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

* 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>

tests/models/llama/test_modeling_llama.py

@@ -47,6 +47,7 @@ if is_torch_available():
         LlamaForCausalLM,
         LlamaForQuestionAnswering,
         LlamaForSequenceClassification,
+        LlamaForTokenClassification,
         LlamaModel,
         LlamaTokenizer,
     )
@@ -286,7 +287,13 @@ class LlamaModelTester:
 @require_torch
 class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification, LlamaForQuestionAnswering)
+        (
+            LlamaModel,
+            LlamaForCausalLM,
+            LlamaForSequenceClassification,
+            LlamaForQuestionAnswering,
+            LlamaForTokenClassification,
+        )
         if is_torch_available()
         else ()
     )
@@ -298,6 +305,7 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
             "text-generation": LlamaForCausalLM,
             "zero-shot": LlamaForSequenceClassification,
             "question-answering": LlamaForQuestionAnswering,
+            "token-classification": LlamaForTokenClassification,
         }
         if is_torch_available()
         else {}
@@ -370,6 +378,21 @@ class LlamaModelTest(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))
 
+    def test_llama_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 = LlamaForTokenClassification(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("Llama buffers include complex numbers, which breaks this test")
     def test_save_load_fast_init_from_base(self):
         pass