cf0c79d52e
* fix: update chat_template * fix: handle gemma3 showing a lot of no content for turn 0 * fix: remove unknown config from examples * fix: test * fix: temporary disable gemma2 test * fix: stop overwriting config.text_config unnecessarily * fix: handling of set cache to the text_config section * feat: add liger gemma support and bump liger to 0.5.5 * fix: add double use_cache setting * fix: add support for final_logit_softcap in CCE for gemma2/3 * fix: set use_cache before model load * feat: add missing layernorm override * fix: handle gemma3 rmsnorm * fix: use wrapper to pass dim as hidden_size * fix: change dim to positional * fix: patch with wrong mlp * chore: refactor use_cache handling * fix import issues * fix tests.e2e.utils import --------- Co-authored-by: Wing Lian <wing@axolotl.ai>
141 lines
4.3 KiB
Python
141 lines
4.3 KiB
Python
"""
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E2E tests for multigpu post-training use Ray Train
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"""
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import logging
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import os
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from pathlib import Path
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import pytest
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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 tests.e2e.utils import check_tensorboard, require_torch_lt_2_6_0
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LOG = logging.getLogger(__name__)
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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 TestMultiGPURay:
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"""
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Test cases for AnyScale Ray post training
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"""
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@require_torch_lt_2_6_0
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def test_lora_ddp(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": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 2048,
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"adapter": "lora",
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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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"val_set_size": 0.05,
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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": "tatsu-lab/alpaca",
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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": 2,
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"micro_batch_size": 4,
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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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"use_tensorboard": True,
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"use_ray": True,
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"ray_num_workers": 2,
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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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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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"--use-ray",
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"--ray-num-workers",
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"2",
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]
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)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
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)
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@require_torch_lt_2_6_0
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@pytest.mark.parametrize(
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"gradient_accumulation_steps",
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[1, 2],
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)
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def test_ds_zero2_packed(self, temp_dir, gradient_accumulation_steps):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sample_packing": True,
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"pad_to_sequence_len": True,
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"sequence_len": 2048,
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"val_set_size": 0.05,
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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": "tatsu-lab/alpaca",
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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": 2,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": gradient_accumulation_steps,
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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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"flash_attention": True,
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"deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero2.json"),
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"use_tensorboard": True,
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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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"axolotl",
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"train",
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str(Path(temp_dir) / "config.yaml"),
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"--use-ray",
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"--ray-num-workers",
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"2",
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]
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)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
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)
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