682a9cf79b
* fixes for delinearization, and make qlora work with fsdp2 * Add back mistakenly removed lm_eval * typo [skip ci] * patch evals for torch.compile + fsdp2 * also check torch_compile w fsdp2 * lots of fixes for flex attn with llama4 * fix patch check and patch llama4 too * attempt to make the patches stick * use transformers 4.51.2 * update configs and README for llama4 * remove torch.compile for CI test * cleanup any existing singletons * set singleton cache to None instead of deleting * use importlib reload with monkeypatch * don't worry about transformers version, mark inputs with grads, fix regex * make sure embeds aren't on cpu * logging and mem improvements * vllm version and add to docker, make sure to save processor on conversion * fix ambiguous tensor bool check * fix vllm to not use v1, upgrade hf transformers * fix tests * make flex_attn_compile_kwargs configurable, since this depends on model params --------- Co-authored-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
"""
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E2E tests for multigpu lora tinyllama
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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 huggingface_hub import snapshot_download
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from transformers.testing_utils import get_torch_dist_unique_port
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from transformers.utils import is_torch_bf16_gpu_available
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from axolotl.utils.dict import DictDefault
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from tests.e2e.utils import check_tensorboard, require_torch_2_6_0
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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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@pytest.fixture(scope="session", autouse=True)
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def download_model():
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# download the model
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snapshot_download("HuggingFaceTB/SmolLM2-135M")
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class TestPackedFlex:
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"""
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Test case for Packed training of llama models
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"""
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@require_torch_2_6_0
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def test_loss_llama(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": 1024,
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"sample_packing": True,
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"flex_attention": True,
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"val_set_size": 0.0,
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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": "vicgalle/alpaca-gpt4",
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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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"micro_batch_size": 2,
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"gradient_accumulation_steps": 2,
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"gradient_checkpointing": True,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch_fused",
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"lr_scheduler": "cosine",
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"max_steps": 2,
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"use_tensorboard": True,
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"save_strategy": "no",
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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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# 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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"--num-processes",
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"2",
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"--main-process-port",
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f"{get_torch_dist_unique_port()}",
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]
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)
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
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)
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