Add INC model loading test (#348)

Author: echarlaix

tests/neural_compressor/test_modeling.py

@@ -0,0 +1,68 @@
+#  Copyright 2021 The HuggingFace 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.
+
+
+import os
+import unittest
+
+from parameterized import parameterized
+from transformers import set_seed
+
+from optimum.exporters import TasksManager
+from optimum.intel import (  # noqa
+    INCConfig,
+    INCModelForCausalLM,
+    INCModelForMaskedLM,
+    INCModelForQuestionAnswering,
+    INCModelForSeq2SeqLM,
+    INCModelForSequenceClassification,
+    INCModelForTokenClassification,
+    INCQuantizer,
+    INCSeq2SeqTrainer,
+    INCStableDiffusionPipeline,
+    INCTrainer,
+)
+from optimum.intel.neural_compressor.utils import _HEAD_TO_AUTOMODELS
+
+
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+set_seed(1009)
+
+
+MODEL_NAMES_TO_TASK = (
+    ("echarlaix/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic", "text-classification"),
+    ("echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1", "text-classification"),
+    ("hf-internal-testing/tiny-random-bert", "fill-mask"),
+    ("Intel/bert-base-uncased-squad-int8-static", "question-answering"),
+    ("hf-internal-testing/tiny-random-gpt2", "text-generation"),
+    ("Intel/t5-small-xsum-int8-dynamic", "text2text-generation"),
+    # ("echarlaix/stable-diffusion-v1-5-inc-int8-dynamic", "stable-diffusion")
+)
+
+
+class INCModelingTest(unittest.TestCase):
+    @parameterized.expand(MODEL_NAMES_TO_TASK)
+    def test_modeling(self, model_id, task):
+        inc_model = eval(_HEAD_TO_AUTOMODELS[task]).from_pretrained(model_id)  # TRANSFORMERS_AUTO_CLASS
+        model_type = inc_model.config.model_type.replace("_", "-")
+        config_class = TasksManager.get_exporter_config_constructor(
+            exporter="onnx",
+            model=inc_model,
+            task=task,
+            model_name=model_id,
+            model_type=model_type,
+        )
+        config = config_class(inc_model.config)
+        model_inputs = config.generate_dummy_inputs(framework="pt")
+        inc_model(**model_inputs)