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add unit tests for testing model training #1449
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Original file line number | Diff line number | Diff line change |
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@@ -1,5 +1,7 @@ | ||
import torchtext | ||
import torch | ||
from torch.nn import functional as torch_F | ||
import copy | ||
from ..common.torchtext_test_case import TorchtextTestCase | ||
from ..common.assets import get_asset_path | ||
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@@ -126,3 +128,49 @@ def test_roberta_bundler_from_config(self): | |
encoder_state_dict['encoder.' + k] = v | ||
model = torchtext.models.RobertaModelBundle.from_config(encoder_conf=dummy_encoder_conf, head=dummy_classifier_head, checkpoint=encoder_state_dict) | ||
self.assertEqual(model.state_dict(), dummy_classifier.state_dict()) | ||
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def test_roberta_bundler_train(self): | ||
from torchtext.models import RobertaEncoderConf, RobertaClassificationHead, RobertaModel, RobertaModelBundle | ||
dummy_encoder_conf = RobertaEncoderConf(vocab_size=10, embedding_dim=16, ffn_dimension=64, num_attention_heads=2, num_encoder_layers=2) | ||
from torch.optim import SGD | ||
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def _train(model): | ||
optim = SGD(model.parameters(), lr=1) | ||
model_input = torch.tensor([[0, 1, 2, 3, 4, 5]]) | ||
target = torch.tensor([0]) | ||
logits = model(model_input) | ||
loss = torch_F.cross_entropy(logits, target) | ||
loss.backward() | ||
optim.step() | ||
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# does not freeze encoder | ||
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16) | ||
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head) | ||
model = RobertaModelBundle.from_config(encoder_conf=dummy_encoder_conf, | ||
head=dummy_classifier_head, | ||
freeze_encoder=False, | ||
checkpoint=dummy_classifier.state_dict()) | ||
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encoder_current_state_dict = copy.deepcopy(model.encoder.state_dict()) | ||
head_current_state_dict = copy.deepcopy(model.head.state_dict()) | ||
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_train(model) | ||
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self.assertNotEqual(model.encoder.state_dict(), encoder_current_state_dict) | ||
self.assertNotEqual(model.head.state_dict(), head_current_state_dict) | ||
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# freeze encoder | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I would split the test into two separate tests. |
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dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16) | ||
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head) | ||
model = RobertaModelBundle.from_config(encoder_conf=dummy_encoder_conf, | ||
head=dummy_classifier_head, | ||
freeze_encoder=True, | ||
checkpoint=dummy_classifier.state_dict()) | ||
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encoder_current_state_dict = copy.deepcopy(model.encoder.state_dict()) | ||
head_current_state_dict = copy.deepcopy(model.head.state_dict()) | ||
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_train(model) | ||
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self.assertEqual(model.encoder.state_dict(), encoder_current_state_dict) | ||
self.assertNotEqual(model.head.state_dict(), head_current_state_dict) |
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Since this is a rather complicated for unit test, can you add doc string?
There are many style for writing docstring for tests, but one should be able to describe a test like "Given A, B should be met."
If the purpose of the test is just ensuring no error happens, then I call that kind of tests as "smoke test". (Could be different from what the "smoke test" means in industry though)
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Thanks @mthrok, I agree on all the points mentioned. This would go in integration/smoke tests. Let me organize all the model tests in follow-up PR. There are other model test (not introduced in this PR) too that accordingly need to be separated.