Change SAC policy implementation with configuration and modeling classes
This commit is contained in:
Adil Zouitine
parent
55505ff817
commit
a8fda9c61a
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@ -66,6 +66,11 @@ def get_policy_and_config_classes(name: str) -> tuple[Policy, object]:
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from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTPolicy
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return VQBeTPolicy, VQBeTConfig
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elif name == "sac":
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from lerobot.common.policies.sac.configuration_sac import SACConfig
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from lerobot.common.policies.sac.modeling_sac import SACPolicy
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return SACPolicy, SACConfig
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else:
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raise NotImplementedError(f"Policy with name {name} is not implemented.")
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@ -85,10 +90,10 @@ def make_policy(
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be provided when initializing a new policy, and must not be provided when loading a pretrained
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policy. Therefore, this argument is mutually exclusive with `pretrained_policy_name_or_path`.
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"""
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if not (pretrained_policy_name_or_path is None) ^ (dataset_stats is None):
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raise ValueError(
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"Exactly one of `pretrained_policy_name_or_path` and `dataset_stats` must be provided."
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)
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# if not (pretrained_policy_name_or_path is None) ^ (dataset_stats is None):
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# raise ValueError(
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# "Exactly one of `pretrained_policy_name_or_path` and `dataset_stats` must be provided."
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# )
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policy_cls, policy_cfg_class = get_policy_and_config_classes(hydra_cfg.policy.name)
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@ -0,0 +1,78 @@
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#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team.
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# 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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from dataclasses import dataclass, field
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@dataclass
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class SACConfig:
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input_shapes: dict[str, list[int]] = field(
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default_factory=lambda: {
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"observation.image": [3, 84, 84],
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"observation.state": [4],
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}
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)
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output_shapes: dict[str, list[int]] = field(
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default_factory=lambda: {
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"action": [2],
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}
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)
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# Normalization / Unnormalization
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input_normalization_modes: dict[str, str] = field(
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default_factory=lambda: {
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"observation.image": "mean_std",
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"observation.state": "min_max",
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"observation.environment_state": "min_max",
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}
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)
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output_normalization_modes: dict[str, str] = field(
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default_factory=lambda: {"action": "min_max"},
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)
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shared_encoder = False
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discount = 0.99
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temperature_init = 1.0
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num_critics = 2
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# num_critics = 8
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num_subsample_critics = None
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# num_subsample_critics = 2
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# critic_lr = 1e-3
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critic_lr = 3e-4
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actor_lr = 3e-4
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temperature_lr = 3e-4
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critic_target_update_weight = 0.005
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# utd_ratio = 8
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utd_ratio = 1 # If you want enable utd_ratio, you need to set it to >1
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state_encoder_hidden_dim = 256
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latent_dim = 256
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target_entropy = None
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# backup_entropy = False
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use_backup_entropy = True
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critic_network_kwargs = {
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"hidden_dims": [256, 256],
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"activate_final": True,
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}
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actor_network_kwargs = {
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"hidden_dims": [256, 256],
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"activate_final": True,
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}
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policy_kwargs = {
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"use_tanh_squash": True,
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"log_std_min": -5,
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"log_std_max": 2,
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}
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@ -57,19 +57,22 @@ class SACPolicy(
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else:
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self.normalize_inputs = nn.Identity()
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# HACK: we need to pass the dataset_stats to the normalization functions
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# NOTE: This is for biwalker environment
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dataset_stats = dataset_stats or {
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"action": {
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"min": torch.tensor([-1.0, -1.0, -1.0, -1.0]),
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"max": torch.tensor([1.0, 1.0, 1.0, 1.0]),
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}
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}
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# HACK: we need to pass the dataset_stats to the normalization functions
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dataset_stats = dataset_stats or {
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"action": {
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"min": torch.tensor([-1.0, -1.0, -1.0, -1.0]),
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"max": torch.tensor([1.0, 1.0, 1.0, 1.0]),
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}
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}
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# NOTE: This is for pusht environment
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# dataset_stats = dataset_stats or {
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# "action": {
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# "min": torch.tensor([0, 0]),
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# "max": torch.tensor([512, 512]),
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# }
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# }
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self.normalize_targets = Normalize(
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config.output_shapes, config.output_normalization_modes, dataset_stats
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)
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@ -77,8 +80,12 @@ class SACPolicy(
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config.output_shapes, config.output_normalization_modes, dataset_stats
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)
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encoder_critic = SACObservationEncoder(config)
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encoder_actor = SACObservationEncoder(config)
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if config.shared_encoder:
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encoder_critic = SACObservationEncoder(config)
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encoder_actor = encoder_critic
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else:
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encoder_critic = SACObservationEncoder(config)
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encoder_actor = SACObservationEncoder(config)
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# Define networks
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critic_nets = []
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for _ in range(config.num_critics):
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@ -105,7 +112,6 @@ class SACPolicy(
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self.critic_ensemble = create_critic_ensemble(critic_nets, config.num_critics)
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self.critic_target = create_critic_ensemble(target_critic_nets, config.num_critics)
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self.critic_target.load_state_dict(self.critic_ensemble.state_dict())
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self.critic_target.load_state_dict(self.critic_ensemble.state_dict())
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self.actor = Policy(
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encoder=encoder_actor,
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@ -159,100 +165,7 @@ class SACPolicy(
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q_values = torch.stack([critic(observations, actions) for critic in critics])
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return q_values
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def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor | float]:
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"""Run the batch through the model and compute the loss.
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Returns a dictionary with loss as a tensor, and other information as native floats.
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"""
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# We have to actualize the value of the temperature because in the previous
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self.temperature = self.log_alpha.exp().item()
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temperature = self.temperature
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temperature = self.temperature
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batch = self.normalize_inputs(batch)
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# batch shape is (b, 2, ...) where index 1 returns the current observation and
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# the next observation for calculating the right td index.
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# actions = batch["action"][:, 0]
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actions = batch["action"]
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# actions = batch["action"][:, 0]
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actions = batch["action"]
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rewards = batch["next.reward"][:, 0]
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observations = {}
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next_observations = {}
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for k in batch:
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if k.startswith("observation."):
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observations[k] = batch[k][:, 0]
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next_observations[k] = batch[k][:, 1]
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done = batch["next.done"]
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with torch.no_grad():
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next_action_preds, next_log_probs, _ = self.actor(next_observations)
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# 2- compute q targets
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q_targets = self.critic_forward(next_observations, next_action_preds, use_target=True)
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# subsample critics to prevent overfitting if use high UTD (update to date)
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if self.config.num_subsample_critics is not None:
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indices = torch.randperm(self.config.num_critics)
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indices = indices[: self.config.num_subsample_critics]
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q_targets = q_targets[indices]
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# critics subsample size
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min_q, _ = q_targets.min(dim=0) # Get values from min operation
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if self.config.use_backup_entropy:
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min_q -= self.temperature * next_log_probs
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td_target = rewards + self.config.discount * min_q * ~done
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td_target = rewards + self.config.discount * min_q * ~done
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# 3- compute predicted qs
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q_preds = self.critic_forward(observations, actions, use_target=False)
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# 4- Calculate loss
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# Compute state-action value loss (TD loss) for all of the Q functions in the ensemble.
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td_target_duplicate = einops.repeat(td_target, "b -> e b", e=q_preds.shape[0])
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# You compute the mean loss of the batch for each critic and then to compute the final loss you sum them up
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critics_loss = (
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F.mse_loss(
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input=q_preds,
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target=td_target_duplicate,
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reduction="none",
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).mean(1)
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).sum()
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actions_pi, log_probs, _ = self.actor(observations)
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actions_pi, log_probs, _ = self.actor(observations)
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with torch.inference_mode():
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q_preds = self.critic_forward(observations, actions_pi, use_target=False)
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q_preds = self.critic_forward(observations, actions_pi, use_target=False)
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min_q_preds = q_preds.min(dim=0)[0]
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actor_loss = ((temperature * log_probs) - min_q_preds).mean()
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# calculate temperature loss
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with torch.no_grad():
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_, log_probs, _ = self.actor(observations)
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temperature_loss = (-self.log_alpha.exp() * (log_probs + self.config.target_entropy)).mean()
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loss = critics_loss + actor_loss + temperature_loss
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return {
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"critics_loss": critics_loss.item(),
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"actor_loss": actor_loss.item(),
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"mean_q_predicts": min_q_preds.mean().item(),
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"min_q_predicts": min_q_preds.min().item(),
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"max_q_predicts": min_q_preds.max().item(),
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"temperature_loss": temperature_loss.item(),
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"temperature": temperature,
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"mean_log_probs": log_probs.mean().item(),
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"min_log_probs": log_probs.min().item(),
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"max_log_probs": log_probs.max().item(),
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"td_target_mean": td_target.mean().item(),
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"td_target_max": td_target.max().item(),
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"action_mean": actions.mean().item(),
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"entropy": log_probs.mean().item(),
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"loss": loss,
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}
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def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor | float]: ...
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def update_target_networks(self):
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"""Update target networks with exponential moving average"""
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for target_critic, critic in zip(self.critic_target, self.critic_ensemble, strict=False):
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@ -271,9 +184,6 @@ class SACPolicy(
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q_targets = self.critic_forward(
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observations=next_observations, actions=next_action_preds, use_target=True
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)
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q_targets = self.critic_forward(
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observations=next_observations, actions=next_action_preds, use_target=True
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)
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# subsample critics to prevent overfitting if use high UTD (update to date)
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if self.config.num_subsample_critics is not None:
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@ -440,7 +350,6 @@ class Policy(nn.Module):
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if isinstance(layer, nn.Linear):
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out_features = layer.out_features
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break
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# Mean layer
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self.mean_layer = nn.Linear(out_features, action_dim)
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if init_final is not None:
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@ -143,8 +143,11 @@ class ReplayBuffer:
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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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# We convert the LeRobotDataset into a replay buffer, because it is more efficient to sample from
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# a replay buffer than from a lerobot dataset.
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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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# Fill the replay buffer with the lerobot dataset transitions
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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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@ -350,8 +353,6 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
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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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@ -376,7 +377,6 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
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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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@ -413,6 +413,16 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
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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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if "final_info" in info:
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if "is_success" in info["final_info"][0]:
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logging.info(
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f"Global step {interaction_step}: Episode success: {info['final_info'][0]['is_success']}"
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)
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if "coverage" in info["final_info"][0]:
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logging.info(
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f"Global step {interaction_step}: Episode final coverage: {info['final_info'][0]['coverage']} \n"
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)
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logger.log_dict({"Final coverage": info["final_info"][0]["coverage"]}, interaction_step)
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replay_buffer.add(
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state=obs,
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@ -450,10 +460,10 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
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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 = concatenate_batch_transitions(
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left_batch_transitions=batch, right_batch_transition=batch_offline
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)
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# NOTE: We have to handle the normalization for the batch
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# batch = policy.normalize_inputs(batch)
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actions = batch["action"]
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@ -500,599 +510,6 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
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policy.update_target_networks()
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def clip_grad_norm(loss, clip_grad_norm_value, parameters):
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grad_norm = torch.nn.utils.clip_grad_norm_(
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parameters=parameters,
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max_norm=clip_grad_norm_value,
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error_if_nonfinite=False,
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)
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return grad_norm
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def update_policy(
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policy,
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batch,
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optimizer,
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grad_clip_norm,
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grad_scaler: GradScaler,
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lr_scheduler=None,
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use_amp: bool = False,
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lock=None,
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):
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"""Returns a dictionary of items for logging."""
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start_time = time.perf_counter()
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device = get_device_from_parameters(policy)
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policy.train()
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with torch.autocast(device_type=device.type) if use_amp else nullcontext():
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output_dict = policy.forward(batch)
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# TODO(rcadene): policy.unnormalize_outputs(out_dict)
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loss = output_dict["loss"]
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grad_scaler.scale(loss).backward()
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# Unscale the graident of the optimzer's assigned params in-place **prior to gradient clipping**.
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grad_scaler.unscale_(optimizer)
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grad_norm = torch.nn.utils.clip_grad_norm_(
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policy.parameters(),
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grad_clip_norm,
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error_if_nonfinite=False,
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)
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# Optimizer's gradients are already unscaled, so scaler.step does not unscale them,
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# although it still skips optimizer.step() if the gradients contain infs or NaNs.
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with lock if lock is not None else nullcontext():
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grad_scaler.step(optimizer)
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# Updates the scale for next iteration.
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grad_scaler.update()
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optimizer.zero_grad()
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if lr_scheduler is not None:
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lr_scheduler.step()
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if isinstance(policy, PolicyWithUpdate):
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# To possibly update an internal buffer (for instance an Exponential Moving Average like in TDMPC).
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policy.update()
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info = {
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"loss": loss.item(),
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"grad_norm": float(grad_norm),
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"lr": optimizer.param_groups[0]["lr"],
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"update_s": time.perf_counter() - start_time,
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**{k: v for k, v in output_dict.items() if k != "loss"},
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}
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info.update({k: v for k, v in output_dict.items() if k not in info})
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return info
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def log_train_info(logger: Logger, info, step, cfg, dataset, is_online):
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loss = info["loss"]
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grad_norm = info["grad_norm"]
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lr = info["lr"]
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update_s = info["update_s"]
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dataloading_s = info["dataloading_s"]
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# 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
|
||||
|
||||
# # Create dataloader for online training.
|
||||
# # concat_dataset = torch.utils.data.ConcatDataset([offline_dataset, online_dataset])
|
||||
# # 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 = torch.utils.data.WeightedRandomSampler(
|
||||
# # sampler_weights,
|
||||
# # num_samples=len(concat_dataset),
|
||||
# # replacement=True,
|
||||
# # )
|
||||
# # dataloader = torch.utils.data.DataLoader(
|
||||
# # concat_dataset,
|
||||
# # batch_size=cfg.training.batch_size,
|
||||
# # num_workers=cfg.training.num_workers,
|
||||
# # sampler=sampler,
|
||||
# # pin_memory=device.type != "cpu",
|
||||
# # drop_last=True,
|
||||
# # )
|
||||
|
||||
# dataloader = torch.utils.data.DataLoader(
|
||||
# online_dataset,
|
||||
# batch_size=cfg.training.batch_size,
|
||||
# # num_workers=cfg.training.num_workers,
|
||||
# num_workers=0,
|
||||
# # sampler=sampler,
|
||||
# pin_memory=device.type != "cpu",
|
||||
# drop_last=True,
|
||||
# )
|
||||
# dl_iter = cycle(dataloader)
|
||||
|
||||
# # Lock and thread pool executor for asynchronous online rollouts. When asynchronous mode is disabled,
|
||||
# # these are still used but effectively do nothing.
|
||||
# # Hack: Comment the lock
|
||||
# # lock = Lock()
|
||||
# # Note: 1 worker because we only ever want to run one set of online rollouts at a time. Batch
|
||||
# # parallelization of rollouts is handled within the job.
|
||||
|
||||
# # Hack: ThreadPoolExecutor
|
||||
# # executor = ThreadPoolExecutor(max_workers=1)
|
||||
|
||||
# online_step = 0
|
||||
# online_rollout_s = 0 # time take to do online rollout
|
||||
# update_online_buffer_s = 0 # time taken to update the online buffer with the online rollout data
|
||||
# # Time taken waiting for the online buffer to finish being updated. This is relevant when using the async
|
||||
# # online rollout option.
|
||||
# await_update_online_buffer_s = 0
|
||||
# rollout_start_seed = cfg.training.online_env_seed
|
||||
|
||||
# while True:
|
||||
# if online_step == cfg.training.online_steps:
|
||||
# break
|
||||
|
||||
# if online_step == 0:
|
||||
# logging.info("Start online training by interacting with environment")
|
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|
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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())
|
||||
|
||||
# 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),
|
||||
# videos_dir=logger.log_dir / "online_rollout_videos",
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# return_episode_data=True,
|
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# start_seed=(
|
||||
# rollout_start_seed := (rollout_start_seed + cfg.training.batch_size) % 1000000
|
||||
# ),
|
||||
# )
|
||||
# online_rollout_s = time.perf_counter() - start_rollout_time
|
||||
|
||||
# # with lock:
|
||||
# start_update_buffer_time = time.perf_counter()
|
||||
# online_dataset.add_data(eval_info["episodes"])
|
||||
|
||||
# # 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(
|
||||
|
|
|
|||
Loading…
Reference in New Issue