first commit

tests/models/hunyuan_v1_moe/test_modeling_hunyuan_v1_moe.py

@@ -0,0 +1,105 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch HunYuanMoEV1 model."""
+
+import unittest
+
+import pytest
+import torch
+
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    from transformers import AutoTokenizer, HunYuanMoEV1ForCausalLM, HunYuanMoEV1Model
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+class HunYuanMoEV1ModelTester(CausalLMModelTester):
+    if is_torch_available():
+        base_model_class = HunYuanMoEV1Model
+
+
+@require_torch
+class HunYuanMoEV1ModelTest(CausalLMModelTest, unittest.TestCase):
+    test_all_params_have_gradient = False
+    model_tester_class = HunYuanMoEV1ModelTester
+
+    def is_pipeline_test_to_skip(
+        self,
+        pipeline_test_case_name,
+        config_class,
+        model_architecture,
+        tokenizer_name,
+        image_processor_name,
+        feature_extractor_name,
+        processor_name,
+    ):
+        return True
+
+    @unittest.skip("Hunyuan model Unsupported")
+    @pytest.mark.torch_compile_test
+    def test_generate_compilation_all_outputs(self):
+        pass
+
+    @unittest.skip("Hunyuan model Unsupported")
+    @pytest.mark.torch_compile_test
+    def test_generate_compile_model_forward(self):
+        pass
+
+    @unittest.skip("Hunyuan model Unsupported")
+    def test_generate_from_inputs_embeds_with_static_cache(self):
+        pass
+
+    @unittest.skip("Hunyuan model Unsupported")
+    def test_generate_with_static_cache(self):
+        pass
+
+
+@require_torch
+class HunYuanMoEV1IntegrationTest(unittest.TestCase):
+    def setUp(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_model_generation(self):
+        EXPECTED_ANSWER = "\nOkay, I need to write a"
+        prompt = "Write a short summary of the benefits of regular exercise"
+        tokenizer = AutoTokenizer.from_pretrained("tencent/Hunyuan-A13B-Instruct")
+        model = HunYuanMoEV1ForCausalLM.from_pretrained(
+            "tencent/Hunyuan-A13B-Instruct", device_map="auto", dtype=torch.bfloat16
+        )
+        messages = [
+            {"role": "user", "content": prompt},
+        ]
+        tokenized_chat = tokenizer.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_tensors="pt",
+        ).to(model.device)
+        generated_ids = model.generate(**tokenized_chat, max_new_tokens=10, top_k=1)
+        text = tokenizer.decode(generated_ids[0])
+        output = text.split("<think>")[1]
+        self.assertEqual(EXPECTED_ANSWER, output)