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transformers/tests/models/hunyuan_v1_moe/test_modeling_hunyuan_v1_moe.py

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+# 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
+from parameterized import parameterized
+
+from transformers import HunYuanMoEV1Config, is_torch_available
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    from transformers import (
+        AutoModelForCausalLM,
+        AutoTokenizer,
+        HunYuanMoEV1ForCausalLM,
+        HunYuanMoEV1ForSequenceClassification,
+        HunYuanMoEV1Model,
+    )
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+class HunYuanMoEV1ModelTester(CausalLMModelTester):
+    config_class = HunYuanMoEV1Config
+    if is_torch_available():
+        base_model_class = HunYuanMoEV1Model
+        causal_lm_class = HunYuanMoEV1ForCausalLM
+        sequence_class = HunYuanMoEV1ForSequenceClassification
+
+
+@require_torch
+class HunYuanMoEV1ModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (
+            HunYuanMoEV1Model,
+            HunYuanMoEV1ForCausalLM,
+            HunYuanMoEV1ForSequenceClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    test_headmasking = False
+    test_pruning = False
+    test_all_params_have_gradient = False
+    model_tester_class = HunYuanMoEV1ModelTester
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": HunYuanMoEV1Model,
+            "text-generation": HunYuanMoEV1ForCausalLM,
+            "text-classification": HunYuanMoEV1ForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    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
+
+    @unittest.skip("HunYuanMoEV1's RoPE has custom parameterization")
+    def test_model_rope_scaling_frequencies(self):
+        pass
+
+    @parameterized.expand([("linear",), ("dynamic",), ("yarn",)])
+    @unittest.skip("HunYuanMoEV1's RoPE has custom parameterization")
+    def test_model_rope_scaling_from_config(self, scaling_type):
+        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):
+        # we will compele this when model file change over
+        # pass
+        EXPECTED_ANSWER = "\nOkay, I need to write a short summary about the benefits of regular exercise. Let me start by recalling what I know. First,"
+        prompt = "Write a short summary of the benefits of regular exercise"
+        tokenizer = AutoTokenizer.from_pretrained("tencent/Hunyuan-A13B-Instruct")
+        model = AutoModelForCausalLM.from_pretrained("tencent/Hunyuan-A13B-Instruct", device_map="auto")
+        messages = [
+            {"role": "user", "content": prompt},
+        ]
+        tokenized_chat = tokenizer.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_tensors="pt",
+        )
+        generated_ids = model.generate(tokenized_chat.to(model.device), max_new_tokens=30, top_k=1)
+        text = tokenizer.decode(generated_ids[0])
+        output = text.split("<think>")[1]
+        self.assertEqual(EXPECTED_ANSWER, output)