fix: num_return_sequences should be less than num_beams, not top_k (#5280)

* formatting

* remove top_k variable

* add pytest

* add numbers

* string formatting

* fix formatting

* revert

* extend tests with assertions for num_return_sequences

---------

Co-authored-by: Julian Risch <julian.risch@deepset.ai>

haystack/nodes/prompt/invocation_layer/hugging_face.py

@@ -196,7 +196,6 @@ class HFLocalInvocationLayer(PromptModelInvocationLayer):
         """
         output: List[Dict[str, str]] = []
         stop_words = kwargs.pop("stop_words", None)
-        top_k = kwargs.pop("top_k", None)
         # either stream is True (will use default handler) or stream_handler is provided for custom handler
         stream = kwargs.get("stream", self.stream)
         stream_handler = kwargs.get("stream_handler", self.stream_handler)
@@ -241,12 +240,21 @@ class HFLocalInvocationLayer(PromptModelInvocationLayer):
             if stop_words:
                 sw = StopWordsCriteria(tokenizer=self.pipe.tokenizer, stop_words=stop_words, device=self.pipe.device)
                 model_input_kwargs["stopping_criteria"] = StoppingCriteriaList([sw])
-            if top_k:
-                model_input_kwargs["num_return_sequences"] = top_k
-                if "num_beams" not in model_input_kwargs or model_input_kwargs["num_beams"] < top_k:
-                    if "num_beams" in model_input_kwargs:
-                        logger.warning("num_beams should not be less than top_k, hence setting it to %s", top_k)
-                    model_input_kwargs["num_beams"] = top_k
+
+            if "num_beams" in model_input_kwargs:
+                num_beams = model_input_kwargs["num_beams"]
+                if (
+                    "num_return_sequences" in model_input_kwargs
+                    and model_input_kwargs["num_return_sequences"] > num_beams
+                ):
+                    num_return_sequences = model_input_kwargs["num_return_sequences"]
+                    logger.warning(
+                        "num_return_sequences %s should not be larger than num_beams %s, hence setting it equal to num_beams",
+                        num_return_sequences,
+                        num_beams,
+                    )
+                    model_input_kwargs["num_return_sequences"] = num_beams
+
             # max_new_tokens is used for text-generation and max_length for text2text-generation
             if is_text_generation:
                 model_input_kwargs["max_new_tokens"] = model_input_kwargs.pop("max_length", self.max_length)

test/prompt/invocation_layer/test_hugging_face.py

@@ -204,6 +204,48 @@ def test_ensure_token_limit_negative(caplog):
     assert caplog.records[0].message == expected_message
 
 
+@pytest.mark.unit
+def test_num_return_sequences_no_larger_than_num_beams(mock_pipeline, mock_get_task, caplog):
+    """
+    Test that num_return_sequences cannot be larger than num_beams and that a warning is logged
+    """
+
+    layer = HFLocalInvocationLayer("google/flan-t5-base")
+
+    with patch.object(layer.pipe, "run_single", MagicMock()):
+        layer.invoke(prompt="What does 42 mean?", generation_kwargs={"num_beams": 5, "num_return_sequences": 8})
+
+    expected_message = (
+        "num_return_sequences 8 should not be larger than num_beams 5, hence setting it equal to num_beams"
+    )
+    # check that the warning is logged
+    assert caplog.records[0].message == expected_message
+
+    # check that num_return_sequences is set to num_beams
+    _, kwargs = layer.pipe.call_args
+    assert kwargs["num_beams"] == 5
+    assert kwargs["num_return_sequences"] == 5
+
+
+@pytest.mark.unit
+def test_num_beams_larger_than_num_return_sequences(mock_pipeline, mock_get_task, caplog):
+    """
+    Test that num_beams can be larger than num_return_sequences and that no warning is logged
+    """
+    layer = HFLocalInvocationLayer("google/flan-t5-base")
+
+    with patch.object(layer.pipe, "run_single", MagicMock()):
+        layer.invoke(prompt="What does 42 mean?", generation_kwargs={"num_beams": 8, "num_return_sequences": 5})
+
+    # check that no warning is logged
+    assert not caplog.records
+
+    # check that num_return_sequences remains unchanged
+    _, kwargs = layer.pipe.call_args
+    assert kwargs["num_beams"] == 8
+    assert kwargs["num_return_sequences"] == 5
+
+
 @pytest.mark.unit
 def test_constructor_with_custom_pretrained_model(mock_pipeline, mock_get_task):
     """