1116 lines
45 KiB
Python
1116 lines
45 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import time
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from contextlib import nullcontext
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from copy import deepcopy
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from pathlib import Path
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from pprint import pformat
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import random
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from typing import Optional, Sequence, TypedDict
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import hydra
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import numpy as np
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import torch
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from deepdiff import DeepDiff
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from omegaconf import DictConfig, ListConfig, OmegaConf
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from termcolor import colored
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from torch import nn
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from torch.cuda.amp import GradScaler
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from tqdm import tqdm
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from lerobot.common.datasets.factory import make_dataset, resolve_delta_timestamps
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from lerobot.common.datasets.lerobot_dataset import MultiLeRobotDataset, LeRobotDataset
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from lerobot.common.datasets.online_buffer import OnlineBuffer, compute_sampler_weights
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from lerobot.common.datasets.sampler import EpisodeAwareSampler
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from lerobot.common.datasets.utils import cycle
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from lerobot.common.envs.factory import make_env
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from lerobot.common.envs.utils import preprocess_observation
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from lerobot.common.logger import Logger, log_output_dir
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from lerobot.common.policies.factory import make_policy
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from lerobot.common.policies.policy_protocol import PolicyWithUpdate
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from lerobot.common.policies.sac.modeling_sac import SACPolicy
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from lerobot.common.policies.utils import get_device_from_parameters
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from lerobot.common.utils.utils import (
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format_big_number,
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get_safe_torch_device,
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init_hydra_config,
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init_logging,
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set_global_seed,
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)
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from lerobot.scripts.eval import eval_policy
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def make_optimizers_and_scheduler(cfg, policy):
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optimizer_actor = torch.optim.Adam(
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params=policy.actor.parameters(),
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lr=policy.config.actor_lr,
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)
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optimizer_critic = torch.optim.Adam(
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params=policy.critic_ensemble.parameters(), lr=policy.config.critic_lr
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)
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# We wrap policy log temperature in list because this is a torch tensor and not a nn.Module
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optimizer_temperature = torch.optim.Adam(params=[policy.log_alpha], lr=policy.config.critic_lr)
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lr_scheduler = None
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optimizers = {
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"actor": optimizer_actor,
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"critic": optimizer_critic,
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"temperature": optimizer_temperature,
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}
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return optimizers, lr_scheduler
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# def update_policy(policy, batch, optimizers, grad_clip_norm):
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# NOTE: This is temporary, online buffer or query lerobot dataset is not performant enough yet
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class Transition(TypedDict):
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state: dict[str, torch.Tensor]
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action: torch.Tensor
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reward: float
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next_state: dict[str, torch.Tensor]
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done: bool
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complementary_info: dict[str, torch.Tensor] = None
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class BatchTransition(TypedDict):
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state: dict[str, torch.Tensor]
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action: torch.Tensor
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reward: torch.Tensor
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next_state: dict[str, torch.Tensor]
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done: torch.Tensor
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class ReplayBuffer:
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def __init__(self, capacity: int, device: str = "cuda:0", state_keys: Optional[Sequence[str]] = None):
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"""
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Args:
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capacity (int): Maximum number of transitions to store in the buffer.
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device (str): The device where the tensors will be moved ("cuda:0" or "cpu").
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state_keys (List[str]): The list of keys that appear in `state` and `next_state`.
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"""
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self.capacity = capacity
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self.device = device
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self.memory: list[Transition] = []
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self.position = 0
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# If no state_keys provided, default to an empty list
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# (you can handle this differently if needed)
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self.state_keys = state_keys if state_keys is not None else []
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def add(
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self,
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state: dict[str, torch.Tensor],
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action: torch.Tensor,
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reward: float,
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next_state: dict[str, torch.Tensor],
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done: bool,
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complementary_info: Optional[dict[str, torch.Tensor]] = None,
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):
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"""Saves a transition."""
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if len(self.memory) < self.capacity:
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self.memory.append(None)
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# Create and store the Transition
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self.memory[self.position] = Transition(
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state=state,
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action=action,
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reward=reward,
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next_state=next_state,
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done=done,
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complementary_info=complementary_info,
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)
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self.position = (self.position + 1) % self.capacity
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@classmethod
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def from_lerobot_dataset(
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cls,
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lerobot_dataset: LeRobotDataset,
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device: str = "cuda:0",
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state_keys: Optional[Sequence[str]] = None,
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) -> "ReplayBuffer":
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replay_buffer = cls(capacity=len(lerobot_dataset), device=device, state_keys=state_keys)
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list_transition = cls._lerobotdataset_to_transitions(dataset=lerobot_dataset, state_keys=state_keys)
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for data in list_transition:
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replay_buffer.add(
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state=data["state"],
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action=data["action"],
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reward=data["reward"],
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next_state=data["next_state"],
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done=data["done"],
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)
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return replay_buffer
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@staticmethod
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def _lerobotdataset_to_transitions(
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dataset: LeRobotDataset,
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state_keys: Optional[Sequence[str]] = None,
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) -> list[Transition]:
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"""
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Convert a LeRobotDataset into a list of RL (s, a, r, s', done) transitions.
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Args:
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dataset (LeRobotDataset):
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The dataset to convert. Each item in the dataset is expected to have
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at least the following keys:
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{
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"action": ...
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"next.reward": ...
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"next.done": ...
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"episode_index": ...
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}
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plus whatever your 'state_keys' specify.
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state_keys (Optional[Sequence[str]]):
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The dataset keys to include in 'state' and 'next_state'. Their names
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will be kept as-is in the output transitions. E.g.
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["observation.state", "observation.environment_state"].
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If None, you must handle or define default keys.
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Returns:
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transitions (List[Transition]):
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A list of Transition dictionaries with the same length as `dataset`.
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"""
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# If not provided, you can either raise an error or define a default:
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if state_keys is None:
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raise ValueError("You must provide a list of keys in `state_keys` that define your 'state'.")
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transitions: list[Transition] = []
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num_frames = len(dataset)
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for i in tqdm(range(num_frames)):
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current_sample = dataset[i]
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# ----- 1) Current state -----
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current_state: dict[str, torch.Tensor] = {}
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for key in state_keys:
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val = current_sample[key]
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current_state[key] = val.unsqueeze(0) # Add batch dimension
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# ----- 2) Action -----
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action = current_sample["action"].unsqueeze(0) # Add batch dimension
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# ----- 3) Reward and done -----
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reward = float(current_sample["next.reward"].item()) # ensure float
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done = bool(current_sample["next.done"].item()) # ensure bool
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# ----- 4) Next state -----
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# If not done and the next sample is in the same episode, we pull the next sample's state.
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# Otherwise (done=True or next sample crosses to a new episode), next_state = current_state.
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next_state = current_state # default
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if not done and (i < num_frames - 1):
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next_sample = dataset[i + 1]
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if next_sample["episode_index"] == current_sample["episode_index"]:
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# Build next_state from the same keys
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next_state_data: dict[str, torch.Tensor] = {}
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for key in state_keys:
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val = next_sample[key]
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next_state_data[key] = val.unsqueeze(0) # Add batch dimension
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next_state = next_state_data
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# ----- Construct the Transition -----
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transition = Transition(
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state=current_state,
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action=action,
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reward=reward,
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next_state=next_state,
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done=done,
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)
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transitions.append(transition)
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return transitions
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def sample(self, batch_size: int) -> BatchTransition:
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"""Sample a random batch of transitions and collate them into batched tensors."""
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list_of_transitions = random.sample(self.memory, batch_size)
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# -- Build batched states --
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batch_state = {}
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for key in self.state_keys:
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batch_state[key] = torch.cat([t["state"][key] for t in list_of_transitions], dim=0).to(
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self.device
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)
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# -- Build batched actions --
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batch_actions = torch.cat([t["action"] for t in list_of_transitions]).to(self.device)
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# -- Build batched rewards --
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batch_rewards = torch.tensor([t["reward"] for t in list_of_transitions], dtype=torch.float32).to(
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self.device
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)
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# -- Build batched next states --
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batch_next_state = {}
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for key in self.state_keys:
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batch_next_state[key] = torch.cat([t["next_state"][key] for t in list_of_transitions], dim=0).to(
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self.device
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)
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# -- Build batched dones --
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batch_dones = torch.tensor([t["done"] for t in list_of_transitions], dtype=torch.float32).to(
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self.device
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)
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batch_dones = torch.tensor([t["done"] for t in list_of_transitions], dtype=torch.float32).to(
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self.device
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)
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# Return a BatchTransition typed dict
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return BatchTransition(
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state=batch_state,
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action=batch_actions,
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reward=batch_rewards,
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next_state=batch_next_state,
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done=batch_dones,
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)
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def concatenate_batch_transitions(
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left_batch_transitions: BatchTransition, right_batch_transition: BatchTransition
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) -> BatchTransition:
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"""Be careful it change the left_batch_transitions in place"""
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left_batch_transitions["state"] = {
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key: torch.cat([left_batch_transitions["state"][key], right_batch_transition["state"][key]], dim=0)
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for key in left_batch_transitions["state"]
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}
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left_batch_transitions["action"] = torch.cat(
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[left_batch_transitions["action"], right_batch_transition["action"]], dim=0
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)
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left_batch_transitions["reward"] = torch.cat(
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[left_batch_transitions["reward"], right_batch_transition["reward"]], dim=0
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)
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left_batch_transitions["next_state"] = {
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key: torch.cat(
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[left_batch_transitions["next_state"][key], right_batch_transition["next_state"][key]], dim=0
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)
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for key in left_batch_transitions["next_state"]
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}
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left_batch_transitions["done"] = torch.cat(
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[left_batch_transitions["done"], right_batch_transition["done"]], dim=0
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)
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return left_batch_transitions
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def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = None):
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if out_dir is None:
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raise NotImplementedError()
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if job_name is None:
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raise NotImplementedError()
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init_logging()
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logging.info(pformat(OmegaConf.to_container(cfg)))
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# Create an env dedicated to online episodes collection from policy rollout.
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# online_env = make_env(cfg, n_envs=cfg.training.online_rollout_batch_size)
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# NOTE: Off policy algorithm are efficient enought to use a single environment
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logging.info("make_env online")
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online_env = make_env(cfg, n_envs=1)
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if cfg.training.eval_freq > 0:
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logging.info("make_env eval")
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eval_env = make_env(cfg, n_envs=1)
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# TODO: Add a way to resume training
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# log metrics to terminal and wandb
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logger = Logger(cfg, out_dir, wandb_job_name=job_name)
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set_global_seed(cfg.seed)
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# Check device is available
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device = get_safe_torch_device(cfg.device, log=True)
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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logging.info("make_policy")
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# TODO: At some point we should just need make sac policy
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policy: SACPolicy = make_policy(
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hydra_cfg=cfg,
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# dataset_stats=offline_dataset.meta.stats if not cfg.resume else None,
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# Hack: But if we do online traning, we do not need dataset_stats
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dataset_stats=None,
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pretrained_policy_name_or_path=str(logger.last_pretrained_model_dir) if cfg.resume else None,
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)
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assert isinstance(policy, nn.Module)
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optimizers, lr_scheduler = make_optimizers_and_scheduler(cfg, policy)
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step = 0 # number of policy updates (forward + backward + optim)
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# TODO: Handle resume
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num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
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num_total_params = sum(p.numel() for p in policy.parameters())
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log_output_dir(out_dir)
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logging.info(f"{cfg.env.task=}")
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# TODO: Handle offline steps
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# logging.info(f"{cfg.training.offline_steps=} ({format_big_number(cfg.training.offline_steps)})")
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logging.info(f"{cfg.training.online_steps=}")
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# logging.info(f"{offline_dataset.num_frames=} ({format_big_number(offline_dataset.num_frames)})")
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# logging.info(f"{offline_dataset.num_episodes=}")
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logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
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logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
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obs, info = online_env.reset()
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obs = preprocess_observation(obs)
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obs = {key: obs[key].to(device, non_blocking=True) for key in obs}
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replay_buffer = ReplayBuffer(
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capacity=cfg.training.online_buffer_capacity, device=device, state_keys=cfg.policy.input_shapes.keys()
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)
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breakpoint()
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batch_size = cfg.training.batch_size
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# if cfg.training.online_steps > 0 and isinstance(cfg.dataset_repo_id, ListConfig):
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# raise NotImplementedError("Online training with LeRobotMultiDataset is not implemented.")
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if cfg.dataset_repo_id is not None:
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logging.info("make_dataset offline buffer")
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offline_dataset = make_dataset(cfg)
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logging.info("Convertion to a offline replay buffer")
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offline_replay_buffer = ReplayBuffer.from_lerobot_dataset(
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offline_dataset, device=device, state_keys=cfg.policy.input_shapes.keys()
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)
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batch_size: int = batch_size // 2 # We will sample from both replay buffer
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# NOTE: For the moment we will solely handle the case of a single environment
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sum_reward_episode = 0
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for interaction_step in range(cfg.training.online_steps):
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# NOTE: At some point we should use a wrapper to handle the observation
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if interaction_step >= cfg.training.online_step_before_learning:
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action = policy.select_action(batch=obs)
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next_obs, reward, done, truncated, info = online_env.step(action.cpu().numpy())
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else:
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action = online_env.action_space.sample()
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next_obs, reward, done, truncated, info = online_env.step(action)
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# HACK
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action = torch.tensor(action, dtype=torch.float32).to(device, non_blocking=True)
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next_obs = preprocess_observation(next_obs)
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next_obs = {key: next_obs[key].to(device, non_blocking=True) for key in obs}
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sum_reward_episode += float(reward[0])
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# Because we are using a single environment
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# we can safely assume that the episode is done
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if done[0] or truncated[0]:
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logging.info(f"Global step {interaction_step}: Episode reward: {sum_reward_episode}")
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logger.log_dict({"Sum episode reward": sum_reward_episode}, interaction_step)
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sum_reward_episode = 0
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replay_buffer.add(
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state=obs,
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action=action,
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reward=float(reward[0]),
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next_state=next_obs,
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done=done[0],
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)
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obs = next_obs
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if interaction_step >= cfg.training.online_step_before_learning:
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for _ in range(cfg.policy.utd_ratio - 1):
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batch = replay_buffer.sample(batch_size)
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if cfg.dataset_repo_id is not None:
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batch_offline = offline_replay_buffer.sample(batch_size)
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batch = concatenate_batch_transitions(batch, batch_offline)
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actions = batch["action"]
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rewards = batch["reward"]
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observations = batch["state"]
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next_observations = batch["next_state"]
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done = batch["done"]
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loss_critic = policy.compute_loss_critic(
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observations=observations,
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actions=actions,
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rewards=rewards,
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next_observations=next_observations,
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done=done,
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)
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optimizers["critic"].zero_grad()
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loss_critic.backward()
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optimizers["critic"].step()
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batch = replay_buffer.sample(batch_size)
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if cfg.dataset_repo_id is not None:
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batch_offline = offline_replay_buffer.sample(batch_size)
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batch = concatenate_batch_transitions(batch, batch_offline)
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# 'observation.state', 'action', 'next.reward', 'next.done'
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# TODO: (azouitine) interface to refine
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# TODO: At some point we should find a way to normalize the inputs
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# batch = policy.normalize_inputs(batch)
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actions = batch["action"]
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rewards = batch["reward"]
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observations = batch["state"]
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next_observations = batch["next_state"]
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done = batch["done"]
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loss_critic = policy.compute_loss_critic(
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observations=observations,
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actions=actions,
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rewards=rewards,
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next_observations=next_observations,
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done=done,
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)
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optimizers["critic"].zero_grad()
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loss_critic.backward()
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optimizers["critic"].step()
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training_infos = {}
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training_infos["loss_critic"] = loss_critic.item()
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if interaction_step % cfg.training.policy_update_freq == 0:
|
|
# TD3 Trick
|
|
for _ in range(cfg.training.policy_update_freq):
|
|
loss_actor = policy.compute_loss_actor(observations=observations)
|
|
|
|
optimizers["actor"].zero_grad()
|
|
loss_actor.backward()
|
|
optimizers["actor"].step()
|
|
|
|
training_infos["loss_actor"] = loss_actor.item()
|
|
|
|
loss_temperature = policy.compute_loss_temperature(observations=observations)
|
|
optimizers["temperature"].zero_grad()
|
|
loss_temperature.backward()
|
|
optimizers["temperature"].step()
|
|
|
|
training_infos["loss_temperature"] = loss_temperature.item()
|
|
|
|
if interaction_step % cfg.training.log_freq == 0:
|
|
logger.log_dict(training_infos, interaction_step, mode="train")
|
|
|
|
policy.update_target_networks()
|
|
|
|
|
|
def clip_grad_norm(loss, clip_grad_norm_value, parameters):
|
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
|
parameters=parameters,
|
|
max_norm=clip_grad_norm_value,
|
|
error_if_nonfinite=False,
|
|
)
|
|
return grad_norm
|
|
|
|
|
|
def update_policy(
|
|
policy,
|
|
batch,
|
|
optimizer,
|
|
grad_clip_norm,
|
|
grad_scaler: GradScaler,
|
|
lr_scheduler=None,
|
|
use_amp: bool = False,
|
|
lock=None,
|
|
):
|
|
"""Returns a dictionary of items for logging."""
|
|
start_time = time.perf_counter()
|
|
device = get_device_from_parameters(policy)
|
|
policy.train()
|
|
with torch.autocast(device_type=device.type) if use_amp else nullcontext():
|
|
output_dict = policy.forward(batch)
|
|
# TODO(rcadene): policy.unnormalize_outputs(out_dict)
|
|
loss = output_dict["loss"]
|
|
grad_scaler.scale(loss).backward()
|
|
|
|
# Unscale the graident of the optimzer's assigned params in-place **prior to gradient clipping**.
|
|
grad_scaler.unscale_(optimizer)
|
|
|
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
|
policy.parameters(),
|
|
grad_clip_norm,
|
|
error_if_nonfinite=False,
|
|
)
|
|
|
|
# Optimizer's gradients are already unscaled, so scaler.step does not unscale them,
|
|
# although it still skips optimizer.step() if the gradients contain infs or NaNs.
|
|
with lock if lock is not None else nullcontext():
|
|
grad_scaler.step(optimizer)
|
|
# Updates the scale for next iteration.
|
|
grad_scaler.update()
|
|
|
|
optimizer.zero_grad()
|
|
|
|
if lr_scheduler is not None:
|
|
lr_scheduler.step()
|
|
|
|
if isinstance(policy, PolicyWithUpdate):
|
|
# To possibly update an internal buffer (for instance an Exponential Moving Average like in TDMPC).
|
|
policy.update()
|
|
|
|
info = {
|
|
"loss": loss.item(),
|
|
"grad_norm": float(grad_norm),
|
|
"lr": optimizer.param_groups[0]["lr"],
|
|
"update_s": time.perf_counter() - start_time,
|
|
**{k: v for k, v in output_dict.items() if k != "loss"},
|
|
}
|
|
info.update({k: v for k, v in output_dict.items() if k not in info})
|
|
|
|
return info
|
|
|
|
|
|
def log_train_info(logger: Logger, info, step, cfg, dataset, is_online):
|
|
loss = info["loss"]
|
|
grad_norm = info["grad_norm"]
|
|
lr = info["lr"]
|
|
update_s = info["update_s"]
|
|
dataloading_s = info["dataloading_s"]
|
|
|
|
# A sample is an (observation,action) pair, where observation and action
|
|
# can be on multiple timestamps. In a batch, we have `batch_size`` number of samples.
|
|
num_samples = (step + 1) * cfg.training.batch_size
|
|
avg_samples_per_ep = dataset.num_frames / dataset.num_episodes
|
|
num_episodes = num_samples / avg_samples_per_ep
|
|
num_epochs = num_samples / dataset.num_frames
|
|
log_items = [
|
|
f"step:{format_big_number(step)}",
|
|
# number of samples seen during training
|
|
f"smpl:{format_big_number(num_samples)}",
|
|
# number of episodes seen during training
|
|
f"ep:{format_big_number(num_episodes)}",
|
|
# number of time all unique samples are seen
|
|
f"epch:{num_epochs:.2f}",
|
|
f"loss:{loss:.3f}",
|
|
f"grdn:{grad_norm:.3f}",
|
|
f"lr:{lr:0.1e}",
|
|
# in seconds
|
|
f"updt_s:{update_s:.3f}",
|
|
f"data_s:{dataloading_s:.3f}", # if not ~0, you are bottlenecked by cpu or io
|
|
]
|
|
logging.info(" ".join(log_items))
|
|
|
|
info["step"] = step
|
|
info["num_samples"] = num_samples
|
|
info["num_episodes"] = num_episodes
|
|
info["num_epochs"] = num_epochs
|
|
info["is_online"] = is_online
|
|
|
|
logger.log_dict(info, step, mode="train")
|
|
|
|
|
|
def log_eval_info(logger, info, step, cfg, dataset, is_online):
|
|
eval_s = info["eval_s"]
|
|
avg_sum_reward = info["avg_sum_reward"]
|
|
pc_success = info["pc_success"]
|
|
|
|
# A sample is an (observation,action) pair, where observation and action
|
|
# can be on multiple timestamps. In a batch, we have `batch_size`` number of samples.
|
|
num_samples = (step + 1) * cfg.training.batch_size
|
|
avg_samples_per_ep = dataset.num_frames / dataset.num_episodes
|
|
num_episodes = num_samples / avg_samples_per_ep
|
|
num_epochs = num_samples / dataset.num_frames
|
|
log_items = [
|
|
f"step:{format_big_number(step)}",
|
|
# number of samples seen during training
|
|
f"smpl:{format_big_number(num_samples)}",
|
|
# number of episodes seen during training
|
|
f"ep:{format_big_number(num_episodes)}",
|
|
# number of time all unique samples are seen
|
|
f"epch:{num_epochs:.2f}",
|
|
f"∑rwrd:{avg_sum_reward:.3f}",
|
|
f"success:{pc_success:.1f}%",
|
|
f"eval_s:{eval_s:.3f}",
|
|
]
|
|
logging.info(" ".join(log_items))
|
|
|
|
info["step"] = step
|
|
info["num_samples"] = num_samples
|
|
info["num_episodes"] = num_episodes
|
|
info["num_epochs"] = num_epochs
|
|
info["is_online"] = is_online
|
|
|
|
logger.log_dict(info, step, mode="eval")
|
|
|
|
|
|
# def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = None):
|
|
# if out_dir is None:
|
|
# raise NotImplementedError()
|
|
# if job_name is None:
|
|
# raise NotImplementedError()
|
|
|
|
# init_logging()
|
|
# logging.info(pformat(OmegaConf.to_container(cfg)))
|
|
|
|
# if cfg.training.online_steps > 0 and isinstance(cfg.dataset_repo_id, ListConfig):
|
|
# raise NotImplementedError("Online training with LeRobotMultiDataset is not implemented.")
|
|
|
|
# # Create an env dedicated to online episodes collection from policy rollout.
|
|
# online_env = make_env(cfg, n_envs=cfg.training.online_rollout_batch_size)
|
|
|
|
# if cfg.training.eval_freq > 0:
|
|
# logging.info("make_env")
|
|
# eval_env = make_env(cfg)
|
|
|
|
# # If we are resuming a run, we need to check that a checkpoint exists in the log directory, and we need
|
|
# # to check for any differences between the provided config and the checkpoint's config.
|
|
# if cfg.resume:
|
|
# if not Logger.get_last_checkpoint_dir(out_dir).exists():
|
|
# raise RuntimeError(
|
|
# "You have set resume=True, but there is no model checkpoint in "
|
|
# f"{Logger.get_last_checkpoint_dir(out_dir)}"
|
|
# )
|
|
# checkpoint_cfg_path = str(Logger.get_last_pretrained_model_dir(out_dir) / "config.yaml")
|
|
# logging.info(
|
|
# colored(
|
|
# "You have set resume=True, indicating that you wish to resume a run",
|
|
# color="yellow",
|
|
# attrs=["bold"],
|
|
# )
|
|
# )
|
|
# # Get the configuration file from the last checkpoint.
|
|
# checkpoint_cfg = init_hydra_config(checkpoint_cfg_path)
|
|
# # Check for differences between the checkpoint configuration and provided configuration.
|
|
# # Hack to resolve the delta_timestamps ahead of time in order to properly diff.
|
|
# resolve_delta_timestamps(cfg)
|
|
# diff = DeepDiff(OmegaConf.to_container(checkpoint_cfg), OmegaConf.to_container(cfg))
|
|
# # Ignore the `resume` and parameters.
|
|
# if "values_changed" in diff and "root['resume']" in diff["values_changed"]:
|
|
# del diff["values_changed"]["root['resume']"]
|
|
# # Log a warning about differences between the checkpoint configuration and the provided
|
|
# # configuration.
|
|
# if len(diff) > 0:
|
|
# logging.warning(
|
|
# "At least one difference was detected between the checkpoint configuration and "
|
|
# f"the provided configuration: \n{pformat(diff)}\nNote that the checkpoint configuration "
|
|
# "takes precedence.",
|
|
# )
|
|
# # Use the checkpoint config instead of the provided config (but keep `resume` parameter).
|
|
# cfg = checkpoint_cfg
|
|
# cfg.resume = True
|
|
# elif Logger.get_last_checkpoint_dir(out_dir).exists():
|
|
# raise RuntimeError(
|
|
# f"The configured output directory {Logger.get_last_checkpoint_dir(out_dir)} already exists. If "
|
|
# "you meant to resume training, please use `resume=true` in your command or yaml configuration."
|
|
# )
|
|
|
|
# if cfg.eval.batch_size > cfg.eval.n_episodes:
|
|
# raise ValueError(
|
|
# "The eval batch size is greater than the number of eval episodes "
|
|
# f"({cfg.eval.batch_size} > {cfg.eval.n_episodes}). As a result, {cfg.eval.batch_size} "
|
|
# f"eval environments will be instantiated, but only {cfg.eval.n_episodes} will be used. "
|
|
# "This might significantly slow down evaluation. To fix this, you should update your command "
|
|
# f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={cfg.eval.batch_size}`), "
|
|
# f"or lower the batch size (e.g. `eval.batch_size={cfg.eval.n_episodes}`)."
|
|
# )
|
|
|
|
# # log metrics to terminal and wandb
|
|
# logger = Logger(cfg, out_dir, wandb_job_name=job_name)
|
|
|
|
# set_global_seed(cfg.seed)
|
|
|
|
# # Check device is available
|
|
# device = get_safe_torch_device(cfg.device, log=True)
|
|
|
|
# torch.backends.cudnn.benchmark = True
|
|
# torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
# logging.info("make_dataset")
|
|
# # offline_dataset = make_dataset(cfg)
|
|
# # TODO (michel-aractingi): temporary fix to avoid datasets with task_index key that doesn't exist in online environment
|
|
# # i.e., pusht
|
|
# # if "task_index" in offline_dataset.hf_dataset[0]:
|
|
# # offline_dataset.hf_dataset = offline_dataset.hf_dataset.remove_columns(["task_index"])
|
|
|
|
# # if isinstance(offline_dataset, MultiLeRobotDataset):
|
|
# # logging.info(
|
|
# # "Multiple datasets were provided. Applied the following index mapping to the provided datasets: "
|
|
# # f"{pformat(offline_dataset.repo_id_to_index , indent=2)}"
|
|
# # )
|
|
|
|
# # Create environment used for evaluating checkpoints during training on simulation data.
|
|
# # On real-world data, no need to create an environment as evaluations are done outside train.py,
|
|
# # using the eval.py instead, with gym_dora environment and dora-rs.
|
|
# eval_env = None
|
|
# if cfg.training.eval_freq > 0:
|
|
# logging.info("make_env")
|
|
# eval_env = make_env(cfg)
|
|
|
|
# logging.info("make_policy")
|
|
# policy = make_policy(
|
|
# hydra_cfg=cfg,
|
|
# # dataset_stats=offline_dataset.meta.stats if not cfg.resume else None,
|
|
# # Hack: But if we do online traning, we do not need dataset_stats
|
|
# dataset_stats=None,
|
|
# pretrained_policy_name_or_path=str(logger.last_pretrained_model_dir) if cfg.resume else None,
|
|
# )
|
|
# assert isinstance(policy, nn.Module)
|
|
# # Create optimizer and scheduler
|
|
# # Temporary hack to move optimizer out of policy
|
|
# optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
|
|
# grad_scaler = GradScaler(enabled=cfg.use_amp)
|
|
|
|
# step = 0 # number of policy updates (forward + backward + optim)
|
|
|
|
# if cfg.resume:
|
|
# step = logger.load_last_training_state(optimizer, lr_scheduler)
|
|
|
|
# num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
|
|
# num_total_params = sum(p.numel() for p in policy.parameters())
|
|
|
|
# log_output_dir(out_dir)
|
|
# logging.info(f"{cfg.env.task=}")
|
|
# logging.info(f"{cfg.training.offline_steps=} ({format_big_number(cfg.training.offline_steps)})")
|
|
# logging.info(f"{cfg.training.online_steps=}")
|
|
# # logging.info(f"{offline_dataset.num_frames=} ({format_big_number(offline_dataset.num_frames)})")
|
|
# # logging.info(f"{offline_dataset.num_episodes=}")
|
|
# logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
|
|
# logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
|
|
|
|
# # Note: this helper will be used in offline and online training loops.
|
|
# def evaluate_and_checkpoint_if_needed(step, is_online):
|
|
# _num_digits = max(6, len(str(cfg.training.offline_steps + cfg.training.online_steps)))
|
|
# step_identifier = f"{step:0{_num_digits}d}"
|
|
|
|
# if cfg.training.eval_freq > 0 and step % cfg.training.eval_freq == 0:
|
|
# logging.info(f"Eval policy at step {step}")
|
|
# with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
|
|
# assert eval_env is not None
|
|
# eval_info = eval_policy(
|
|
# eval_env,
|
|
# policy,
|
|
# cfg.eval.n_episodes,
|
|
# videos_dir=Path(out_dir) / "eval" / f"videos_step_{step_identifier}",
|
|
# max_episodes_rendered=4,
|
|
# start_seed=cfg.seed,
|
|
# )
|
|
# # log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_online=is_online)
|
|
# log_eval_info(logger, eval_info["aggregated"], step, cfg, online_dataset, is_online=is_online)
|
|
# if cfg.wandb.enable:
|
|
# logger.log_video(eval_info["video_paths"][0], step, mode="eval")
|
|
# logging.info("Resume training")
|
|
|
|
# if cfg.training.save_checkpoint and (
|
|
# step % cfg.training.save_freq == 0
|
|
# or step == cfg.training.offline_steps + cfg.training.online_steps
|
|
# ):
|
|
# logging.info(f"Checkpoint policy after step {step}")
|
|
# # Note: Save with step as the identifier, and format it to have at least 6 digits but more if
|
|
# # needed (choose 6 as a minimum for consistency without being overkill).
|
|
# logger.save_checkpoint(
|
|
# step,
|
|
# policy,
|
|
# optimizer,
|
|
# lr_scheduler,
|
|
# identifier=step_identifier,
|
|
# )
|
|
# logging.info("Resume training")
|
|
|
|
# # create dataloader for offline training
|
|
# # if cfg.training.get("drop_n_last_frames"):
|
|
# # shuffle = False
|
|
# # sampler = EpisodeAwareSampler(
|
|
# # offline_dataset.episode_data_index,
|
|
# # drop_n_last_frames=cfg.training.drop_n_last_frames,
|
|
# # shuffle=True,
|
|
# # )
|
|
# # else:
|
|
# # shuffle = True
|
|
# # sampler = None
|
|
# # dataloader = torch.utils.data.DataLoader(
|
|
# # offline_dataset,
|
|
# # num_workers=cfg.training.num_workers,
|
|
# # batch_size=cfg.training.batch_size,
|
|
# # shuffle=shuffle,
|
|
# # sampler=sampler,
|
|
# # pin_memory=device.type != "cpu",
|
|
# # drop_last=False,
|
|
# # )
|
|
# # dl_iter = cycle(dataloader)
|
|
|
|
# policy.train()
|
|
# # offline_step = 0
|
|
# # for _ in range(step, cfg.training.offline_steps):
|
|
# # if offline_step == 0:
|
|
# # logging.info("Start offline training on a fixed dataset")
|
|
|
|
# # start_time = time.perf_counter()
|
|
# # batch = next(dl_iter)
|
|
# # dataloading_s = time.perf_counter() - start_time
|
|
|
|
# # for key in batch:
|
|
# # batch[key] = batch[key].to(device, non_blocking=True)
|
|
|
|
# # train_info = update_policy(
|
|
# # policy,
|
|
# # batch,
|
|
# # optimizer,
|
|
# # cfg.training.grad_clip_norm,
|
|
# # grad_scaler=grad_scaler,
|
|
# # lr_scheduler=lr_scheduler,
|
|
# # use_amp=cfg.use_amp,
|
|
# # )
|
|
|
|
# # train_info["dataloading_s"] = dataloading_s
|
|
|
|
# # if step % cfg.training.log_freq == 0:
|
|
# # log_train_info(logger, train_info, step, cfg, offline_dataset, is_online=False)
|
|
|
|
# # # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
|
|
# # # so we pass in step + 1.
|
|
# # evaluate_and_checkpoint_if_needed(step + 1, is_online=False)
|
|
|
|
# # step += 1
|
|
# # offline_step += 1 # noqa: SIM113
|
|
|
|
# # if cfg.training.online_steps == 0:
|
|
# # if eval_env:
|
|
# # eval_env.close()
|
|
# # logging.info("End of training")
|
|
# # return
|
|
|
|
# # Online training.
|
|
|
|
# # Create an env dedicated to online episodes collection from policy rollout.
|
|
# online_env = make_env(cfg, n_envs=cfg.training.online_rollout_batch_size)
|
|
# resolve_delta_timestamps(cfg)
|
|
# online_buffer_path = logger.log_dir / "online_buffer"
|
|
# if cfg.resume and not online_buffer_path.exists():
|
|
# # If we are resuming a run, we default to the data shapes and buffer capacity from the saved online
|
|
# # buffer.
|
|
# logging.warning(
|
|
# "When online training is resumed, we load the latest online buffer from the prior run, "
|
|
# "and this might not coincide with the state of the buffer as it was at the moment the checkpoint "
|
|
# "was made. This is because the online buffer is updated on disk during training, independently "
|
|
# "of our explicit checkpointing mechanisms."
|
|
# )
|
|
# online_dataset = OnlineBuffer(
|
|
# online_buffer_path,
|
|
# data_spec={
|
|
# **{k: {"shape": v, "dtype": np.dtype("float32")} for k, v in policy.config.input_shapes.items()},
|
|
# **{k: {"shape": v, "dtype": np.dtype("float32")} for k, v in policy.config.output_shapes.items()},
|
|
# "next.reward": {"shape": (), "dtype": np.dtype("float32")},
|
|
# "next.done": {"shape": (), "dtype": np.dtype("?")},
|
|
# "next.success": {"shape": (), "dtype": np.dtype("?")},
|
|
# },
|
|
# buffer_capacity=cfg.training.online_buffer_capacity,
|
|
# fps=online_env.unwrapped.metadata["render_fps"],
|
|
# delta_timestamps=cfg.training.delta_timestamps,
|
|
# )
|
|
|
|
# # If we are doing online rollouts asynchronously, deepcopy the policy to use for online rollouts (this
|
|
# # makes it possible to do online rollouts in parallel with training updates).
|
|
# online_rollout_policy = deepcopy(policy) if cfg.training.do_online_rollout_async else policy
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# # Create dataloader for online training.
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# # concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset])
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# # sampler_weights = compute_sampler_weights(
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# # offline_dataset,
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# # offline_drop_n_last_frames=cfg.training.get("drop_n_last_frames", 0),
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# # online_dataset=online_dataset,
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# # # +1 because online rollouts return an extra frame for the "final observation". Note: we don't have
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# # # this final observation in the offline datasets, but we might add them in future.
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# # online_drop_n_last_frames=cfg.training.get("drop_n_last_frames", 0) + 1,
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# # online_sampling_ratio=cfg.training.online_sampling_ratio,
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# # )
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# # sampler = torch.utils.data.WeightedRandomSampler(
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# # sampler_weights,
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# # num_samples=len(concat_dataset),
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# # replacement=True,
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# # )
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# # dataloader = torch.utils.data.DataLoader(
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# # concat_dataset,
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# # batch_size=cfg.training.batch_size,
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# # num_workers=cfg.training.num_workers,
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# # sampler=sampler,
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# # pin_memory=device.type != "cpu",
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# # drop_last=True,
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# # )
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# dataloader = torch.utils.data.DataLoader(
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# online_dataset,
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# batch_size=cfg.training.batch_size,
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# # num_workers=cfg.training.num_workers,
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# num_workers=0,
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# # sampler=sampler,
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# pin_memory=device.type != "cpu",
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# drop_last=True,
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# )
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# dl_iter = cycle(dataloader)
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# # Lock and thread pool executor for asynchronous online rollouts. When asynchronous mode is disabled,
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# # these are still used but effectively do nothing.
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# # Hack: Comment the lock
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# # lock = Lock()
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# # Note: 1 worker because we only ever want to run one set of online rollouts at a time. Batch
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# # parallelization of rollouts is handled within the job.
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# # Hack: ThreadPoolExecutor
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# # executor = ThreadPoolExecutor(max_workers=1)
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# online_step = 0
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# online_rollout_s = 0 # time take to do online rollout
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# update_online_buffer_s = 0 # time taken to update the online buffer with the online rollout data
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# # Time taken waiting for the online buffer to finish being updated. This is relevant when using the async
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# # online rollout option.
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# await_update_online_buffer_s = 0
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# rollout_start_seed = cfg.training.online_env_seed
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|
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# while True:
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# if online_step == cfg.training.online_steps:
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# break
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# if online_step == 0:
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# logging.info("Start online training by interacting with environment")
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# def sample_trajectory_and_update_buffer():
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# nonlocal rollout_start_seed
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# # with lock:
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# online_rollout_policy.load_state_dict(policy.state_dict())
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# online_rollout_policy.eval()
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# start_rollout_time = time.perf_counter()
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# with torch.no_grad():
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# eval_info = eval_policy(
|
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# online_env,
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# online_rollout_policy,
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# n_episodes=cfg.training.online_rollout_n_episodes,
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# max_episodes_rendered=min(10, cfg.training.online_rollout_n_episodes),
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# videos_dir=logger.log_dir / "online_rollout_videos",
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# return_episode_data=True,
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# start_seed=(
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# rollout_start_seed := (rollout_start_seed + cfg.training.batch_size) % 1000000
|
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# ),
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# )
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# online_rollout_s = time.perf_counter() - start_rollout_time
|
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|
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# # with lock:
|
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# start_update_buffer_time = time.perf_counter()
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# online_dataset.add_data(eval_info["episodes"])
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|
|
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# # Update the concatenated dataset length used during sampling.
|
|
# # concat_dataset.cumulative_sizes = concat_dataset.cumsum(concat_dataset.datasets)
|
|
# # HACK: We do only online training, so we don't need update dataset length because
|
|
# # we do not concatenate offline and online datasets.
|
|
# # online_dataset.cumulative_sizes = online_dataset.cumsum(online_dataset.datasets)
|
|
|
|
# # Update the sampling weights.
|
|
# # sampler.weights = compute_sampler_weights(
|
|
# # offline_dataset,
|
|
# # offline_drop_n_last_frames=cfg.training.get("drop_n_last_frames", 0),
|
|
# # online_dataset=online_dataset,
|
|
# # # +1 because online rollouts return an extra frame for the "final observation". Note: we don't have
|
|
# # # this final observation in the offline datasets, but we might add them in future.
|
|
# # online_drop_n_last_frames=cfg.training.get("drop_n_last_frames", 0) + 1,
|
|
# # online_sampling_ratio=cfg.training.online_sampling_ratio,
|
|
# # )
|
|
# # sampler.num_frames = len(concat_dataset)
|
|
|
|
# update_online_buffer_s = time.perf_counter() - start_update_buffer_time
|
|
|
|
# return online_rollout_s, update_online_buffer_s
|
|
|
|
# # Hack:Comment it
|
|
# # future = executor.submit(sample_trajectory_and_update_buffer)
|
|
# # sample_trajectory_and_update_buffer()
|
|
# # If we aren't doing async rollouts, or if we haven't yet gotten enough examples in our buffer, wait
|
|
# # here until the rollout and buffer update is done, before proceeding to the policy update steps.
|
|
# if (
|
|
# not cfg.training.do_online_rollout_async
|
|
# or len(online_dataset) <= cfg.training.online_buffer_seed_size
|
|
# ):
|
|
# # online_rollout_s, update_online_buffer_s = future.result()
|
|
# online_rollout_s, update_online_buffer_s = sample_trajectory_and_update_buffer()
|
|
|
|
# if len(online_dataset) <= cfg.training.online_buffer_seed_size:
|
|
# logging.info(
|
|
# f"Seeding online buffer: {len(online_dataset)}/{cfg.training.online_buffer_seed_size}"
|
|
# )
|
|
# continue
|
|
|
|
# policy.train()
|
|
# for _ in range(cfg.training.online_steps_between_rollouts):
|
|
# # Hack: Comment the lock and reindent
|
|
# # with lock:
|
|
# start_time = time.perf_counter()
|
|
# batch = next(dl_iter)
|
|
# dataloading_s = time.perf_counter() - start_time
|
|
|
|
# for key in batch:
|
|
# batch[key] = batch[key].to(cfg.device, non_blocking=True)
|
|
|
|
# train_info = update_policy(
|
|
# policy,
|
|
# batch,
|
|
# optimizer,
|
|
# cfg.training.grad_clip_norm,
|
|
# grad_scaler=grad_scaler,
|
|
# lr_scheduler=lr_scheduler,
|
|
# use_amp=cfg.use_amp,
|
|
# # lock=lock,
|
|
# # Hack: Comment the lock
|
|
# lock=None,
|
|
# )
|
|
|
|
# train_info["dataloading_s"] = dataloading_s
|
|
# train_info["online_rollout_s"] = online_rollout_s
|
|
# train_info["update_online_buffer_s"] = update_online_buffer_s
|
|
# train_info["await_update_online_buffer_s"] = await_update_online_buffer_s
|
|
# # Hack: Comment the lock and reindent
|
|
# # with lock:
|
|
# train_info["online_buffer_size"] = len(online_dataset)
|
|
|
|
# if step % cfg.training.log_freq == 0:
|
|
# log_train_info(logger, train_info, step, cfg, online_dataset, is_online=True)
|
|
|
|
# # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
|
|
# # so we pass in step + 1.
|
|
# evaluate_and_checkpoint_if_needed(step + 1, is_online=True)
|
|
|
|
# step += 1
|
|
# online_step += 1
|
|
|
|
# # If we're doing async rollouts, we should now wait until we've completed them before proceeding
|
|
# # to do the next batch of rollouts.
|
|
# # Hack: comment it
|
|
# # if future.running():
|
|
# start = time.perf_counter()
|
|
# # online_rollout_s, update_online_buffer_s = future.result()
|
|
# online_rollout_s, update_online_buffer_s = sample_trajectory_and_update_buffer()
|
|
# await_update_online_buffer_s = time.perf_counter() - start
|
|
|
|
# if online_step >= cfg.training.online_steps:
|
|
# break
|
|
|
|
# if eval_env:
|
|
# eval_env.close()
|
|
# logging.info("End of training")
|
|
|
|
|
|
@hydra.main(version_base="1.2", config_name="default", config_path="../configs")
|
|
def train_cli(cfg: dict):
|
|
train(
|
|
cfg,
|
|
out_dir=hydra.core.hydra_config.HydraConfig.get().run.dir,
|
|
job_name=hydra.core.hydra_config.HydraConfig.get().job.name,
|
|
)
|
|
|
|
|
|
def train_notebook(out_dir=None, job_name=None, config_name="default", config_path="../configs"):
|
|
from hydra import compose, initialize
|
|
|
|
hydra.core.global_hydra.GlobalHydra.instance().clear()
|
|
initialize(config_path=config_path)
|
|
cfg = compose(config_name=config_name)
|
|
train(cfg, out_dir=out_dir, job_name=job_name)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
train_cli()
|