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Adding [T5/MT5/UMT5]ForTokenClassification (#28443)

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* Adding [T5/MT5/UMT5]ForTokenClassification

* Add auto mappings for T5ForTokenClassification and variants

* Adding ForTokenClassification to the list of models

* Adding attention_mask param to the T5ForTokenClassification test

* Remove outdated comment in test

* Adding EncoderOnly and Token Classification tests for MT5 and UMT5

* Fix typo in umt5 string

* Add tests for all the existing MT5 models

* Fix wrong comment in dependency_versions_table

* Reverting change to common test for _keys_to_ignore_on_load_missing

The test is correctly picking up redundant keys in _keys_to_ignore_on_load_missing.

* Removing _keys_to_ignore_on_missing from MT5 since the key is not used in the model

* Add fix-copies to MT5ModelTest
This commit is contained in:
JB (Don)
2024-02-01 10:53:49 +08:00
committed by GitHub
parent 7b2bd1fbbd
commit 0d26abdd3a
18 changed files with 1579 additions and 54 deletions
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38
tests/models/t5/test_modeling_t5.py
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@@ -52,6 +52,7 @@ if is_torch_available():
T5ForConditionalGeneration,
T5ForQuestionAnswering,
T5ForSequenceClassification,
T5ForTokenClassification,
T5Model,
T5Tokenizer,
)
@@ -586,9 +587,11 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
# `QAPipelineTests` is not working well with slow tokenizers (for some models) and we don't want to touch the file
# `src/transformers/data/processors/squad.py` (where this test fails for this model)
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
if tokenizer_name is None:
return True
if pipeline_test_case_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
@@ -998,6 +1001,22 @@ class T5EncoderOnlyModelTester:
output = model(input_ids, attention_mask=attention_mask)["last_hidden_state"]
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_with_token_classification_head(
self,
config,
input_ids,
attention_mask,
):
labels = torch.tensor([1] * self.seq_length * self.batch_size, dtype=torch.long, device=torch_device)
model = T5ForTokenClassification(config=config).to(torch_device).eval()
outputs = model(
input_ids=input_ids,
labels=labels,
attention_mask=attention_mask,
)
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.seq_length, config.num_labels))
self.parent.assertEqual(outputs["loss"].size(), ())
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
@@ -1013,11 +1032,18 @@ class T5EncoderOnlyModelTester:
return config, inputs_dict
class T5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (T5EncoderModel,) if is_torch_available() else ()
class T5EncoderOnlyModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (T5EncoderModel, T5ForTokenClassification) if is_torch_available() else ()
test_pruning = False
test_resize_embeddings = False
test_model_parallel = True
pipeline_model_mapping = (
{
"token-classification": T5ForTokenClassification,
}
if is_torch_available()
else {}
)
all_parallelizable_model_classes = (T5EncoderModel,) if is_torch_available() else ()
def setUp(self):
@@ -1036,6 +1062,10 @@ class T5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)
def test_with_token_classification_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_with_token_classification_head(*config_and_inputs)
def use_task_specific_params(model, task):
model.config.update(model.config.task_specific_params[task])