From 52a18de38f31065e2e60b5f172db666c26997aef Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Tue, 23 Aug 2022 03:01:51 +0000
Subject: [PATCH 001/159] fix multi-layer-inherit
---
.../transformers/tokenizer_utils_base.py | 7 +++
tests/transformers/test_tokenizer_util.py | 57 +++++++++++++++++++
2 files changed, 64 insertions(+)
create mode 100644 tests/transformers/test_tokenizer_util.py
diff --git a/paddlenlp/transformers/tokenizer_utils_base.py b/paddlenlp/transformers/tokenizer_utils_base.py
index bccbb02b2ecc..04dafd6acb43 100644
--- a/paddlenlp/transformers/tokenizer_utils_base.py
+++ b/paddlenlp/transformers/tokenizer_utils_base.py
@@ -1353,8 +1353,15 @@ class PretrainedTokenizerBase(SpecialTokensMixin):
truncation_side: str = "right"
slow_tokenizer_class = None
+ # tag for init_kwargs
+ _have_done_init: bool = False
+
def __init__(self, **kwargs):
# inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
+ if self._have_done_init:
+ return
+ self._have_done_init = True
+
self.init_inputs = ()
self.init_kwargs = copy.deepcopy(kwargs)
self.name_or_path = kwargs.pop("name_or_path", "")
diff --git a/tests/transformers/test_tokenizer_util.py b/tests/transformers/test_tokenizer_util.py
new file mode 100644
index 000000000000..c222ad5fd461
--- /dev/null
+++ b/tests/transformers/test_tokenizer_util.py
@@ -0,0 +1,57 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import unittest
+import json
+import tempfile
+
+from paddlenlp.transformers.tokenizer_utils import PretrainedTokenizer
+
+
+class EmptyTokenizer(PretrainedTokenizer):
+
+ def __init__(self, a=1, b=2):
+ pass
+
+
+class SubEmptyTokenizer(EmptyTokenizer):
+
+ def __init__(self, c=3, d=4):
+ super().__init__(a=c, b=d)
+
+
+class TokenizerUtilsTest(unittest.TestCase):
+
+ def test_multi_inherit(self):
+ tokenizer = SubEmptyTokenizer()
+
+ self.assertIn('c', tokenizer.init_kwargs)
+ self.assertEqual(tokenizer.init_kwargs['c'], 3)
+
+ def test_config(self):
+ tmpdirname = tempfile.mkdtemp()
+
+ tokenizer = SubEmptyTokenizer()
+ tokenizer.save_pretrained(tmpdirname)
+
+ with open(os.path.join(tmpdirname, "tokenizer_config.json"),
+ 'r',
+ encoding='utf-8') as f:
+ data = json.load(f)
+
+ self.assertIn('c', data)
+ self.assertEqual(data['c'], 3)
+ self.assertEqual(data['tokenizer_class'], "SubEmptyTokenizer")
From 7b50b5450a5cffd58c415b2126eb82bae39c073c Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Wed, 24 Aug 2022 06:29:58 +0000
Subject: [PATCH 002/159] update bert model unittest
---
tests/transformers/bert/test_modeling.py | 156 +++++++++++++----------
1 file changed, 88 insertions(+), 68 deletions(-)
diff --git a/tests/transformers/bert/test_modeling.py b/tests/transformers/bert/test_modeling.py
index 7b2a7e093a86..c95a3597c950 100644
--- a/tests/transformers/bert/test_modeling.py
+++ b/tests/transformers/bert/test_modeling.py
@@ -15,44 +15,45 @@
import unittest
import paddle
+from parameterized import parameterized_class
from paddlenlp.transformers import BertModel, BertForQuestionAnswering, BertForSequenceClassification,\
BertForTokenClassification, BertForPretraining, BertForMultipleChoice, BertForMaskedLM, BertPretrainedModel
-from ..test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
-from ...testing_utils import slow
+
+from tests.testing_utils import slow
+from tests.transformers.test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
class BertModelTester:
- 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,
- initializer_range=0.02,
- pad_token_id=0,
- pool_act="tanh",
- fuse=False,
- type_sequence_label_size=2,
- num_labels=3,
- num_choices=4,
- num_classes=3,
- scope=None,
- ):
+ 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,
+ initializer_range=0.02,
+ pad_token_id=0,
+ pool_act="tanh",
+ fuse=False,
+ type_sequence_label_size=2,
+ num_labels=3,
+ num_choices=4,
+ num_classes=3,
+ scope=None,
+ return_dict=False):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
@@ -79,6 +80,7 @@ def __init__(
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
+ self.return_dict = return_dict
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
@@ -140,8 +142,10 @@ def create_and_check_model(
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)
+ result = model(input_ids,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ result = model(input_ids, return_dict=self.return_dict)
self.parent.assertEqual(
result[0].shape,
[self.batch_size, self.seq_length, self.hidden_size])
@@ -163,7 +167,8 @@ def create_and_check_for_masked_lm(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- labels=token_labels)
+ labels=token_labels,
+ return_dict=self.return_dict)
self.parent.assertEqual(
result[1].shape,
[self.batch_size, self.seq_length, self.vocab_size])
@@ -181,12 +186,15 @@ def create_and_check_model_past_large_inputs(
model = BertModel(**config)
model.eval()
+ self.return_dict = False
+
# first forward pass
outputs = model(input_ids,
attention_mask=input_mask,
use_cache=True,
- return_dict=True)
- past_key_values = outputs.past_key_values
+ return_dict=self.return_dict)
+ past_key_values = outputs.past_key_values if self.return_dict else outputs[
+ 2]
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), self.vocab_size)
@@ -196,15 +204,26 @@ def create_and_check_model_past_large_inputs(
next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1)
- output_from_no_past = model(next_input_ids,
- attention_mask=next_attention_mask,
- output_hidden_states=True,
- return_dict=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,
- return_dict=True)["hidden_states"][0]
+ outputs = model(next_input_ids,
+ attention_mask=next_attention_mask,
+ output_hidden_states=True,
+ return_dict=self.return_dict)
+
+ if self.return_dict:
+ output_from_no_past = outputs['hidden_states'][0]
+ else:
+ output_from_no_past = outputs[2][0]
+
+ outputs = model(next_tokens,
+ attention_mask=next_attention_mask,
+ past_key_values=past_key_values,
+ output_hidden_states=True,
+ return_dict=self.return_dict)
+
+ if self.return_dict:
+ output_from_past = outputs['hidden_states'][0]
+ else:
+ output_from_past = outputs[2][0]
# select random slice
random_slice_idx = ids_tensor((1, ), output_from_past.shape[-1]).item()
@@ -235,13 +254,12 @@ def create_and_check_for_pretraining(
):
model = BertForPretraining(BertModel(**config))
model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- labels=token_labels,
- next_sentence_label=sequence_labels,
- )
+ result = model(input_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ labels=token_labels,
+ next_sentence_label=sequence_labels,
+ return_dict=self.return_dict)
self.parent.assertEqual(
result[1].shape,
[self.batch_size, self.seq_length, self.vocab_size])
@@ -266,12 +284,11 @@ def create_and_check_for_multiple_choice(
[-1, self.num_choices, -1])
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(
[-1, self.num_choices, -1])
- result = model(
- multiple_choice_inputs_ids,
- attention_mask=multiple_choice_input_mask,
- token_type_ids=multiple_choice_token_type_ids,
- labels=choice_labels,
- )
+ result = model(multiple_choice_inputs_ids,
+ attention_mask=multiple_choice_input_mask,
+ token_type_ids=multiple_choice_token_type_ids,
+ labels=choice_labels,
+ return_dict=self.return_dict)
self.parent.assertEqual(result[1].shape,
[self.batch_size, self.num_choices])
@@ -287,13 +304,12 @@ def create_and_check_for_question_answering(
):
model = BertForQuestionAnswering(BertModel(**config))
model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- start_positions=sequence_labels,
- end_positions=sequence_labels,
- )
+ result = model(input_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ start_positions=sequence_labels,
+ end_positions=sequence_labels,
+ return_dict=self.return_dict)
self.parent.assertEqual(result[1].shape,
[self.batch_size, self.seq_length])
self.parent.assertEqual(result[2].shape,
@@ -315,7 +331,8 @@ def create_and_check_for_sequence_classification(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- labels=sequence_labels)
+ labels=sequence_labels,
+ return_dict=self.return_dict)
self.parent.assertEqual(result[1].shape,
[self.batch_size, self.num_classes])
@@ -335,7 +352,8 @@ def create_and_check_for_token_classification(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- labels=token_labels)
+ labels=token_labels,
+ return_dict=self.return_dict)
self.parent.assertEqual(
result[1].shape,
[self.batch_size, self.seq_length, self.num_classes])
@@ -359,8 +377,10 @@ def prepare_config_and_inputs_for_common(self):
return config, inputs_dict
+@parameterized_class(("return_dict", ), [[True], [False]])
class BertModelTest(ModelTesterMixin, unittest.TestCase):
base_model_class = BertModel
+ return_dict = False
all_model_classes = (
BertModel,
@@ -373,7 +393,7 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
)
def setUp(self):
- self.model_tester = BertModelTester(self)
+ self.model_tester = BertModelTester(self, return_dict=self.return_dict)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
From b1629c0cdfdc5c96e83dc733f5325500697d457f Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Wed, 24 Aug 2022 06:35:49 +0000
Subject: [PATCH 003/159] update requirements.txt
---
requirements.txt | 1 +
1 file changed, 1 insertion(+)
diff --git a/requirements.txt b/requirements.txt
index f92e71484d98..ab13cb3a4db1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,3 +10,4 @@ paddlefsl
sentencepiece
paddle2onnx
protobuf>=3.1.0, <=3.20.0
+parameterized
\ No newline at end of file
From d6f58c836f3de7cca2f3d14a249dfbd3d4b41ed2 Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Wed, 24 Aug 2022 06:57:47 +0000
Subject: [PATCH 004/159] update ernie modeling test
---
tests/transformers/ernie/test_modeling.py | 61 +++++++++++++++--------
1 file changed, 40 insertions(+), 21 deletions(-)
diff --git a/tests/transformers/ernie/test_modeling.py b/tests/transformers/ernie/test_modeling.py
index d6735344263a..7cd6ab575143 100644
--- a/tests/transformers/ernie/test_modeling.py
+++ b/tests/transformers/ernie/test_modeling.py
@@ -15,9 +15,11 @@
import unittest
import paddle
+from parameterized import parameterized_class
from paddlenlp.transformers import ErnieModel, ErnieForQuestionAnswering, ErnieForSequenceClassification,\
ErnieForTokenClassification, ErnieForPretraining, ErnieForMultipleChoice, ErnieForMaskedLM, ErniePretrainedModel
+
from ...transformers.test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
from ...testing_utils import slow
@@ -49,6 +51,7 @@ def __init__(
num_choices=4,
num_classes=3,
scope=None,
+ return_dict=False,
):
self.parent = parent
self.batch_size = batch_size
@@ -73,6 +76,7 @@ def __init__(
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
+ self.return_dict = return_dict
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
@@ -118,9 +122,12 @@ def create_and_check_model(
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)
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ result = model(input_ids,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ result = model(input_ids, return_dict=self.return_dict)
self.parent.assertEqual(
result[0].shape,
[self.batch_size, self.seq_length, self.hidden_size])
@@ -138,7 +145,11 @@ def create_and_check_for_masked_lm(
model.eval()
result = model(input_ids,
attention_mask=input_mask,
- token_type_ids=token_type_ids)
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(
result.shape, [self.batch_size, self.seq_length, self.vocab_size])
@@ -158,11 +169,12 @@ def create_and_check_for_multiple_choice(
[-1, self.num_choices, -1])
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(
[-1, self.num_choices, -1])
- result = model(
- multiple_choice_inputs_ids,
- attention_mask=multiple_choice_input_mask,
- token_type_ids=multiple_choice_token_type_ids,
- )
+ result = model(multiple_choice_inputs_ids,
+ attention_mask=multiple_choice_input_mask,
+ token_type_ids=multiple_choice_token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
self.parent.assertEqual(result.shape,
[self.batch_size, self.num_choices])
@@ -170,11 +182,10 @@ def create_and_check_for_question_answering(self, config, input_ids,
token_type_ids, input_mask):
model = ErnieForQuestionAnswering(ErnieModel(**config))
model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- )
+ result = model(input_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
self.parent.assertEqual(result[0].shape,
[self.batch_size, self.seq_length])
self.parent.assertEqual(result[1].shape,
@@ -190,11 +201,13 @@ def create_and_check_for_sequence_classification(
model = ErnieForSequenceClassification(ErnieModel(**config),
num_classes=self.num_classes)
model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- )
+ result = model(input_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(result.shape,
[self.batch_size, self.num_classes])
@@ -210,7 +223,11 @@ def create_and_check_for_token_classification(
model.eval()
result = model(input_ids,
attention_mask=input_mask,
- token_type_ids=token_type_ids)
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(
result.shape, [self.batch_size, self.seq_length, self.num_classes])
@@ -230,8 +247,10 @@ def prepare_config_and_inputs_for_common(self):
return config, inputs_dict
+@parameterized_class(("return_dict", ), [[False], [True]])
class ErnieModelTest(ModelTesterMixin, unittest.TestCase):
base_model_class = ErnieModel
+ return_dict = False
all_model_classes = (
ErnieModel,
@@ -244,7 +263,7 @@ class ErnieModelTest(ModelTesterMixin, unittest.TestCase):
)
def setUp(self):
- self.model_tester = ErnieModelTester(self)
+ self.model_tester = ErnieModelTester(self, return_dict=self.return_dict)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
From 522becff954ae2b851b582b7bd06d16f8d69f22a Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Wed, 24 Aug 2022 07:25:47 +0000
Subject: [PATCH 005/159] update roberta unittest
---
paddlenlp/transformers/roberta/modeling.py | 8 ++-
tests/transformers/roberta/test_modeling.py | 75 ++++++++++++++-------
2 files changed, 57 insertions(+), 26 deletions(-)
diff --git a/paddlenlp/transformers/roberta/modeling.py b/paddlenlp/transformers/roberta/modeling.py
index 50584a7376d7..f852663a7932 100644
--- a/paddlenlp/transformers/roberta/modeling.py
+++ b/paddlenlp/transformers/roberta/modeling.py
@@ -810,9 +810,13 @@ def forward(self,
loss = loss_fct(logits.reshape((-1, self.num_classes)),
labels.reshape((-1, )))
if not return_dict:
+
output = (logits, ) + outputs[2:]
- return ((loss, ) + output) if loss is not None else (
- output[0] if len(output) == 1 else output)
+ if loss is not None:
+ return (loss, ) + output
+ if len(output) == 1:
+ return output[0]
+ return output
return TokenClassifierOutput(
loss=loss,
diff --git a/tests/transformers/roberta/test_modeling.py b/tests/transformers/roberta/test_modeling.py
index 66e350c411ec..f38dbc0277b1 100644
--- a/tests/transformers/roberta/test_modeling.py
+++ b/tests/transformers/roberta/test_modeling.py
@@ -15,6 +15,7 @@
import unittest
import paddle
+from parameterized import parameterized_class
from paddlenlp.transformers import (
RobertaPretrainedModel,
@@ -35,10 +36,7 @@
class RobertaModelTester:
- def __init__(
- self,
- parent,
- ):
+ def __init__(self, parent, return_dict: bool = False):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
@@ -64,6 +62,7 @@ def __init__(
self.num_labels = 3
self.num_choices = 4
self.scope = None
+ self.return_dict = return_dict
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
@@ -121,14 +120,23 @@ def create_and_check_model(self, config, input_ids, token_type_ids,
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, return_dict=True)
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ result = model(input_ids,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ result = model(input_ids, return_dict=self.return_dict)
+
+ if self.return_dict:
+ last_hidden_state = result.last_hidden_state
+ pooler_output = result.pooler_output
+ else:
+ last_hidden_state, pooler_output = result[0], result[1]
self.parent.assertEqual(
- result.last_hidden_state.shape,
+ last_hidden_state.shape,
[self.batch_size, self.seq_length, self.hidden_size])
- self.parent.assertEqual(result.pooler_output.shape,
+ self.parent.assertEqual(pooler_output.shape,
[self.batch_size, self.hidden_size])
def create_and_check_for_causal_lm(
@@ -143,10 +151,12 @@ def create_and_check_for_causal_lm(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=True)
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(
- result.logits.shape,
- [self.batch_size, self.seq_length, self.vocab_size])
+ result.shape, [self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_for_masked_lm(
self,
@@ -160,10 +170,12 @@ def create_and_check_for_masked_lm(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=True)
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(
- result.logits.shape,
- [self.batch_size, self.seq_length, self.vocab_size])
+ result.shape, [self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_for_token_classification(self, config, input_ids,
token_type_ids, input_mask):
@@ -174,10 +186,13 @@ def create_and_check_for_token_classification(self, config, input_ids,
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=True)
+ return_dict=self.return_dict)
+
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(
- result.logits.shape,
- [self.batch_size, self.seq_length, self.num_labels])
+ result.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):
@@ -192,8 +207,11 @@ def create_and_check_for_multiple_choice(self, config, input_ids,
result = model(multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
- return_dict=True)
- self.parent.assertEqual(result.logits.shape,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
+ self.parent.assertEqual(result.shape,
[self.batch_size, self.num_choices])
def create_and_check_for_question_answering(self, config, input_ids,
@@ -203,10 +221,16 @@ def create_and_check_for_question_answering(self, config, input_ids,
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=True)
- self.parent.assertEqual(result.start_logits.shape,
+ return_dict=self.return_dict)
+
+ if self.return_dict:
+ start_logits, end_logits = result.start_logits, result.end_logits
+ else:
+ start_logits, end_logits = result[0], result[1]
+
+ self.parent.assertEqual(start_logits.shape,
[self.batch_size, self.seq_length])
- self.parent.assertEqual(result.end_logits.shape,
+ self.parent.assertEqual(end_logits.shape,
[self.batch_size, self.seq_length])
def prepare_config_and_inputs_for_common(self):
@@ -225,8 +249,10 @@ def prepare_config_and_inputs_for_common(self):
return config, inputs_dict
+@parameterized_class(("return_dict", ), [[True], [False]])
class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
base_model_class = RobertaModel
+ return_dict = False
all_model_classes = (
RobertaForCausalLM,
@@ -240,7 +266,8 @@ class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
all_generative_model_classes = (RobertaForCausalLM, )
def setUp(self):
- self.model_tester = RobertaModelTester(self)
+ self.model_tester = RobertaModelTester(self,
+ return_dict=self.return_dict)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
From 0f133c04acda5b49325562dd12446854218256a0 Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Wed, 24 Aug 2022 08:37:49 +0000
Subject: [PATCH 006/159] update roformer modeling testing
---
tests/transformers/roformer/test_modeling.py | 109 ++++++++++++-------
1 file changed, 70 insertions(+), 39 deletions(-)
diff --git a/tests/transformers/roformer/test_modeling.py b/tests/transformers/roformer/test_modeling.py
index 23a46ddc3c93..786dad6819b2 100644
--- a/tests/transformers/roformer/test_modeling.py
+++ b/tests/transformers/roformer/test_modeling.py
@@ -18,12 +18,14 @@
from dataclasses import dataclass, fields, Field
import paddle
+from parameterized import parameterized_class
-from paddlenlp.transformers import (
- RoFormerModel, RoFormerPretrainedModel, RoFormerForPretraining,
- RoFormerForSequenceClassification, RoFormerForTokenClassification,
- RoFormerForQuestionAnswering, RoFormerForMultipleChoice,
- RoFormerForMaskedLM)
+from paddlenlp.transformers import (RoFormerModel, RoFormerPretrainedModel,
+ RoFormerForSequenceClassification,
+ RoFormerForTokenClassification,
+ RoFormerForQuestionAnswering,
+ RoFormerForMultipleChoice,
+ RoFormerForMaskedLM)
from ..test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
from ...testing_utils import slow
@@ -75,11 +77,10 @@ class RoFormerModelTestConfig(RoFormerModelTestModelConfig):
class RoFormerModelTester:
- def __init__(
- self,
- parent,
- config: Optional[RoFormerModelTestConfig] = None,
- ):
+ def __init__(self,
+ parent,
+ config: Optional[RoFormerModelTestConfig] = None,
+ return_dict: bool = False):
self.parent = parent
self.config: RoFormerModelTestConfig = config or RoFormerModelTestConfig(
)
@@ -87,6 +88,7 @@ def __init__(
self.is_training = self.config.is_training
self.num_classes = self.config.num_classes
self.num_choices = self.config.num_choices
+ self.return_dict = return_dict
def prepare_config_and_inputs(self):
config = self.config
@@ -109,6 +111,11 @@ def prepare_config_and_inputs(self):
def get_config(self) -> dict:
return self.config.model_kwargs
+ def __getattr__(self, key: str):
+ if not hasattr(self.config, key):
+ raise AttributeError(f'attribute <{key}> not exist')
+ return getattr(self.config, key)
+
def create_and_check_model(
self,
config,
@@ -120,9 +127,16 @@ def create_and_check_model(
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)
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ result = model(input_ids,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ result = model(input_ids, return_dict=self.return_dict)
+
+ if self.return_dict:
+ result = [result.last_hidden_state, result.pooler_output]
+
self.parent.assertEqual(result[0].shape, [
self.config.batch_size, self.config.seq_length,
self.config.hidden_size
@@ -151,11 +165,12 @@ def create_and_check_for_multiple_choice(
input_mask = input_mask.unsqueeze(1).expand(
[-1, self.config.num_choices, -1])
- result = model(
- multiple_choice_inputs_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- )
+ result = model(multiple_choice_inputs_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
self.parent.assertEqual(
result.shape, [self.config.batch_size, self.config.num_choices])
@@ -163,15 +178,20 @@ def create_and_check_for_question_answering(self, config, input_ids,
token_type_ids, input_mask):
model = RoFormerForQuestionAnswering(RoFormerModel(**config))
model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- )
+ result = model(input_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ start_logits, end_logits = result.start_logits, result.end_logits
+ else:
+ start_logits, end_logits = result[0], result[1]
+
self.parent.assertEqual(
- result[0].shape, [self.config.batch_size, self.config.seq_length])
+ start_logits.shape,
+ [self.config.batch_size, self.config.seq_length])
self.parent.assertEqual(
- result[1].shape, [self.config.batch_size, self.config.seq_length])
+ end_logits.shape, [self.config.batch_size, self.config.seq_length])
def create_and_check_for_token_classification(
self,
@@ -185,7 +205,11 @@ def create_and_check_for_token_classification(
model.eval()
result = model(input_ids,
attention_mask=input_mask,
- token_type_ids=token_type_ids)
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(result.shape, [
self.config.batch_size, self.config.seq_length,
self.config.num_classes
@@ -202,7 +226,11 @@ def create_and_check_for_masked_lm(
model.eval()
result = model(input_ids,
attention_mask=input_mask,
- token_type_ids=token_type_ids)
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(result.shape, [
self.config.batch_size, self.config.seq_length,
self.config.vocab_size
@@ -218,11 +246,13 @@ def create_and_check_for_sequence_classification(
model = RoFormerForSequenceClassification(
RoFormerModel(**config), num_classes=self.config.num_classes)
model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- )
+ result = model(input_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+
self.parent.assertEqual(
result.shape, [self.config.batch_size, self.config.num_classes])
@@ -242,18 +272,19 @@ def prepare_config_and_inputs_for_common(self):
return config, inputs_dict
+@parameterized_class(("return_dict", ), [[True], [False]])
class RoFormerModelTest(ModelTesterMixin, unittest.TestCase):
base_model_class = RoFormerModel
+ return_dict: bool = False
- all_model_classes = (
- RoFormerModel,
- RoFormerForMultipleChoice,
- RoFormerForPretraining,
- RoFormerForSequenceClassification,
- )
+ all_model_classes = (RoFormerModel, RoFormerForSequenceClassification,
+ RoFormerForTokenClassification,
+ RoFormerForQuestionAnswering,
+ RoFormerForMultipleChoice, RoFormerForMaskedLM)
def setUp(self):
- self.model_tester = RoFormerModelTester(self)
+ self.model_tester = RoFormerModelTester(self,
+ return_dict=self.return_dict)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
From d4b41227bb9746fe4a964dd7428e44beeee2de6a Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Wed, 24 Aug 2022 13:07:19 +0000
Subject: [PATCH 007/159] complete ernie label loss
---
tests/transformers/ernie/test_modeling.py | 140 ++++++++++++++++------
1 file changed, 104 insertions(+), 36 deletions(-)
diff --git a/tests/transformers/ernie/test_modeling.py b/tests/transformers/ernie/test_modeling.py
index 7cd6ab575143..468c9818ca65 100644
--- a/tests/transformers/ernie/test_modeling.py
+++ b/tests/transformers/ernie/test_modeling.py
@@ -20,39 +20,38 @@
from paddlenlp.transformers import ErnieModel, ErnieForQuestionAnswering, ErnieForSequenceClassification,\
ErnieForTokenClassification, ErnieForPretraining, ErnieForMultipleChoice, ErnieForMaskedLM, ErniePretrainedModel
-from ...transformers.test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
+from ..test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
from ...testing_utils import slow
class ErnieModelTester:
- def __init__(
- self,
- parent,
- batch_size=13,
- seq_length=7,
- is_training=True,
- use_input_mask=True,
- use_token_type_ids=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=2,
- initializer_range=0.02,
- pad_token_id=0,
- type_sequence_label_size=2,
- num_labels=3,
- num_choices=4,
- num_classes=3,
- scope=None,
- return_dict=False,
- ):
+ def __init__(self,
+ parent,
+ batch_size=13,
+ seq_length=7,
+ is_training=True,
+ use_input_mask=True,
+ use_token_type_ids=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=2,
+ initializer_range=0.02,
+ pad_token_id=0,
+ type_sequence_label_size=2,
+ num_labels=3,
+ num_choices=4,
+ num_classes=3,
+ scope=None,
+ return_dict: bool = False,
+ use_labels: bool = False):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
@@ -77,6 +76,7 @@ def __init__(
self.num_choices = num_choices
self.scope = scope
self.return_dict = return_dict
+ self.use_labels = use_labels
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
@@ -92,8 +92,19 @@ def prepare_config_and_inputs(self):
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
+ return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return {
@@ -117,6 +128,9 @@ def create_and_check_model(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = ErnieModel(**config)
model.eval()
@@ -140,15 +154,21 @@ def create_and_check_for_masked_lm(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = ErnieForMaskedLM(ErnieModel(**config))
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ labels=token_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(
result.shape, [self.batch_size, self.seq_length, self.vocab_size])
@@ -159,6 +179,9 @@ def create_and_check_for_multiple_choice(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = ErnieForMultipleChoice(ErnieModel(**config),
num_choices=self.num_choices)
@@ -172,23 +195,45 @@ def create_and_check_for_multiple_choice(
result = model(multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
+ labels=choice_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
+
self.parent.assertEqual(result.shape,
[self.batch_size, self.num_choices])
- def create_and_check_for_question_answering(self, config, input_ids,
- token_type_ids, input_mask):
+ def create_and_check_for_question_answering(
+ self,
+ config,
+ input_ids,
+ token_type_ids,
+ input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
+ ):
model = ErnieForQuestionAnswering(ErnieModel(**config))
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ start_positions=sequence_labels,
+ end_positions=sequence_labels,
return_dict=self.return_dict)
- self.parent.assertEqual(result[0].shape,
+
+ if self.return_dict:
+ start_logits, end_logits = result.start_logits, result.end_logits
+ elif self.use_labels:
+ start_logits, end_logits = result[1], result[2]
+ else:
+ start_logits, end_logits = result[0], result[1]
+
+ self.parent.assertEqual(start_logits.shape,
[self.batch_size, self.seq_length])
- self.parent.assertEqual(result[1].shape,
+ self.parent.assertEqual(end_logits.shape,
[self.batch_size, self.seq_length])
def create_and_check_for_sequence_classification(
@@ -197,6 +242,9 @@ def create_and_check_for_sequence_classification(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = ErnieForSequenceClassification(ErnieModel(**config),
num_classes=self.num_classes)
@@ -204,9 +252,12 @@ def create_and_check_for_sequence_classification(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ labels=sequence_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(result.shape,
[self.batch_size, self.num_classes])
@@ -217,6 +268,9 @@ def create_and_check_for_token_classification(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = ErnieForTokenClassification(ErnieModel(**config),
num_classes=self.num_classes)
@@ -224,9 +278,12 @@ def create_and_check_for_token_classification(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ labels=token_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(
result.shape, [self.batch_size, self.seq_length, self.num_classes])
@@ -238,6 +295,9 @@ def prepare_config_and_inputs_for_common(self):
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
@@ -247,10 +307,16 @@ def prepare_config_and_inputs_for_common(self):
return config, inputs_dict
-@parameterized_class(("return_dict", ), [[False], [True]])
+@parameterized_class(("return_dict", "use_labels"), [
+ [False, False],
+ [False, True],
+ [True, False],
+ [True, True],
+])
class ErnieModelTest(ModelTesterMixin, unittest.TestCase):
base_model_class = ErnieModel
- return_dict = False
+ return_dict: bool = False
+ use_labels: bool = False
all_model_classes = (
ErnieModel,
@@ -263,7 +329,9 @@ class ErnieModelTest(ModelTesterMixin, unittest.TestCase):
)
def setUp(self):
- self.model_tester = ErnieModelTester(self, return_dict=self.return_dict)
+ self.model_tester = ErnieModelTester(self,
+ use_labels=self.use_labels,
+ return_dict=self.return_dict)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
From 200af6de8d728d0c9819ad540633b28cdff1da76 Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Thu, 25 Aug 2022 02:45:09 +0000
Subject: [PATCH 008/159] complete ernie/roberta/roformer unittest
---
tests/transformers/roberta/test_modeling.py | 150 ++++++++++++++++---
tests/transformers/roformer/test_modeling.py | 90 +++++++++--
2 files changed, 212 insertions(+), 28 deletions(-)
diff --git a/tests/transformers/roberta/test_modeling.py b/tests/transformers/roberta/test_modeling.py
index f38dbc0277b1..f63eb6bfacab 100644
--- a/tests/transformers/roberta/test_modeling.py
+++ b/tests/transformers/roberta/test_modeling.py
@@ -28,7 +28,7 @@
RobertaModel,
)
-from ...transformers.test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
+from ..test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
from ...testing_utils import slow
ROBERTA_TINY = "sshleifer/tiny-distilroberta-base"
@@ -36,7 +36,10 @@
class RobertaModelTester:
- def __init__(self, parent, return_dict: bool = False):
+ def __init__(self,
+ parent,
+ return_dict: bool = False,
+ use_labels: bool = False):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
@@ -63,6 +66,7 @@ def __init__(self, parent, return_dict: bool = False):
self.num_choices = 4
self.scope = None
self.return_dict = return_dict
+ self.use_labels = use_labels
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
@@ -78,8 +82,18 @@ def prepare_config_and_inputs(self):
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
+ return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return {
@@ -105,6 +119,9 @@ def prepare_config_and_inputs_for_decoder(self):
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
) = self.prepare_config_and_inputs()
return (
@@ -112,10 +129,21 @@ def prepare_config_and_inputs_for_decoder(self):
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
)
- def create_and_check_model(self, config, input_ids, token_type_ids,
- input_mask):
+ def create_and_check_model(
+ self,
+ config,
+ input_ids,
+ token_type_ids,
+ input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
+ ):
model = RobertaModel(**config)
model.eval()
result = model(input_ids,
@@ -145,15 +173,21 @@ def create_and_check_for_causal_lm(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = RobertaForCausalLM(RobertaModel(**config))
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ labels=token_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(
result.shape, [self.batch_size, self.seq_length, self.vocab_size])
@@ -164,21 +198,35 @@ def create_and_check_for_masked_lm(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = RobertaForMaskedLM(RobertaModel(**config))
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ labels=token_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(
result.shape, [self.batch_size, self.seq_length, self.vocab_size])
- def create_and_check_for_token_classification(self, config, input_ids,
- token_type_ids, input_mask):
+ def create_and_check_for_token_classification(
+ self,
+ config,
+ input_ids,
+ token_type_ids,
+ input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
+ ):
model = RobertaForTokenClassification(RobertaModel(**config),
num_classes=self.num_labels,
dropout=None)
@@ -186,16 +234,53 @@ def create_and_check_for_token_classification(self, config, input_ids,
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
+ return_dict=self.return_dict,
+ labels=token_labels)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(
result.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):
+ def create_and_check_for_sequence_classification(
+ self,
+ config,
+ input_ids,
+ token_type_ids,
+ input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
+ ):
+ model = RobertaForSequenceClassification(RobertaModel(**config),
+ num_classes=self.num_labels)
+ model.eval()
+ result = model(input_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ labels=sequence_labels,
+ return_dict=self.return_dict)
+ if self.return_dict:
+ result = result.logits
+ elif self.use_labels:
+ result = result[1]
+
+ self.parent.assertEqual(result.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,
+ ):
model = RobertaForMultipleChoice(RobertaModel(**config))
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(
@@ -207,24 +292,40 @@ def create_and_check_for_multiple_choice(self, config, input_ids,
result = model(multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
- return_dict=self.return_dict)
+ return_dict=self.return_dict,
+ labels=choice_labels)
+
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(result.shape,
[self.batch_size, self.num_choices])
- def create_and_check_for_question_answering(self, config, input_ids,
- token_type_ids, input_mask):
+ def create_and_check_for_question_answering(
+ self,
+ config,
+ input_ids,
+ token_type_ids,
+ input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
+ ):
model = RobertaForQuestionAnswering(RobertaModel(**config))
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
+ return_dict=self.return_dict,
+ start_positions=sequence_labels,
+ end_positions=sequence_labels)
if self.return_dict:
start_logits, end_logits = result.start_logits, result.end_logits
+ elif self.use_labels:
+ start_logits, end_logits = result[1], result[2]
else:
start_logits, end_logits = result[0], result[1]
@@ -240,6 +341,9 @@ def prepare_config_and_inputs_for_common(self):
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
@@ -249,10 +353,16 @@ def prepare_config_and_inputs_for_common(self):
return config, inputs_dict
-@parameterized_class(("return_dict", ), [[True], [False]])
+@parameterized_class(("return_dict", "use_labels"), [
+ [False, False],
+ [False, True],
+ [True, False],
+ [True, True],
+])
class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
base_model_class = RobertaModel
- return_dict = False
+ return_dict: bool = False
+ use_labels: bool = False
all_model_classes = (
RobertaForCausalLM,
@@ -267,7 +377,8 @@ class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
def setUp(self):
self.model_tester = RobertaModelTester(self,
- return_dict=self.return_dict)
+ return_dict=self.return_dict,
+ use_labels=self.use_labels)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -282,6 +393,11 @@ 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_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(
diff --git a/tests/transformers/roformer/test_modeling.py b/tests/transformers/roformer/test_modeling.py
index 786dad6819b2..17f36d107825 100644
--- a/tests/transformers/roformer/test_modeling.py
+++ b/tests/transformers/roformer/test_modeling.py
@@ -69,6 +69,7 @@ class RoFormerModelTestConfig(RoFormerModelTestModelConfig):
is_training: bool = False
use_input_mask: bool = False
use_token_type_ids: bool = True
+ type_sequence_label_size: int = 2
# used for sequence classification
num_classes: int = 3
@@ -80,7 +81,8 @@ class RoFormerModelTester:
def __init__(self,
parent,
config: Optional[RoFormerModelTestConfig] = None,
- return_dict: bool = False):
+ return_dict: bool = False,
+ use_labels: bool = False):
self.parent = parent
self.config: RoFormerModelTestConfig = config or RoFormerModelTestConfig(
)
@@ -88,7 +90,10 @@ def __init__(self,
self.is_training = self.config.is_training
self.num_classes = self.config.num_classes
self.num_choices = self.config.num_choices
+
+ self.type_sequence_label_size = self.config.type_sequence_label_size
self.return_dict = return_dict
+ self.use_labels = use_labels
def prepare_config_and_inputs(self):
config = self.config
@@ -105,8 +110,19 @@ def prepare_config_and_inputs(self):
token_type_ids = ids_tensor([config.batch_size, config.seq_length],
config.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_classes)
+ choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
config = self.get_config()
- return config, input_ids, token_type_ids, input_mask
+ return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self) -> dict:
return self.config.model_kwargs
@@ -122,6 +138,9 @@ def create_and_check_model(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = RoFormerModel(**config)
model.eval()
@@ -150,6 +169,9 @@ def create_and_check_for_multiple_choice(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = RoFormerForMultipleChoice(RoFormerModel(**config),
num_choices=self.config.num_choices)
@@ -168,22 +190,40 @@ def create_and_check_for_multiple_choice(
result = model(multiple_choice_inputs_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
+ return_dict=self.return_dict,
+ labels=choice_labels)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
+
self.parent.assertEqual(
result.shape, [self.config.batch_size, self.config.num_choices])
- def create_and_check_for_question_answering(self, config, input_ids,
- token_type_ids, input_mask):
+ def create_and_check_for_question_answering(
+ self,
+ config,
+ input_ids,
+ token_type_ids,
+ input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
+ ):
model = RoFormerForQuestionAnswering(RoFormerModel(**config))
model.eval()
- result = model(input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- return_dict=self.return_dict)
+ result = model(
+ input_ids,
+ attention_mask=input_mask,
+ token_type_ids=token_type_ids,
+ return_dict=self.return_dict,
+ start_positions=sequence_labels,
+ end_positions=sequence_labels,
+ )
if self.return_dict:
start_logits, end_logits = result.start_logits, result.end_logits
+ elif self.use_labels:
+ start_logits, end_logits = result[1], result[2]
else:
start_logits, end_logits = result[0], result[1]
@@ -199,6 +239,9 @@ def create_and_check_for_token_classification(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = RoFormerForTokenClassification(RoFormerModel(**config),
num_classes=self.num_classes)
@@ -206,9 +249,12 @@ def create_and_check_for_token_classification(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ labels=token_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(result.shape, [
self.config.batch_size, self.config.seq_length,
@@ -221,15 +267,21 @@ def create_and_check_for_masked_lm(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = RoFormerForMaskedLM(RoFormerModel(**config))
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ labels=token_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(result.shape, [
self.config.batch_size, self.config.seq_length,
@@ -242,6 +294,9 @@ def create_and_check_for_sequence_classification(
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
):
model = RoFormerForSequenceClassification(
RoFormerModel(**config), num_classes=self.config.num_classes)
@@ -249,9 +304,12 @@ def create_and_check_for_sequence_classification(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
+ labels=sequence_labels,
return_dict=self.return_dict)
if self.return_dict:
result = result.logits
+ elif self.use_labels:
+ result = result[1]
self.parent.assertEqual(
result.shape, [self.config.batch_size, self.config.num_classes])
@@ -263,6 +321,9 @@ def prepare_config_and_inputs_for_common(self):
input_ids,
token_type_ids,
input_mask,
+ sequence_labels,
+ token_labels,
+ choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
@@ -272,10 +333,16 @@ def prepare_config_and_inputs_for_common(self):
return config, inputs_dict
-@parameterized_class(("return_dict", ), [[True], [False]])
+@parameterized_class(("return_dict", "use_labels"), [
+ [False, False],
+ [False, True],
+ [True, False],
+ [True, True],
+])
class RoFormerModelTest(ModelTesterMixin, unittest.TestCase):
base_model_class = RoFormerModel
return_dict: bool = False
+ use_labels: bool = False
all_model_classes = (RoFormerModel, RoFormerForSequenceClassification,
RoFormerForTokenClassification,
@@ -284,7 +351,8 @@ class RoFormerModelTest(ModelTesterMixin, unittest.TestCase):
def setUp(self):
self.model_tester = RoFormerModelTester(self,
- return_dict=self.return_dict)
+ return_dict=self.return_dict,
+ use_labels=self.use_labels)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
From 26daca4cf15e36ccd1ef73f1d08441d272a917fa Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Thu, 25 Aug 2022 12:09:05 +0000
Subject: [PATCH 009/159] update label/loss
---
tests/transformers/bert/test_modeling.py | 45 +++++----
tests/transformers/ernie/test_modeling.py | 75 +++++++-------
tests/transformers/roberta/test_modeling.py | 100 +++++++++----------
tests/transformers/roformer/test_modeling.py | 80 +++++++--------
4 files changed, 145 insertions(+), 155 deletions(-)
diff --git a/tests/transformers/bert/test_modeling.py b/tests/transformers/bert/test_modeling.py
index 9e841d46f139..81ef6ded0f2d 100644
--- a/tests/transformers/bert/test_modeling.py
+++ b/tests/transformers/bert/test_modeling.py
@@ -169,8 +169,13 @@ def create_and_check_for_masked_lm(
token_type_ids=token_type_ids,
labels=token_labels,
return_dict=self.return_dict)
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
+
self.parent.assertEqual(
- result[1].shape,
+ result[0].shape,
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_model_past_large_inputs(
@@ -186,8 +191,6 @@ def create_and_check_model_past_large_inputs(
model = BertModel(**config)
model.eval()
- self.return_dict = False
-
# first forward pass
outputs = model(input_ids,
attention_mask=input_mask,
@@ -209,10 +212,7 @@ def create_and_check_model_past_large_inputs(
output_hidden_states=True,
return_dict=self.return_dict)
- if self.return_dict:
- output_from_no_past = outputs['hidden_states'][0]
- else:
- output_from_no_past = outputs[2][0]
+ output_from_no_past = outputs[2][0]
outputs = model(next_tokens,
attention_mask=next_attention_mask,
@@ -220,10 +220,7 @@ def create_and_check_model_past_large_inputs(
output_hidden_states=True,
return_dict=self.return_dict)
- if self.return_dict:
- output_from_past = outputs['hidden_states'][0]
- else:
- output_from_past = outputs[2][0]
+ output_from_past = outputs[2][0]
# select random slice
random_slice_idx = ids_tensor((1, ), output_from_past.shape[-1]).item()
@@ -257,13 +254,11 @@ def create_and_check_for_pretraining(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- labels=token_labels,
- next_sentence_label=sequence_labels,
return_dict=self.return_dict)
self.parent.assertEqual(
- result[1].shape,
+ result[0].shape,
[self.batch_size, self.seq_length, self.vocab_size])
- self.parent.assertEqual(result[2].shape, [self.batch_size, 2])
+ self.parent.assertEqual(result[1].shape, [self.batch_size, 2])
def create_and_check_for_multiple_choice(
self,
@@ -289,7 +284,12 @@ def create_and_check_for_multiple_choice(
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
return_dict=self.return_dict)
- self.parent.assertEqual(result[1].shape,
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
+
+ self.parent.assertEqual(result[0].shape,
[self.batch_size, self.num_choices])
def create_and_check_for_question_answering(
@@ -310,6 +310,11 @@ def create_and_check_for_question_answering(
start_positions=sequence_labels,
end_positions=sequence_labels,
return_dict=self.return_dict)
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
+
self.parent.assertEqual(result[1].shape,
[self.batch_size, self.seq_length])
self.parent.assertEqual(result[2].shape,
@@ -377,10 +382,16 @@ def prepare_config_and_inputs_for_common(self):
return config, inputs_dict
-@parameterized_class(("return_dict", ), [[True], [False]])
+@parameterized_class(("return_dict", "use_labels"), [
+ [False, False],
+ [False, True],
+ [True, False],
+ [True, True],
+])
class BertModelTest(ModelTesterMixin, unittest.TestCase):
base_model_class = BertModel
return_dict = False
+ use_labels = False
all_model_classes = (
BertModel,
diff --git a/tests/transformers/ernie/test_modeling.py b/tests/transformers/ernie/test_modeling.py
index 468c9818ca65..69f072897bc0 100644
--- a/tests/transformers/ernie/test_modeling.py
+++ b/tests/transformers/ernie/test_modeling.py
@@ -49,9 +49,7 @@ def __init__(self,
num_labels=3,
num_choices=4,
num_classes=3,
- scope=None,
- return_dict: bool = False,
- use_labels: bool = False):
+ scope=None):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
@@ -75,8 +73,6 @@ def __init__(self,
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
- self.return_dict = return_dict
- self.use_labels = use_labels
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
@@ -96,7 +92,7 @@ def prepare_config_and_inputs(self):
token_labels = None
choice_labels = None
- if self.use_labels:
+ if self.parent.use_labels:
sequence_labels = ids_tensor([self.batch_size],
self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length],
@@ -137,11 +133,11 @@ def create_and_check_model(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
+ return_dict=self.parent.return_dict)
result = model(input_ids,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
- result = model(input_ids, return_dict=self.return_dict)
+ return_dict=self.parent.return_dict)
+ result = model(input_ids, return_dict=self.parent.return_dict)
self.parent.assertEqual(
result[0].shape,
[self.batch_size, self.seq_length, self.hidden_size])
@@ -164,14 +160,16 @@ def create_and_check_for_masked_lm(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
self.parent.assertEqual(
- result.shape, [self.batch_size, self.seq_length, self.vocab_size])
+ result[0].shape,
+ [self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_for_multiple_choice(
self,
@@ -196,13 +194,13 @@ def create_and_check_for_multiple_choice(
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+ if choice_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
- self.parent.assertEqual(result.shape,
+ self.parent.assertEqual(result[0].shape,
[self.batch_size, self.num_choices])
def create_and_check_for_question_answering(
@@ -222,11 +220,9 @@ def create_and_check_for_question_answering(
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
- return_dict=self.return_dict)
+ return_dict=self.parent.return_dict)
- if self.return_dict:
- start_logits, end_logits = result.start_logits, result.end_logits
- elif self.use_labels:
+ if sequence_labels is not None:
start_logits, end_logits = result[1], result[2]
else:
start_logits, end_logits = result[0], result[1]
@@ -253,13 +249,13 @@ def create_and_check_for_sequence_classification(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+ if sequence_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
- self.parent.assertEqual(result.shape,
+ self.parent.assertEqual(result[0].shape,
[self.batch_size, self.num_classes])
def create_and_check_for_token_classification(
@@ -279,14 +275,15 @@ def create_and_check_for_token_classification(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
self.parent.assertEqual(
- result.shape, [self.batch_size, self.seq_length, self.num_classes])
+ result[0].shape,
+ [self.batch_size, self.seq_length, self.num_classes])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
@@ -329,9 +326,7 @@ class ErnieModelTest(ModelTesterMixin, unittest.TestCase):
)
def setUp(self):
- self.model_tester = ErnieModelTester(self,
- use_labels=self.use_labels,
- return_dict=self.return_dict)
+ self.model_tester = ErnieModelTester(self)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
diff --git a/tests/transformers/roberta/test_modeling.py b/tests/transformers/roberta/test_modeling.py
index f63eb6bfacab..b7a2d7a8610f 100644
--- a/tests/transformers/roberta/test_modeling.py
+++ b/tests/transformers/roberta/test_modeling.py
@@ -36,10 +36,7 @@
class RobertaModelTester:
- def __init__(self,
- parent,
- return_dict: bool = False,
- use_labels: bool = False):
+ def __init__(self, parent):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
@@ -65,8 +62,6 @@ def __init__(self,
self.num_labels = 3
self.num_choices = 4
self.scope = None
- self.return_dict = return_dict
- self.use_labels = use_labels
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
@@ -85,7 +80,7 @@ def prepare_config_and_inputs(self):
sequence_labels = None
token_labels = None
choice_labels = None
- if self.use_labels:
+ if self.parent.use_labels:
sequence_labels = ids_tensor([self.batch_size],
self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length],
@@ -149,22 +144,16 @@ def create_and_check_model(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
+ return_dict=self.parent.return_dict)
result = model(input_ids,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
- result = model(input_ids, return_dict=self.return_dict)
-
- if self.return_dict:
- last_hidden_state = result.last_hidden_state
- pooler_output = result.pooler_output
- else:
- last_hidden_state, pooler_output = result[0], result[1]
+ return_dict=self.parent.return_dict)
+ result = model(input_ids, return_dict=self.parent.return_dict)
self.parent.assertEqual(
- last_hidden_state.shape,
+ result[0].shape,
[self.batch_size, self.seq_length, self.hidden_size])
- self.parent.assertEqual(pooler_output.shape,
+ self.parent.assertEqual(result[1].shape,
[self.batch_size, self.hidden_size])
def create_and_check_for_causal_lm(
@@ -183,14 +172,15 @@ def create_and_check_for_causal_lm(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
self.parent.assertEqual(
- result.shape, [self.batch_size, self.seq_length, self.vocab_size])
+ result[0].shape,
+ [self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_for_masked_lm(
self,
@@ -208,14 +198,16 @@ def create_and_check_for_masked_lm(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
self.parent.assertEqual(
- result.shape, [self.batch_size, self.seq_length, self.vocab_size])
+ result[0].shape,
+ [self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_for_token_classification(
self,
@@ -234,16 +226,17 @@ def create_and_check_for_token_classification(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict,
+ return_dict=self.parent.return_dict,
labels=token_labels)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
self.parent.assertEqual(
- result.shape, [self.batch_size, self.seq_length, self.num_labels])
+ result[0].shape,
+ [self.batch_size, self.seq_length, self.num_labels])
def create_and_check_for_sequence_classification(
self,
@@ -262,13 +255,14 @@ def create_and_check_for_sequence_classification(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
- self.parent.assertEqual(result.shape,
+ self.parent.assertEqual(result[0].shape,
[self.batch_size, self.num_labels])
def create_and_check_for_multiple_choice(
@@ -292,15 +286,15 @@ def create_and_check_for_multiple_choice(
result = model(multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
- return_dict=self.return_dict,
+ return_dict=self.parent.return_dict,
labels=choice_labels)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
- self.parent.assertEqual(result.shape,
+ self.parent.assertEqual(result[0].shape,
[self.batch_size, self.num_choices])
def create_and_check_for_question_answering(
@@ -318,13 +312,11 @@ def create_and_check_for_question_answering(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict,
+ return_dict=self.parent.return_dict,
start_positions=sequence_labels,
end_positions=sequence_labels)
- if self.return_dict:
- start_logits, end_logits = result.start_logits, result.end_logits
- elif self.use_labels:
+ if sequence_labels is not None:
start_logits, end_logits = result[1], result[2]
else:
start_logits, end_logits = result[0], result[1]
@@ -376,9 +368,7 @@ class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
all_generative_model_classes = (RobertaForCausalLM, )
def setUp(self):
- self.model_tester = RobertaModelTester(self,
- return_dict=self.return_dict,
- use_labels=self.use_labels)
+ self.model_tester = RobertaModelTester(self)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
diff --git a/tests/transformers/roformer/test_modeling.py b/tests/transformers/roformer/test_modeling.py
index 17f36d107825..a26b4a46cb2e 100644
--- a/tests/transformers/roformer/test_modeling.py
+++ b/tests/transformers/roformer/test_modeling.py
@@ -80,9 +80,7 @@ class RoFormerModelTester:
def __init__(self,
parent,
- config: Optional[RoFormerModelTestConfig] = None,
- return_dict: bool = False,
- use_labels: bool = False):
+ config: Optional[RoFormerModelTestConfig] = None):
self.parent = parent
self.config: RoFormerModelTestConfig = config or RoFormerModelTestConfig(
)
@@ -92,8 +90,6 @@ def __init__(self,
self.num_choices = self.config.num_choices
self.type_sequence_label_size = self.config.type_sequence_label_size
- self.return_dict = return_dict
- self.use_labels = use_labels
def prepare_config_and_inputs(self):
config = self.config
@@ -114,7 +110,7 @@ def prepare_config_and_inputs(self):
token_labels = None
choice_labels = None
- if self.use_labels:
+ if self.parent.use_labels:
sequence_labels = ids_tensor([self.batch_size],
self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length],
@@ -147,14 +143,11 @@ def create_and_check_model(
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
+ return_dict=self.parent.return_dict)
result = model(input_ids,
token_type_ids=token_type_ids,
- return_dict=self.return_dict)
- result = model(input_ids, return_dict=self.return_dict)
-
- if self.return_dict:
- result = [result.last_hidden_state, result.pooler_output]
+ return_dict=self.parent.return_dict)
+ result = model(input_ids, return_dict=self.parent.return_dict)
self.parent.assertEqual(result[0].shape, [
self.config.batch_size, self.config.seq_length,
@@ -190,15 +183,16 @@ def create_and_check_for_multiple_choice(
result = model(multiple_choice_inputs_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict,
+ return_dict=self.parent.return_dict,
labels=choice_labels)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
self.parent.assertEqual(
- result.shape, [self.config.batch_size, self.config.num_choices])
+ result[0].shape, [self.config.batch_size, self.config.num_choices])
def create_and_check_for_question_answering(
self,
@@ -216,13 +210,12 @@ def create_and_check_for_question_answering(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
- return_dict=self.return_dict,
+ return_dict=self.parent.return_dict,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
- if self.return_dict:
- start_logits, end_logits = result.start_logits, result.end_logits
- elif self.use_labels:
+
+ if sequence_labels is not None:
start_logits, end_logits = result[1], result[2]
else:
start_logits, end_logits = result[0], result[1]
@@ -250,13 +243,14 @@ def create_and_check_for_token_classification(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
- self.parent.assertEqual(result.shape, [
+ self.parent.assertEqual(result[0].shape, [
self.config.batch_size, self.config.seq_length,
self.config.num_classes
])
@@ -277,13 +271,14 @@ def create_and_check_for_masked_lm(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
- self.parent.assertEqual(result.shape, [
+ self.parent.assertEqual(result[0].shape, [
self.config.batch_size, self.config.seq_length,
self.config.vocab_size
])
@@ -305,14 +300,15 @@ def create_and_check_for_sequence_classification(
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
- return_dict=self.return_dict)
- if self.return_dict:
- result = result.logits
- elif self.use_labels:
- result = result[1]
+ return_dict=self.parent.return_dict)
+
+ if token_labels is not None:
+ result = result[1:]
+ elif paddle.is_tensor(result):
+ result = [result]
self.parent.assertEqual(
- result.shape, [self.config.batch_size, self.config.num_classes])
+ result[0].shape, [self.config.batch_size, self.config.num_classes])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
@@ -350,9 +346,7 @@ class RoFormerModelTest(ModelTesterMixin, unittest.TestCase):
RoFormerForMultipleChoice, RoFormerForMaskedLM)
def setUp(self):
- self.model_tester = RoFormerModelTester(self,
- return_dict=self.return_dict,
- use_labels=self.use_labels)
+ self.model_tester = RoFormerModelTester(self)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
From 3a90faa9164793d01182cb9a0ab029c4010af833 Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Tue, 30 Aug 2022 11:52:02 +0000
Subject: [PATCH 010/159] update refactor code
---
paddlenlp/transformers/tokenizer_utils_base.py | 11 +++--------
1 file changed, 3 insertions(+), 8 deletions(-)
diff --git a/paddlenlp/transformers/tokenizer_utils_base.py b/paddlenlp/transformers/tokenizer_utils_base.py
index 244a3a809eab..88f66541b959 100644
--- a/paddlenlp/transformers/tokenizer_utils_base.py
+++ b/paddlenlp/transformers/tokenizer_utils_base.py
@@ -1353,17 +1353,12 @@ class PretrainedTokenizerBase(SpecialTokensMixin):
truncation_side: str = "right"
slow_tokenizer_class = None
- # tag for init_kwargs
- _have_done_init: bool = False
-
def __init__(self, **kwargs):
# inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
- if self._have_done_init:
- return
- self._have_done_init = True
-
self.init_inputs = ()
- self.init_kwargs = copy.deepcopy(kwargs)
+
+ self.init_kwargs = getattr(self, "init_kwargs",
+ None) or copy.deepcopy(kwargs)
self.name_or_path = kwargs.pop("name_or_path", "")
self._processor_class = kwargs.pop("processor_class", None)
From 13a13aff17b3b2cc35ba33c0520bf1b64193e470 Mon Sep 17 00:00:00 2001
From: wj-Mcat <1435130236@qq.com>
Date: Wed, 31 Aug 2022 08:02:01 +0000
Subject: [PATCH 011/159] remove unrelated requirements
---
requirements.txt | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/requirements.txt b/requirements.txt
index ab13cb3a4db1..474b2ca14a5e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,5 +9,4 @@ tqdm
paddlefsl
sentencepiece
paddle2onnx
-protobuf>=3.1.0, <=3.20.0
-parameterized
\ No newline at end of file
+protobuf>=3.1.0, <=3.20.0
\ No newline at end of file
From 58e5e7e4bb0dccf61eb32725cdc9619c34f579a8 Mon Sep 17 00:00:00 2001
From: yujun <50394665+JunnYu@users.noreply.github.com>
Date: Tue, 6 Sep 2022 19:28:05 +0800
Subject: [PATCH 012/159] add license
---
.../stable_diffusion_utils/LICENSE | 82 +++++++++++++++++++
.../stable_diffusion_utils/README.md | 78 ++++++++++++++++++
2 files changed, 160 insertions(+)
create mode 100644 paddlenlp/transformers/stable_diffusion_utils/LICENSE
create mode 100644 paddlenlp/transformers/stable_diffusion_utils/README.md
diff --git a/paddlenlp/transformers/stable_diffusion_utils/LICENSE b/paddlenlp/transformers/stable_diffusion_utils/LICENSE
new file mode 100644
index 000000000000..928aa738f243
--- /dev/null
+++ b/paddlenlp/transformers/stable_diffusion_utils/LICENSE
@@ -0,0 +1,82 @@
+Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
+
+CreativeML Open RAIL-M
+dated August 22, 2022
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
+
+Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
+
+In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
+
+Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
+
+This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+1. Definitions
+
+- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
+- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
+- "Output" means the results of operating a Model as embodied in informational content resulting therefrom.
+- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
+- "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
+- "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
+- "Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
+- "Licensor" means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
+- "You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, image generator.
+- "Third Parties" means individuals or legal entities that are not under common control with Licensor or You.
+- "Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
+- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
+
+Section II: INTELLECTUAL PROPERTY RIGHTS
+
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
+3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
+Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
+You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
+You must cause any modified files to carry prominent notices stating that You changed the files;
+You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
+6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
+
+Section IV: OTHER PROVISIONS
+
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License, update the Model through electronic means, or modify the Output of the Model based on updates. You shall undertake reasonable efforts to use the latest version of the Model.
+8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
+9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
+10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+
+END OF TERMS AND CONDITIONS
+
+
+
+
+Attachment A
+
+Use Restrictions
+
+You agree not to use the Model or Derivatives of the Model:
+- In any way that violates any applicable national, federal, state, local or international law or regulation;
+- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
+- To generate or disseminate personal identifiable information that can be used to harm an individual;
+- To defame, disparage or otherwise harass others;
+- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
+- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
+- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
+- To provide medical advice and medical results interpretation;
+- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
\ No newline at end of file
diff --git a/paddlenlp/transformers/stable_diffusion_utils/README.md b/paddlenlp/transformers/stable_diffusion_utils/README.md
new file mode 100644
index 000000000000..92fd00ba260d
--- /dev/null
+++ b/paddlenlp/transformers/stable_diffusion_utils/README.md
@@ -0,0 +1,78 @@
+## Stable Diffusion模型
+
+**Stable Diffusion** 是由 **CompVis**、**Stability AI** 和 **LAION** 的研究人员和工程师开源的文图生成模型。他们使用**LAION-5B** 数据库子集的512x512大小的图像进行训练。**LAION-5B** 是目前存在的最大、可免费访问的数据集。
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