Add multitask diffusion transformer policy

2026-05-18 10:10:08 +00:00 · 2025-11-12 16:20:59 -08:00
parent a5b29d4301
commit 14a7a4d7d4
13 changed files with 2080 additions and 4 deletions
@@ -0,0 +1,377 @@
+#!/usr/bin/env python
+
+# Copyright 2025 Bryson Jones and 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.
+
+"""Test script for Multi-Task DiT policy.
+
+To run tests with GPU on Modal (temporary script):
+    modal run run_tests_modal.py
+
+To run tests locally:
+    python -m pytest tests/policies/test_multi_task_dit_policy.py -v
+"""
+
+import pytest
+import torch
+from torch import Tensor
+
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.policies.multi_task_dit.configuration_multi_task_dit import (
+    DiffusionConfig,
+    FlowMatchingConfig,
+    MultiTaskDiTConfig,
+)
+from lerobot.policies.multi_task_dit.modeling_multi_task_dit import MultiTaskDiTPolicy
+from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
+from lerobot.utils.random_utils import seeded_context, set_seed
+
+
+@pytest.fixture(autouse=True)
+def set_random_seed():
+    seed = 17
+    set_seed(seed)
+
+
+def create_train_batch(
+    batch_size: int = 2,
+    n_obs_steps: int = 2,
+    horizon: int = 16,
+    state_dim: int = 10,
+    action_dim: int = 10,
+    height: int = 224,
+    width: int = 224,
+) -> dict[str, Tensor]:
+    """Create a training batch with visual input and text."""
+    return {
+        "observation.state": torch.randn(batch_size, n_obs_steps, state_dim),
+        f"{OBS_IMAGES}.laptop": torch.rand(batch_size, n_obs_steps, 3, height, width),
+        ACTION: torch.randn(batch_size, horizon, action_dim),
+        "task": ["pick up the cube"] * batch_size,
+    }
+
+
+def create_observation_batch(
+    batch_size: int = 2, state_dim: int = 10, height: int = 224, width: int = 224
+) -> dict:
+    """Create observation batch for inference for a single timestep."""
+    return {
+        "observation.state": torch.randn(batch_size, state_dim),
+        f"{OBS_IMAGES}.laptop": torch.rand(batch_size, 3, height, width),
+        "task": ["pick up the red cube"] * batch_size,
+    }
+
+
+def create_config(
+    state_dim: int = 10,
+    action_dim: int = 10,
+    n_obs_steps: int = 2,
+    horizon: int = 16,
+    n_action_steps: int = 8,
+    with_visual: bool = True,
+    height: int = 224,
+    width: int = 224,
+) -> MultiTaskDiTConfig:
+    """Create a MultiTaskDiT config for testing.
+
+    Args:
+        state_dim: Dimension of state observations
+        action_dim: Dimension of actions
+        n_obs_steps: Number of observation steps
+        horizon: Action prediction horizon
+        n_action_steps: Number of action steps to execute
+        with_visual: Whether to include visual input (default: True)
+        height: Image height (only used if with_visual=True)
+        width: Image width (only used if with_visual=True)
+    """
+    input_features = {OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))}
+
+    if with_visual:
+        input_features[f"{OBS_IMAGES}.laptop"] = PolicyFeature(
+            type=FeatureType.VISUAL, shape=(3, height, width)
+        )
+
+    config = MultiTaskDiTConfig(
+        input_features=input_features,
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+
+    # Use smaller model for faster tests
+    config.transformer.hidden_dim = 128
+    config.transformer.num_layers = 2
+    config.transformer.num_heads = 4
+
+    config.validate_features()
+    return config
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 10, 10), (1, 6, 6)])
+def test_multi_task_dit_policy_forward(batch_size: int, state_dim: int, action_dim: int):
+    """Test forward pass (training mode)."""
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.train()
+
+    batch = create_train_batch(
+        batch_size=batch_size,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        state_dim=state_dim,
+        action_dim=action_dim,
+    )
+
+    # Test forward pass
+    loss, _ = policy.forward(batch)
+    assert loss is not None
+    assert loss.item() is not None
+    assert loss.shape == ()
+
+    # Test backward pass
+    loss.backward()
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 10, 10), (1, 6, 6)])
+def test_multi_task_dit_policy_select_action(batch_size: int, state_dim: int, action_dim: int):
+    """Test select_action (inference mode)."""
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.eval()
+    policy.reset()  # Reset queues before inference
+
+    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)
+
+
+def test_multi_task_dit_policy_diffusion_objective():
+    """Test policy with diffusion objective."""
+    batch_size = 2
+    state_dim = 10
+    action_dim = 10
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+    config.objective = DiffusionConfig(
+        noise_scheduler_type="DDPM",
+        num_train_timesteps=100,
+        num_inference_steps=10,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.train()
+
+    batch = create_train_batch(
+        batch_size=batch_size,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        state_dim=state_dim,
+        action_dim=action_dim,
+    )
+
+    # Test forward pass
+    loss, _ = policy.forward(batch)
+    assert loss is not None
+    assert loss.item() is not None
+
+    # Test inference
+    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)
+
+
+def test_multi_task_dit_policy_flow_matching_objective():
+    """Test policy with flow matching objective."""
+    batch_size = 2
+    state_dim = 10
+    action_dim = 10
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+    config.objective = FlowMatchingConfig(
+        sigma_min=0.0,
+        num_integration_steps=10,  # Use fewer steps for faster tests
+        integration_method="euler",
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.train()
+
+    batch = create_train_batch(
+        batch_size=batch_size,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        state_dim=state_dim,
+        action_dim=action_dim,
+    )
+
+    # Test forward pass
+    loss, _ = policy.forward(batch)
+    assert loss is not None
+    assert loss.item() is not None
+
+    # Test inference
+    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)
+
+
+def test_multi_task_dit_policy_save_and_load(tmp_path):
+    """Test that the policy can be saved and loaded correctly."""
+    root = tmp_path / "test_multi_task_dit_save_and_load"
+
+    state_dim = 10
+    action_dim = 10
+    batch_size = 2
+    n_obs_steps = 2
+    horizon = 16
+    n_action_steps = 8
+
+    config = create_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        n_action_steps=n_action_steps,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    policy.eval()
+
+    # Get device before saving
+    device = next(policy.parameters()).device
+
+    policy.save_pretrained(root)
+    loaded_policy = MultiTaskDiTPolicy.from_pretrained(root, config=config)
+
+    # Explicitly move loaded_policy to the same device
+    loaded_policy.to(device)
+    loaded_policy.eval()
+
+    batch = create_train_batch(
+        batch_size=batch_size,
+        n_obs_steps=n_obs_steps,
+        horizon=horizon,
+        state_dim=state_dim,
+        action_dim=action_dim,
+    )
+
+    # Move batch to the same device as the policy
+    for key in batch:
+        if isinstance(batch[key], torch.Tensor):
+            batch[key] = batch[key].to(device)
+
+    with torch.no_grad():
+        with seeded_context(12):
+            # Collect policy values before saving
+            loss, _ = policy.forward(batch)
+
+            observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+            # Move observation batch to device
+            for key in observation_batch:
+                if isinstance(observation_batch[key], torch.Tensor):
+                    observation_batch[key] = observation_batch[key].to(device)
+            actions = policy.select_action(observation_batch)
+
+        with seeded_context(12):
+            # Collect policy values after loading
+            loaded_loss, _ = loaded_policy.forward(batch)
+
+            loaded_observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+            # Move observation batch to device
+            for key in loaded_observation_batch:
+                if isinstance(loaded_observation_batch[key], torch.Tensor):
+                    loaded_observation_batch[key] = loaded_observation_batch[key].to(device)
+            loaded_actions = loaded_policy.select_action(loaded_observation_batch)
+
+        # Compare state dicts
+        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
+        assert torch.allclose(loss, loaded_loss)
+        assert torch.allclose(actions, loaded_actions)
+
+
+def test_multi_task_dit_policy_get_optim_params():
+    """Test that the policy returns correct optimizer parameter groups."""
+    config = create_config(
+        state_dim=10,
+        action_dim=10,
+        n_obs_steps=2,
+        horizon=16,
+        n_action_steps=8,
+    )
+
+    policy = MultiTaskDiTPolicy(config=config)
+    param_groups = policy.get_optim_params()
+
+    # Should have 2 parameter groups: non-vision and vision encoder
+    assert len(param_groups) == 2
+
+    # First group is non-vision params (no lr specified, will use default)
+    assert "params" in param_groups[0]
+    assert len(param_groups[0]["params"]) > 0
+
+    # Second group is vision encoder params with different lr
+    assert "params" in param_groups[1]
+    assert "lr" in param_groups[1]
+    expected_lr = config.optimizer_lr * config.observation_encoder.vision.lr_multiplier
+    assert param_groups[1]["lr"] == expected_lr