transformers/examples/test_examples.py

# coding=utf-8
# Copyright 2018 HuggingFace Inc..
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import argparse
import logging
import os
import sys
from unittest.mock import patch

import torch

from transformers.file_utils import is_apex_available
from transformers.testing_utils import TestCasePlus, require_torch_non_multi_gpu_but_fix_me, torch_device


SRC_DIRS = [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-generation",
        "text-classification",
        "token-classification",
        "language-modeling",
        "multiple-choice",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
    import run_clm
    import run_generation
    import run_glue
    import run_mlm
    import run_ner
    import run_qa as run_squad
    import run_swag


logging.basicConfig(level=logging.DEBUG)

logger = logging.getLogger()


def get_setup_file():
    parser = argparse.ArgumentParser()
    parser.add_argument("-f")
    args = parser.parse_args()
    return args.f


def is_cuda_and_apex_available():
    is_using_cuda = torch.cuda.is_available() and torch_device == "cuda"
    return is_using_cuda and is_apex_available()


class ExamplesTests(TestCasePlus):
    @require_torch_non_multi_gpu_but_fix_me
    def test_run_glue(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

        tmp_dir = self.get_auto_remove_tmp_dir()
        testargs = f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

        if is_cuda_and_apex_available():
            testargs.append("--fp16")

        with patch.object(sys, "argv", testargs):
            result = run_glue.main()
            del result["eval_loss"]
            for value in result.values():
                self.assertGreaterEqual(value, 0.75)

    @require_torch_non_multi_gpu_but_fix_me
    def test_run_clm(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

        tmp_dir = self.get_auto_remove_tmp_dir()
        testargs = f"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

        if torch.cuda.device_count() > 1:
            # Skipping because there are not enough batches to train the model + would need a drop_last to work.
            return

        if torch_device != "cuda":
            testargs.append("--no_cuda")

        with patch.object(sys, "argv", testargs):
            result = run_clm.main()
            self.assertLess(result["perplexity"], 100)

    @require_torch_non_multi_gpu_but_fix_me
    def test_run_mlm(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

        tmp_dir = self.get_auto_remove_tmp_dir()
        testargs = f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
        """.split()

        if torch_device != "cuda":
            testargs.append("--no_cuda")

        with patch.object(sys, "argv", testargs):
            result = run_mlm.main()
            self.assertLess(result["perplexity"], 42)

    @require_torch_non_multi_gpu_but_fix_me
    def test_run_ner(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

        tmp_dir = self.get_auto_remove_tmp_dir()
        testargs = f"""
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs=2
        """.split()

        if torch_device != "cuda":
            testargs.append("--no_cuda")

        with patch.object(sys, "argv", testargs):
            result = run_ner.main()
            self.assertGreaterEqual(result["eval_accuracy_score"], 0.75)
            self.assertGreaterEqual(result["eval_precision"], 0.75)
            self.assertLess(result["eval_loss"], 0.5)

    @require_torch_non_multi_gpu_but_fix_me
    def test_run_squad(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

        tmp_dir = self.get_auto_remove_tmp_dir()
        testargs = f"""
            run_squad.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

        with patch.object(sys, "argv", testargs):
            result = run_squad.main()
            self.assertGreaterEqual(result["f1"], 30)
            self.assertGreaterEqual(result["exact"], 30)

    @require_torch_non_multi_gpu_but_fix_me
    def test_run_swag(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

        tmp_dir = self.get_auto_remove_tmp_dir()
        testargs = f"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

        with patch.object(sys, "argv", testargs):
            result = run_swag.main()
            self.assertGreaterEqual(result["eval_accuracy"], 0.8)

    @require_torch_non_multi_gpu_but_fix_me
    def test_generation(self):
        stream_handler = logging.StreamHandler(sys.stdout)
        logger.addHandler(stream_handler)

        testargs = ["run_generation.py", "--prompt=Hello", "--length=10", "--seed=42"]

        if is_cuda_and_apex_available():
            testargs.append("--fp16")

        model_type, model_name = (
            "--model_type=gpt2",
            "--model_name_or_path=sshleifer/tiny-gpt2",
        )
        with patch.object(sys, "argv", testargs + [model_type, model_name]):
            result = run_generation.main()
            self.assertGreaterEqual(len(result[0]), 10)