mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +06:00
c82e017aa9
1. Fixes evaluation errors popping up when you train/eval on squad v2 (one was newly encountered and one that was previously reported Running SQuAD 1.0 sample command raises IndexError #15401 but not completely fixed). 2. Removes boolean arguments that don't use store_true. Please, don't use these: *ANY non-empty string is being converted to True in this case and this clearly is not the desired behavior (and it creates a LOT of confusion). 3. All no-trainer test scripts are now saving metric values in the same way (with the right prefix eval_), which is consistent with the trainer-based versions. 4. Adds forgotten model.eval() in the no-trainer versions. This improved some results, but not everything (see the discussion in the end). Please, see the F1 scores and the discussion below.
348 lines
13 KiB
Python
348 lines
13 KiB
Python
# 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 json
|
|
import logging
|
|
import os
|
|
import sys
|
|
from unittest.mock import patch
|
|
|
|
import torch
|
|
|
|
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow, torch_device
|
|
from transformers.utils import is_apex_available
|
|
|
|
|
|
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",
|
|
"summarization",
|
|
"translation",
|
|
"image-classification",
|
|
"speech-recognition",
|
|
"audio-classification",
|
|
"speech-pretraining",
|
|
"image-pretraining",
|
|
"semantic-segmentation",
|
|
]
|
|
]
|
|
sys.path.extend(SRC_DIRS)
|
|
|
|
|
|
if SRC_DIRS is not None:
|
|
import run_clm_no_trainer
|
|
import run_glue_no_trainer
|
|
import run_image_classification_no_trainer
|
|
import run_mlm_no_trainer
|
|
import run_ner_no_trainer
|
|
import run_qa_no_trainer as run_squad_no_trainer
|
|
import run_semantic_segmentation_no_trainer
|
|
import run_summarization_no_trainer
|
|
import run_swag_no_trainer
|
|
import run_translation_no_trainer
|
|
|
|
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 get_results(output_dir):
|
|
results = {}
|
|
path = os.path.join(output_dir, "all_results.json")
|
|
if os.path.exists(path):
|
|
with open(path, "r") as f:
|
|
results = json.load(f)
|
|
else:
|
|
raise ValueError(f"can't find {path}")
|
|
return results
|
|
|
|
|
|
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()
|
|
|
|
|
|
stream_handler = logging.StreamHandler(sys.stdout)
|
|
logger.addHandler(stream_handler)
|
|
|
|
|
|
class ExamplesTestsNoTrainer(TestCasePlus):
|
|
def test_run_glue_no_trainer(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_glue_no_trainer.py
|
|
--model_name_or_path distilbert-base-uncased
|
|
--output_dir {tmp_dir}
|
|
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
|
|
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--learning_rate=1e-4
|
|
--seed=42
|
|
--checkpointing_steps epoch
|
|
--with_tracking
|
|
""".split()
|
|
|
|
if is_cuda_and_apex_available():
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_glue_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "glue_no_trainer")))
|
|
|
|
def test_run_clm_no_trainer(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_clm_no_trainer.py
|
|
--model_name_or_path distilgpt2
|
|
--train_file ./tests/fixtures/sample_text.txt
|
|
--validation_file ./tests/fixtures/sample_text.txt
|
|
--block_size 128
|
|
--per_device_train_batch_size 5
|
|
--per_device_eval_batch_size 5
|
|
--num_train_epochs 2
|
|
--output_dir {tmp_dir}
|
|
--checkpointing_steps epoch
|
|
--with_tracking
|
|
""".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
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_clm_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertLess(result["perplexity"], 100)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "clm_no_trainer")))
|
|
|
|
def test_run_mlm_no_trainer(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_mlm_no_trainer.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}
|
|
--num_train_epochs=1
|
|
--checkpointing_steps epoch
|
|
--with_tracking
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_mlm_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertLess(result["perplexity"], 42)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "mlm_no_trainer")))
|
|
|
|
def test_run_ner_no_trainer(self):
|
|
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
|
|
epochs = 7 if get_gpu_count() > 1 else 2
|
|
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_ner_no_trainer.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}
|
|
--learning_rate=2e-4
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=2
|
|
--num_train_epochs={epochs}
|
|
--seed 7
|
|
--checkpointing_steps epoch
|
|
--with_tracking
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_ner_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
|
|
self.assertLess(result["train_loss"], 0.5)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "ner_no_trainer")))
|
|
|
|
def test_run_squad_no_trainer(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_qa_no_trainer.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}
|
|
--max_train_steps=10
|
|
--num_warmup_steps=2
|
|
--learning_rate=2e-4
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--checkpointing_steps epoch
|
|
--with_tracking
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_squad_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
|
|
self.assertGreaterEqual(result["eval_f1"], 30)
|
|
self.assertGreaterEqual(result["eval_exact"], 30)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "qa_no_trainer")))
|
|
|
|
def test_run_swag_no_trainer(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_swag_no_trainer.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}
|
|
--max_train_steps=20
|
|
--num_warmup_steps=2
|
|
--learning_rate=2e-4
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--with_tracking
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_swag_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_accuracy"], 0.8)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "swag_no_trainer")))
|
|
|
|
@slow
|
|
def test_run_summarization_no_trainer(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_summarization_no_trainer.py
|
|
--model_name_or_path t5-small
|
|
--train_file tests/fixtures/tests_samples/xsum/sample.json
|
|
--validation_file tests/fixtures/tests_samples/xsum/sample.json
|
|
--output_dir {tmp_dir}
|
|
--max_train_steps=50
|
|
--num_warmup_steps=8
|
|
--learning_rate=2e-4
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--checkpointing_steps epoch
|
|
--with_tracking
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_summarization_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_rouge1"], 10)
|
|
self.assertGreaterEqual(result["eval_rouge2"], 2)
|
|
self.assertGreaterEqual(result["eval_rougeL"], 7)
|
|
self.assertGreaterEqual(result["eval_rougeLsum"], 7)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "summarization_no_trainer")))
|
|
|
|
@slow
|
|
def test_run_translation_no_trainer(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_translation_no_trainer.py
|
|
--model_name_or_path sshleifer/student_marian_en_ro_6_1
|
|
--source_lang en
|
|
--target_lang ro
|
|
--train_file tests/fixtures/tests_samples/wmt16/sample.json
|
|
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
|
|
--output_dir {tmp_dir}
|
|
--max_train_steps=50
|
|
--num_warmup_steps=8
|
|
--learning_rate=3e-3
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--source_lang en_XX
|
|
--target_lang ro_RO
|
|
--checkpointing_steps epoch
|
|
--with_tracking
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_translation_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_bleu"], 30)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "translation_no_trainer")))
|
|
|
|
@slow
|
|
def test_run_semantic_segmentation_no_trainer(self):
|
|
stream_handler = logging.StreamHandler(sys.stdout)
|
|
logger.addHandler(stream_handler)
|
|
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_semantic_segmentation_no_trainer.py
|
|
--dataset_name huggingface/semantic-segmentation-test-sample
|
|
--output_dir {tmp_dir}
|
|
--max_train_steps=10
|
|
--num_warmup_steps=2
|
|
--learning_rate=2e-4
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--checkpointing_steps epoch
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_semantic_segmentation_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_overall_accuracy"], 0.10)
|
|
|
|
def test_run_image_classification_no_trainer(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_image_classification_no_trainer.py
|
|
--dataset_name huggingface/image-classification-test-sample
|
|
--output_dir {tmp_dir}
|
|
--num_warmup_steps=8
|
|
--learning_rate=3e-3
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--checkpointing_steps epoch
|
|
--with_tracking
|
|
--seed 42
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_image_classification_no_trainer.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_accuracy"], 0.50)
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
|
|
self.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_classification_no_trainer")))
|