138 lines
4.9 KiB
Python
138 lines
4.9 KiB
Python
"""
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E2E smoke test for diffusion training plugin
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"""
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from axolotl.common.datasets import load_datasets
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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from tests.e2e.utils import check_model_output_exists
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class TestDiffusion:
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"""
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Test case for diffusion training plugin
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"""
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def test_diffusion_smoke_test(self, temp_dir):
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"""
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Smoke test for diffusion training to ensure the plugin loads and trains without error.
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"""
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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"trust_remote_code": True,
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"sequence_len": 256,
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"val_set_size": 0.1,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 3, # Very short for smoke test
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.0001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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"bf16": True,
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"save_safetensors": True,
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"save_first_step": False,
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"logging_steps": 1,
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"eval_steps": 3,
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"plugins": ["axolotl.integrations.diffusion.DiffusionPlugin"],
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# Diffusion-specific config
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"diffusion_mask_token_id": 32000,
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"diffusion_eps": 1e-3,
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"diffusion_importance_weighting": False,
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}
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)
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# Normalize and validate config
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cfg = normalize_config(cfg)
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cfg = validate_config(cfg)
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# Load datasets to ensure they work with diffusion training
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datasets_meta = load_datasets(cfg=cfg, cli_args=DictDefault({}))
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assert datasets_meta.train_dataset is not None
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assert len(datasets_meta.train_dataset) > 0
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# Run training
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train(cfg=cfg, cli_args=DictDefault({}), dataset_meta=datasets_meta)
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# Check that model was saved
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check_model_output_exists(cfg)
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def test_diffusion_sft_labels(self, temp_dir):
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"""
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Test that diffusion training properly handles SFT data with labels.
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"""
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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"trust_remote_code": True,
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"sequence_len": 256,
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"val_set_size": 0.1,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 2, # Very short for smoke test
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.0001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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"bf16": True,
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"save_safetensors": True,
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"save_first_step": False,
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"logging_steps": 1,
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"eval_steps": 2,
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"plugins": ["axolotl.integrations.diffusion.DiffusionPlugin"],
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# Diffusion-specific config
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"diffusion_mask_token_id": 32000,
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"diffusion_eps": 1e-3,
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"diffusion_importance_weighting": True, # Test importance weighting
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# Ensure we have proper SFT labels
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"train_on_inputs": False, # This ensures prompt tokens get -100 labels
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}
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)
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# Normalize and validate config
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cfg = normalize_config(cfg)
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cfg = validate_config(cfg)
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# Load datasets
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datasets_meta = load_datasets(cfg=cfg, cli_args=DictDefault({}))
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# Verify that the dataset has labels
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sample = datasets_meta.train_dataset[0]
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assert "labels" in sample, "SFT dataset should have labels"
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# Check that some labels are -100 (prompt tokens)
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labels = sample["labels"]
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if hasattr(labels, "tolist"):
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labels = labels.tolist()
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assert -100 in labels, "SFT dataset should have -100 labels for prompt tokens"
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# Run training
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train(cfg=cfg, cli_args=DictDefault({}), dataset_meta=datasets_meta)
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# Check that model was saved
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check_model_output_exists(cfg) |