[legacy] clean up legacy code (#4743)

* [legacy] remove outdated codes of pipeline (#4692)

* [legacy] remove cli of benchmark and update optim (#4690)

* [legacy] remove cli of benchmark and update optim

* [doc] fix cli doc test

* [legacy] fix engine clip grad norm

* [legacy] remove outdated colo tensor (#4694)

* [legacy] remove outdated colo tensor

* [test] fix test import

* [legacy] move outdated zero to legacy (#4696)

* [legacy] clean up utils (#4700)

* [legacy] clean up utils

* [example] update examples

* [legacy] clean up amp

* [legacy] fix amp module

* [legacy] clean up gpc (#4742)

* [legacy] clean up context

* [legacy] clean core, constants and global vars

* [legacy] refactor initialize

* [example] fix examples ci

* [example] fix examples ci

* [legacy] fix tests

* [example] fix gpt example

* [example] fix examples ci

* [devops] fix ci installation

* [example] fix examples ci

colossalai/legacy/amp/torch_amp/__init__.py

@@ -0,0 +1,45 @@
+from typing import Optional
+
+import torch.nn as nn
+from torch.nn.modules.loss import _Loss
+from torch.optim import Optimizer
+
+from colossalai.context import Config
+
+from .torch_amp import TorchAMPLoss, TorchAMPModel, TorchAMPOptimizer
+
+
+def convert_to_torch_amp(model: nn.Module,
+                         optimizer: Optimizer,
+                         criterion: Optional[_Loss] = None,
+                         amp_config: Optional[Config] = None):
+    """A helper function to wrap training components with Pytorch AMP modules
+
+    Args:
+        model (:class:`torch.nn.Module`): your model object.
+        optimizer (:class:`torch.optim.Optimizer`): your optimizer object
+        criterion (:class:`torch.nn.modules.loss._Loss`, optional): your loss function object
+        amp_config (:class:`colossalai.context.Config` or dict, optional): configuration for Pytorch AMP.
+
+    The ``amp_config`` should include parameters below:
+    ::
+
+        init_scale (float, optional, default=2.**16)
+        growth_factor (float, optional, default=2.0)
+        backoff_factor (float, optional, default=0.5)
+        growth_interval (int, optional, default=2000)
+        enabled (bool, optional, default=True)
+
+    Returns:
+        A tuple (model, optimizer, criterion)
+    """
+    model = TorchAMPModel(model)
+    if amp_config is None:
+        amp_config = dict()
+    optimizer = TorchAMPOptimizer(optimizer, **amp_config)
+    if criterion:
+        criterion = TorchAMPLoss(criterion)
+    return model, optimizer, criterion
+
+
+__all__ = ['convert_to_torch_amp', 'TorchAMPModel', 'TorchAMPLoss', 'TorchAMPOptimizer']