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examples/pytorch/test_accelerate_examples.py

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+# 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 shutil
+import sys
+import tempfile
+import unittest
+from unittest import mock
+
+from accelerate.utils import write_basic_config
+
+from transformers.testing_utils import (
+    TestCasePlus,
+    backend_device_count,
+    run_command,
+    slow,
+    torch_device,
+)
+
+
+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) 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 ExamplesTestsNoTrainer(TestCasePlus):
+    @classmethod
+    def setUpClass(cls):
+        # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
+        cls.tmpdir = tempfile.mkdtemp()
+        cls.configPath = os.path.join(cls.tmpdir, "default_config.yml")
+        write_basic_config(save_location=cls.configPath)
+        cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]
+
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.tmpdir)
+
+    @slow
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_glue_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
+            --model_name_or_path distilbert/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
+            --num_warmup_steps=2
+            --checkpointing_steps epoch
+            --with_tracking
+        """.split()
+
+        run_command(self._launch_args + testargs)
+        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")))
+
+    @unittest.skip("Zach is working on this.")
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_clm_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
+            --model_name_or_path distilbert/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 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
+
+        run_command(self._launch_args + testargs)
+        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")))
+
+    @unittest.skip("Zach is working on this.")
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_mlm_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.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}
+            --num_train_epochs=1
+            --checkpointing_steps epoch
+            --with_tracking
+        """.split()
+
+        run_command(self._launch_args + testargs)
+        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")))
+
+    @slow
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    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 backend_device_count(torch_device) > 1 else 2
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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}
+            --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()
+
+        run_command(self._launch_args + testargs)
+        result = get_results(tmp_dir)
+        self.assertGreaterEqual(result["eval_accuracy"], 0.75)
+        self.assertLess(result["train_loss"], 0.6)
+        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")))
+
+    @slow
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_squad_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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}
+            --seed=42
+            --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()
+
+        run_command(self._launch_args + testargs)
+        result = get_results(tmp_dir)
+        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
+        self.assertGreaterEqual(result["eval_f1"], 28)
+        self.assertGreaterEqual(result["eval_exact"], 28)
+        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")))
+
+    @slow
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_swag_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.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}
+            --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()
+
+        run_command(self._launch_args + testargs)
+        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
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_summarization_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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}
+            --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()
+
+        run_command(self._launch_args + testargs)
+        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
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_translation_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/translation/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
+            --num_beams=6
+            --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()
+
+        run_command(self._launch_args + testargs)
+        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"""
+            {self.examples_dir}/pytorch/semantic-segmentation/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()
+
+        run_command(self._launch_args + testargs)
+        result = get_results(tmp_dir)
+        self.assertGreaterEqual(result["eval_overall_accuracy"], 0.10)
+
+    @slow
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_image_classification_no_trainer(self):
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
+            --model_name_or_path google/vit-base-patch16-224-in21k
+            --dataset_name hf-internal-testing/cats_vs_dogs_sample
+            --learning_rate 1e-4
+            --per_device_train_batch_size 2
+            --per_device_eval_batch_size 1
+            --max_train_steps 2
+            --train_val_split 0.1
+            --seed 42
+            --output_dir {tmp_dir}
+            --with_tracking
+            --checkpointing_steps 1
+            --label_column_name labels
+        """.split()
+
+        run_command(self._launch_args + testargs)
+        result = get_results(tmp_dir)
+        # The base model scores a 25%
+        self.assertGreaterEqual(result["eval_accuracy"], 0.4)
+        self.assertTrue(os.path.exists(os.path.join(tmp_dir, "step_1")))
+        self.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_classification_no_trainer")))
+
+    @slow
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_object_detection_no_trainer(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/object-detection/run_object_detection_no_trainer.py
+            --model_name_or_path qubvel-hf/detr-resnet-50-finetuned-10k-cppe5
+            --dataset_name qubvel-hf/cppe-5-sample
+            --output_dir {tmp_dir}
+            --max_train_steps=10
+            --num_warmup_steps=2
+            --learning_rate=1e-6
+            --per_device_train_batch_size=2
+            --per_device_eval_batch_size=1
+            --checkpointing_steps epoch
+        """.split()
+
+        run_command(self._launch_args + testargs)
+        result = get_results(tmp_dir)
+        self.assertGreaterEqual(result["test_map"], 0.10)
+
+    @slow
+    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline", "DVCLIVE_TEST": "true"})
+    def test_run_instance_segmentation_no_trainer(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            {self.examples_dir}/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py
+            --model_name_or_path qubvel-hf/finetune-instance-segmentation-ade20k-mini-mask2former
+            --output_dir {tmp_dir}
+            --dataset_name qubvel-hf/ade20k-nano
+            --do_reduce_labels
+            --image_height 256
+            --image_width 256
+            --num_train_epochs 1
+            --per_device_train_batch_size 2
+            --per_device_eval_batch_size 1
+            --seed 1234
+        """.split()
+
+        run_command(self._launch_args + testargs)
+        result = get_results(tmp_dir)
+        self.assertGreaterEqual(result["test_map"], 0.1)