feat(imperative): add traced module

GitOrigin-RevId: 28c3503f2e

imperative/python/megengine/__init__.py

@@ -130,3 +130,4 @@ import megengine.optimizer
 import megengine.quantization
 import megengine.random
 import megengine.utils
+import megengine.experimental

imperative/python/megengine/experimental/__init__.py

@@ -6,4 +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.
+from . import traced_module
 from .weight_scaler import get_scaled_model

imperative/python/megengine/experimental/traced_module/__init__.py

@@ -5,3 +5,15 @@
 # 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.
+
+from ...core._imperative_rt.core2 import set_cpp_apply_module_trace
+from .traced_module import (
+    TracedModule,
+    _register_all_builtin_module,
+    cpp_apply_module_trace,
+    register_as_builtin,
+    trace_module,
+)
+
+_register_all_builtin_module()
+set_cpp_apply_module_trace(cpp_apply_module_trace)

imperative/python/megengine/experimental/traced_module/expr.py

@@ -0,0 +1,215 @@
+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
+
+import collections
+from typing import List
+
+from ...core._imperative_rt import OpDef
+from ...core._imperative_rt.core2 import Tensor as RawTensor
+from ...core._imperative_rt.core2 import apply, set_module_tracing, unset_module_tracing
+from ...core.ops.special import Const
+from ...tensor import Tensor
+from .module_tracer import active_module_tracer
+from .node import ModuleNode, Node, NodeMixin, TensorNode
+
+
+class Expr:
+    """
+    ``Expr`` represents the operations(i.e. Call, Apply, GetAttr, Input, Constant) on ``Node``.
+    """
+
+    inputs = None  # type: List[Node]
+    outputs = None  # type: List[Node]
+
+
+# expr: None (i.e. fake expression which is used to mark input)
+class Input(Expr):
+    name = None
+
+    def __init__(self, name=None, type=None):
+        self.inputs = []
+        node_cls = type if type else Node
+        self.outputs = [
+            node_cls(self, name=name),
+        ]
+        self.name = name
+
+    @classmethod
+    def make(cls, *args, **kwargs):
+        expr = cls(*args, **kwargs)
+        active_module_tracer().current_scope().add_input(expr.outputs[0])
+        return expr.outputs[0]
+
+    def __repr__(self):
+        return "{} = Input({})".format(self.outputs[0], self.name)
+
+
+# expr: outputs = getattr(inputs[0], self.name)
+class GetAttr(Expr):
+    name = None
+
+    def __init__(self, module, name, type=None):
+        assert isinstance(module, ModuleNode)
+        self.inputs = [
+            module,
+        ]
+        self.name = name
+        node_cls = type if type else Node
+        self.outputs = [
+            node_cls(self),
+        ]
+
+    @classmethod
+    def make(cls, *args, **kwargs):
+        expr = cls(*args, **kwargs)
+        active_module_tracer().current_scope().insert(expr)
+        expr.outputs[0]._name = expr.name
+        return expr.outputs[0]
+
+    def interpret(self, *inputs):
+        return (getattr(inputs[0], self.name),)
+
+    def __repr__(self):
+        return '{} = GetAttr({}, "{}")'.format(
+            self.outputs[0], self.inputs[0], self.name
+        )
+
+
+# expr: outputs = inputs[0].__call__(*inputs[1:])
+class Call(Expr):
+    def __init__(self, module):
+        assert isinstance(module, ModuleNode)
+        self.inputs = [
+            module,
+        ]
+
+    def add_input(self, node):
+        self.inputs.append(node)
+
+    def add_outputs(self, references):
+        self.outputs = []
+        if not isinstance(references, collections.Sequence):
+            references = (references,)
+
+        for i in references:
+            self.outputs.append(NodeMixin.get_wrapped_type(i)(self))
+
+    @classmethod
+    def make(cls, *args, **kwargs):
+        expr = cls(*args, **kwargs)
+        active_module_tracer().current_scope().insert(expr)
+        return expr
+
+    def interpret(self, *inputs):
+        mod = inputs[0]
+        args = inputs[1:]
+        outputs = mod(*args)
+        if isinstance(outputs, RawTensor):
+            outputs = (outputs,)
+        return outputs
+
+    def __repr__(self):
+        return "{} = Call({})({})".format(
+            ", ".join(str(i) for i in self.outputs),
+            self.inputs[0],
+            ", ".join(str(i) for i in self.inputs[1:]),
+        )
+
+
+# expr: outputs = apply(self.opdef, *inputs)
+class Apply(Expr):
+    opdef = None
+
+    def __init__(self, opdef):
+        assert isinstance(opdef, OpDef)
+        self.opdef = opdef
+        self.inputs = []
+
+    def add_input(self, node):
+        self.inputs.append(node)
+
+    def add_outputs(self, references):
+        self.outputs = []
+        if not isinstance(references, collections.Sequence):
+            references = (references,)
+
+        for i in references:
+            self.outputs.append(NodeMixin.get_wrapped_type(i)(self))
+
+    @classmethod
+    def make(cls, *args, **kwargs):
+        expr = cls(*args, **kwargs)
+        active_module_tracer().current_scope().insert(expr)
+        return expr
+
+    def interpret(self, *inputs):
+        return apply(self.opdef, *inputs)
+
+    def __repr__(self):
+        return "{} = {}({})".format(
+            ", ".join(str(i) for i in self.outputs),
+            self.opdef,
+            ", ".join(str(i) for i in self.inputs),
+        )
+
+    @classmethod
+    def apply_module_trace_hook(cls, opdef, *inputs):
+        for i in inputs:
+            node = NodeMixin.get(i, None)
+            if node is None:  # capture as constant
+                NodeMixin.wrap_safe(i, Constant.make(i))
+        apply_node = cls.make(opdef)
+        for i in inputs:
+            apply_node.add_input(NodeMixin.get(i))
+
+        unset_module_tracing()
+        outputs = apply(opdef, *inputs)
+        set_module_tracing()
+
+        apply_node.add_outputs(outputs)
+        for n, v in zip(apply_node.outputs, outputs):
+            NodeMixin.wrap_safe(v, n)
+        return list(outputs)
+
+
+# expr outputs = self.value
+class Constant(Expr):
+    value = None
+    # TODO: constant cache to reduce the size of dumped model
+    _constant_cache = {}
+
+    def __init__(self, c):
+        # TODO: type check, since not all types should be captured as constant
+        self.value = c
+        self.inputs = []
+        node_cls = NodeMixin.get_wrapped_type(c)
+        self.outputs = [
+            node_cls(self),
+        ]
+
+    @classmethod
+    def make(cls, *args, **kwargs):
+        expr = cls(*args, **kwargs)
+        active_module_tracer().current_scope().insert(expr)
+        return expr.outputs[0]
+
+    def interpret(self, *inputs):
+        if isinstance(self.value, RawTensor):
+            return Const(self.value.numpy())()
+        return (self.value,)
+
+    def __repr__(self):
+        return "{} = Constant({})".format(self.outputs[0], self.value)
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        if isinstance(self.value, RawTensor):
+            state["value"] = Tensor(self.value)
+        return state

imperative/python/megengine/experimental/traced_module/module_tracer.py

@@ -0,0 +1,52 @@
+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
+from ...module import Module
+
+_active_module_tracer = None
+
+
+def active_module_tracer():
+    return _active_module_tracer
+
+
+def set_active_module_tracer(tracer):
+    global _active_module_tracer
+    _active_module_tracer = tracer
+
+
+class module_tracer:
+
+    _opaque_types = set()
+
+    _active_scopes = None
+
+    def __init__(self):
+        self._active_scopes = []
+
+    @classmethod
+    def register_as_builtin(cls, mod):
+        assert issubclass(mod, Module)
+        cls._opaque_types.add(mod)
+        return mod
+
+    @classmethod
+    def is_builtin(cls, mod):
+        return type(mod) in cls._opaque_types
+
+    def push_scope(self, scope):
+        self._active_scopes.append(scope)
+
+    def pop_scope(self):
+        self._active_scopes.pop()
+
+    def current_scope(self):
+        if self._active_scopes:
+            return self._active_scopes[-1]
+        return None

imperative/python/megengine/experimental/traced_module/node.py

@@ -0,0 +1,123 @@
+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+from typing import Any, Dict, Tuple, Type
+
+import numpy
+
+from ...core._imperative_rt.core2 import Tensor as RawTensor
+from ...module import Module
+from ...tensor import Tensor
+
+
+class Node:
+    """
+    ``Node`` represents the variables （Tensor/Module/other python object) used in Module's forward method. They are inputs/outputs of Expr(the operations on variables).
+
+    param expr: the Expr which produces the node
+    param name: the name of the node
+    """
+
+    expr = None
+    __total_id = 0
+    _id = None
+    _name = None
+
+    def __init__(self, expr: "Expr", name: str = None):
+        self.expr = expr
+        self._id = Node.__total_id
+        Node.__total_id += 1
+        self._name = name
+
+    def __repr__(self):
+        if self._name is None:
+            return "%{}".format(self._id)
+        else:
+            return "%{}".format(self._name)
+
+
+class ModuleNode(Node):
+    """
+    ``ModuleNode`` represents the Module objects.
+
+    Attributes:
+        module_type: type of the Module correspending to the ModuleNode
+        graph: the InternalGraph which will be interpreted when call Module's forward method
+        attr_type_map: record the type of Module's attributes
+    """
+
+    module_type = Module  # type: Type[Module]
+    graph = None
+    attr_type_map = None  # type: Dict[str, Type[Any]]
+
+    def __repr__(self):
+        if self._name is None:
+            return "%{}({})".format(self._id, self.module_type.__name__)
+        else:
+            return "%{}({})".format(self._name, self.module_type.__name__)
+
+
+class TensorNode(Node):
+    """
+    ``TensorNode`` represents the Tensor objects.
+    """
+
+    shape = None  # type: Tuple[int]
+    dtype = None  # type: numpy.dtype
+
+    def __repr__(self):
+        if self._name is None:
+            return "%{}(Tensor)".format(self._id)
+        else:
+            return "%{}(Tensor)".format(self._name)
+
+
+class NodeMixin:
+    __node = None
+
+    @classmethod
+    def wrap(cls, value, node):
+        if isinstance(value, (NodeMixin, RawTensor)):
+            if isinstance(node, Node):
+                if isinstance(value, RawTensor):
+                    node.dtype = value.dtype
+                    node.shape = (
+                        value._tuple_shape if isinstance(value, Tensor) else value.shape
+                    )
+                setattr(value, "_NodeMixin__node", node)
+            else:
+                assert callable(node)
+                n = node()
+                if isinstance(value, RawTensor):
+                    n.dtype = value.dtype
+                    n.shape = (
+                        value._tuple_shape if isinstance(value, Tensor) else value.shape
+                    )
+                setattr(value, "_NodeMixin__node", n)
+
+    @classmethod
+    def wrap_safe(cls, value, node):
+        assert isinstance(value, (NodeMixin, RawTensor))
+        if isinstance(value, RawTensor):
+            node.dtype = value.dtype
+            node.shape = (
+                value._tuple_shape if isinstance(value, Tensor) else value.shape
+            )
+        setattr(value, "_NodeMixin__node", node)
+
+    @classmethod
+    def get(cls, value, *default):
+        return getattr(value, "_NodeMixin__node", *default)
+
+    @classmethod
+    def get_wrapped_type(cls, value):
+        if isinstance(value, RawTensor):
+            return TensorNode
+        if isinstance(value, (Module, NodeMixin)):
+            return ModuleNode
+        return Node

imperative/python/megengine/experimental/traced_module/traced_module.py

@@ -0,0 +1,295 @@
+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import collections
+import copy
+from typing import List, Type
+
+from ... import module as M
+from ...core._imperative_rt.core2 import set_module_tracing, unset_module_tracing
+from ...module import Module
+from ...tensor import Tensor
+from .expr import Apply, Call, Constant, Expr, GetAttr, Input
+from .module_tracer import active_module_tracer, module_tracer, set_active_module_tracer
+from .node import ModuleNode, Node, NodeMixin, TensorNode
+
+
+class InternalGraph:
+    """
+    ``InternalGraph`` is a graph consist of ``Node`` and  ``Expr``, it is used to represent the execution procedure of Module's forward method.
+
+    Attributes:
+    _exprs: List of Exprs in order of execution
+    _inputs: Input Nodes of InternalGraph
+    _outputs: Output Nodes of InternalGraph
+    """
+
+    _exprs = None  # type: List[Expr]
+    _inputs = None  # type: List[Node]
+    _outputs = None  # type: List[Node]
+
+    def __init__(self):
+        self._exprs = []
+        self._inputs = []
+        self._outputs = []
+
+    def insert(self, expr):
+        self._exprs.append(expr)
+
+    def add_input(self, i):
+        self._inputs.append(i)
+
+    def add_output(self, o):
+        self._outputs.append(o)
+
+    def interpret(self, *inputs):
+        # TODO: support kwargs ?
+        # TODO: skip expressions which are independent and have no side effect
+        node2value = {}
+        for n, v in zip(self._inputs, inputs):
+            node2value[n] = v
+        for expr in self._exprs:
+            values = expr.interpret(*list(node2value[i] for i in expr.inputs))
+            for n, v in zip(expr.outputs, values):
+                node2value[n] = v
+        return list(node2value[i] for i in self._outputs)
+
+    def __repr__(self):
+        return "InternalGraph ({}) {{\n\t{}\n\treturn {}\n}}".format(
+            ", ".join(str(i) for i in self._inputs),
+            "\n\t".join(str(i) for i in self._exprs),
+            ", ".join(str(i) for i in self._outputs),
+        )
+
+
+class TracedModuleBuilder(NodeMixin):
+
+    _mod = None  # type: Module
+    _body = None  # type: InternalGraph
+    _is_builtin = None  # type: bool
+
+    __builder_attributes__ = [
+        "_mod",
+        "_body",
+        "_NodeMixin__node",
+        "_is_builtin",
+        "_is_traced",
+        "build",
+    ]
+
+    def __init__(self, mod):
+        super(TracedModuleBuilder, self).__init__()
+        self._mod = mod
+        self._body = InternalGraph()
+        self._is_traced = False
+        self._is_builtin = module_tracer.is_builtin(mod)
+
+    def build(self):
+        if self._is_builtin:
+            node = NodeMixin.get(self)
+            node.module_type = type(self._mod)
+            return self._mod
+        else:
+            node = NodeMixin.get(self)
+            node.graph = self._body
+            node.attr_type_map = {}
+            traced_module = TracedModule(node)
+            for k, v in self.__dict__.items():
+                if k not in TracedModuleBuilder.__builder_attributes__:
+                    if isinstance(v, TracedModuleBuilder):
+                        v = v.build()
+                    setattr(traced_module, k, v)
+                    traced_module.m_node.attr_type_map[k] = type(v)
+            return traced_module
+
+    def __call__(self, *inputs, **kwargs):
+        assert isinstance(self._mod, Module)
+
+        # prepare args and kwargs for inner graph
+        def mark_constant(x):
+            node = NodeMixin.get(x, None)
+            if node is None:  # capture as constant
+                NodeMixin.wrap(x, lambda: Constant.make(x))
+
+        for i in inputs:
+            mark_constant(i)
+        for k, v in kwargs.items():
+            mark_constant(v)
+        callnode = Call.make(NodeMixin.get(self))
+
+        def add_input(x):
+            callnode.add_input(NodeMixin.get(x))
+
+        for i in inputs:
+            add_input(i)
+        for k, v in kwargs.items():
+            add_input(v)
+
+        if self._is_builtin or self._is_traced:
+            unset_module_tracing()
+            outputs = self._mod(*inputs, **kwargs)
+            set_module_tracing()
+            if self._is_builtin:
+                self._body = None
+        else:
+            active_module_tracer().push_scope(self._body)
+            # rebind self to new input node
+            orig_self = NodeMixin.get(self)
+            NodeMixin.wrap_safe(
+                self, Input.make("self", NodeMixin.get_wrapped_type(self))
+            )
+            # prepare args and kwargs for inner graph
+            def wrap(x):
+                wrapped = copy.copy(x)  # FIXME
+                NodeMixin.wrap(
+                    wrapped,
+                    lambda: Input.make(type=NodeMixin.get_wrapped_type(wrapped)),
+                )
+                return wrapped
+
+            args = []
+            for i in inputs:
+                args.append(wrap(i))
+            for k, v in kwargs.items():
+                kwargs[k] = wrap(v)
+
+            outputs = type(self._mod).forward(self, *args, **kwargs)
+
+            for i in (
+                outputs if isinstance(outputs, collections.abc.Sequence) else (outputs,)
+            ):
+                active_module_tracer().current_scope().add_output(NodeMixin.get(i))
+
+            NodeMixin.wrap_safe(self, orig_self)
+            self._is_traced = True
+            active_module_tracer().pop_scope()
+
+        # rebind output to outer graph
+        callnode.add_outputs(outputs)
+        for i, node in zip(
+            outputs if isinstance(outputs, collections.abc.Sequence) else (outputs,),
+            callnode.outputs,
+        ):
+            NodeMixin.wrap_safe(i, node)
+        return outputs
+
+    def __getattr__(self, name):
+        if name not in self._mod.__dict__:
+            attr = getattr(type(self._mod), name).__get__(self, type(self))
+        else:
+            attr = getattr(self._mod, name)
+            if isinstance(attr, Module):
+                attr = TracedModuleBuilder(attr)
+            setattr(self, name, attr)
+            NodeMixin.wrap(
+                attr,
+                lambda: GetAttr.make(
+                    NodeMixin.get(self), name, type=NodeMixin.get_wrapped_type(attr)
+                ),
+            )
+        return attr
+
+    def __getattribute__(self, name):
+        if name in TracedModuleBuilder.__builder_attributes__:
+            return super().__getattribute__(name)
+        else:
+            wrapped = super().__getattribute__(name)
+            if name in self._mod.__dict__ and not NodeMixin.get(wrapped, None):
+                assert not self._is_builtin
+                NodeMixin.wrap(
+                    wrapped,
+                    lambda: GetAttr.make(
+                        NodeMixin.get(self),
+                        name,
+                        type=NodeMixin.get_wrapped_type(wrapped),
+                    ),
+                )
+            return wrapped
+
+
+class TracedModule(Module):
+    """
+    `TracedModule` is the Module created by tracing normal module. It owns a ModuleNode(m_node), and will interpret the m_node.graph when it is called.
+    """
+
+    m_node = None  # type: ModuleNode
+
+    def __init__(self, node):
+        super(TracedModule, self).__init__()
+        self.m_node = node
+
+    def forward(self, *inputs):
+        rst = self.m_node.graph.interpret(self, *inputs)
+        if len(rst) == 1:
+            rst = rst[0]
+        return rst
+
+    def __getstate__(self):
+        d = self.__dict__
+        for k in Module.__dict__:
+            d.pop(k, None)
+        return d
+
+
+def cpp_apply_module_trace(opdef, *args):
+    return Apply.apply_module_trace_hook(opdef, *args)
+
+
+def register_as_builtin(mod_cls: Type[Module]) -> None:
+    """
+    Registers class ``mod_cls`` (subclass of megengine.module.Module) as builtin module.
+
+    param mod_cls: the Module class which will be threated as builtin module in tracing
+    """
+    module_tracer.register_as_builtin(mod_cls)
+
+
+def _register_all_builtin_module():
+    from inspect import getmembers, isclass
+
+    for sub_mod in [M, M.qat, M.quantized]:
+        for m in getmembers(sub_mod):
+            if (
+                isclass(m[1])
+                and issubclass(m[1], M.Module)
+                and m[1] is not M.Sequential
+            ):
+                module_tracer.register_as_builtin(m[1])
+
+
+def trace_module(mod: Module, *inputs: Tensor, **kwargs: Tensor) -> TracedModule:
+    """
+    Traces module ``mod`` and returns corresponding TracedModule.
+
+    param mod: the module will be converted to TracedModule
+    param input: the positional arguments passed to forward method of ``mod``
+    param kwargs: the keyword arguments passed to forward method of ``mod``
+    """
+    assert active_module_tracer() is None
+    try:
+        set_module_tracing()
+        set_active_module_tracer(module_tracer())
+        global_scope = InternalGraph()
+
+        active_module_tracer().push_scope(global_scope)
+
+        builder = TracedModuleBuilder(mod)
+        NodeMixin.wrap_safe(builder, Input.make("TopModule", ModuleNode))
+
+        for _, i in enumerate(inputs):
+            NodeMixin.wrap_safe(i, Input.make("arg_{}".format(_)))
+        for k, v in kwargs.items():
+            NodeMixin.wrap_safe(v, Input.make("kwarg_{}".format(k)))
+
+        builder(*inputs, **kwargs)
+        active_module_tracer().pop_scope()
+
+        return builder.build()
+    finally:
+        set_active_module_tracer(None)
+        unset_module_tracing()