131 lines
4.1 KiB
Python
131 lines
4.1 KiB
Python
"""
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E2E tests for lora llama
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"""
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import json
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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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from transformers.utils import is_torch_bf16_gpu_available
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from axolotl.cli import do_merge_lora, load_datasets
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from axolotl.cli.merge_lora import modify_cfg_for_merge
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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 TestLoraLlama(unittest.TestCase):
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"""
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Test case for Llama models using LoRA
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"""
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@with_temp_dir
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def test_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": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 1024,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 32,
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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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": 8,
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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_torch",
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"lr_scheduler": "cosine",
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"max_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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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) / "adapter_model.bin").exists()
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@with_temp_dir
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def test_lora_merge(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": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 1024,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 32,
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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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": 8,
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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_torch",
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"lr_scheduler": "cosine",
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"max_steps": 10,
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"bf16": "auto",
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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) / "adapter_model.bin").exists()
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cfg.lora_model_dir = cfg.output_dir
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cfg.load_in_4bit = False
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cfg.load_in_8bit = False
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cfg.flash_attention = False
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cfg.deepspeed = None
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cfg.fsdp = None
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cfg = modify_cfg_for_merge(cfg)
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cfg.merge_lora = True
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cli_args = TrainerCliArgs(merge_lora=True)
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do_merge_lora(cfg=cfg, cli_args=cli_args)
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assert (Path(temp_dir) / "merged/pytorch_model.bin").exists()
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with open(
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Path(temp_dir) / "merged/config.json", "r", encoding="utf-8"
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) as f_handle:
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config = f_handle.read()
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config = json.loads(config)
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if is_torch_bf16_gpu_available():
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assert config["torch_dtype"] == "bfloat16"
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else:
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assert config["torch_dtype"] == "float16"
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