Fixup
This commit is contained in:
Eugene Mironov
parent
12f570292c
commit
3655d26a27
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@ -493,15 +493,10 @@ def add_actor_information_and_train(
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logging.debug("[LEARNER] Starting optimization loop")
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time_for_one_optimization_step = time.time()
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for _ in range(utd_ratio):
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# profiler = cProfile.Profile()
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# profiler.enable()
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for _ in range(utd_ratio - 1):
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batch = replay_buffer.sample(batch_size)
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# profiler.disable()
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# profiler.dump_stats("sample_buffer.prof")
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if cfg.dataset_repo_id is not None:
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if 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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@ -530,6 +525,40 @@ def add_actor_information_and_train(
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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 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(
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left_batch_transitions=batch, right_batch_transition=batch_offline
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)
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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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check_nan_in_transition(
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observations=observations, actions=actions, next_state=next_observations
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)
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observation_features, next_observation_features = get_observation_features(
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policy, observations, next_observations
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)
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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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observation_features=observation_features,
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next_observation_features=next_observation_features,
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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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@ -566,6 +595,7 @@ def add_actor_information_and_train(
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logger.log_dict(
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d=training_infos, mode="train", custom_step_key="Optimization step"
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)
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# logging.info(f"Training infos: {training_infos}")
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time_for_one_optimization_step = time.time() - time_for_one_optimization_step
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frequency_for_one_optimization_step = 1 / (
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@ -593,8 +623,6 @@ def add_actor_information_and_train(
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optimization_step % save_freq == 0 or optimization_step == online_steps
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):
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logging.info(f"Checkpoint policy after step {optimization_step}")
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# Note: Save with step as the identifier, and format it to have at least 6 digits but more if
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# needed (choose 6 as a minimum for consistency without being overkill).
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_num_digits = max(6, len(str(online_steps)))
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step_identifier = f"{optimization_step:0{_num_digits}d}"
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interaction_step = (
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@ -624,203 +652,6 @@ def add_actor_information_and_train(
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logging.info("Resume training")
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if shutdown_event is not None and shutdown_event.is_set():
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logging.info("[LEARNER] Shutdown signal received. Exiting...")
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break
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logging.debug("[LEARNER] Waiting for transitions")
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while not transition_queue.empty() and not shutdown_event.is_set():
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transition_list = transition_queue.get()
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transition_list = bytes_to_transitions(transition_list)
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for transition in transition_list:
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transition = move_transition_to_device(transition, device=device)
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replay_buffer.add(**transition)
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if transition.get("complementary_info", {}).get("is_intervention"):
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offline_replay_buffer.add(**transition)
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logging.debug("[LEARNER] Received transitions")
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logging.debug("[LEARNER] Waiting for interactions")
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while not interaction_message_queue.empty() and not shutdown_event.is_set():
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interaction_message = interaction_message_queue.get()
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interaction_message = bytes_to_python_object(interaction_message)
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# If cfg.resume, shift the interaction step with the last checkpointed step in order to not break the logging
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interaction_message["Interaction step"] += interaction_step_shift
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logger.log_dict(
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interaction_message, mode="train", custom_step_key="Interaction step"
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)
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logging.debug("[LEARNER] Received interactions")
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if len(replay_buffer) < cfg.training.online_step_before_learning:
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continue
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logging.debug("[LEARNER] Starting optimization loop")
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time_for_one_optimization_step = time.time()
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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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check_nan_in_transition(
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observations=observations, actions=actions, next_state=next_observations
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)
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observation_features, next_observation_features = get_observation_features(
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policy, observations, next_observations
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)
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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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observation_features=observation_features,
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next_observation_features=next_observation_features,
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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(
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left_batch_transitions=batch, right_batch_transition=batch_offline
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)
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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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check_nan_in_transition(
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observations=observations, actions=actions, next_state=next_observations
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)
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observation_features, next_observation_features = get_observation_features(
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policy, observations, next_observations
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)
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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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observation_features=observation_features,
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next_observation_features=next_observation_features,
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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 optimization_step % cfg.training.policy_update_freq == 0:
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for _ in range(cfg.training.policy_update_freq):
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loss_actor = policy.compute_loss_actor(
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observations=observations,
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observation_features=observation_features,
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)
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optimizers["actor"].zero_grad()
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loss_actor.backward()
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optimizers["actor"].step()
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training_infos["loss_actor"] = loss_actor.item()
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loss_temperature = policy.compute_loss_temperature(
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observations=observations,
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observation_features=observation_features,
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)
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optimizers["temperature"].zero_grad()
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loss_temperature.backward()
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optimizers["temperature"].step()
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training_infos["loss_temperature"] = loss_temperature.item()
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if (
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time.time() - last_time_policy_pushed
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> cfg.actor_learner_config.policy_parameters_push_frequency
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):
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push_actor_policy_to_queue(parameters_queue, policy)
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last_time_policy_pushed = time.time()
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policy.update_target_networks()
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if optimization_step % cfg.training.log_freq == 0:
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training_infos["Optimization step"] = optimization_step
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logger.log_dict(
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d=training_infos, mode="train", custom_step_key="Optimization step"
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)
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# logging.info(f"Training infos: {training_infos}")
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time_for_one_optimization_step = time.time() - time_for_one_optimization_step
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frequency_for_one_optimization_step = 1 / (
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time_for_one_optimization_step + 1e-9
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)
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logging.info(
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f"[LEARNER] Optimization frequency loop [Hz]: {frequency_for_one_optimization_step}"
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)
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logger.log_dict(
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{
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"Optimization frequency loop [Hz]": frequency_for_one_optimization_step,
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"Optimization step": optimization_step,
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},
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mode="train",
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custom_step_key="Optimization step",
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)
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optimization_step += 1
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if optimization_step % cfg.training.log_freq == 0:
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logging.info(f"[LEARNER] Number of optimization step: {optimization_step}")
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if cfg.training.save_checkpoint and (
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optimization_step % cfg.training.save_freq == 0
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or optimization_step == cfg.training.online_steps
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):
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logging.info(f"Checkpoint policy after step {optimization_step}")
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# Note: Save with step as the identifier, and format it to have at least 6 digits but more if
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# needed (choose 6 as a minimum for consistency without being overkill).
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_num_digits = max(6, len(str(cfg.training.online_steps)))
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step_identifier = f"{optimization_step:0{_num_digits}d}"
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interaction_step = (
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interaction_message["Interaction step"]
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if interaction_message is not None
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else 0
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)
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logger.save_checkpoint(
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optimization_step,
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policy,
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optimizers,
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scheduler=None,
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identifier=step_identifier,
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interaction_step=interaction_step,
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)
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# TODO : temporarly save replay buffer here, remove later when on the robot
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# We want to control this with the keyboard inputs
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dataset_dir = logger.log_dir / "dataset"
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if dataset_dir.exists() and dataset_dir.is_dir():
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shutil.rmtree(
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dataset_dir,
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)
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replay_buffer.to_lerobot_dataset(
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cfg.dataset_repo_id, fps=cfg.fps, root=logger.log_dir / "dataset"
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)
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logging.info("Resume training")
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def make_optimizers_and_scheduler(cfg, policy: nn.Module):
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"""
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