Enabling Tapex in table question answering pipeline. (huggingface#1…

…6663)

* 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 @@ def __init__(self, args_parser=TableQuestionAnsweringArgumentHandler(), *args, *
  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 @@ def _sanitize_parameters(self, sequential=None, padding=None, truncation=None, *
  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")
@@ -347,45 +354,55 @@ def preprocess(self, pipeline_input, sequential=None, padding=True, truncation="
 
  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
 
  def postprocess(self, model_outputs):
  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 @@ def test_integration_sqa_tf(self):
  {"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)