Enable dynamic parameter updates for the FARMReader (#650)

haystack/pipeline.py

@@ -62,6 +62,10 @@ class Pipeline:
                 input_edge_name = "output_1"
             self.graph.add_edge(input_node_name, name, label=input_edge_name)
 
+    def get_node(self, name: str):
+        component = self.graph.nodes[name]["component"]
+        return component
+
     def run(self, **kwargs):
         has_next_node = True
         current_node_id = self.root_node_id
@@ -120,11 +124,17 @@ class Pipeline:
 
 
 class BaseStandardPipeline:
+    pipeline: Pipeline
+
     def add_node(self, component, name: str, inputs: List[str]):
-        self.pipeline.add_node(component=component, name=name, inputs=inputs)  # type: ignore
+        self.pipeline.add_node(component=component, name=name, inputs=inputs)
+
+    def get_node(self, name: str):
+        component = self.pipeline.get_node(name)
+        return component
 
     def draw(self, path: Path = Path("pipeline.png")):
-        self.pipeline.draw(path)  # type: ignore
+        self.pipeline.draw(path)
 
 
 class ExtractiveQAPipeline(BaseStandardPipeline):

haystack/reader/farm.py

@@ -44,7 +44,8 @@ class FARMReader(BaseReader):
         context_window_size: int = 150,
         batch_size: int = 50,
         use_gpu: bool = True,
-        no_ans_boost: Optional[float] = None,
+        no_ans_boost: float = 0.0,
+        return_no_answer: bool = False,
         top_k_per_candidate: int = 3,
         top_k_per_sample: int = 1,
         num_processes: Optional[int] = None,
@@ -63,9 +64,10 @@ class FARMReader(BaseReader):
                            to a value so only a single batch is used.
         :param use_gpu: Whether to use GPU (if available)
         :param no_ans_boost: How much the no_answer logit is boosted/increased.
-        If set to None (default), disables returning "no answer" predictions.
+        If set to 0 (default), the no_answer logit is not changed.
         If a negative number, there is a lower chance of "no_answer" being predicted.
         If a positive number, there is an increased chance of "no_answer"
+        :param return_no_answer: Whether to include no_answer predictions in the results.
         :param top_k_per_candidate: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
         Note that this is not the number of "final answers" you will receive
         (see `top_k` in FARMReader.predict() or Finder.get_answers() for that)
@@ -85,11 +87,7 @@ class FARMReader(BaseReader):
 
         """
 
-        if no_ans_boost is None:
+        self.return_no_answers = return_no_answer
-            no_ans_boost = 0
-            self.return_no_answers = False
-        else:
-            self.return_no_answers = True
         self.top_k_per_candidate = top_k_per_candidate
         self.inferencer = QAInferencer.load(model_name_or_path, batch_size=batch_size, gpu=use_gpu,
                                           task_type="question_answering", max_seq_len=max_seq_len,
@@ -233,6 +231,29 @@ class FARMReader(BaseReader):
         self.inferencer.model = trainer.train()
         self.save(Path(save_dir))
 
+    def update_parameters(
+        self,
+        context_window_size: Optional[int] = None,
+        no_ans_boost: Optional[float] = None,
+        return_no_answer: Optional[bool] = None,
+        max_seq_len: Optional[int] = None,
+        doc_stride: Optional[int] = None,
+    ):
+        """
+        Hot update parameters of a loaded Reader. It may not to be safe when processing concurrent requests.
+        """
+        if no_ans_boost is not None:
+            self.inferencer.model.prediction_heads[0].no_ans_boost = no_ans_boost
+        if return_no_answer is not None:
+            self.return_no_answers = return_no_answer
+        if doc_stride is not None:
+            self.inferencer.processor.doc_stride = doc_stride
+        if context_window_size is not None:
+            self.inferencer.model.prediction_heads[0].context_window_size = context_window_size
+        if max_seq_len is not None:
+            self.inferencer.processor.max_seq_len = max_seq_len
+            self.max_seq_len = max_seq_len
+
     def save(self, directory: Path):
         """
         Saves the Reader model so that it can be reused at a later point in time.

test/conftest.py

@@ -199,6 +199,7 @@ def no_answer_reader(request):
             use_gpu=False,
             top_k_per_sample=5,
             no_ans_boost=0,
+            return_no_answer=True,
             num_processes=0
         )
     if request.param == "transformers":

test/test_reader.py

@@ -115,3 +115,51 @@ def test_top_k(reader, test_docs_xs, top_k):
         reader.inferencer.model.prediction_heads[0].n_best_per_sample = old_top_k_per_sample
     except:
         print("WARNING: Could not set `top_k_per_sample` in FARM. Please update FARM version.")
+
+
+def test_farm_reader_update_params(test_docs_xs):
+    reader = FARMReader(
+        model_name_or_path="deepset/roberta-base-squad2",
+        use_gpu=False,
+        no_ans_boost=0,
+        num_processes=0
+    )
+
+    docs = [Document.from_dict(d) if isinstance(d, dict) else d for d in test_docs_xs]
+
+    # original reader
+    prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=3)
+    assert len(prediction["answers"]) == 3
+    assert prediction["answers"][0]["answer"] == "Carla"
+
+    # update no_ans_boost
+    reader.update_parameters(
+        context_window_size=100, no_ans_boost=100, return_no_answer=True, max_seq_len=384, doc_stride=128
+    )
+    prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=3)
+    assert len(prediction["answers"]) == 3
+    assert prediction["answers"][0]["answer"] is None
+
+    # update no_ans_boost
+    reader.update_parameters(
+        context_window_size=100, no_ans_boost=0, return_no_answer=False, max_seq_len=384, doc_stride=128
+    )
+    prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=3)
+    assert len(prediction["answers"]) == 3
+    assert None not in [ans["answer"] for ans in prediction["answers"]]
+
+    # update context_window_size
+    reader.update_parameters(context_window_size=6, no_ans_boost=-10, max_seq_len=384, doc_stride=128)
+    prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=3)
+    assert len(prediction["answers"]) == 3
+    assert len(prediction["answers"][0]["context"]) == 6
+
+    # update doc_stride with invalid value
+    with pytest.raises(Exception):
+        reader.update_parameters(context_window_size=100, no_ans_boost=-10, max_seq_len=384, doc_stride=999)
+        reader.predict(query="Who lives in Berlin?", documents=docs, top_k=3)
+
+    # update max_seq_len with invalid value
+    with pytest.raises(Exception):
+        reader.update_parameters(context_window_size=6, no_ans_boost=-10, max_seq_len=99, doc_stride=128)
+        reader.predict(query="Who lives in Berlin?", documents=docs, top_k=3)