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Add TokenClassification for Mistral, Mixtral and Qwen2 (#29878)

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* Add MistralForTokenClassification

* Add tests and docs

* Add token classification for Mixtral and Qwen2

* Save llma for token classification draft

* Add token classification support for Llama, Gemma, Persimmon, StableLm and StarCoder2

* Formatting

* Add token classification support for Qwen2Moe model

* Add dropout layer to each ForTokenClassification model

* Add copied from in tests

* Update src/transformers/models/llama/modeling_llama.py

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Propagate suggested changes

* Style

---------

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
This commit is contained in:
Joseph Enguehard
2024-05-20 09:06:57 +01:00
committed by GitHub
parent 481a957814
commit 07bf2dff78
39 changed files with 1174 additions and 19 deletions
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31
tests/models/gemma/test_modeling_gemma.py
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@@ -41,7 +41,13 @@ from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GemmaForCausalLM, GemmaForSequenceClassification, GemmaModel, GemmaTokenizer
from transformers import (
GemmaForCausalLM,
GemmaForSequenceClassification,
GemmaForTokenClassification,
GemmaModel,
GemmaTokenizer,
)
class GemmaModelTester:
@@ -284,12 +290,17 @@ class GemmaModelTester:
@require_torch
class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GemmaModel, GemmaForCausalLM, GemmaForSequenceClassification) if is_torch_available() else ()
all_model_classes = (
(GemmaModel, GemmaForCausalLM, GemmaForSequenceClassification, GemmaForTokenClassification)
if is_torch_available()
else ()
)
all_generative_model_classes = (GemmaForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": GemmaModel,
"text-classification": GemmaForSequenceClassification,
"token-classification": GemmaForTokenClassification,
"text-generation": GemmaForCausalLM,
"zero-shot": GemmaForSequenceClassification,
}
@@ -370,6 +381,22 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_token_classification_model with Llama->Gemma,llama->Gemma
def test_Gemma_token_classification_model(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels)
model = GemmaForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=token_labels)
self.assertEqual(
result.logits.shape,
(self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels),
)
@unittest.skip("Gemma buffers include complex numbers, which breaks this test")
def test_save_load_fast_init_from_base(self):
pass