f62e23737b
* wip on multimodal sample packing support * wip on multimodal packing support * llama-1b-yml * setup logging for test * yml * yml * yml * fix for __len__ for eval sample packing * reverted irrelavant changes * reformatted, reverted log message * reverted unnecessary changes * added e2e multigpu testing for eval sample packing * formatting * fixed e2e test_eval params * fix test_eval e2e multigpu * fix test_eval e2e multigpu * Update tests/e2e/multigpu/test_eval.py Co-authored-by: Wing Lian <wing.lian@gmail.com> * Update tests/e2e/multigpu/test_eval.py Co-authored-by: Wing Lian <wing.lian@gmail.com> --------- Co-authored-by: Wing Lian <wing.lian@gmail.com>
156 lines
5.0 KiB
Python
156 lines
5.0 KiB
Python
"""
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E2E tests for multigpu eval
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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 yaml
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from accelerate.test_utils import execute_subprocess_async
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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.multigpu")
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os.environ["WANDB_DISABLED"] = "true"
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AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
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class TestMultiGPUEval(unittest.TestCase):
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"""
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Test case for MultiGPU Eval Sample Packing
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"""
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@with_temp_dir
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def test_eval_sample_packing(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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"load_in_8bit": False,
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"load_in_4bit": True,
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"strict": False,
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"sequence_len": 2048,
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"adapter": "qlora",
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"sample_packing": True,
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"eval_sample_packing": True,
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"pad_to_sequence_len": True,
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"lora_modules_to_save": ["embed_tokens", "lm_head"],
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"val_set_size": 0.1,
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"special_tokens": {"pad_token": "<|end_of_text|>"},
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"datasets": [
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{
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"path": "teknium/GPT4-LLM-Cleaned",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 5,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 4,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"loss_watchdog_threshold": 5.0,
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"loss_watchdog_patience": 3,
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"bf16": "auto",
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"warmup_steps": 1,
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"evals_per_epoch": 2,
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"eval_max_new_tokens": 128,
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"saves_per_epoch": 1,
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"logging_steps": 1,
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"weight_decay": 0.0,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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@with_temp_dir
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def test_eval(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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"load_in_8bit": False,
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"load_in_4bit": True,
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"strict": False,
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"sequence_len": 2048,
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"adapter": "qlora",
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"sample_packing": True,
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"eval_sample_packing": False,
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"pad_to_sequence_len": True,
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"lora_modules_to_save": ["embed_tokens", "lm_head"],
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"val_set_size": 0.1,
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"special_tokens": {"pad_token": "<|end_of_text|>"},
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"datasets": [
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{
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"path": "teknium/GPT4-LLM-Cleaned",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"max_steps": 5,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 4,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"loss_watchdog_threshold": 5.0,
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"loss_watchdog_patience": 3,
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"bf16": "auto",
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"warmup_steps": 1,
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"evals_per_epoch": 2,
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"eval_max_new_tokens": 128,
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"saves_per_epoch": 1,
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"logging_steps": 1,
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"weight_decay": 0.0,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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