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tests/models/tapas/test_modeling_tapas.py
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Ilyas Moutawwakil
[PoC] HF exporters (#41992)
02 июл 2026, 21:11
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02 июл 2026, 21:11
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# Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import unittest from functools import cached_property import numpy as np import pandas as pd from transformers import ( MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TapasConfig, is_torch_available, ) from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasTokenizer, ) from transformers.models.tapas.modeling_tapas import ( IndexMap, ProductIndexMap, flatten, gather, range_index_map, reduce_max, reduce_mean, reduce_sum, ) class TapasModelTester: """You can also import this e.g from .test_modeling_tapas import TapasModelTester""" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, max_position_embeddings=512, type_vocab_sizes=[3, 256, 256, 2, 256, 256, 10], type_sequence_label_size=2, positive_weight=10.0, num_aggregation_labels=4, num_labels=2, aggregation_loss_importance=0.8, use_answer_as_supervision=True, answer_loss_importance=0.001, use_normalized_answer_loss=False, huber_loss_delta=25.0, temperature=1.0, agg_temperature=1.0, use_gumbel_for_cells=False, use_gumbel_for_agg=False, average_approximation_function="ratio", cell_selection_preference=0.5, answer_loss_cutoff=100, max_num_rows=64, max_num_columns=32, average_logits_per_cell=True, select_one_column=True, allow_empty_column_selection=False, init_cell_selection_weights_to_zero=True, reset_position_index_per_cell=True, disable_per_token_loss=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.max_position_embeddings = max_position_embeddings self.type_vocab_sizes = type_vocab_sizes self.type_sequence_label_size = type_sequence_label_size self.positive_weight = positive_weight self.num_aggregation_labels = num_aggregation_labels self.num_labels = num_labels self.aggregation_loss_importance = aggregation_loss_importance self.use_answer_as_supervision = use_answer_as_supervision self.answer_loss_importance = answer_loss_importance self.use_normalized_answer_loss = use_normalized_answer_loss self.huber_loss_delta = huber_loss_delta self.temperature = temperature self.agg_temperature = agg_temperature self.use_gumbel_for_cells = use_gumbel_for_cells self.use_gumbel_for_agg = use_gumbel_for_agg self.average_approximation_function = average_approximation_function self.cell_selection_preference = cell_selection_preference self.answer_loss_cutoff = answer_loss_cutoff self.max_num_rows = max_num_rows self.max_num_columns = max_num_columns self.average_logits_per_cell = average_logits_per_cell self.select_one_column = select_one_column self.allow_empty_column_selection = allow_empty_column_selection self.init_cell_selection_weights_to_zero = init_cell_selection_weights_to_zero self.reset_position_index_per_cell = reset_position_index_per_cell self.disable_per_token_loss = disable_per_token_loss self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).to(torch_device) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]).to(torch_device) token_type_ids = [] for type_vocab_size in self.type_vocab_sizes: token_type_ids.append(ids_tensor(shape=[self.batch_size, self.seq_length], vocab_size=type_vocab_size)) token_type_ids = torch.stack(token_type_ids, dim=2).to(torch_device) sequence_labels = None token_labels = None labels = None numeric_values = None numeric_values_scale = None float_answer = None aggregation_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size).to(torch_device) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels).to(torch_device) labels = ids_tensor([self.batch_size, self.seq_length], vocab_size=2).to(torch_device) numeric_values = floats_tensor([self.batch_size, self.seq_length]).to(torch_device) numeric_values_scale = floats_tensor([self.batch_size, self.seq_length]).to(torch_device) float_answer = floats_tensor([self.batch_size]).to(torch_device) aggregation_labels = ids_tensor([self.batch_size], self.num_aggregation_labels).to(torch_device) config = self.get_config() return ( config, input_ids, input_mask, token_type_ids, sequence_labels, token_labels, labels, numeric_values, numeric_values_scale, float_answer, aggregation_labels, ) def get_config(self): return TapasConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_sizes=self.type_vocab_sizes, initializer_range=self.initializer_range, positive_weight=self.positive_weight, num_aggregation_labels=self.num_aggregation_labels, num_labels=self.num_labels, aggregation_loss_importance=self.aggregation_loss_importance, use_answer_as_supervision=self.use_answer_as_supervision, answer_loss_importance=self.answer_loss_importance, use_normalized_answer_loss=self.use_normalized_answer_loss, huber_loss_delta=self.huber_loss_delta, temperature=self.temperature, agg_temperature=self.agg_temperature, use_gumbel_for_cells=self.use_gumbel_for_cells, use_gumbel_for_agg=self.use_gumbel_for_agg, average_approximation_function=self.average_approximation_function, cell_selection_preference=self.cell_selection_preference, answer_loss_cutoff=self.answer_loss_cutoff, max_num_rows=self.max_num_rows, max_num_columns=self.max_num_columns, average_logits_per_cell=self.average_logits_per_cell, select_one_column=self.select_one_column, allow_empty_column_selection=self.allow_empty_column_selection, init_cell_selection_weights_to_zero=self.init_cell_selection_weights_to_zero, reset_position_index_per_cell=self.reset_position_index_per_cell, disable_per_token_loss=self.disable_per_token_loss, ) def create_and_check_model( self, config, input_ids, input_mask, token_type_ids, sequence_labels, token_labels, labels, numeric_values, numeric_values_scale, float_answer, aggregation_labels, ): model = TapasModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, input_mask, token_type_ids, sequence_labels, token_labels, labels, numeric_values, numeric_values_scale, float_answer, aggregation_labels, ): model = TapasForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_question_answering( self, config, input_ids, input_mask, token_type_ids, sequence_labels, token_labels, labels, numeric_values, numeric_values_scale, float_answer, aggregation_labels, ): # inference: without aggregation head (SQA). Model only returns logits sqa_config = copy.copy(config) sqa_config.num_aggregation_labels = 0 sqa_config.use_answer_as_supervision = False model = TapasForQuestionAnswering(config=sqa_config) model.to(torch_device) model.eval() result = model( input_ids=input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) # inference: with aggregation head (WTQ, WikiSQL-supervised). Model returns logits and aggregation logits model = TapasForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids=input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels)) # training: can happen in 3 main ways # case 1: conversational (SQA) model = TapasForQuestionAnswering(config=sqa_config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=labels, ) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) # case 2: weak supervision for aggregation (WTQ) model = TapasForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids=input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale, float_answer=float_answer, ) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels)) # case 3: strong supervision for aggregation (WikiSQL-supervised) wikisql_config = copy.copy(config) wikisql_config.use_answer_as_supervision = False model = TapasForQuestionAnswering(config=wikisql_config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=labels, aggregation_labels=aggregation_labels, ) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.logits_aggregation.shape, (self.batch_size, self.num_aggregation_labels)) def create_and_check_for_sequence_classification( self, config, input_ids, input_mask, token_type_ids, sequence_labels, token_labels, labels, numeric_values, numeric_values_scale, float_answer, aggregation_labels, ): config.num_labels = self.num_labels model = TapasForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, token_type_ids, sequence_labels, token_labels, labels, numeric_values, numeric_values_scale, float_answer, aggregation_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class TapasModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TapasModel, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, ) if is_torch_available() else None ) pipeline_model_mapping = ( { "feature-extraction": TapasModel, "fill-mask": TapasForMaskedLM, "table-question-answering": TapasForQuestionAnswering, "text-classification": TapasForSequenceClassification, "zero-shot": TapasForSequenceClassification, } if is_torch_available() else {} ) test_resize_embeddings = True test_torch_exportable = False # data-dependent aggregation logic def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() if isinstance(v, torch.Tensor) and v.ndim > 1 else v for k, v in inputs_dict.items() } if return_labels: if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device) elif model_class in get_values(MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["aggregation_labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["numeric_values"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.float, device=torch_device, ) inputs_dict["numeric_values_scale"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.float, device=torch_device, ) inputs_dict["float_answer"] = torch.zeros( self.model_tester.batch_size, dtype=torch.float, device=torch_device ) elif model_class in [ *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING), ]: inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class in [ *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING), *get_values(MODEL_FOR_CAUSAL_LM_MAPPING), *get_values(MODEL_FOR_MASKED_LM_MAPPING), *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING), ]: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): return True def setUp(self): self.model_tester = TapasModelTester(self) self.config_tester = ConfigTester(self, config_class=TapasConfig, dim=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def prepare_tapas_single_inputs_for_inference(): # Here we prepare a single table-question pair to test TAPAS inference on: data = { "Footballer": ["Lionel Messi", "Cristiano Ronaldo"], "Age": ["33", "35"], } queries = "Which footballer is 33 years old?" table = pd.DataFrame.from_dict(data) return table, queries def prepare_tapas_batch_inputs_for_inference(): # Here we prepare a batch of 2 table-question pairs to test TAPAS inference on: data = { "Footballer": ["Lionel Messi", "Cristiano Ronaldo"], "Age": ["33", "35"], "Number of goals": ["712", "750"], } queries = ["Which footballer is 33 years old?", "How many goals does Ronaldo have?"] table = pd.DataFrame.from_dict(data) return table, queries def prepare_tapas_batch_inputs_for_training(): # Here we prepare a DIFFERENT batch of 2 table-question pairs to test TAPAS training on: data = { "Footballer": ["Lionel Messi", "Cristiano Ronaldo"], "Age": ["33", "35"], "Number of goals": ["712", "750"], } queries = ["Which footballer is 33 years old?", "What's the total number of goals?"] table = pd.DataFrame.from_dict(data) answer_coordinates = [[(0, 0)], [(0, 2), (1, 2)]] answer_text = [["Lionel Messi"], ["1462"]] float_answer = [float("NaN"), float("1462")] return table, queries, answer_coordinates, answer_text, float_answer @require_torch class TapasModelIntegrationTest(unittest.TestCase): @cached_property def default_tokenizer(self): return TapasTokenizer.from_pretrained("google/tapas-base-finetuned-wtq") @slow def test_inference_no_head(self): model = TapasModel.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device) tokenizer = self.default_tokenizer table, queries = prepare_tapas_single_inputs_for_inference() inputs = tokenizer(table=table, queries=queries, return_tensors="pt") inputs = {k: v.to(torch_device) for k, v in inputs.items()} with torch.no_grad(): outputs = model(**inputs) # test the sequence output expected_slice = torch.tensor( [ [ [-0.141581565, -0.599805772, 0.747186482], [-0.143664181, -0.602008104, 0.749218345], [-0.15169853, -0.603363097, 0.741370678], ] ], device=torch_device, ) torch.testing.assert_close(outputs.last_hidden_state[:, :3, :3], expected_slice, rtol=0.0005, atol=0.0005) # test the pooled output expected_slice = torch.tensor([[0.987518311, -0.970520139, -0.994303405]], device=torch_device) torch.testing.assert_close(outputs.pooler_output[:, :3], expected_slice, rtol=0.0005, atol=0.0005) @unittest.skip(reason="Model not available yet") def test_inference_masked_lm(self): pass # TapasForQuestionAnswering has 3 possible ways of being fine-tuned: # - conversational set-up (SQA) # - weak supervision for aggregation (WTQ, WikiSQL) # - strong supervision for aggregation (WikiSQL-supervised) # We test all of them: @slow def test_inference_question_answering_head_conversational(self): # note that google/tapas-base-finetuned-sqa should correspond to tapas_sqa_inter_masklm_base_reset model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-sqa").to(torch_device) tokenizer = self.default_tokenizer table, queries = prepare_tapas_single_inputs_for_inference() inputs = tokenizer(table=table, queries=queries, return_tensors="pt") inputs = {k: v.to(torch_device) for k, v in inputs.items()} with torch.no_grad(): outputs = model(**inputs) # test the logits logits = outputs.logits expected_shape = torch.Size((1, 21)) self.assertEqual(logits.shape, expected_shape) expected_tensor = torch.tensor( [ [ -9997.22461, -9997.22461, -9997.22461, -9997.22461, -9997.22461, -9997.22461, -9997.22461, -9997.22461, -9997.22461, -16.2628059, -10004.082, 15.4330549, 15.4330549, 15.4330549, -9990.42, -16.3270779, -16.3270779, -16.3270779, -16.3270779, -16.3270779, -10004.8506, ] ], device=torch_device, ) torch.testing.assert_close(logits, expected_tensor, rtol=0.015, atol=0.015) @slow def test_inference_question_answering_head_conversational_absolute_embeddings(self): # note that google/tapas-small-finetuned-sqa should correspond to tapas_sqa_inter_masklm_small_reset # however here we test the version with absolute position embeddings model = TapasForQuestionAnswering.from_pretrained("google/tapas-small-finetuned-sqa", revision="no_reset").to( torch_device ) tokenizer = self.default_tokenizer table, queries = prepare_tapas_single_inputs_for_inference() inputs = tokenizer(table=table, queries=queries, return_tensors="pt") inputs = {k: v.to(torch_device) for k, v in inputs.items()} with torch.no_grad(): outputs = model(**inputs) # test the logits logits = outputs.logits expected_shape = torch.Size((1, 21)) self.assertEqual(logits.shape, expected_shape) expected_tensor = torch.tensor( [ [ -10014.7793, -10014.7793, -10014.7793, -10014.7793, -10014.7793, -10014.7793, -10014.7793, -10014.7793, -10014.7793, -18.8419304, -10018.0391, 17.7848816, 17.7848816, 17.7848816, -9981.02832, -16.4005489, -16.4005489, -16.4005489, -16.4005489, -16.4005489, -10013.4736, ] ], device=torch_device, ) torch.testing.assert_close(logits, expected_tensor, rtol=0.01, atol=0.01) @slow def test_inference_question_answering_head_weak_supervision(self): # note that google/tapas-base-finetuned-wtq should correspond to tapas_wtq_wikisql_sqa_inter_masklm_base_reset model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device) tokenizer = self.default_tokenizer # let's test on a batch table, queries = prepare_tapas_batch_inputs_for_inference() inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt") inputs_on_device = {k: v.to(torch_device) for k, v in inputs.items()} with torch.no_grad(): outputs = model(**inputs_on_device) # test the logits logits = outputs.logits expected_shape = torch.Size((2, 28)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor( [ [-160.375504, -160.375504, -160.375504, -10072.3965, -10070.9414, -10094.9736], [-9861.6123, -9861.6123, -9861.6123, -9861.6123, -9891.01172, 146.600677], ], device=torch_device, ) torch.testing.assert_close(logits[:, -6:], expected_slice, rtol=0.4, atol=0.4) # test the aggregation logits logits_aggregation = outputs.logits_aggregation expected_shape = torch.Size((2, 4)) self.assertEqual(logits_aggregation.shape, expected_shape) expected_tensor = torch.tensor( [[18.8545208, -9.76614857, -6.3128891, -2.93525243], [-4.05782509, 40.0351, -5.35329962, 23.3978653]], device=torch_device, ) torch.testing.assert_close(logits_aggregation, expected_tensor, rtol=0.001, atol=0.001) # test the predicted answer coordinates and aggregation indices EXPECTED_PREDICTED_ANSWER_COORDINATES = [[(0, 0)], [(1, 2)]] EXPECTED_PREDICTED_AGGREGATION_INDICES = [0, 1] predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions( inputs, outputs.logits.detach().cpu(), outputs.logits_aggregation.detach().cpu() ) self.assertEqual(EXPECTED_PREDICTED_ANSWER_COORDINATES, predicted_answer_coordinates) self.assertEqual(EXPECTED_PREDICTED_AGGREGATION_INDICES, predicted_aggregation_indices) @slow def test_training_question_answering_head_weak_supervision(self): # note that google/tapas-base-finetuned-wtq should correspond to tapas_wtq_wikisql_sqa_inter_masklm_base_reset model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq").to(torch_device) model.to(torch_device) tokenizer = self.default_tokenizer # let's test on a batch table, queries, answer_coordinates, answer_text, float_answer = prepare_tapas_batch_inputs_for_training() inputs = tokenizer( table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding="longest", return_tensors="pt", ) # prepare data (created by the tokenizer) and move to torch_device input_ids = inputs["input_ids"].to(torch_device) attention_mask = inputs["attention_mask"].to(torch_device) token_type_ids = inputs["token_type_ids"].to(torch_device) labels = inputs["labels"].to(torch_device) numeric_values = inputs["numeric_values"].to(torch_device) numeric_values_scale = inputs["numeric_values_scale"].to(torch_device) # the answer should be prepared by the user float_answer = torch.FloatTensor(float_answer).to(torch_device) # forward pass to get loss + logits: with torch.no_grad(): outputs = model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale, float_answer=float_answer, ) # test the loss loss = outputs.loss expected_loss = torch.tensor(3.3527612686157227e-08, device=torch_device) torch.testing.assert_close(loss, expected_loss, rtol=1e-6, atol=1e-6) # test the logits on the first example logits = outputs.logits expected_shape = torch.Size((2, 29)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor( [ -160.0156, -160.0156, -160.0156, -160.0156, -160.0156, -10072.2266, -10070.8896, -10092.6006, -10092.6006, ], device=torch_device, ) torch.testing.assert_close(logits[0, -9:], expected_slice, rtol=1e-6, atol=1e-6) # test the aggregation logits on the second example logits_aggregation = outputs.logits_aggregation expected_shape = torch.Size((2, 4)) self.assertEqual(logits_aggregation.shape, expected_shape) expected_slice = torch.tensor([-4.0538, 40.0304, -5.3554, 23.3965], device=torch_device) torch.testing.assert_close(logits_aggregation[1, -4:], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_question_answering_head_strong_supervision(self): # note that google/tapas-base-finetuned-wikisql-supervised should correspond to tapas_wikisql_sqa_inter_masklm_base_reset model = TapasForQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wikisql-supervised").to( torch_device ) tokenizer = self.default_tokenizer table, queries = prepare_tapas_single_inputs_for_inference() inputs = tokenizer(table=table, queries=queries, return_tensors="pt") inputs = {k: v.to(torch_device) for k, v in inputs.items()} with torch.no_grad(): outputs = model(**inputs) # test the logits logits = outputs.logits expected_shape = torch.Size((1, 21)) self.assertEqual(logits.shape, expected_shape) expected_tensor = torch.tensor( [ [ -10011.1084, -10011.1084, -10011.1084, -10011.1084, -10011.1084, -10011.1084, -10011.1084, -10011.1084, -10011.1084, -18.6185989, -10008.7969, 17.6355762, 17.6355762, 17.6355762, -10002.4404, -18.7111301, -18.7111301, -18.7111301, -18.7111301, -18.7111301, -10007.0977, ] ], device=torch_device, ) torch.testing.assert_close(logits, expected_tensor, rtol=0.02, atol=0.02) # test the aggregation logits logits_aggregation = outputs.logits_aggregation expected_shape = torch.Size((1, 4)) self.assertEqual(logits_aggregation.shape, expected_shape) expected_tensor = torch.tensor( [[16.5659733, -3.06624889, -2.34152961, -0.970244825]], device=torch_device ) # PyTorch model outputs [[16.5679, -3.0668, -2.3442, -0.9674]] torch.testing.assert_close(logits_aggregation, expected_tensor, rtol=0.003, atol=0.003) @slow def test_inference_classification_head(self): # note that google/tapas-base-finetuned-tabfact should correspond to tapas_tabfact_inter_masklm_base_reset model = TapasForSequenceClassification.from_pretrained("google/tapas-base-finetuned-tabfact").to(torch_device) tokenizer = self.default_tokenizer table, queries = prepare_tapas_single_inputs_for_inference() inputs = tokenizer(table=table, queries=queries, padding="longest", return_tensors="pt") inputs = {k: v.to(torch_device) for k, v in inputs.items()} with torch.no_grad(): outputs = model(**inputs) # test the classification logits logits = outputs.logits expected_shape = torch.Size((1, 2)) self.assertEqual(logits.shape, expected_shape) expected_tensor = torch.tensor( [[0.795137286, 9.5572]], device=torch_device ) # Note that the PyTorch model outputs [[0.8057, 9.5281]] torch.testing.assert_close(outputs.logits, expected_tensor, rtol=0.05, atol=0.05) @require_torch class TapasUtilitiesTest(unittest.TestCase): def _prepare_tables(self): """Prepares two tables, both with three distinct rows. The first table has two columns: 1.0, 2.0 | 3.0 2.0, 0.0 | 1.0 1.0, 3.0 | 4.0 The second table has three columns: 1.0 | 2.0 | 3.0 2.0 | 0.0 | 1.0 1.0 | 3.0 | 4.0 Returns: SegmentedTensors with the tables. """ values = torch.tensor( [ [[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]], [[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]], ] ) row_index = IndexMap( indices=torch.tensor( [ [[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 0, 0], [1, 1, 1], [2, 2, 2]], ] ), num_segments=3, batch_dims=1, ) col_index = IndexMap( indices=torch.tensor( [ [[0, 0, 1], [0, 0, 1], [0, 0, 1]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]], ] ), num_segments=3, batch_dims=1, ) return values, row_index, col_index def test_product_index(self): _, row_index, col_index = self._prepare_tables() cell_index = ProductIndexMap(row_index, col_index) row_index_proj = cell_index.project_outer(cell_index) col_index_proj = cell_index.project_inner(cell_index) ind = cell_index.indices self.assertEqual(cell_index.num_segments, 9) # Projections should give back the original indices. np.testing.assert_array_equal(row_index.indices.numpy(), row_index_proj.indices.numpy()) self.assertEqual(row_index.num_segments, row_index_proj.num_segments) self.assertEqual(row_index.batch_dims, row_index_proj.batch_dims) np.testing.assert_array_equal(col_index.indices.numpy(), col_index_proj.indices.numpy()) self.assertEqual(col_index.batch_dims, col_index_proj.batch_dims) # The first and second "column" are identified in the first table. for i in range(3): self.assertEqual(ind[0, i, 0], ind[0, i, 1]) self.assertNotEqual(ind[0, i, 0], ind[0, i, 2]) # All rows are distinct in the first table. for i, i_2 in zip(range(3), range(3)): for j, j_2 in zip(range(3), range(3)): if i != i_2 and j != j_2: self.assertNotEqual(ind[0, i, j], ind[0, i_2, j_2]) # All cells are distinct in the second table. for i, i_2 in zip(range(3), range(3)): for j, j_2 in zip(range(3), range(3)): if i != i_2 or j != j_2: self.assertNotEqual(ind[1, i, j], ind[1, i_2, j_2]) def test_flatten(self): _, row_index, col_index = self._prepare_tables() row_index_flat = flatten(row_index) col_index_flat = flatten(col_index) shape = [3, 4, 5] batched_index = IndexMap(indices=torch.zeros(shape).type(torch.LongTensor), num_segments=1, batch_dims=3) batched_index_flat = flatten(batched_index) np.testing.assert_array_equal( row_index_flat.indices.numpy(), [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5] ) np.testing.assert_array_equal( col_index_flat.indices.numpy(), [0, 0, 1, 0, 0, 1, 0, 0, 1, 3, 4, 5, 3, 4, 5, 3, 4, 5] ) self.assertEqual(batched_index_flat.num_segments.numpy(), np.prod(shape)) np.testing.assert_array_equal(batched_index_flat.indices.numpy(), range(np.prod(shape))) def test_range_index_map(self): batch_shape = [3, 4] num_segments = 5 index = range_index_map(batch_shape, num_segments) self.assertEqual(num_segments, index.num_segments) self.assertEqual(2, index.batch_dims) indices = index.indices np.testing.assert_array_equal(list(indices.size()), [3, 4, 5]) for i in range(batch_shape[0]): for j in range(batch_shape[1]): np.testing.assert_array_equal(indices[i, j, :].numpy(), range(num_segments)) def test_reduce_sum(self): values, row_index, col_index = self._prepare_tables() cell_index = ProductIndexMap(row_index, col_index) row_sum, _ = reduce_sum(values, row_index) col_sum, _ = reduce_sum(values, col_index) cell_sum, _ = reduce_sum(values, cell_index) np.testing.assert_allclose(row_sum.numpy(), [[6.0, 3.0, 8.0], [6.0, 3.0, 8.0]]) np.testing.assert_allclose(col_sum.numpy(), [[9.0, 8.0, 0.0], [4.0, 5.0, 8.0]]) np.testing.assert_allclose( cell_sum.numpy(), [[3.0, 3.0, 0.0, 2.0, 1.0, 0.0, 4.0, 4.0, 0.0], [1.0, 2.0, 3.0, 2.0, 0.0, 1.0, 1.0, 3.0, 4.0]], ) def test_reduce_mean(self): values, row_index, col_index = self._prepare_tables() cell_index = ProductIndexMap(row_index, col_index) row_mean, _ = reduce_mean(values, row_index) col_mean, _ = reduce_mean(values, col_index) cell_mean, _ = reduce_mean(values, cell_index) np.testing.assert_allclose( row_mean.numpy(), [[6.0 / 3.0, 3.0 / 3.0, 8.0 / 3.0], [6.0 / 3.0, 3.0 / 3.0, 8.0 / 3.0]] ) np.testing.assert_allclose(col_mean.numpy(), [[9.0 / 6.0, 8.0 / 3.0, 0.0], [4.0 / 3.0, 5.0 / 3.0, 8.0 / 3.0]]) np.testing.assert_allclose( cell_mean.numpy(), [ [3.0 / 2.0, 3.0, 0.0, 2.0 / 2.0, 1.0, 0.0, 4.0 / 2.0, 4.0, 0.0], [1.0, 2.0, 3.0, 2.0, 0.0, 1.0, 1.0, 3.0, 4.0], ], ) def test_reduce_max(self): values = torch.as_tensor([2.0, 1.0, 0.0, 3.0]) index = IndexMap(indices=torch.as_tensor([0, 1, 0, 1]), num_segments=2) maximum, _ = reduce_max(values, index) np.testing.assert_array_equal(maximum.numpy(), [2, 3]) def test_reduce_sum_vectorized(self): values = torch.as_tensor([[1.0, 2.0, 3.0], [2.0, 3.0, 4.0], [3.0, 4.0, 5.0]]) index = IndexMap(indices=torch.as_tensor([[0, 0, 1]]), num_segments=2, batch_dims=0) sums, new_index = reduce_sum(values, index) np.testing.assert_allclose(sums.numpy(), [3.0, 3.0]) np.testing.assert_array_equal(new_index.indices.numpy(), [0, 1]) np.testing.assert_array_equal(new_index.num_segments.numpy(), 2) np.testing.assert_array_equal(new_index.batch_dims, 0) def test_gather(self): values, row_index, col_index = self._prepare_tables() cell_index = ProductIndexMap(row_index, col_index) # Compute sums and then gather. The result should have the same shape as # the original table and each element should contain the sum the values in # its cell. sums, _ = reduce_sum(values, cell_index) cell_sum = gather(sums, cell_index) assert cell_sum.size() == values.size() np.testing.assert_allclose( cell_sum.numpy(), [[[3.0, 3.0, 3.0], [2.0, 2.0, 1.0], [4.0, 4.0, 4.0]], [[1.0, 2.0, 3.0], [2.0, 0.0, 1.0], [1.0, 3.0, 4.0]]], ) def test_gather_vectorized(self): values = torch.as_tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) index = IndexMap(indices=torch.as_tensor([[0, 1], [1, 0]]), num_segments=2, batch_dims=1) result = gather(values, index) np.testing.assert_array_equal(result.numpy(), [[[1, 2], [3, 4]], [[7, 8], [5, 6]]])