Implement training time rtc for pi0, pi0.5 and smolvla

tests/policies/rtc/test_training_time_rtc.py

@@ -0,0 +1,51 @@
+#!/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.
+
+"""Tests for training-time RTC helpers."""
+
+import torch
+
+from lerobot.configs.types import RTCTrainingDelayDistribution
+from lerobot.policies.rtc.configuration_rtc import RTCTrainingConfig
+from lerobot.policies.rtc.training_time import apply_rtc_training_time, sample_rtc_delay
+
+
+def test_rtc_training_config_defaults():
+    config = RTCTrainingConfig()
+    assert config.enabled is False
+    assert config.min_delay == 0
+    assert config.max_delay == 0
+    assert config.delay_distribution == RTCTrainingDelayDistribution.UNIFORM
+    assert config.exp_decay == 1.0
+
+
+def test_sample_rtc_delay_uniform_range():
+    cfg = RTCTrainingConfig(enabled=True, min_delay=1, max_delay=4)
+    delays = sample_rtc_delay(cfg, batch_size=100, device=torch.device("cpu"))
+    assert delays.min().item() >= 1
+    assert delays.max().item() <= 4
+
+
+def test_apply_rtc_training_time_prefix_mask():
+    time = torch.tensor([0.5])
+    delays = torch.tensor([2])
+    time_tokens, postfix_mask = apply_rtc_training_time(time, delays, seq_len=4)
+    assert time_tokens.shape == (1, 4)
+    assert postfix_mask.shape == (1, 4)
+    # Delay=2 means the first two steps are prefix (time forced to 0.0) and only the last two are postfix.
+    assert torch.allclose(time_tokens[0], torch.tensor([0.0, 0.0, 0.5, 0.5]))
+    assert torch.equal(postfix_mask[0], torch.tensor([False, False, True, True]))
+