[legacy] move communication and nn to legacy and refactor logger (#4671)

* [legacy] move communication to legacy (#4640)

* [legacy] refactor logger and clean up legacy codes (#4654)

* [legacy] make logger independent to gpc

* [legacy] make optim independent to registry

* [legacy] move test engine to legacy

* [legacy] move nn to legacy (#4656)

* [legacy] move nn to legacy

* [checkpointio] fix save hf config

* [test] remove useledd rpc pp test

* [legacy] fix nn init

* [example] skip tutorial hybriad parallel example

* [devops] test doc check

* [devops] test doc check

colossalai/legacy/nn/metric/accuracy_3d.py

@@ -0,0 +1,35 @@
+import torch
+from torch import nn
+
+from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
+from colossalai.legacy.nn.layer.parallel_3d import reduce_by_batch_3d, split_tensor_3d
+from colossalai.legacy.nn.layer.parallel_3d._utils import get_parallel_mode_from_env
+
+from ._utils import calc_acc
+
+
+class Accuracy3D(nn.Module):
+    """Accuracy for 3D parallelism
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
+        self.weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
+
+    def forward(self, logits, targets):
+        """Calculate the accuracy of predicted labels.
+
+        Args:
+            logits (:class:`torch.tensor`): Predicted labels.
+            targets (:class:`torch.tensor`): True labels from data.
+
+        Returns:
+            float: the accuracy of prediction.
+         """
+        with torch.no_grad():
+            targets = split_tensor_3d(targets, 0, self.weight_parallel_mode)
+            targets = split_tensor_3d(targets, 0, self.input_parallel_mode)
+            correct = calc_acc(logits, targets)
+            correct = reduce_by_batch_3d(correct, self.input_parallel_mode, self.weight_parallel_mode)
+        return correct