Reasoning_parserでマッチしなかった時にlm_outputがNoneではなく空文字列になるように変更

flexeval/core/language_model/hf_lm.py

@@ -436,13 +436,17 @@ def _batch_generate_chat_response(
                 if lm_output.raw_text is None:
                     lm_output.raw_text = lm_output.text
                 reasoning = self.reasoning_parser(lm_output.text)
-                lm_output.text = reasoning.text
+                # `reasoning.text` is None when the reasoning pattern fails to match.
+                # LMOutput.text must never be None (see LMOutput.__post_init__), so fall back to "".
+                lm_output.text = reasoning.text if reasoning.text is not None else ""
                 lm_output.reasoning_text = reasoning.reasoning_text
 
             if self.tool_parser and tools is not None:
                 parsed_tool_calling_message = self.tool_parser(lm_output.text)
                 lm_output.tool_calls = parsed_tool_calling_message.tool_call_dicts
-                lm_output.text = parsed_tool_calling_message.text
+                lm_output.text = (
+                    parsed_tool_calling_message.text if parsed_tool_calling_message.text is not None else ""
+                )
                 if lm_output.raw_text is None:
                     lm_output.raw_text = parsed_tool_calling_message.raw_text
                 lm_output.tool_call_validation_result = parsed_tool_calling_message.validation_result

flexeval/core/language_model/vllm_model.py

@@ -284,13 +284,17 @@ def _batch_generate_chat_response(
                 if lm_output.raw_text is None:
                     lm_output.raw_text = lm_output.text
                 reasoning = self.reasoning_parser(lm_output.text)
-                lm_output.text = reasoning.text
+                # `reasoning.text` is None when the reasoning pattern fails to match.
+                # LMOutput.text must never be None (see LMOutput.__post_init__), so fall back to "".
+                lm_output.text = reasoning.text if reasoning.text is not None else ""
                 lm_output.reasoning_text = reasoning.reasoning_text
 
             if self.tool_parser and tools is not None:
                 parsed_tool_calling_message = self.tool_parser(lm_output.text)
                 lm_output.tool_calls = parsed_tool_calling_message.tool_call_dicts
-                lm_output.text = parsed_tool_calling_message.text
+                lm_output.text = (
+                    parsed_tool_calling_message.text if parsed_tool_calling_message.text is not None else ""
+                )
                 if lm_output.raw_text is None:
                     lm_output.raw_text = parsed_tool_calling_message.raw_text
                 lm_output.tool_call_validation_result = parsed_tool_calling_message.validation_result

tests/core/language_model/test_hf_lm.py

@@ -565,7 +565,7 @@ def test_reasoning_parser_handles_batch_chat_response(chat_lm: HuggingFaceLM) ->
         chat_lm.reasoning_parser = original_parser
 
 
-def test_reasoning_parser_unmatched_pattern_sets_none(chat_lm: HuggingFaceLM) -> None:
+def test_reasoning_parser_unmatched_pattern_sets_empty_text(chat_lm: HuggingFaceLM) -> None:
     reasoning_parser = UnifiedRegexReasoningParser(pattern=r"<think>(?P<reasoning_content>.*?)</think>(?P<content>.*)")
     raw_output = "no think tags here"
     original_parser = chat_lm.reasoning_parser
@@ -576,7 +576,28 @@ def test_reasoning_parser_unmatched_pattern_sets_none(chat_lm: HuggingFaceLM) ->
         ):
             response = chat_lm.generate_chat_response([{"role": "user", "content": "test"}], max_new_tokens=1)
         assert response.raw_text == raw_output
-        assert response.text is None
+        # LMOutput.text must never be None (see LMOutput.__post_init__), so a failed match falls back to "".
+        assert response.text == ""
+        assert response.reasoning_text is None
+    finally:
+        chat_lm.reasoning_parser = original_parser
+
+
+def test_reasoning_parser_unclosed_think_tag_sets_empty_text(chat_lm: HuggingFaceLM) -> None:
+    reasoning_parser = UnifiedRegexReasoningParser(pattern=r"<think>(?P<reasoning_content>.*?)</think>(?P<content>.*)")
+    # Simulates generation truncated (e.g. by max_new_tokens) before the closing </think> tag.
+    raw_output = "<think>reasoning that got cut off"
+    original_parser = chat_lm.reasoning_parser
+    chat_lm.reasoning_parser = reasoning_parser
+    try:
+        with patch.object(
+            chat_lm, "_batch_complete_text", return_value=[LMOutput(text=raw_output, finish_reason="length")]
+        ):
+            response = chat_lm.generate_chat_response([{"role": "user", "content": "test"}], max_new_tokens=1)
+        assert response.raw_text == raw_output
+        # The pattern requires a closing </think>, so an unclosed tag fails to match just like no tags at all,
+        # and the unfinished reasoning must not leak into `text` (see LMOutput.__post_init__ for the None -> "" rule).
+        assert response.text == ""
         assert response.reasoning_text is None
     finally:
         chat_lm.reasoning_parser = original_parser

tests/core/language_model/vllm/test_vllm_specific.py

@@ -314,6 +314,46 @@ def test_reasoning_parser_handles_batch_chat_response(chat_lm: VLLM) -> None:
         chat_lm.reasoning_parser = original_parser
 
 
+@pytest.mark.skipif(not is_vllm_enabled(), reason="vllm library is not installed")
+def test_reasoning_parser_unmatched_pattern_sets_empty_text(chat_lm: VLLM) -> None:
+    reasoning_parser = UnifiedRegexReasoningParser(pattern=r"<think>(?P<reasoning_content>.*?)</think>(?P<content>.*)")
+    raw_output = "no think tags here"
+    original_parser = chat_lm.reasoning_parser
+    chat_lm.reasoning_parser = reasoning_parser
+    try:
+        with patch.object(
+            chat_lm, "_batch_complete_text", return_value=[LMOutput(text=raw_output, finish_reason="stop")]
+        ):
+            response = chat_lm.generate_chat_response([{"role": "user", "content": "test"}], max_new_tokens=1)
+        assert response.raw_text == raw_output
+        # LMOutput.text must never be None (see LMOutput.__post_init__), so a failed match falls back to "".
+        assert response.text == ""
+        assert response.reasoning_text is None
+    finally:
+        chat_lm.reasoning_parser = original_parser
+
+
+@pytest.mark.skipif(not is_vllm_enabled(), reason="vllm library is not installed")
+def test_reasoning_parser_unclosed_think_tag_sets_empty_text(chat_lm: VLLM) -> None:
+    reasoning_parser = UnifiedRegexReasoningParser(pattern=r"<think>(?P<reasoning_content>.*?)</think>(?P<content>.*)")
+    # Simulates generation truncated (e.g. by max_new_tokens) before the closing </think> tag.
+    raw_output = "<think>reasoning that got cut off"
+    original_parser = chat_lm.reasoning_parser
+    chat_lm.reasoning_parser = reasoning_parser
+    try:
+        with patch.object(
+            chat_lm, "_batch_complete_text", return_value=[LMOutput(text=raw_output, finish_reason="length")]
+        ):
+            response = chat_lm.generate_chat_response([{"role": "user", "content": "test"}], max_new_tokens=1)
+        assert response.raw_text == raw_output
+        # The pattern requires a closing </think>, so an unclosed tag fails to match just like no tags at all,
+        # and the unfinished reasoning must not leak into `text` (see LMOutput.__post_init__ for the None -> "" rule).
+        assert response.text == ""
+        assert response.reasoning_text is None
+    finally:
+        chat_lm.reasoning_parser = original_parser
+
+
 @pytest.mark.skipif(not is_vllm_enabled(), reason="vllm library is not installed")
 def test_no_reasoning_parser_leaves_chat_output_unchanged(chat_lm: VLLM) -> None:
     assert chat_lm.reasoning_parser is None