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added GPTNeoForTokenClassification (#22908)

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* added GPTNeoForTokenClassification

* add to top-level init

* fixup

* test

* more fixup

* add to gpt_neo.mdx

* repo consistency

* dummy copy

* fix copies

* optax >= 0.1.5 assumes jax.Array exists - which it doesn't for jax <= 0.3.6

* merge with main made this superfluous

* added classifier_dropout

* remove legacy code

* removed fmt:on/off
removed expected_outputs

* doc style fix

* classifier_dropout is always in config

---------

Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
This commit is contained in:
peter-sk
2023-04-27 18:10:03 +02:00
committed by GitHub
parent 614e191c4d
commit d65b14ed67
9 changed files with 129 additions and 2 deletions
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20
tests/models/gpt_neo/test_modeling_gpt_neo.py
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@@ -35,6 +35,7 @@ if is_torch_available():
GPT2Tokenizer,
GPTNeoForCausalLM,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
)
@@ -334,6 +335,16 @@ class GPTNeoModelTester:
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_gpt_neo_for_token_classification(
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
):
config.num_labels = self.num_labels
model = GPTNeoForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_forward_and_backwards(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False
):
@@ -374,13 +385,16 @@ class GPTNeoModelTester:
@require_torch
class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(GPTNeoModel, GPTNeoForCausalLM, GPTNeoForSequenceClassification) if is_torch_available() else ()
(GPTNeoModel, GPTNeoForCausalLM, GPTNeoForSequenceClassification, GPTNeoForTokenClassification)
if is_torch_available()
else ()
)
all_generative_model_classes = (GPTNeoForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": GPTNeoModel,
"text-classification": GPTNeoForSequenceClassification,
"token-classification": GPTNeoForTokenClassification,
"text-generation": GPTNeoForCausalLM,
"zero-shot": GPTNeoForSequenceClassification,
}
@@ -428,6 +442,10 @@ class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_for_sequence_classification(*config_and_inputs)
def test_gpt_neo_token_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_neo_for_token_classification(*config_and_inputs)
def test_gpt_neo_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)