Fix conflicts

model.py

@@ -148,6 +148,9 @@ class StaticGraphAdapter(object):
 
         base = os.path.basename(path)
         assert base != "", "path should be of 'dirname/filename' format"
+        dir_name = os.path.dirname(path)
+        if dir_name and not os.path.exists(dir_name):
+            os.makedirs(dir_name)
         param_path = path + ".pdparams"
         _save(self.model.state_dict(), param_path)
         prog = self._progs.get('train', None)
@@ -161,9 +164,7 @@ class StaticGraphAdapter(object):
         }
         if not optim:
             return
-        # HACK this is contrived, optimizer state is not the same for
-        # static/dynamic graph mode
-        optim['__static_graph_only__'] = True
+
         _save(optim, optim_path)
 
     def load(self, path):
@@ -198,8 +199,6 @@ class StaticGraphAdapter(object):
         optim_state = _load(optim_path)
         if optim_state is None:
             return
-        assert '__static_graph_only__' in optim_state, \
-            "optimizer saved in dygraph mode is not usable in static graph"
 
         assert self._executor
         self._load_optimizer(optim_state)
@@ -214,10 +213,66 @@ class StaticGraphAdapter(object):
                                             global_scope(),
                                             self._executor._default_executor)
 
+        converted_state = dict(state)
         for var in optim:
-            assert var.name in state, \
+            if var.name in ["@LR_DECAY_COUNTER@", "global_step"]:
+                # When using learning rate scheduler, dygraph would name the
+                # global step var as "global_step" to save, while static-graph
+                # would has a state var named as "@LR_DECAY_COUNTER@".
+                # NOTE: dygraph saved global_step is 1 larger than that in
+                # static-graph, since the time of global_step to increase is
+                # different.
+                state_val = (
+                    np.array(converted_state.pop("global_step")) - 1
+                ) if "global_step" in converted_state else converted_state.pop(
+                    "@LR_DECAY_COUNTER@", None)
+                if state_val is not None:
+                    converted_state[var.name] = state_val
+            elif var.name.startswith("learning_rate_"):
+                # When using static learning rate, static-graph would make it
+                # a persistable var named 'unique_name.generate("learning_rate")',
+                # However, dygraph wouldn't save it.
+                if var.name not in state: continue
+            else:
+                # moment and other accumulators
+                if var.name not in converted_state:
+                    # try to convert from dygraph name
+                    opt_name = self.model._optimizer._name
+                    opt_cls_name = self.model._optimizer.__class__.__name__
+                    opt_unq_name = None
+                    for name in self.model._optimizer._accumulators.keys():
+                        accum_name = name if opt_name is None else name[len(
+                            opt_name) + 1:]
+                        for param_name, state_var in self.model._optimizer._accumulators[
+                                name].items():
+                            if opt_unq_name is None:
+                                # can not infer out the exact unique(opt_name),
+                                # thus try to extract rather than generate
+                                for state_key in sorted(
+                                        state.keys(),
+                                        key=lambda x: len(x),
+                                        reverse=True):
+                                    prefix = param_name + "_" + (
+                                        opt_cls_name if opt_name is None else
+                                        opt_name) + "_"
+                                    if state_key.startswith(prefix):
+                                        prefix_offset = state_key[len(
+                                            prefix):].find("_") + len(prefix)
+                                        opt_unq_name = state_key[len(
+                                            param_name + "_"):prefix_offset]
+                                        # TODO: assert
+                                        # assert opt_unq_name is None
+                                    # gen(param.name + "_" + gen(opt_name) + "_" + accum_name)
+                                    # always end with "_0" since the unique optimizer._name
+                            dy_state_name = (param_name + "_" + opt_unq_name +
+                                             "_" + accum_name + "_0")
+                            converted_state[
+                                state_var.name] = converted_state.pop(
+                                    dy_state_name)
+
+            assert var.name in converted_state, \
                 "variable [{}] is not in optimizer state file".format(var.name)
-            self._set_var(var, state[var.name])
+            self._set_var(var, converted_state[var.name])
 
     def _set_var(self, var, ndarray):
         t = global_scope().find_var(var.name).get_tensor()
@@ -275,6 +330,17 @@ class StaticGraphAdapter(object):
 
     def _make_program(self, inputs):
         prog = self._orig_prog.clone()
+        # change inputs to the same var in cloned program
+        inputs = fluid.layers.utils.map_structure(
+            lambda var: prog.global_block().var(var.name), inputs)
+        # NOTE: When defining learning rate scheduling in static-graph, ops to
+        # increase the global step var and calculate learning rate would be
+        # prepended into _orig_prog. test program maked by `_orig_prog.clone`
+        # also would include these ops. Thus must prune these ops in test
+        # program, otherwise the global step would be changed in test.
+        if self.mode != 'train':
+            for op in list(prog.global_block().ops):
+                prog.global_block()._remove_op(0)
         if self.mode == 'train' and self.model._optimizer._learning_rate_map:
             # HACK workaround learning rate map issue
             lr_var = self.model._optimizer._learning_rate_map[self._orig_prog]
@@ -432,7 +498,50 @@ class DynamicGraphAdapter(object):
         self.model.set_dict(params)
         if self.model._optimizer is None or optim is None:
             return
-        self.model._optimizer.set_dict(optim)
+
+        # If optimizer performs set_dict when state vars haven't been created,
+        # which would happen when set_dict before minimize, the state would be
+        # stored in optimizer._accumulators_holder and loaded lazily.
+        # To contrive this when loading from static-graph saved states, extend
+        # state dict to include keys named accoring to dygraph naming rules.
+        # TODO: if len(self.model._optimizer._accumulators) > 0
+        converted_state = dict(optim)
+        opt_unq_name = self.model._optimizer._name
+        opt_cls_name = self.model._optimizer.__class__.__name__
+        opt_name = opt_unq_name[:opt_unq_name.rfind("_")]  # remove suffix idx
+        param_names = [param.name for param in self.model.parameters()]
+        for var_name, state_var in sorted(
+                optim.items(), key=lambda x: len(x[0]), reverse=True):
+            if var_name in ["@LR_DECAY_COUNTER@", "global_step"]:
+                # NOTE: dygraph saved global_step is 1 larger than that in
+                # static-graph, since the time of global_step to increase is
+                # different.
+                if var_name == "@LR_DECAY_COUNTER@":
+                    converted_state["global_step"] = np.array(
+                        converted_state.pop("@LR_DECAY_COUNTER@")) + 1
+            else:
+                # moment and other accumulators
+                # extend state dict to include promising dygraph names
+                for param_name in param_names:
+                    if var_name.startswith(param_name + "_" + opt_name):
+                        # when init optimizer with name
+                        accum_name = var_name[len(param_name + "_" + opt_name +
+                                                  "_"):]
+                    elif var_name.startswith(param_name +
+                                             "_") and opt_name == opt_cls_name:
+                        # when init optimizer without name
+                        accum_name = var_name[len(param_name + "_"):]
+                    else:
+                        continue
+                    # remove suffix idx
+                    accum_name = accum_name[:accum_name.rfind("_")]
+                    # state names always end with "_0" in dygraph because of the
+                    # unique optimizer._name
+                    dy_state_name = (param_name + "_" + opt_unq_name + "_" +
+                                     accum_name + "_0")
+                    converted_state[dy_state_name] = state_var
+
+        self.model._optimizer.set_dict(converted_state)
 
 
 class Model(fluid.dygraph.Layer):