[jit] don't try to set training after ScriptModule has been initialized.

Now when initializing a ScriptModule during the torch.jit.load() process, there is already a cpp module backing the thing. That means that setting training will overwrite whatever the initialized ScriptModule had. This PR splits apart the common "set up internal state" part of the Module __init__ and calls that from ScriptModule.__init__ and Module.__init__, leaving the "nn.Module-specific" part (setting `self.training`) for the nn.Module __init__ ghstack-source-id: 9b2ba8a15c43cf230363e4cd10ba4ad3ac4931f7 Pull Request resolved: https://github.com/pytorch/pytorch/pull/23680

[jit] don't try to set training after ScriptModule has been initialized.
Now when initializing a ScriptModule during the torch.jit.load() process, there is already a cpp module backing the thing. That means that setting training will overwrite whatever the initialized ScriptModule had. This PR splits apart the common "set up internal state" part of the Module __init__ and calls that from ScriptModule.__init__ and Module.__init__, leaving the "nn.Module-specific" part (setting `self.training`) for the nn.Module __init__ ghstack-source-id: 9b2ba8a15c43cf230363e4cd10ba4ad3ac4931f7 Pull Request resolved: https://github.com/pytorch/pytorch/pull/23680
7c404fa5 · Michael Suo · 7a7cfcbf · 7c404fa5 · 7c404fa5
--- a/torch/jit/__init__.py
+++ b/torch/jit/__init__.py
@@ -1517,7 +1517,9 @@ if _enabled:
            else:
                self.__dict__['_c'] = torch._C.ScriptModule(_qualified_name, _compilation_unit, True)

-            Module.__init__(self)
+            Module._construct(self)
+            Module.__setattr__(self, "training", True)
+
            self._parameters = OrderedParameterDict(self._c)
            self._buffers = OrderedBufferDict(self._c)
            self._modules = OrderedModuleDict(self._c)
@@ -1564,7 +1566,7 @@ if _enabled:
                # to improve invocation performance
                self.__dict__[attr] = script_method
                return script_method
-            return Module.__getattr__(self, attr)
+            return super(ScriptModule, self).__getattr__(attr)

        def __setattr__(self, attr, value):
            if attr not in self._constants_set:

--- a/torch/nn/modules/module.py
+++ b/torch/nn/modules/module.py
@@ -69,6 +69,15 @@ class Module(object):
    _version = 1

    def __init__(self):
+        self._construct()
+        # initialize self.training separately from the rest of the internal
+        # state, as it is managed differently by nn.Module and ScriptModule
+        self.training = True
+
+    def _construct(self):
+        """
+        Initializes internal Module state, shared by both nn.Module and ScriptModule.
+        """
        torch._C._log_api_usage_once("python.nn_module")
        self._backend = thnn_backend
        self._parameters = OrderedDict()
@@ -79,7 +88,6 @@ class Module(object):
        self._state_dict_hooks = OrderedDict()
        self._load_state_dict_pre_hooks = OrderedDict()
        self._modules = OrderedDict()
-        self.training = True

    def forward(self, *input):
        r"""Defines the computation performed at every call.