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1647 lines
78 KiB
Python
1647 lines
78 KiB
Python
# 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 unittest
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from functools import cached_property
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import pytest
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from transformers import T5Config, is_torch_available
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from transformers.testing_utils import (
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Expectations,
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cleanup,
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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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require_torch_accelerator,
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slow,
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torch_device,
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)
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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, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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import torch.nn.functional as F
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from transformers import (
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ByT5Tokenizer,
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GenerationConfig,
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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 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 get_config_v1_1(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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# V1.1 related params: uses gated-gelu and `tie_word_embeddings=False` as an
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# indicator to not scale decoder outputs
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feed_forward_proj="gated-gelu",
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tie_word_embeddings=False,
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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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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))
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def create_and_check_model_fp16_forward(
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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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|
):
|
|
model = T5Model(config=config).to(torch_device).half().eval()
|
|
output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"]
|
|
self.parent.assertFalse(torch.isnan(output).any().item())
|
|
|
|
def check_resize_embeddings_t5_v1_1(
|
|
self,
|
|
config,
|
|
):
|
|
prev_vocab_size = config.vocab_size
|
|
|
|
# V1.1 related params: uses gated-gelu and `tie_word_embeddings=False`
|
|
config.feed_forward_proj = "gated-gelu"
|
|
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,
|
|
}
|
|
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 ()
|
|
)
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": T5Model,
|
|
"text-classification": T5ForSequenceClassification,
|
|
"zero-shot": T5ForSequenceClassification,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
|
|
test_resize_embeddings = 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,
|
|
image_processor_name,
|
|
feature_extractor_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
|
|
|
|
# overwrite because T5 doesn't accept position ids as input and expects `decoder_input_ids`
|
|
def test_custom_4d_attention_mask(self):
|
|
for model_class in self.all_generative_model_classes:
|
|
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
model = model_class(config).to(device=torch_device, dtype=torch.float32)
|
|
|
|
(
|
|
input_ids,
|
|
_,
|
|
input_ids_shared_prefix,
|
|
mask_shared_prefix,
|
|
_,
|
|
) = self._get_custom_4d_mask_test_data()
|
|
|
|
logits = model.forward(
|
|
decoder_input_ids=input_ids,
|
|
input_ids=input_dict["input_ids"][:3],
|
|
).logits
|
|
# logits.shape == torch.Size([3, 4, ...])
|
|
|
|
logits_shared_prefix = model(
|
|
input_ids=input_dict["input_ids"][:1],
|
|
decoder_input_ids=input_ids_shared_prefix,
|
|
decoder_attention_mask=mask_shared_prefix,
|
|
)[0]
|
|
# logits_shared_prefix.shape == torch.Size([1, 6, ...])
|
|
|
|
out_last_tokens = logits[:, -1, :] # last tokens in each batch line
|
|
out_shared_prefix_last_tokens = logits_shared_prefix[0, -3:, :] # last three tokens
|
|
|
|
# comparing softmax-normalized logits:
|
|
normalized_0 = F.softmax(out_last_tokens)
|
|
normalized_1 = F.softmax(out_shared_prefix_last_tokens)
|
|
torch.testing.assert_close(normalized_0, normalized_1, rtol=1e-3, atol=1e-4)
|
|
|
|
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.assertTrue(config_and_inputs[0].scale_decoder_outputs)
|
|
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()
|
|
config_v1 = self.model_tester.get_config_v1_1()
|
|
config_and_inputs = list(config_and_inputs)
|
|
config_and_inputs[0] = config_v1
|
|
|
|
self.assertFalse(config_and_inputs[0].scale_decoder_outputs)
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
# 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)
|
|
|
|
@unittest.skipIf(torch_device == "cpu", "Can't 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(reason="T5 has no separate base model without a head.")
|
|
def test_model_base_model_prefix(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 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_resize_embeddings = False
|
|
pipeline_model_mapping = (
|
|
{
|
|
"token-classification": T5ForTokenClassification,
|
|
}
|
|
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", "Can't 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 is_pipeline_test_to_skip(
|
|
self,
|
|
pipeline_test_case_name,
|
|
config_class,
|
|
model_architecture,
|
|
tokenizer_name,
|
|
image_processor_name,
|
|
feature_extractor_name,
|
|
processor_name,
|
|
):
|
|
if tokenizer_name is None:
|
|
return True
|
|
|
|
# `T5EncoderOnlyModelTest` 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)
|
|
if pipeline_test_case_name == "TokenClassificationPipelineTests" and not tokenizer_name.endswith("Fast"):
|
|
return True
|
|
|
|
return False
|
|
|
|
|
|
def use_task_specific_params(model, task):
|
|
task_params = model.config.task_specific_params[task]
|
|
|
|
# Get all valid GenerationConfig attributes
|
|
temp_config = GenerationConfig()
|
|
generation_config_attrs = set(temp_config.to_dict().keys())
|
|
|
|
for key, value in task_params.items():
|
|
if key in generation_config_attrs:
|
|
setattr(model.generation_config, key, value)
|
|
else:
|
|
setattr(model.config, key, value)
|
|
|
|
|
|
@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", 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", 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", 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",
|
|
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 without using `accelerate`
|
|
model = T5ForConditionalGeneration.from_pretrained(
|
|
"google-t5/t5-small",
|
|
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 using `accelerate`
|
|
model = T5ForConditionalGeneration.from_pretrained(
|
|
"google-t5/t5-small", 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):
|
|
def tearDown(self):
|
|
# See LlamaIntegrationTest.tearDown(). Can be removed once LlamaIntegrationTest.tearDown() is removed.
|
|
cleanup(torch_device, gc_collect=False)
|
|
|
|
@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)
|
|
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, max_length=8, num_beams=1, do_sample=False)
|
|
|
|
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 comparison 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 = Expectations(
|
|
{
|
|
(None, None): -19.0845,
|
|
("rocm", (9, 4)): -19.0846,
|
|
}
|
|
).get_expectation()
|
|
torch.testing.assert_close(
|
|
mtf_score,
|
|
EXPECTED_SCORE,
|
|
atol=1e-4,
|
|
rtol=0.0,
|
|
)
|
|
|
|
@slow
|
|
def test_small_v1_1_integration_test(self):
|
|
"""
|
|
For comparison 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
|
|
torch.testing.assert_close(
|
|
mtf_score,
|
|
EXPECTED_SCORE,
|
|
atol=1e-4,
|
|
rtol=0.0,
|
|
)
|
|
|
|
@slow
|
|
def test_small_byt5_integration_test(self):
|
|
"""
|
|
For comparison 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
|
|
torch.testing.assert_close(
|
|
mtf_score,
|
|
EXPECTED_SCORE,
|
|
atol=1e-4,
|
|
rtol=0.0,
|
|
)
|
|
|
|
@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 = [
|
|
"<pad> "
|
|
'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 ."
|
|
"</s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s>",
|
|
"<pad> "
|
|
"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 ."
|
|
"</s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s>",
|
|
"<pad> "
|
|
"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 ."
|
|
"</s>",
|
|
"<pad> "
|
|
"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 ."
|
|
"</s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s></s>",
|
|
]
|
|
|
|
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,
|
|
max_length=512,
|
|
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)
|
|
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 = (
|
|
'<pad> "Luigi sagte mir oft, dass er nie wollte, dass die Brüder am Gericht sitzen", schrieb sie.</s>'
|
|
)
|
|
|
|
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])
|
|
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])
|
|
new_truncated_translation = (
|
|
"<pad> "
|
|
"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."
|
|
"</s>"
|
|
)
|
|
|
|
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 = (
|
|
"<pad> Taco Bell a declarat că intenţionează să adauge 2 000 de locaţii în SUA până în 2022.</s>"
|
|
)
|
|
|
|
inputs = tok(model.config.prefix + en_text, return_tensors="pt").to(torch_device)
|
|
output = model.generate(**inputs)
|
|
translation = tok.decode(output[0])
|
|
self.assertEqual(translation, expected_translation)
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
@pytest.mark.torch_compile_test
|
|
def test_compile_static_cache(self):
|
|
NUM_TOKENS_TO_GENERATE = 40
|
|
EXPECTED_TEXT_COMPLETION = [
|
|
"theory of relativity states that 1) the speed of light is constant in all inertial reference frames . the laws of physics are the same for all inertial reference frames .",
|
|
"ketchup is my favorite condiment .",
|
|
]
|
|
|
|
prompts = [
|
|
"summarize: Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial "
|
|
"reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
|
|
"theory of relativity is not hard to grasp.",
|
|
"summarize: My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, "
|
|
"my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my pizza.",
|
|
]
|
|
model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small").to(torch_device)
|
|
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
|
inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
|
|
|
|
# Dynamic Cache
|
|
generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
|
|
dynamic_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
|
|
self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text)
|
|
|
|
# Static Cache
|
|
generated_ids = model.generate(
|
|
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
|
|
)
|
|
static_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
|
|
self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text)
|
|
|
|
# Static Cache + compile
|
|
model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
|
|
generated_ids = model.generate(
|
|
**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static"
|
|
)
|
|
static_compiled_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
|
|
self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
@pytest.mark.torch_compile_test
|
|
def test_compile_static_cache_encoder(self):
|
|
prompts = [
|
|
"summarize: Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial "
|
|
"reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
|
|
"theory of relativity is not hard to grasp.",
|
|
"summarize: My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, "
|
|
"my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my pizza.",
|
|
]
|
|
model = T5EncoderModel.from_pretrained("google-t5/t5-small").to(torch_device)
|
|
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
|
inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
|
|
|
|
logits = model(**inputs)
|
|
|
|
model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
|
|
logits_compiled = model(**inputs)
|
|
torch.testing.assert_close(logits[0][:, -3:, -3], logits_compiled[0][:, -3:, -3], rtol=1e-5, atol=1e-5)
|
|
|
|
@pytest.mark.torch_export_test
|
|
@slow
|
|
def test_export_encoder(self):
|
|
"""Test exporting T5EncoderModel to torch export format."""
|
|
|
|
from transformers.integrations.executorch import Seq2SeqLMEncoderExportableModule
|
|
|
|
model_id = "google-t5/t5-small"
|
|
device = "cpu"
|
|
example_input_ids = torch.ones((1, 10), dtype=torch.long).to(device)
|
|
|
|
# Load model
|
|
model = T5EncoderModel.from_pretrained(model_id).to(device=device).eval()
|
|
|
|
# Get original output for comparison
|
|
with torch.no_grad():
|
|
original_output = model(input_ids=example_input_ids).last_hidden_state
|
|
|
|
encoder_model = Seq2SeqLMEncoderExportableModule(model)
|
|
|
|
# Export the encoder_model
|
|
with torch.no_grad():
|
|
seq_len_dim = torch.export.Dim("sequence_length", max=4096)
|
|
|
|
exported_program = torch.export.export(
|
|
encoder_model, (example_input_ids,), dynamic_shapes={"input_ids": {1: seq_len_dim}}, strict=True
|
|
)
|
|
|
|
# Test the exported model
|
|
with torch.no_grad():
|
|
exported_output = exported_program.module()(example_input_ids)
|
|
|
|
# Verify outputs are close enough
|
|
self.assertTrue(torch.allclose(original_output, exported_output, atol=1e-5))
|
|
|
|
@pytest.mark.torch_export_test
|
|
@slow
|
|
def test_export_decoder(self):
|
|
"""Test exporting T5 decoder with static cache to torch export format."""
|
|
|
|
from transformers import AutoModelForSeq2SeqLM, T5ForConditionalGeneration
|
|
from transformers.integrations.executorch import Seq2SeqLMDecoderExportableModuleWithStaticCache
|
|
|
|
model_id = "google-t5/t5-small"
|
|
|
|
# Configuration for static cache
|
|
batch_size = 1
|
|
max_cache_len = 123
|
|
device = "cpu"
|
|
|
|
full_model = AutoModelForSeq2SeqLM.from_pretrained(model_id).to(device)
|
|
self.assertIsInstance(full_model, T5ForConditionalGeneration)
|
|
decoder_model = (
|
|
Seq2SeqLMDecoderExportableModuleWithStaticCache(full_model, max_cache_len, batch_size).to(device).eval()
|
|
)
|
|
|
|
# Prepare test inputs
|
|
example_decoder_input_ids = torch.tensor([[0]], dtype=torch.long) # Start token
|
|
example_cache_position = torch.tensor([0], dtype=torch.long)
|
|
|
|
# For T5-small, hidden size is 512
|
|
example_encoder_hidden_states = torch.zeros((batch_size, 10, 512), dtype=torch.float32)
|
|
|
|
# Export the model
|
|
with torch.no_grad():
|
|
encoder_sequence_length_dim = torch.export.Dim("encoder_sequence_length", max=4096)
|
|
|
|
exported_program = torch.export.export(
|
|
decoder_model,
|
|
(example_decoder_input_ids, example_encoder_hidden_states, example_cache_position),
|
|
dynamic_shapes={
|
|
"decoder_input_ids": None,
|
|
"encoder_hidden_states": {1: encoder_sequence_length_dim},
|
|
"cache_position": None,
|
|
},
|
|
strict=True,
|
|
)
|
|
|
|
# We won't directly verify outputs here as it's complicated with caching,
|
|
# but we'll check the export was successful
|
|
self.assertIsNotNone(exported_program)
|
|
|
|
# Verify cache buffers existence and shapes
|
|
cache_buffers = [
|
|
(name, buffer)
|
|
for name, buffer in exported_program.named_buffers()
|
|
if name.startswith("key_cache_") or name.startswith("value_cache_")
|
|
]
|
|
|
|
# Verify cache buffers
|
|
self.assertTrue(len(cache_buffers) > 0, "No cache buffers found in exported model")
|
|
for name, buffer in cache_buffers:
|
|
# Verify cache buffers are 3D
|
|
self.assertEqual(buffer.shape[2], max_cache_len)
|
|
|
|
@pytest.mark.torch_export_test
|
|
@slow
|
|
def test_export_t5_summarization(self):
|
|
"""Test composing exported T5 encoder and decoder for summarization."""
|
|
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5ForConditionalGeneration
|
|
from transformers.integrations.executorch import Seq2SeqLMExportableModule
|
|
|
|
device = torch_device
|
|
batch_size = 1
|
|
max_cache_length = 1234
|
|
max_hidden_seq_length = 5678
|
|
model_id = "google-t5/t5-small"
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
full_model = AutoModelForSeq2SeqLM.from_pretrained(model_id).to(device).eval()
|
|
self.assertIsInstance(full_model, T5ForConditionalGeneration)
|
|
wrapped_model = Seq2SeqLMExportableModule(
|
|
full_model,
|
|
batch_size=batch_size,
|
|
max_hidden_seq_length=max_hidden_seq_length,
|
|
max_cache_length=max_cache_length,
|
|
)
|
|
|
|
exported_t5 = wrapped_model.export()
|
|
|
|
# Test Summarization with Composed Models
|
|
prompts = [
|
|
"summarize: Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial "
|
|
"reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
|
|
"theory of relativity is not hard to grasp."
|
|
]
|
|
input_ids = tokenizer(prompts, return_tensors="pt").input_ids
|
|
|
|
generated_ids = exported_t5.generate(prompt_token_ids=input_ids, max_new_tokens=max_cache_length)
|
|
generated_summary = tokenizer.decode(generated_ids, skip_special_tokens=True)
|
|
|
|
# Also run original model for comparison
|
|
original_model = T5ForConditionalGeneration.from_pretrained(model_id).eval()
|
|
with torch.no_grad():
|
|
original_outputs = original_model.generate(input_ids, max_length=50, num_beams=1)
|
|
original_summary = tokenizer.decode(original_outputs[0], skip_special_tokens=True)
|
|
|
|
# Basic verification that we got a reasonable summary
|
|
self.assertEqual(generated_summary, original_summary)
|
|
|
|
|
|
@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
|