612 lines
21 KiB
Python
612 lines
21 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 json
|
|
import logging
|
|
import os
|
|
import sys
|
|
from unittest.mock import patch
|
|
|
|
from transformers import ViTMAEForPreTraining, Wav2Vec2ForPreTraining
|
|
from transformers.testing_utils import (
|
|
CaptureLogger,
|
|
TestCasePlus,
|
|
backend_device_count,
|
|
is_torch_fp16_available_on_device,
|
|
slow,
|
|
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",
|
|
"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_audio_classification
|
|
import run_clm
|
|
import run_generation
|
|
import run_glue
|
|
import run_image_classification
|
|
import run_mae
|
|
import run_mlm
|
|
import run_ner
|
|
import run_qa as run_squad
|
|
import run_semantic_segmentation
|
|
import run_seq2seq_qa as run_squad_seq2seq
|
|
import run_speech_recognition_ctc
|
|
import run_speech_recognition_ctc_adapter
|
|
import run_speech_recognition_seq2seq
|
|
import run_summarization
|
|
import run_swag
|
|
import run_translation
|
|
import run_wav2vec2_pretraining_no_trainer
|
|
|
|
|
|
logging.basicConfig(level=logging.DEBUG)
|
|
|
|
logger = logging.getLogger()
|
|
|
|
|
|
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
|
|
|
|
|
|
stream_handler = logging.StreamHandler(sys.stdout)
|
|
logger.addHandler(stream_handler)
|
|
|
|
|
|
class ExamplesTests(TestCasePlus):
|
|
def test_run_glue(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_glue.py
|
|
--model_name_or_path distilbert/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_torch_fp16_available_on_device(torch_device):
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_glue.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
|
|
|
|
def test_run_clm(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_clm.py
|
|
--model_name_or_path distilbert/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 backend_device_count(torch_device) > 1:
|
|
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
|
|
return
|
|
|
|
if torch_device == "cpu":
|
|
testargs.append("--use_cpu")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_clm.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertLess(result["perplexity"], 100)
|
|
|
|
def test_run_clm_config_overrides(self):
|
|
# test that config_overrides works, despite the misleading dumps of default un-updated
|
|
# config via tokenizer
|
|
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_clm.py
|
|
--model_type gpt2
|
|
--tokenizer_name openai-community/gpt2
|
|
--train_file ./tests/fixtures/sample_text.txt
|
|
--output_dir {tmp_dir}
|
|
--config_overrides n_embd=10,n_head=2
|
|
""".split()
|
|
|
|
if torch_device == "cpu":
|
|
testargs.append("--use_cpu")
|
|
|
|
logger = run_clm.logger
|
|
with patch.object(sys, "argv", testargs):
|
|
with CaptureLogger(logger) as cl:
|
|
run_clm.main()
|
|
|
|
self.assertIn('"n_embd": 10', cl.out)
|
|
self.assertIn('"n_head": 2', cl.out)
|
|
|
|
def test_run_mlm(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_mlm.py
|
|
--model_name_or_path distilbert/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 == "cpu":
|
|
testargs.append("--use_cpu")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_mlm.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertLess(result["perplexity"], 42)
|
|
|
|
def test_run_ner(self):
|
|
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
|
|
epochs = 7 if backend_device_count(torch_device) > 1 else 2
|
|
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_ner.py
|
|
--model_name_or_path google-bert/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={epochs}
|
|
--seed 7
|
|
""".split()
|
|
|
|
if torch_device == "cpu":
|
|
testargs.append("--use_cpu")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_ner.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
|
|
self.assertLess(result["eval_loss"], 0.5)
|
|
|
|
def test_run_squad(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_qa.py
|
|
--model_name_or_path google-bert/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):
|
|
run_squad.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_f1"], 30)
|
|
self.assertGreaterEqual(result["eval_exact"], 30)
|
|
|
|
def test_run_squad_seq2seq(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_seq2seq_qa.py
|
|
--model_name_or_path google-t5/t5-small
|
|
--context_column context
|
|
--question_column question
|
|
--answer_column answers
|
|
--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
|
|
--predict_with_generate
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_squad_seq2seq.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_f1"], 30)
|
|
self.assertGreaterEqual(result["eval_exact"], 30)
|
|
|
|
def test_run_swag(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_swag.py
|
|
--model_name_or_path google-bert/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):
|
|
run_swag.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_accuracy"], 0.8)
|
|
|
|
def test_generation(self):
|
|
testargs = ["run_generation.py", "--prompt=Hello", "--length=10", "--seed=42"]
|
|
|
|
if is_torch_fp16_available_on_device(torch_device):
|
|
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)
|
|
|
|
@slow
|
|
def test_run_summarization(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_summarization.py
|
|
--model_name_or_path google-t5/t5-small
|
|
--train_file tests/fixtures/tests_samples/xsum/sample.json
|
|
--validation_file tests/fixtures/tests_samples/xsum/sample.json
|
|
--output_dir {tmp_dir}
|
|
--overwrite_output_dir
|
|
--max_steps=50
|
|
--warmup_steps=8
|
|
--do_train
|
|
--do_eval
|
|
--learning_rate=2e-4
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--predict_with_generate
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_summarization.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)
|
|
|
|
@slow
|
|
def test_run_translation(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_translation.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}
|
|
--overwrite_output_dir
|
|
--max_steps=50
|
|
--warmup_steps=8
|
|
--do_train
|
|
--do_eval
|
|
--learning_rate=3e-3
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--predict_with_generate
|
|
--source_lang en_XX
|
|
--target_lang ro_RO
|
|
--max_source_length 512
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_translation.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_bleu"], 30)
|
|
|
|
def test_run_image_classification(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_image_classification.py
|
|
--output_dir {tmp_dir}
|
|
--model_name_or_path google/vit-base-patch16-224-in21k
|
|
--dataset_name hf-internal-testing/cats_vs_dogs_sample
|
|
--do_train
|
|
--do_eval
|
|
--learning_rate 1e-4
|
|
--per_device_train_batch_size 2
|
|
--per_device_eval_batch_size 1
|
|
--remove_unused_columns False
|
|
--overwrite_output_dir True
|
|
--dataloader_num_workers 16
|
|
--metric_for_best_model accuracy
|
|
--max_steps 10
|
|
--train_val_split 0.1
|
|
--seed 42
|
|
--label_column_name labels
|
|
""".split()
|
|
|
|
if is_torch_fp16_available_on_device(torch_device):
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_image_classification.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_accuracy"], 0.8)
|
|
|
|
def test_run_speech_recognition_ctc(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_speech_recognition_ctc.py
|
|
--output_dir {tmp_dir}
|
|
--model_name_or_path hf-internal-testing/tiny-random-wav2vec2
|
|
--dataset_name hf-internal-testing/librispeech_asr_dummy
|
|
--dataset_config_name clean
|
|
--train_split_name validation
|
|
--eval_split_name validation
|
|
--do_train
|
|
--do_eval
|
|
--learning_rate 1e-4
|
|
--per_device_train_batch_size 2
|
|
--per_device_eval_batch_size 1
|
|
--remove_unused_columns False
|
|
--overwrite_output_dir True
|
|
--preprocessing_num_workers 16
|
|
--max_steps 10
|
|
--seed 42
|
|
""".split()
|
|
|
|
if is_torch_fp16_available_on_device(torch_device):
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_speech_recognition_ctc.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertLess(result["eval_loss"], result["train_loss"])
|
|
|
|
def test_run_speech_recognition_ctc_adapter(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_speech_recognition_ctc_adapter.py
|
|
--output_dir {tmp_dir}
|
|
--model_name_or_path hf-internal-testing/tiny-random-wav2vec2
|
|
--dataset_name hf-internal-testing/librispeech_asr_dummy
|
|
--dataset_config_name clean
|
|
--train_split_name validation
|
|
--eval_split_name validation
|
|
--do_train
|
|
--do_eval
|
|
--learning_rate 1e-4
|
|
--per_device_train_batch_size 2
|
|
--per_device_eval_batch_size 1
|
|
--remove_unused_columns False
|
|
--overwrite_output_dir True
|
|
--preprocessing_num_workers 16
|
|
--max_steps 10
|
|
--target_language tur
|
|
--seed 42
|
|
""".split()
|
|
|
|
if is_torch_fp16_available_on_device(torch_device):
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_speech_recognition_ctc_adapter.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "./adapter.tur.safetensors")))
|
|
self.assertLess(result["eval_loss"], result["train_loss"])
|
|
|
|
def test_run_speech_recognition_seq2seq(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_speech_recognition_seq2seq.py
|
|
--output_dir {tmp_dir}
|
|
--model_name_or_path hf-internal-testing/tiny-random-speech-encoder-decoder
|
|
--dataset_name hf-internal-testing/librispeech_asr_dummy
|
|
--dataset_config_name clean
|
|
--train_split_name validation
|
|
--eval_split_name validation
|
|
--do_train
|
|
--do_eval
|
|
--learning_rate 1e-4
|
|
--per_device_train_batch_size 2
|
|
--per_device_eval_batch_size 4
|
|
--remove_unused_columns False
|
|
--overwrite_output_dir True
|
|
--preprocessing_num_workers 16
|
|
--max_steps 10
|
|
--seed 42
|
|
""".split()
|
|
|
|
if is_torch_fp16_available_on_device(torch_device):
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_speech_recognition_seq2seq.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertLess(result["eval_loss"], result["train_loss"])
|
|
|
|
def test_run_audio_classification(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_audio_classification.py
|
|
--output_dir {tmp_dir}
|
|
--model_name_or_path hf-internal-testing/tiny-random-wav2vec2
|
|
--dataset_name anton-l/superb_demo
|
|
--dataset_config_name ks
|
|
--train_split_name test
|
|
--eval_split_name test
|
|
--audio_column_name audio
|
|
--label_column_name label
|
|
--do_train
|
|
--do_eval
|
|
--learning_rate 1e-4
|
|
--per_device_train_batch_size 2
|
|
--per_device_eval_batch_size 1
|
|
--remove_unused_columns False
|
|
--overwrite_output_dir True
|
|
--num_train_epochs 10
|
|
--max_steps 50
|
|
--seed 42
|
|
""".split()
|
|
|
|
if is_torch_fp16_available_on_device(torch_device):
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_audio_classification.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertLess(result["eval_loss"], result["train_loss"])
|
|
|
|
def test_run_wav2vec2_pretraining(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_wav2vec2_pretraining_no_trainer.py
|
|
--output_dir {tmp_dir}
|
|
--model_name_or_path hf-internal-testing/tiny-random-wav2vec2
|
|
--dataset_name hf-internal-testing/librispeech_asr_dummy
|
|
--dataset_config_names clean
|
|
--dataset_split_names validation
|
|
--learning_rate 1e-4
|
|
--per_device_train_batch_size 4
|
|
--per_device_eval_batch_size 4
|
|
--preprocessing_num_workers 16
|
|
--max_train_steps 2
|
|
--validation_split_percentage 5
|
|
--seed 42
|
|
""".split()
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_wav2vec2_pretraining_no_trainer.main()
|
|
model = Wav2Vec2ForPreTraining.from_pretrained(tmp_dir)
|
|
self.assertIsNotNone(model)
|
|
|
|
def test_run_vit_mae_pretraining(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_mae.py
|
|
--output_dir {tmp_dir}
|
|
--dataset_name hf-internal-testing/cats_vs_dogs_sample
|
|
--do_train
|
|
--do_eval
|
|
--learning_rate 1e-4
|
|
--per_device_train_batch_size 2
|
|
--per_device_eval_batch_size 1
|
|
--remove_unused_columns False
|
|
--overwrite_output_dir True
|
|
--dataloader_num_workers 16
|
|
--metric_for_best_model accuracy
|
|
--max_steps 10
|
|
--train_val_split 0.1
|
|
--seed 42
|
|
""".split()
|
|
|
|
if is_torch_fp16_available_on_device(torch_device):
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_mae.main()
|
|
model = ViTMAEForPreTraining.from_pretrained(tmp_dir)
|
|
self.assertIsNotNone(model)
|
|
|
|
def test_run_semantic_segmentation(self):
|
|
tmp_dir = self.get_auto_remove_tmp_dir()
|
|
testargs = f"""
|
|
run_semantic_segmentation.py
|
|
--output_dir {tmp_dir}
|
|
--dataset_name huggingface/semantic-segmentation-test-sample
|
|
--do_train
|
|
--do_eval
|
|
--remove_unused_columns False
|
|
--overwrite_output_dir True
|
|
--max_steps 10
|
|
--learning_rate=2e-4
|
|
--per_device_train_batch_size=2
|
|
--per_device_eval_batch_size=1
|
|
--seed 32
|
|
""".split()
|
|
|
|
if is_torch_fp16_available_on_device(torch_device):
|
|
testargs.append("--fp16")
|
|
|
|
with patch.object(sys, "argv", testargs):
|
|
run_semantic_segmentation.main()
|
|
result = get_results(tmp_dir)
|
|
self.assertGreaterEqual(result["eval_overall_accuracy"], 0.1)
|