chore(release): bump version

GitOrigin-RevId: 4047219962

dnn/scripts/gen_flatbuffers_schema.py

@@ -52,14 +52,11 @@ class FlatBuffersWriter(IndentWriterBase):
             name = p + e
             e = self._enums[(p, e)]
             self._write_doc(e.name)
-            self._write("enum %s%s : uint {", p, e.name, indent=1)
+            attribute = "(bit_flags)" if e.combined else ""
+            self._write("enum %s%s : uint %s {", p, e.name, attribute, indent=1)
             for idx, member in enumerate(e.members):
                 self._write_doc(member)
-                if e.combined:
-                    self._write("%s=%d,", scramble_enum_member_name(str(member)),
-                            1<<idx)
-                else:
-                    self._write("%s,", scramble_enum_member_name(str(member)))
+                self._write("%s,", scramble_enum_member_name(str(member)))
             self._write("}\n", indent=-1)
 
     def _write_doc(self, doc):
@@ -97,8 +94,11 @@ class FlatBuffersWriter(IndentWriterBase):
             return
         self._write_doc(e.name)
         self._used_enum.add(key)
-        self._write("%s:%s%s = %s;", e.name_field, p.name, e.name,
-                    scramble_enum_member_name(str(e.members[e.default])))
+        if e.combined:
+            default = e.compose_combined_enum(e.default)
+        else:
+            default = scramble_enum_member_name(str(e.members[e.default]))
+        self._write("%s:%s%s = %s;", e.name_field, p.name, e.name, default)
 
     def _resolve_const(self, v):
         while v in self._cur_const_val:
@@ -120,9 +120,12 @@ class FlatBuffersWriter(IndentWriterBase):
             return
         self._used_enum.add((e.src_class, e.src_name))
         enum_name = e.src_class + e.src_name
-        self._write(
-            "%s:%s = %s;", e.name_field, enum_name,
-            scramble_enum_member_name(str(e.src_enum.members[e.get_default()])))
+        s = e.src_enum
+        if s.combined:
+            default = s.compose_combined_enum(e.get_default())
+        else:
+            default = scramble_enum_member_name(str(s.members[e.get_default()]))
+        self._write("%s:%s = %s;", e.name_field, enum_name, default)
 
     def _get_fb_default(self, cppdefault):
         if not isinstance(cppdefault, str):

dnn/scripts/gen_param_defs.py

@@ -73,11 +73,21 @@ class member_defs:
         """define an enum; the result would contain both an enum class def and its
         corresponding data field
 
-        :param default: index of default member value
+        :param default:
+            for normal enum class: index of default member value
+            for bit combined class: tuple of index of default member value
+
+            For example, following representations of the default value for bit
+            combined class are all equivalent:
+                Enum(members=('a', 'b', 'c'), default=('a', 'b'), ...)
+                Enum(members=('a', 'b', 'c'), default=(0, 1), ...)
+                Enum(members=('a', 'b', 'c'), default=(1 << 0) | (1 << 1), ...)
 
         :attr name_field: name of the data field of this enum in the param
             struct
-        :attr member_alias: list of (member, alias) pairs
+        :attr member_alias:
+            for normal enum class: list of (member, alias) pairs
+            for bit combined class: list of (tuple of members, alias) paris
         """
         __slots__ = ['name', 'name_field', 'members', 'default',
                      'member_alias', 'combined']
@@ -90,17 +100,11 @@ class member_defs:
             name = member_defs.Doc.make(name)
             assert name.id[0].isupper()
             members = tuple(map(member_defs.Doc.make, members))
-            if isinstance(default, str):
-                if default not in name_field:
-                    raise ValueError(
-                        "Default value '{}' does not exist.".format(default))
-                default = name_field.index(default)
-            assert isinstance(default, int)
             self.name = name
             self.combined = combined
             self.name_field = self.get_name_field(name.id, name_field)
             self.members = members
-            self.default = default
+            self.default = self.normalize_enum_value(default)
 
             self.all_enums[(param_name, name.id)] = self
 
@@ -114,6 +118,43 @@ class member_defs:
             assert isinstance(name_field, str)
             return name_field
 
+        def normalize_enum_value(self, value):
+            def normalize(v):
+                if isinstance(v, str):
+                    if v not in self.members:
+                        raise ValueError(
+                            "enum member '{}' does not exist.".format(v))
+                    v = self.members.index(v)
+                assert isinstance(v, int)
+                return v
+            if self.combined:
+                if isinstance(value, int):
+                    value = self.decompose_combined_enum(value)
+                assert isinstance(value, tuple)
+                value = tuple(normalize(i) for i in value)
+                return value
+            else:
+                return normalize(value)
+
+        @staticmethod
+        def decompose_combined_enum(v):
+            """Integer => tuple of the indexes of the enum members"""
+            assert isinstance(v, int)
+            idx = 0
+            members = []
+            while v > 0:
+                if v & 1:
+                    members.append(idx)
+                idx += 1
+                v >>= 1
+            return tuple(members)
+
+        def compose_combined_enum(self, v):
+            """tuple of members => Integer"""
+            assert self.combined and isinstance(v, tuple)
+            norm_v = self.normalize_enum_value(v)
+            return sum(1 << i for i in norm_v)
+
     class Field(Base):
         """define a normal data field"""
         __slots__ = ['name', 'dtype', 'default']
@@ -146,6 +187,10 @@ class member_defs:
                 src_name = name
             self.src_name = src_name
             self.default = default
+            # TODO: remove this assertion if needed; adding mock param_defs in
+            # current testing framework is too complicated, and currently we
+            # only allow aliasing of normal enum
+            assert not self.src_enum.combined
 
         @property
         def src_enum(self):
@@ -157,7 +202,7 @@ class member_defs:
             set"""
             if self.default is None:
                 return self.src_enum.default
-            return self.default
+            return self.src_enum.normalize_enum_value(self.default)
 
 
 class ParamDef:
@@ -198,7 +243,7 @@ class ParamDef:
             self.name.id, name, name_field, members, default, member_alias))
         return self
 
-    def add_bit_combination_enum(self, name, *members, default=0,
+    def add_bit_combination_enum(self, name, *members, default=tuple(),
                  name_field=None, member_alias=[]):
         self.members.append(member_defs.Enum(
             self.name.id, name, name_field, members, default, member_alias, True))
@@ -322,11 +367,13 @@ class PyWriter(IndentWriterBase):
             '       for idx, v in enumerate(pdata):\n'
             '           if isinstance(v, _EnumBase):\n'
             '               pdata[idx] = _enum_member2num[id(v)]\n'
+            '           elif isinstance(v, _BitCombinedEnumBase):\n'
+            '               pdata[idx] = v._value_\n'
             '       return tag + self._packer.pack(*pdata)\n'
             '\n'
         )
-        self._write(
-            'class _EnumBase(enum.Enum):\n'
+        # it's hard to mix custom implemention into enum, just do copy-paste instead
+        classbody = (
             '   @classmethod\n'
             '   def __normalize(cls, val):\n'
             '       if isinstance(val, str):\n'
@@ -349,6 +396,12 @@ class PyWriter(IndentWriterBase):
             '       return super()._missing_(value)\n'
             '\n'
         )
+        self._write(
+            'class _EnumBase(enum.Enum):\n' + classbody
+        )
+        self._write(
+            'class _BitCombinedEnumBase(enum.Flag):\n' + classbody
+        )
         if not self._imperative:
             self._write(
                 'def _as_dtype_num(dtype):\n'
@@ -464,30 +517,42 @@ class SerializedDType(_ParamDefBase):
     def _on_member_enum(self, e):
         qualname = '{}.{}'.format(self._cur_param_name, e.name)
 
-        self._write('class %s(_EnumBase):', e.name, indent=1)
+        if e.combined:
+            self._write('class %s(_BitCombinedEnumBase):', e.name, indent=1)
+        else:
+            self._write('class %s(_EnumBase):', e.name, indent=1)
+
         self._write_doc(e.name)
 
         for idx, emem in enumerate(e.members):
-            self._write('%s = "%s"', emem, emem)
-            self._write_doc(emem)
             if e.combined:
-                self._enum_member2num.append('id({}.{}):{}'.format(
-                    qualname, emem, 1<<idx))
+                self._write('%s = 1 << %d', emem, idx)
+                self._write_doc(emem)
             else:
+                self._write('%s = "%s"', emem, emem)
+                self._write_doc(emem)
                 self._enum_member2num.append('id({}.{}):{}'.format(
                     qualname, emem, idx))
 
-        for emem, emem_alis in e.member_alias:
-            self._write('%s = %s', emem_alis, emem)
+        for emem, emem_alias in e.member_alias:
+            if e.combined:
+                self._write('%s = %s', emem_alias, e.compose_combined_enum(emem))
+            else:
+                self._write('%s = %s', emem_alias, emem)
 
         self._unindent()
         self._write('')
 
+        if e.combined:
+            default = e.compose_combined_enum(e.default)
+        else:
+            default = "'{}'".format(e.members[e.default])
+
         self._cur_fields.append(self.FieldDef(
             name=e.name_field,
             cvt='{}.convert({})'.format(qualname, e.name_field),
             fmt='I',
-            default="'{}'".format(e.members[e.default]),
+            default=default,
             type=qualname,
             doc=None))
 
@@ -495,11 +560,16 @@ class SerializedDType(_ParamDefBase):
         self._write('%s = %s.%s', e.name, e.src_class, e.src_name)
         s = e.src_enum
         qualname = '{}.{}'.format(e.src_class, e.src_name)
+
+        if s.combined:
+            default = s.compose_combined_enum(e.get_default())
+        else:
+            default = "'{}'".format(s.members[e.get_default()])
         self._cur_fields.append(self.FieldDef(
             name=e.name_field,
             cvt='{}.convert({})'.format(qualname, e.name_field),
             fmt='I',
-            default="'{}'".format(s.members[e.get_default()]),
+            default=default,
             type=qualname,
             doc=None))
 
@@ -639,14 +709,19 @@ class CPPWriter(IndentWriterBase):
                 v += ','
             self._write(v)
         for mem, alias in e.member_alias:
-            self._write('%s = %s,', alias, mem)
+            if e.combined:
+                self._write('%s = %s,', alias, e.compose_combined_enum(mem))
+            else:
+                self._write('%s = %s,', alias, mem)
         self._write('};', indent=-1)
         self._non_static_members.append(e)
         self._write('static MEGDNN_CONSTEXPR uint32_t %s_NR_MEMBER = %d;',
                     str(e.name).upper(), len(e.members))
-        self._add_ctor_args(e.name,
-                            '{}::{}'.format(e.name, e.members[e.default]),
-                            e.name_field)
+        if e.combined:
+            default = 'static_cast<{}>({})'.format(e.name, e.compose_combined_enum(e.default))
+        else:
+            default = '{}::{}'.format(e.name, e.members[e.default])
+        self._add_ctor_args(e.name, default, e.name_field)
 
     def _on_member_enum_alias(self, e):
         s = e.src_enum
@@ -654,10 +729,11 @@ class CPPWriter(IndentWriterBase):
         self._non_static_members.append(e)
         self._write('static MEGDNN_CONSTEXPR uint32_t %s_NR_MEMBER = %d;',
                     str(e.name).upper(), len(s.members))
-        self._add_ctor_args(e.name,
-                            '{}::{}'.format(e.name,
-                                            s.members[e.get_default()]),
-                            e.name_field)
+        if s.combined:
+            default = 'static_cast<{}>({})'.format(e.name, s.compose_combined_enum(e.default))
+        else:
+            default = '{}::{}'.format(e.name, s.members[e.get_default()])
+        self._add_ctor_args(e.name, default, e.name_field)
 
     def _on_member_field(self, f):
         self._non_static_members.append(f)

dnn/scripts/gen_tablegen.py

@@ -106,7 +106,12 @@ class ConverterWriter(IndentWriterBase):
             return
 
         # wrapped with default value
-        default_val = "static_cast<{}::{}>({})".format(fullname, e.name, e.default)
+        if e.combined:
+            default_val = "static_cast<{}::{}>({})".format(
+                    fullname, e.name, e.compose_combined_enum(e.default))
+        else:
+            default_val = "{}::{}::{}".format(fullname, e.name, e.members[e.default])
+
         wrapped = self._wrapped_with_default_value(td_class, default_val)
 
         self._current_tparams.append("{}:${}".format(wrapped, e.name_field))
@@ -124,7 +129,13 @@ class ConverterWriter(IndentWriterBase):
         self._write("def {} : {};".format(td_class, enum_def))
 
         # wrapped with default value
-        default_val = "static_cast<{}::{}>({})".format(fullname, e.name, e.get_default())
+        s = e.src_enum
+        if s.combined:
+            default_val = "static_cast<{}::{}>({})".format(
+                    fullname, e.name, s.compose_combined_enum(e.get_default()))
+        else:
+            default_val = "{}::{}::{}".format(fullname, e.name, s.members[e.get_default()])
+
         wrapped = self._wrapped_with_default_value(td_class, default_val)
 
         self._current_tparams.append("{}:${}".format(wrapped, e.name_field))

dnn/src/x86/conv_bias/opr_impl.cpp

@@ -185,7 +185,7 @@ SmallVector<AlgoCategory> ConvBiasImpl::suggest_algo_category_order(
     }
     //! conv1x1
     im2col_prefer |= (FH == 1 && FW == 1);
-    //! x86 8x8x16 not optmized, so it will use fallback im2col+matmul
+    //! x86 8x8x16 not optimized, so it will use fallback im2col+matmul
     if (param.deduce_algo_data_type() == AlgoDataType::INT8X8X16) {
         im2col_prefer = true;
     }

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/dtype.py

@@ -5,6 +5,7 @@
 # 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 re
 from collections import namedtuple
 from typing import Union
 
@@ -22,6 +23,12 @@ from .._imperative_rt.common import (
 )
 
 
+def get_dtype_bit(dtype_name: str):
+    numbers = re.findall(r"\d+", dtype_name)
+    assert len(numbers) == 1, "Unsupport dtype name with more than one number."
+    return int(numbers[0])
+
+
 # normal dtype related
 def is_lowbit(dtype):
     return (dtype is intb1) or (dtype is intb2) or (dtype is intb4)

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/debug_param.py

@@ -40,7 +40,7 @@ def set_execution_strategy(option):
         * HEURISTIC uses heuristic to choose the fastest algorithm.
         * PROFILE runs possible algorithms on real device to find the best one.
         * REPRODUCIBLE uses the algorithms that is reproducible.
-        * OPTMIZED uses the algorithms that is optimized.
+        * OPTIMIZED uses the algorithms that is optimized.
 
         The default strategy is HEURISTIC, this options can be combined to
         form a combination option, e.g. PROFILE | REPRODUCIBLE

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/module/module.py

@@ -607,10 +607,10 @@ class Module(metaclass=ABCMeta):
 
     def __getattribute__(self, name: str):
         value = super().__getattribute__(name)
-        if name == "_name":
+        if name == "__dict__":
             return value
-        if isinstance(value, (Tensor, Module)):
-            value._name = name
+        for prefix, variable in _expand_structure(name, value):
+            variable._name = prefix
         return value
 
     def __setattr__(self, name: str, value):

imperative/python/megengine/module/quantized/concat.py

@@ -23,7 +23,7 @@ class Concat(QuantizedModule):
         self.output_dtype = dtype
 
     def forward(self, inps: Iterable[Tensor], axis: int = 0):
-        new_inps = (x.astype(self.output_dtype) for x in inps)
+        new_inps = tuple(x.astype(self.output_dtype) for x in inps)
         return F.concat(new_inps, axis)
 
     @classmethod

imperative/python/megengine/module/sequential.py

@@ -92,6 +92,7 @@ class Sequential(Module):
         return [getattr(self, key) for key in self.layer_keys]
 
     def forward(self, inp):
-        for layer in self.layer_values:
+        # avoid layer_values as a name prefix, see Module.__getattribute__
+        for layer in [getattr(self, key) for key in self.layer_keys]:
             inp = layer(inp)
         return inp

imperative/python/megengine/optimizer/optimizer.py

@@ -6,6 +6,7 @@
 # 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 copy
 from abc import ABCMeta, abstractmethod
 from collections.abc import Iterable
 from typing import Dict
@@ -197,10 +198,11 @@ class Optimizer(metaclass=ABCMeta):
                     cur_id += 1
 
         for param, st in self._state.items():
+            _st = copy.copy(st)
             if not keep_var:
                 for k, v in st.items():
-                    st[k] = v.numpy()
-            state[param2id[param]] = st
+                    _st[k] = v.numpy()
+            state[param2id[param]] = _st
 
         for group in self.param_groups:
             param_group = {k: v for k, v in group.items() if k != "params"}

imperative/python/megengine/quantization/__init__.py

@@ -6,8 +6,16 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 
-from .fake_quant import FakeQuantize
-from .observer import Observer
+from .fake_quant import TQT, FakeQuantize
+from .observer import (
+    ExponentialMovingAverageObserver,
+    HistogramObserver,
+    MinMaxObserver,
+    Observer,
+    PassiveObserver,
+    SyncExponentialMovingAverageObserver,
+    SyncMinMaxObserver,
+)
 from .qconfig import (
     QConfig,
     calibration_qconfig,

imperative/python/megengine/tools/network_visualize.py

@@ -8,14 +8,19 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import argparse
 import logging
+import re
 
 import numpy as np
 
 from megengine.core.tensor.dtype import is_quantize
 from megengine.logger import _imperative_rt_logger, get_logger, set_mgb_log_level
 from megengine.utils.module_stats import (
-    print_flops_stats,
-    print_params_stats,
+    enable_receptive_field,
+    get_op_stats,
+    get_param_stats,
+    print_op_stats,
+    print_param_stats,
+    print_summary,
     sizeof_fmt,
 )
 from megengine.utils.network import Network
@@ -40,34 +45,41 @@ def visualize(
     :param log_params: whether print and record params size.
     :param log_flops: whether print and record op flops.
     """
-    try:
-        from tensorboard.compat.proto.attr_value_pb2 import AttrValue
-        from tensorboard.compat.proto.config_pb2 import RunMetadata
-        from tensorboard.compat.proto.graph_pb2 import GraphDef
-        from tensorboard.compat.proto.node_def_pb2 import NodeDef
-        from tensorboard.compat.proto.step_stats_pb2 import (
-            AllocatorMemoryUsed,
-            DeviceStepStats,
-            NodeExecStats,
-            StepStats,
-        )
-        from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
-        from tensorboard.compat.proto.versions_pb2 import VersionDef
-        from tensorboardX import SummaryWriter
-    except ImportError:
-        logger.error(
-            "TensorBoard and TensorboardX are required for visualize.", exc_info=True
-        )
-        return
+    if log_path:
+        try:
+            from tensorboard.compat.proto.attr_value_pb2 import AttrValue
+            from tensorboard.compat.proto.config_pb2 import RunMetadata
+            from tensorboard.compat.proto.graph_pb2 import GraphDef
+            from tensorboard.compat.proto.node_def_pb2 import NodeDef
+            from tensorboard.compat.proto.step_stats_pb2 import (
+                AllocatorMemoryUsed,
+                DeviceStepStats,
+                NodeExecStats,
+                StepStats,
+            )
+            from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto
+            from tensorboard.compat.proto.versions_pb2 import VersionDef
+            from tensorboardX import SummaryWriter
+        except ImportError:
+            logger.error(
+                "TensorBoard and TensorboardX are required for visualize.",
+                exc_info=True,
+            )
+            return
     # FIXME: remove this after resolving "span dist too large" warning
     old_level = set_mgb_log_level(logging.ERROR)
 
+    enable_receptive_field()
+
     graph = Network.load(model_path)
-    writer = SummaryWriter(log_path)
 
     def process_name(name):
-        return name.replace(".", "/").encode(encoding="utf-8")
+        # nodes that start with point or contain float const will lead to display bug
+        if not re.match(r"^[+-]?\d*\.\d*", name):
+            name = name.replace(".", "/")
+        return name.encode(encoding="utf-8")
 
+    summary = [["item", "value"]]
     node_list = []
     flops_list = []
     params_list = []
@@ -84,78 +96,90 @@ def visualize(
             node_oup = node.outputs[0]
 
         inp_list = [process_name(var.owner.name) for var in node.inputs]
-        attr = {
-            "_output_shapes": AttrValue(
-                list=AttrValue.ListValue(
-                    shape=[
-                        TensorShapeProto(
-                            dim=[TensorShapeProto.Dim(size=d) for d in node_oup.shape]
-                        )
-                    ]
-                )
-            ),
-        }
-        if hasattr(node, "calc_flops"):
-            flops_num = node.calc_flops()
+        if log_path:
+            # detail format see tensorboard/compat/proto/attr_value.proto
+            attr = {
+                "_output_shapes": AttrValue(
+                    list=AttrValue.ListValue(
+                        shape=[
+                            TensorShapeProto(
+                                dim=[
+                                    TensorShapeProto.Dim(size=d) for d in node_oup.shape
+                                ]
+                            )
+                        ]
+                    )
+                ),
+                "params": AttrValue(s=str(node.params).encode(encoding="utf-8")),
+                "dtype": AttrValue(s=str(node_oup.dtype).encode(encoding="utf-8")),
+            }
+        flops_stats = get_op_stats(node, node.inputs, node.outputs)
+        if flops_stats is not None:
             # add op flops attr
-            attr["flops"] = AttrValue(s=sizeof_fmt(flops_num).encode(encoding="utf-8"))
-            flops_list.append(
-                dict(
-                    name=node.name,
-                    class_name=node.type,
-                    input_shapes=[i.shape for i in node.inputs],
-                    output_shapes=[o.shape for o in node.outputs],
-                    flops_num=flops_num,
-                    flops_cum=0,
+            if log_path and hasattr(flops_stats, "flops_num"):
+                attr["flops"] = AttrValue(
+                    s=sizeof_fmt(flops_stats["flops"]).encode(encoding="utf-8")
                 )
-            )
+            flops_stats["name"] = node.name
+            flops_stats["class_name"] = node.type
+            flops_list.append(flops_stats)
+
         if node.type == "ImmutableTensor":
-            param_dim = np.prod(node_oup.shape)
-            # TODO: consider other quantize dtypes
-            param_bytes = 1 if is_quantize(node_oup.dtype) else 4
+            param_stats = get_param_stats(node.numpy())
             # add tensor size attr
-            attr["size"] = AttrValue(
-                s=sizeof_fmt(param_dim * param_bytes).encode(encoding="utf-8")
-            )
-            params_list.append(
-                dict(
-                    name=node.name,
-                    shape=node_oup.shape,
-                    param_dim=param_dim,
-                    bits=param_bytes * 8,
-                    size=param_dim * param_bytes,
-                    size_cum=0,
-                    mean="{:.2g}".format(node.numpy().mean()),
-                    std="{:.2g}".format(node.numpy().std()),
+            if log_path:
+                attr["size"] = AttrValue(
+                    s=sizeof_fmt(param_stats["size"]).encode(encoding="utf-8")
+                )
+            param_stats["name"] = node.name
+            params_list.append(param_stats)
+
+        if log_path:
+            node_list.append(
+                NodeDef(
+                    name=process_name(node.name),
+                    op=node.type,
+                    input=inp_list,
+                    attr=attr,
                 )
             )
-        # FIXME(MGE-2165): nodes outside network module may lead to unknown display bug
-        if not len(node.name.split(".")) > 2 and not node in graph.input_vars:
-            continue
-        node_list.append(
-            NodeDef(
-                name=process_name(node.name), op=node.type, input=inp_list, attr=attr,
-            )
-        )
+    # summary
+    extra_info = {
+        "#ops": len(graph.all_oprs),
+        "#params": len(params_list),
+    }
 
-    total_flops, total_params = 0, 0
+    total_flops, total_param_dims, total_param_size = 0, 0, 0
     if log_params:
-        total_params = print_params_stats(params_list, bar_length_max)
+        total_param_dims, total_param_size = print_param_stats(
+            params_list, bar_length_max
+        )
+        extra_info["total_param_dims"] = sizeof_fmt(total_param_dims)
+        extra_info["total_param_size"] = sizeof_fmt(total_param_size)
     if log_flops:
-        total_flops = print_flops_stats(flops_list, bar_length_max)
+        total_flops = print_op_stats(flops_list, bar_length_max)
+        extra_info["total_flops"] = sizeof_fmt(total_flops, suffix="OPs")
+    if log_params and log_flops:
+        extra_info["flops/param_size"] = "{:3.3f}".format(
+            total_flops / total_param_size
+        )
 
-    graph_def = GraphDef(node=node_list, versions=VersionDef(producer=22))
+    if log_path:
+        graph_def = GraphDef(node=node_list, versions=VersionDef(producer=22))
 
-    device = "/device:CPU:0"
-    stepstats = RunMetadata(
-        step_stats=StepStats(dev_stats=[DeviceStepStats(device=device)])
-    )
-    writer._get_file_writer().add_graph((graph_def, stepstats))
+        device = "/device:CPU:0"
+        stepstats = RunMetadata(
+            step_stats=StepStats(dev_stats=[DeviceStepStats(device=device)])
+        )
+        writer = SummaryWriter(log_path)
+        writer._get_file_writer().add_graph((graph_def, stepstats))
+
+    print_summary(**extra_info)
 
     # FIXME: remove this after resolving "span dist too large" warning
     _imperative_rt_logger.set_log_level(old_level)
 
-    return total_params, total_flops
+    return total_param_size, total_flops
 
 
 def main():
@@ -164,7 +188,7 @@ def main():
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
     parser.add_argument("model_path", help="dumped model path.")
-    parser.add_argument("log_path", help="tensorboard log path.")
+    parser.add_argument("--log_path", help="tensorboard log path.")
     parser.add_argument(
         "--bar_length_max",
         type=int,
@@ -179,7 +203,20 @@ def main():
     parser.add_argument(
         "--log_flops", action="store_true", help="whether print and record op flops.",
     )
-    visualize(**vars(parser.parse_args()))
+    parser.add_argument(
+        "--all",
+        action="store_true",
+        help="whether print all stats. Tensorboard logs will be placed in './log' if not specified.",
+    )
+    args = parser.parse_args()
+    if args.all:
+        args.log_params = True
+        args.log_flops = True
+        if not args.log_path:
+            args.log_path = "./log"
+    kwargs = vars(args)
+    kwargs.pop("all")
+    visualize(**kwargs)
 
 
 if __name__ == "__main__":

imperative/python/megengine/utils/module_stats.py

@@ -5,16 +5,17 @@
 # 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 contextlib
 from functools import partial
 
 import numpy as np
 import tabulate
 
 import megengine as mge
-import megengine.core.tensor.dtype as dtype
 import megengine.module as m
 import megengine.module.qat as qatm
 import megengine.module.quantized as qm
+from megengine.core.tensor.dtype import get_dtype_bit
 from megengine.functional.tensor import zeros
 
 try:
@@ -26,61 +27,99 @@ logger = mge.get_logger(__name__)
 logger.setLevel("INFO")
 
 
-CALC_FLOPS = {}
+_calc_flops_dict = {}
+_calc_receptive_field_dict = {}
 
 
-def _register_modules(*modules):
+def _receptive_field_fallback(module, inputs, outputs):
+    if not _receptive_field_enabled:
+        return
+    assert not hasattr(module, "_rf")
+    assert not hasattr(module, "_stride")
+    if len(inputs) == 0:
+        # TODO: support other dimension
+        module._rf = (1, 1)
+        module._stride = (1, 1)
+        return module._rf, module._stride
+    rf, stride = preprocess_receptive_field(module, inputs, outputs)
+    module._rf = rf
+    module._stride = stride
+    return rf, stride
+
+
+# key tuple, impl_dict, fallback
+_iter_list = [
+    ("flops_num", _calc_flops_dict, None),
+    (
+        ("receptive_field", "stride"),
+        _calc_receptive_field_dict,
+        _receptive_field_fallback,
+    ),
+]
+
+_receptive_field_enabled = False
+
+
+def _register_dict(*modules, dict=None):
     def callback(impl):
         for module in modules:
-            CALC_FLOPS[module] = impl
+            dict[module] = impl
         return impl
 
     return callback
 
 
-@_register_modules(
-    m.Conv2d,
-    m.ConvTranspose2d,
-    m.LocalConv2d,
-    qm.Conv2d,
-    qm.ConvRelu2d,
-    qm.ConvBn2d,
-    qm.ConvBnRelu2d,
-    qatm.Conv2d,
-    qatm.ConvRelu2d,
-    qatm.ConvBn2d,
-    qatm.ConvBnRelu2d,
+def register_flops(*modules):
+    return _register_dict(*modules, dict=_calc_flops_dict)
+
+
+def register_receptive_field(*modules):
+    return _register_dict(*modules, dict=_calc_receptive_field_dict)
+
+
+def enable_receptive_field():
+    global _receptive_field_enabled
+    _receptive_field_enabled = True
+
+
+def disable_receptive_field():
+    global _receptive_field_enabled
+    _receptive_field_enabled = False
+
+
+@register_flops(
+    m.Conv1d, m.Conv2d, m.Conv3d, m.ConvTranspose2d, m.LocalConv2d, m.DeformableConv2d
 )
-def count_convNd(module, input, output):
+def flops_convNd(module: m.Conv2d, inputs, outputs):
     bias = 1 if module.bias is not None else 0
-    group = module.groups
-    ic = input[0].shape[1]
-    oc = output[0].shape[1]
-    goc = oc // group
-    gic = ic // group
-    N = output[0].shape[0]
-    HW = np.prod(output[0].shape[2:])
     # N x Cout x H x W x  (Cin x Kw x Kh + bias)
-    return N * HW * goc * (gic * np.prod(module.kernel_size) + bias)
+    return np.prod(outputs[0].shape) * (
+        module.in_channels // module.groups * np.prod(module.kernel_size) + bias
+    )
 
 
-@_register_modules(m.ConvTranspose2d)
-def count_deconvNd(module, input, output):
-    return np.prod(input[0].shape) * output[0].shape[1] * np.prod(module.kernel_size)
+@register_flops(m.Linear)
+def flops_linear(module: m.Linear, inputs, outputs):
+    bias = module.out_features if module.bias is not None else 0
+    return np.prod(outputs[0].shape) * module.in_features + bias
 
 
-@_register_modules(m.Linear, qatm.Linear, qm.Linear)
-def count_linear(module, input, output):
-    return np.prod(output[0].shape) * module.in_features
+@register_flops(m.BatchMatMulActivation)
+def flops_batchmatmul(module: m.BatchMatMulActivation, inputs, outputs):
+    bias = 1 if module.bias is not None else 0
+    x = inputs[0]
+    w = module.weight
+    batch_size = x.shape[0]
+    n, p = x.shape[1:]
+    _, m = w.shape[1:]
+    return n * (p + bias) * m * batch_size
 
 
 # does not need import qat and quantized module since they inherit from float module.
 hook_modules = (
-    m.Conv2d,
-    m.ConvTranspose2d,
-    m.LocalConv2d,
-    m.BatchNorm2d,
+    m.conv._ConvNd,
     m.Linear,
+    m.BatchMatMulActivation,
 )
 
 
@@ -106,22 +145,63 @@ def sizeof_fmt(num, suffix="B"):
     return "{}{:.1f} {}{}".format(sign_str, num, "Yi", suffix)
 
 
-def print_flops_stats(flops, bar_length_max=20):
-    flops_list = [i["flops_num"] for i in flops]
-    max_flops_num = max(flops_list + [0])
-    # calc total flops and set flops_cum
+def preprocess_receptive_field(module, inputs, outputs):
+    # TODO: support other dimensions
+    pre_rf = (
+        max(getattr(i.owner, "_rf", (1, 1))[0] for i in inputs),
+        max(getattr(i.owner, "_rf", (1, 1))[1] for i in inputs),
+    )
+    pre_stride = (
+        max(getattr(i.owner, "_stride", (1, 1))[0] for i in inputs),
+        max(getattr(i.owner, "_stride", (1, 1))[1] for i in inputs),
+    )
+    return pre_rf, pre_stride
+
+
+def get_op_stats(module, inputs, outputs):
+    rst = {
+        "input_shapes": [i.shape for i in inputs],
+        "output_shapes": [o.shape for o in outputs],
+    }
+    valid_flag = False
+    for key, _dict, fallback in _iter_list:
+        for _type in _dict:
+            if isinstance(module, _type):
+                value = _dict[_type](module, inputs, outputs)
+                valid_flag = True
+                break
+        else:
+            if fallback is not None:
+                value = fallback(module, inputs, outputs)
+            continue
+
+        if isinstance(key, tuple):
+            assert isinstance(value, tuple)
+            for k, v in zip(key, value):
+                rst[k] = v
+        else:
+            rst[key] = value
+
+    if valid_flag:
+        return rst
+    else:
+        return None
+    return
+
+
+def print_op_stats(flops, bar_length_max=20):
+    max_flops_num = max([i["flops_num"] for i in flops] + [0])
     total_flops_num = 0
     for d in flops:
         total_flops_num += int(d["flops_num"])
         d["flops_cum"] = sizeof_fmt(total_flops_num, suffix="OPs")
 
-    for i in flops:
-        f = i["flops_num"]
-        i["flops"] = sizeof_fmt(f, suffix="OPs")
-        r = i["ratio"] = f / total_flops_num
-        i["percentage"] = "{:.2f}%".format(r * 100)
-        bar_length = int(f / max_flops_num * bar_length_max)
-        i["bar"] = "#" * bar_length
+    for d in flops:
+        ratio = d["ratio"] = d["flops_num"] / total_flops_num
+        d["percentage"] = "{:.2f}%".format(ratio * 100)
+        bar_length = int(d["flops_num"] / max_flops_num * bar_length_max)
+        d["bar"] = "#" * bar_length
+        d["flops"] = sizeof_fmt(d["flops_num"], suffix="OPs")
 
     header = [
         "name",
@@ -133,10 +213,13 @@ def print_flops_stats(flops, bar_length_max=20):
         "percentage",
         "bar",
     ]
+    if _receptive_field_enabled:
+        header.insert(4, "receptive_field")
+        header.insert(5, "stride")
 
     total_flops_str = sizeof_fmt(total_flops_num, suffix="OPs")
     total_var_size = sum(
-        sum(s[1] if len(s) > 1 else 0 for s in i["output_shapes"]) for i in flops
+        sum(s[1] if len(s) > 1 else 0 for s in d["output_shapes"]) for d in flops
     )
     flops.append(
         dict(name="total", flops=total_flops_str, output_shapes=total_var_size)
@@ -147,30 +230,44 @@ def print_flops_stats(flops, bar_length_max=20):
     return total_flops_num
 
 
-def print_params_stats(params, bar_length_max=20):
+def get_param_stats(param: np.ndarray):
+    nbits = get_dtype_bit(param.dtype.name)
+    shape = param.shape
+    param_dim = np.prod(param.shape)
+    param_size = param_dim * nbits // 8
+    return {
+        "dtype": param.dtype,
+        "shape": shape,
+        "mean": "{:.3g}".format(param.mean()),
+        "std": "{:.3g}".format(param.std()),
+        "param_dim": param_dim,
+        "nbits": nbits,
+        "size": param_size,
+    }
+
+
+def print_param_stats(params, bar_length_max=20):
+    max_size = max([d["size"] for d in params] + [0])
     total_param_dims, total_param_size = 0, 0
     for d in params:
         total_param_dims += int(d["param_dim"])
         total_param_size += int(d["size"])
-        d["size"] = sizeof_fmt(d["size"])
         d["size_cum"] = sizeof_fmt(total_param_size)
 
     for d in params:
-        ratio = d["param_dim"] / total_param_dims
+        ratio = d["size"] / total_param_size
         d["ratio"] = ratio
         d["percentage"] = "{:.2f}%".format(ratio * 100)
-
-    # construct bar
-    max_ratio = max([d["ratio"] for d in params])
-    for d in params:
-        bar_length = int(d["ratio"] / max_ratio * bar_length_max)
+        bar_length = int(d["size"] / max_size * bar_length_max)
         d["size_bar"] = "#" * bar_length
+        d["size"] = sizeof_fmt(d["size"])
 
     param_size = sizeof_fmt(total_param_size)
     params.append(dict(name="total", param_dim=total_param_dims, size=param_size,))
 
     header = [
         "name",
+        "dtype",
         "shape",
         "mean",
         "std",
@@ -186,7 +283,13 @@ def print_params_stats(params, bar_length_max=20):
         "param stats: \n" + tabulate.tabulate(dict2table(params, header=header))
     )
 
-    return total_param_size
+    return total_param_dims, total_param_size
+
+
+def print_summary(**kwargs):
+    data = [["item", "value"]]
+    data.extend(list(kwargs.items()))
+    logger.info("summary\n" + tabulate.tabulate(data))
 
 
 def module_stats(
@@ -205,71 +308,53 @@ def module_stats(
     :param log_params: whether print and record params size.
     :param log_flops: whether print and record op flops.
     """
+    disable_receptive_field()
 
-    def get_byteswidth(tensor):
-        if dtype.is_quantize(tensor.dtype):
-            return 1
-        # elif dtype.is_bfloat16(tensor.dtype):
-        #      return 2
-        else:
-            return 4
-
-    def module_stats_hook(module, input, output, name=""):
+    def module_stats_hook(module, inputs, outputs, name=""):
         class_name = str(module.__class__).split(".")[-1].split("'")[0]
 
-        flops_fun = CALC_FLOPS.get(type(module))
-        if callable(flops_fun):
-            flops_num = flops_fun(module, input, output)
-
-            if not isinstance(output, (list, tuple)):
-                output = [output]
-
-            flops.append(
-                dict(
-                    name=name,
-                    class_name=class_name,
-                    input_shapes=[i.shape for i in input],
-                    output_shapes=[o.shape for o in output],
-                    flops_num=flops_num,
-                    flops_cum=0,
-                )
-            )
+        flops_stats = get_op_stats(module, inputs, outputs)
+        if flops_stats is not None:
+            flops_stats["name"] = name
+            flops_stats["class_name"] = class_name
+            flops.append(flops_stats)
 
         if hasattr(module, "weight") and module.weight is not None:
             w = module.weight
-            value = w.numpy()
-            param_dim = np.prod(w.shape)
-            param_bytes = get_byteswidth(w)
-            params.append(
-                dict(
-                    name=name + "-w",
-                    shape=w.shape,
-                    param_dim=param_dim,
-                    bits=param_bytes * 8,
-                    size=param_dim * param_bytes,
-                    size_cum=0,
-                    mean="{:.2g}".format(value.mean()),
-                    std="{:.2g}".format(value.std()),
-                )
-            )
+            param_stats = get_param_stats(w.numpy())
+            param_stats["name"] = name + "-w"
+            params.append(param_stats)
 
         if hasattr(module, "bias") and module.bias is not None:
             b = module.bias
-            value = b.numpy()
-            param_dim = np.prod(b.shape)
-            param_bytes = get_byteswidth(b)
-            params.append(
-                dict(
-                    name=name + "-b",
-                    shape=b.shape,
-                    param_dim=param_dim,
-                    bits=param_bytes * 8,
-                    size=param_dim * param_bytes,
-                    size_cum=0,
-                    mean="{:.2g}".format(value.mean()),
-                    std="{:.2g}".format(value.std()),
-                )
-            )
+            param_stats = get_param_stats(b.numpy())
+            param_stats["name"] = name + "-b"
+            params.append(param_stats)
+
+    @contextlib.contextmanager
+    def adjust_stats(module, training=False):
+        """Adjust module to training/eval mode temporarily.
+
+        Args:
+            module (M.Module): used module.
+            training (bool): training mode. True for train mode, False fro eval mode.
+        """
+
+        def recursive_backup_stats(module, mode):
+            for m in module.modules():
+                # save prev status to _prev_training
+                m._prev_training = m.training
+                m.train(mode, recursive=False)
+
+        def recursive_recover_stats(module):
+            for m in module.modules():
+                # recover prev status and delete attribute
+                m.training = m._prev_training
+                delattr(m, "_prev_training")
+
+        recursive_backup_stats(module, mode=training)
+        yield module
+        recursive_recover_stats(module)
 
     # multiple inputs to the network
     if not isinstance(input_size[0], tuple):
@@ -286,15 +371,28 @@ def module_stats(
             )
 
     inputs = [zeros(in_size, dtype=np.float32) for in_size in input_size]
-    model.eval()
-    model(*inputs)
+    with adjust_stats(model, training=False) as model:
+        model(*inputs)
+
     for h in hooks:
         h.remove()
 
-    total_flops, total_params = 0, 0
+    extra_info = {
+        "#params": len(params),
+    }
+    total_flops, total_param_dims, total_param_size = 0, 0, 0
     if log_params:
-        total_params = print_params_stats(params, bar_length_max)
+        total_param_dims, total_param_size = print_param_stats(params, bar_length_max)
+        extra_info["total_param_dims"] = sizeof_fmt(total_param_dims)
+        extra_info["total_param_size"] = sizeof_fmt(total_param_size)
     if log_flops:
-        total_flops = print_flops_stats(flops, bar_length_max)
+        total_flops = print_op_stats(flops, bar_length_max)
+        extra_info["total_flops"] = sizeof_fmt(total_flops, suffix="OPs")
+    if log_params and log_flops:
+        extra_info["flops/param_size"] = "{:3.3f}".format(
+            total_flops / total_param_size
+        )
+
+    print_summary(**extra_info)
 
-    return total_params, total_flops
+    return total_param_size, total_flops

imperative/python/megengine/utils/network.py

@@ -11,12 +11,14 @@ import fnmatch
 import itertools
 import re
 from collections import OrderedDict
-from typing import Dict, List
+from typing import Dict, List, Sequence
 
 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 ..logger import get_logger
 from .comp_graph_tools import get_dep_vars, get_opr_type, get_oprs_seq
 from .network_node import (
     Host2DeviceCopy,
@@ -27,6 +29,8 @@ from .network_node import (
     str_to_mge_class,
 )
 
+logger = get_logger(__name__)
+
 
 class Network:
     def __init__(self):
@@ -60,12 +64,12 @@ 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
 
@@ -83,6 +87,58 @@ class Network:
             for o in opr.outputs:
                 self.all_vars_map[o.var.id] = o
 
+    def optimize_for_inference(self, dest_vars, **kwargs):
+        r"""
+        Applies optimize_for_inference pass for operator graph.
+
+            :param dest_vars: list of output vars in the operator graph
+
+            :Keyword Arguments:
+
+                * enable_io16xc32 --
+                    whether to use float16 for I/O between oprs and use
+                    float32 as internal computation precision. Note the output var would be
+                    changed to float16.
+                * enable_ioc16 --
+                    whether to use float16 for both I/O and computation
+                    precision.
+
+                * enable_hwcd4 --
+                    whether to use NHWCD4 data layout. This is faster on some
+                    OpenCL backend.
+                * enable_nchw88 --
+                    whether to use NCHW88 data layout, currently
+                    used in X86 AVX backend.
+                * enable_nchw44 --
+                    whether to use NCHW44 data layout, currently
+                    used in arm backend.
+                * enable_nchw44_dot --
+                    whether to use NCHW44_dot data layout, currently
+                    used in armv8.2+dotprod backend.
+                * enable_nchw4 --
+                    whether to use NCHW4 data layout, currently
+                    used in nvidia backend(based on cudnn).
+                * enable_nchw32 --
+                    whether to use NCHW32 data layout, currently
+                    used in nvidia backend with tensorcore(based on cudnn).
+                * enable_chwn4 --
+                    whether to use CHWN4 data layout, currently
+                    used in nvidia backend with tensorcore.
+
+                * enable_fuse_conv_bias_nonlinearity: whether to fuse conv+bias+nonlinearty
+                    into one opr.
+                * enable_fuse_conv_bias_with_z: whether to fuse conv_bias with z
+                    input for inference on nvidia backend(this optimization pass will
+                    result in mismatch of the precision of output of training and
+                    inference)
+        """
+
+        if not isinstance(dest_vars, Sequence):
+            dest_vars = [dest_vars]
+        dest_vars = list(G.VarNode(var.var) for var in dest_vars)
+        new_vars = G.optimize_for_inference(dest_vars, **kwargs)
+        return list(self._get_var(var) for var in new_vars)
+
     def dump(
         self,
         file,
@@ -122,47 +178,22 @@ class Network:
 
         :Keyword Arguments:
 
-            * enable_io16xc32 --
-                whether to use float16 for I/O between oprs and use
-                float32 as internal computation precision. Note the output var would be
-                changed to float16.
-            * enable_ioc16 --
-                whether to use float16 for both I/O and computation
-                precision.
-
-            * enable_hwcd4 --
-                whether to use NHWCD4 data layout. This is faster on some
-                OpenCL backend.
-            * enable_nchw88 --
-                whether to use NCHW88 data layout, currently
-                used in X86 AVX backend.
-            * enable_nchw44 --
-                whether to use NCHW44 data layout, currently
-                used in arm backend.
-            * enable_nchw44_dot --
-                whether to use NCHW44_dot data layout, currently
-                used in armv8.2+dotprod backend.
-            * enable_nchw4 --
-                whether to use NCHW4 data layout, currently
-                used in nvidia backend(based on cudnn).
-            * enable_nchw32 --
-                whether to use NCHW32 data layout, currently
-                used in nvidia backend with tensorcore(based on cudnn).
-            * enable_chwn4 --
-                whether to use CHWN4 data layout, currently
-                used in nvidia backend with tensorcore.
-
-            * enable_fuse_conv_bias_nonlinearity: whether to fuse conv+bias+nonlinearty
-                into one opr.
-            * enable_fuse_conv_bias_with_z: whether to fuse conv_bias with z
-                input for inference on nvidia backend(this optimization pass will
-                result in mismatch of the precision of output of training and
-                inference)
+            See also :py:meth:`optimize_for_inference`.
+
         """
 
         self._compile()
         out = [G.VarNode(var.var) for var in self.output_vars]
 
+        if kwargs.pop("arg_names", False):
+            logger.warning(
+                '"arg_names" is not supported in Network.dump, rename input vars directly'
+            )
+        if kwargs.pop("output_names", False):
+            logger.warning(
+                '"output_names" is not supported in Network.dump, rename output vars directly'
+            )
+
         if optimize_for_inference:
             out = G.optimize_for_inference(out, **kwargs)
 
@@ -197,6 +228,8 @@ 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 +242,25 @@ 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 +312,9 @@ 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 +322,7 @@ 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 +338,7 @@ 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 +423,16 @@ 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 +444,7 @@ 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 +699,7 @@ 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
 
 
@@ -663,6 +710,7 @@ class NodeFilterName(NodeFilter):
 
     def __init__(self, node_iter, pattern, ignorecase):
         super().__init__(node_iter)
+        self.pattern = pattern
         self._re = self.make_re(pattern, ignorecase)
 
     @classmethod
@@ -676,5 +724,5 @@ class NodeFilterName(NodeFilter):
 
     def __iter__(self):
         for i in self._iter:
-            if self._re.match(i.name):
+            if self.pattern == i.name or self._re.match(i.name):
                 yield i

imperative/python/megengine/utils/network_node.py

@@ -6,27 +6,41 @@
 # 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 (
+    preprocess_receptive_field,
+    register_flops,
+    register_receptive_field,
+)
 
 
 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)
@@ -53,6 +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
 
@@ -130,7 +178,7 @@ 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
 
@@ -168,8 +216,8 @@ 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)
@@ -177,7 +225,7 @@ 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
 
@@ -225,8 +273,21 @@ class Elemwise(OpNode):
     type = "Elemwise"
     opdef = builtin.Elemwise
 
-    def calc_flops(self):
-        return np.prod(self.outputs[0].shape)
+
+class ElemwiseMultiType(OpNode):
+    type = "ElemwiseMultiType"
+    opdef = builtin.ElemwiseMultiType
+
+    @classmethod
+    def load(cls, opr):
+        obj = super(ElemwiseMultiType, cls).load(opr)
+        obj.params["dtype"] = opr.outputs[0].dtype
+        return obj
+
+
+@register_flops(Elemwise, ElemwiseMultiType)
+def flops_elemwise(opnode: Elemwise, inputs, outputs):
+    return np.prod(outputs[0].shape)
 
 
 class Reduce(OpNode):
@@ -255,20 +316,24 @@ class MatrixMul(OpNode):
     type = "MatrixMul"
     opdef = builtin.MatrixMul
 
-    def calc_flops(self):
-        assert len(self.inputs[0].shape) == 2 and len(self.outputs[0].shape) == 2
-        mid_shape = self.inputs[0].shape[1]
-        return np.prod(self.outputs[0].shape) * mid_shape
+
+@register_flops(MatrixMul)
+def flops_matmul(opnode: MatrixMul, inputs, outputs):
+    assert len(inputs[0].shape) == 2 and len(outputs[0].shape) == 2
+    mid_shape = inputs[0].shape[1]
+    return np.prod(outputs[0].shape) * mid_shape
 
 
 class BatchedMatrixMul(OpNode):
     type = "BatchedMatmul"
     opdef = builtin.BatchedMatrixMul
 
-    def calc_flops(self):
-        assert len(self.inputs[0].shape) == 3 and len(self.outputs[0].shape) == 3
-        mid_shape = self.inputs[0].shape[2]
-        return np.prod(self.outputs[0].shape) * mid_shape
+
+@register_flops(BatchedMatrixMul)
+def flops_batchmatmul(opnode: BatchedMatrixMul, inputs, outputs):
+    assert len(inputs[0].shape) == 3 and len(outputs[0].shape) == 3
+    mid_shape = inputs[0].shape[2]
+    return np.prod(outputs[0].shape) * mid_shape
 
 
 class Dot(OpNode):
@@ -285,18 +350,6 @@ class ConvolutionForward(OpNode):
     type = "Convolution"
     opdef = builtin.Convolution
 
-    def calc_flops(self):
-        param_W_shape = self.inputs[1].shape
-        kh = param_W_shape[-2]
-        kw = param_W_shape[-1]
-        if len(param_W_shape) == 5:
-            num_input = param_W_shape[2]
-        else:
-            num_input = param_W_shape[1]
-        NCHW = np.prod(self.outputs[0].shape)
-        # N x Cout x H x W x  (Cin x Kw x Kh)
-        return NCHW * (num_input * kw * kh)
-
 
 class ConvolutionBackwardData(OpNode):
     type = "ConvTranspose"
@@ -343,17 +396,41 @@ class ConvBiasForward(OpNode):
         obj.params["dtype"] = opr.outputs[0].dtype
         return obj
 
-    def calc_flops(self):
-        param_W_shape = self.inputs[1].shape
-        kh = param_W_shape[-2]
-        kw = param_W_shape[-1]
-        if len(param_W_shape) == 5:
-            num_input = param_W_shape[2]
-        else:
-            num_input = param_W_shape[1]
-        NCHW = np.prod(self.outputs[0].shape)
-        # N x Cout x H x W x  (Cin x Kw x Kh + bias)
-        return NCHW * (num_input * kw * kh + 1)
+
+@register_flops(
+    ConvolutionForward, ConvBiasForward,
+)
+def flops_conv(opnode: ConvolutionForward, inputs, outputs):
+    param_W_shape = inputs[1].shape
+    kh = param_W_shape[-2]
+    kw = param_W_shape[-1]
+    if len(param_W_shape) == 5:
+        num_input = param_W_shape[2]
+    else:
+        num_input = param_W_shape[1]
+    NCHW = np.prod(outputs[0].shape)
+    bias = 1 if isinstance(opnode, ConvBiasForward) else 0
+    # N x Cout x H x W x  (Cin x Kw x Kh)
+    return NCHW * (num_input * kw * kh + bias)
+
+
+@register_receptive_field(ConvolutionForward, ConvBiasForward)
+def receptive_field(opnode: ConvolutionForward, inputs, outputs):
+    pre_rf, pre_stride = preprocess_receptive_field(opnode, inputs, outputs)
+    param_W_shape = inputs[1].shape
+    kh = param_W_shape[-2]
+    kw = param_W_shape[-1]
+    rf = (
+        kh * pre_stride[0] + pre_rf[0] - pre_stride[0],
+        kw * pre_stride[1] + pre_rf[1] - pre_stride[1],
+    )
+    stride = (
+        opnode.params["stride_h"] * pre_stride[0],
+        opnode.params["stride_w"] * pre_stride[1],
+    )
+    opnode._rf = rf
+    opnode._stride = stride
+    return rf, stride
 
 
 class BatchConvBiasForward(OpNode):
@@ -652,20 +729,6 @@ class AssertEqual(OpNode):
     opdef = builtin.AssertEqual
 
 
-class ElemwiseMultiType(OpNode):
-    type = "ElemwiseMultiType"
-    opdef = builtin.ElemwiseMultiType
-
-    @classmethod
-    def load(cls, opr):
-        obj = super(ElemwiseMultiType, cls).load(opr)
-        obj.params["dtype"] = opr.outputs[0].dtype
-        return obj
-
-    def calc_flops(self):
-        return np.prod(self.outputs[0].shape)
-
-
 class CvtColorForward(OpNode):
     type = "CvtColor"
     opdef = builtin.CvtColor

imperative/python/src/graph_rt.cpp

@@ -266,7 +266,7 @@ void init_graph_rt(py::module m) {
                 {"HEURISTIC", [&]() { stg = _AlgoStrategy::HEURISTIC; }},
                 {"PROFILE", [&]() { stg = _AlgoStrategy::PROFILE; }},
                 {"REPRODUCIBLE", [&]() { stg = _AlgoStrategy::REPRODUCIBLE; }},
-                {"OPTMIZED", [&]() { stg = _AlgoStrategy::OPTMIZED; }},
+                {"OPTIMIZED", [&]() { stg = _AlgoStrategy::OPTIMIZED; }},
         };
         auto it = m.find(strategy);
         mgb_assert(it != m.end(), "Invalid strategy string!");

imperative/python/src/ops.cpp

@@ -87,9 +87,13 @@ struct pyobj_convert_generic {
     }
 };
 
+template<typename T, typename SFINAE=void>
+struct EnumTrait;
+
 template <typename T>
-struct EnumTrait {
+struct EnumTrait<T, std::enable_if_t<std::is_enum_v<T>>> {
     static constexpr bool is_bit_combined = false;
+    static constexpr std::underlying_type_t<T> max = 0;
 };
 
 template <typename T>
@@ -264,18 +268,25 @@ struct BitCombinedEnumWrapper {
             return ret;
         }
     }
-    static PyObject* py_new_combined_enum(PyTypeObject* type, PyObject*, PyObject*) {
-        PyObject* obj = type->tp_alloc(type, 0);
-        reinterpret_cast<BitCombinedEnumWrapper*>(obj)->value = static_cast<T>(1);
-        return obj;
-    }
-    static int py_init(PyObject* self, PyObject* args, PyObject*) {
-        int input = 1;
-        if (PyArg_ParseTuple(args, "|i", &input)){
-            reinterpret_cast<BitCombinedEnumWrapper*>(self)->value =
-                    static_cast<T>(input);
+    static PyObject* py_new_combined_enum(PyTypeObject* type, PyObject* args, PyObject*) {
+        if (!PyTuple_Size(args)) {
+            PyObject* obj = type->tp_alloc(type, 0);
+            reinterpret_cast<BitCombinedEnumWrapper*>(obj)->value = T();
+            return obj;
+        }
+        else {
+            PyObject* input;
+            if (!PyArg_ParseTuple(args, "|O", &input)) {
+                return nullptr;
+            }
+            T value;
+            try {
+                value = pyobj_convert_generic<T>::from(input);
+            } CATCH_ALL(nullptr);
+            PyObject* obj = type->tp_alloc(type, 0);
+            reinterpret_cast<BitCombinedEnumWrapper*>(obj)->value = value;
+            return obj;
         }
-        return 0;
     }
     static PyObject* py_repr(PyObject* self) {
         return pyobj_convert_generic<std::string>::to(
@@ -325,6 +336,12 @@ struct pyobj_convert_generic<T,
     static T from(PyObject* obj) {
         if (PyObject_TypeCheck(obj, &Wrapper::type)) {
             return reinterpret_cast<Wrapper*>(obj)->value;
+        } else if(PyLong_Check(obj)) {
+            auto value = pyobj_convert_generic<std::underlying_type_t<T>>::from(obj);
+            mgb_throw_if(value > EnumTrait<T>::max, mgb::MegBrainError,
+                    "out of range, cannot convert %zu to %s",
+                    static_cast<uint32_t>(value), Wrapper::name);
+            return static_cast<T>(value);
         }
         // try as string
         // TODO: type checkcd

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/integration/test_optimizer.py

@@ -104,6 +104,10 @@ def _test_optimizer(opt_str, test_case, check_class, update_lr=False):
             )
             step += 1
             check_func(ori_params, net.parameters(), step)
+            try_state_dict = {
+                "net": net.state_dict(),
+                "opt": opt.state_dict(),
+            }
 
 
 def test_sgd():

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)
@@ -309,14 +415,19 @@ def test_broadcast():
     output2_shape = (20, 10, 20)
     data2 = np.random.random(input2_shape).astype(np.float32)
 
+    input3_shape = (10, 10)
+    output3_shape = (10, 10)
+    data3 = np.random.random(input3_shape).astype(np.float32)
+
     def compare_fn(x, y):
         assert x.shape[0] == y
 
     cases = [
         {"input": [data1, output1_shape], "output": output1_shape},
         {"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):
@@ -329,35 +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])
 
 
@@ -377,35 +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(
@@ -420,7 +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)
 
@@ -432,7 +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,
     )
 
 
@@ -445,14 +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/quantization/test_module.py

@@ -30,7 +30,10 @@ min_max_fakequant_qconfig = QConfig(
     act_fake_quant=partial(FakeQuantize, dtype="qint8"),
 )
 
-inp_scale = np.float32(np.random.rand() + 1)
+
+def gen_inp_scale():
+    return np.float32(np.random.rand() + 1)
+
 
 min_val = np.random.randint(-127, 0, size=(2,)).astype("float32")
 max_val = np.random.randint(1, 127, size=(2,)).astype("float32")
@@ -116,6 +119,7 @@ def test_dequant_stub():
     q_net.eval()
 
     x = mge.tensor(np.random.normal(size=(3, 3)).astype("float32"))
+    inp_scale = gen_inp_scale()
     x = fake_quant_act(x, inp_scale)
     x.qparams.scale = inp_scale
 
@@ -192,6 +196,7 @@ def test_linear():
     init_qat_net(qat_net)
 
     x = mge.tensor(np.random.normal(size=(3, 3)).astype("float32"))
+    inp_scale = gen_inp_scale()
     x = fake_quant_act(x, inp_scale)
     x.qparams.update(create_qparams(QuantMode.SYMMERTIC, "qint8", inp_scale))
 
@@ -235,6 +240,7 @@ def test_conv(module):
     init_qat_net(qat_net)
 
     x = mge.tensor(np.random.normal(size=(1, 3, 3, 3)).astype("float32"))
+    inp_scale = gen_inp_scale()
     x = fake_quant_act(x, inp_scale)
     x.qparams.update(create_qparams(QuantMode.SYMMERTIC, "qint8", inp_scale))
 
@@ -269,3 +275,41 @@ def test_conv(module):
     np.testing.assert_allclose(qat_without_fakequant, normal, atol=1e-5)
     np.testing.assert_allclose(qat, fake_quant_normal, atol=act_scale)
     np.testing.assert_allclose(q, fake_quant_normal.numpy(), atol=act_scale)
+
+
+def test_concat():
+    normal_net = Float.Concat()
+    normal_net.eval()
+
+    qat_net = QAT.Concat()
+    qat_net.eval()
+    disable_observer(qat_net)
+
+    propagate_qconfig(qat_net, min_max_fakequant_qconfig)
+    init_qat_net(qat_net)
+
+    inps = []
+    inps_int8 = []
+    for i in range(3):
+        inp_scale = gen_inp_scale()
+        inps.append(mge.tensor(np.random.normal(size=(3, 3)).astype("float32")))
+        inps[i] = fake_quant_act(inps[i], inp_scale)
+        inps[i].qparams.update(create_qparams(QuantMode.SYMMERTIC, "qint8", inp_scale))
+        inps_int8.append(quant(inps[i], inp_scale))
+
+    qat_from_float = QAT.Concat.from_float_module(normal_net)
+    qat_from_float.eval()
+    disable_fake_quant(qat_from_float)
+    disable_observer(qat_from_float)
+
+    q_net = Q.Concat.from_qat_module(qat_net)
+    q_net.eval()
+
+    normal = normal_net(inps)
+    qat_without_fakequant = qat_from_float(inps)
+    fake_quant_normal = fake_quant_act(normal_net(inps), act_scale)
+    qat = qat_net(inps)
+    q = q_net(inps_int8).numpy() * act_scale
+    np.testing.assert_allclose(qat_without_fakequant, normal)
+    np.testing.assert_allclose(qat, fake_quant_normal.numpy())
+    np.testing.assert_allclose(q, fake_quant_normal.numpy())

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

@@ -124,6 +124,35 @@ def test_with_submodule(symbolic):
 
 
 @pytest.mark.parametrize("symbolic", [False, True])
+def test_with_submodule_in_container(symbolic):
+    class Simple(M.Module):
+        def __init__(self, name):
+            super().__init__()
+            self.name = name
+            self.l0 = [M.Linear(3, 3) for _ in range(2)]
+            self.l1 = tuple(self.l0)
+            self.l2 = dict(zip(["l2-0", "l2-1"], self.l0))
+
+        def forward(self, x):
+            for i in range(2):
+                x = self.l0[i](x)
+                x = self.l1[i](x)
+                x = self.l2["l2-%d" % i](x)
+            return x
+
+    m = Simple("simple")
+
+    ops = _dump_and_load(m, symbolic)
+    assert ops[-1].outputs[0].name == "simple.l2.l2-1.ADD"
+    assert ops[-1].name == "simple.l2.l2-1.ADD"
+    assert ops[-2].name == "simple.l2.l2-1.MatrixMul"
+    assert ops[-3].name == "simple.l1.1.ADD"
+    assert ops[-4].name == "simple.l1.1.MatrixMul"
+    assert ops[-5].name == "simple.l0.1.ADD"
+    assert ops[-6].name == "simple.l0.1.MatrixMul"
+
+
+@pytest.mark.parametrize("symbolic", [False, True])
 def test_named_submodule(symbolic):
     class Simple(M.Module):
         def __init__(self, name):
@@ -264,4 +293,4 @@ def test_quantized_module_user_naming_param(symbolic):
     (matrix_mul_op,) = [op for op in ops if op.name == "simple.linear.MatrixMul"]
     for var in matrix_mul_op.inputs:
         assert var.name in ("simple.quant.TypeCvt", "simple.linear.user-weight")
-    # BUG bias' name does not meet expectations because of astype operator after quantization
+    # WONTFIX: bias' name does not meet expectations because of astype operator after quantization

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)

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

@@ -466,6 +466,19 @@ def test_topk():
     check_pygraph_dump(fwd, [x], [top, indices])
 
 
+def test_nvof():
+    if not is_cuda_available():
+        return
+    src_shape = (4, 5, 224, 224, 4)
+    src = np.random.randint(0, 255, src_shape).astype("uint8")
+    src = Tensor(src)
+
+    @trace(symbolic=True, capture_as_const=True)
+    def fwd(src):
+        return F.nn.nvof(src, precision=1)
+
+    result = fwd(src)
+    check_pygraph_dump(fwd, [src], [result])
 
 
 def test_random():

imperative/python/version_template.py

@@ -6,5 +6,5 @@
 # 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.
-__version__ = "1.3.0.dev"
+__version__ = "1.3.1"
 

imperative/src/impl/ops/broadcast.cpp

@@ -24,14 +24,14 @@ std::shared_ptr<OpDef> make_from_op_node(cg::OperatorNodeBase* node_) {
     return Broadcast::make();
 }
 
-cg::OperatorNodeBase* apply_on_var_node(
+auto apply_on_var_node(
         const OpDef& def,
         const VarNodeArray& inputs) {
     auto&& op = def.cast_final_safe<Broadcast>();
     size_t nr_inp = inputs.size();
     mgb_assert(nr_inp == 2, "Broadcast expects 2 inputs; got %lu actually", nr_inp);
     OperatorNodeConfig config{op.make_name()};
-    return opr::Broadcast::make(inputs[0], inputs[1], config).node()->owner_opr();
+    return opr::Broadcast::make(inputs[0], inputs[1], config);
 }
 
 bool valid_broadcast(const TensorShape& src_shape,

imperative/tablegen/CMakeLists.txt

@@ -1,6 +1,7 @@
 # mgb tablegen executable
 set(TABLE_TARGET mgb-mlir-autogen)
-add_executable(${TABLE_TARGET} autogen.cpp)
+file(GLOB_RECURSE SRCS ${CMAKE_CURRENT_SOURCE_DIR}/*.h ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+add_executable(${TABLE_TARGET} ${SRCS})
 target_include_directories(${TABLE_TARGET} PRIVATE ${MLIR_LLVM_INCLUDE_DIR})
 target_link_libraries(${TABLE_TARGET} PRIVATE LLVMTableGen MLIRTableGen LLVMSupport)
 set(MGB_TABLEGEN_EXE ${TABLE_TARGET})

imperative/tablegen/autogen.cpp

@@ -1,8 +1,17 @@
-#include <iostream>
-#include <unordered_map>
-#include <functional>
-
-#include "./helper.h"
+/**
+ * \file imperative/tablegen/autogen.cpp
+ * 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.
+ */
+
+#include "./targets/cpp_class.h"
+#include "./targets/pybind11.h"
+#include "./targets/python_c_extension.h"
 
 using llvm::raw_ostream;
 using llvm::RecordKeeper;
@@ -27,731 +36,7 @@ llvm::cl::opt<ActionType> action(
                      clEnumValN(CPython, "gen-python-c-extension",
                                 "Generate python c extensions")));
 
-using MgbAttrWrapper = mlir::tblgen::MgbAttrWrapperBase;
-using MgbEnumAttr = mlir::tblgen::MgbEnumAttrMixin;
-using MgbHashableAttr = mlir::tblgen::MgbHashableAttrMixin;
-using MgbAliasAttr = mlir::tblgen::MgbAliasAttrMixin;
-using MgbOp = mlir::tblgen::MgbOpBase;
-using MgbHashableOp = mlir::tblgen::MgbHashableOpMixin;
-
-llvm::StringRef attr_to_ctype(const mlir::tblgen::Attribute& attr_) {
-    // Note: we have already registered the corresponding attr wrappers
-    // for following basic ctypes so we needn't handle them here
-    /* auto&& attr_type_name = attr.getAttrDefName();
-    if (attr_type_name == "UI32Attr") {
-        return "uint32_t";
-    }
-    if (attr_type_name == "UI64Attr") {
-        return "uint64_t";
-    }
-    if (attr_type_name == "I32Attr") {
-        return "int32_t";
-    }
-    if (attr_type_name == "F32Attr") {
-        return "float";
-    }
-    if (attr_type_name == "F64Attr") {
-        return "double";
-    }
-    if (attr_type_name == "StrAttr") {
-        return "std::string";
-    }
-    if (attr_type_name == "BoolAttr") {
-        return "bool";
-    }*/
-
-    auto&& attr = llvm::cast<MgbAttrWrapper>(attr_);
-    if (auto e = llvm::dyn_cast<MgbEnumAttr>(&attr)) {
-        return e->getEnumName();
-    }
-    return attr.getUnderlyingType();
-}
-
-static void gen_op_def_c_header_single(raw_ostream &os, MgbOp& op) {
-    os << formatv(
-        "class {0} : public OpDefImplBase<{0}> {{\n"
-        "    MGB_DYN_TYPE_OBJ_FINAL_DECL;\n\n"
-        "public:\n",
-        op.getCppClassName()
-    );
-    // handle enum alias
-    for (auto &&i : op.getMgbAttributes()) {
-        if (auto attr = llvm::dyn_cast<MgbEnumAttr>(&i.attr)) {
-            os << formatv(
-                "    using {0} = {1};\n",
-                attr->getEnumName(), attr->getUnderlyingType()
-            );
-        }
-    }
-    for (auto &&i : op.getMgbAttributes()) {
-        auto defaultValue = i.attr.getDefaultValue().str();
-        if (!defaultValue.empty()) {
-            defaultValue = formatv(" = {0}", defaultValue);
-        }
-        os << formatv(
-            "    {0} {1}{2};\n",
-            attr_to_ctype(i.attr), i.name, defaultValue
-        );
-    }
-
-    auto gen_ctor = [&](auto&& paramList, auto&& memInitList, auto&& body) {
-        os << formatv(
-            "    {0}({1}){2}{3}\n",
-            op.getCppClassName(), paramList, memInitList, body
-        );
-    };
-
-    gen_ctor("", "", " = default;");
-
-    if (!op.getMgbAttributes().empty()) {
-        std::vector<std::string> paramList, initList;
-        for (auto &&i : op.getMgbAttributes()) {
-            paramList.push_back(formatv(
-                "{0} {1}_", attr_to_ctype(i.attr), i.name
-            ));
-            initList.push_back(formatv(
-                "{0}({0}_)", i.name
-            ));
-        }
-        paramList.push_back("std::string scope_ = {}");
-        gen_ctor(llvm::join(paramList, ", "),
-                 ": " + llvm::join(initList, ", "),
-                 " { set_scope(scope_); }");
-    }
-
-    auto packedParams = op.getPackedParams();
-    if (!packedParams.empty()) {
-        std::vector<std::string> paramList, initList;
-        for (auto &&p : packedParams) {
-            auto&& paramFields = p.getFields();
-            auto&& paramType = p.getFullName();
-            auto&& paramName = formatv("packed_param_{0}", paramList.size());
-            paramList.push_back(
-                paramFields.empty() ? paramType.str()
-                    : formatv("{0} {1}", paramType, paramName)
-            );
-            for (auto&& i : paramFields) {
-                initList.push_back(formatv(
-                    "{0}({1}.{0})", i.name, paramName
-                ));
-            }
-        }
-        for (auto&& i : op.getExtraArguments()) {
-            paramList.push_back(formatv(
-                "{0} {1}_", attr_to_ctype(i.attr), i.name
-            ));
-            initList.push_back(formatv(
-                "{0}({0}_)", i.name
-            ));
-        }
-        gen_ctor(llvm::join(paramList, ", "),
-                 initList.empty() ? "" : ": " + llvm::join(initList, ", "),
-                 " {}");
-    }
-
-    if (!packedParams.empty()) {
-        for (auto&& p : packedParams) {
-            auto accessor = p.getAccessor();
-            if (!accessor.empty()) {
-                os << formatv(
-                    "    {0} {1}() const {{\n",
-                    p.getFullName(), accessor
-                );
-                std::vector<llvm::StringRef> fields;
-                for (auto&& i : p.getFields()) {
-                    fields.push_back(i.name);
-                }
-                os << formatv(
-                    "        return {{{0}};\n",
-                    llvm::join(fields, ", ")
-                );
-                os << "    }\n";
-            }
-        }
-    }
-
-    if (auto decl = op.getExtraOpdefDecl()) {
-        os << decl.getValue();
-    }
-
-    os << formatv(
-        "};\n\n"
-    );
-}
-
-static void gen_to_string_trait_for_enum(raw_ostream &os, MgbOp& op) {
-    for (auto &&i : op.getMgbAttributes()) {
-        if (auto attr = llvm::dyn_cast<MgbEnumAttr>(&i.attr)) {
-            if (attr->supportToString()) {
-                std::vector<std::string> case_body;
-                std::string ename = formatv("{0}::{1}",
-                    op.getCppClassName(), attr->getEnumName());
-                llvm::for_each(attr->getEnumMembers(), [&](auto&& v){
-                    case_body.push_back(formatv(
-                        "case {0}::{1}: return \"{1}\";", ename, v));
-                });
-                os << formatv(R"(
-template <>
-struct ToStringTrait<{0}> {
-    std::string operator()({0} e) const {
-        switch (e) {
-            {1}
-            default:
-                return "{0}::Unknown";
-        }
-    }
-};
-)", ename, llvm::join(case_body, "\n"));
-            }
-        }
-    }
-}
-
-static void gen_op_def_c_body_single(raw_ostream &os, MgbOp& op) {
-    auto&& className = op.getCppClassName();
-    os << formatv(
-        "MGB_DYN_TYPE_OBJ_FINAL_IMPL({0});\n\n", className
-    );
-    auto formatMethImpl = [&](auto&& meth) {
-        return formatv(
-            "{0}_{1}_impl", className, meth
-        );
-    };
-    std::vector<std::string> methods;
-    if (auto hashable = llvm::dyn_cast<MgbHashableOp>(&op)) {
-        os << "namespace {\n";
-
-        // generate hash()
-        mlir::tblgen::FmtContext ctx;
-        os << formatv(
-            "size_t {0}(const OpDef& def_) {{\n",
-            formatMethImpl("hash")
-        );
-        os << formatv(
-            "    auto&& op_ = def_.cast_final_safe<{0}>();\n"
-            "    static_cast<void>(op_);\n",
-            className
-        );
-        ctx.withSelf("op_");
-        os << mlir::tblgen::tgfmt(hashable->getHashFunctionTemplate(), &ctx);
-        os << "}\n";
-
-        // generate is_same_st()
-        os << formatv(
-            "bool {0}(const OpDef& lhs_, const OpDef& rhs_) {{\n",
-            formatMethImpl("is_same_st")
-        );
-        os << formatv(
-            "    auto &&a_ = lhs_.cast_final_safe<{0}>(),\n"
-            "         &&b_ = rhs_.cast_final_safe<{0}>();\n"
-            "    static_cast<void>(a_);\n"
-            "    static_cast<void>(b_);\n",
-            className
-        );
-        os << mlir::tblgen::tgfmt(hashable->getCmpFunctionTemplate(), &ctx, "a_", "b_");
-        os << "}\n";
-
-        // generate props()
-        os << formatv(
-            "std::vector<std::pair<const char*, std::string>> {0}(const OpDef& def_) {{\n",
-            formatMethImpl("props")
-        );
-        os << formatv(
-            "    auto&& op_ = def_.cast_final_safe<{0}>();\n"
-            "    static_cast<void>(op_);\n",
-            className
-        );
-        ctx.withSelf("op_");
-        os << mlir::tblgen::tgfmt(hashable->getPropsFunctionTemplate(), &ctx);
-        os << "}\n";
-
-        // generate make_name()
-        os << formatv(
-            "std::string {0}(const OpDef& def_) {{\n", formatMethImpl("make_name")
-        );
-        os << formatv(
-            "    auto&& op_ = def_.cast_final_safe<{0}>();\n"
-            "    static_cast<void>(op_);\n",
-            className
-        );
-        ctx.withSelf("op_");
-        os << mlir::tblgen::tgfmt(op.getNameFunctionTemplate(), &ctx);
-        os << "}\n";
-
-        os << "} // anonymous namespace\n";
-
-        methods.push_back("hash");
-        methods.push_back("is_same_st");
-        methods.push_back("props");
-        methods.push_back("make_name");
-    }
-    if (!methods.empty()) {
-        os << formatv(
-            "OP_TRAIT_REG({0}, {0})", op.getCppClassName()
-        );
-        for (auto&& i : methods) {
-            os << formatv(
-                "\n    .{0}({1})", i, formatMethImpl(i)
-            );
-        }
-        os << ";\n\n";
-    }
-}
-
-struct EnumContext {
-    std::unordered_map<unsigned int, std::pair<llvm::StringRef, llvm::StringRef>> enumAlias;
-};
-
-static void gen_op_def_pybind11_single(raw_ostream &os, MgbOp& op, EnumContext& ctx) {
-    auto className = op.getCppClassName();
-    os << formatv(
-        "py::class_<{0}, std::shared_ptr<{0}>, OpDef> {0}Inst(m, \"{0}\");\n\n",
-        className
-    );
-    for (auto&& i : op.getMgbAttributes()) {
-        if (auto attr = llvm::dyn_cast<MgbEnumAttr>(&i.attr)) {
-            unsigned int enumID;
-            if (auto alias = llvm::dyn_cast<MgbAliasAttr>(attr)) {
-                auto&& aliasBase = alias->getAliasBase();
-                enumID =
-                    llvm::cast<MgbEnumAttr>(aliasBase)
-                            .getBaseRecord()->getID();
-            } else {
-                enumID = attr->getBaseRecord()->getID();
-            }
-            auto&& enumAlias = ctx.enumAlias;
-            auto&& iter = enumAlias.find(enumID);
-            if (iter == enumAlias.end()) {
-                os << formatv(
-                    "py::enum_<{0}::{1}>({0}Inst, \"{1}\")",
-                    className, attr->getEnumName()
-                );
-                std::vector<std::string> body;
-                for (auto&& i: attr->getEnumMembers()) {
-                    os << formatv(
-                        "\n    .value(\"{2}\", {0}::{1}::{2})",
-                        className, attr->getEnumName(), i
-                    );
-                    body.push_back(formatv(
-                        "if (str == \"{2}\") return {0}::{1}::{2};",
-                        className, attr->getEnumName(), i
-                    ));
-                }
-                if (attr->getEnumCombinedFlag()) {
-                    //! define operator |
-                    os << formatv(
-                            "\n    .def(\"__or__\", []({0}::{1} s0, {0}::{1} s1) {{ "
-                            "\n         return static_cast<{0}::{1}>(uint32_t(s0) | uint32_t(s1));"
-                            "\n      })",
-                            className, attr->getEnumName());
-                    //! define operator &
-                    os << formatv(
-                            "\n    .def(\"__and__\", []({0}::{1} s0, {0}::{1} s1) {{"
-                            "\n         return static_cast<{0}::{1}>(uint32_t(s0) & uint32_t(s1));"
-                            "\n    })",
-                            className, attr->getEnumName());
-                }
-                os << formatv(
-                    "\n    .def(py::init([](const std::string& in) {"
-                    "\n        auto&& str = normalize_enum(in);"
-                    "\n        {0}"
-                    "\n        throw py::cast_error(\"invalid enum value \" + in);"
-                    "\n    }));\n",
-                    llvm::join(body, "\n        ")
-                );
-                os << formatv(
-                    "py::implicitly_convertible<std::string, {0}::{1}>();\n\n",
-                    className, attr->getEnumName()
-                );
-                enumAlias.emplace(enumID,
-                    std::make_pair(className, attr->getEnumName()));
-            } else {
-                os << formatv(
-                    "{0}Inst.attr(\"{1}\") = {2}Inst.attr(\"{3}\");\n\n",
-                    className, attr->getEnumName(),
-                    iter->second.first, iter->second.second
-                );
-            }
-        }
-    }
-    // generate op class binding
-    os << formatv("{0}Inst", className);
-    bool hasDefaultCtor = op.getMgbAttributes().empty();
-    if (!hasDefaultCtor) {
-        os << "\n    .def(py::init<";
-        std::vector<llvm::StringRef> targs;
-        for (auto &&i : op.getMgbAttributes()) {
-            targs.push_back(i.attr.getReturnType());
-        }
-        os << llvm::join(targs, ", ");
-        os << ", std::string>()";
-        for (auto &&i : op.getMgbAttributes()) {
-            os << formatv(", py::arg(\"{0}\")", i.name);
-            auto defaultValue = i.attr.getDefaultValue();
-            if (!defaultValue.empty()) {
-                os << formatv(" = {0}", defaultValue);
-            } else {
-                hasDefaultCtor = true;
-            }
-        }
-        os << ", py::arg(\"scope\") = {})";
-    }
-    if (hasDefaultCtor) {
-        os << "\n    .def(py::init<>())";
-    }
-    for (auto &&i : op.getMgbAttributes()) {
-        os << formatv(
-            "\n    .def_readwrite(\"{0}\", &{1}::{0})",
-            i.name, className
-        );
-    }
-    os << ";\n\n";
-}
-
-static std::string gen_op_def_python_c_extension_enum(
-        raw_ostream& os, EnumContext& ctx, MgbEnumAttr* attr,
-        llvm::StringRef className) {
-    std::string body;
-    unsigned int enumID;
-    if (auto alias = llvm::dyn_cast<MgbAliasAttr>(attr)) {
-        auto&& aliasBase = alias->getAliasBase();
-        enumID = llvm::cast<MgbEnumAttr>(aliasBase).getBaseRecord()->getID();
-    } else {
-        enumID = attr->getBaseRecord()->getID();
-    }
-    auto&& enumAlias = ctx.enumAlias;
-    auto&& iter = enumAlias.find(enumID);
-    auto enumName = attr->getEnumName();
-    body += "{\n";
-    body += formatv("auto& e_type = EnumWrapper<{0}::{1}>::type;", className,
-                    enumName);
-    if (iter == enumAlias.end()) {
-        os << formatv(
-                "template<> PyTypeObject EnumWrapper<{0}::{1}>::type={{};\n",
-                className, enumName);
-        os << formatv(
-                "template<> const char* EnumWrapper<{0}::{1}>::name = "
-                "\"{0}.{1}\";\n",
-                className, enumName);
-        std::vector<std::string> pairStr;
-        for (auto&& i : attr->getEnumMembers()) {
-            pairStr.push_back(
-                    formatv("{{normalize_enum(\"{2}\"), {0}::{1}::{2}}",
-                            className, enumName, i));
-        }
-        os << formatv(R"(
-template<> std::unordered_map<std::string, {0}::{1}>
-EnumWrapper<{0}::{1}>::str2type = {{
-    {2}
-};
-)",
-                      className, enumName, llvm::join(pairStr, ", "));
-        pairStr.clear();
-        for (auto&& i : attr->getEnumMembers()) {
-            pairStr.push_back(
-                    formatv("{{{0}::{1}::{2}, normalize_enum(\"{2}\")}",
-                            className, enumName, i));
-        }
-        os << formatv(R"(
-template<> std::unordered_map<{0}::{1}, std::string>
-EnumWrapper<{0}::{1}>::type2str = {{
-    {2}
-};
-)",
-                      className, enumName, llvm::join(pairStr, ", "));
-        body += formatv(R"(
-    e_type = {{PyVarObject_HEAD_INIT(NULL, 0)};
-    e_type.tp_name = "megengine.core._imperative_rt.ops.{0}.{1}";
-    e_type.tp_basicsize = sizeof(EnumWrapper<{0}::{1}>);
-    e_type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE;
-    e_type.tp_doc = "{0}.{1}";
-    e_type.tp_base = &PyBaseObject_Type;
-    e_type.tp_repr = EnumWrapper<{0}::{1}>::py_repr;
-    e_type.tp_richcompare = EnumWrapper<{0}::{1}>::tp_richcompare;
-    mgb_assert(PyType_Ready(&e_type) >= 0);
-)",
-                        className, enumName);
-        for (auto&& i : attr->getEnumMembers()) {
-            body += formatv(R"({{
-    PyObject* inst = e_type.tp_alloc(&e_type, 0);
-    reinterpret_cast<EnumWrapper<{0}::{1}>*>(inst)->value = {0}::{1}::{2};
-    mgb_assert(PyDict_SetItemString(e_type.tp_dict, "{2}", inst) >= 0);
-})",
-                            className, enumName, i);
-        }
-        enumAlias.emplace(enumID, std::make_pair(className, enumName));
-    }
-    body += formatv(R"(
-    PyType_Modified(&e_type);
-    mgb_assert(PyDict_SetItemString(
-        py_type.tp_dict, "{0}", reinterpret_cast<PyObject*>(&e_type)) >= 0);
-)",
-                    enumName);
-    body += "}\n";
-    return body;
-}
-
-static std::string gen_op_def_python_c_extension_bit_combined_enum(
-        raw_ostream& os, EnumContext& ctx, MgbEnumAttr* attr,
-        llvm::StringRef className) {
-    std::string body;
-    unsigned int enumID;
-    if (auto alias = llvm::dyn_cast<MgbAliasAttr>(attr)) {
-        auto&& aliasBase = alias->getAliasBase();
-        enumID = llvm::cast<MgbEnumAttr>(aliasBase).getBaseRecord()->getID();
-    } else {
-        enumID = attr->getBaseRecord()->getID();
-    }
-    auto&& enumAlias = ctx.enumAlias;
-    auto&& iter = enumAlias.find(enumID);
-    auto enumName = attr->getEnumName();
-    body += "{\n";
-    body += formatv("auto& e_type = BitCombinedEnumWrapper<{0}::{1}>::type;",
-                    className, enumName);
-    if (iter == enumAlias.end()) {
-        os << formatv(
-                "template<> PyTypeObject "
-                "BitCombinedEnumWrapper<{0}::{1}>::type={{};\n",
-                className, enumName);
-        os << formatv(
-                "template<> PyNumberMethods "
-                "BitCombinedEnumWrapper<{0}::{1}>::number_methods={{};\n",
-                className, enumName);
-        os << formatv(
-                "template<> const char* BitCombinedEnumWrapper<{0}::{1}>::name "
-                "= \"{0}.{1}\";\n",
-                className, enumName);
-        os << formatv(
-                "template<> struct EnumTrait<{0}::{1}> {{  static constexpr "
-                "bool is_bit_combined = true;};\n",
-                className, enumName);
-        std::vector<std::string> pairStr;
-        for (auto&& i : attr->getEnumMembers()) {
-            pairStr.push_back(
-                    formatv("{{normalize_enum(\"{2}\"), {0}::{1}::{2}}",
-                            className, enumName, i));
-        }
-        os << formatv(R"(
-template<> std::unordered_map<std::string, {0}::{1}>
-BitCombinedEnumWrapper<{0}::{1}>::str2type = {{
-    {2}
-};
-)",
-                      className, enumName, llvm::join(pairStr, ", "));
-        pairStr.clear();
-        for (auto&& i : attr->getEnumMembers()) {
-            pairStr.push_back(
-                    formatv("{{{0}::{1}::{2}, normalize_enum(\"{2}\")}",
-                            className, enumName, i));
-        }
-        os << formatv(R"(
-template<> std::unordered_map<{0}::{1}, std::string>
-BitCombinedEnumWrapper<{0}::{1}>::type2str = {{
-    {2}
-};
-)",
-                      className, enumName, llvm::join(pairStr, ", "));
-        body += formatv(R"(
-    e_type = {{PyVarObject_HEAD_INIT(NULL, 0)};
-    e_type.tp_name = "megengine.core._imperative_rt.ops.{0}.{1}";
-    e_type.tp_basicsize = sizeof(BitCombinedEnumWrapper<{0}::{1}>);
-    e_type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE;
-    e_type.tp_doc = "{0}.{1}";
-    e_type.tp_base = &PyBaseObject_Type;
-    e_type.tp_new = BitCombinedEnumWrapper<{0}::{1}>::py_new_combined_enum;
-    e_type.tp_init = BitCombinedEnumWrapper<{0}::{1}>::py_init;
-    e_type.tp_repr = BitCombinedEnumWrapper<{0}::{1}>::py_repr;
-    e_type.tp_richcompare = BitCombinedEnumWrapper<{0}::{1}>::tp_richcompare;
-    auto& number_method = BitCombinedEnumWrapper<{0}::{1}>::number_methods;
-    number_method.nb_or = BitCombinedEnumWrapper<{0}::{1}>::py_or;
-    number_method.nb_and = BitCombinedEnumWrapper<{0}::{1}>::py_and;
-    e_type.tp_as_number = &number_method;
-    mgb_assert(PyType_Ready(&e_type) >= 0);
-)",
-                        className, enumName);
-        for (auto&& i : attr->getEnumMembers()) {
-            body += formatv(R"({{
-    PyObject* inst = e_type.tp_alloc(&e_type, 0);
-    reinterpret_cast<BitCombinedEnumWrapper<{0}::{1}>*>(inst)->value = {0}::{1}::{2};
-    mgb_assert(PyDict_SetItemString(e_type.tp_dict, "{2}", inst) >= 0);
-})",
-                            className, enumName, i);
-        }
-        enumAlias.emplace(enumID, std::make_pair(className, enumName));
-    }
-    body += formatv(R"(
-    PyType_Modified(&e_type);
-    mgb_assert(PyDict_SetItemString(
-        py_type.tp_dict, "{0}", reinterpret_cast<PyObject*>(&e_type)) >= 0);
-)",
-                    enumName);
-    body += "}\n";
-    return body;
-}
-
-static void gen_op_def_python_c_extension_single(raw_ostream &os, MgbOp& op, EnumContext& ctx) {
-    auto className = op.getCppClassName();
-    std::string body;
-
-    // generate PyType for enum class member
-    for (auto&& i : op.getMgbAttributes()) {
-        if (auto attr = llvm::dyn_cast<MgbEnumAttr>(&i.attr)) {
-            if (attr->getEnumCombinedFlag()) {
-                body += gen_op_def_python_c_extension_bit_combined_enum(
-                        os, ctx, attr, className);
-            } else {
-                body += gen_op_def_python_c_extension_enum(os, ctx, attr,
-                                                           className);
-            }
-        }
-    }
-
-    // generate getsetters
-    std::vector<std::string> getsetters;
-    for (auto &&i : op.getMgbAttributes()) {
-        getsetters.push_back(formatv(
-            "{{const_cast<char*>(\"{1}\"), py_get_generic({0}, {1}), py_set_generic({0}, {1}), const_cast<char*>(\"{1}\"), NULL},",
-            className, i.name));
-    }
-
-    // generate tp_init
-    std::string initBody;
-    if (!op.getMgbAttributes().empty()) {
-        initBody += "static const char* kwlist[] = {";
-
-        std::vector<llvm::StringRef> attr_name_list;
-        llvm::for_each(op.getMgbAttributes(), [&](auto&& attr) {
-            attr_name_list.push_back(attr.name);
-        });
-        attr_name_list.push_back("scope");
-
-        llvm::for_each(attr_name_list, [&](auto&& attr) {
-            initBody += formatv("\"{0}\", ", attr);
-        });
-        initBody += "NULL};\n";
-        initBody += "    PyObject ";
-        std::vector<std::string> attr_init;
-        llvm::for_each(attr_name_list, [&](auto&& attr) {
-            attr_init.push_back(formatv("*{0} = NULL", attr));
-        });
-        initBody += llvm::join(attr_init, ", ") + ";\n";
-        initBody += "    if (!PyArg_ParseTupleAndKeywords(args, kwds, \"|";
-        // an extra slot created for name
-        initBody += std::string(attr_name_list.size(), 'O');
-        initBody += "\", const_cast<char**>(kwlist)";
-        llvm::for_each(attr_name_list, [&](auto&& attr) {
-            initBody += formatv(", &{0}", attr);
-        });
-        initBody += "))\n";
-        initBody += "    return -1;\n";
-
-        llvm::for_each(op.getMgbAttributes(), [&](auto&& attr) {
-            initBody += formatv(R"(
-    if ({1}) {{
-        try {{
-            reinterpret_cast<PyOp({0})*>(self)->inst().{1} =
-                pyobj_convert_generic<decltype({0}::{1})>::from({1});
-        } CATCH_ALL(-1)
-    }
-)", className, attr.name);
-        });
-
-        initBody += formatv(R"(
-    if (scope) {{
-        try {{
-            reinterpret_cast<PyOp(OpDef)*>(self)->op
-                ->set_scope(pyobj_convert_generic<std::string>::from(scope));
-        } CATCH_ALL(-1)
-    }
-)", className);
-
-    }
-    initBody += "\n    return 0;";
-
-    os << formatv(R"(
-PyOpDefBegin({0}) // {{
-    static PyGetSetDef py_getsetters[];
-    static int py_init(PyObject *self, PyObject *args, PyObject *kwds);
-// };
-PyOpDefEnd({0})
-PyGetSetDef PyOp({0})::py_getsetters[] = {{
-    {1}
-    {{NULL}  /* Sentinel */
-};
-int PyOp({0})::py_init(PyObject *self, PyObject *args, PyObject *kwds) {{
-    {2}
-}
-
-void _init_py_{0}(py::module m) {{
-    using py_op = PyOp({0});
-    auto& py_type = PyOpType({0});
-    py_type = {{PyVarObject_HEAD_INIT(NULL, 0)};
-    py_type.tp_name = "megengine.core._imperative_rt.ops.{0}";
-    py_type.tp_basicsize = sizeof(PyOp({0}));
-    py_type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE;
-    py_type.tp_doc = "{0}";
-    py_type.tp_base = &PyOpType(OpDef);
-    py_type.tp_dealloc = py_dealloc_generic<py_op>;
-    py_type.tp_new = py_new_generic<py_op>;
-    py_type.tp_init = py_op::py_init;
-    py_type.tp_getset = py_op::py_getsetters;
-    mgb_assert(PyType_Ready(&py_type) >= 0);
-    {3}
-    PyType_Modified(&py_type);
-    m.add_object("{0}", reinterpret_cast<PyObject*>(&py_type));
-    mgb_assert(PyOp(OpDef)::ctype2pytype.emplace({0}::typeinfo(), &py_type).second);
-}
-)",
-    op.getCppClassName(), llvm::join(getsetters, "\n    "), initBody, body);
-}
-
-static void for_each_operator(raw_ostream &os, RecordKeeper &keeper,
-        std::function<void(raw_ostream&, MgbOp&)> callback) {
-    auto op_base_class = keeper.getClass("Op");
-    ASSERT(op_base_class, "could not find base class Op");
-    for (auto&& i: keeper.getDefs()) {
-        auto&& r = i.second;
-        if (r->isSubClassOf(op_base_class)) {
-            auto op = mlir::tblgen::Operator(r.get());
-            if (op.getDialectName().str() == "mgb") {
-                std::cerr << "\033[34;15m" << "Generating " << r->getName().str() << "\033[0m" << std::endl;
-                callback(os, llvm::cast<MgbOp>(op));
-            }
-        }
-    }
-}
-
-static bool gen_op_def_c_header(raw_ostream &os, RecordKeeper &keeper) {
-    for_each_operator(os, keeper, gen_op_def_c_header_single);
-    for_each_operator(os, keeper, gen_to_string_trait_for_enum);
-    return false;
-}
-
-static bool gen_op_def_c_body(raw_ostream &os, RecordKeeper &keeper) {
-    for_each_operator(os, keeper, gen_op_def_c_body_single);
-    return false;
-}
-
-static bool gen_op_def_pybind11(raw_ostream &os, RecordKeeper &keeper) {
-    EnumContext ctx;
-    using namespace std::placeholders;
-    for_each_operator(os, keeper,
-        std::bind(gen_op_def_pybind11_single, _1, _2, std::ref(ctx)));
-    return false;
-}
-
-static bool gen_op_def_python_c_extension(raw_ostream &os, RecordKeeper &keeper) {
-    EnumContext ctx;
-    using namespace std::placeholders;
-    for_each_operator(os, keeper,
-        std::bind(gen_op_def_python_c_extension_single, _1, _2, std::ref(ctx)));
-    os << "#define INIT_ALL_OP(m)";
-    for_each_operator(os, keeper, [&](raw_ostream& os, MgbOp& op) {
-        os << formatv(" \\\n    _init_py_{0}(m);", op.getCppClassName());
-    });
-    os << "\n";
-    return false;
-}
+using namespace mlir::tblgen;
 
 int main(int argc, char **argv) {
     llvm::InitLLVM y(argc, argv);

imperative/tablegen/emitter.h

@@ -0,0 +1,40 @@
+/**
+ * \file imperative/tablegen/emitter.h
+ * 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.
+ */
+#pragma once
+
+#include <unordered_map>
+#include <stdexcept>
+
+#include "llvm/ADT/StringRef.h"
+#include "llvm/Support/raw_ostream.h"
+
+namespace mlir::tblgen {
+
+struct Environment {
+    std::unordered_map<unsigned int, std::pair<llvm::StringRef, llvm::StringRef>> enumAlias;
+};
+
+struct EmitterBase {
+    EmitterBase(raw_ostream& os_): os(os_) {}
+    EmitterBase(raw_ostream& os_, Environment& env): os(os_), env_p(&env) {}
+protected:
+    void newline() { os << "\n"; }
+    Environment& env() {
+        if (env_p) {
+            return *env_p;
+        }
+        throw std::runtime_error("access global environment via non-environment emitter");
+    }
+    raw_ostream& os;
+    Environment* env_p = nullptr;
+};
+
+} // namespace mlir::tblgen

imperative/tablegen/helper.h

@@ -1,3 +1,16 @@
+/**
+ * \file imperative/tablegen/helper.h
+ * 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.
+ */
+#pragma once
+
+#include <iostream>
 #include <string>
 #include <vector>
 
@@ -278,5 +291,28 @@ public:
     }
 };
 
+using MgbAttrWrapper = mlir::tblgen::MgbAttrWrapperBase;
+using MgbEnumAttr = mlir::tblgen::MgbEnumAttrMixin;
+using MgbHashableAttr = mlir::tblgen::MgbHashableAttrMixin;
+using MgbAliasAttr = mlir::tblgen::MgbAliasAttrMixin;
+using MgbOp = mlir::tblgen::MgbOpBase;
+using MgbHashableOp = mlir::tblgen::MgbHashableOpMixin;
+
+static inline void foreach_operator(llvm::RecordKeeper &keeper,
+        std::function<void(MgbOp&)> callback) {
+    auto op_base_class = keeper.getClass("Op");
+    ASSERT(op_base_class, "could not find base class Op");
+    for (auto&& i: keeper.getDefs()) {
+        auto&& r = i.second;
+        if (r->isSubClassOf(op_base_class)) {
+            auto op = mlir::tblgen::Operator(r.get());
+            if (op.getDialectName().str() == "mgb") {
+                std::cerr << "\033[34;15m" << "Generating " << r->getName().str() << "\033[0m" << std::endl;
+                callback(llvm::cast<MgbOp>(op));
+            }
+        }
+    }
+}
+
 } // namespace tblgen
 } // namespace mlir

imperative/tablegen/targets/cpp_class.cpp

@@ -0,0 +1,309 @@
+/**
+ * \file imperative/tablegen/targets/cpp_class.cpp
+ * 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.
+ */
+
+#include "./cpp_class.h"
+#include "../emitter.h"
+
+namespace mlir::tblgen {
+namespace {
+llvm::StringRef attr_to_ctype(const mlir::tblgen::Attribute& attr_) {
+    // Note: we have already registered the corresponding attr wrappers
+    // for following basic ctypes so we needn't handle them here
+    /* auto&& attr_type_name = attr.getAttrDefName();
+    if (attr_type_name == "UI32Attr") {
+        return "uint32_t";
+    }
+    if (attr_type_name == "UI64Attr") {
+        return "uint64_t";
+    }
+    if (attr_type_name == "I32Attr") {
+        return "int32_t";
+    }
+    if (attr_type_name == "F32Attr") {
+        return "float";
+    }
+    if (attr_type_name == "F64Attr") {
+        return "double";
+    }
+    if (attr_type_name == "StrAttr") {
+        return "std::string";
+    }
+    if (attr_type_name == "BoolAttr") {
+        return "bool";
+    }*/
+
+    auto&& attr = llvm::cast<MgbAttrWrapper>(attr_);
+    if (auto e = llvm::dyn_cast<MgbEnumAttr>(&attr)) {
+        return e->getEnumName();
+    }
+    return attr.getUnderlyingType();
+}
+
+class OpDefEmitter final: public EmitterBase {
+public:
+    OpDefEmitter(MgbOp& op_, raw_ostream& os_):
+        EmitterBase(os_), op(op_) {}
+    void emit_header();
+    void emit_tpl_spl();
+    void emit_body();
+private:
+    MgbOp& op;
+};
+
+void OpDefEmitter::emit_header() {
+    os << formatv(
+        "class {0} : public OpDefImplBase<{0}> {{\n"
+        "    MGB_DYN_TYPE_OBJ_FINAL_DECL;\n\n"
+        "public:\n",
+        op.getCppClassName()
+    );
+    // handle enum alias
+    for (auto &&i : op.getMgbAttributes()) {
+        if (auto attr = llvm::dyn_cast<MgbEnumAttr>(&i.attr)) {
+            os << formatv(
+                "    using {0} = {1};\n",
+                attr->getEnumName(), attr->getUnderlyingType()
+            );
+        }
+    }
+    for (auto &&i : op.getMgbAttributes()) {
+        auto defaultValue = i.attr.getDefaultValue().str();
+        if (!defaultValue.empty()) {
+            defaultValue = formatv(" = {0}", defaultValue);
+        }
+        os << formatv(
+            "    {0} {1}{2};\n",
+            attr_to_ctype(i.attr), i.name, defaultValue
+        );
+    }
+
+    auto gen_ctor = [&](auto&& paramList, auto&& memInitList, auto&& body) {
+        os << formatv(
+            "    {0}({1}){2}{3}\n",
+            op.getCppClassName(), paramList, memInitList, body
+        );
+    };
+
+    gen_ctor("", "", " = default;");
+
+    if (!op.getMgbAttributes().empty()) {
+        std::vector<std::string> paramList, initList;
+        for (auto &&i : op.getMgbAttributes()) {
+            paramList.push_back(formatv(
+                "{0} {1}_", attr_to_ctype(i.attr), i.name
+            ));
+            initList.push_back(formatv(
+                "{0}({0}_)", i.name
+            ));
+        }
+        paramList.push_back("std::string scope_ = {}");
+        gen_ctor(llvm::join(paramList, ", "),
+                 ": " + llvm::join(initList, ", "),
+                 " { set_scope(scope_); }");
+    }
+
+    auto packedParams = op.getPackedParams();
+    if (!packedParams.empty()) {
+        std::vector<std::string> paramList, initList;
+        for (auto &&p : packedParams) {
+            auto&& paramFields = p.getFields();
+            auto&& paramType = p.getFullName();
+            auto&& paramName = formatv("packed_param_{0}", paramList.size());
+            paramList.push_back(
+                paramFields.empty() ? paramType.str()
+                    : formatv("{0} {1}", paramType, paramName)
+            );
+            for (auto&& i : paramFields) {
+                initList.push_back(formatv(
+                    "{0}({1}.{0})", i.name, paramName
+                ));
+            }
+        }
+        for (auto&& i : op.getExtraArguments()) {
+            paramList.push_back(formatv(
+                "{0} {1}_", attr_to_ctype(i.attr), i.name
+            ));
+            initList.push_back(formatv(
+                "{0}({0}_)", i.name
+            ));
+        }
+        gen_ctor(llvm::join(paramList, ", "),
+                 initList.empty() ? "" : ": " + llvm::join(initList, ", "),
+                 " {}");
+    }
+
+    if (!packedParams.empty()) {
+        for (auto&& p : packedParams) {
+            auto accessor = p.getAccessor();
+            if (!accessor.empty()) {
+                os << formatv(
+                    "    {0} {1}() const {{\n",
+                    p.getFullName(), accessor
+                );
+                std::vector<llvm::StringRef> fields;
+                for (auto&& i : p.getFields()) {
+                    fields.push_back(i.name);
+                }
+                os << formatv(
+                    "        return {{{0}};\n",
+                    llvm::join(fields, ", ")
+                );
+                os << "    }\n";
+            }
+        }
+    }
+
+    if (auto decl = op.getExtraOpdefDecl()) {
+        os << decl.getValue();
+    }
+
+    os << formatv(
+        "};\n\n"
+    );
+}
+
+void OpDefEmitter::emit_tpl_spl() {
+    for (auto &&i : op.getMgbAttributes()) {
+        if (auto attr = llvm::dyn_cast<MgbEnumAttr>(&i.attr)) {
+            if (attr->supportToString()) {
+                std::vector<std::string> case_body;
+                std::string ename = formatv("{0}::{1}",
+                    op.getCppClassName(), attr->getEnumName());
+                llvm::for_each(attr->getEnumMembers(), [&](auto&& v){
+                    case_body.push_back(formatv(
+                        "case {0}::{1}: return \"{1}\";", ename, v));
+                });
+                os << formatv(R"(
+template <>
+struct ToStringTrait<{0}> {
+    std::string operator()({0} e) const {
+        switch (e) {
+            {1}
+            default:
+                return "{0}::Unknown";
+        }
+    }
+};
+)", ename, llvm::join(case_body, "\n"));
+            }
+        }
+    }
+}
+
+void OpDefEmitter::emit_body() {
+    auto&& className = op.getCppClassName();
+    os << formatv(
+        "MGB_DYN_TYPE_OBJ_FINAL_IMPL({0});\n\n", className
+    );
+    auto formatMethImpl = [&](auto&& meth) {
+        return formatv(
+            "{0}_{1}_impl", className, meth
+        );
+    };
+    std::vector<std::string> methods;
+    if (auto hashable = llvm::dyn_cast<MgbHashableOp>(&op)) {
+        os << "namespace {\n";
+
+        // generate hash()
+        mlir::tblgen::FmtContext ctx;
+        os << formatv(
+            "size_t {0}(const OpDef& def_) {{\n",
+            formatMethImpl("hash")
+        );
+        os << formatv(
+            "    auto&& op_ = def_.cast_final_safe<{0}>();\n"
+            "    static_cast<void>(op_);\n",
+            className
+        );
+        ctx.withSelf("op_");
+        os << mlir::tblgen::tgfmt(hashable->getHashFunctionTemplate(), &ctx);
+        os << "}\n";
+
+        // generate is_same_st()
+        os << formatv(
+            "bool {0}(const OpDef& lhs_, const OpDef& rhs_) {{\n",
+            formatMethImpl("is_same_st")
+        );
+        os << formatv(
+            "    auto &&a_ = lhs_.cast_final_safe<{0}>(),\n"
+            "         &&b_ = rhs_.cast_final_safe<{0}>();\n"
+            "    static_cast<void>(a_);\n"
+            "    static_cast<void>(b_);\n",
+            className
+        );
+        os << mlir::tblgen::tgfmt(hashable->getCmpFunctionTemplate(), &ctx, "a_", "b_");
+        os << "}\n";
+
+        // generate props()
+        os << formatv(
+            "std::vector<std::pair<const char*, std::string>> {0}(const OpDef& def_) {{\n",
+            formatMethImpl("props")
+        );
+        os << formatv(
+            "    auto&& op_ = def_.cast_final_safe<{0}>();\n"
+            "    static_cast<void>(op_);\n",
+            className
+        );
+        ctx.withSelf("op_");
+        os << mlir::tblgen::tgfmt(hashable->getPropsFunctionTemplate(), &ctx);
+        os << "}\n";
+
+        // generate make_name()
+        os << formatv(
+            "std::string {0}(const OpDef& def_) {{\n", formatMethImpl("make_name")
+        );
+        os << formatv(
+            "    auto&& op_ = def_.cast_final_safe<{0}>();\n"
+            "    static_cast<void>(op_);\n",
+            className
+        );
+        ctx.withSelf("op_");
+        os << mlir::tblgen::tgfmt(op.getNameFunctionTemplate(), &ctx);
+        os << "}\n";
+
+        os << "} // anonymous namespace\n";
+
+        methods.push_back("hash");
+        methods.push_back("is_same_st");
+        methods.push_back("props");
+        methods.push_back("make_name");
+    }
+    if (!methods.empty()) {
+        os << formatv(
+            "OP_TRAIT_REG({0}, {0})", op.getCppClassName()
+        );
+        for (auto&& i : methods) {
+            os << formatv(
+                "\n    .{0}({1})", i, formatMethImpl(i)
+            );
+        }
+        os << ";\n\n";
+    }
+}
+} // namespace
+
+bool gen_op_def_c_header(raw_ostream &os, llvm::RecordKeeper &keeper) {
+    foreach_operator(keeper, [&](MgbOp& op) {
+        OpDefEmitter emitter(op, os);
+        emitter.emit_header();
+        emitter.emit_tpl_spl();
+    });
+    return false;
+}
+
+bool gen_op_def_c_body(raw_ostream &os, llvm::RecordKeeper &keeper) {
+    foreach_operator(keeper, [&](MgbOp& op) {
+        OpDefEmitter emitter(op, os);
+        emitter.emit_body();
+    });
+    return false;
+}
+} // namespace mlir::tblgen

imperative/tablegen/targets/cpp_class.h

@@ -0,0 +1,21 @@
+/**
+ * \file imperative/tablegen/targets/cpp_class.h
+ * 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.
+ */
+#pragma once
+
+#include "../helper.h"
+
+namespace mlir::tblgen {
+
+bool gen_op_def_c_header(raw_ostream &os, llvm::RecordKeeper &keeper);
+
+bool gen_op_def_c_body(raw_ostream &os, llvm::RecordKeeper &keeper);
+
+} // namespace mlir::tblgen

imperative/tablegen/targets/pybind11.cpp

@@ -0,0 +1,142 @@
+/**
+ * \file imperative/tablegen/targets/pybind11.cpp
+ * 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.
+ */
+
+#include "./pybind11.h"
+#include "../emitter.h"
+
+namespace mlir::tblgen {
+namespace {
+class OpDefEmitter final: public EmitterBase {
+public:
+    OpDefEmitter(MgbOp& op_, raw_ostream& os_, Environment& env_):
+        EmitterBase(os_, env_), op(op_) {}
+
+    void emit();
+private:
+    MgbOp& op;
+};
+
+void OpDefEmitter::emit() {
+    auto className = op.getCppClassName();
+    os << formatv(
+        "py::class_<{0}, std::shared_ptr<{0}>, OpDef> {0}Inst(m, \"{0}\");\n\n",
+        className
+    );
+    for (auto&& i : op.getMgbAttributes()) {
+        if (auto attr = llvm::dyn_cast<MgbEnumAttr>(&i.attr)) {
+            unsigned int enumID;
+            if (auto alias = llvm::dyn_cast<MgbAliasAttr>(attr)) {
+                auto&& aliasBase = alias->getAliasBase();
+                enumID =
+                    llvm::cast<MgbEnumAttr>(aliasBase)
+                            .getBaseRecord()->getID();
+            } else {
+                enumID = attr->getBaseRecord()->getID();
+            }
+            auto&& enumAlias = env().enumAlias;
+            auto&& iter = enumAlias.find(enumID);
+            if (iter == enumAlias.end()) {
+                os << formatv(
+                    "py::enum_<{0}::{1}>({0}Inst, \"{1}\")",
+                    className, attr->getEnumName()
+                );
+                std::vector<std::string> body;
+                for (auto&& i: attr->getEnumMembers()) {
+                    os << formatv(
+                        "\n    .value(\"{2}\", {0}::{1}::{2})",
+                        className, attr->getEnumName(), i
+                    );
+                    body.push_back(formatv(
+                        "if (str == \"{2}\") return {0}::{1}::{2};",
+                        className, attr->getEnumName(), i
+                    ));
+                }
+                if (attr->getEnumCombinedFlag()) {
+                    //! define operator |
+                    os << formatv(
+                            "\n    .def(\"__or__\", []({0}::{1} s0, {0}::{1} s1) {{ "
+                            "\n         return static_cast<{0}::{1}>(uint32_t(s0) | uint32_t(s1));"
+                            "\n      })",
+                            className, attr->getEnumName());
+                    //! define operator &
+                    os << formatv(
+                            "\n    .def(\"__and__\", []({0}::{1} s0, {0}::{1} s1) {{"
+                            "\n         return static_cast<{0}::{1}>(uint32_t(s0) & uint32_t(s1));"
+                            "\n    })",
+                            className, attr->getEnumName());
+                }
+                os << formatv(
+                    "\n    .def(py::init([](const std::string& in) {"
+                    "\n        auto&& str = normalize_enum(in);"
+                    "\n        {0}"
+                    "\n        throw py::cast_error(\"invalid enum value \" + in);"
+                    "\n    }));\n",
+                    llvm::join(body, "\n        ")
+                );
+                os << formatv(
+                    "py::implicitly_convertible<std::string, {0}::{1}>();\n\n",
+                    className, attr->getEnumName()
+                );
+                enumAlias.emplace(enumID,
+                    std::make_pair(className, attr->getEnumName()));
+            } else {
+                os << formatv(
+                    "{0}Inst.attr(\"{1}\") = {2}Inst.attr(\"{3}\");\n\n",
+                    className, attr->getEnumName(),
+                    iter->second.first, iter->second.second
+                );
+            }
+        }
+    }
+    // generate op class binding
+    os << formatv("{0}Inst", className);
+    bool hasDefaultCtor = op.getMgbAttributes().empty();
+    if (!hasDefaultCtor) {
+        os << "\n    .def(py::init<";
+        std::vector<llvm::StringRef> targs;
+        for (auto &&i : op.getMgbAttributes()) {
+            targs.push_back(i.attr.getReturnType());
+        }
+        os << llvm::join(targs, ", ");
+        os << ", std::string>()";
+        for (auto &&i : op.getMgbAttributes()) {
+            os << formatv(", py::arg(\"{0}\")", i.name);
+            auto defaultValue = i.attr.getDefaultValue();
+            if (!defaultValue.empty()) {
+                os << formatv(" = {0}", defaultValue);
+            } else {
+                hasDefaultCtor = true;
+            }
+        }
+        os << ", py::arg(\"scope\") = {})";
+    }
+    if (hasDefaultCtor) {
+        os << "\n    .def(py::init<>())";
+    }
+    for (auto &&i : op.getMgbAttributes()) {
+        os << formatv(
+            "\n    .def_readwrite(\"{0}\", &{1}::{0})",
+            i.name, className
+        );
+    }
+    os << ";\n\n";
+}
+} // namespace
+
+bool gen_op_def_pybind11(raw_ostream &os, llvm::RecordKeeper &keeper) {
+    Environment env;
+    using namespace std::placeholders;
+    foreach_operator(keeper, [&](MgbOp& op) {
+        OpDefEmitter(op, os, env).emit();
+    });
+    return false;
+}
+} // namespace mlir::tblgen

imperative/tablegen/targets/pybind11.h

@@ -0,0 +1,19 @@
+/**
+ * \file imperative/tablegen/targets/pybind11.h
+ * 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.
+ */
+#pragma once
+
+#include "../helper.h"
+
+namespace mlir::tblgen {
+
+bool gen_op_def_pybind11(raw_ostream &os, llvm::RecordKeeper &keeper);
+
+} // namespace mlir::tblgen

imperative/tablegen/targets/python_c_extension.cpp

@@ -0,0 +1,314 @@
+/**
+ * \file imperative/tablegen/targets/python_c_extension.cpp
+ * 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.
+ */
+
+#include "python_c_extension.h"
+#include "../emitter.h"
+
+namespace mlir::tblgen {
+namespace {
+struct Initproc {
+    std::string func;
+    Initproc(std::string&& s): func(std::move(s)) {}
+    std::string operator()(std::string argument) {
+        return formatv("{0}({1})", func, argument);
+    }
+};
+
+class OpDefEmitter: public EmitterBase {
+public:
+    OpDefEmitter(MgbOp& op_, raw_ostream& os_, Environment& env_):
+        EmitterBase(os_, env_), op(op_) {
+        ctx.withSelf(op.getCppClassName());
+    }
+
+    Initproc emit();
+private:
+    void emit_class();
+    void emit_py_init();
+    void emit_py_getsetters();
+    Initproc emit_initproc();
+
+    MgbOp& op;
+    std::vector<Initproc> subclasses;
+    mlir::tblgen::FmtContext ctx;
+};
+
+class EnumAttrEmitter: public EmitterBase {
+public:
+    EnumAttrEmitter(llvm::StringRef parent, MgbEnumAttr* attr_, raw_ostream& os_, Environment& env_):
+        EmitterBase(os_, env_), attr(attr_) {
+        unsigned int enumID;
+        if (auto alias = llvm::dyn_cast<MgbAliasAttr>(attr)) {
+            auto&& aliasBase = alias->getAliasBase();
+            enumID = llvm::cast<MgbEnumAttr>(aliasBase).getBaseRecord()->getID();
+        } else {
+            enumID = attr->getBaseRecord()->getID();
+        }
+        ctx.addSubst("enumTpl", attr->getEnumCombinedFlag() ? "BitCombinedEnumWrapper" : "EnumWrapper");
+        ctx.addSubst("opClass", parent);
+        ctx.addSubst("enumClass", attr->getEnumName());
+        firstOccur = env().enumAlias.emplace(enumID, std::make_pair(parent, attr->getEnumName())).second;
+    }
+
+    Initproc emit();
+protected:
+    void emit_tpl_spl();
+    Initproc emit_initproc();
+
+    MgbEnumAttr* attr;
+    bool firstOccur;
+    mlir::tblgen::FmtContext ctx;
+};
+
+Initproc EnumAttrEmitter::emit() {
+    emit_tpl_spl();
+    return emit_initproc();
+}
+
+void EnumAttrEmitter::emit_tpl_spl() {
+    if (!firstOccur) return;
+
+    os << tgfmt(
+            "template<> PyTypeObject $enumTpl<$opClass::$enumClass>::type={};\n",
+            &ctx);
+
+    os << tgfmt(
+            "template<> const char* $enumTpl<$opClass::$enumClass>::name = "
+            "\"$opClass.$enumClass\";\n", 
+            &ctx);
+
+    if (attr->getEnumCombinedFlag()) {
+        os << tgfmt(
+                "template<> PyNumberMethods "
+                "$enumTpl<$opClass::$enumClass>::number_methods={};\n",
+                &ctx);
+        os << tgfmt(R"(
+template<> struct EnumTrait<$opClass::$enumClass> {
+    static constexpr bool is_bit_combined = true;
+    static constexpr std::underlying_type_t<$opClass::$enumClass> max = (1llu << $0) - 1;
+};
+)", &ctx, attr->getEnumMembers().size());
+    }
+
+    auto str2type = [&](auto&& i) -> std::string {
+        return tgfmt("{normalize_enum(\"$0\"), $opClass::$enumClass::$0}", &ctx, i);
+    };
+    os << tgfmt(R"(
+template<> std::unordered_map<std::string, $opClass::$enumClass>
+$enumTpl<$opClass::$enumClass>::str2type = {$0};
+)", &ctx, llvm::join(llvm::map_range(attr->getEnumMembers(), str2type), ", "));
+
+    auto type2str = [&](auto&& i) -> std::string {
+        return tgfmt("{$opClass::$enumClass::$0, normalize_enum(\"$0\")}", &ctx, i);
+    };
+    os << tgfmt(R"(
+template<> std::unordered_map<$opClass::$enumClass, std::string>
+$enumTpl<$opClass::$enumClass>::type2str = {$0};
+)", &ctx, llvm::join(llvm::map_range(attr->getEnumMembers(), type2str), ", "));
+}
+
+Initproc EnumAttrEmitter::emit_initproc() {
+    std::string initproc = formatv("_init_py_{0}_{1}",
+        ctx.getSubstFor("opClass"), ctx.getSubstFor("enumClass"));
+
+    os << tgfmt(R"(
+void $0(PyTypeObject& py_type) {
+    auto& e_type = $enumTpl<$opClass::$enumClass>::type;
+)", &ctx, initproc);
+
+    if (firstOccur) {
+        os << tgfmt(R"(
+    e_type = {PyVarObject_HEAD_INIT(NULL, 0)};
+    e_type.tp_name = "megengine.core._imperative_rt.ops.$opClass.$enumClass";
+    e_type.tp_basicsize = sizeof($enumTpl<$opClass::$enumClass>);
+    e_type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE;
+    e_type.tp_doc = "$opClass.$enumClass";
+    e_type.tp_base = &PyBaseObject_Type;
+    e_type.tp_repr = $enumTpl<$opClass::$enumClass>::py_repr;
+    e_type.tp_richcompare = $enumTpl<$opClass::$enumClass>::tp_richcompare;
+)", &ctx);
+        if (attr->getEnumCombinedFlag()) {
+            // only bit combined enum could new instance because bitwise operation,
+            // others should always use singleton
+            os << tgfmt(R"(
+    e_type.tp_new = $enumTpl<$opClass::$enumClass>::py_new_combined_enum;
+    auto& number_method = $enumTpl<$opClass::$enumClass>::number_methods;
+    number_method.nb_or = $enumTpl<$opClass::$enumClass>::py_or;
+    number_method.nb_and = $enumTpl<$opClass::$enumClass>::py_and;
+    e_type.tp_as_number = &number_method;
+)", &ctx);
+        }
+
+        os << "    mgb_assert(PyType_Ready(&e_type) >= 0);\n";
+
+
+        for (auto&& i : attr->getEnumMembers()) {
+            os << tgfmt(R"({
+    PyObject* inst = e_type.tp_alloc(&e_type, 0);
+    reinterpret_cast<$enumTpl<$opClass::$enumClass>*>(inst)->value = $opClass::$enumClass::$0;
+    mgb_assert(PyDict_SetItemString(e_type.tp_dict, "$0", inst) >= 0);
+    PyType_Modified(&e_type);
+})", &ctx, i);
+        }
+    }
+
+    os << tgfmt(R"(
+    mgb_assert(PyDict_SetItemString(
+        py_type.tp_dict, "$enumClass", reinterpret_cast<PyObject*>(&e_type)) >= 0);
+)", &ctx);
+    os << "}\n";
+    return initproc;
+}   
+
+Initproc OpDefEmitter::emit() {
+    for (auto&& i : op.getMgbAttributes()) {
+        if (auto attr = llvm::dyn_cast<MgbEnumAttr>(&i.attr)) {
+            subclasses.push_back(EnumAttrEmitter(op.getCppClassName(), attr, os, env()).emit());
+        }
+    }
+
+    emit_class();
+    emit_py_init();
+    emit_py_getsetters();
+    return emit_initproc();
+}
+
+void OpDefEmitter::emit_class() {
+    os << tgfmt(R"(
+PyOpDefBegin($_self) // {
+    static PyGetSetDef py_getsetters[];
+    static int py_init(PyObject *self, PyObject *args, PyObject *kwds);
+// };
+PyOpDefEnd($_self)
+)", &ctx);
+}
+
+void OpDefEmitter::emit_py_init() {
+    std::string initBody;
+    if (!op.getMgbAttributes().empty()) {
+        initBody += "static const char* kwlist[] = {";
+
+        std::vector<llvm::StringRef> attr_name_list;
+        llvm::for_each(op.getMgbAttributes(), [&](auto&& attr) {
+            attr_name_list.push_back(attr.name);
+        });
+        attr_name_list.push_back("scope");
+
+        llvm::for_each(attr_name_list, [&](auto&& attr) {
+            initBody += formatv("\"{0}\", ", attr);
+        });
+        initBody += "NULL};\n";
+        initBody += "    PyObject ";
+        auto initializer = [&](auto&& attr) -> std::string {
+            return formatv("*{0} = NULL", attr);
+        };
+        initBody += llvm::join(llvm::map_range(attr_name_list, initializer), ", ") + ";\n";
+        initBody += "    if (!PyArg_ParseTupleAndKeywords(args, kwds, \"|";
+        // an extra slot created for name
+        initBody += std::string(attr_name_list.size(), 'O');
+        initBody += "\", const_cast<char**>(kwlist)";
+        llvm::for_each(attr_name_list, [&](auto&& attr) {
+            initBody += formatv(", &{0}", attr);
+        });
+        initBody += "))\n";
+        initBody += "    return -1;\n";
+
+        llvm::for_each(op.getMgbAttributes(), [&](auto&& attr) {
+            initBody += tgfmt(R"(
+    if ($0) {
+        try {
+            reinterpret_cast<PyOp($_self)*>(self)->inst().$0 =
+                pyobj_convert_generic<decltype($_self::$0)>::from($0);
+        } CATCH_ALL(-1)
+    }
+)", &ctx, attr.name);
+        });
+
+        initBody += tgfmt(R"(
+    if (scope) {
+        try {
+            reinterpret_cast<PyOp(OpDef)*>(self)->op
+                ->set_scope(pyobj_convert_generic<std::string>::from(scope));
+        } CATCH_ALL(-1)
+    }
+)", &ctx);
+
+    }
+    initBody += "\n    return 0;";
+
+
+    os << tgfmt(R"(
+int PyOp($_self)::py_init(PyObject *self, PyObject *args, PyObject *kwds) {
+    $0
+}
+)", &ctx, initBody);
+}
+
+void OpDefEmitter::emit_py_getsetters() {
+    auto f = [&](auto&& attr) -> std::string {
+        return tgfmt(
+            "{const_cast<char*>(\"$0\"), py_get_generic($_self, $0), py_set_generic($_self, $0), const_cast<char*>(\"$0\"), NULL},",
+            &ctx, attr.name);
+    };
+    os << tgfmt(R"(
+PyGetSetDef PyOp($_self)::py_getsetters[] = {
+    $0
+    {NULL}  /* Sentinel */
+};
+)", &ctx, llvm::join(llvm::map_range(op.getMgbAttributes(), f), "\n    "));
+}
+
+Initproc OpDefEmitter::emit_initproc() {
+    std::string initproc = formatv("_init_py_{0}", op.getCppClassName());
+    std::string subclass_init_call;
+    for (auto&& i : subclasses) {
+        subclass_init_call += formatv("    {0};\n", i("py_type"));
+    }
+    os << tgfmt(R"(
+void $0(py::module m) {
+    using py_op = PyOp($_self);
+    auto& py_type = PyOpType($_self);
+    py_type = {PyVarObject_HEAD_INIT(NULL, 0)};
+    py_type.tp_name = "megengine.core._imperative_rt.ops.$_self";
+    py_type.tp_basicsize = sizeof(PyOp($_self));
+    py_type.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE;
+    py_type.tp_doc = "$_self";
+    py_type.tp_base = &PyOpType(OpDef);
+    py_type.tp_dealloc = py_dealloc_generic<py_op>;
+    py_type.tp_new = py_new_generic<py_op>;
+    py_type.tp_init = py_op::py_init;
+    py_type.tp_getset = py_op::py_getsetters;
+    mgb_assert(PyType_Ready(&py_type) >= 0);
+    $1
+    PyType_Modified(&py_type);
+    m.add_object("$_self", reinterpret_cast<PyObject*>(&py_type));
+    mgb_assert(PyOp(OpDef)::ctype2pytype.emplace($_self::typeinfo(), &py_type).second);
+}
+)", &ctx, initproc, subclass_init_call);
+    return initproc;
+}
+} // namespace
+
+bool gen_op_def_python_c_extension(raw_ostream &os, llvm::RecordKeeper &keeper) {
+    Environment env;
+    using namespace std::placeholders;
+    std::vector<Initproc> initprocs;
+    foreach_operator(keeper, [&](MgbOp& op) {
+        initprocs.emplace_back(OpDefEmitter(op, os, env).emit());
+    });
+    os << "#define INIT_ALL_OP(m)";
+    for(auto&& init : initprocs) {
+        os << formatv(" \\\n    {0};", init("m"));
+    }
+    os << "\n";
+    return false;
+}
+} // namespace mlir::tblgen

imperative/tablegen/targets/python_c_extension.h

@@ -0,0 +1,19 @@
+/**
+ * \file imperative/tablegen/targets/python_c_extension.h
+ * 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.
+ */
+#pragma once
+
+#include "../helper.h"
+
+namespace mlir::tblgen {
+
+bool gen_op_def_python_c_extension(raw_ostream &os, llvm::RecordKeeper &keeper);
+
+} // namespace mlir::tblgen

sdk/load-and-run/src/mgblar.cpp

@@ -709,7 +709,7 @@ void run_test_st(Args &env) {
             strategy = S::PROFILE;
         }
     } else if (env.use_fast_run) {
-        strategy = S::PROFILE | S::OPTMIZED;
+        strategy = S::PROFILE | S::OPTIMIZED;
     } else if (env.reproducible) {
         strategy = S::HEURISTIC | S::REPRODUCIBLE;
     }

src/core/include/megbrain/utils/thread_impl_1.h

@@ -365,14 +365,16 @@ namespace mgb {
                     if (!m_free_task_block.empty()) {
                         ret = std::move(m_free_task_block.back());
                         m_free_task_block.pop_back();
+                        break;
                     } else if (m_block_quota > 0) {
                         ret = std::make_unique<TaskBlock>();
                         m_block_quota--;
+                        break;
                     } else {
                         m_cv.wait(m_mutex);
                         continue;
                     }
-                } while (false);
+                } while (true);
                 ret->first_tid = m_new_block_first_tid;
                 m_new_block_first_tid += BLOCK_SIZE;
                 ret->prev = prev;

src/core/include/megbrain/version.h

@@ -12,8 +12,8 @@
 #pragma once
 
 #define MGB_MAJOR   8
-#define MGB_MINOR   9999
-#define MGB_PATCH   0
+#define MGB_MINOR   10
+#define MGB_PATCH   1
 //! whether it is development version
 #ifndef MGB_IS_DEV
 #define MGB_IS_DEV  0

src/gopt/impl/inference.cpp

@@ -1565,7 +1565,7 @@ std::unique_ptr<ConvertFormatPass> ConvertFormatPass::make_nhwcd4_converter() {
                 if (new_inp[i]->shape()[1] % 4 != 0) {
                     can_exec_cd4 = false;
                 }
-                //! cd4 elemwise with scaler is supported
+                //! cd4 elemwise with scaler is unsupported
             } else if (!new_inp[i]->shape().is_scalar()) {
                 can_exec_cd4 = false;
             }
@@ -1627,6 +1627,7 @@ std::unique_ptr<ConvertFormatPass> ConvertFormatPass::make_nhwcd4_converter() {
     replace_func[opr::Broadcast::typeinfo()] = relayout_inp_to_chw;
     replace_func[opr::IncrSubtensor::typeinfo()] = relayout_inp_to_chw;
     replace_func[opr::AxisAddRemove::typeinfo()] = relayout_inp_to_chw;
+    replace_func[opr::TypeCvt::typeinfo()] = replace_elemwise_opr;
     replace_func[opr::ResizeForward::typeinfo()] = replace_resize_opr;
     replace_func[opr::WarpPerspectiveForward::typeinfo()] =
             replace_warp_perspective_opr;

src/gopt/test/inference.cpp

@@ -1265,6 +1265,55 @@ TEST(TestGoptInference, ConvertFormatNHWCD4Elemwise) {
     MGB_ASSERT_TENSOR_NEAR(host_y, host_y_opt, 1e-3);
 }
 
+TEST(TestGoptInference, ConvertFormatNHWCD4TypeCvt) {
+    NaiveMegDNNHandleScope naive_megdnn_handle;
+
+    HostTensorGenerator<> gen;
+    auto cn = CompNode::load("cpu0");
+    auto graph = ComputingGraph::make();
+    graph->options().graph_opt_level = 0;
+    auto mkcvar = [&](const char* name, const TensorShape& shp) {
+        return opr::SharedDeviceTensor::make(*graph, *gen(shp, cn))
+                .rename(name);
+    };
+    auto host_x = gen({8, 8, 8, 8}, cn);
+    auto x = opr::Host2DeviceCopy::make(*graph, host_x);
+
+    opr::Convolution::Param param;
+
+    param.pad_h = param.pad_w = 0;
+    auto w1 = mkcvar("w1", {8, 8, 3, 3}),
+         conv1 = opr::Convolution::make(x, w1, param),
+         tcvt1 = opr::TypeCvt::make(conv1, dtype::Float16());
+    auto w2 = mkcvar("w2", {8, 8, 3, 3}),
+         conv2 = opr::Convolution::make(x, w2, param),
+         tcvt2 = opr::TypeCvt::make(conv2, dtype::Float16());
+    auto y = opr::Elemwise::make({tcvt1, tcvt2}, opr::Elemwise::Param::Mode::ADD);
+
+    SymbolVar y_opt;
+    auto options = gopt::OptimizeForInferenceOptions{};
+    options.enable_nhwcd4();
+    unpack_vector(gopt::optimize_for_inference({y}, options), y_opt);
+
+    ASSERT_EQ(opr::Convolution::Param::Format::NHWCD4,
+              find_opr<opr::Convolution>(y_opt).param().format);
+
+    graph->compile({{y_opt, {}}})
+            ->to_json()
+            ->writeto_fpath(output_file(
+                "TestGoptInference.ConvertFormatNHWCD4TypeCvt.json"));
+
+    HostTensorND host_y_opt, host_y;
+    auto func = graph->compile({make_callback_copy(y, host_y),
+                                make_callback_copy(y_opt, host_y_opt)});
+    func->execute();
+    MGB_ASSERT_TENSOR_EQ(host_y, host_y_opt);
+
+    *host_x = *gen({8, 8, 16, 16}, cn);
+    func->execute();
+    MGB_ASSERT_TENSOR_EQ(host_y, host_y_opt);
+}
+
 TEST(TestGoptInference, ConvertFormatNHWCD4LOCAL) {
     // hwcd4 is only supported in naive handle
     NaiveMegDNNHandleScope naive_megdnn_handle;
@@ -1707,8 +1756,8 @@ TEST(TestGoptInference, FastProfileCache) {
     using S = opr::Convolution::ExecutionPolicy::Strategy;
     ASSERT_EQ(S::HEURISTIC, conv.execution_policy_transient().strategy);
     gopt::modify_opr_algo_strategy_inplace({z + 2.3f},
-                                           S::PROFILE | S::OPTMIZED);
-    ASSERT_EQ(S::PROFILE | S::OPTMIZED, conv.execution_policy().strategy);
+                                           S::PROFILE | S::OPTIMIZED);
+    ASSERT_EQ(S::PROFILE | S::OPTIMIZED, conv.execution_policy().strategy);
 }
 
 TEST(TestGoptInference, AlgoWorkspaceLimit) {

src/opr/impl/dnn/dnn.oprdecl

@@ -6,7 +6,7 @@ decl_opr('Convolution',
                      'convolution kernel in '
                      '(out channel, in channel, kern row, kern col) format')],
          params=[('param', 'ConvolutionV0'),
-                 ('execution_polity', 'ExecutionPolicy')],
+                 ('execution_polity', 'ExecutionPolicyV0')],
          desc='batched convolution on channeled 2D images')
 
 decl_opr('Convolution',
@@ -28,7 +28,7 @@ decl_opr('ConvolutionBackwardData',
                      'convolution kernel in '
                      '(out channel, in channel, kern row, kern col) format')],
          params=[('param', 'ConvolutionV0'),
-                 ('execution_polity', 'ExecutionPolicy')],
+                 ('execution_polity', 'ExecutionPolicyV0')],
          body=[
              'a, b = all_inputs',
              'all_inputs = [b, a]'
@@ -201,7 +201,7 @@ decl_opr('ConvBiasForward',
              Doc('bias', 'bias'),
          ],
          params=[('param', 'ConvBiasV1'),
-                 ('execution_policy', 'ExecutionPolicy')],
+                 ('execution_policy', 'ExecutionPolicyV0')],
          desc=('activation(convolution(src, filter) + bias) with specified '
                'dtype'),
          has_out_dtype=True)

src/opr/impl/search_policy/algo_chooser.cpp

@@ -283,7 +283,7 @@ std::vector<megdnn::Algorithm::SearchItem> flatten_search_space(
 static bool algo_attribute_match_strategy(AlgoAttribute attribute,
                                           ExecutionStrategy selected_strategy) {
     bool ret = true;
-    if (selected_strategy & ExecutionStrategy::OPTMIZED) {
+    if (selected_strategy & ExecutionStrategy::OPTIMIZED) {
         ret &= (!static_cast<bool>(AlgoAttribute::NAIVE & attribute));
     } else if (selected_strategy & ExecutionStrategy::REPRODUCIBLE) {
         ret &= static_cast<bool>(AlgoAttribute::REPRODUCIBLE & attribute);

src/opr/test/dnn/convolution.cpp

@@ -357,7 +357,7 @@ TEST(TestOprDNN, ConvBiasExePolicy) {
 #if MGB_ENABLE_FASTRUN
     for (auto strategy :
          SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
-          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
+          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
 #else
     for (auto strategy :
          SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {
@@ -444,7 +444,7 @@ TEST(TestOprDNN, ConvolutionExePolicy) {
 #if MGB_ENABLE_FASTRUN
     for (auto strategy :
          SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
-          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
+          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
 #else
     for (auto strategy :
          SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {
@@ -1717,7 +1717,7 @@ TEST(TestOprDNN, LocalShareForwardExecPolicy) {
 #if MGB_ENABLE_FASTRUN
     for (auto strategy :
          SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
-          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
+          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
 #else
     for (auto strategy :
          SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {
@@ -1828,7 +1828,7 @@ TEST(TestOprDNN, DeformableConvForward) {
 #if MGB_ENABLE_FASTRUN
     for (auto strategy :
          SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
-          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
+          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
 #else
     for (auto strategy :
          SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {
@@ -1997,7 +1997,7 @@ TEST(TestOprDNN, BatchConvBiasForward) {
 #if MGB_ENABLE_FASTRUN
     for (auto strategy :
          SmallVector<S>{S::PROFILE, S::HEURISTIC, S::PROFILE | S::REPRODUCIBLE,
-          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTMIZED}) {
+          S::PROFILE | S::HEURISTIC, S::PROFILE | S::OPTIMIZED}) {
 #else
     for (auto strategy :
          SmallVector<S>{S : HEURISTIC, S::PROFILE | S::HEURISTIC}) {

src/serialization/impl/extern_c_opr.cpp

@@ -290,11 +290,8 @@ ExternCOprRunner::ExternCOprRunner(std::string& name,
           m_dump_name{name},
           m_param{nullptr} {
     mgb_assert(m_desc->size == sizeof(MGBOprDesc),
-               "invalid MGBOprDesc size: expect=%zu got=%u, may caused by "
-               "extern_c_opr.h mismatch, please confirm that the "
-               "extern_c_opr.h used when compiling the loader is consistent "
-               "with the runtime caller build used",
-               sizeof(MGBOprDesc), m_desc->size);
+               "invalid MGBOprDesc size: expect=%zu got=%u", sizeof(MGBOprDesc),
+               m_desc->size);
     for (auto i : inputs) {
         add_input({i});
     }

tools/mlir/mgb-file-check/CMakeLists.txt

@@ -0,0 +1,8 @@
+add_custom_command(
+    OUTPUT link_sh
+    COMMAND ${CMAKE_COMMAND} -E create_symlink
+        ${PROJECT_SOURCE_DIR}/tools/mlir/mgb-file-check/mgb-file-check.sh
+        ${PROJECT_BINARY_DIR}/tools/mlir/mgb-file-check/mgb-file-check
+)
+
+add_custom_target(mgb-file-check DEPENDS link_sh)

tools/mlir/mgb-file-check/mgb-file-check.sh

@@ -0,0 +1,3 @@
+#!/bin/bash -e
+
+FileCheck --enable-var-scope --dump-input=fail "$@"

tools/mlir/mgb-opt/CMakeLists.txt

@@ -0,0 +1,23 @@
+get_property(dialect_libs GLOBAL PROPERTY MLIR_DIALECT_LIBS)
+get_property(conversion_libs GLOBAL PROPERTY MLIR_CONVERSION_LIBS)
+set(LIBS
+        ${dialect_libs}
+        ${conversion_libs}
+        LLVMSupport
+        MLIROptLib
+        MLIRIR
+        MLIRPass
+        MLIRSupport
+        )
+add_executable(mgb-opt mgb-opt.cpp)
+
+target_include_directories(
+  mgb-opt
+  PRIVATE ${MLIR_LLVM_INCLUDE_DIR} ${PROJECT_SOURCE_DIR}/src/jit/include
+          ${PROJECT_BINARY_DIR}/src/jit/include)
+
+add_dependencies(mgb-opt mgb_dialect)
+
+target_link_libraries(mgb-opt PRIVATE ${LIBS} megbrain megdnn ${MGE_CUDA_LIBS})
+
+llvm_update_compile_flags(mgb-opt)

tools/mlir/mgb-opt/mgb-opt.cpp

@@ -0,0 +1,85 @@
+/**
+ * \file tools/mlir/mgb-opt/mgb-opt.cpp
+ * 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.
+ */
+
+#include "megbrain/jit/mlir/ir/dialect.h"
+#include "megbrain/jit/mlir/ir/passes.h"
+
+#include <llvm/Support/CommandLine.h>
+#include <llvm/Support/InitLLVM.h>
+#include <llvm/Support/PrettyStackTrace.h>
+#include <llvm/Support/SourceMgr.h>
+#include <llvm/Support/ToolOutputFile.h>
+
+#include <mlir/Dialect/Affine/IR/AffineOps.h>
+#include <mlir/Dialect/LLVMIR/LLVMDialect.h>
+#include <mlir/IR/AsmState.h>
+#include <mlir/InitAllDialects.h>
+#include <mlir/InitAllPasses.h>
+#include <mlir/Pass/Pass.h>
+#include <mlir/Pass/PassManager.h>
+#include <mlir/Support/FileUtilities.h>
+#include <mlir/Support/MlirOptMain.h>
+
+using namespace llvm;
+using namespace mlir;
+
+//! TODO: Implement a custom MlirOptMain that supports the following flags.
+static cl::opt<bool> print_mlir{
+        "print-mlir",
+        cl::desc("Prints MLIR IR after translation"),
+        cl::init(false),
+};
+
+static cl::list<std::string> input_values{
+        "input-value",
+        cl::desc("Input shapes and optional values"),
+        cl::ZeroOrMore,
+};
+
+static cl::opt<std::string> input_values_file{
+        "input-value-file",
+        cl::desc("Provides a file for input shapes and optional values (see "
+                 "ParseToVariantListFromFile in vm_util.h for details)"),
+        cl::init(""),
+};
+
+static cl::opt<bool> run{
+        "run",
+        cl::desc("Runs the module (vs. just compiling and verifing)"),
+        cl::init(true),
+};
+
+static cl::list<std::string> run_args{
+        "run-arg",
+        cl::desc("Argument passed to the execution flag parser"),
+        cl::ZeroOrMore,
+};
+
+namespace mgb {
+namespace jit {
+void register_test_mgb_to_affine_lowering_pass();
+void register_test_affine_to_llvm_lowering_pass();
+}  // namespace jit
+}  // namespace mgb
+
+int main(int argc, char** argv) {
+    mlir::registerAllPasses();
+
+    mlir::DialectRegistry registry;
+    mlir::registerAllDialects(registry);
+    registry.insert<mgb::jit::MgbDialect>();
+
+    mgb::jit::register_test_mgb_to_affine_lowering_pass();
+    mgb::jit::register_test_affine_to_llvm_lowering_pass();
+
+    return failed(MlirOptMain(argc, argv, "MLIR modular optimizer driver", registry));
+}

tools/param_defs/mgb_opr_param_defs.py

@@ -41,8 +41,14 @@ pdef('PersistentOutputStorage').add_fields(
           Doc('REPRODUCIBLE',
               'when profile or heuristic algo selection it require the algos'
               'must be reproducible'),
-          Doc('OPTMIZED',
-              'profile require algos are optmized to achieve fast-profile')).
+          Doc('OPTIMIZED',
+              'profile require algos are optmized to achieve fast-profile'),
+          default=('HEURISTIC',),
+          member_alias=[(('HEURISTIC', 'REPRODUCIBLE'), 'HEURISTIC_REPRODUCIBLE'),
+                        (('PROFILE', 'REPRODUCIBLE'), 'PROFILE_REPRODUCIBLE'),
+                        (('PROFILE', 'HEURISTIC'), 'PROFILE_HEURISTIC'),
+                        (('OPTIMIZED',), 'OPTMIZED'),
+                        ]).
  add_fields('uint64',
             Doc('workspace_limit', 'workspace limit in bytes'),
             str(2**64-1)+'ull'))