chore: import upstream snapshot with attribution

decoding/GAD/fairseq/optim/sgd.py

@@ -0,0 +1,43 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch.optim
+
+from . import LegacyFairseqOptimizer, register_optimizer
+
+
+@register_optimizer("sgd")
+class SGD(LegacyFairseqOptimizer):
+    def __init__(self, args, params):
+        super().__init__(args)
+        self._optimizer = torch.optim.SGD(params, **self.optimizer_config)
+
+    @staticmethod
+    def add_args(parser):
+        """Add optimizer-specific arguments to the parser."""
+        # fmt: off
+        parser.add_argument('--momentum', default=0.0, type=float, metavar='M',
+                            help='momentum factor')
+        parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
+                            help='weight decay')
+        # fmt: on
+
+    @property
+    def optimizer_config(self):
+        """
+        Return a kwarg dictionary that will be used to override optimizer
+        args stored in checkpoints. This allows us to load a checkpoint and
+        resume training using a different set of optimizer args, e.g., with a
+        different learning rate.
+        """
+        return {
+            "lr": self.args.lr[0],
+            "momentum": self.args.momentum,
+            "weight_decay": self.args.weight_decay,
+        }
+
+    @property
+    def supports_flat_params(self):
+        return True