correct the tests

tests/models/chatglm/test_modeling_chatglm.py

@@ -24,7 +24,6 @@
     require_bitsandbytes,
     require_flash_attn,
     require_torch,
-    require_torch_accelerator,
     require_torch_gpu,
     slow,
     torch_device,
@@ -43,11 +42,11 @@
         ChatGlmForCausalLM,
         ChatGlmForSequenceClassification,
         ChatGlmModel,
-        CodeLlamaTokenizer,
         LlamaTokenizer,
     )
 
 
+# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester with Llama->ChatGlm
 class ChatGlmModelTester:
     def __init__(
         self,
@@ -275,11 +274,22 @@ def prepare_config_and_inputs_for_common(self):
 
 
 @require_torch
+# Copied from tests.models.llama.test_modeling_llama.LlamaModelTest with Llama->ChatGlm
 class ChatGlmModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
         (ChatGlmModel, ChatGlmForCausalLM, ChatGlmForSequenceClassification) if is_torch_available() else ()
     )
     all_generative_model_classes = (ChatGlmForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ChatGlmModel,
+            "text-classification": ChatGlmForSequenceClassification,
+            "text-generation": ChatGlmForCausalLM,
+            "zero-shot": ChatGlmForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
     test_headmasking = False
     test_pruning = False
 
@@ -300,7 +310,7 @@ def test_model_various_embeddings(self):
             config_and_inputs[0].position_embedding_type = type
             self.model_tester.create_and_check_model(*config_and_inputs)
 
-    def test_chatglm_sequence_classification_model(self):
+    def test_llama_sequence_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"]
@@ -312,7 +322,7 @@ def test_chatglm_sequence_classification_model(self):
         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_chatglm_sequence_classification_model_for_single_label(self):
+    def test_llama_sequence_classification_model_for_single_label(self):
         config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
         config.num_labels = 3
         config.problem_type = "single_label_classification"
@@ -325,7 +335,7 @@ def test_chatglm_sequence_classification_model_for_single_label(self):
         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_chatglm_sequence_classification_model_for_multi_label(self):
+    def test_llama_sequence_classification_model_for_multi_label(self):
         config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
         config.num_labels = 3
         config.problem_type = "multi_label_classification"
@@ -385,12 +395,12 @@ def test_flash_attn_2_generate_padding_right(self):
         Overwritting the common test as the test is flaky on tiny models
         """
         model = ChatGlmForCausalLM.from_pretrained(
-            "meta-chatglm/ChatGlm-2-7b-hf",
+            "meta-llama/ChatGlm-2-7b-hf",
             load_in_4bit=True,
             device_map={"": 0},
         )
 
-        tokenizer = LlamaTokenizer.from_pretrained("meta-chatglm/ChatGlm-2-7b-hf")
+        tokenizer = ChatGlmTokenizer.from_pretrained("meta-llama/ChatGlm-2-7b-hf")
 
         texts = ["hi", "Hello this is a very long sentence"]
 
@@ -403,7 +413,7 @@ def test_flash_attn_2_generate_padding_right(self):
         output_native = tokenizer.batch_decode(output_native)
 
         model = ChatGlmForCausalLM.from_pretrained(
-            "meta-chatglm/ChatGlm-2-7b-hf", load_in_4bit=True, device_map={"": 0}, use_flash_attention_2=True
+            "meta-llama/ChatGlm-2-7b-hf", load_in_4bit=True, device_map={"": 0}, use_flash_attention_2=True
         )
 
         output_fa_2 = model.generate(**inputs, max_new_tokens=20, do_sample=False)