[Auto Parallel] Make Engine class callable (#46416)

* [Auto Parallel] Imporve the user-defined fetches and logging

* [Auto Parallel] Make Engine class callable

* [Auto Parallel] Update the data loading of tuner

python/paddle/distributed/auto_parallel/dist_loader.py

@@ -34,7 +34,7 @@ class DistributedDataLoader(metaclass=abc.ABCMeta):
 
         self.dataset = dataset
         self.epochs = epochs
-        self.drop_lost = drop_last
+        self.drop_last = drop_last
 
         if batch_size is None:
             self.batch_size = None
@@ -105,7 +105,7 @@ class NonIterableGeneratorLoader(DistributedDataLoader):
             self.collate_fn = collate_fn or default_convert_fn
         self.dataset_fetcher = _DatasetKind.create_fetcher(
             self.dataset_kind, self.dataset, self.auto_collate_batch,
-            self.collate_fn, self.drop_lost)
+            self.collate_fn, self.drop_last)
 
         self._steps = self._infer_steps()
         self._inner_dataloader = self._create_inner_dataloader()
@@ -153,7 +153,7 @@ class NonIterableGeneratorLoader(DistributedDataLoader):
                     self.dataset_fetcher = _DatasetKind.create_fetcher(
                         self.dataset_kind, self.dataset,
                         self.auto_collate_batch, self.collate_fn,
-                        self.drop_lost)
+                        self.drop_last)
                     break
 
                 partial_data = []

python/paddle/distributed/auto_parallel/engine.py

@@ -49,7 +49,7 @@ from .utils import get_logger, get_dist_attr
 from .process_group import new_process_group, get_all_process_groups
 from .dist_context import DistributedContext, get_default_distributed_context
 from .strategy import Strategy
-from .interface import _get_fetches
+from .interface import CollectionNames, get_collection
 
 
 class Engine:
@@ -197,7 +197,7 @@ class Engine:
         self._dygraph_mode = False
         self._tuning = self._strategy.tuning
 
-    def _prepare_single_mode(self, mode):
+    def _prepare_program(self, mode):
         # Do the build process
         self._build(mode)
         # Do the planning process
@@ -208,6 +208,62 @@ class Engine:
         self._initialize(mode)
         self._mode_init_states[mode] = True
 
+    def _prepare_feed(self, user_feeds=None, mode="train"):
+        if user_feeds is not None:
+            assert isinstance(user_feeds, dict), \
+                "user_feeds must be a dict, but receive {}".format(type(user_feeds).__name__)
+        feeds = {}
+        # TODO: add inputs and labels feed dict
+        for name, var in get_collection(CollectionNames.FEEDS):
+            assert name is not None, "No name defined for feed var"
+            feeds[name] = var
+        if user_feeds is not None:
+            for name, var in user_feeds.items():
+                feeds[name] = var
+        return feeds
+
+    def _prepare_fetch(self, user_fetches=None, mode="train"):
+        if user_fetches is not None:
+            assert isinstance(user_fetches, list), \
+                "user_fetches must be a list, but receive {}".format(type(user_fetches).__name__)
+        fetch_names = []
+        fetch_new_names = []
+        fetch_sections = {}
+        cnt = 0
+
+        def _process_section(section_name, var_list):
+            nonlocal cnt
+            section_start = cnt
+            for var in var_list:
+                new_name = None
+                # Rename the loss
+                if section_name == "loss":
+                    new_name = "loss"
+                if isinstance(var, tuple):
+                    assert len(var) == 2, "Length of tuple {} must be 2".format(
+                        var)
+                    new_name, var = var
+                if self._is_local_var(var) and var.name not in fetch_names:
+                    fetch_names.append(var.name)
+                    fetch_new_names.append(var.name)
+                    cnt += 1
+                if self._is_local_var(var) and new_name is not None:
+                    fetch_new_names[fetch_names.index(var.name)] = new_name
+            section_end = cnt
+            fetch_sections[section_name] = (section_start, section_end)
+
+        for name, var_list in self._fetch_vars[mode].items():
+            if name == "loss" and mode != "predict":
+                _process_section("loss", var_list)
+            if name == "metrics" and mode != "predict":
+                _process_section("metrics", var_list)
+            if name == "outputs" and mode == "predict":
+                _process_section("metrics", var_list)
+        var_list = (get_collection(CollectionNames.FETCHES)
+                    or []) + (user_fetches or [])
+        _process_section("user_fetches", var_list)
+        return fetch_names, fetch_new_names, fetch_sections
+
     def _build(self, mode):
         if _non_static_mode() or self._dygraph_mode:
             paddle.disable_static()
@@ -427,30 +483,32 @@ class Engine:
             dist_startup_prog = self._dist_startup_progs[mode][self._cur_rank]
             self._executor.run(dist_startup_prog)
 
-    def _infer_sample_spec(self, data, batch_size, split):
+    def _split_sample_item(self, data, split):
         if isinstance(data, paddle.io.IterableDataset):
             if split is None:
-                input, label = next(iter(data))
+                inputs, labels = next(iter(data))
             else:
                 sample = next(iter(data))
-                input = sample[:split]
-                label = sample[split:]
+                inputs = sample[:split]
+                labels = sample[split:]
         elif isinstance(data, paddle.io.Dataset):
             if split is None:
-                input, label = data[0]
+                inputs, labels = data[0]
             else:
                 sample = data[0]
-                input = sample[:split]
-                label = sample[split:]
+                inputs = sample[:split]
+                labels = sample[split:]
         else:
             raise ValueError(
                 "Data should be a Dataset or IterableDatset, but received {}.".
                 format(type(data).__name__))
+        inputs = to_list(inputs)
+        labels = to_list(labels)
+        return inputs, labels
 
+    def _infer_sample_spec(self, inputs, labels, batch_size):
         self.inputs_spec = []
         self.labels_spec = []
-        input_list = to_list(input)
-        label_list = to_list(label)
 
         def _infer_item_spec(item, name, batch_size, specs):
             if isinstance(item, np.ndarray):
@@ -468,13 +526,13 @@ class Engine:
             else:
                 specs.append(InputSpec([batch_size], type(item), name))
 
-        if input_list is not None:
-            for i, item in enumerate(input_list):
+        if inputs is not None:
+            for i, item in enumerate(inputs):
                 assert item is not None, "Receive None input."
                 name = "input" + str(i)
                 _infer_item_spec(item, name, batch_size, self.inputs_spec)
-        if label_list is not None:
-            for i, item in enumerate(label_list):
+        if labels is not None:
+            for i, item in enumerate(labels):
                 assert item is not None, "Receive None input."
                 name = "label" + str(i)
                 _infer_item_spec(item, name, batch_size, self.labels_spec)
@@ -482,6 +540,65 @@ class Engine:
         self.inputs_spec = self._validate_spec(self.inputs_spec)
         self.labels_spec = self._validate_spec(self.labels_spec)
 
+    def __call__(self,
+                 inputs=None,
+                 labels=None,
+                 feeds=None,
+                 fetches=None,
+                 mode="train"):
+        feed_dict = self._prepare_feed(feeds, mode)
+        fetch_list, fetch_new_names, fetch_sections = self._prepare_fetch(
+            fetches, mode)
+        try:
+            outs = self._executor.run(
+                self.main_program,
+                feed=feed_dict,
+                fetch_list=fetch_list,
+                use_program_cache=self._strategy.use_cache,
+                return_numpy=self._strategy.return_numpy)
+        except core.EOFException:
+            pass
+        self._print_log(outs, self.mode, None, None, None, fetch_new_names,
+                        fetch_sections)
+        return outs
+
+    # TODO: need a better to print the log
+    def _print_log(self,
+                   outs,
+                   mode="train",
+                   epoch=None,
+                   step=None,
+                   lr=None,
+                   fetch_new_names=None,
+                   fetch_sections=None):
+        prefix = "[{}] ".format(mode)
+        logs = {}
+        if epoch is not None:
+            logs["epoch: {:d} "] = epoch
+        if step is not None:
+            logs["step: {:d} "] = step
+        if lr is not None:
+            logs["lr: {:5e} "] = lr
+        if fetch_sections is not None:
+            assert fetch_new_names is not None
+            for section_name, section in fetch_sections.items():
+                section_start, section_end = section
+                if section_name == "metrics" and section_start < section_end:
+                    metric_out = outs[section_start:section_end]
+                    for metric in self._metrics:
+                        metric.update(*metric_out)
+                        results = metric.accumulate()
+                        for i, res in enumerate(to_list(results)):
+                            logs[metric.name()[i] + ": {:8f} "] = res
+                elif section_name == "loss" and section_start < section_end:
+                    for i in range(section_start, section_end):
+                        logs[fetch_new_names[i] + ": {:8f} "] = outs[i][0]
+                else:
+                    for i in range(section_start, section_end):
+                        logs[fetch_new_names[i] + ": {} "] = outs[i]
+        string = prefix + ''.join(list(logs.keys()))
+        self._logger.info(string.format(*list(logs.values())))
+
     def fit(self,
             train_data,
             train_sample_split=None,
@@ -561,28 +678,24 @@ class Engine:
                            batch_size=64)
         """
         self.mode = 'train'
-        self._infer_sample_spec(train_data, batch_size, train_sample_split)
+        inputs, labels = self._split_sample_item(train_data, train_sample_split)
+        self._infer_sample_spec(inputs, labels, batch_size)
         if not self._mode_init_states[self.mode]:
-            self._prepare_single_mode(self.mode)
+            self._prepare_program(self.mode)
         else:
             self._switch_mode("train")
 
         assert self.mode in self._dist_main_progs, \
-            "train model is not ready, please call `engine._prepare_single_mode('train')` first."
-        train_dataloader = self._create_dataloader(train_data, batch_size,
-                                                   epochs, steps_per_epoch,
-                                                   collate_fn)
-
-        fetch_loss = self._validate_fetches(self.fetch_vars["loss"])
-        fetch_metrics = self._validate_fetches(self.fetch_vars["metrics"])
-        inner_fetch = dict(fetch_loss, **fetch_metrics)
-        usr_fetch = self._validate_fetches(_get_fetches())
-        fetch_list, fetch_map = self._fetch_map(inner_fetch, usr_fetch)
+            "train model is not ready, please call `engine._prepare_program('train')` first."
+        train_dataloader = self._prepare_dataloader(train_data, batch_size,
+                                                    epochs, steps_per_epoch,
+                                                    collate_fn)
+
+        fetch_list, fetch_new_names, fetch_sections = self._prepare_fetch(
+            mode=self.mode)
         lr_scheduler = self._get_lr_scheduler(self.main_program)
 
-        outputs = defaultdict(list)
         for epoch in range(epochs):
-            train_logs = {"epoch: {:d} ": epoch}
             for step, _ in enumerate(train_dataloader):
                 try:
                     outs = self._executor.run(
@@ -592,32 +705,11 @@ class Engine:
                         return_numpy=self._strategy.return_numpy)
                 except core.EOFException:
                     break
-                train_logs["step: {:d} "] = step
-                # update lr
                 if lr_scheduler and step % self._k_steps == 0:
                     lr_scheduler.step()
-                train_logs["lr: {:5e} "] = self._get_lr(self._lr_optimizer)
-                # inner fetches
-                if fetch_loss:
-                    train_logs["loss: {:8f} "] = outs[0][0]
-                    outputs["loss"].append(outs[0][0])
-                # Metric
-                if fetch_metrics:
-                    metric_out = outs[len(fetch_loss):len(inner_fetch)]
-                    for metric in self._metrics:
-                        metric.update(*metric_out)
-                        results = metric.accumulate()
-                        for i, res in enumerate(to_list(results)):
-                            train_logs[metric.name()[i] + ": {:8f} "] = res
-                            outputs[metric.name()[i]].append(outs[0][0])
-                # user fetches
-                user_outs = outs[len(inner_fetch):]
-                user_fetch_list = fetch_list[len(inner_fetch):]
-                for i, out in enumerate(user_outs):
-                    train_logs[fetch_map[user_fetch_list[i]] + ": {}"] = out
-                # logger
-                string = '[train] ' + ''.join(list(train_logs.keys()))
-                self._logger.info(string.format(*list(train_logs.values())))
+                lr = self._get_lr(self._lr_optimizer)
+                self._print_log(outs, self.mode, epoch, step, lr,
+                                fetch_new_names, fetch_sections)
 
             if valid_data and epoch % valid_freq == 0:
                 self.evaluate(valid_data, valid_sample_split, batch_size,
@@ -625,7 +717,7 @@ class Engine:
                 self._switch_mode("train")
             else:
                 self._reset_metrics()
-        return outputs
+        return outs
 
     def evaluate(self,
                  valid_data,
@@ -652,7 +744,7 @@ class Engine:
                 the sample list, None for only stack each fields of sample in axis
                 0. Default None.
             callbacks (Callback|None, optional): A list of `Callback` instances to apply
-                during evaling. Default: None. (Unused for now)
+                during evaluating. Default: None. (Unused for now)
 
         Returns:
             None
@@ -681,24 +773,22 @@ class Engine:
 
         """
         self.mode = 'eval'
-        self._infer_sample_spec(valid_data, batch_size, valid_sample_split)
+        inputs, labels = self._split_sample_item(valid_data, valid_sample_split)
+        self._infer_sample_spec(inputs, labels, batch_size)
         if not self._mode_init_states[self.mode]:
-            self._prepare_single_mode(self.mode)
+            self._prepare_program(self.mode)
         else:
             self._switch_mode("eval")
 
         assert self.mode in self._dist_main_progs, \
-            "eval model is not ready, please call `engine._prepare_single_mode('eval')` first."
-        valid_dataloader = self._create_dataloader(valid_data,
-                                                   batch_size,
-                                                   steps_per_epoch=steps,
-                                                   collate_fn=collate_fn)
+            "eval model is not ready, please call `engine._prepare_program('eval')` first."
+        valid_dataloader = self._prepare_dataloader(valid_data,
+                                                    batch_size,
+                                                    steps_per_epoch=steps,
+                                                    collate_fn=collate_fn)
 
-        fetch_loss = self._validate_fetches(self.fetch_vars["loss"])
-        fetch_metrics = self._validate_fetches(self.fetch_vars["metrics"])
-        inner_fetch = dict(fetch_loss, **fetch_metrics)
-        usr_fetch = self._validate_fetches(_get_fetches())
-        fetch_list, fetch_map = self._fetch_map(inner_fetch, usr_fetch)
+        fetch_list, fetch_new_names, fetch_sections = self._prepare_fetch(
+            mode=self.mode)
 
         outputs = defaultdict(list)
         for step, _ in enumerate(valid_dataloader):
@@ -710,28 +800,8 @@ class Engine:
                     return_numpy=self._strategy.return_numpy)
             except core.EOFException:
                 break
-            eval_logs = {"step: {:d} ": step}
-            # inner fetches
-            if fetch_loss:
-                eval_logs["loss: {:8f} "] = outs[0][0]
-                outputs["eval_loss"].append(outs[0][0])
-            # Metric
-            if fetch_metrics:
-                metric_out = outs[len(fetch_loss):len(inner_fetch)]
-                for metric in self._metrics:
-                    metric.update(*metric_out)
-                    results = metric.accumulate()
-                    for i, res in enumerate(to_list(results)):
-                        eval_logs[metric.name()[i] + ": {:8f} "] = res
-                        outputs["eval_" + metric.name()[i]].append(res)
-            # user fetches
-            usr_outs = outs[len(inner_fetch):]
-            usr_fetch_list = fetch_list[len(inner_fetch):]
-            for i, out in enumerate(usr_outs):
-                eval_logs[fetch_map[usr_fetch_list[i]] + ": {}"] = out
-            # logger
-            string = '[eval] ' + ''.join(list(eval_logs.keys()))
-            self._logger.info(string.format(*list(eval_logs.values())))
+            self._print_log(outs, self.mode, None, step, None, fetch_new_names,
+                            fetch_sections)
         self._reset_metrics()
         return outputs
 
@@ -786,24 +856,23 @@ class Engine:
                 engine.predict(valid_dataset, batch_size=64)
         """
         self.mode = 'predict'
-        self._infer_sample_spec(test_data, batch_size, test_sample_split)
+        inputs, labels = self._split_sample_item(test_data, test_sample_split)
+        self._infer_sample_spec(inputs, labels, batch_size)
         if not self._mode_init_states[self.mode]:
-            self._prepare_single_mode(self.mode)
+            self._prepare_program(self.mode)
         else:
             self._switch_mode("predict")
 
         assert self.mode in self._dist_main_progs, \
-            "predict model is not ready, please call `engine._prepare_single_mode('predict')` first."
-        test_dataloader = self._create_dataloader(test_data,
-                                                  batch_size,
-                                                  steps_per_epoch=steps,
-                                                  collate_fn=collate_fn)
+            "predict model is not ready, please call `engine._prepare_program('predict')` first."
+        test_dataloader = self._prepare_dataloader(test_data,
+                                                   batch_size,
+                                                   steps_per_epoch=steps,
+                                                   collate_fn=collate_fn)
 
-        fetch_outputs = self._validate_fetches(self.fetch_vars["outputs"])
-        usr_fetch = self._validate_fetches(_get_fetches())
-        fetch_list, fetch_map = self._fetch_map(fetch_outputs, usr_fetch)
+        fetch_list, fetch_new_names, fetch_sections = self._prepare_fetch(
+            mode=self.mode)
 
-        outputs = []
         for step, _ in enumerate(test_dataloader):
             try:
                 outs = self._executor.run(
@@ -813,27 +882,44 @@ class Engine:
                     return_numpy=self._strategy.return_numpy)
             except core.EOFException:
                 break
-            predict_logs = {"step: {:d} ": step}
-            outputs.append(outs[:len(fetch_outputs)])
-            for i, out in enumerate(outs):
-                predict_logs[fetch_map[fetch_list[i]] + ": {}"] = out
-            # logger
-            string = '[pred] ' + ''.join(list(predict_logs.keys()))
-            self._logger.info(string.format(*list(predict_logs.values())))
+            self._print_log(outs, self.mode, None, step, None, fetch_new_names,
+                            fetch_sections)
 
-        return outputs
+        return outs
 
     def _tune(self, tune_data, tune_sample_split=None, batch_size=1):
         self.mode = 'train'
-        self._infer_sample_spec(tune_data, batch_size, tune_sample_split)
+        inputs, labels = self._split_sample_item(tune_data, tune_sample_split)
+        self._infer_sample_spec(inputs, labels, batch_size)
         self._optimization_tuning(self.mode, tune_data, batch_size)
 
-    def _create_dataloader(self,
-                           dataset,
-                           batch_size,
-                           epochs=1,
-                           steps_per_epoch=None,
-                           collate_fn=None):
+    def dataloader(self,
+                   dataset,
+                   sample_split=1,
+                   batch_size=1,
+                   epochs=1,
+                   steps_per_epoch=None,
+                   collate_fn=None,
+                   mode="train",
+                   from_generator=True):
+        assert from_generator, "Only support from_generator for now"
+        self.mode = mode
+        inputs, labels = self._split_sample_item(dataset, sample_split)
+        self._infer_sample_spec(inputs, labels, batch_size)
+        if not self._mode_init_states[self.mode]:
+            self._prepare_program(self.mode)
+        else:
+            self._switch_mode("train")
+        dataloader = self._prepare_dataloader(dataset, batch_size, epochs,
+                                              steps_per_epoch, collate_fn)
+        return dataloader
+
+    def _prepare_dataloader(self,
+                            dataset,
+                            batch_size,
+                            epochs=1,
+                            steps_per_epoch=None,
+                            collate_fn=None):
 
         if self._strategy.gradient_merge and batch_size is not None:
             assert batch_size % self._k_steps == 0, \
@@ -921,32 +1007,6 @@ class Engine:
         var_name = _to_name_str(var)
         return var_name in self.main_program.global_block().vars
 
-    def _validate_fetches(self, fetches):
-        # 1. Check user-defined fetches type
-        # 2. Prepare fetches_dict like {user_defined_name: var_name}
-        if not fetches:
-            return {}
-        if isinstance(fetches, dict):
-            fetch_var_names = list(map(_to_name_str, fetches.values()))
-            fetches_dict = dict(zip(fetch_var_names, list(fetches.keys())))
-        elif isinstance(fetches, list):
-            fetch_var_names = list(map(_to_name_str, fetches))
-            fetches_dict = dict(zip(fetch_var_names, fetch_var_names))
-        else:
-            raise TypeError("'fetches' only support 'dict' and 'list', "
-                            "but got '{}'".format(str(type(fetches))))
-        return dict(
-            filter(lambda x: self._is_local_var(x[0]), fetches_dict.items()))
-
-    def _fetch_map(self, inner_fetch, usr_fetch):
-        # replace inner fetch name if usr set for it
-        for iname in inner_fetch:
-            if iname in usr_fetch:
-                inner_fetch[iname] = usr_fetch[iname]
-                usr_fetch.pop(iname)
-        fetches = dict(inner_fetch, **usr_fetch)
-        return list(fetches.keys()), fetches
-
     def _get_input_split_info(self, var, dist_context):
         # deduce how the input data is split among the cluster
         from .utils import _get_comm_group, _get_corresponding_rank
@@ -1066,7 +1126,7 @@ class Engine:
         """
         if training:
             assert 'train' in self._serial_main_progs, \
-                "training model is not ready, please call `engine._prepare_single_mode('train')` first."
+                "training model is not ready, please call `engine._prepare_program('train')` first."
             serial_program = self._serial_main_progs["train"]
             dist_main_prog = self._dist_main_progs["train"][self._cur_rank]
             dist_context = self._dist_contexts["train"]
@@ -1097,7 +1157,7 @@ class Engine:
                 the parameter in file storing model states of or receives a
                 mismatch shape). Default: False.
             load_optimizer (bool, optional): If True, the stored optimizer
-                states is restored. Otherwise, the optimizer states is intialized
+                states is restored. Otherwise, the optimizer states is initialized
                 from scratch. Default: False.
 
         Returns:

python/paddle/distributed/auto_parallel/interface.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections import defaultdict
+
 import paddle
 from paddle.fluid import core
 from .process_mesh import ProcessMesh
@@ -196,15 +198,42 @@ def recompute(op):
     return RecomputeOperator(op)
 
 
-_g_fetched_tensors = {}
+# _g_fetched_tensors = {}
 
+# def fetch(tensor, name=None):
+#     if name is None:
+#         _g_fetched_tensors[tensor.name] = tensor
+#     else:
+#         _g_fetched_tensors[name] = tensor
 
-def fetch(tensor, name=None):
-    if name is None:
-        _g_fetched_tensors[tensor.name] = tensor
+# def _get_fetches():
+#     return _g_fetched_tensors
+
+_g_collections = {}
+
+
+class CollectionNames(object):
+    FEEDS = "feeds"
+    FETCHES = "fetches"
+
+
+def get_collection(name):
+    collection = _g_collections.get(name, None)
+    if collection is None:
+        collection = []
+        _g_collections[name] = collection
+    return _g_collections[name]
+
+
+def add_to_collection(collection_name, value, value_name=None):
+    if collection_name not in _g_collections:
+        _g_collections[collection_name] = []
     else:
-        _g_fetched_tensors[name] = tensor
+        if value_name is not None:
+            _g_collections[collection_name].append((value_name, value))
+        else:
+            _g_collections[collection_name].append((None, value))
 
 
-def _get_fetches():
-    return _g_fetched_tensors
+def fetch(tensor, name=None):
+    add_to_collection(CollectionNames.FETCHES, tensor, name)

python/paddle/fluid/tests/unittests/auto_parallel/engine_api.py

@@ -97,7 +97,7 @@ class MLPLayer(nn.Layer):
         out = self.dropout(out)
         out = self.linear2(out)
         if is_fetch:
-            auto.fetch(out, "out")
+            auto.fetch(out, "my_out")
         return out
 
 
@@ -145,6 +145,57 @@ def train(fetch):
     temp_dir.cleanup()
 
 
+def train_callable():
+    mlp = MLPLayer(hidden_size=hidden_size,
+                   intermediate_size=4 * hidden_size,
+                   dropout_ratio=0.1,
+                   initializer_range=0.02)
+    loss = paddle.nn.CrossEntropyLoss()
+    optimizer = paddle.optimizer.Adam(learning_rate=0.00001,
+                                      beta1=0.9,
+                                      beta2=0.999,
+                                      epsilon=1e-08,
+                                      grad_clip=None)
+    metric = paddle.metric.Accuracy()
+
+    strategy = auto.Strategy()
+    strategy.auto_mode = "semi"
+
+    engine = auto.Engine(mlp, loss, optimizer, metric, strategy=strategy)
+
+    # train
+    train_dataset = MyDataset(batch_num * batch_size)
+    train_dataloader = engine.dataloader(train_dataset,
+                                         batch_size=batch_size,
+                                         mode="train")
+    for _ in train_dataloader:
+        outs = engine(mode="train")
+
+    # eval
+    eval_dataset2 = MyDataset(batch_size)
+    eval_dataloader = engine.dataloader(eval_dataset2,
+                                        batch_size=batch_size,
+                                        mode="eval")
+    for _ in eval_dataloader:
+        outs = engine(mode="eval")
+
+    # predict
+    test_dataset = MyDataset(batch_size)
+    predict_dataloader = engine.dataloader(test_dataset,
+                                           batch_size=batch_size,
+                                           mode="predict")
+    for _ in predict_dataloader:
+        outs = engine(mode="predict")
+
+    # save
+    temp_dir = tempfile.TemporaryDirectory()
+    model_filename = os.path.join(temp_dir.name, 'mlp')
+    engine.save(model_filename, training=True)
+    engine.load(model_filename)
+    temp_dir.cleanup()
+
+
 if __name__ == "__main__":
     train(fetch=True)
     train(fetch=False)
+    train_callable()