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""" |
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E2E tests for llama w/ S2 attn |
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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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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 TestLlamaShiftedSparseAttention(unittest.TestCase): |
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""" |
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Test case for Llama models using S2 Attn |
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""" |
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@with_temp_dir |
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def test_lora_s2_attn(self, temp_dir): |
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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": 16384, |
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"sample_packing": False, |
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"flash_attention": True, |
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"s2_attention": True, |
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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": 16, |
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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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"special_tokens": {}, |
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"datasets": [ |
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{ |
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"path": "Yukang/LongAlpaca-12k", |
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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": 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_torch", |
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"lr_scheduler": "cosine", |
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"max_steps": 10, |
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"save_steps": 5, |
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"eval_steps": 5, |
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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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@with_temp_dir |
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def test_fft_s2_attn(self, temp_dir): |
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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": 16384, |
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"sample_packing": False, |
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"flash_attention": True, |
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"s2_attention": True, |
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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": "Yukang/LongAlpaca-12k", |
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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": 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_torch", |
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"lr_scheduler": "cosine", |
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"max_steps": 10, |
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"save_steps": 5, |
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"eval_steps": 5, |
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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) / "pytorch_model.bin").exists() |
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