update to unittesting (#1983)

* update to unittesting

Signed-off-by: Oleksii Kuchaiev <okuchaiev@nvidia.com>

* expanding unittesting

Signed-off-by: Oleksii Kuchaiev <okuchaiev@nvidia.com>

* Update test_megatron.py

Signed-off-by: ericharper <complex451@gmail.com>

* unskip export test

Signed-off-by: ericharper <complex451@gmail.com>

* try get test

Signed-off-by: ericharper <complex451@gmail.com>

* add check to megatron test to make sure it is in
ourt CI environment

Signed-off-by: Oleksii Kuchaiev <okuchaiev@nvidia.com>

Co-authored-by: Eric Harper <complex451@gmail.com>

CONTRIBUTING.md

@@ -5,20 +5,31 @@ We do all of NeMo's development in the open. Contributions from NeMo community a
 
 # Pull Requests (PR) Guidelines
 
+**Send your PRs to the `main` branch**
+
 1) Make sure your PR does one thing. Have a clear answer to "What does this PR do?".
 2) Read General Principles and style guide below
-3) Make sure unittest pass on your machine
-4) Make sure you sign your commits. E.g. use ``git commit -s`` when before your commit
-5) Make sure all unittests finish successfully before sending PR ``pytest`` or (if yor dev box does not have GPU) ``pytest --cpu`` from NeMo's root folder
-6) Send your PR and request a review
+3) Make sure you sign your commits. E.g. use ``git commit -s`` when before your commit
+4) Make sure all unittests finish successfully before sending PR ``pytest`` or (if yor dev box does not have GPU) ``pytest --cpu`` from NeMo's root folder
+5) Send your PR and request a review
 
-Send your PR to the `main` branch
+## Unit tests
+Quick tests (locally, while developing)
+```
+pytest
+# If you don't have NVIDIA GPU do:
+# pytest --cpu
+```
+Full tests, including pre-trained model downloads
+```
+pytest --with_downloads
+```
 
-Whom should you ask for review:
-1. For changes to NeMo's core: @okuchaiev, @blisc, @titu1994, @tkornuta-nvidia, or @ericharper
-1. For changes to NeMo's ASR collection: @okuchaiev, @titu1994, @redoctopus, @blisc, or @vsl9
-1. For changes to NeMo's NLP collection: @ekmb, @yzhang123, @VahidooX, @vladgets, or @ericharper
-1. For changes to NeMo's TTS collection: @blisc or @stasbel
+## Whom should you ask for review:
+1. For changes to NeMo's core: @ericharper, @titu1994, @blisc, or @okuchaiev  
+1. For changes to NeMo's ASR collection: @titu1994, @redoctopus, @jbalam-nv, or @okuchaiev
+1. For changes to NeMo's NLP collection: @MaximumEntropy, @ericharper, @ekmb, @yzhang123, @VahidooX, @vladgets, or @okuchaiev 
+1. For changes to NeMo's TTS collection: @blisc or @stasbel, or @okuchaiev
 
 Note that some people may self-assign to review your PR - in which case, please wait for them to add a review.
 
@@ -27,7 +38,7 @@ Your  pull requests must pass all checks and peer-review before they can be merg
 # General principles
 1. **User-oriented**: make it easy for end users, even at the cost of writing more code in the background
 1. **Robust**: make it hard for users to make mistakes.
-1. **Supporting of both training and inferencing**: if a module can only be used for training, write a companion module to be used during inference.
+1. **Well-tested**: please add simple, fast unittests. Consider adding CI tests for end-to-end functionality.
 1. **Reusable**: for every piece of code, think about how it can be reused in the future and make it easy to be reused.
 1. **Readable**: code should be easier to read.
 1. **Legal**: if you copy even one line of code from the Internet, make sure that the code allows the license that NeMo supports. Give credit and link back to the code.

Jenkinsfile

@@ -54,7 +54,7 @@ pipeline {
 
     stage('L0: Unit Tests GPU') {
       steps {
-        sh 'pytest -m "unit and not skipduringci and not pleasefixme"'
+        sh 'pytest -m "not pleasefixme" --with_downloads'
       }
     }
 
@@ -66,7 +66,7 @@ pipeline {
         }
       }
       steps {
-        sh 'CUDA_VISIBLE_DEVICES="" pytest -m "unit and not pleasefixme" --cpu'
+        sh 'CUDA_VISIBLE_DEVICES="" pytest -m "not pleasefixme" --cpu --with_downloads'
       }
     }
 

tests/collections/nlp/test_huggingface.py

@@ -40,30 +40,35 @@ class TestHuggingFace(TestCase):
         pretrained_lm_models = nemo_nlp.modules.get_pretrained_lm_models_list()
         self.assertTrue(len(pretrained_lm_models) > 0)
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_get_pretrained_bert_model(self):
         model = nemo_nlp.modules.get_lm_model(pretrained_model_name='bert-base-uncased')
         assert isinstance(model, nemo_nlp.modules.BertEncoder)
         do_export(model, "bert-base-uncased")
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_get_pretrained_distilbert_model(self):
         model = nemo_nlp.modules.get_lm_model(pretrained_model_name='distilbert-base-uncased')
         assert isinstance(model, nemo_nlp.modules.DistilBertEncoder)
         do_export(model, "distilbert-base-uncased")
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_get_pretrained_roberta_model(self):
         model = nemo_nlp.modules.get_lm_model(pretrained_model_name='roberta-base')
         assert isinstance(model, nemo_nlp.modules.RobertaEncoder)
         do_export(model, "roberta-base-uncased")
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_get_pretrained_albert_model(self):
         model = nemo_nlp.modules.get_lm_model(pretrained_model_name='albert-base-v1')
         assert isinstance(model, nemo_nlp.modules.AlbertEncoder)
         do_export(model, "albert-base-v1")
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_get_pretrained_chinese_bert_wwm_model(self):
         model_name = 'hfl/chinese-bert-wwm'
@@ -72,6 +77,7 @@ class TestHuggingFace(TestCase):
         tokenizer = get_tokenizer(tokenizer_name=model_name)
         assert isinstance(tokenizer, AutoTokenizer)
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_get_pretrained_arabic_model(self):
         model_name = 'asafaya/bert-base-arabic'

tests/collections/nlp/test_megatron.py

@@ -31,6 +31,14 @@ import nemo.collections.nlp as nemo_nlp
 from nemo.core.classes import typecheck
 
 
+def get_pretrained_bert_345m_uncased_model():
+    model_name = "megatron-bert-345m-uncased"
+    model = nemo_nlp.modules.get_lm_model(pretrained_model_name=model_name)
+    if torch.cuda.is_available():
+        model = model.cuda()
+    return model
+
+
 class TestMegatron(TestCase):
     @pytest.mark.run_only_on('GPU')
     @pytest.mark.unit
@@ -38,33 +46,31 @@ class TestMegatron(TestCase):
         pretrained_lm_models = nemo_nlp.modules.get_pretrained_lm_models_list()
         self.assertTrue(len(pretrained_lm_models) > 0)
 
+    @pytest.mark.skipif(not os.path.exists('/home/TestData/nlp'), reason='Not a Jenkins machine')
+    @pytest.mark.with_downloads()
     @pytest.mark.run_only_on('GPU')
     @pytest.mark.unit
-    def test_get_pretrained_bert_345m_uncased_model(self):
-        model_name = "megatron-bert-345m-uncased"
-        model = nemo_nlp.modules.get_lm_model(pretrained_model_name=model_name)
-        if torch.cuda.is_available():
-            model = model.cuda()
-
+    def test_get_model(self):
+        model = get_pretrained_bert_345m_uncased_model()
         assert isinstance(model, nemo_nlp.modules.MegatronBertEncoder)
 
         typecheck.set_typecheck_enabled(enabled=False)
         inp = model.input_example()
         out = model.forward(*inp)
         typecheck.set_typecheck_enabled(enabled=True)
-        self.model = model
 
     @pytest.mark.run_only_on('GPU')
     @pytest.mark.unit
     @pytest.mark.skip('ONNX export is broken in PyTorch')
     def test_onnx_export(self):
-        assert self.model
+        model = get_pretrained_bert_345m_uncased_model()
+        assert model
         with tempfile.TemporaryDirectory() as tmpdir:
             # Generate filename in the temporary directory.
             # Test export.
-            self.model.export(os.path.join(tmpdir, "megatron.onnx"))
+            model.export(os.path.join(tmpdir, "megatron.onnx"))
 
 
 if __name__ == "__main__":
     t = TestMegatron()
-    t.test_get_pretrained_bert_345m_uncased_model()
+    t.test_get_model()

tests/collections/nlp/test_pretrained_models_performance.py

@@ -55,6 +55,7 @@ def data_exists(data_dir):
 
 
 class TestPretrainedModelPerformance(TestCase):
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     @pytest.mark.run_only_on('GPU')
     @pytest.mark.skipif(
@@ -83,6 +84,7 @@ class TestPretrainedModelPerformance(TestCase):
     @pytest.mark.skipif(
         not data_exists('/home/TestData/nlp/token_classification_punctuation/fisher'), reason='Not a Jenkins machine'
     )
+    @pytest.mark.with_downloads()
     def test_punct_capit_with_distilbert(self):
         data_dir = '/home/TestData/nlp/token_classification_punctuation/fisher'
         model = models.PunctuationCapitalizationModel.from_pretrained("punctuation_en_distilbert")
@@ -93,6 +95,7 @@ class TestPretrainedModelPerformance(TestCase):
         assert abs(metrics['punct_f1'] - 52.4225) < 0.001
         assert int(model.punct_class_report.total_examples) == 128
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     @pytest.mark.run_only_on('GPU')
     @pytest.mark.skipif(

tests/conftest.py

@@ -42,6 +42,11 @@ def pytest_addoption(parser):
         action='store_true',
         help="pass that argument to use local test data/skip downloading from URL/GitHub (DEFAULT: False)",
     )
+    parser.addoption(
+        '--with_downloads',
+        action='store_true',
+        help="pass this argument to active tests which download models from the cloud.",
+    )
 
 
 @pytest.fixture
@@ -60,6 +65,15 @@ def run_only_on_device_fixture(request, device):
             pytest.skip('skipped on this device: {}'.format(device))
 
 
+@pytest.fixture(autouse=True)
+def downloads_weights(request, device):
+    if request.node.get_closest_marker('with_downloads'):
+        if not request.config.getoption("--with_downloads"):
+            pytest.skip(
+                'To run this test, pass --with_downloads option. It will download (and cache) models from cloud.'
+            )
+
+
 @pytest.fixture
 def cleanup_local_folder():
     # Asserts in fixture are not recommended, but I'd rather stop users from deleting expensive training runs

tests/core/test_save_restore.py

@@ -120,24 +120,28 @@ class TestSaveRestore:
 
             return model_copy
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_EncDecCTCModel(self):
         # TODO: Switch to using named configs because here we don't really care about weights
         qn = EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
         self.__test_restore_elsewhere(model=qn, attr_for_eq_check=set(["decoder._feat_in", "decoder._num_classes"]))
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_EncDecCTCModelBPE(self):
         # TODO: Switch to using named configs because here we don't really care about weights
         cn = EncDecCTCModelBPE.from_pretrained(model_name="stt_en_citrinet_256")
         self.__test_restore_elsewhere(model=cn, attr_for_eq_check=set(["decoder._feat_in", "decoder._num_classes"]))
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_EncDecCTCModelBPE_v2(self):
         # TODO: Switch to using named configs because here we don't really care about weights
         cn = EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_small")
         self.__test_restore_elsewhere(model=cn, attr_for_eq_check=set(["decoder._feat_in", "decoder._num_classes"]))
 
+    @pytest.mark.with_downloads()
     @pytest.mark.unit
     def test_PunctuationCapitalization(self):
         # TODO: Switch to using named configs because here we don't really care about weights