732851f105
* restore to current phi modeling code from phi-2 * enable gradient checkpointing * don't cast everything to float32 all the time * gradient checkpointing for phi2 ParallelBlock module too * fix enabling flash attn for phi2 * add comment about import * fix phi2 example * fix model type check for tokenizer * revert float32 -> bf16 casting changes * support fused dense flash attn * fix the repo for flash-attn * add package name for subdir pkg * fix the data collator when not using sample packing * install packaging for pytests in ci * also fix setup to not install flash attn fused dense subdir if not extras * split out the fused-dense-lib in extra requires * don't train w group_by_length for phi * update integration test to use phi2 * set max steps and save steps for phi e2e tests * try to workaround ssave issue in ci * skip phi2 e2e test for now
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
"""
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E2E tests for lora llama
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"""
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import logging
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import os
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import unittest
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from pathlib import Path
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import pytest
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from axolotl.cli import load_datasets
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.train import train
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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 .utils import with_temp_dir
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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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class TestPhi(unittest.TestCase):
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"""
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Test case for Phi2 models
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"""
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@pytest.mark.skip(reason="fixme later")
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@with_temp_dir
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def test_phi2_ft(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": "microsoft/phi-2",
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"trust_remote_code": True,
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"model_type": "AutoModelForCausalLM",
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"tokenizer_type": "AutoTokenizer",
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"sequence_len": 512,
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"sample_packing": False,
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"load_in_8bit": False,
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"adapter": None,
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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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"dataset_shard_num": 10,
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"dataset_shard_idx": 0,
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"num_epochs": 1,
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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": 0.00001,
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"optimizer": "paged_adamw_8bit",
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"lr_scheduler": "cosine",
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"bf16": True,
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"flash_attention": True,
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"max_steps": 10,
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"save_steps": 10,
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"eval_steps": 10,
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"save_safetensors": True,
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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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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (Path(temp_dir) / "pytorch_model.bin").exists()
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@pytest.mark.skip(reason="multipack no longer supported atm")
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@with_temp_dir
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def test_ft_packed(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": "microsoft/phi-2",
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"trust_remote_code": True,
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"model_type": "PhiForCausalLM",
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"tokenizer_type": "AutoTokenizer",
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"sequence_len": 512,
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"sample_packing": True,
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"load_in_8bit": False,
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"adapter": None,
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"val_set_size": 0.1,
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"special_tokens": {
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"unk_token": "<|endoftext|>",
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"bos_token": "<|endoftext|>",
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"eos_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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"dataset_shard_num": 10,
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"dataset_shard_idx": 0,
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"num_epochs": 1,
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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": 0.00001,
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"optimizer": "adamw_bnb_8bit",
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"lr_scheduler": "cosine",
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"bf16": True,
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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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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (Path(temp_dir) / "pytorch_model.bin").exists()
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