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Unverified Commit d4e92f1a authored by Lysandre Debut's avatar Lysandre Debut Committed by GitHub
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Remove add-new-model in favor of add-new-model-like (#30424) 

* Remove add-new-model in favor of add-new-model-like

* nits
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templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py deleted 100644 → 0
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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
{% if cookiecutter.is_encoder_decoder_model == "False" %}
import unittest
from transformers import is_tf_available, {{cookiecutter.camelcase_modelname}}Config
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import (
TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
TF{{cookiecutter.camelcase_modelname}}Model,
)
class TF{{cookiecutter.camelcase_modelname}}ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = True
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 5
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_causal_lm_base_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
# Also check the case where encoder outputs are not passed
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_causal_lm_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
prediction_scores = model(inputs)["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_causal_lm_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
prediction_scores = result["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_causal_lm_model_past(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and attn_mask
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0]
output_from_past = model(
next_tokens, past_key_values=past_key_values, output_hidden_states=True
).hidden_states[0]
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
def create_and_check_causal_lm_model_past_with_attn_mask(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
# create attention mask
half_seq_length = self.seq_length // 2
attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
# first forward pass
outputs = model(input_ids, attention_mask=attn_mask, use_cache=True)
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
past_key_values = outputs.past_key_values
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
condition = tf.transpose(
tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
)
input_ids = tf.where(condition, random_other_next_tokens, input_ids)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
attn_mask = tf.concat(
[attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
axis=1,
)
output_from_no_past = model(
next_input_ids,
attention_mask=attn_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True
).hidden_states[0]
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
def create_and_check_causal_lm_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
input_ids = input_ids[:1, :]
input_mask = input_mask[:1, :]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
).hidden_states[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
input_ids = input_ids[:1, :]
input_mask = input_mask[:1, :]
encoder_hidden_states = encoder_hidden_states[:1, :, :]
encoder_attention_mask = encoder_attention_mask[:1, :]
self.batch_size = 1
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
).hidden_states[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
TF{{cookiecutter.camelcase_modelname}}Model,
TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
)
if is_tf_available()
else ()
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
"""Test the base model"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="Template classes interact badly with this test.")
def test_keras_fit(self):
pass
def test_causal_lm_base_model(self):
"""Test the base model of the causal LM model
is_deocder=True, no cross_attention, no encoder outputs
"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs)
def test_model_as_decoder(self):
"""Test the base model as a decoder (of an encoder-decoder architecture)
is_deocder=True + cross_attention + pass encoder outputs
"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm(self):
"""Test the causal LM model"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model(*config_and_inputs)
def test_causal_lm_model_as_decoder(self):
"""Test the causal LM model as a decoder"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs)
def test_causal_lm_model_past(self):
"""Test causal LM model with `past_key_values`"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs)
def test_causal_lm_model_past_with_attn_mask(self):
"""Test the causal LM model with `past_key_values` and `attention_mask`"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)
def test_causal_lm_model_past_with_large_inputs(self):
"""Test the causal LM model with `past_key_values` and a longer decoder sequence length"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
"""Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
self.assertIsNotNone(model)
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 32000
expected_shape = [1, 6, vocab_size]
self.assertEqual(output.shape, expected_shape)
print(output[:, :3, :3])
# TODO Replace values below with what was printed above.
expected_slice = tf.constant(
[
[
[-0.05243197, -0.04498899, 0.05512108],
[-0.07444685, -0.01064632, 0.04352357],
[-0.05020351, 0.05530146, 0.00700043],
]
]
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
{% else %}
import unittest
from transformers import (
is_tf_available,
{{cookiecutter.camelcase_modelname}}Config,
{{cookiecutter.camelcase_modelname}}Tokenizer,
)
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
if is_tf_available():
import tensorflow as tf
from transformers import (
TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
TF{{cookiecutter.camelcase_modelname}}Model,
)
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelTester:
config_cls = {{cookiecutter.camelcase_modelname}}Config
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
input_ids = tf.concat([input_ids, eos_tensor], axis=1)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.config_cls(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_ids=[2],
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.pad_token_id,
**self.config_updates,
)
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = TF{{cookiecutter.camelcase_modelname}}Model(config=config).get_decoder()
input_ids = inputs_dict["input_ids"]
input_ids = input_ids[:1, :]
attention_mask = inputs_dict["attention_mask"][:1, :]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if attention_mask is None:
attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int32)
if decoder_attention_mask is None:
decoder_attention_mask = tf.concat([tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int32), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int32)], axis=-1)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model) if is_tf_available() else ()
all_generative_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_tf_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
@unittest.skip(reason="Template classes interact badly with this test.")
def test_keras_fit(self):
pass
def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if tf.debugging.assert_near(a, b, atol=atol):
return True
raise
except Exception:
if len(prefix) > 0:
prefix = f"{prefix}: "
raise AssertionError(f"{prefix}{a} != {b}")
def _long_tensor(tok_lst):
return tf.constant(tok_lst, dtype=tf.int32)
TOLERANCE = 1e-4
@slow
@require_sentencepiece
@require_tokenizers
@require_tf
class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
def test_inference_no_head(self):
model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
# change to intended input here
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
output = model(**inputs_dict)[0]
expected_shape = (1, 11, 1024)
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = tf.Tensor(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]],
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
def test_inference_with_head(self):
model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
# change to intended input here
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
output = model(**inputs_dict)[0]
expected_shape = (1, 11, 1024)
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = tf.Tensor(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]],
)
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE)
def test_seq_to_seq_generation(self):
hf = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
batch_input = [
# string 1,
# string 2,
# string 3,
# string 4,
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
dct = tok.batch_encode_plus(
batch_input,
max_length=512,
padding="max_length",
truncation_strategy="only_first",
truncation=True,
return_tensors="tf",
)
hypotheses_batch = hf.generate(
input_ids=dct["input_ids"],
attention_mask=dct["attention_mask"],
num_beams=2,
)
EXPECTED = [
# here expected 1,
# here expected 2,
# here expected 3,
# here expected 4,
]
generated = tok.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated == EXPECTED
{%- endif %}
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_{{cookiecutter.lowercase_modelname}}.py deleted 100644 → 0
View file @ 0eb8fbcd
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch {{cookiecutter.modelname}} model. """
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
import unittest
from ...test_modeling_common import floats_tensor
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers import {{cookiecutter.camelcase_modelname}}Config
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForMaskedLM,
{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}ForTokenClassification,
{{cookiecutter.camelcase_modelname}}Model,
)
from transformers.models.{{cookiecutter.lowercase_modelname}}.modeling_{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.uppercase_modelname}} )
class {{cookiecutter.camelcase_modelname}}ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = {{cookiecutter.camelcase_modelname}}Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = {{cookiecutter.camelcase_modelname}}Model(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = {{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = {{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = {{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = {{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = {{cookiecutter.camelcase_modelname}}ForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = {{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = {{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}ForMaskedLM,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}ForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = ({{cookiecutter.camelcase_modelname}}ForCausalLM,) if is_torch_available() else ()
def setUp(self):
self.model_tester = {{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
@slow
def test_model_from_pretrained(self):
model_name = "{{coockiecutter.checkpoint_identifier}}"
model = {{cookiecutter.camelcase_modelname}}Model.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class {{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = {{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 32000
expected_shape = torch.Size((1, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
# TODO Replace values below with what was printed above.
expected_slice = torch.tensor(
[[[-0.0483, 0.1188, -0.0313], [-0.0606, 0.1435, 0.0199], [-0.0235, 0.1519, 0.0175]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
{% else -%}
import copy
import tempfile
import unittest
from transformers import is_torch_available
from transformers.utils import cached_property
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import (
{{cookiecutter.camelcase_modelname}}Config,
{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}Tokenizer,
)
from transformers.models.{{cookiecutter.lowercase_modelname}}.modeling_{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.camelcase_modelname}}Decoder,
{{cookiecutter.camelcase_modelname}}Encoder,
)
def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
@require_torch
class {{cookiecutter.camelcase_modelname}}ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = {{cookiecutter.camelcase_modelname}}Model(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = {{cookiecutter.camelcase_modelname}}Model(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = {{cookiecutter.camelcase_modelname}}Encoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = {{cookiecutter.camelcase_modelname}}Decoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
({{cookiecutter.camelcase_modelname}}Model, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.camelcase_modelname}}ForSequenceClassification, {{cookiecutter.camelcase_modelname}}ForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = ({{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_torch_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_missing_keys = False
def setUp(self):
self.model_tester = {{cookiecutter.camelcase_modelname}}ModelTester(self)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
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_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
# {{cookiecutter.camelcase_modelname}}ForSequenceClassification 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 ({{cookiecutter.camelcase_modelname}}Model, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.camelcase_modelname}}ForQuestionAnswering):
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_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
TOLERANCE = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class {{cookiecutter.camelcase_modelname}}ModelIntegrationTests(unittest.TestCase):
@cached_property
def default_tokenizer(self):
return {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
def test_inference_no_head(self):
model = {{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}').to(torch_device)
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[2, 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 11, 1024))
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = torch.tensor(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def test_inference_head(self):
model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}').to(torch_device)
# change to intended input
input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 11, model.config.vocab_size))
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = torch.tensor(
[[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def test_seq_to_seq_generation(self):
hf = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}').to(torch_device)
tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
batch_input = [
# string 1,
# string 2,
# string 3,
# string 4,
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
dct = tok.batch_encode_plus(
batch_input,
max_length=512,
padding="max_length",
truncation_strategy="only_first",
truncation=True,
return_tensors="pt",
)
hypotheses_batch = hf.generate(
input_ids=dct["input_ids"].to(torch_device),
attention_mask=dct["attention_mask"].to(torch_device),
num_beams=2,
)
EXPECTED = [
# here expected 1,
# here expected 2,
# here expected 3,
# here expected 4,
]
generated = tok.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated == EXPECTED
class {{cookiecutter.camelcase_modelname}}StandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=4,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = {{cookiecutter.camelcase_modelname}}Config(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = {{cookiecutter.camelcase_modelname}}Decoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = {{cookiecutter.camelcase_modelname}}Decoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class {{cookiecutter.camelcase_modelname}}StandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = ({{cookiecutter.camelcase_modelname}}Decoder, {{cookiecutter.camelcase_modelname}}ForCausalLM) if is_torch_available() else ()
all_generative_model_classes = ({{cookiecutter.camelcase_modelname}}ForCausalLM,) if is_torch_available() else ()
test_pruning = False
is_encoder_decoder = False
def setUp(
self,
):
self.model_tester = {{cookiecutter.camelcase_modelname}}StandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config)
def test_config(self):
self.config_tester.run_common_tests()
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_attn_mask_past(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_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
{% endif -%}
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/to_replace_{{cookiecutter.lowercase_modelname}}.py deleted 100644 → 0
View file @ 0eb8fbcd
## Copyright 2022 The HuggingFace Team. All rights reserved.
##
## Licensed under the Apache License, Version 2.0 (the "License");
## you may not use this file except in compliance with the License.
## You may obtain a copy of the License at
##
## http://www.apache.org/licenses/LICENSE-2.0
##
## Unless required by applicable law or agreed to in writing, software
## distributed under the License is distributed on an "AS IS" BASIS,
## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
## See the License for the specific language governing permissions and
## limitations under the License.
## This file is made so that specific statements may be copied inside existing files. This is useful to copy
## import statements in __init__.py, or to complete model lists in the AUTO files.
##
## It is to be used as such:
## Put '# To replace in: "FILE_PATH"' in order to indicate the contents will be copied in the file at path FILE_PATH
## Put '# Below: "STATEMENT"' in order to copy the contents below **the first occurrence** of that line in the file at FILE_PATH
## Put '# Replace with:' followed by the lines containing the content to define the content
## End a statement with '# End.'. If starting a new statement without redefining the FILE_PATH, it will continue pasting
## content in that file.
##
## Put '## COMMENT' to comment on the file.
# To replace in: "src/transformers/__init__.py"
# Below: " # PyTorch models structure" if generating PyTorch
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"{{cookiecutter.camelcase_modelname}}ForCausalLM",
"{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"{{cookiecutter.camelcase_modelname}}Layer",
"{{cookiecutter.camelcase_modelname}}Model",
"{{cookiecutter.camelcase_modelname}}PreTrainedModel",
"load_tf_weights_in_{{cookiecutter.lowercase_modelname}}",
]
)
{% else %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"{{cookiecutter.camelcase_modelname}}ForCausalLM",
"{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"{{cookiecutter.camelcase_modelname}}Model",
"{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% endif -%}
# End.
# Below: " # TensorFlow models structure" if generating TensorFlow
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"TF{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"TF{{cookiecutter.camelcase_modelname}}ForCausalLM",
"TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"TF{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"TF{{cookiecutter.camelcase_modelname}}Layer",
"TF{{cookiecutter.camelcase_modelname}}Model",
"TF{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% else %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"TF{{cookiecutter.camelcase_modelname}}Model",
"TF{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% endif -%}
# End.
# Below: " # Flax models structure" if generating Flax
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM",
"Flax{{cookiecutter.camelcase_modelname}}ForCausalLM",
"Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice",
"Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification",
"Flax{{cookiecutter.camelcase_modelname}}Layer",
"Flax{{cookiecutter.camelcase_modelname}}Model",
"Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% else %}
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].extend(
[
"Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration",
"Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering",
"Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification",
"Flax{{cookiecutter.camelcase_modelname}}Model",
"Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel",
]
)
{% endif -%}
# End.
# Below: " # Fast tokenizers structure"
# Replace with:
_import_structure["models.{{cookiecutter.lowercase_modelname}}"].append("{{cookiecutter.camelcase_modelname}}TokenizerFast")
# End.
# Below: " # Models"
# Replace with:
"models.{{cookiecutter.lowercase_modelname}}": ["{{cookiecutter.camelcase_modelname}}Config", "{{cookiecutter.camelcase_modelname}}Tokenizer"],
# End.
# To replace in: "src/transformers/__init__.py"
# Below: " # PyTorch model imports" if generating PyTorch
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
from .models.{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.camelcase_modelname}}ForMaskedLM,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}ForTokenClassification,
{{cookiecutter.camelcase_modelname}}Layer,
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}PreTrainedModel,
load_tf_weights_in_{{cookiecutter.lowercase_modelname}},
)
{% else %}
from .models.{{cookiecutter.lowercase_modelname}} import (
{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
{{cookiecutter.camelcase_modelname}}ForCausalLM,
{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
{{cookiecutter.camelcase_modelname}}Model,
{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif -%}
# End.
# Below: " # TensorFlow model imports" if generating TensorFlow
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
from .models.{{cookiecutter.lowercase_modelname}} import (
TF_{{cookiecutter.uppercase_modelname}} TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
TF{{cookiecutter.camelcase_modelname}}Layer,
TF{{cookiecutter.camelcase_modelname}}Model,
TF{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% else %}
from .models.{{cookiecutter.lowercase_modelname}} import (
TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
TF{{cookiecutter.camelcase_modelname}}Model,
TF{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif -%}
# End.
# Below: " # Flax model imports" if generating Flax
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" %}
from .models.{{cookiecutter.lowercase_modelname}} import (
Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM,
Flax{{cookiecutter.camelcase_modelname}}ForCausalLM,
Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification,
Flax{{cookiecutter.camelcase_modelname}}Layer,
Flax{{cookiecutter.camelcase_modelname}}Model,
Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% else %}
from .models.{{cookiecutter.lowercase_modelname}} import (
Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,
Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
Flax{{cookiecutter.camelcase_modelname}}Model,
Flax{{cookiecutter.camelcase_modelname}}PreTrainedModel,
)
{% endif -%}
# End.
# Below: " # Fast tokenizers imports"
# Replace with:
from .models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}TokenizerFast
# End.
# Below: " from .models.albert import AlbertConfig"
# Replace with:
from .models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.uppercase_modelname}}{{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}Tokenizer
# End.
# To replace in: "src/transformers/models/__init__.py"
# Below: "from . import ("
# Replace with:
{{cookiecutter.lowercase_modelname}},
# End.
# To replace in: "src/transformers/models/auto/configuration_auto.py"
# Below: "# Add configs here"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}Config"),
# End.
# Below: "# Add full (and cased) model names here"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}"),
# End.
# To replace in: "src/transformers/models/auto/modeling_auto.py" if generating PyTorch
# Below: "# Base model mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}Model"),
# End.
# Below: "# Model with LM heads mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# Below: "# Model for Causal LM mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForCausalLM"),
# End.
# Below: "# Model for Masked LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Sequence Classification mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForSequenceClassification"),
# End.
# Below: "# Model for Question Answering mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"),
# End.
# Below: "# Model for Token Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForTokenClassification"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Multiple Choice mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForMultipleChoice"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Seq2Seq Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else %}
("{{cookiecutter.lowercase_modelname}}", "{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# To replace in: "src/transformers/models/auto/modeling_tf_auto.py" if generating TensorFlow
# Below: "# Base model mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}Model"),
# End.
# Below: "# Model with LM heads mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# Below: "# Model for Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForCausalLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Masked LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Sequence Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Question Answering mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Token Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForTokenClassification"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Multiple Choice mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Seq2Seq Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else %}
("{{cookiecutter.lowercase_modelname}}", "TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# To replace in: "src/transformers/models/auto/modeling_flax_auto.py" if generating Flax
# Below: "# Base model mapping"
# Replace with:
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}Model"),
# End.
# Below: "# Model for Masked LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# Below: "# Model for Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForCausalLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Masked LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Sequence Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification"),
{% endif -%}
# End.
# Below: "# Model for Question Answering mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"),
{% else %}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering"),
{% endif -%}
# End.
# Below: "# Model for Token Classification mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Multiple Choice mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice"),
{% else -%}
{% endif -%}
# End.
# Below: "# Model for Seq2Seq Causal LM mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else %}
("{{cookiecutter.lowercase_modelname}}", "Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration"),
{% endif -%}
# End.
# To replace in: "utils/check_repo.py" if generating PyTorch
# Below: "models to ignore for model xxx mapping"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else -%}
"{{cookiecutter.camelcase_modelname}}Encoder",
"{{cookiecutter.camelcase_modelname}}Decoder",
"{{cookiecutter.camelcase_modelname}}DecoderWrapper",
{% endif -%}
# End.
# Below: "models to ignore for not tested"
# Replace with:
{% if cookiecutter.is_encoder_decoder_model == "False" -%}
{% else -%}
"{{cookiecutter.camelcase_modelname}}Encoder", # Building part of bigger (tested) model.
"{{cookiecutter.camelcase_modelname}}Decoder", # Building part of bigger (tested) model.
"{{cookiecutter.camelcase_modelname}}DecoderWrapper", # Building part of bigger (tested) model.
{% endif -%}
# End.
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_fast_{{cookiecutter.lowercase_modelname}}.py deleted 100644 → 0
View file @ 0eb8fbcd
# coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for {{cookiecutter.modelname}}."""
{%- if cookiecutter.tokenizer_type == "Based on BERT" %}
from ...utils import logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
}
}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(BertTokenizerFast):
r"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
[`~{{cookiecutter.camelcase_modelname}}TokenizerFast`] is identical to [`BertTokenizerFast`] and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BertTokenizerFast`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
{%- elif cookiecutter.tokenizer_type == "Based on BART" %}
from ...utils import logging
from ..bart.tokenization_bart_fast import BartTokenizerFast
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(BartTokenizerFast):
r"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
[`~{{cookiecutter.camelcase_modelname}}TokenizerFast`] is identical to [`BartTokenizerFast`] and runs
end-to-end tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BartTokenizerFast`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
{%- elif cookiecutter.tokenizer_type == "Standalone" %}
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
def __init__(
self,
vocab_file,
merges_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
trim_offsets=True,
**kwargs
):
super().__init__(
ByteLevelBPETokenizer(
vocab_file=vocab_file,
merges_file=merges_file,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
),
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
**kwargs,
)
self.add_prefix_space = add_prefix_space
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
if token_ids_1 is None:
return output
return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
{% endif %}
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py deleted 100644 → 0
View file @ 0eb8fbcd
# coding=utf-8
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for {{cookiecutter.modelname}}."""
{%- if cookiecutter.tokenizer_type == "Based on BERT" %}
from ...utils import logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
}
}
class {{cookiecutter.camelcase_modelname}}Tokenizer(BertTokenizer):
r"""
Construct a {{cookiecutter.modelname}} tokenizer.
[`~{{cookiecutter.camelcase_modelname}}Tokenizer`] is identical to [`BertTokenizer`] and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
{%- elif cookiecutter.tokenizer_type == "Based on BART" %}
from ...utils import logging
from ..bart.tokenization_bart import BartTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
class {{cookiecutter.camelcase_modelname}}Tokenizer(BartTokenizer):
"""
Construct a {{cookiecutter.modelname}} tokenizer.
[`~{{cookiecutter.camelcase_modelname}}Tokenizer`] is identical to [`BartTokenizer`] and runs end-to-end
tokenization: punctuation splitting and wordpiece.
Refer to superclass [`BartTokenizer`] for usage examples and documentation concerning
parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
{%- elif cookiecutter.tokenizer_type == "Standalone" %}
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
class {{cookiecutter.camelcase_modelname}}Tokenizer(PreTrainedTokenizer):
"""
Construct a {{cookiecutter.modelname}} tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
**kwargs
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
""" Initialisation """
@property
def vocab_size(self):
""" Returns vocab size """
def get_vocab(self):
""" Returns vocab as a dict """
def _tokenize(self, text):
""" Returns a tokenized string. """
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
def save_vocabulary(self, save_directory):
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A {{cookiecutter.modelname}} sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
text = " " + text
return (text, kwargs)
class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
merges_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
trim_offsets=True,
**kwargs
):
super().__init__(
ByteLevelBPETokenizer(
vocab_file=vocab_file,
merges_file=merges_file,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
),
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
**kwargs,
)
self.add_prefix_space = add_prefix_space
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
if token_ids_1 is None:
return output
return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
{{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
{% endif %}
templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/{{cookiecutter.lowercase_modelname}}.md deleted 100644 → 0
View file @ 0eb8fbcd
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# {{cookiecutter.modelname}}
## Overview
The {{cookiecutter.modelname}} model was proposed in [<INSERT PAPER NAME HERE>](<INSERT PAPER LINK HERE>) by <INSERT AUTHORS HERE>. <INSERT SHORT SUMMARY HERE>
The abstract from the paper is the following:
*<INSERT PAPER ABSTRACT HERE>*
Tips:
<INSERT TIPS ABOUT MODEL HERE>
This model was contributed by [INSERT YOUR HF USERNAME HERE](<https://huggingface.co/<INSERT YOUR HF USERNAME HERE>). The original code can be found [here](<INSERT LINK TO GITHUB REPO HERE>).
## {{cookiecutter.camelcase_modelname}}Config
[[autodoc]] {{cookiecutter.camelcase_modelname}}Config
## {{cookiecutter.camelcase_modelname}}Tokenizer
[[autodoc]] {{cookiecutter.camelcase_modelname}}Tokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## {{cookiecutter.camelcase_modelname}}TokenizerFast
[[autodoc]] {{cookiecutter.camelcase_modelname}}TokenizerFast
{% if "PyTorch" in cookiecutter.generate_tensorflow_pytorch_and_flax -%}
## {{cookiecutter.camelcase_modelname}}Model
[[autodoc]] {{cookiecutter.camelcase_modelname}}Model
- forward
{% if cookiecutter.is_encoder_decoder_model == "False" %}
## {{cookiecutter.camelcase_modelname}}ForCausalLM
[[autodoc]] {{cookiecutter.camelcase_modelname}}ForCausalLM
- forward
## {{cookiecutter.camelcase_modelname}}ForMaskedLM
[[autodoc]] {{cookiecutter.camelcase_modelname}}ForMaskedLM
- forward
## {{cookiecutter.camelcase_modelname}}ForSequenceClassification
[[autodoc]] transformers.{{cookiecutter.camelcase_modelname}}ForSequenceClassification
- forward
## {{cookiecutter.camelcase_modelname}}ForMultipleChoice
[[autodoc]] transformers.{{cookiecutter.camelcase_modelname}}ForMultipleChoice
- forward
## {{cookiecutter.camelcase_modelname}}ForTokenClassification
[[autodoc]] transformers.{{cookiecutter.camelcase_modelname}}ForTokenClassification
- forward
## {{cookiecutter.camelcase_modelname}}ForQuestionAnswering
[[autodoc]] {{cookiecutter.camelcase_modelname}}ForQuestionAnswering
- forward
{%- else %}
## {{cookiecutter.camelcase_modelname}}ForConditionalGeneration
[[autodoc]] {{cookiecutter.camelcase_modelname}}ForConditionalGeneration
- forward
## {{cookiecutter.camelcase_modelname}}ForSequenceClassification
[[autodoc]] {{cookiecutter.camelcase_modelname}}ForSequenceClassification
- forward
## {{cookiecutter.camelcase_modelname}}ForQuestionAnswering
[[autodoc]] {{cookiecutter.camelcase_modelname}}ForQuestionAnswering
- forward
## {{cookiecutter.camelcase_modelname}}ForCausalLM
[[autodoc]] {{cookiecutter.camelcase_modelname}}ForCausalLM
- forward
{% endif -%}
{% endif -%}
{% if "TensorFlow" in cookiecutter.generate_tensorflow_pytorch_and_flax -%}
## TF{{cookiecutter.camelcase_modelname}}Model
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}Model
- call
{% if cookiecutter.is_encoder_decoder_model == "False" %}
## TF{{cookiecutter.camelcase_modelname}}ForMaskedLM
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}ForMaskedLM
- call
## TF{{cookiecutter.camelcase_modelname}}ForCausalLM
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}ForCausalLM
- call
## TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification
- call
## TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice
- call
## TF{{cookiecutter.camelcase_modelname}}ForTokenClassification
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}ForTokenClassification
- call
## TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering
- call
{%- else %}
## TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
[[autodoc]] TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
- call
{% endif -%}
{% endif -%}
{% if "Flax" in cookiecutter.generate_tensorflow_pytorch_and_flax -%}
## Flax{{cookiecutter.camelcase_modelname}}Model
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}Model
- call
{% if cookiecutter.is_encoder_decoder_model == "False" %}
## Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM
- call
## Flax{{cookiecutter.camelcase_modelname}}ForCausalLM
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForCausalLM
- call
## Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification
- call
## Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice
- call
## Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification
- call
## Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering
- call
{%- else %}
## Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification
- call
## Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering
- call
## Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
[[autodoc]] Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
- call
{% endif -%}
{% endif -%}
templates/adding_a_new_model/cookiecutter.json deleted 100644 → 0
View file @ 0eb8fbcd
{
"modelname": "BrandNewBERT",
"uppercase_modelname": "BRAND_NEW_BERT",
"lowercase_modelname": "brand_new_bert",
"camelcase_modelname": "BrandNewBert",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "brand-new-bert-base-cased",
"tokenizer_type": ["Based on BERT", "Based on BART", "Standalone"],
"generate_tensorflow_pytorch_and_flax": [
"PyTorch, TensorFlow and Flax",
"PyTorch & TensorFlow",
"PyTorch & Flax",
"TensorFlow & Flax",
"PyTorch",
"TensorFlow",
"Flax"
],
"is_encoder_decoder_model": ["True", "False"]
}
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{
"modelname": "Template",
"uppercase_modelname": "TEMPLATE",
"lowercase_modelname": "template",
"camelcase_modelname": "Template",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "brand-new-bert-base-cased",
"tokenizer_type": "Based on BERT",
"generate_tensorflow_pytorch_and_flax": "PyTorch, TensorFlow and Flax",
"is_encoder_decoder_model": "False"
}
templates/adding_a_new_model/tests/flax-encoder-bert-tokenizer.json deleted 100644 → 0
View file @ 0eb8fbcd
{
"modelname": "TemplateFLAX",
"uppercase_modelname": "TEMPLATE_FLAX",
"lowercase_modelname": "template_flax",
"camelcase_modelname": "TemplateFlax",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "brand-new-bert-base-cased",
"tokenizer_type": "Based on BERT",
"generate_tensorflow_pytorch_and_flax": "Flax",
"is_encoder_decoder_model": "False"
}
templates/adding_a_new_model/tests/flax-seq-2-seq-bart-tokenizer.json deleted 100644 → 0
View file @ 0eb8fbcd
{
"modelname": "FlaxNewENCDEC",
"uppercase_modelname": "FLAX_NEW_ENC_DEC",
"lowercase_modelname": "flax_new_enc_dec_template",
"camelcase_modelname": "FlaxNewEncDec",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "new-flax-enc-dec-base",
"tokenizer_type": "Based on BART",
"generate_tensorflow_pytorch_and_flax": "Flax",
"is_encoder_decoder_model": "True"
}
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{
"modelname": "TemplatePT",
"uppercase_modelname": "TEMPLATE_PT",
"lowercase_modelname": "template_pt",
"camelcase_modelname": "TemplatePt",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "brand-new-bert-base-cased",
"tokenizer_type": "Based on BERT",
"generate_tensorflow_pytorch_and_flax": "PyTorch",
"is_encoder_decoder_model": "False"
}
templates/adding_a_new_model/tests/pt-seq-2-seq-bart-tokenizer.json deleted 100644 → 0
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{
"modelname": "PTNewENCDEC",
"uppercase_modelname": "PT_NEW_ENC_DEC",
"lowercase_modelname": "pt_new_enc_dec_template",
"camelcase_modelname": "PtNewEncDec",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "pt-new-enc-dec-base",
"tokenizer_type": "Based on BART",
"generate_tensorflow_pytorch_and_flax": "PyTorch",
"is_encoder_decoder_model": "True"
}
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{
"modelname": "TemplateBI",
"uppercase_modelname": "TEMPLATE_BI",
"lowercase_modelname": "template_bi",
"camelcase_modelname": "TemplateBi",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "bi-brand-new-bert-base-cased",
"tokenizer_type": "Standalone",
"generate_tensorflow_pytorch_and_flax": "PyTorch, TensorFlow and Flax",
"is_encoder_decoder_model": "False"
}
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{
"modelname": "TemplateTF",
"uppercase_modelname": "TEMPLATE_TF",
"lowercase_modelname": "template_tf",
"camelcase_modelname": "TemplateTf",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "brand-new-bert-base-cased",
"tokenizer_type": "Based on BERT",
"generate_tensorflow_pytorch_and_flax": "TensorFlow",
"is_encoder_decoder_model": "False"
}
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{
"modelname": "NewTFENCDEC",
"uppercase_modelname": "NEW_TF_ENC_DEC",
"lowercase_modelname": "new_tf_enc_dec_template",
"camelcase_modelname": "NewTFEncDec",
"authors": "The HuggingFace Team",
"checkpoint_identifier": "new-tf-enc-dec-base_template",
"tokenizer_type": "Based on BART",
"generate_tensorflow_pytorch_and_flax": "TensorFlow",
"is_encoder_decoder_model": "True"
}
utils/not_doctested.txt
View file @ d4e92f1a
......@@ -335,7 +335,6 @@ src/transformers/benchmark/benchmark_args_tf.py
src/transformers/benchmark/benchmark_args_utils.py
src/transformers/benchmark/benchmark_tf.py
src/transformers/benchmark/benchmark_utils.py
src/transformers/commands/add_new_model.py
src/transformers/commands/add_new_model_like.py
src/transformers/commands/convert.py
src/transformers/commands/download.py
......
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