87455e7f32
* swaps to use newer sample packing for mistral * fix multipack patch test * patch the common fa utils * update for refactor of flash attn unpad * remove un-needed drop attn mask for mistral * bump transformers to main to pick up latest mistral fix for 12b and refactor of fa2 * update test
98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
"""
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E2E smoke tests to check that the monkeypatches are in place for certain configurations
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"""
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import unittest
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import transformers
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.utils.config import normalize_config
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.models import load_model, load_tokenizer
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from ..utils import with_temp_dir
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class TestModelPatches(unittest.TestCase):
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"""
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TestCases for the multipack monkey patches
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"""
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@with_temp_dir
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def test_mixtral_multipack(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "hf-internal-testing/Mixtral-tiny",
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"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
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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": 0.1,
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"special_tokens": {},
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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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}
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)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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tokenizer = load_tokenizer(cfg)
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model, _ = load_model(cfg, tokenizer, inference=cli_args.inference)
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assert (
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"MixtralFlashAttention2"
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in model.model.layers[0].self_attn.__class__.__name__
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)
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@with_temp_dir
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def test_mistral_multipack(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "openaccess-ai-collective/tiny-mistral",
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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": 0.1,
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"special_tokens": {},
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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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}
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)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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tokenizer = load_tokenizer(cfg)
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load_model(cfg, tokenizer, inference=cli_args.inference)
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assert (
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"torch.jit"
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in transformers.modeling_flash_attention_utils._get_unpad_data.__module__ # pylint: disable=protected-access
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)
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