Fix metric calculation in examples and setup tests to run on multi-gpu for no_trainer scripts (huggingface#17331)

* Fix length in no_trainer examples

* Add setup and teardown

* Use new accelerator config generator to automatically make tests able to run based on environment

Author: muellerzr

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -489,7 +489,7 @@ def collate_fn(examples):
             predictions, references = accelerator.gather((predictions, batch["labels"]))
             # If we are in a multiprocess environment, the last batch has duplicates
             if accelerator.num_processes > 1:
-                if step == len(eval_dataloader):
+                if step == len(eval_dataloader) - 1:
                     predictions = predictions[: len(eval_dataloader.dataset) - samples_seen]
                     references = references[: len(eval_dataloader.dataset) - samples_seen]
                 else:

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -574,7 +574,7 @@ def preprocess_function(examples):
             predictions, references = accelerator.gather((predictions, batch["labels"]))
             # If we are in a multiprocess environment, the last batch has duplicates
             if accelerator.num_processes > 1:
-                if step == len(eval_dataloader):
+                if step == len(eval_dataloader) - 1:
                     predictions = predictions[: len(eval_dataloader.dataset) - samples_seen]
                     references = references[: len(eval_dataloader.dataset) - samples_seen]
                 else:

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -591,7 +591,7 @@ def preprocess_val(example_batch):
 
             # If we are in a multiprocess environment, the last batch has duplicates
             if accelerator.num_processes > 1:
-                if step == len(eval_dataloader):
+                if step == len(eval_dataloader) - 1:
                     predictions = predictions[: len(eval_dataloader.dataset) - samples_seen]
                     references = references[: len(eval_dataloader.dataset) - samples_seen]
                 else:

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -310,7 +310,9 @@ def parse_args():
 
 def main():
     args = parse_args()
-
+    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
+    # If we're using tracking, we also need to initialize it here and it will pick up all supported trackers in the environment
+    accelerator = Accelerator(log_with="all", logging_dir=args.output_dir) if args.with_tracking else Accelerator()
     if args.source_prefix is None and args.model_name_or_path in [
         "t5-small",
         "t5-base",
@@ -322,9 +324,6 @@ def main():
             "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
             "`--source_prefix 'summarize: ' `"
         )
-    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
-    # If we're using tracking, we also need to initialize it here and it will pick up all supported trackers in the environment
-    accelerator = Accelerator(log_with="all", logging_dir=args.output_dir) if args.with_tracking else Accelerator()
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -675,11 +674,11 @@ def postprocess_text(preds, labels):
                 decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
                 # If we are in a multiprocess environment, the last batch has duplicates
                 if accelerator.num_processes > 1:
-                    if step == len(eval_dataloader):
+                    if step == len(eval_dataloader) - 1:
                         decoded_preds = decoded_preds[: len(eval_dataloader.dataset) - samples_seen]
                         decoded_labels = decoded_labels[: len(eval_dataloader.dataset) - samples_seen]
                     else:
-                        samples_seen += decoded_labels.shape[0]
+                        samples_seen += len(decoded_labels)
 
                 metric.add_batch(
                     predictions=decoded_preds,

examples/pytorch/test_accelerate_examples.py

@@ -18,49 +18,18 @@
 import json
 import logging
 import os
+import shutil
+import subprocess
 import sys
-from unittest.mock import patch
+import tempfile
 
 import torch
 
+from accelerate.utils import write_basic_config
 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()
@@ -94,10 +63,22 @@ def is_cuda_and_apex_available():
 
 
 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)
+
     def test_run_glue_no_trainer(self):
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
-            run_glue_no_trainer.py
+            {self.examples_dir}/pytorch/text-classification/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
@@ -113,17 +94,16 @@ def test_run_glue_no_trainer(self):
         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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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
+            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
             --model_name_or_path distilgpt2
             --train_file ./tests/fixtures/sample_text.txt
             --validation_file ./tests/fixtures/sample_text.txt
@@ -140,17 +120,16 @@ def test_run_clm_no_trainer(self):
             # 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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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
+            {self.examples_dir}/pytorch/language-modeling/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
@@ -160,20 +139,19 @@ def test_run_mlm_no_trainer(self):
             --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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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
+            {self.examples_dir}/pytorch/token-classification/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
@@ -187,18 +165,17 @@ def test_run_ner_no_trainer(self):
             --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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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
+            {self.examples_dir}/pytorch/question-answering/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
@@ -213,19 +190,18 @@ def test_run_squad_no_trainer(self):
             --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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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
+            {self.examples_dir}/pytorch/multiple-choice/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
@@ -238,17 +214,16 @@ def test_run_swag_no_trainer(self):
             --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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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
+            {self.examples_dir}/pytorch/summarization/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
@@ -262,21 +237,20 @@ def test_run_summarization_no_trainer(self):
             --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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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
+            {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
@@ -294,12 +268,11 @@ def test_run_translation_no_trainer(self):
             --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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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):
@@ -308,7 +281,7 @@ def test_run_semantic_segmentation_no_trainer(self):
 
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
-            run_semantic_segmentation_no_trainer.py
+            {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
@@ -319,15 +292,14 @@ def test_run_semantic_segmentation_no_trainer(self):
             --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)
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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
+            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
             --dataset_name huggingface/image-classification-test-sample
             --output_dir {tmp_dir}
             --num_warmup_steps=8
@@ -339,9 +311,8 @@ def test_run_image_classification_no_trainer(self):
             --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")))
+        _ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
+        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")))

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -528,7 +528,7 @@ def preprocess_function(examples):
             predictions, references = accelerator.gather((predictions, batch["labels"]))
             # If we are in a multiprocess environment, the last batch has duplicates
             if accelerator.num_processes > 1:
-                if step == len(eval_dataloader):
+                if step == len(eval_dataloader) - 1:
                     predictions = predictions[: len(eval_dataloader.dataset) - samples_seen]
                     references = references[: len(eval_dataloader.dataset) - samples_seen]
                 else: