334 lines
13 KiB
Python
334 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2018 HuggingFace Inc..
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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 argparse
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import json
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import logging
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import os
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import shutil
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import sys
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import tempfile
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import unittest
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from unittest import mock
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from accelerate.utils import write_basic_config
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from transformers.testing_utils import (
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TestCasePlus,
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backend_device_count,
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run_command,
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slow,
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torch_device,
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)
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger()
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def get_setup_file():
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parser = argparse.ArgumentParser()
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parser.add_argument("-f")
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args = parser.parse_args()
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return args.f
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def get_results(output_dir):
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results = {}
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path = os.path.join(output_dir, "all_results.json")
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if os.path.exists(path):
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with open(path, "r") as f:
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results = json.load(f)
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else:
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raise ValueError(f"can't find {path}")
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return results
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stream_handler = logging.StreamHandler(sys.stdout)
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logger.addHandler(stream_handler)
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class ExamplesTestsNoTrainer(TestCasePlus):
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@classmethod
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def setUpClass(cls):
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# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
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cls.tmpdir = tempfile.mkdtemp()
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cls.configPath = os.path.join(cls.tmpdir, "default_config.yml")
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write_basic_config(save_location=cls.configPath)
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cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]
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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.tmpdir)
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_glue_no_trainer(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
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--model_name_or_path distilbert-base-uncased
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--output_dir {tmp_dir}
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--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
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--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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--learning_rate=1e-4
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--seed=42
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--num_warmup_steps=2
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--checkpointing_steps epoch
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--with_tracking
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["eval_accuracy"], 0.75)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "glue_no_trainer")))
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@unittest.skip("Zach is working on this.")
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_clm_no_trainer(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
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--model_name_or_path distilgpt2
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--train_file ./tests/fixtures/sample_text.txt
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--validation_file ./tests/fixtures/sample_text.txt
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--block_size 128
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--per_device_train_batch_size 5
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--per_device_eval_batch_size 5
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--num_train_epochs 2
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--output_dir {tmp_dir}
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--checkpointing_steps epoch
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--with_tracking
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""".split()
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if backend_device_count(torch_device) > 1:
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# Skipping because there are not enough batches to train the model + would need a drop_last to work.
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return
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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self.assertLess(result["perplexity"], 100)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "clm_no_trainer")))
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@unittest.skip("Zach is working on this.")
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_mlm_no_trainer(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
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--model_name_or_path distilroberta-base
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--train_file ./tests/fixtures/sample_text.txt
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--validation_file ./tests/fixtures/sample_text.txt
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--output_dir {tmp_dir}
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--num_train_epochs=1
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--checkpointing_steps epoch
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--with_tracking
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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self.assertLess(result["perplexity"], 42)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "mlm_no_trainer")))
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_ner_no_trainer(self):
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# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
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epochs = 7 if backend_device_count(torch_device) > 1 else 2
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
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--model_name_or_path bert-base-uncased
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--train_file tests/fixtures/tests_samples/conll/sample.json
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--validation_file tests/fixtures/tests_samples/conll/sample.json
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--output_dir {tmp_dir}
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=2
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--num_train_epochs={epochs}
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--seed 7
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--checkpointing_steps epoch
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--with_tracking
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["eval_accuracy"], 0.75)
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self.assertLess(result["train_loss"], 0.6)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "ner_no_trainer")))
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_squad_no_trainer(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
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--model_name_or_path bert-base-uncased
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--version_2_with_negative
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--train_file tests/fixtures/tests_samples/SQUAD/sample.json
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--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
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--output_dir {tmp_dir}
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--seed=42
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--max_train_steps=10
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--num_warmup_steps=2
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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--checkpointing_steps epoch
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--with_tracking
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
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self.assertGreaterEqual(result["eval_f1"], 28)
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self.assertGreaterEqual(result["eval_exact"], 28)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "qa_no_trainer")))
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_swag_no_trainer(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
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--model_name_or_path bert-base-uncased
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--train_file tests/fixtures/tests_samples/swag/sample.json
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--validation_file tests/fixtures/tests_samples/swag/sample.json
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--output_dir {tmp_dir}
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--max_train_steps=20
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--num_warmup_steps=2
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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--with_tracking
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["eval_accuracy"], 0.8)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "swag_no_trainer")))
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@slow
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_summarization_no_trainer(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
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--model_name_or_path t5-small
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--train_file tests/fixtures/tests_samples/xsum/sample.json
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--validation_file tests/fixtures/tests_samples/xsum/sample.json
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--output_dir {tmp_dir}
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--max_train_steps=50
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--num_warmup_steps=8
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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--checkpointing_steps epoch
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--with_tracking
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["eval_rouge1"], 10)
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self.assertGreaterEqual(result["eval_rouge2"], 2)
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self.assertGreaterEqual(result["eval_rougeL"], 7)
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self.assertGreaterEqual(result["eval_rougeLsum"], 7)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "summarization_no_trainer")))
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@slow
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_translation_no_trainer(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
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--model_name_or_path sshleifer/student_marian_en_ro_6_1
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--source_lang en
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--target_lang ro
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--train_file tests/fixtures/tests_samples/wmt16/sample.json
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--validation_file tests/fixtures/tests_samples/wmt16/sample.json
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--output_dir {tmp_dir}
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--max_train_steps=50
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--num_warmup_steps=8
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--num_beams=6
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--learning_rate=3e-3
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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--source_lang en_XX
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--target_lang ro_RO
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--checkpointing_steps epoch
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--with_tracking
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["eval_bleu"], 30)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "translation_no_trainer")))
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@slow
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def test_run_semantic_segmentation_no_trainer(self):
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stream_handler = logging.StreamHandler(sys.stdout)
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logger.addHandler(stream_handler)
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
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--dataset_name huggingface/semantic-segmentation-test-sample
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--output_dir {tmp_dir}
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--max_train_steps=10
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--num_warmup_steps=2
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--learning_rate=2e-4
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--per_device_train_batch_size=2
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--per_device_eval_batch_size=1
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--checkpointing_steps epoch
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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self.assertGreaterEqual(result["eval_overall_accuracy"], 0.10)
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@mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
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def test_run_image_classification_no_trainer(self):
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tmp_dir = self.get_auto_remove_tmp_dir()
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testargs = f"""
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{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
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--model_name_or_path google/vit-base-patch16-224-in21k
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--dataset_name hf-internal-testing/cats_vs_dogs_sample
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--learning_rate 1e-4
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--per_device_train_batch_size 2
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--per_device_eval_batch_size 1
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--max_train_steps 2
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--train_val_split 0.1
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--seed 42
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--output_dir {tmp_dir}
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--with_tracking
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--checkpointing_steps 1
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--label_column_name labels
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""".split()
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run_command(self._launch_args + testargs)
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result = get_results(tmp_dir)
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# The base model scores a 25%
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self.assertGreaterEqual(result["eval_accuracy"], 0.4)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "step_1")))
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_classification_no_trainer")))
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