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Pepijn
141 lines
4.5 KiB
Python
141 lines
4.5 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. 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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"""Unit coverage for Pi052 training-time RTC conditioning."""
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from types import SimpleNamespace
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import pytest
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import torch
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from torch import nn
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pytest.importorskip("transformers")
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from lerobot.policies.pi05.modeling_pi05 import ( # noqa: E402
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_build_flow_matching_inputs,
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_prepare_trained_rtc_prefix,
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create_sinusoidal_pos_embedding,
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)
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from lerobot.policies.pi052.configuration_pi052 import PI052Config # noqa: E402
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from lerobot.policies.pi052.modeling_pi052 import ( # noqa: E402
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PI05Pytorch as PI052Pytorch,
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_flow_loss_per_sample,
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_reduce_flow_loss,
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)
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def test_training_rtc_uses_clean_prefix_and_per_token_time():
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actions = torch.tensor([[[1.0], [2.0], [3.0], [4.0]]])
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noise = torch.tensor([[[10.0], [20.0], [30.0], [40.0]]])
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time = torch.tensor([0.25])
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prefix_mask = torch.tensor([[True, True, False, False]])
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x_t, model_time = _build_flow_matching_inputs(actions, noise, time, prefix_mask)
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assert model_time.tolist() == [[0.0, 0.0, 0.25, 0.25]]
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assert torch.equal(x_t[:, :2], actions[:, :2])
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assert torch.equal(x_t[:, 2:], 0.25 * noise[:, 2:] + 0.75 * actions[:, 2:])
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def test_training_rtc_loss_averages_over_postfix_only():
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flow_loss = torch.tensor([[[100.0], [100.0], [2.0], [4.0]]])
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prefix_mask = torch.tensor([[True, True, False, False]])
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per_sample = _flow_loss_per_sample(flow_loss, prefix_mask)
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assert per_sample.tolist() == [3.0]
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def test_training_rtc_mean_loss_uses_global_postfix_normalization():
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flow_loss = torch.tensor(
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[
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[[1.0], [1.0], [1.0], [1.0]],
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[[9.0], [9.0], [9.0], [9.0]],
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]
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)
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prefix_mask = torch.tensor(
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[
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[False, False, False, False],
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[True, True, True, False],
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]
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)
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loss = _reduce_flow_loss(flow_loss, prefix_mask, predict_actions_t=None, reduction="mean")
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assert loss.item() == pytest.approx(13 / 5)
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def test_per_token_time_embedding_preserves_action_axis():
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time = torch.tensor([[0.0, 0.5, 1.0]])
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embedding = create_sinusoidal_pos_embedding(time, 8, 4e-3, 4.0, time.device)
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assert embedding.shape == (1, 3, 8)
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assert not torch.equal(embedding[:, 0], embedding[:, 1])
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def test_action_expert_embeds_per_token_flow_times():
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model = PI052Pytorch.__new__(PI052Pytorch)
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nn.Module.__init__(model)
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model.config = SimpleNamespace(chunk_size=3, min_period=4e-3, max_period=4.0)
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model.gradient_checkpointing_enabled = False
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model.action_in_proj = nn.Linear(2, 8)
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model.time_mlp_in = nn.Linear(8, 8)
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model.time_mlp_out = nn.Linear(8, 8)
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suffix, _, _, adarms_cond = model.embed_suffix(
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torch.randn(2, 3, 2),
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torch.tensor([[0.0, 0.5, 0.5], [0.0, 0.0, 0.5]]),
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)
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assert suffix.shape == (2, 3, 8)
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assert adarms_cond.shape == (2, 3, 8)
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def test_trained_rtc_prefix_is_padded_and_masked():
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x_t = torch.randn(1, 5, 4)
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previous = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
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prefix, mask = _prepare_trained_rtc_prefix(x_t, previous, inference_delay=2, training_max_delay=3)
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assert prefix.shape == x_t.shape
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assert mask.shape == x_t.shape
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assert torch.equal(prefix[0, :2, :2], previous[:2])
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assert torch.count_nonzero(prefix[0, :2, 2:]) == 0
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assert mask[0, :2].all()
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assert not mask[0, 2:].any()
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def test_trained_rtc_rejects_delay_outside_training_distribution():
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with pytest.raises(ValueError, match="exceeds the checkpoint"):
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_prepare_trained_rtc_prefix(
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torch.randn(1, 5, 4),
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torch.randn(4, 2),
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inference_delay=4,
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training_max_delay=3,
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)
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@pytest.mark.parametrize("non_finite", [float("nan"), float("inf")])
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def test_trained_rtc_rejects_non_finite_prefix(non_finite):
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previous = torch.randn(4, 2)
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previous[0, 0] = non_finite
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with pytest.raises(ValueError, match="NaN or Inf"):
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_prepare_trained_rtc_prefix(
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torch.randn(1, 5, 4),
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previous,
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inference_delay=2,
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training_max_delay=3,
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)
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@pytest.mark.parametrize("max_delay", [-1, 5])
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def test_pi052_config_rejects_invalid_training_rtc_delay(max_delay):
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with pytest.raises(ValueError, match="rtc_training_max_delay"):
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PI052Config(chunk_size=5, n_action_steps=5, rtc_training_max_delay=max_delay)
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