110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
"""
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E2E tests for falcon
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"""
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import unittest
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import pytest
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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 TestFalconPatched(unittest.TestCase):
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"""
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Test case for Falcon models
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"""
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@pytest.mark.skip(reason="no tiny models for testing with safetensors")
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@with_temp_dir
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def test_qlora(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "illuin/tiny-random-FalconForCausalLM",
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"flash_attention": True,
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"sample_packing": True,
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"sequence_len": 2048,
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"load_in_4bit": True,
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"adapter": "qlora",
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"lora_r": 16,
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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"lora_target_linear": True,
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"lora_modules_to_save": ["word_embeddings", "lm_head"],
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"val_set_size": 0.05,
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"special_tokens": {
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"bos_token": "<|endoftext|>",
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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": 2,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 1,
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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": 20,
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"save_steps": 10,
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"eval_steps": 10,
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"bf16": "auto",
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"save_first_step": False,
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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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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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@pytest.mark.skip(reason="no tiny models for testing with safetensors")
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@with_temp_dir
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def test_ft(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "illuin/tiny-random-FalconForCausalLM",
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"flash_attention": True,
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"sample_packing": True,
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"sequence_len": 2048,
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"val_set_size": 0.05,
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"special_tokens": {
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"bos_token": "<|endoftext|>",
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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": 2,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 1,
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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": 20,
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"save_steps": 10,
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"eval_steps": 10,
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"bf16": "auto",
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"save_first_step": False,
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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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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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