mirror of
https://github.com/huggingface/lerobot.git
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pepijn
3f6909fb63
- Record batch_size in training_step.json alongside num_processes and feed the checkpoint's value into compute_sampler_state on resume; warn when it differs (per-rank sample-exactness needs the same batch size). - Document the set_epoch vs __iter__ auto-advance coupling on EpisodeAwareSampler (callers should rely on exactly one mechanism per run). - Note the broadened (reproducibility-breaking) sampler guard and the no-generator distributed sharding correctness in lerobot_train.py. - Add load_training_batch_size + parallel tests. Co-authored-by: Cursor <cursoragent@cursor.com>
139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 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 pathlib import Path
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from unittest.mock import Mock, patch
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from lerobot.common.train_utils import (
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get_step_checkpoint_dir,
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get_step_identifier,
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load_training_batch_size,
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load_training_num_processes,
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load_training_state,
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load_training_step,
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save_checkpoint,
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save_training_state,
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save_training_step,
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update_last_checkpoint,
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)
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from lerobot.utils.constants import (
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CHECKPOINTS_DIR,
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LAST_CHECKPOINT_LINK,
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OPTIMIZER_PARAM_GROUPS,
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OPTIMIZER_STATE,
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RNG_STATE,
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SCHEDULER_STATE,
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TRAINING_STATE_DIR,
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TRAINING_STEP,
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)
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def test_get_step_identifier():
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assert get_step_identifier(5, 1000) == "000005"
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assert get_step_identifier(123, 100_000) == "000123"
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assert get_step_identifier(456789, 1_000_000) == "0456789"
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def test_get_step_checkpoint_dir():
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output_dir = Path("/checkpoints")
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step_dir = get_step_checkpoint_dir(output_dir, 1000, 5)
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assert step_dir == output_dir / CHECKPOINTS_DIR / "000005"
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def test_save_load_training_step(tmp_path):
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save_training_step(5000, tmp_path)
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assert (tmp_path / TRAINING_STEP).is_file()
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def test_load_training_step(tmp_path):
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step = 5000
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save_training_step(step, tmp_path)
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loaded_step = load_training_step(tmp_path)
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assert loaded_step == step
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def test_save_training_state_records_num_processes(tmp_path, optimizer, scheduler):
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save_training_state(tmp_path, 10, optimizer, scheduler, num_processes=4)
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assert load_training_num_processes(tmp_path) == 4
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def test_load_training_num_processes_absent_returns_none(tmp_path, optimizer, scheduler):
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# Checkpoints written before the world size was recorded must still load (back-compat).
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save_training_state(tmp_path, 10, optimizer, scheduler)
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assert load_training_num_processes(tmp_path) is None
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def test_save_training_state_records_batch_size(tmp_path, optimizer, scheduler):
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save_training_state(tmp_path, 10, optimizer, scheduler, batch_size=32)
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assert load_training_batch_size(tmp_path) == 32
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def test_load_training_batch_size_absent_returns_none(tmp_path, optimizer, scheduler):
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# Checkpoints written before the batch size was recorded must still load (back-compat).
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save_training_state(tmp_path, 10, optimizer, scheduler)
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assert load_training_batch_size(tmp_path) is None
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def test_update_last_checkpoint(tmp_path):
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checkpoint = tmp_path / "0005"
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checkpoint.mkdir()
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update_last_checkpoint(checkpoint)
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last_checkpoint = tmp_path / LAST_CHECKPOINT_LINK
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assert last_checkpoint.is_symlink()
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assert last_checkpoint.resolve() == checkpoint
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@patch("lerobot.common.train_utils.save_training_state")
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def test_save_checkpoint(mock_save_training_state, tmp_path, optimizer):
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policy = Mock()
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cfg = Mock()
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save_checkpoint(tmp_path, 10, cfg, policy, optimizer)
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policy.save_pretrained.assert_called_once()
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cfg.save_pretrained.assert_called_once()
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mock_save_training_state.assert_called_once()
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@patch("lerobot.common.train_utils.save_training_state")
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def test_save_checkpoint_peft(mock_save_training_state, tmp_path, optimizer):
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policy = Mock()
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policy.config = Mock()
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policy.config.save_pretrained = Mock()
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cfg = Mock()
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cfg.use_peft = True
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save_checkpoint(tmp_path, 10, cfg, policy, optimizer)
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policy.save_pretrained.assert_called_once()
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cfg.save_pretrained.assert_called_once()
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policy.config.save_pretrained.assert_called_once()
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mock_save_training_state.assert_called_once()
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def test_save_training_state(tmp_path, optimizer, scheduler):
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save_training_state(tmp_path, 10, optimizer, scheduler)
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assert (tmp_path / TRAINING_STATE_DIR).is_dir()
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assert (tmp_path / TRAINING_STATE_DIR / TRAINING_STEP).is_file()
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assert (tmp_path / TRAINING_STATE_DIR / RNG_STATE).is_file()
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assert (tmp_path / TRAINING_STATE_DIR / OPTIMIZER_STATE).is_file()
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assert (tmp_path / TRAINING_STATE_DIR / OPTIMIZER_PARAM_GROUPS).is_file()
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assert (tmp_path / TRAINING_STATE_DIR / SCHEDULER_STATE).is_file()
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def test_save_load_training_state(tmp_path, optimizer, scheduler):
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save_training_state(tmp_path, 10, optimizer, scheduler)
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loaded_step, loaded_optimizer, loaded_scheduler = load_training_state(tmp_path, optimizer, scheduler)
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assert loaded_step == 10
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assert loaded_optimizer is optimizer
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assert loaded_scheduler is scheduler
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