Port HIL SERL (#644)

Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Eugene Mironov <helper2424@gmail.com>
Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com>
Co-authored-by: Ke Wang <superwk1017@gmail.com>
Co-authored-by: Yoel Chornton <yoel.chornton@gmail.com>
Co-authored-by: imstevenpmwork <steven.palma@huggingface.co>
Co-authored-by: Simon Alibert <simon.alibert@huggingface.co>

tests/policies/test_sac_policy.py

@@ -0,0 +1,541 @@
+# !/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+
+import pytest
+import torch
+from torch import Tensor, nn
+
+from lerobot.common.policies.sac.configuration_sac import SACConfig
+from lerobot.common.policies.sac.modeling_sac import MLP, SACPolicy
+from lerobot.common.utils.random_utils import seeded_context, set_seed
+from lerobot.configs.types import FeatureType, PolicyFeature
+
+try:
+    import transformers  # noqa: F401
+
+    TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    TRANSFORMERS_AVAILABLE = False
+
+
+@pytest.fixture(autouse=True)
+def set_random_seed():
+    seed = 42
+    set_seed(seed)
+
+
+def test_mlp_with_default_args():
+    mlp = MLP(input_dim=10, hidden_dims=[256, 256])
+
+    x = torch.randn(10)
+    y = mlp(x)
+    assert y.shape == (256,)
+
+
+def test_mlp_with_batch_dim():
+    mlp = MLP(input_dim=10, hidden_dims=[256, 256])
+    x = torch.randn(2, 10)
+    y = mlp(x)
+    assert y.shape == (2, 256)
+
+
+def test_forward_with_empty_hidden_dims():
+    mlp = MLP(input_dim=10, hidden_dims=[])
+    x = torch.randn(1, 10)
+    assert mlp(x).shape == (1, 10)
+
+
+def test_mlp_with_dropout():
+    mlp = MLP(input_dim=10, hidden_dims=[256, 256, 11], dropout_rate=0.1)
+    x = torch.randn(1, 10)
+    y = mlp(x)
+    assert y.shape == (1, 11)
+
+    drop_out_layers_count = sum(isinstance(layer, nn.Dropout) for layer in mlp.net)
+    assert drop_out_layers_count == 2
+
+
+def test_mlp_with_custom_final_activation():
+    mlp = MLP(input_dim=10, hidden_dims=[256, 256], final_activation=torch.nn.Tanh())
+    x = torch.randn(1, 10)
+    y = mlp(x)
+    assert y.shape == (1, 256)
+    assert (y >= -1).all() and (y <= 1).all()
+
+
+def test_sac_policy_with_default_args():
+    with pytest.raises(ValueError, match="should be an instance of class `PreTrainedConfig`"):
+        SACPolicy()
+
+
+def create_dummy_state(batch_size: int, state_dim: int = 10) -> Tensor:
+    return {
+        "observation.state": torch.randn(batch_size, state_dim),
+    }
+
+
+def create_dummy_with_visual_input(batch_size: int, state_dim: int = 10) -> Tensor:
+    return {
+        "observation.image": torch.randn(batch_size, 3, 84, 84),
+        "observation.state": torch.randn(batch_size, state_dim),
+    }
+
+
+def create_dummy_action(batch_size: int, action_dim: int = 10) -> Tensor:
+    return torch.randn(batch_size, action_dim)
+
+
+def create_default_train_batch(
+    batch_size: int = 8, state_dim: int = 10, action_dim: int = 10
+) -> dict[str, Tensor]:
+    return {
+        "action": create_dummy_action(batch_size, action_dim),
+        "reward": torch.randn(batch_size),
+        "state": create_dummy_state(batch_size, state_dim),
+        "next_state": create_dummy_state(batch_size, state_dim),
+        "done": torch.randn(batch_size),
+    }
+
+
+def create_train_batch_with_visual_input(
+    batch_size: int = 8, state_dim: int = 10, action_dim: int = 10
+) -> dict[str, Tensor]:
+    return {
+        "action": create_dummy_action(batch_size, action_dim),
+        "reward": torch.randn(batch_size),
+        "state": create_dummy_with_visual_input(batch_size, state_dim),
+        "next_state": create_dummy_with_visual_input(batch_size, state_dim),
+        "done": torch.randn(batch_size),
+    }
+
+
+def create_observation_batch(batch_size: int = 8, state_dim: int = 10) -> dict[str, Tensor]:
+    return {
+        "observation.state": torch.randn(batch_size, state_dim),
+    }
+
+
+def create_observation_batch_with_visual_input(batch_size: int = 8, state_dim: int = 10) -> dict[str, Tensor]:
+    return {
+        "observation.state": torch.randn(batch_size, state_dim),
+        "observation.image": torch.randn(batch_size, 3, 84, 84),
+    }
+
+
+def make_optimizers(policy: SACPolicy, has_discrete_action: bool = False) -> dict[str, torch.optim.Optimizer]:
+    """Create optimizers for the SAC policy."""
+    optimizer_actor = torch.optim.Adam(
+        # Handle the case of shared encoder where the encoder weights are not optimized with the actor gradient
+        params=[
+            p
+            for n, p in policy.actor.named_parameters()
+            if not policy.config.shared_encoder or not n.startswith("encoder")
+        ],
+        lr=policy.config.actor_lr,
+    )
+    optimizer_critic = torch.optim.Adam(
+        params=policy.critic_ensemble.parameters(),
+        lr=policy.config.critic_lr,
+    )
+    optimizer_temperature = torch.optim.Adam(
+        params=[policy.log_alpha],
+        lr=policy.config.critic_lr,
+    )
+
+    optimizers = {
+        "actor": optimizer_actor,
+        "critic": optimizer_critic,
+        "temperature": optimizer_temperature,
+    }
+
+    if has_discrete_action:
+        optimizers["discrete_critic"] = torch.optim.Adam(
+            params=policy.discrete_critic.parameters(),
+            lr=policy.config.critic_lr,
+        )
+
+    return optimizers
+
+
+def create_default_config(
+    state_dim: int, continuous_action_dim: int, has_discrete_action: bool = False
+) -> SACConfig:
+    action_dim = continuous_action_dim
+    if has_discrete_action:
+        action_dim += 1
+
+    config = SACConfig(
+        input_features={"observation.state": PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
+        output_features={"action": PolicyFeature(type=FeatureType.ACTION, shape=(continuous_action_dim,))},
+        dataset_stats={
+            "observation.state": {
+                "min": [0.0] * state_dim,
+                "max": [1.0] * state_dim,
+            },
+            "action": {
+                "min": [0.0] * continuous_action_dim,
+                "max": [1.0] * continuous_action_dim,
+            },
+        },
+    )
+    config.validate_features()
+    return config
+
+
+def create_config_with_visual_input(
+    state_dim: int, continuous_action_dim: int, has_discrete_action: bool = False
+) -> SACConfig:
+    config = create_default_config(
+        state_dim=state_dim,
+        continuous_action_dim=continuous_action_dim,
+        has_discrete_action=has_discrete_action,
+    )
+    config.input_features["observation.image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(3, 84, 84))
+    config.dataset_stats["observation.image"] = {
+        "mean": torch.randn(3, 1, 1),
+        "std": torch.randn(3, 1, 1),
+    }
+
+    # Let make tests a little bit faster
+    config.state_encoder_hidden_dim = 32
+    config.latent_dim = 32
+
+    config.validate_features()
+    return config
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
+def test_sac_policy_with_default_config(batch_size: int, state_dim: int, action_dim: int):
+    batch = create_default_train_batch(batch_size=batch_size, action_dim=action_dim, state_dim=state_dim)
+    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
+
+    policy = SACPolicy(config=config)
+    policy.train()
+
+    optimizers = make_optimizers(policy)
+
+    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
+    assert cirtic_loss.item() is not None
+    assert cirtic_loss.shape == ()
+    cirtic_loss.backward()
+    optimizers["critic"].step()
+
+    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
+    assert actor_loss.item() is not None
+    assert actor_loss.shape == ()
+
+    actor_loss.backward()
+    optimizers["actor"].step()
+
+    temperature_loss = policy.forward(batch, model="temperature")["loss_temperature"]
+    assert temperature_loss.item() is not None
+    assert temperature_loss.shape == ()
+
+    temperature_loss.backward()
+    optimizers["temperature"].step()
+
+    policy.eval()
+    with torch.no_grad():
+        observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+        selected_action = policy.select_action(observation_batch)
+        assert selected_action.shape == (batch_size, action_dim)
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
+def test_sac_policy_with_visual_input(batch_size: int, state_dim: int, action_dim: int):
+    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
+    policy = SACPolicy(config=config)
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
+    )
+
+    policy.train()
+
+    optimizers = make_optimizers(policy)
+
+    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
+    assert cirtic_loss.item() is not None
+    assert cirtic_loss.shape == ()
+    cirtic_loss.backward()
+    optimizers["critic"].step()
+
+    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
+    assert actor_loss.item() is not None
+    assert actor_loss.shape == ()
+
+    actor_loss.backward()
+    optimizers["actor"].step()
+
+    temperature_loss = policy.forward(batch, model="temperature")["loss_temperature"]
+    assert temperature_loss.item() is not None
+    assert temperature_loss.shape == ()
+
+    temperature_loss.backward()
+    optimizers["temperature"].step()
+
+    policy.eval()
+    with torch.no_grad():
+        observation_batch = create_observation_batch_with_visual_input(
+            batch_size=batch_size, state_dim=state_dim
+        )
+        selected_action = policy.select_action(observation_batch)
+        assert selected_action.shape == (batch_size, action_dim)
+
+
+# Let's check best candidates for pretrained encoders
+@pytest.mark.parametrize(
+    "batch_size,state_dim,action_dim,vision_encoder_name",
+    [(1, 6, 6, "helper2424/resnet10"), (1, 6, 6, "facebook/convnext-base-224")],
+)
+@pytest.mark.skipif(not TRANSFORMERS_AVAILABLE, reason="Transformers are not installed")
+def test_sac_policy_with_pretrained_encoder(
+    batch_size: int, state_dim: int, action_dim: int, vision_encoder_name: str
+):
+    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
+    config.vision_encoder_name = vision_encoder_name
+    policy = SACPolicy(config=config)
+    policy.train()
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
+    )
+
+    optimizers = make_optimizers(policy)
+
+    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
+    assert cirtic_loss.item() is not None
+    assert cirtic_loss.shape == ()
+    cirtic_loss.backward()
+    optimizers["critic"].step()
+
+    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
+    assert actor_loss.item() is not None
+    assert actor_loss.shape == ()
+
+
+def test_sac_policy_with_shared_encoder():
+    batch_size = 2
+    action_dim = 10
+    state_dim = 10
+    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
+    config.shared_encoder = True
+
+    policy = SACPolicy(config=config)
+    policy.train()
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
+    )
+
+    policy.train()
+
+    optimizers = make_optimizers(policy)
+
+    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
+    assert cirtic_loss.item() is not None
+    assert cirtic_loss.shape == ()
+    cirtic_loss.backward()
+    optimizers["critic"].step()
+
+    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
+    assert actor_loss.item() is not None
+    assert actor_loss.shape == ()
+
+    actor_loss.backward()
+    optimizers["actor"].step()
+
+
+def test_sac_policy_with_discrete_critic():
+    batch_size = 2
+    continuous_action_dim = 9
+    full_action_dim = continuous_action_dim + 1  # the last action is discrete
+    state_dim = 10
+    config = create_config_with_visual_input(
+        state_dim=state_dim, continuous_action_dim=continuous_action_dim, has_discrete_action=True
+    )
+
+    num_discrete_actions = 5
+    config.num_discrete_actions = num_discrete_actions
+
+    policy = SACPolicy(config=config)
+    policy.train()
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=full_action_dim
+    )
+
+    policy.train()
+
+    optimizers = make_optimizers(policy, has_discrete_action=True)
+
+    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
+    assert cirtic_loss.item() is not None
+    assert cirtic_loss.shape == ()
+    cirtic_loss.backward()
+    optimizers["critic"].step()
+
+    discrete_critic_loss = policy.forward(batch, model="discrete_critic")["loss_discrete_critic"]
+    assert discrete_critic_loss.item() is not None
+    assert discrete_critic_loss.shape == ()
+    discrete_critic_loss.backward()
+    optimizers["discrete_critic"].step()
+
+    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
+    assert actor_loss.item() is not None
+    assert actor_loss.shape == ()
+
+    actor_loss.backward()
+    optimizers["actor"].step()
+
+    policy.eval()
+    with torch.no_grad():
+        observation_batch = create_observation_batch_with_visual_input(
+            batch_size=batch_size, state_dim=state_dim
+        )
+        selected_action = policy.select_action(observation_batch)
+        assert selected_action.shape == (batch_size, full_action_dim)
+
+        discrete_actions = selected_action[:, -1].long()
+        discrete_action_values = set(discrete_actions.tolist())
+
+        assert all(action in range(num_discrete_actions) for action in discrete_action_values), (
+            f"Discrete action {discrete_action_values} is not in range({num_discrete_actions})"
+        )
+
+
+def test_sac_policy_with_default_entropy():
+    config = create_default_config(continuous_action_dim=10, state_dim=10)
+    policy = SACPolicy(config=config)
+    assert policy.target_entropy == -5.0
+
+
+def test_sac_policy_default_target_entropy_with_discrete_action():
+    config = create_config_with_visual_input(state_dim=10, continuous_action_dim=6, has_discrete_action=True)
+    policy = SACPolicy(config=config)
+    assert policy.target_entropy == -3.0
+
+
+def test_sac_policy_with_predefined_entropy():
+    config = create_default_config(state_dim=10, continuous_action_dim=6)
+    config.target_entropy = -3.5
+
+    policy = SACPolicy(config=config)
+    assert policy.target_entropy == pytest.approx(-3.5)
+
+
+def test_sac_policy_update_temperature():
+    config = create_default_config(continuous_action_dim=10, state_dim=10)
+    policy = SACPolicy(config=config)
+
+    assert policy.temperature == pytest.approx(1.0)
+    policy.log_alpha.data = torch.tensor([math.log(0.1)])
+    policy.update_temperature()
+    assert policy.temperature == pytest.approx(0.1)
+
+
+def test_sac_policy_update_target_network():
+    config = create_default_config(state_dim=10, continuous_action_dim=6)
+    config.critic_target_update_weight = 1.0
+
+    policy = SACPolicy(config=config)
+    policy.train()
+
+    for p in policy.critic_ensemble.parameters():
+        p.data = torch.ones_like(p.data)
+
+    policy.update_target_networks()
+    for p in policy.critic_target.parameters():
+        assert torch.allclose(p.data, torch.ones_like(p.data)), (
+            f"Target network {p.data} is not equal to {torch.ones_like(p.data)}"
+        )
+
+
+@pytest.mark.parametrize("num_critics", [1, 3])
+def test_sac_policy_with_critics_number_of_heads(num_critics: int):
+    batch_size = 2
+    action_dim = 10
+    state_dim = 10
+    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
+    config.num_critics = num_critics
+
+    policy = SACPolicy(config=config)
+    policy.train()
+
+    assert len(policy.critic_ensemble.critics) == num_critics
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
+    )
+
+    policy.train()
+
+    optimizers = make_optimizers(policy)
+
+    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
+    assert cirtic_loss.item() is not None
+    assert cirtic_loss.shape == ()
+    cirtic_loss.backward()
+    optimizers["critic"].step()
+
+
+def test_sac_policy_save_and_load(tmp_path):
+    root = tmp_path / "test_sac_save_and_load"
+
+    state_dim = 10
+    action_dim = 10
+    batch_size = 2
+
+    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
+    policy = SACPolicy(config=config)
+    policy.eval()
+    policy.save_pretrained(root)
+    loaded_policy = SACPolicy.from_pretrained(root, config=config)
+    loaded_policy.eval()
+
+    batch = create_default_train_batch(batch_size=1, state_dim=10, action_dim=10)
+
+    with torch.no_grad():
+        with seeded_context(12):
+            # Collect policy values before saving
+            cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
+            actor_loss = policy.forward(batch, model="actor")["loss_actor"]
+            temperature_loss = policy.forward(batch, model="temperature")["loss_temperature"]
+
+            observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+            actions = policy.select_action(observation_batch)
+
+        with seeded_context(12):
+            # Collect policy values after loading
+            loaded_cirtic_loss = loaded_policy.forward(batch, model="critic")["loss_critic"]
+            loaded_actor_loss = loaded_policy.forward(batch, model="actor")["loss_actor"]
+            loaded_temperature_loss = loaded_policy.forward(batch, model="temperature")["loss_temperature"]
+
+            loaded_observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+            loaded_actions = loaded_policy.select_action(loaded_observation_batch)
+
+        assert policy.state_dict().keys() == loaded_policy.state_dict().keys()
+        for k in policy.state_dict():
+            assert torch.allclose(policy.state_dict()[k], loaded_policy.state_dict()[k], atol=1e-6)
+
+        # Compare values before and after saving and loading
+        # They should be the same
+        assert torch.allclose(cirtic_loss, loaded_cirtic_loss)
+        assert torch.allclose(actor_loss, loaded_actor_loss)
+        assert torch.allclose(temperature_loss, loaded_temperature_loss)
+        assert torch.allclose(actions, loaded_actions)