Fix problems found when running hugging face text summariztion model on large input.

evadb/executor/delete_executor.py

@@ -45,31 +45,31 @@ def predicate_node_to_filter_clause(
         left = predicate_node.get_child(0)
         right = predicate_node.get_child(1)
 
-        if type(left) == TupleValueExpression:
+        if isinstance(left, TupleValueExpression):
             column = left.name
             x = table.columns[column]
-        elif type(left) == ConstantValueExpression:
+        elif isinstance(left, ConstantValueExpression):
             value = left.value
             x = value
         else:
             left_filter_clause = self.predicate_node_to_filter_clause(table, left)
 
-        if type(right) == TupleValueExpression:
+        if isinstance(right, TupleValueExpression):
             column = right.name
             y = table.columns[column]
-        elif type(right) == ConstantValueExpression:
+        elif isinstance(right, ConstantValueExpression):
             value = right.value
             y = value
         else:
             right_filter_clause = self.predicate_node_to_filter_clause(table, right)
 
-        if type(predicate_node) == LogicalExpression:
+        if isinstance(predicate_node, LogicalExpression):
             if predicate_node.etype == ExpressionType.LOGICAL_AND:
                 filter_clause = and_(left_filter_clause, right_filter_clause)
             elif predicate_node.etype == ExpressionType.LOGICAL_OR:
                 filter_clause = or_(left_filter_clause, right_filter_clause)
 
-        elif type(predicate_node) == ComparisonExpression:
+        elif isinstance(predicate_node, ComparisonExpression):
             assert (
                 predicate_node.etype != ExpressionType.COMPARE_CONTAINS
                 and predicate_node.etype != ExpressionType.COMPARE_IS_CONTAINED

evadb/optimizer/rules/rules.py

@@ -872,6 +872,7 @@ def apply(self, before: LogicalGet, context: OptimizerContext):
         # read in a batch from storage engine.
         # Todo: Experiment heuristics.
         after = SeqScanPlan(None, before.target_list, before.alias)
+        batch_mem_size = context.db.config.get_value("executor", "batch_mem_size")
         after.append_child(
             StoragePlan(
                 before.table_obj,
@@ -880,6 +881,7 @@ def apply(self, before: LogicalGet, context: OptimizerContext):
                 sampling_rate=before.sampling_rate,
                 sampling_type=before.sampling_type,
                 chunk_params=before.chunk_params,
+                batch_mem_size=batch_mem_size,
             )
         )
         yield after

evadb/third_party/huggingface/model.py

@@ -33,6 +33,12 @@ class TextHFModel(AbstractHFUdf):
     Base Model for all HF Models that take in text as input
     """
 
+    def __call__(self, *args, **kwargs):
+        # Use truncation=True to handle the case where num of tokens is larger
+        # than limit
+        # Ref: https://stackoverflow.com/questions/66954682/token-indices-sequence-length-is-longer-than-the-specified-maximum-sequence-leng
+        return self.forward(args[0], truncation=True)
+
     def input_formatter(self, inputs: Any):
         return inputs.values.flatten().tolist()
 

test/optimizer/rules/test_batch_mem_size.py

@@ -0,0 +1,60 @@
+# coding=utf-8
+# Copyright 2018-2023 EvaDB
+#
+# 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 unittest
+from test.util import get_evadb_for_testing
+
+from mock import ANY, patch
+
+from evadb.server.command_handler import execute_query_fetch_all
+from evadb.storage.sqlite_storage_engine import SQLStorageEngine
+
+
+class BatchMemSizeTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.evadb = get_evadb_for_testing()
+        # reset the catalog manager before running each test
+        cls.evadb.catalog().reset()
+
+    @classmethod
+    def tearDownClass(cls):
+        execute_query_fetch_all(cls.evadb, "DROP TABLE IF EXISTS MyCSV;")
+
+    def test_batch_mem_size_for_sqlite_storage_engine(self):
+        """
+            This testcase make sure that the `batch_mem_size` is correctly passed to
+        the storage engine.
+        """
+        test_batch_mem_size = 100
+        self.evadb.config.update_value(
+            "executor", "batch_mem_size", test_batch_mem_size
+        )
+        create_table_query = """
+            CREATE TABLE IF NOT EXISTS MyCSV (
+                id INTEGER UNIQUE,
+                frame_id INTEGER,
+                video_id INTEGER,
+                dataset_name TEXT(30),
+                label TEXT(30),
+                bbox NDARRAY FLOAT32(4),
+                object_id INTEGER
+            );"""
+        execute_query_fetch_all(self.evadb, create_table_query)
+
+        select_table_query = "SELECT * FROM MyCSV;"
+        with patch.object(SQLStorageEngine, "read") as mock_read:
+            mock_read.__iter__.return_value = []
+            execute_query_fetch_all(self.evadb, select_table_query)
+            mock_read.assert_called_with(ANY, test_batch_mem_size)

test/udfs/test_hugging_face.py

@@ -0,0 +1,47 @@
+# coding=utf-8
+# Copyright 2018-2023 EvaDB
+#
+# 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 unittest
+
+import pandas as pd
+from mock import MagicMock
+
+from evadb.third_party.huggingface.model import TextHFModel
+
+
+class TestTextHFModel(TextHFModel):
+    @property
+    def default_pipeline_args(self) -> dict:
+        # We need to improve the hugging face interface, passing
+        # UdfCatalogEntry into UDF is not ideal.
+        return {
+            "task": "summarization",
+            "model": "sshleifer/distilbart-cnn-12-6",
+            "min_length": 5,
+            "max_length": 100,
+        }
+
+
+class HuggingFaceTest(unittest.TestCase):
+    def test_hugging_face_with_large_input(self):
+        udf_obj = MagicMock()
+        udf_obj.metadata = []
+        text_summarization_model = TestTextHFModel(udf_obj)
+
+        large_text = pd.DataFrame([{"text": "hello" * 4096}])
+        try:
+            text_summarization_model(large_text)
+        except IndexError:
+            self.fail("hugging face with large input raised IndexError.")