bcc78d8fa3
* bump transformers and update attention class map name * also run the tests in docker * add mixtral e2e smoke test * fix base name for docker image in test * mixtral lora doesn't seem to work, at least check qlora * add testcase for mixtral w sample packing * check monkeypatch for flash attn multipack * also run the e2e tests in docker * use all gpus to run tests in docker ci * use privileged mode too for docker w gpus * rename the docker e2e actions for gh ci * set privileged mode for docker and update mixtral model self attn check * use fp16/bf16 for mixtral w fa2 * skip e2e tests on docker w gpus for now * tests to validate mistral and mixtral patches * fix rel import
124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
"""
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E2E tests for mixtral
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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 transformers.utils import is_torch_bf16_gpu_available
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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 TestMixtral(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_qlora(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": "hf-internal-testing/Mixtral-tiny",
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"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
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"flash_attention": True,
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"sequence_len": 2048,
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"load_in_4bit": True,
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"adapter": "qlora",
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"lora_r": 16,
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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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": "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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"sample_packing": True,
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}
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)
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if is_torch_bf16_gpu_available():
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cfg.bf16 = True
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else:
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cfg.fp16 = True
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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_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": "hf-internal-testing/Mixtral-tiny",
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"tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
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"flash_attention": 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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"sample_packing": True,
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}
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)
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if is_torch_bf16_gpu_available():
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cfg.bf16 = True
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else:
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cfg.fp16 = True
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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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model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (
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"axolotl.monkeypatch.mixtral.modeling_mixtral"
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in model.model.layers[0].self_attn.__class__.__module__
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)
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assert (
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"MixtralMultipackFlashAttention2"
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in model.model.layers[0].self_attn.__class__.__name__
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)
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assert (Path(temp_dir) / "pytorch_model.bin").exists()
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