        if getattr(args, k):
-            kwargs[k] = True
-
-    if args.optimize_for_inference:
-        outputs = G.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="enable 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)",
-    )
-    parser.add_argument(
-        "--enable-fuse-preprocess",
-        action="store_true",
-        help="fuse astype\pad_channel\dimshuffle and etc opr "
-        "from h2d opr",
-    )
-    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)
-
-    strip_info_file = args.output + ".json" if args.output_strip_info else None
-
-    with open(args.output, "wb") as fout:
-        fout.write(b"mgbtest0")
-        fout.write(struct.pack("I", len(feeds["testcases"])))
-        dump_content, stat = G.dump_graph(
-            output_mgbvars,
-            append_json=True,
-            strip_info_file=strip_info_file,
-            **sereg_kwargs,
-        )
-        fout.write(dump_content)
-
-        logger.info(
-            "graph dump sizes: tot_size={:.3f}KiB overhead={:.3f}KiB".format(
-                stat.tot_bytes / 1024, (stat.tot_bytes - stat.tensor_value_bytes) / 1024
-            )
-        )
-
-    def make_dev_tensor(value, dtype=None, device=None):
-        return 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:
-            dump_content, _ = G.dump_graph(
-                output_mgbvars, strip_info_file=strip_info_file, append_json=True
-            )
-            fout.write(dump_content)
-
-
-if __name__ == "__main__":
-    main()