add ut

Signed-off-by: n1ck-guo <[email protected]>

tests/CI/test_kv_cache_pruning.py

@@ -0,0 +1,58 @@
+# Copyright (c) 2024 Intel Corporation
+#
+# 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 sys
+import unittest
+
+from transformers import AutoTokenizer
+from intel_extension_for_transformers.transformers.kv_cache_compression import (
+    H2OKVPruner,
+    H2OConfig,
+    LlamaForCausalLM
+)
+
+MODLE_NAME = "Maykeye/TinyLLama-v0"
+
+class TestH2O(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        pass
+
+    def test_h2o(self):
+        h2o_config = H2OConfig(
+            heavy_ratio=0.1,
+            recent_ratio=0.1,
+            h2o_min_seqlen=-1,
+            real_drop=True,
+            mean=False
+        )
+        model = LlamaForCausalLM.from_pretrained(MODLE_NAME, prune_config=h2o_config)
+        tokenizer = AutoTokenizer.from_pretrained(MODLE_NAME)
+        prompt_text = "In a small, bustling cafe nestled in the heart of a vibrant city,"
+        input = tokenizer(prompt_text, add_special_tokens=False, return_tensors='pt').input_ids.to(model.device)
+        generate_ids = model.generate(input, max_new_tokens=20)
+        result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+        h2o_config = H2OConfig(
+            heavy_ratio=0.1,
+            recent_ratio=0.1,
+            h2o_min_seqlen=-1,
+            real_drop=False,
+            mean=True
+        )
+        model = LlamaForCausalLM.from_pretrained(MODLE_NAME, prune_config=h2o_config)
+        output = model(input)
+
+if __name__ == "__main__":
+    unittest.main()