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pepijn
727f98021b
Align tokenizer fitting and loss reduction with the effective training dataset, and fail early when FAST supervision cannot be produced safely. Co-authored-by: Cursor <cursoragent@cursor.com>
107 lines
3.5 KiB
Python
107 lines
3.5 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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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from types import SimpleNamespace
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import pytest
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from lerobot.policies import factory
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from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
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from lerobot.policies.pi052 import fit_fast_tokenizer as fit_module
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def test_pi0_fast_resolves_dataset_specific_tokenizer(monkeypatch, tmp_path):
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config = PI0FastConfig(
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auto_fit_fast_tokenizer=True,
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action_tokenizer_name="base-tokenizer",
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fast_tokenizer_cache_dir=str(tmp_path),
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fast_tokenizer_fit_samples=17,
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chunk_size=12,
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n_action_steps=12,
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)
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received = {}
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def fake_fit(**kwargs):
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received.update(kwargs)
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return "/cache/fitted-tokenizer"
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monkeypatch.setattr(fit_module, "fit_fast_tokenizer", fake_fit)
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assert fit_module.resolve_fast_tokenizer(config, "user/dataset") == "/cache/fitted-tokenizer"
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assert received == {
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"dataset_repo_id": "user/dataset",
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"cache_dir": tmp_path,
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"base_tokenizer_name": "base-tokenizer",
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"n_samples": 17,
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"chunk_size": 12,
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"dataset_root": None,
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"dataset_revision": None,
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"episodes": None,
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"exclude_episodes": None,
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"normalization_mode": config.normalization_mapping["ACTION"],
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"action_stats": None,
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"use_relative_actions": False,
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"relative_action_mask": None,
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}
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def test_fast_fit_failure_is_not_silently_replaced(monkeypatch, tmp_path):
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config = PI0FastConfig(auto_fit_fast_tokenizer=True, fast_tokenizer_cache_dir=str(tmp_path))
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monkeypatch.setattr(
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fit_module,
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"fit_fast_tokenizer",
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lambda **kwargs: (_ for _ in ()).throw(RuntimeError("fit failed")),
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)
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with pytest.raises(RuntimeError, match="fit failed"):
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fit_module.resolve_fast_tokenizer(config, "user/dataset")
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def test_only_global_rank_zero_fits_shared_tokenizer(monkeypatch):
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monkeypatch.setenv("RANK", "8")
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monkeypatch.setenv("LOCAL_RANK", "0")
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assert not fit_module._is_global_leader()
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monkeypatch.setenv("RANK", "0")
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assert fit_module._is_global_leader()
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def test_pretrained_pi0_fast_overrides_only_fitted_tokenizer(monkeypatch):
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config = PI0FastConfig(auto_fit_fast_tokenizer=True)
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calls = []
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monkeypatch.setattr(
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fit_module,
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"resolve_fast_tokenizer",
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lambda config, dataset_repo_id, *args: "/cache/fitted-tokenizer",
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)
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def fake_from_pretrained(cls, *args, **kwargs):
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calls.append(kwargs)
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return SimpleNamespace(steps=[])
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monkeypatch.setattr(factory.PolicyProcessorPipeline, "from_pretrained", classmethod(fake_from_pretrained))
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factory.make_pre_post_processors(
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config,
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pretrained_path="checkpoint",
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dataset_repo_id="user/dataset",
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)
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assert calls[0]["overrides"] == {
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"action_tokenizer_processor": {"action_tokenizer_name": "/cache/fitted-tokenizer"}
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}
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