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transformers/tests/models/xmod/test_modeling_xmod.py

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# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 unittest
from transformers import XLMRobertaTokenizer, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
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 (
XmodConfig,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
)
from transformers.models.xmod.modeling_xmod import XmodEmbeddings, create_position_ids_from_input_ids
class XmodModelTester:
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,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
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.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return XmodConfig(
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_size=self.type_vocab_size,
initializer_range=self.initializer_range,
default_language="en_XX",
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = XmodModel(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_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = XmodModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_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_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = XmodForCausalLM(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_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = XmodForCausalLM(config=config).to(torch_device).eval()
# make sure that ids don't start with pad token
mask = input_ids.ne(config.pad_token_id).long()
input_ids = input_ids * mask
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
# make sure that ids don't start with pad token
mask = next_tokens.ne(config.pad_token_id).long()
next_tokens = next_tokens * mask
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = XmodForMaskedLM(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_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = XmodForTokenClassification(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.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = XmodForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = XmodForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_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 XmodModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
XmodForCausalLM,
XmodForMaskedLM,
XmodModel,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodForMultipleChoice,
XmodForQuestionAnswering,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (XmodForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": XmodModel,
"fill-mask": XmodForMaskedLM,
"question-answering": XmodForQuestionAnswering,
"text-classification": XmodForSequenceClassification,
"text-generation": XmodForCausalLM,
"token-classification": XmodForTokenClassification,
"zero-shot": XmodForSequenceClassification,
}
if is_torch_available()
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
def setUp(self):
self.model_tester = XmodModelTester(self)
self.config_tester = ConfigTester(self, config_class=XmodConfig, hidden_size=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_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_past_with_large_inputs_relative_pos_emb(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
config_and_inputs[0].position_embedding_type = "relative_key"
self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*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_create_position_ids_respects_padding_index(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is XmodEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
model = XmodEmbeddings(config=config)
input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
expected_positions = torch.as_tensor(
[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
)
position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
def test_create_position_ids_from_inputs_embeds(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is XmodEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
embeddings = XmodEmbeddings(config=config)
inputs_embeds = torch.empty(2, 4, 30)
expected_single_positions = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
def test_set_default_language(self):
config = self.model_tester.prepare_config_and_inputs()[0]
model = XmodForMaskedLM(config=config)
model.set_default_language("en_XX")
self.assertEqual(model.config.default_language, "en_XX")
with self.assertRaises(ValueError):
model.set_default_language("xx_XX")
def test_freeze_embeddings_and_language_adapters(self):
config = self.model_tester.prepare_config_and_inputs()[0]
model = XmodForMaskedLM(config=config)
num_trainable_params_before = sum(p.numel() for p in model.parameters() if p.requires_grad)
model.freeze_embeddings_and_language_adapters()
num_trainable_params_after = sum(p.numel() for p in model.parameters() if p.requires_grad)
self.assertLess(num_trainable_params_after, num_trainable_params_before)
@require_sentencepiece
@require_tokenizers
@require_torch
class XmodModelIntegrationTest(unittest.TestCase):
@slow
def test_xmod_base(self):
model = XmodModel.from_pretrained("facebook/xmod-base")
# language en_XX
model.set_default_language("en_XX")
input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
expected_output_shape = torch.Size((1, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
expected_output_values_last_dim = torch.tensor(
[[-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724]]
)
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
# language de_DE
model.set_default_language("de_DE")
input_ids = torch.tensor([[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2315, 58761, 18391, 5, 2]])
# Der Hund ist niedlich und wohnt in einem Gartenhaus.
expected_output_shape = torch.Size((1, 16, 768)) # batch_size, sequence_length, embedding_vector_dim
# fmt: off
expected_output_values_last_dim = torch.tensor(
[[0.0162, 0.0075, -0.1882, 0.2335, -0.0952, -0.3994, -0.0317, -0.1174, 0.0177, 0.4280, -0.0240, -0.2138,
0.0785, -0.1045, -0.2811, -0.3220]]
)
# fmt: on
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
@slow
def test_xmod_large_prenorm(self):
model = XmodModel.from_pretrained("facebook/xmod-large-prenorm")
# language en_XX
model.set_default_language("en_XX")
input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
expected_output_shape = torch.Size((1, 12, 1024)) # batch_size, sequence_length, embedding_vector_dim
# fmt: off
expected_output_values_last_dim = torch.tensor(
[[-0.0121, -0.0194, -0.0240, -0.0160, -0.0205, -0.0159, -0.0243, -0.0206, -0.0161, -0.0335, -0.0196,
-0.0141]]
)
# fmt: on
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
# language de_DE
model.set_default_language("de_DE")
input_ids = torch.tensor([[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2315, 58761, 18391, 5, 2]])
# Der Hund ist niedlich und wohnt in einem Gartenhaus.
expected_output_shape = torch.Size((1, 16, 1024)) # batch_size, sequence_length, embedding_vector_dim
# fmt: off
expected_output_values_last_dim = torch.tensor(
[[-0.0120, -0.0262, -0.0253, -0.0112, -0.0128, -0.0164, -0.0080, -0.0081, -0.0192, -0.0117, -0.0170,
-0.0120, -0.0210, -0.0173, -0.0078, -0.0122]]
)
# fmt: on
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
@slow
def test_multilingual_batch(self):
model = XmodModel.from_pretrained("facebook/xmod-base")
# fmt: off
input_ids = torch.tensor([
[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2],
[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2],
[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2],
[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2],
])
# fmt: on
lang_ids = torch.LongTensor([0, 8, 8, 0])
expected_output_shape = torch.Size((4, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
# fmt: off
expected_output_values_last_dim = torch.tensor([
[-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724],
[-0.2668, -0.0235, -0.1739, 0.2266, -0.0901, -0.3482, 0.0105, -0.1915, 0.0397, 0.3822, 0.1836, -0.3407],
[-0.2668, -0.0235, -0.1739, 0.2266, -0.0901, -0.3482, 0.0105, -0.1915, 0.0397, 0.3822, 0.1836, -0.3407],
[-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724],
])
# fmt: on
output = model(input_ids, lang_ids=lang_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
@slow
def test_end_to_end_mask_fill(self):
tokenizer = XLMRobertaTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")
model = XmodForMaskedLM.from_pretrained("facebook/xmod-base", default_language="en_XX")
model.to(torch_device)
sentences = [
"Hello, my dog is a little <mask>.",
"Hi <mask>!",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
outputs = model(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
)
probs = outputs.logits.softmax(dim=-1)
_, predictions = probs.topk(1)
predictions = predictions.squeeze(-1)
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model(input_ids=inputs_non_padded)
probs_non_padded = output_non_padded.logits.softmax(dim=-1)
_, predictions_non_padded = probs_non_padded.topk(1)
predictions_non_padded = predictions_non_padded.squeeze(-1)
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model(input_ids=inputs_padded)
probs_padded = output_padded.logits.softmax(dim=-1)
_, predictions_padded = probs_padded.topk(1)
predictions_padded = predictions_padded.squeeze(-1)
batch_out_sentence = tokenizer.batch_decode(predictions, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(predictions_non_padded[0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(predictions_padded[0], skip_special_tokens=True)
expected_output_sentence = [
"Hello, my dog is a little girl.",
"Hi everyone!",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])
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