72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
"""
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E2E tests for QAT
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"""
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import unittest
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from pathlib import Path
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from axolotl.cli.args import TrainerCliArgs
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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 .utils import check_model_output_exists, with_temp_dir
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class TestQATLlama(unittest.TestCase):
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"""
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Test case for QAT Llama models
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"""
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@with_temp_dir
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def test_qat_lora(self, temp_dir):
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# pylint: disable=duplicate-code
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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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"sequence_len": 1024,
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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": "mlabonne/FineTome-100k",
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"type": "chat_template",
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"field_messages": "conversations",
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"message_property_mappings": {
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"role": "from",
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"content": "value",
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},
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"drop_system_message": True,
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"split": "train[:1%]",
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},
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],
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"chat_template": "chatml",
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"qat": {
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"quantize_embedding": True,
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"activation_dtype": "int8",
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"weight_dtype": "int8",
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"group_size": 8,
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},
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"num_epochs": 1,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_bnb_8bit",
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"lr_scheduler": "cosine",
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"max_steps": 5,
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"save_safetensors": True,
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"bf16": True,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(Path(temp_dir) / "checkpoint-5", cfg)
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