chore: import upstream snapshot with attribution

2026-07-13 13:24:13 +08:00
commit 1037506f2e
6050 changed files with 1731598 additions and 0 deletions
@@ -0,0 +1,76 @@
+# 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.
+
+from dataclasses import dataclass, field
+from typing import Optional, List
+from omegaconf import II
+
+from fairseq.dataclass import FairseqDataclass
+from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
+
+
+@dataclass
+class FixedLRScheduleConfig(FairseqDataclass):
+    force_anneal: Optional[int] = field(
+        default=None,
+        metadata={"help": "force annealing at specified epoch"},
+    )
+    lr_shrink: float = field(
+        default=0.1,
+        metadata={"help": "shrink factor for annealing, lr_new = (lr * lr_shrink)"},
+    )
+    warmup_updates: int = field(
+        default=0,
+        metadata={"help": "warmup the learning rate linearly for the first N updates"},
+    )
+    lr: List[float] = II("optimization.lr")
+
+
+@register_lr_scheduler("fixed", dataclass=FixedLRScheduleConfig)
+class FixedLRSchedule(FairseqLRScheduler):
+    """Decay the LR on a fixed schedule."""
+
+    def __init__(self, cfg: FixedLRScheduleConfig, optimizer):
+        super().__init__(cfg, optimizer)
+
+        self.lr = cfg.lr[0]
+        if cfg.warmup_updates > 0:
+            self.warmup_factor = 1.0 / cfg.warmup_updates
+        else:
+            self.warmup_factor = 1
+
+    def state_dict(self):
+        return {"lr": self.lr}
+
+    def load_state_dict(self, state_dict):
+        if "lr" in state_dict:
+            self.lr = state_dict["lr"]
+
+    def get_next_lr(self, epoch):
+        lrs = self.cfg.lr
+        if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal:
+            # use fixed LR schedule
+            next_lr = lrs[min(epoch - 1, len(lrs) - 1)]
+        else:
+            # annneal based on lr_shrink
+            next_lr = lrs[-1] * self.cfg.lr_shrink ** (
+                epoch + 1 - self.cfg.force_anneal
+            )
+        return next_lr
+
+    def step_begin_epoch(self, epoch):
+        """Update the learning rate at the beginning of the given epoch."""
+        self.lr = self.get_next_lr(epoch)
+        self.optimizer.set_lr(self.warmup_factor * self.lr)
+        return self.optimizer.get_lr()
+
+    def step_update(self, num_updates):
+        """Update the learning rate after each update."""
+        if self.cfg.warmup_updates > 0 and num_updates < self.cfg.warmup_updates:
+            self.warmup_factor = (num_updates + 1) / float(self.cfg.warmup_updates)
+            self.optimizer.set_lr(self.warmup_factor * self.lr)
+        else:
+            self.optimizer.set_lr(self.lr)
+        return self.optimizer.get_lr()