133 lines
4.9 KiB
Python
133 lines
4.9 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import sys
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from pathlib import Path
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from unittest.mock import patch
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from parameterized import parameterized
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from run_eval import run_generate
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from run_eval_search import run_search
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from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
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from utils import ROUGE_KEYS
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger()
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def _dump_articles(path: Path, articles: list):
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content = "\n".join(articles)
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Path(path).open("w").writelines(content)
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T5_TINY = "patrickvonplaten/t5-tiny-random"
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BART_TINY = "sshleifer/bart-tiny-random"
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MBART_TINY = "sshleifer/tiny-mbart"
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stream_handler = logging.StreamHandler(sys.stdout)
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logger.addHandler(stream_handler)
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logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
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class TestTheRest(TestCasePlus):
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def run_eval_tester(self, model):
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input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
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output_file_name = input_file_name.parent / "utest_output.txt"
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assert not output_file_name.exists()
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articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
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_dump_articles(input_file_name, articles)
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score_path = str(Path(self.get_auto_remove_tmp_dir()) / "scores.json")
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task = "translation_en_to_de" if model == T5_TINY else "summarization"
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testargs = f"""
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run_eval_search.py
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{model}
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{input_file_name}
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{output_file_name}
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--score_path {score_path}
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--task {task}
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--num_beams 2
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--length_penalty 2.0
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""".split()
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with patch.object(sys, "argv", testargs):
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run_generate()
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assert Path(output_file_name).exists()
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# os.remove(Path(output_file_name))
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# test one model to quickly (no-@slow) catch simple problems and do an
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# extensive testing of functionality with multiple models as @slow separately
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def test_run_eval(self):
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self.run_eval_tester(T5_TINY)
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# any extra models should go into the list here - can be slow
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@parameterized.expand([BART_TINY, MBART_TINY])
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@slow
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def test_run_eval_slow(self, model):
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self.run_eval_tester(model)
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# testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
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@parameterized.expand([T5_TINY, MBART_TINY])
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@slow
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def test_run_eval_search(self, model):
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input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
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output_file_name = input_file_name.parent / "utest_output.txt"
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assert not output_file_name.exists()
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text = {
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"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
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"de": [
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"Maschinelles Lernen ist großartig, oder?",
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"Ich esse gerne Bananen",
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"Morgen ist wieder ein toller Tag!",
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],
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}
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tmp_dir = Path(self.get_auto_remove_tmp_dir())
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score_path = str(tmp_dir / "scores.json")
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reference_path = str(tmp_dir / "val.target")
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_dump_articles(input_file_name, text["en"])
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_dump_articles(reference_path, text["de"])
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task = "translation_en_to_de" if model == T5_TINY else "summarization"
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testargs = f"""
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run_eval_search.py
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{model}
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{str(input_file_name)}
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{str(output_file_name)}
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--score_path {score_path}
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--reference_path {reference_path}
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--task {task}
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""".split()
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testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"])
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with patch.object(sys, "argv", testargs):
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with CaptureStdout() as cs:
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run_search()
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expected_strings = [" num_beams | length_penalty", model, "Best score args"]
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un_expected_strings = ["Info"]
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if "translation" in task:
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expected_strings.append("bleu")
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else:
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expected_strings.extend(ROUGE_KEYS)
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for w in expected_strings:
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assert w in cs.out
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for w in un_expected_strings:
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assert w not in cs.out
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assert Path(output_file_name).exists()
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os.remove(Path(output_file_name))
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