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/__init__.py

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+from .optimizers import OptimizerConfig as OptimizerConfig

lerobot/common/optim/factory.py

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+#!/usr/bin/env python
+
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+
+import torch
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+
+from lerobot.common.logger import TRAINING_STATE
+from lerobot.common.policies.pretrained import PreTrainedPolicy
+from lerobot.common.utils.utils import get_global_random_state, set_global_random_state
+from lerobot.configs.train import TrainPipelineConfig
+
+
+def make_optimizer_and_scheduler(
+    cfg: TrainPipelineConfig, policy: PreTrainedPolicy
+) -> tuple[Optimizer, LRScheduler | None]:
+    """Generates the optimizer and scheduler based on configs.
+
+    Args:
+        cfg (TrainPipelineConfig): The training config that contains optimizer and scheduler configs
+        policy (PreTrainedPolicy): The policy config from which parameters and presets must be taken from.
+
+    Returns:
+        tuple[Optimizer, LRScheduler | None]: The couple (Optimizer, Scheduler). Scheduler can be `None`.
+    """
+    params = policy.get_optim_params() if cfg.use_policy_training_preset else policy.parameters()
+    optimizer = cfg.optimizer.build(params)
+    lr_scheduler = cfg.scheduler.build(optimizer, cfg.offline.steps) if cfg.scheduler is not None else None
+    return optimizer, lr_scheduler
+
+
+def load_training_state(checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None) -> int:
+    """
+    Given the checkpoint directory, load the optimizer state, scheduler state, and random state, and
+    return the global training step.
+    """
+    # TODO(aliberts): use safetensors instead as weights_only=False is unsafe
+    training_state = torch.load(checkpoint_dir / TRAINING_STATE, weights_only=False)
+    optimizer.load_state_dict(training_state["optimizer"])
+    if scheduler is not None:
+        scheduler.load_state_dict(training_state["scheduler"])
+    elif "scheduler" in training_state:
+        raise ValueError("The checkpoint contains a scheduler state_dict, but no LRScheduler was provided.")
+    # Small HACK to get the expected keys: use `get_global_random_state`.
+    set_global_random_state({k: training_state[k] for k in get_global_random_state()})
+    return training_state["step"], optimizer, scheduler

lerobot/common/optim/optimizers.py

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+import abc
+from dataclasses import asdict, dataclass
+
+import draccus
+import torch
+
+
+@dataclass
+class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC):
+    lr: float
+    betas: tuple[float, float]
+    eps: float
+    weight_decay: float
+    grad_clip_norm: float
+
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
+
+    @classmethod
+    def default_choice_name(cls) -> str | None:
+        return "adam"
+
+    @abc.abstractmethod
+    def build(self) -> torch.optim.Optimizer:
+        raise NotImplementedError
+
+
+@OptimizerConfig.register_subclass("adam")
+@dataclass
+class AdamConfig(OptimizerConfig):
+    lr: float = 1e-3
+    betas: tuple[float, float] = (0.9, 0.999)
+    eps: float = 1e-8
+    weight_decay: float = 0.0
+    grad_clip_norm: float = 10.0
+
+    def build(self, params: dict) -> torch.optim.Optimizer:
+        kwargs = asdict(self)
+        kwargs.pop("grad_clip_norm")
+        return torch.optim.Adam(params, **kwargs)
+
+
+@OptimizerConfig.register_subclass("adamw")
+@dataclass
+class AdamWConfig(OptimizerConfig):
+    lr: float = 1e-3
+    betas: tuple[float, float] = (0.9, 0.999)
+    eps: float = 1e-8
+    weight_decay: float = 1e-2
+    grad_clip_norm: float = 10.0
+
+    def build(self, params: dict) -> torch.optim.Optimizer:
+        kwargs = asdict(self)
+        kwargs.pop("grad_clip_norm")
+        return torch.optim.AdamW(params, **kwargs)

lerobot/common/optim/schedulers.py

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+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)