Simplify configs (#550)

Co-authored-by: Remi <remi.cadene@huggingface.co>
Co-authored-by: HUANG TZU-CHUN <137322177+tc-huang@users.noreply.github.com>

lerobot/common/optim/schedulers.py

@@ -0,0 +1,56 @@
+import abc
+import math
+from dataclasses import asdict, dataclass
+
+import draccus
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LambdaLR, LRScheduler
+
+
+@dataclass
+class LRSchedulerConfig(draccus.ChoiceRegistry, abc.ABC):
+    num_warmup_steps: int
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
+
+    @abc.abstractmethod
+    def build(self, optimizer: Optimizer, num_training_steps: int) -> LRScheduler | None:
+        raise NotImplementedError
+
+
+@LRSchedulerConfig.register_subclass("diffuser")
+@dataclass
+class DiffuserSchedulerConfig(LRSchedulerConfig):
+    name: str = "cosine"
+    num_warmup_steps: int | None = None
+
+    def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR:
+        from diffusers.optimization import get_scheduler
+
+        kwargs = {**asdict(self), "num_training_steps": num_training_steps, "optimizer": optimizer}
+        return get_scheduler(**kwargs)
+
+
+@LRSchedulerConfig.register_subclass("vqbet")
+@dataclass
+class VQBeTSchedulerConfig(LRSchedulerConfig):
+    num_warmup_steps: int
+    num_vqvae_training_steps: int
+    num_cycles: float = 0.5
+
+    def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR:
+        def lr_lambda(current_step):
+            if current_step < self.num_vqvae_training_steps:
+                return float(1)
+            else:
+                adjusted_step = current_step - self.num_vqvae_training_steps
+                if adjusted_step < self.num_warmup_steps:
+                    return float(adjusted_step) / float(max(1, self.num_warmup_steps))
+                progress = float(adjusted_step - self.num_warmup_steps) / float(
+                    max(1, num_training_steps - self.num_warmup_steps)
+                )
+                return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(self.num_cycles) * 2.0 * progress)))
+
+        return LambdaLR(optimizer, lr_lambda, -1)