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import json
import os
import sys
import numpy as np
import pandas as pd
import pytest
import yaml
from transformers.testing_utils import execute_subprocess_async
from llm_studio.app_utils.default_datasets import (
prepare_default_dataset_causal_language_modeling,
)
def get_experiment_status(path: str) -> str:
"""Get status information from experiment.
Args:
path: path to experiment folder
Returns:
experiment status
"""
try:
flag_json_path = os.path.join(path, "flags.json")
if not os.path.exists(flag_json_path):
return "none"
with open(flag_json_path) as file:
flags = json.load(file)
status = flags.get("status", "none")
return status
except Exception:
return "none"
@pytest.mark.parametrize(
"config_name",
[
"test_causal_language_modeling_oasst_cfg",
"test_sequence_to_sequence_modeling_oasst_cfg",
],
)
@pytest.mark.parametrize(
"metric",
[
"Perplexity",
"BLEU",
],
)
def test_oasst_training_gpu(tmp_path, config_name, metric):
run_oasst(tmp_path, config_name, metric)
@pytest.mark.parametrize(
"settings",
[
# ["AUC", "test_causal_binary_classification_modeling_cfg"],
# ["LogLoss", "test_causal_binary_classification_modeling_cfg"],
# ["Accuracy", "test_causal_binary_classification_modeling_cfg"],
# ["AUC", "test_causal_multiclass_classification_modeling_cfg"],
# ["LogLoss", "test_causal_multiclass_classification_modeling_cfg"],
# ["Accuracy", "test_causal_multiclass_classification_modeling_cfg"],
["AUC", "test_causal_multilabel_classification_modeling_cfg"],
["LogLoss", "test_causal_multilabel_classification_modeling_cfg"],
["Accuracy", "test_causal_multilabel_classification_modeling_cfg"],
],
)
def test_oasst_classification_training_gpu(tmp_path, settings):
metric, config_name = settings
run_oasst(
tmp_path,
config_name=config_name,
metric=metric,
)
@pytest.mark.parametrize(
"settings",
[
["MSE", "test_causal_regression_modeling_cfg"],
["MAE", "test_causal_regression_modeling_cfg"],
],
)
def test_oasst_regression_training_gpu(tmp_path, settings):
metric, config_name = settings
run_oasst(
tmp_path,
config_name=config_name,
metric=metric,
)
@pytest.mark.parametrize(
"settings",
[
["MSE", "test_causal_regression_modeling_cpu_cfg"],
["MAE", "test_causal_regression_modeling_cpu_cfg"],
],
)
def test_oasst_regression_training_cpu(tmp_path, settings):
metric, config_name = settings
run_oasst(
tmp_path,
config_name=config_name,
metric=metric,
)
@pytest.mark.parametrize(
"settings",
[
["AUC", "test_causal_binary_classification_modeling_cpu_cfg"],
["LogLoss", "test_causal_multiclass_classification_modeling_cpu_cfg"],
["Accuracy", "test_causal_multilabel_classification_modeling_cpu_cfg"],
],
)
def test_oasst_classification_training_cpu(tmp_path, settings):
metric, config_name = settings
run_oasst(
tmp_path,
config_name=config_name,
metric=metric,
)
@pytest.mark.parametrize(
"config_name",
[
"test_causal_language_modeling_oasst_cpu_cfg",
"test_sequence_to_sequence_modeling_oasst_cpu_cfg",
],
)
@pytest.mark.parametrize(
"metric",
[
"Perplexity",
"BLEU",
],
)
def test_oasst_training_cpu(tmp_path, config_name, metric):
run_oasst(tmp_path, config_name, metric)
def run_oasst(tmp_path, config_name, metric):
"""
Test training on OASST dataset.
Pytest keeps around the last 3 test runs in the tmp_path fixture.
"""
prepare_default_dataset_causal_language_modeling(tmp_path)
train_path = os.path.join(tmp_path, "train_full.pq")
# create dummy labels for classification problem type,
# unused for other problem types
df = pd.read_parquet(train_path)
df["multiclass_label"] = np.random.choice(["0", "1", "2"], size=len(df))
df["binary_label"] = np.random.choice(["0", "1"], size=len(df))
df["regression_label"] = np.random.uniform(0, 1, size=len(df))
df["regression_label2"] = np.random.uniform(0, 1, size=len(df))
df.to_parquet(train_path)
with open(
os.path.join(
os.path.dirname(os.path.realpath(__file__)), f"{config_name}.yaml"
),
"r",
) as fp:
cfg = yaml.load(fp, Loader=yaml.FullLoader)
# set paths and save in tmp folder
cfg["dataset"]["train_dataframe"] = train_path
cfg["output_directory"] = os.path.join(tmp_path, "output")
# set metric
cfg["prediction"]["metric"] = metric
cfg["prediction"]["max_length_inference"] = 2
modifed_config_path = os.path.join(tmp_path, "cfg.yaml")
with open(modifed_config_path, "w") as fp:
yaml.dump(cfg, fp)
# llm studio directory (relative to this file)
llm_studio_dir = os.path.abspath(
os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../llm_studio/")
)
cmd = [
f"{sys.executable}",
os.path.join(llm_studio_dir, "train.py"),
"-Y",
f"{modifed_config_path}",
]
execute_subprocess_async(cmd)
assert os.path.exists(cfg["output_directory"])
status = get_experiment_status(path=cfg["output_directory"])
assert status == "finished"
assert os.path.exists(os.path.join(cfg["output_directory"], "charts.db"))
assert os.path.exists(os.path.join(cfg["output_directory"], "checkpoint.pth"))
assert os.path.exists(os.path.join(cfg["output_directory"], "logs.log"))
assert os.path.exists(
os.path.join(cfg["output_directory"], "validation_predictions.csv")
)
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