forked from tangger/lerobot
Remove latency, tdmpc policy passes tests (TODO: make it work with online RL)
This commit is contained in:
Cadene
@@ -1,14 +1,11 @@
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import pytest
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from tensordict import TensorDict
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from tensordict.nn import TensorDictModule
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import torch
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from torchrl.data import UnboundedContinuousTensorSpec
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from torchrl.envs import EnvBase
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from lerobot.common.datasets.utils import cycle
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from lerobot.common.envs.utils import postprocess_action, preprocess_observation
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from lerobot.common.policies.factory import make_policy
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from lerobot.common.envs.factory import make_env
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from lerobot.common.datasets.factory import make_dataset
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from lerobot.common.policies.abstract import AbstractPolicy
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from lerobot.common.utils import init_hydra_config
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from .utils import DEVICE, DEFAULT_CONFIG_PATH
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@@ -16,22 +13,23 @@ from .utils import DEVICE, DEFAULT_CONFIG_PATH
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"env_name,policy_name,extra_overrides",
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[
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("simxarm", "tdmpc", ["policy.mpc=true"]),
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("pusht", "tdmpc", ["policy.mpc=false"]),
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#("pusht", "tdmpc", ["policy.mpc=false"]),
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("pusht", "diffusion", []),
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("aloha", "act", ["env.task=sim_insertion", "dataset_id=aloha_sim_insertion_human"]),
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("aloha", "act", ["env.task=sim_insertion", "dataset_id=aloha_sim_insertion_scripted"]),
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("aloha", "act", ["env.task=sim_transfer_cube", "dataset_id=aloha_sim_transfer_cube_human"]),
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("aloha", "act", ["env.task=sim_transfer_cube", "dataset_id=aloha_sim_transfer_cube_scripted"]),
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# ("aloha", "act", ["env.task=sim_insertion", "dataset_id=aloha_sim_insertion_human"]),
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#("aloha", "act", ["env.task=sim_insertion", "dataset_id=aloha_sim_insertion_scripted"]),
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#("aloha", "act", ["env.task=sim_transfer_cube", "dataset_id=aloha_sim_transfer_cube_human"]),
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#("aloha", "act", ["env.task=sim_transfer_cube", "dataset_id=aloha_sim_transfer_cube_scripted"]),
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# TODO(aliberts): simxarm not working with diffusion
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# ("simxarm", "diffusion", []),
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],
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)
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def test_concrete_policy(env_name, policy_name, extra_overrides):
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def test_policy(env_name, policy_name, extra_overrides):
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"""
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Tests:
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- Making the policy object.
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- Updating the policy.
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- Using the policy to select actions at inference time.
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- Test the action can be applied to the policy
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"""
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cfg = init_hydra_config(
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DEFAULT_CONFIG_PATH,
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@@ -46,91 +44,43 @@ def test_concrete_policy(env_name, policy_name, extra_overrides):
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policy = make_policy(cfg)
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# Check that we run select_actions and get the appropriate output.
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dataset = make_dataset(cfg)
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env = make_env(cfg, transform=dataset.transform)
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env = make_env(cfg, num_parallel_envs=2)
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if env_name != "aloha":
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# TODO(alexander-soare): Fix this part of the test. PrioritizedSliceSampler raises NotImplementedError:
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# seq_length as a list is not supported for now.
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policy.update(dataset, torch.tensor(0, device=DEVICE))
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action = policy(
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env.observation_spec.rand()["observation"].to(DEVICE),
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torch.tensor(0, device=DEVICE),
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dataloader = torch.utils.data.DataLoader(
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dataset,
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num_workers=4,
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batch_size=cfg.policy.batch_size,
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shuffle=True,
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pin_memory=DEVICE != "cpu",
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drop_last=True,
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)
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assert action.shape == env.action_spec.shape
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dl_iter = cycle(dataloader)
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batch = next(dl_iter)
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def test_abstract_policy_forward():
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"""
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Given an underlying policy that produces an action trajectory with n_action_steps actions, checks that:
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- The policy is invoked the expected number of times during a rollout.
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- The environment's termination condition is respected even when part way through an action trajectory.
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- The observations are returned correctly.
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"""
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for key in batch:
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batch[key] = batch[key].to(DEVICE, non_blocking=True)
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n_action_steps = 8 # our test policy will output 8 action step horizons
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terminate_at = 10 # some number that is more than n_action_steps but not a multiple
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rollout_max_steps = terminate_at + 1 # some number greater than terminate_at
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# Test updating the policy
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policy(batch, step=0)
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# A minimal environment for testing.
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class StubEnv(EnvBase):
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# reset the policy and environment
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policy.reset()
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observation, _ = env.reset(seed=cfg.seed)
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def __init__(self):
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super().__init__()
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self.action_spec = UnboundedContinuousTensorSpec(shape=(1,))
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self.reward_spec = UnboundedContinuousTensorSpec(shape=(1,))
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# apply transform to normalize the observations
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observation = preprocess_observation(observation, dataset.transform)
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def _step(self, tensordict: TensorDict) -> TensorDict:
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self.invocation_count += 1
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return TensorDict(
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{
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"observation": torch.tensor([self.invocation_count]),
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"reward": torch.tensor([self.invocation_count]),
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"terminated": torch.tensor(
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tensordict["action"].item() == terminate_at
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),
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}
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)
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# send observation to device/gpu
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observation = {key: observation[key].to(DEVICE, non_blocking=True) for key in observation}
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def _reset(self, tensordict: TensorDict) -> TensorDict:
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self.invocation_count = 0
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return TensorDict(
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{
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"observation": torch.tensor([self.invocation_count]),
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"reward": torch.tensor([self.invocation_count]),
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}
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)
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# get the next action for the environment
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with torch.inference_mode():
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action = policy.select_action(observation, step=0)
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def _set_seed(self, seed: int | None):
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return
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# apply inverse transform to unnormalize the action
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action = postprocess_action(action, dataset.transform)
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class StubPolicy(AbstractPolicy):
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name = "stub"
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# Test step through policy
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env.step(action)
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def __init__(self):
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super().__init__(n_action_steps)
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self.n_policy_invocations = 0
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def update(self):
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pass
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def select_actions(self):
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self.n_policy_invocations += 1
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return torch.stack(
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[torch.tensor([i]) for i in range(self.n_action_steps)]
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).unsqueeze(0)
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env = StubEnv()
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policy = StubPolicy()
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policy = TensorDictModule(
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policy,
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in_keys=[],
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out_keys=["action"],
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)
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# Keep track to make sure the policy is called the expected number of times
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rollout = env.rollout(rollout_max_steps, policy)
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assert len(rollout) == terminate_at + 1 # +1 for the reset observation
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assert policy.n_policy_invocations == (terminate_at // n_action_steps) + 1
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assert torch.equal(rollout["observation"].flatten(), torch.arange(terminate_at + 1))
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