Add T5 Encoder for Feature Extraction (#8717)
* Add T5 Encoder class for feature extraction * fix T5 encoder add_start_docstrings indent * update init with T5 encoder * update init with TFT5ModelEncoder * remove TFT5ModelEncoder * change T5ModelEncoder order in init * add T5ModelEncoder to transformers init * clean T5ModelEncoder * update init with TFT5ModelEncoder * add TFModelEncoder for Tensorflow * update init with TFT5ModelEncoder * Update src/transformers/models/t5/modeling_t5.py change output from Seq2SeqModelOutput to BaseModelOutput Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * remove encoder_outputs 1. remove encoder_outputs from the function call. 2. remove the encoder_outputs If statement. 3. remove isinstance from return_dict. * Authorize missing decoder keys * remove unnecessary input parameters remove pask_key_values and use_cache * remove use_cache remove use_cache from the forward method * add doctoring for T5 encoder add doctoring for T5 encoder with T5_ENCODER_INPUTS_DOCSTRING * change return_dict to dot access * add T5_ENCODER_INPUTS_DOCSTRING for TF T5 * change TFT5Encoder output type to BaseModelOutput * remove unnecessary parameters for TFT5Encoder * remove unnecessary if statement * add import BaseModelOutput * fix BaseModelOutput typo to TFBaseModelOutput * update T5 doc with T5ModelEncoder * add T5ModelEncoder to tests * finish pytorch * finish docs and mt5 * add mtf to init * fix init * remove n_positions * finish PR * Update src/transformers/models/mt5/modeling_mt5.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/models/t5/modeling_t5.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/models/t5/modeling_tf_t5.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/models/mt5/modeling_tf_mt5.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * make style Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Ahmed Elnaggar
@@ -30,7 +30,7 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor
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if is_torch_available():
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import torch
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from transformers import T5Config, T5ForConditionalGeneration, T5Model, T5Tokenizer
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from transformers import T5Config, T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Tokenizer
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from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
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@@ -39,7 +39,6 @@ class T5ModelTester:
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self,
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parent,
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vocab_size=99,
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n_positions=14,
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batch_size=13,
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encoder_seq_length=7,
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decoder_seq_length=9,
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@@ -71,7 +70,6 @@ class T5ModelTester:
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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.n_positions = n_positions
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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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@@ -104,7 +102,6 @@ class T5ModelTester:
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config = T5Config(
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vocab_size=self.vocab_size,
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n_positions=self.n_positions,
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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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@@ -559,6 +556,144 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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)
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class T5EncoderOnlyModelTester:
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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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# For common tests
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use_attention_mask=True,
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hidden_size=32,
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num_hidden_layers=5,
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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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is_training=False,
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dropout_rate=0.1,
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initializer_factor=0.002,
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is_encoder_decoder=False,
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eos_token_id=1,
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pad_token_id=0,
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scope=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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# For common tests
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self.seq_length = self.encoder_seq_length
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self.use_attention_mask = use_attention_mask
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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.is_encoder_decoder = is_encoder_decoder
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self.scope = None
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self.is_training = is_training
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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)
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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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config = 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_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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is_encoder_decoder=self.is_encoder_decoder,
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)
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return (
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config,
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input_ids,
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attention_mask,
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)
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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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attention_mask,
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):
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model = T5EncoderModel(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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attention_mask=attention_mask,
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)
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result = model(input_ids=input_ids)
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encoder_output = result.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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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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attention_mask,
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):
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model = T5EncoderModel(config=config).to(torch_device).half().eval()
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output = model(input_ids, attention_mask=attention_mask)["last_hidden_state"]
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self.parent.assertFalse(torch.isnan(output).any().item())
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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attention_mask,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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}
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return config, inputs_dict
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class T5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (T5EncoderModel,) if is_torch_available() else ()
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test_pruning = False
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test_torchscript = True
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test_resize_embeddings = False
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def setUp(self):
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self.model_tester = T5EncoderOnlyModelTester(self)
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self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
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def test_model_fp16_forward(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)
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def use_task_specific_params(model, task):
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model.config.update(model.config.task_specific_params[task])
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