Enabling `Tapex` in table question answering pipeline. (#16663)

* Enabling `Tapex` in table question answering pipeline.

* Questions are independant for Tapex, making the test respect that.

* Missing extra space.

src/transformers/pipelines/table_question_answering.py

@@ -105,9 +105,10 @@ class TableQuestionAnsweringPipeline(Pipeline):
             else MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING
         )
 
-        self.aggregate = bool(getattr(self.model.config, "aggregation_labels")) and bool(
-            getattr(self.model.config, "num_aggregation_labels")
+        self.aggregate = bool(getattr(self.model.config, "aggregation_labels", None)) and bool(
+            getattr(self.model.config, "num_aggregation_labels", None)
         )
+        self.type = "tapas" if hasattr(self.model.config, "aggregation_labels") else None
 
     def batch_inference(self, **inputs):
         return self.model(**inputs)
@@ -335,7 +336,13 @@ class TableQuestionAnsweringPipeline(Pipeline):
             forward_params["sequential"] = sequential
         return preprocess_params, forward_params, {}
 
-    def preprocess(self, pipeline_input, sequential=None, padding=True, truncation="drop_rows_to_fit"):
+    def preprocess(self, pipeline_input, sequential=None, padding=True, truncation=None):
+        if truncation is None:
+            if self.type == "tapas":
+                truncation = "drop_rows_to_fit"
+            else:
+                truncation = "do_not_truncate"
+
         table, query = pipeline_input["table"], pipeline_input["query"]
         if table.empty:
             raise ValueError("table is empty")
@@ -347,7 +354,14 @@ class TableQuestionAnsweringPipeline(Pipeline):
 
     def _forward(self, model_inputs, sequential=False):
         table = model_inputs.pop("table")
-        outputs = self.sequential_inference(**model_inputs) if sequential else self.batch_inference(**model_inputs)
+
+        if self.type == "tapas":
+            if sequential:
+                outputs = self.sequential_inference(**model_inputs)
+            else:
+                outputs = self.batch_inference(**model_inputs)
+        else:
+            outputs = self.model.generate(**model_inputs)
         model_outputs = {"model_inputs": model_inputs, "table": table, "outputs": outputs}
         return model_outputs
 
@@ -355,37 +369,40 @@ class TableQuestionAnsweringPipeline(Pipeline):
         inputs = model_outputs["model_inputs"]
         table = model_outputs["table"]
         outputs = model_outputs["outputs"]
-        if self.aggregate:
-            logits, logits_agg = outputs[:2]
-            predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits, logits_agg)
-            answer_coordinates_batch, agg_predictions = predictions
-            aggregators = {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(agg_predictions)}
-
-            no_agg_label_index = self.model.config.no_aggregation_label_index
-            aggregators_prefix = {
-                i: aggregators[i] + " > " for i, pred in enumerate(agg_predictions) if pred != no_agg_label_index
-            }
+        if self.type == "tapas":
+            if self.aggregate:
+                logits, logits_agg = outputs[:2]
+                predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits, logits_agg)
+                answer_coordinates_batch, agg_predictions = predictions
+                aggregators = {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(agg_predictions)}
+
+                no_agg_label_index = self.model.config.no_aggregation_label_index
+                aggregators_prefix = {
+                    i: aggregators[i] + " > " for i, pred in enumerate(agg_predictions) if pred != no_agg_label_index
+                }
+            else:
+                logits = outputs[0]
+                predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits)
+                answer_coordinates_batch = predictions[0]
+                aggregators = {}
+                aggregators_prefix = {}
+            answers = []
+            for index, coordinates in enumerate(answer_coordinates_batch):
+                cells = [table.iat[coordinate] for coordinate in coordinates]
+                aggregator = aggregators.get(index, "")
+                aggregator_prefix = aggregators_prefix.get(index, "")
+                answer = {
+                    "answer": aggregator_prefix + ", ".join(cells),
+                    "coordinates": coordinates,
+                    "cells": [table.iat[coordinate] for coordinate in coordinates],
+                }
+                if aggregator:
+                    answer["aggregator"] = aggregator
+
+                answers.append(answer)
+            if len(answer) == 0:
+                raise PipelineException("Empty answer")
         else:
-            logits = outputs[0]
-            predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits)
-            answer_coordinates_batch = predictions[0]
-            aggregators = {}
-            aggregators_prefix = {}
-
-        answers = []
-        for index, coordinates in enumerate(answer_coordinates_batch):
-            cells = [table.iat[coordinate] for coordinate in coordinates]
-            aggregator = aggregators.get(index, "")
-            aggregator_prefix = aggregators_prefix.get(index, "")
-            answer = {
-                "answer": aggregator_prefix + ", ".join(cells),
-                "coordinates": coordinates,
-                "cells": [table.iat[coordinate] for coordinate in coordinates],
-            }
-            if aggregator:
-                answer["aggregator"] = aggregator
-
-            answers.append(answer)
-        if len(answer) == 0:
-            raise PipelineException("Empty answer")
+            answers = [{"answer": answer} for answer in self.tokenizer.batch_decode(outputs, skip_special_tokens=True)]
+
         return answers if len(answers) > 1 else answers[0]

tests/pipelines/test_pipelines_table_question_answering.py

@@ -632,3 +632,31 @@ class TQAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
             {"answer": "28 november 1967", "coordinates": [(2, 3)], "cells": ["28 november 1967"]},
         ]
         self.assertListEqual(results, expected_results)
+
+    @slow
+    @require_torch
+    def test_large_model_pt_tapex(self):
+        model_id = "microsoft/tapex-large-finetuned-wtq"
+        table_querier = pipeline(
+            "table-question-answering",
+            model=model_id,
+        )
+        data = {
+            "Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"],
+            "Age": ["56", "45", "59"],
+            "Number of movies": ["87", "53", "69"],
+            "Date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
+        }
+        queries = [
+            "How many movies has George Clooney played in?",
+            "How old is Mr Clooney ?",
+            "What's the date of birth of Leonardo ?",
+        ]
+        results = table_querier(data, queries, sequential=True)
+
+        expected_results = [
+            {"answer": " 69"},
+            {"answer": " 59"},
+            {"answer": " 10 june 1996"},
+        ]
+        self.assertListEqual(results, expected_results)