1b53c49e1a
* diffusion training plugin * cleanup * nits * fixes + improvements * add back in reinit_weights (clobbered?); masking / pretrain fixes * nits * cleanup; tests draft * sample generation, tests fixes * fixes * nits * add inference support; add auto-mask token support * nits * nits * progress * simplify logging * lint * prefix args with diffusion_ * coderabbito * tests fix * nit * nits * cleanup + nits * nits * fix SFT sample gen * fixes * fix * comments * comments * lint * reward model lora fix * cleanup; fix pretraining_dataset case * gradio inference * update cfgs * update cfgs * train, generation parity, cleanup * fix * simplify * test * test fix
239 lines
9.0 KiB
Python
239 lines
9.0 KiB
Python
"""Test streaming configuration and data loading functionality."""
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import unittest
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from unittest.mock import Mock, patch
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from datasets import IterableDataset
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from axolotl.utils.config import validate_config
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from axolotl.utils.data.sft import (
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_prepare_streaming_dataset,
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prepare_datasets,
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)
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from axolotl.utils.dict import DictDefault
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class TestStreamingConfig(unittest.TestCase):
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"""Test streaming configuration and deprecation handling."""
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def test_streaming_multipack_buffer_size_deprecation(self):
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"""Test that pretrain_multipack_buffer_size is properly deprecated."""
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# Test with old config name
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cfg_old = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"pretrain_multipack_buffer_size": 5000,
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"datasets": [{"path": "test/dataset", "type": "alpaca"}],
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"sequence_len": 256,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"learning_rate": 0.0001,
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}
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)
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with self.assertLogs("axolotl.utils.schemas.validation", level="WARNING") as cm:
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validated_cfg = validate_config(cfg_old)
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self.assertIn("pretrain_multipack_buffer_size` is deprecated", cm.output[0])
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self.assertEqual(validated_cfg.streaming_multipack_buffer_size, 5000)
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self.assertIsNone(
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getattr(validated_cfg, "pretrain_multipack_buffer_size", None)
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)
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def test_streaming_multipack_buffer_size_new(self):
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"""Test that new streaming_multipack_buffer_size works correctly."""
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cfg_new = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"streaming_multipack_buffer_size": 7000,
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"datasets": [{"path": "test/dataset", "type": "alpaca"}],
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"sequence_len": 256,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"learning_rate": 0.0001,
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}
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)
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validated_cfg = validate_config(cfg_new)
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self.assertEqual(validated_cfg.streaming_multipack_buffer_size, 7000)
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def test_both_buffer_sizes_raises_error(self):
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"""Test that having both old and new buffer size configs raises an error."""
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cfg_both = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"pretrain_multipack_buffer_size": 5000,
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"streaming_multipack_buffer_size": 7000,
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"datasets": [{"path": "test/dataset", "type": "alpaca"}],
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"sequence_len": 256,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"learning_rate": 0.0001,
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}
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)
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with self.assertRaises(ValueError) as cm:
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validate_config(cfg_both)
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self.assertIn("both are set", str(cm.exception))
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class TestStreamingDatasetPreparation(unittest.TestCase):
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"""Test dataset preparation with streaming configuration."""
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def setUp(self):
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self.tokenizer = Mock()
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self.tokenizer.pad_token_id = 0
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self.tokenizer.eos_token_id = 1
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@patch("axolotl.utils.data.sft._prepare_streaming_dataset")
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def test_prepare_datasets_with_streaming_true(self, mock_prepare_streaming):
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"""Test that streaming=True triggers streaming dataset preparation."""
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cfg = DictDefault(
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{
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"streaming": True,
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"datasets": [{"path": "test/dataset", "type": "alpaca"}],
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}
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)
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mock_prepare_streaming.return_value = (Mock(), None, 100, [])
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prepare_datasets(cfg, self.tokenizer)
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mock_prepare_streaming.assert_called_once_with(cfg, self.tokenizer, None)
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@patch("axolotl.utils.data.sft._prepare_streaming_dataset")
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def test_prepare_datasets_with_pretraining_dataset(self, mock_prepare_streaming):
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"""Test that pretraining_dataset triggers streaming dataset preparation."""
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cfg = DictDefault(
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{
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"pretraining_dataset": "test/dataset",
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}
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)
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mock_prepare_streaming.return_value = (Mock(), None, 100, [])
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prepare_datasets(cfg, self.tokenizer)
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mock_prepare_streaming.assert_called_once_with(cfg, self.tokenizer, None)
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@patch("axolotl.utils.data.sft._prepare_standard_dataset")
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def test_prepare_datasets_without_streaming(self, mock_prepare_standard):
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"""Test that without streaming, standard dataset preparation is used."""
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cfg = DictDefault(
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{
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"datasets": [{"path": "test/dataset", "type": "alpaca"}],
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}
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)
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mock_prepare_standard.return_value = (Mock(), None, 100, [])
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prepare_datasets(cfg, self.tokenizer)
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mock_prepare_standard.assert_called_once_with(cfg, self.tokenizer, None)
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class TestStreamingWithSamplePacking(unittest.TestCase):
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"""Test streaming dataset preparation with sample packing."""
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def setUp(self):
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self.tokenizer = Mock()
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self.tokenizer.pad_token_id = 0
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self.tokenizer.eos_token_id = 1
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@patch("axolotl.utils.data.sft._load_streaming_dataset")
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def test_streaming_sft_with_sample_packing_sets_split(self, mock_load_streaming):
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"""Test that streaming SFT with sample_packing sets default split."""
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cfg = DictDefault(
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{
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"streaming": True,
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"sample_packing": True,
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"datasets": [{"path": "test/dataset", "type": "alpaca"}],
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"sequence_len": 256,
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"micro_batch_size": 1,
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}
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)
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mock_load_streaming.return_value = Mock(spec=IterableDataset)
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with patch("axolotl.utils.data.sft._load_and_prepare_datasets"):
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_prepare_streaming_dataset(cfg, self.tokenizer, None)
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# Check that the dataset config has split set to 'train'
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call_args = mock_load_streaming.call_args
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dataset_config = call_args[0][0]
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self.assertEqual(dataset_config.split, "train")
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def test_multipack_attn_forced_true_for_sft(self):
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"""Test that multipack_attn is forced to True for SFT with sample packing."""
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from axolotl.utils.data.streaming import wrap_streaming_dataset
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cfg = DictDefault(
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{
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"sample_packing": True,
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"pretrain_multipack_attn": False, # Should be overridden for SFT
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"pretraining_dataset": None, # This makes it SFT
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"sequence_len": 256,
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"micro_batch_size": 1,
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"streaming_multipack_buffer_size": 1000,
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"seed": 42,
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}
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)
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mock_dataset = Mock()
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mock_dataset.features = None # For streaming datasets
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mock_dataset.__iter__ = Mock(return_value=iter([])) # Empty iterator
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mock_dataset.map = Mock(return_value=mock_dataset)
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mock_ds_wrapper = Mock()
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with patch(
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"axolotl.utils.data.streaming.PretrainingBatchSamplerDataCollatorForSeq2Seq"
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) as mock_collator:
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with patch("axolotl.utils.data.streaming.encode_packed_streaming"):
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wrap_streaming_dataset(
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mock_dataset, self.tokenizer, cfg, mock_ds_wrapper
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)
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# Check that multipack_attn=True was used in the collator
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mock_collator.assert_called_once()
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call_kwargs = mock_collator.call_args[1]
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self.assertTrue(call_kwargs["multipack_attn"])
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def test_multipack_attn_respects_config_for_pretraining(self):
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"""Test that multipack_attn respects config for pretraining datasets."""
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from axolotl.utils.data.streaming import wrap_streaming_dataset
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cfg = DictDefault(
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{
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"sample_packing": True,
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"pretrain_multipack_attn": False, # Should be respected for pretraining
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"pretraining_dataset": "test/dataset", # This makes it pretraining
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"sequence_len": 256,
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"micro_batch_size": 1,
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"streaming_multipack_buffer_size": 1000,
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"seed": 42,
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}
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)
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mock_dataset = Mock()
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mock_dataset.features = None # For streaming datasets
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mock_dataset.__iter__ = Mock(return_value=iter([])) # Empty iterator
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mock_dataset.map = Mock(return_value=mock_dataset)
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mock_ds_wrapper = Mock()
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with patch(
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"axolotl.utils.data.streaming.PretrainingBatchSamplerDataCollatorForSeq2Seq"
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) as mock_collator:
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with patch("axolotl.utils.data.streaming.encode_packed_streaming"):
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wrap_streaming_dataset(
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mock_dataset, self.tokenizer, cfg, mock_ds_wrapper
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)
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# Check that multipack_attn=False was used (respecting config)
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mock_collator.assert_called_once()
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call_kwargs = mock_collator.call_args[1]
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self.assertFalse(call_kwargs["multipack_attn"])
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if __name__ == "__main__":
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unittest.main()
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