1629 lines
80 KiB
Python
1629 lines
80 KiB
Python
# coding=utf-8
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# Copyright 2018 Google T5 Authors and HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import os
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import pickle
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import tempfile
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import unittest
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from transformers import T5Config, is_torch_available
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from transformers.models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
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from transformers.testing_utils import (
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require_accelerate,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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slow,
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torch_device,
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)
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from transformers.utils import cached_property, is_torch_fx_available
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_fx_available():
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from transformers.utils.fx import symbolic_trace
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if is_torch_available():
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import torch
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from transformers import (
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AutoTokenizer,
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ByT5Tokenizer,
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T5EncoderModel,
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T5ForConditionalGeneration,
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T5ForQuestionAnswering,
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T5ForSequenceClassification,
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T5ForTokenClassification,
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T5Model,
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T5Tokenizer,
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)
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class T5ModelTester:
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def __init__(
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self,
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parent,
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vocab_size=99,
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batch_size=13,
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encoder_seq_length=7,
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decoder_seq_length=7,
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# For common tests
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is_training=True,
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use_attention_mask=True,
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use_labels=True,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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d_ff=37,
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relative_attention_num_buckets=8,
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dropout_rate=0.1,
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initializer_factor=0.002,
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eos_token_id=1,
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pad_token_id=0,
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decoder_start_token_id=0,
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scope=None,
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decoder_layers=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.encoder_seq_length = encoder_seq_length
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self.decoder_seq_length = decoder_seq_length
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# For common tests
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self.seq_length = self.decoder_seq_length
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self.is_training = is_training
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self.use_attention_mask = use_attention_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.d_ff = d_ff
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.dropout_rate = dropout_rate
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self.initializer_factor = initializer_factor
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.decoder_start_token_id = decoder_start_token_id
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self.scope = None
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self.decoder_layers = decoder_layers
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def get_large_model_config(self):
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return T5Config.from_pretrained("google-t5/t5-base")
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size).clamp(2)
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input_ids[:, -1] = self.eos_token_id # Eos Token
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decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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attention_mask = None
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decoder_attention_mask = None
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if self.use_attention_mask:
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attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
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decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
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lm_labels = None
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if self.use_labels:
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lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
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config = self.get_config()
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return (
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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)
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def get_pipeline_config(self):
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return T5Config(
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vocab_size=166, # t5 forces 100 extra tokens
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d_model=self.hidden_size,
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d_ff=self.d_ff,
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d_kv=self.hidden_size // self.num_attention_heads,
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num_layers=self.num_hidden_layers,
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num_decoder_layers=self.decoder_layers,
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num_heads=self.num_attention_heads,
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relative_attention_num_buckets=self.relative_attention_num_buckets,
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dropout_rate=self.dropout_rate,
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initializer_factor=self.initializer_factor,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.pad_token_id,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.decoder_start_token_id,
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)
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def get_config(self):
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return T5Config(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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d_ff=self.d_ff,
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d_kv=self.hidden_size // self.num_attention_heads,
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num_layers=self.num_hidden_layers,
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num_decoder_layers=self.decoder_layers,
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num_heads=self.num_attention_heads,
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relative_attention_num_buckets=self.relative_attention_num_buckets,
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dropout_rate=self.dropout_rate,
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initializer_factor=self.initializer_factor,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.pad_token_id,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.decoder_start_token_id,
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)
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def check_prepare_lm_labels_via_shift_left(
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self,
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = T5Model(config=config)
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model.to(torch_device)
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model.eval()
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# make sure that lm_labels are correctly padded from the right
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lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)
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# add casaul pad token mask
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triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
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lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
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decoder_input_ids = model._shift_right(lm_labels)
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for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
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# first item
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self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
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if i < decoder_input_ids_slice.shape[-1]:
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if i < decoder_input_ids.shape[-1] - 1:
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# items before diagonal
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self.parent.assertListEqual(
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decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
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)
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# pad items after diagonal
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if i < decoder_input_ids.shape[-1] - 2:
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self.parent.assertListEqual(
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decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
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)
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else:
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# all items after square
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self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
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def create_and_check_model(
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self,
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = T5Model(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids=input_ids,
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decoder_input_ids=decoder_input_ids,
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attention_mask=attention_mask,
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decoder_attention_mask=decoder_attention_mask,
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)
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result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
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decoder_output = result.last_hidden_state
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decoder_past = result.past_key_values
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encoder_output = result.encoder_last_hidden_state
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self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
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self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size))
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# There should be `num_layers` key value embeddings stored in decoder_past
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self.parent.assertEqual(len(decoder_past), config.num_layers)
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# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
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self.parent.assertEqual(len(decoder_past[0]), 4)
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def create_and_check_with_lm_head(
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self,
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
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outputs = model(
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input_ids=input_ids,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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labels=lm_labels,
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)
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self.parent.assertEqual(len(outputs), 4)
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self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
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self.parent.assertEqual(outputs["loss"].size(), ())
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def create_and_check_with_sequence_classification_head(
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self,
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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labels = torch.tensor([1] * self.batch_size, dtype=torch.long, device=torch_device)
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model = T5ForSequenceClassification(config=config).to(torch_device).eval()
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outputs = model(
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input_ids=input_ids,
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decoder_input_ids=input_ids,
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labels=labels,
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)
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# self.parent.assertEqual(len(outputs), 4)
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self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, config.num_labels))
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self.parent.assertEqual(outputs["loss"].size(), ())
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def create_and_check_decoder_model_past(
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self,
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = T5Model(config=config).get_decoder().to(torch_device).eval()
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# first forward pass
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outputs = model(input_ids, use_cache=True)
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outputs_use_cache_conf = model(input_ids)
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outputs_no_past = model(input_ids, use_cache=False)
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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output_from_no_past = model(next_input_ids)["last_hidden_state"]
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output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_decoder_model_attention_mask_past(
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self,
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = T5Model(config=config).get_decoder()
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model.to(torch_device)
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model.eval()
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# create attention mask
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attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
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half_seq_length = input_ids.shape[-1] // 2
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attn_mask[:, half_seq_length:] = 0
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# first forward pass
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output, past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True).to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# change a random masked slice from input_ids
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random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
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random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
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input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
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# append to next input_ids and attn_mask
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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attn_mask = torch.cat(
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[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
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dim=1,
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)
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# get two different outputs
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output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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decoder_input_ids,
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attention_mask,
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decoder_attention_mask,
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lm_labels,
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):
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model = T5Model(config=config).get_decoder().to(torch_device).eval()
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([attention_mask, next_mask], dim=-1)
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output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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||
def create_and_check_generate_with_past_key_values(
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||
self,
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||
config,
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||
input_ids,
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decoder_input_ids,
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||
attention_mask,
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||
decoder_attention_mask,
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||
lm_labels,
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):
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model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
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torch.manual_seed(0)
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||
output_without_past_cache = model.generate(
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input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
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)
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torch.manual_seed(0)
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output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
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self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
|
||
|
||
def create_and_check_model_fp16_forward(
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||
self,
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||
config,
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||
input_ids,
|
||
decoder_input_ids,
|
||
attention_mask,
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||
decoder_attention_mask,
|
||
lm_labels,
|
||
):
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||
model = T5Model(config=config).to(torch_device).half().eval()
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||
output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"]
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||
self.parent.assertFalse(torch.isnan(output).any().item())
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||
|
||
def create_and_check_encoder_decoder_shared_weights(
|
||
self,
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||
config,
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||
input_ids,
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||
decoder_input_ids,
|
||
attention_mask,
|
||
decoder_attention_mask,
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||
lm_labels,
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):
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||
for model_class in [T5Model, T5ForConditionalGeneration]:
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||
torch.manual_seed(0)
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||
model = model_class(config=config).to(torch_device).eval()
|
||
# load state dict copies weights but does not tie them
|
||
model.encoder.load_state_dict(model.decoder.state_dict(), strict=False)
|
||
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||
torch.manual_seed(0)
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||
tied_config = copy.deepcopy(config)
|
||
tied_config.tie_encoder_decoder = True
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||
tied_model = model_class(config=tied_config).to(torch_device).eval()
|
||
|
||
model_result = model(
|
||
input_ids=input_ids,
|
||
decoder_input_ids=decoder_input_ids,
|
||
attention_mask=attention_mask,
|
||
decoder_attention_mask=decoder_attention_mask,
|
||
)
|
||
|
||
tied_model_result = tied_model(
|
||
input_ids=input_ids,
|
||
decoder_input_ids=decoder_input_ids,
|
||
attention_mask=attention_mask,
|
||
decoder_attention_mask=decoder_attention_mask,
|
||
)
|
||
|
||
# check that models has less parameters
|
||
self.parent.assertLess(
|
||
sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
|
||
)
|
||
random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()
|
||
|
||
# check that outputs are equal
|
||
self.parent.assertTrue(
|
||
torch.allclose(
|
||
model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4
|
||
)
|
||
)
|
||
|
||
# check that outputs after saving and loading are equal
|
||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||
tied_model.save_pretrained(tmpdirname)
|
||
tied_model = model_class.from_pretrained(tmpdirname)
|
||
tied_model.to(torch_device)
|
||
tied_model.eval()
|
||
|
||
# check that models has less parameters
|
||
self.parent.assertLess(
|
||
sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
|
||
)
|
||
random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()
|
||
|
||
tied_model_result = tied_model(
|
||
input_ids=input_ids,
|
||
decoder_input_ids=decoder_input_ids,
|
||
attention_mask=attention_mask,
|
||
decoder_attention_mask=decoder_attention_mask,
|
||
)
|
||
|
||
# check that outputs are equal
|
||
self.parent.assertTrue(
|
||
torch.allclose(
|
||
model_result[0][0, :, random_slice_idx],
|
||
tied_model_result[0][0, :, random_slice_idx],
|
||
atol=1e-4,
|
||
)
|
||
)
|
||
|
||
def check_resize_embeddings_t5_v1_1(
|
||
self,
|
||
config,
|
||
):
|
||
prev_vocab_size = config.vocab_size
|
||
|
||
config.tie_word_embeddings = False
|
||
model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
|
||
model.resize_token_embeddings(prev_vocab_size - 10)
|
||
|
||
self.parent.assertEqual(model.get_input_embeddings().weight.shape[0], prev_vocab_size - 10)
|
||
self.parent.assertEqual(model.get_output_embeddings().weight.shape[0], prev_vocab_size - 10)
|
||
self.parent.assertEqual(model.config.vocab_size, prev_vocab_size - 10)
|
||
|
||
def prepare_config_and_inputs_for_common(self):
|
||
config_and_inputs = self.prepare_config_and_inputs()
|
||
(
|
||
config,
|
||
input_ids,
|
||
decoder_input_ids,
|
||
attention_mask,
|
||
decoder_attention_mask,
|
||
lm_labels,
|
||
) = config_and_inputs
|
||
|
||
inputs_dict = {
|
||
"input_ids": input_ids,
|
||
"attention_mask": attention_mask,
|
||
"decoder_input_ids": decoder_input_ids,
|
||
"decoder_attention_mask": decoder_attention_mask,
|
||
"use_cache": False,
|
||
}
|
||
return config, inputs_dict
|
||
|
||
|
||
@require_torch
|
||
class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||
all_model_classes = (
|
||
(T5Model, T5ForConditionalGeneration, T5ForSequenceClassification, T5ForQuestionAnswering)
|
||
if is_torch_available()
|
||
else ()
|
||
)
|
||
all_generative_model_classes = (T5ForConditionalGeneration,) if is_torch_available() else ()
|
||
pipeline_model_mapping = (
|
||
{
|
||
"conversational": T5ForConditionalGeneration,
|
||
"feature-extraction": T5Model,
|
||
"question-answering": T5ForQuestionAnswering,
|
||
"summarization": T5ForConditionalGeneration,
|
||
"text-classification": T5ForSequenceClassification,
|
||
"text2text-generation": T5ForConditionalGeneration,
|
||
"translation": T5ForConditionalGeneration,
|
||
"zero-shot": T5ForSequenceClassification,
|
||
}
|
||
if is_torch_available()
|
||
else {}
|
||
)
|
||
all_parallelizable_model_classes = (T5Model, T5ForConditionalGeneration) if is_torch_available() else ()
|
||
fx_compatible = True
|
||
test_pruning = False
|
||
test_resize_embeddings = True
|
||
test_model_parallel = True
|
||
is_encoder_decoder = True
|
||
# The small T5 model needs higher percentages for CPU/MP tests
|
||
model_split_percents = [0.5, 0.8, 0.9]
|
||
|
||
def setUp(self):
|
||
self.model_tester = T5ModelTester(self)
|
||
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
|
||
|
||
# `QAPipelineTests` is not working well with slow tokenizers (for some models) and we don't want to touch the file
|
||
# `src/transformers/data/processors/squad.py` (where this test fails for this model)
|
||
def is_pipeline_test_to_skip(
|
||
self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, processor_name
|
||
):
|
||
if tokenizer_name is None:
|
||
return True
|
||
if pipeline_test_case_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
|
||
return True
|
||
|
||
return False
|
||
|
||
def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
|
||
if not is_torch_fx_available() or not self.fx_compatible:
|
||
return
|
||
|
||
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
||
configs_no_init.return_dict = False
|
||
|
||
for model_class in self.all_model_classes:
|
||
if model_class.__name__ == "T5ForSequenceClassification":
|
||
continue
|
||
model = model_class(config=configs_no_init)
|
||
model.to(torch_device)
|
||
model.eval()
|
||
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)
|
||
|
||
try:
|
||
if model.config.is_encoder_decoder:
|
||
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
|
||
labels = inputs.get("labels", None)
|
||
input_names = [
|
||
"attention_mask",
|
||
"decoder_attention_mask",
|
||
"decoder_input_ids",
|
||
"input_features",
|
||
"input_ids",
|
||
"input_values",
|
||
]
|
||
if labels is not None:
|
||
input_names.append("labels")
|
||
|
||
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
||
input_names = list(filtered_inputs.keys())
|
||
|
||
model_output = model(**filtered_inputs)
|
||
|
||
traced_model = symbolic_trace(model, input_names)
|
||
traced_output = traced_model(**filtered_inputs)
|
||
else:
|
||
input_names = [
|
||
"attention_mask",
|
||
"bbox",
|
||
"input_features",
|
||
"input_ids",
|
||
"input_values",
|
||
"pixel_values",
|
||
"token_type_ids",
|
||
"visual_feats",
|
||
"visual_pos",
|
||
]
|
||
|
||
labels = inputs.get("labels", None)
|
||
start_positions = inputs.get("start_positions", None)
|
||
end_positions = inputs.get("end_positions", None)
|
||
if labels is not None:
|
||
input_names.append("labels")
|
||
if start_positions is not None:
|
||
input_names.append("start_positions")
|
||
if end_positions is not None:
|
||
input_names.append("end_positions")
|
||
|
||
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
||
input_names = list(filtered_inputs.keys())
|
||
|
||
if model.__class__.__name__ in set(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values()) and (
|
||
not hasattr(model.config, "problem_type") or model.config.problem_type is None
|
||
):
|
||
model.config.problem_type = "single_label_classification"
|
||
|
||
traced_model = symbolic_trace(model, input_names)
|
||
traced_output = traced_model(**filtered_inputs)
|
||
model_output = model(**filtered_inputs)
|
||
|
||
except Exception as e:
|
||
self.fail(f"Couldn't trace module: {e}")
|
||
|
||
def flatten_output(output):
|
||
flatten = []
|
||
for x in output:
|
||
if isinstance(x, (tuple, list)):
|
||
flatten += flatten_output(x)
|
||
elif not isinstance(x, torch.Tensor):
|
||
continue
|
||
else:
|
||
flatten.append(x)
|
||
return flatten
|
||
|
||
model_output = flatten_output(model_output)
|
||
traced_output = flatten_output(traced_output)
|
||
num_outputs = len(model_output)
|
||
|
||
for i in range(num_outputs):
|
||
self.assertTrue(
|
||
torch.allclose(model_output[i], traced_output[i]),
|
||
f"traced {i}th output doesn't match model {i}th output for {model_class}",
|
||
)
|
||
|
||
# Test that the model can be serialized and restored properly
|
||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||
pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
|
||
try:
|
||
with open(pkl_file_name, "wb") as f:
|
||
pickle.dump(traced_model, f)
|
||
with open(pkl_file_name, "rb") as f:
|
||
loaded = pickle.load(f)
|
||
except Exception as e:
|
||
self.fail(f"Couldn't serialize / deserialize the traced model: {e}")
|
||
|
||
loaded_output = loaded(**filtered_inputs)
|
||
loaded_output = flatten_output(loaded_output)
|
||
|
||
for i in range(num_outputs):
|
||
self.assertTrue(
|
||
torch.allclose(model_output[i], loaded_output[i]),
|
||
f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
|
||
)
|
||
|
||
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
|
||
# (Even with this call, there are still memory leak by ~0.04MB)
|
||
self.clear_torch_jit_class_registry()
|
||
|
||
def test_config(self):
|
||
self.config_tester.run_common_tests()
|
||
|
||
def test_shift_right(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)
|
||
|
||
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_v1_1(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
# check that gated gelu feed forward and different word embeddings work
|
||
config = config_and_inputs[0]
|
||
config.tie_word_embeddings = False
|
||
config.feed_forward_proj = "gated-gelu"
|
||
self.model_tester.create_and_check_model(config, *config_and_inputs[1:])
|
||
|
||
# T5ForSequenceClassification does not support inputs_embeds
|
||
def test_inputs_embeds(self):
|
||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||
|
||
for model_class in (T5Model, T5ForConditionalGeneration, T5ForQuestionAnswering):
|
||
model = model_class(config)
|
||
model.to(torch_device)
|
||
model.eval()
|
||
|
||
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
|
||
|
||
if not self.is_encoder_decoder:
|
||
input_ids = inputs["input_ids"]
|
||
del inputs["input_ids"]
|
||
else:
|
||
encoder_input_ids = inputs["input_ids"]
|
||
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
|
||
del inputs["input_ids"]
|
||
inputs.pop("decoder_input_ids", None)
|
||
|
||
wte = model.get_input_embeddings()
|
||
if not self.is_encoder_decoder:
|
||
inputs["inputs_embeds"] = wte(input_ids)
|
||
else:
|
||
inputs["inputs_embeds"] = wte(encoder_input_ids)
|
||
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
|
||
|
||
with torch.no_grad():
|
||
model(**inputs)[0]
|
||
|
||
def test_config_and_model_silu_gated(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
config = config_and_inputs[0]
|
||
config.feed_forward_proj = "gated-silu"
|
||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||
|
||
def test_with_lm_head(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_with_lm_head(*config_and_inputs)
|
||
|
||
def test_with_sequence_classification_head(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs)
|
||
|
||
def test_decoder_model_past(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
|
||
|
||
def test_decoder_model_past_with_attn_mask(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
|
||
|
||
def test_decoder_model_past_with_3d_attn_mask(self):
|
||
(
|
||
config,
|
||
input_ids,
|
||
decoder_input_ids,
|
||
attention_mask,
|
||
decoder_attention_mask,
|
||
lm_labels,
|
||
) = self.model_tester.prepare_config_and_inputs()
|
||
|
||
attention_mask = ids_tensor(
|
||
[self.model_tester.batch_size, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length],
|
||
vocab_size=2,
|
||
)
|
||
decoder_attention_mask = ids_tensor(
|
||
[self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length],
|
||
vocab_size=2,
|
||
)
|
||
|
||
self.model_tester.create_and_check_decoder_model_attention_mask_past(
|
||
config,
|
||
input_ids,
|
||
decoder_input_ids,
|
||
attention_mask,
|
||
decoder_attention_mask,
|
||
lm_labels,
|
||
)
|
||
|
||
def test_decoder_model_past_with_large_inputs(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
|
||
|
||
def test_generate_with_past_key_values(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_generate_with_past_key_values(*config_and_inputs)
|
||
|
||
def test_encoder_decoder_shared_weights(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs)
|
||
|
||
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
|
||
def test_model_fp16_forward(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)
|
||
|
||
def test_v1_1_resize_embeddings(self):
|
||
config = self.model_tester.prepare_config_and_inputs()[0]
|
||
self.model_tester.check_resize_embeddings_t5_v1_1(config)
|
||
|
||
@slow
|
||
def test_model_from_pretrained(self):
|
||
model_name = "google-t5/t5-small"
|
||
model = T5Model.from_pretrained(model_name)
|
||
self.assertIsNotNone(model)
|
||
|
||
@unittest.skip("Test has a segmentation fault on torch 1.8.0")
|
||
def test_export_to_onnx(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
model = T5Model(config_and_inputs[0]).to(torch_device)
|
||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||
torch.onnx.export(
|
||
model,
|
||
(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),
|
||
f"{tmpdirname}/t5_test.onnx",
|
||
export_params=True,
|
||
opset_version=9,
|
||
input_names=["input_ids", "decoder_input_ids"],
|
||
)
|
||
|
||
def test_generate_with_head_masking(self):
|
||
attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
config = config_and_inputs[0]
|
||
max_length = config_and_inputs[1].shape[-1] + 3
|
||
model = T5ForConditionalGeneration(config).eval()
|
||
model.to(torch_device)
|
||
|
||
head_masking = {
|
||
"head_mask": torch.zeros(config.num_layers, config.num_heads, device=torch_device),
|
||
"decoder_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device),
|
||
"cross_attn_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device),
|
||
}
|
||
|
||
for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
|
||
head_masks = {name: mask}
|
||
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
|
||
if name == "head_mask":
|
||
head_masks["decoder_head_mask"] = torch.ones(
|
||
config.num_decoder_layers, config.num_heads, device=torch_device
|
||
)
|
||
|
||
out = model.generate(
|
||
config_and_inputs[1],
|
||
num_beams=1,
|
||
max_length=max_length,
|
||
output_attentions=True,
|
||
return_dict_in_generate=True,
|
||
**head_masks,
|
||
)
|
||
# We check the state of decoder_attentions and cross_attentions just from the last step
|
||
attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
|
||
self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0)
|
||
|
||
@unittest.skip("Does not support conversations.")
|
||
def test_pipeline_conversational(self):
|
||
pass
|
||
|
||
|
||
class T5EncoderOnlyModelTester:
|
||
def __init__(
|
||
self,
|
||
parent,
|
||
vocab_size=99,
|
||
batch_size=13,
|
||
encoder_seq_length=7,
|
||
# For common tests
|
||
use_attention_mask=True,
|
||
hidden_size=32,
|
||
num_hidden_layers=2,
|
||
num_attention_heads=4,
|
||
d_ff=37,
|
||
relative_attention_num_buckets=8,
|
||
is_training=False,
|
||
dropout_rate=0.1,
|
||
initializer_factor=0.002,
|
||
is_encoder_decoder=False,
|
||
eos_token_id=1,
|
||
pad_token_id=0,
|
||
scope=None,
|
||
):
|
||
self.parent = parent
|
||
self.batch_size = batch_size
|
||
self.encoder_seq_length = encoder_seq_length
|
||
# For common tests
|
||
self.seq_length = self.encoder_seq_length
|
||
self.use_attention_mask = use_attention_mask
|
||
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.d_ff = d_ff
|
||
self.relative_attention_num_buckets = relative_attention_num_buckets
|
||
self.dropout_rate = dropout_rate
|
||
self.initializer_factor = initializer_factor
|
||
self.eos_token_id = eos_token_id
|
||
self.pad_token_id = pad_token_id
|
||
self.is_encoder_decoder = is_encoder_decoder
|
||
self.scope = None
|
||
self.is_training = is_training
|
||
|
||
def get_large_model_config(self):
|
||
return T5Config.from_pretrained("google-t5/t5-base")
|
||
|
||
def prepare_config_and_inputs(self):
|
||
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
|
||
|
||
attention_mask = None
|
||
if self.use_attention_mask:
|
||
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
|
||
|
||
config = T5Config(
|
||
vocab_size=self.vocab_size,
|
||
d_model=self.hidden_size,
|
||
d_ff=self.d_ff,
|
||
d_kv=self.hidden_size // self.num_attention_heads,
|
||
num_layers=self.num_hidden_layers,
|
||
num_heads=self.num_attention_heads,
|
||
relative_attention_num_buckets=self.relative_attention_num_buckets,
|
||
dropout_rate=self.dropout_rate,
|
||
initializer_factor=self.initializer_factor,
|
||
eos_token_id=self.eos_token_id,
|
||
bos_token_id=self.pad_token_id,
|
||
pad_token_id=self.pad_token_id,
|
||
is_encoder_decoder=self.is_encoder_decoder,
|
||
)
|
||
|
||
return (
|
||
config,
|
||
input_ids,
|
||
attention_mask,
|
||
)
|
||
|
||
def create_and_check_model(
|
||
self,
|
||
config,
|
||
input_ids,
|
||
attention_mask,
|
||
):
|
||
model = T5EncoderModel(config=config)
|
||
model.to(torch_device)
|
||
model.eval()
|
||
result = model(
|
||
input_ids=input_ids,
|
||
attention_mask=attention_mask,
|
||
)
|
||
result = model(input_ids=input_ids)
|
||
encoder_output = result.last_hidden_state
|
||
|
||
self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
|
||
|
||
def create_and_check_model_fp16_forward(
|
||
self,
|
||
config,
|
||
input_ids,
|
||
attention_mask,
|
||
):
|
||
model = T5EncoderModel(config=config).to(torch_device).half().eval()
|
||
output = model(input_ids, attention_mask=attention_mask)["last_hidden_state"]
|
||
self.parent.assertFalse(torch.isnan(output).any().item())
|
||
|
||
def create_and_check_with_token_classification_head(
|
||
self,
|
||
config,
|
||
input_ids,
|
||
attention_mask,
|
||
):
|
||
labels = torch.tensor([1] * self.seq_length * self.batch_size, dtype=torch.long, device=torch_device)
|
||
model = T5ForTokenClassification(config=config).to(torch_device).eval()
|
||
outputs = model(
|
||
input_ids=input_ids,
|
||
labels=labels,
|
||
attention_mask=attention_mask,
|
||
)
|
||
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.seq_length, config.num_labels))
|
||
self.parent.assertEqual(outputs["loss"].size(), ())
|
||
|
||
def prepare_config_and_inputs_for_common(self):
|
||
config_and_inputs = self.prepare_config_and_inputs()
|
||
(
|
||
config,
|
||
input_ids,
|
||
attention_mask,
|
||
) = config_and_inputs
|
||
|
||
inputs_dict = {
|
||
"input_ids": input_ids,
|
||
"attention_mask": attention_mask,
|
||
}
|
||
return config, inputs_dict
|
||
|
||
|
||
class T5EncoderOnlyModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||
all_model_classes = (T5EncoderModel, T5ForTokenClassification) if is_torch_available() else ()
|
||
test_pruning = False
|
||
test_resize_embeddings = False
|
||
test_model_parallel = True
|
||
pipeline_model_mapping = (
|
||
{
|
||
"token-classification": T5ForTokenClassification,
|
||
}
|
||
if is_torch_available()
|
||
else {}
|
||
)
|
||
all_parallelizable_model_classes = (T5EncoderModel,) if is_torch_available() else ()
|
||
|
||
def setUp(self):
|
||
self.model_tester = T5EncoderOnlyModelTester(self)
|
||
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=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)
|
||
|
||
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
|
||
def test_model_fp16_forward(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)
|
||
|
||
def test_with_token_classification_head(self):
|
||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||
self.model_tester.create_and_check_with_token_classification_head(*config_and_inputs)
|
||
|
||
|
||
def use_task_specific_params(model, task):
|
||
model.config.update(model.config.task_specific_params[task])
|
||
|
||
|
||
@require_torch
|
||
@require_accelerate
|
||
@require_tokenizers
|
||
@slow
|
||
class T5ModelFp16Tests(unittest.TestCase):
|
||
def test_fp16_fp32_conversion(self):
|
||
r"""
|
||
A test to check whether the argument `keep_in_fp32_modules` correctly does its job
|
||
"""
|
||
orig_import = __import__
|
||
accelerate_mock = unittest.mock.Mock()
|
||
|
||
# mock import of accelerate
|
||
def import_accelerate_mock(name, *args, **kwargs):
|
||
if name == "accelerate":
|
||
if accelerate_available:
|
||
return accelerate_mock
|
||
else:
|
||
raise ImportError
|
||
return orig_import(name, *args, **kwargs)
|
||
|
||
# Load without using `accelerate`
|
||
with unittest.mock.patch("builtins.__import__", side_effect=import_accelerate_mock):
|
||
accelerate_available = False
|
||
|
||
model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small", torch_dtype=torch.float16)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16)
|
||
|
||
# Load without in bf16
|
||
model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small", torch_dtype=torch.bfloat16)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16)
|
||
|
||
# Load using `accelerate` in bf16
|
||
model = T5ForConditionalGeneration.from_pretrained(
|
||
"google-t5/t5-small", torch_dtype=torch.bfloat16, device_map="auto"
|
||
)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16)
|
||
|
||
# Load using `accelerate` in bf16
|
||
model = T5ForConditionalGeneration.from_pretrained(
|
||
"google-t5/t5-small", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True
|
||
)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.bfloat16)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.bfloat16)
|
||
|
||
# Load without using `accelerate`
|
||
model = T5ForConditionalGeneration.from_pretrained(
|
||
"google-t5/t5-small", torch_dtype=torch.float16, low_cpu_mem_usage=True
|
||
)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16)
|
||
|
||
# Load using `accelerate`
|
||
model = T5ForConditionalGeneration.from_pretrained(
|
||
"google-t5/t5-small", torch_dtype=torch.float16, device_map="auto"
|
||
)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
|
||
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wi.weight.dtype == torch.float16)
|
||
|
||
|
||
@require_torch
|
||
@require_sentencepiece
|
||
@require_tokenizers
|
||
class T5ModelIntegrationTests(unittest.TestCase):
|
||
@cached_property
|
||
def model(self):
|
||
return T5ForConditionalGeneration.from_pretrained("google-t5/t5-base").to(torch_device)
|
||
|
||
@cached_property
|
||
def tokenizer(self):
|
||
return T5Tokenizer.from_pretrained("google-t5/t5-base")
|
||
|
||
@slow
|
||
def test_torch_quant(self):
|
||
r"""
|
||
Test that a simple `torch.quantization.quantize_dynamic` call works on a T5 model.
|
||
"""
|
||
model_name = "google/flan-t5-small"
|
||
tokenizer = T5Tokenizer.from_pretrained(model_name)
|
||
model = T5ForConditionalGeneration.from_pretrained(model_name)
|
||
model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
|
||
input_text = "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
|
||
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
|
||
_ = model.generate(input_ids)
|
||
|
||
@slow
|
||
def test_small_generation(self):
|
||
model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small").to(torch_device)
|
||
model.config.max_length = 8
|
||
model.config.num_beams = 1
|
||
model.config.do_sample = False
|
||
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
||
|
||
input_ids = tokenizer("summarize: Hello there", return_tensors="pt").input_ids.to(torch_device)
|
||
|
||
sequences = model.generate(input_ids)
|
||
|
||
output_str = tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
|
||
self.assertTrue(output_str == "Hello there!")
|
||
|
||
@slow
|
||
def test_small_integration_test(self):
|
||
"""
|
||
For comparision run:
|
||
>>> import t5 # pip install t5==0.7.1
|
||
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
|
||
|
||
>>> path_to_mtf_small_t5_checkpoint = '<fill_in>'
|
||
>>> path_to_mtf_small_spm_model_path = '<fill_in>'
|
||
>>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_checkpoint, batch_size=1, tpu=None)
|
||
>>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100)
|
||
>>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
|
||
"""
|
||
|
||
model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small").to(torch_device)
|
||
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
||
|
||
input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
|
||
labels = tokenizer("Hi I am", return_tensors="pt").input_ids
|
||
|
||
loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
|
||
mtf_score = -(labels.shape[-1] * loss.item())
|
||
|
||
EXPECTED_SCORE = -19.0845
|
||
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
|
||
|
||
@slow
|
||
def test_small_v1_1_integration_test(self):
|
||
"""
|
||
For comparision run:
|
||
>>> import t5 # pip install t5==0.7.1
|
||
>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
|
||
|
||
>>> path_to_mtf_small_t5_v1_1_checkpoint = '<fill_in>'
|
||
>>> path_to_mtf_small_spm_model_path = '<fill_in>'
|
||
>>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_t5_v1_1_checkpoint, batch_size=1, tpu=None)
|
||
>>> vocab = SentencePieceVocabulary(path_to_mtf_small_spm_model_path, extra_ids=100)
|
||
>>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
|
||
"""
|
||
|
||
model = T5ForConditionalGeneration.from_pretrained("google/t5-v1_1-small").to(torch_device)
|
||
tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-small")
|
||
|
||
input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
|
||
labels = tokenizer("Hi I am", return_tensors="pt").input_ids
|
||
|
||
loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
|
||
mtf_score = -(labels.shape[-1] * loss.item())
|
||
|
||
EXPECTED_SCORE = -59.0293
|
||
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
|
||
|
||
@slow
|
||
def test_small_byt5_integration_test(self):
|
||
"""
|
||
For comparision run:
|
||
>>> import t5 # pip install t5==0.9.1
|
||
|
||
>>> path_to_byt5_small_checkpoint = '<fill_in>'
|
||
>>> t5_model = t5.models.MtfModel(model_dir=path_to_tf_checkpoint, batch_size=1, tpu=None)
|
||
>>> vocab = t5.data.ByteVocabulary()
|
||
>>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
|
||
"""
|
||
|
||
model = T5ForConditionalGeneration.from_pretrained("google/byt5-small").to(torch_device)
|
||
tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
|
||
|
||
input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
|
||
labels = tokenizer("Hi I am", return_tensors="pt").input_ids
|
||
|
||
loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
|
||
mtf_score = -(labels.shape[-1] * loss.item())
|
||
|
||
EXPECTED_SCORE = -60.7397
|
||
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
|
||
|
||
@slow
|
||
def test_summarization(self):
|
||
model = self.model
|
||
tok = self.tokenizer
|
||
|
||
FRANCE_ARTICLE = ( # @noqa
|
||
"Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings"
|
||
" Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane."
|
||
' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."'
|
||
' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s'
|
||
" comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video"
|
||
" showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French"
|
||
" Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a"
|
||
" phone at the wreckage site. The two publications described the supposed video, but did not post it on"
|
||
" their websites. The publications said that they watched the video, which was found by a source close to"
|
||
" the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported."
|
||
' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the'
|
||
" cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the"
|
||
' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,'
|
||
" editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said"
|
||
" the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman"
|
||
" in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the"
|
||
' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,'
|
||
' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be'
|
||
" sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by"
|
||
" specialized technicians working hand-in-hand with investigators. But none of the cell phones found so"
|
||
" far have been sent to the institute, Menichini said. Asked whether staff involved in the search could"
|
||
' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin'
|
||
' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match'
|
||
' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered'
|
||
' cell phones from the crash site after Bild and Paris Match published their reports. "That is something'
|
||
" we did not know before. ... Overall we can say many things of the investigation weren't revealed by the"
|
||
' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline'
|
||
" Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the"
|
||
" controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the"
|
||
' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of'
|
||
' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school'
|
||
" discovered in an internal investigation, Lufthansa said, included medical documents he submitted in"
|
||
" connection with resuming his flight training. The announcement indicates that Lufthansa, the parent"
|
||
" company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and"
|
||
" ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%"
|
||
' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was'
|
||
" sharing the information and documents -- including training and medical records -- with public"
|
||
" prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the"
|
||
" past week to recover human remains and plane debris scattered across a steep mountainside. He saw the"
|
||
" crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash"
|
||
" site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late"
|
||
" Tuesday that no visible human remains were left at the site but recovery teams would keep searching."
|
||
" French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all"
|
||
" the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested."
|
||
" In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said."
|
||
" Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew"
|
||
" on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with"
|
||
" the flight school during his training were among several developments as investigators continued to"
|
||
" delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa"
|
||
" spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his"
|
||
' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in'
|
||
" Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at"
|
||
" some point before his aviation career and underwent psychotherapy before he got his pilot's license."
|
||
" Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the"
|
||
" crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to"
|
||
" lose his pilot's license, a European government official briefed on the investigation told CNN on"
|
||
' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being'
|
||
" considered. Another source, a law enforcement official briefed on the investigation, also told CNN that"
|
||
" authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would"
|
||
" not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had"
|
||
" seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded"
|
||
" he had psychological issues, the European government official said. But no matter what details emerge"
|
||
" about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic"
|
||
' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact'
|
||
" that maybe they weren't going to keep doing their job and they're upset about that and so they're"
|
||
' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to'
|
||
" also take that rage and turn it outward on 149 other people who had nothing to do with the person's"
|
||
' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight'
|
||
" 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura"
|
||
" Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine"
|
||
" Amiel and Anna-Maja Rappard contributed to this report."
|
||
)
|
||
SHORTER_ARTICLE = (
|
||
"(CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
|
||
" Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
|
||
" formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
|
||
" The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
|
||
' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
|
||
' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
|
||
" situation in Palestinian territories, paving the way for possible war crimes investigations against"
|
||
" Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
|
||
" the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
|
||
" body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
|
||
' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
|
||
' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
|
||
' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
|
||
" Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
|
||
' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
|
||
" acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
|
||
' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
|
||
' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
|
||
" immediately end their pressure, and countries that support universal acceptance of the court's treaty"
|
||
' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
|
||
" group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
|
||
' decision to join a treaty to which over 100 countries around the world are members." In January, when'
|
||
" the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
|
||
' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
|
||
" disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
|
||
' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
|
||
' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
|
||
' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
|
||
" it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
|
||
' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
|
||
" court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
|
||
' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
|
||
" between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
|
||
" will include alleged war crimes committed since June. The International Criminal Court was set up in"
|
||
" 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
|
||
" and Faith Karimi contributed to this report."
|
||
)
|
||
IRAN_ARTICLE = (
|
||
"(CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran"
|
||
" in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively"
|
||
" block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger."
|
||
" Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli"
|
||
" Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a"
|
||
" letter to the Iranian leadership warning them away from a deal. The debate that has already begun since"
|
||
" the announcement of the new framework will likely result in more heat than light. It will not be helped"
|
||
" by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ."
|
||
" The most misleading assertion, despite universal rejection by experts, is that the negotiations'"
|
||
" objective at the outset was the total elimination of any nuclear program in Iran. That is the position"
|
||
" of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it"
|
||
" had been, there would have been no Iranian team at the negotiating table. Rather, the objective has"
|
||
" always been to structure an agreement or series of agreements so that Iran could not covertly develop a"
|
||
" nuclear arsenal before the United States and its allies could respond. The new framework has exceeded"
|
||
" expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by"
|
||
" two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another"
|
||
" dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite"
|
||
" sharp accusations by some in the United States and its allies, Iran denies having such a program, and"
|
||
" U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's"
|
||
" continued cooperation with International Atomic Energy Agency inspections is further evidence on this"
|
||
" point, and we'll know even more about Iran's program in the coming months and years because of the deal."
|
||
" In fact, the inspections provisions that are part of this agreement are designed to protect against any"
|
||
" covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that"
|
||
" the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter"
|
||
" warning that a deal might be killed by Congress or a future president). This of course is not the case."
|
||
" The talks were between Iran and the five permanent members of the U.N. Security Council (United States,"
|
||
" United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has"
|
||
" played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement"
|
||
" reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran"
|
||
" and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement"
|
||
" contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the"
|
||
" case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased"
|
||
" or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes"
|
||
" Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear"
|
||
" sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going"
|
||
" forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such"
|
||
" a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the"
|
||
' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not'
|
||
" suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New"
|
||
" START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement"
|
||
" with Iran will not be so balanced. The restrictions and obligations in the final framework agreement"
|
||
" will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove"
|
||
" most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally"
|
||
" some insist that any agreement must address Iranian missile programs, human rights violations or support"
|
||
" for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are"
|
||
" unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in"
|
||
" the negotiations would be a poison pill. This agreement should be judged on its merits and on how it"
|
||
" affects the security of our negotiating partners and allies, including Israel. Those judgments should be"
|
||
" fact-based, not based on questionable assertions or dubious assumptions."
|
||
)
|
||
ARTICLE_SUBWAY = (
|
||
"New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A"
|
||
" year later, she got married again in Westchester County, but to a different man and without divorcing"
|
||
" her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos"
|
||
' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married'
|
||
" once more, this time in the Bronx. In an application for a marriage license, she stated it was her"
|
||
' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false'
|
||
' instrument for filing in the first degree," referring to her false statements on the 2010 marriage'
|
||
" license application, according to court documents. Prosecutors said the marriages were part of an"
|
||
" immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to"
|
||
" her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was"
|
||
" arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New"
|
||
" York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total,"
|
||
" Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All"
|
||
" occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be"
|
||
" married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors"
|
||
" said the immigration scam involved some of her husbands, who filed for permanent residence status"
|
||
" shortly after the marriages. Any divorces happened only after such filings were approved. It was"
|
||
" unclear whether any of the men will be prosecuted. The case was referred to the Bronx District"
|
||
" Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's"
|
||
' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,'
|
||
" Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his"
|
||
" native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces"
|
||
" up to four years in prison. Her next court appearance is scheduled for May 18."
|
||
)
|
||
|
||
expected_summaries = [
|
||
'prosecutor: "so far no videos were used in the crash investigation" two magazines claim to have found a'
|
||
" cell phone video of the final seconds . \"one can hear cries of 'My God' in several languages,\" one"
|
||
" magazine says .",
|
||
"the formal accession was marked by a ceremony at The Hague, in the Netherlands . the ICC opened a"
|
||
" preliminary examination into the situation in the occupied Palestinian territory . as members of the"
|
||
" court, Palestinians may be subject to counter-charges as well .",
|
||
"the u.s. and its negotiating partners reached a very strong framework agreement with Iran . aaron miller:"
|
||
" the debate that has already begun since the announcement of the new framework will likely result in more"
|
||
" heat than light . the deal would reduce Iran's low-enriched uranium stockpile, cut centrifuges and"
|
||
" implement a rigorous inspection regime .",
|
||
"prosecutors say the marriages were part of an immigration scam . if convicted, barrientos faces two"
|
||
' criminal counts of "offering a false instrument for filing in the first degree" she has been married 10'
|
||
" times, with nine of her marriages occurring between 1999 and 2002 .",
|
||
]
|
||
|
||
use_task_specific_params(model, "summarization")
|
||
|
||
dct = tok(
|
||
[model.config.prefix + x for x in [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY]],
|
||
padding="max_length",
|
||
truncation=True,
|
||
return_tensors="pt",
|
||
).to(torch_device)
|
||
self.assertEqual(512, dct["input_ids"].shape[1])
|
||
|
||
hypotheses_batch = model.generate(
|
||
**dct,
|
||
num_beams=4,
|
||
length_penalty=2.0,
|
||
max_length=142,
|
||
min_length=56,
|
||
no_repeat_ngram_size=3,
|
||
do_sample=False,
|
||
early_stopping=True,
|
||
)
|
||
|
||
decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
||
self.assertListEqual(
|
||
expected_summaries,
|
||
decoded,
|
||
)
|
||
|
||
@slow
|
||
def test_translation_en_to_de(self):
|
||
model = self.model
|
||
tok = self.tokenizer
|
||
use_task_specific_params(model, "translation_en_to_de")
|
||
|
||
en_text = '"Luigi often said to me that he never wanted the brothers to end up in court", she wrote.'
|
||
expected_translation = (
|
||
'"Luigi sagte mir oft, dass er nie wollte, dass die Brüder am Gericht sitzen", schrieb sie.'
|
||
)
|
||
|
||
input_ids = tok.encode(model.config.prefix + en_text, return_tensors="pt")
|
||
input_ids = input_ids.to(torch_device)
|
||
output = model.generate(input_ids)
|
||
translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
||
self.assertEqual(translation, expected_translation)
|
||
|
||
@slow
|
||
def test_translation_en_to_fr(self):
|
||
model = self.model # google-t5/t5-base
|
||
tok = self.tokenizer
|
||
use_task_specific_params(model, "translation_en_to_fr")
|
||
|
||
en_text = (
|
||
' This image section from an infrared recording by the Spitzer telescope shows a "family portrait" of'
|
||
" countless generations of stars: the oldest stars are seen as blue dots. "
|
||
)
|
||
|
||
input_ids = tok.encode(model.config.prefix + en_text, return_tensors="pt")
|
||
input_ids = input_ids.to(torch_device)
|
||
|
||
output = model.generate(
|
||
input_ids=input_ids,
|
||
num_beams=4,
|
||
length_penalty=2.0,
|
||
max_length=100,
|
||
no_repeat_ngram_size=3,
|
||
do_sample=False,
|
||
early_stopping=True,
|
||
)
|
||
translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
||
new_truncated_translation = (
|
||
"Cette section d'images provenant de l'enregistrement infrarouge effectué par le télescope Spitzer montre "
|
||
"un "
|
||
"« portrait familial » de générations innombrables d’étoiles : les plus anciennes sont observées "
|
||
"sous forme "
|
||
"de points bleus."
|
||
)
|
||
|
||
self.assertEqual(translation, new_truncated_translation)
|
||
|
||
@slow
|
||
def test_translation_en_to_ro(self):
|
||
model = self.model
|
||
tok = self.tokenizer
|
||
use_task_specific_params(model, "translation_en_to_ro")
|
||
en_text = "Taco Bell said it plans to add 2,000 locations in the US by 2022."
|
||
expected_translation = "Taco Bell a declarat că intenţionează să adauge 2 000 de locaţii în SUA până în 2022."
|
||
|
||
inputs = tok(model.config.prefix + en_text, return_tensors="pt").to(torch_device)
|
||
output = model.generate(**inputs)
|
||
translation = tok.decode(output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
||
self.assertEqual(translation, expected_translation)
|
||
|
||
@slow
|
||
def test_contrastive_search_t5(self):
|
||
article = (
|
||
" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A"
|
||
" year later, she got married again in Westchester County, but to a different man and without divorcing"
|
||
" her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos"
|
||
' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married'
|
||
" once more, this time in the Bronx. In an application for a marriage license, she stated it was her"
|
||
' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false'
|
||
' instrument for filing in the first degree," referring to her false statements on the 2010 marriage'
|
||
" license application, according to court documents. Prosecutors said the marriages were part of an"
|
||
" immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to"
|
||
" her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was"
|
||
" arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New"
|
||
" York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total,"
|
||
" Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All"
|
||
" occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be"
|
||
" married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors"
|
||
" said the immigration scam involved some of her husbands, who filed for permanent residence status"
|
||
" shortly after the marriages. Any divorces happened only after such filings were approved. It was"
|
||
" unclear whether any of the men will be prosecuted. The case was referred to the Bronx District"
|
||
" Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's"
|
||
' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,'
|
||
" Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his"
|
||
" native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces"
|
||
" up to four years in prison. Her next court appearance is scheduled for May 18."
|
||
)
|
||
article = "summarize: " + article.strip()
|
||
t5_tokenizer = AutoTokenizer.from_pretrained("flax-community/t5-base-cnn-dm")
|
||
t5_model = T5ForConditionalGeneration.from_pretrained("flax-community/t5-base-cnn-dm").to(torch_device)
|
||
input_ids = t5_tokenizer(
|
||
article, add_special_tokens=False, truncation=True, max_length=512, return_tensors="pt"
|
||
).input_ids.to(torch_device)
|
||
|
||
outputs = t5_model.generate(input_ids, penalty_alpha=0.5, top_k=5, max_length=64)
|
||
generated_text = t5_tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
||
|
||
self.assertListEqual(
|
||
generated_text,
|
||
[
|
||
"Liana Barrientos has been married 10 times, nine of them in the Bronx. Her husbands filed for "
|
||
"permanent residence after the marriages, prosecutors say."
|
||
],
|
||
)
|
||
|
||
|
||
@require_torch
|
||
class TestAsymmetricT5(unittest.TestCase):
|
||
def build_model_and_check_forward_pass(self, **kwargs):
|
||
tester = T5ModelTester(self, **kwargs)
|
||
config, *inputs = tester.prepare_config_and_inputs()
|
||
(
|
||
input_ids,
|
||
decoder_input_ids,
|
||
attention_mask,
|
||
decoder_attention_mask,
|
||
lm_labels,
|
||
) = inputs
|
||
model = T5ForConditionalGeneration(config=config).to(torch_device).eval()
|
||
outputs = model(
|
||
input_ids=input_ids,
|
||
decoder_input_ids=decoder_input_ids,
|
||
decoder_attention_mask=decoder_attention_mask,
|
||
labels=lm_labels,
|
||
)
|
||
# outputs = model(*inputs)
|
||
assert len(outputs) == 4
|
||
assert outputs["logits"].size() == (tester.batch_size, tester.decoder_seq_length, tester.vocab_size)
|
||
assert outputs["loss"].size() == ()
|
||
return model
|
||
|
||
def test_small_decoder(self):
|
||
# num_hidden_layers is passed to T5Config as num_layers
|
||
model = self.build_model_and_check_forward_pass(decoder_layers=1, num_hidden_layers=2)
|
||
assert len(model.encoder.block) == 2
|
||
assert len(model.decoder.block) == 1
|
||
|
||
def test_defaulting_to_symmetry(self):
|
||
# num_hidden_layers is passed to T5Config as num_layers
|
||
model = self.build_model_and_check_forward_pass(num_hidden_layers=2)
|
||
assert len(model.decoder.block) == len(model.encoder.block) == 2
|