[legacy] move trainer to legacy (#4545)

* [legacy] move trainer to legacy

* [doc] update docs related to trainer

* [test] ignore legacy test

colossalai/legacy/trainer/hooks/_lr_scheduler_hook.py

@@ -0,0 +1,48 @@
+from torch import Tensor
+
+from colossalai.registry import HOOKS
+
+from ._metric_hook import LearningRateMetric, MetricHook
+
+
+@HOOKS.register_module
+class LRSchedulerHook(MetricHook):
+    r"""Build LR scheduler for trainer.
+
+    Args:
+        lr_scheduler (:class:`colossalai.nn.lr_scheduler`): The specific LR scheduler
+            in range of ``colossalai.nn.lr_scheduler``, more details about ``lr_scheduler`` could be found in
+            `lr_scheduler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/nn/lr_scheduler>`_.
+        by_epoch (bool): If `True`, the LR will be scheduled every epoch. Else, the LR will be scheduled every batch.
+        store_lr_in_state (bool, optional): If `True`, store the learning rate in each state, defaults to `True`.
+        priority (int, optional): Priority in the printing, hooks with small priority will be printed in front
+            defaults to 1. If different hooks share same priority, the order of printing would
+            depend on the hooks order in the hook list.
+    """
+
+    def __init__(
+        self,
+        lr_scheduler,
+        by_epoch: bool,
+        store_lr_in_state: bool = True,
+        priority: int = 1,
+    ):
+        super().__init__(priority=priority)
+        self.by_epoch = by_epoch
+        self.lr_scheduler = lr_scheduler
+        self.store_lr_in_state = store_lr_in_state
+
+    def after_hook_is_attached(self, trainer):
+        self._check_metric_states_initialization(trainer)
+        trainer.states['metrics']['train']['LR'] = LearningRateMetric(epoch_only=self.by_epoch,
+                                                                      initial_lr=self.lr_scheduler.get_last_lr()[0])
+
+    def after_train_epoch(self, trainer):
+        if self.by_epoch:
+            self.lr_scheduler.step()
+            trainer.states['metrics']['train']['LR'].update(self.lr_scheduler.get_last_lr()[0])
+
+    def after_train_iter(self, trainer, output: Tensor, label: Tensor, loss: Tensor):
+        if not self.by_epoch:
+            self.lr_scheduler.step()
+            trainer.states['metrics']['train']['LR'].update(self.lr_scheduler.get_last_lr()[0])