[ModelZoo] Refactor ERNIE-Layout Usage and add unittest (PaddlePaddle#4170)

* Add unittest

* Apply suggestions from code review

* Delete test_tokenizer.py

* Apply suggestions from code review

Co-authored-by: Sijun He <[email protected]>

Author: linjieccc

model_zoo/ernie-layout/run_cls.py

@@ -13,23 +13,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import collections
+import os
 from functools import partial
 
-import paddle
-from paddlenlp.trainer import PdArgumentParser, TrainingArguments
-from paddlenlp.trainer import get_last_checkpoint
-from paddlenlp.transformers import AutoTokenizer, AutoModelForSequenceClassification
-from paddlenlp.utils.log import logger
-from paddle.metric import Accuracy
-from datasets import load_dataset, load_metric
 import datasets
+import paddle
 from data_collator import DataCollator
-
+from datasets import load_dataset
 from finetune_args import DataArguments, ModelArguments
-from utils import PreProcessor, PostProcessor, get_label_ld
 from layout_trainer import LayoutTrainer
+from paddle.metric import Accuracy
+from utils import PostProcessor, PreProcessor, get_label_ld
+
+from paddlenlp.trainer import PdArgumentParser, TrainingArguments, get_last_checkpoint
+from paddlenlp.transformers import AutoModelForSequenceClassification, AutoTokenizer
+from paddlenlp.utils.log import logger
 
 
 def main():

model_zoo/ernie-layout/run_mrc.py

@@ -13,22 +13,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import collections
+import os
 from functools import partial
 
-import paddle
-from paddlenlp.trainer import PdArgumentParser, TrainingArguments
-from paddlenlp.trainer import get_last_checkpoint
-from paddlenlp.transformers import AutoTokenizer, AutoModelForQuestionAnswering
-from paddlenlp.utils.log import logger
 import datasets
-from datasets import load_dataset, load_metric
+import paddle
 from data_collator import DataCollator
-
+from datasets import load_dataset
 from finetune_args import DataArguments, ModelArguments
-from utils import PreProcessor, PostProcessor, anls_score
 from layout_trainer import LayoutTrainer
+from utils import PostProcessor, PreProcessor, anls_score
+
+from paddlenlp.trainer import PdArgumentParser, TrainingArguments, get_last_checkpoint
+from paddlenlp.transformers import AutoModelForQuestionAnswering, AutoTokenizer
+from paddlenlp.utils.log import logger
 
 
 def main():

model_zoo/ernie-layout/run_ner.py

@@ -13,22 +13,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import collections
+import os
 from functools import partial
 
 import paddle
-from paddlenlp.trainer import PdArgumentParser, TrainingArguments
-from paddlenlp.trainer import get_last_checkpoint
-from paddlenlp.transformers import AutoTokenizer, AutoModelForTokenClassification
-from seqeval.metrics import classification_report
-
-from datasets import load_dataset
 from data_collator import DataCollator
-
+from datasets import load_dataset
 from finetune_args import DataArguments, ModelArguments
-from utils import PreProcessor, PostProcessor, get_label_ld
 from layout_trainer import LayoutTrainer
+from seqeval.metrics import classification_report
+from utils import PostProcessor, PreProcessor, get_label_ld
+
+from paddlenlp.trainer import PdArgumentParser, TrainingArguments, get_last_checkpoint
+from paddlenlp.transformers import AutoModelForTokenClassification, AutoTokenizer
+from paddlenlp.utils.log import logger
 
 
 def main():

paddlenlp/transformers/__init__.py

@@ -77,6 +77,7 @@
 from .ernie_gram.tokenizer import *
 from .ernie_layout.modeling import *
 from .ernie_layout.tokenizer import *
+from .ernie_layout.configuration import *
 from .ernie_m.configuration import *
 from .ernie_m.modeling import *
 from .ernie_m.tokenizer import *

paddlenlp/transformers/ernie_layout/configuration.py

@@ -0,0 +1,207 @@
+# Copyright (c) 2022 PaddlePaddle Authors. 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.
+""" ERNIE-Layout model configuration"""
+
+from typing import Dict
+
+from ..configuration_utils import PretrainedConfig
+
+__all__ = [
+    "ERNIE_LAYOUT_PRETRAINED_INIT_CONFIGURATION",
+    "ErnieLayoutConfig",
+    "ERNIE_LAYOUT_PRETRAINED_RESOURCE_FILES_MAP",
+]
+
+ERNIE_LAYOUT_PRETRAINED_INIT_CONFIGURATION = {
+    "ernie-layoutx-base-uncased": {
+        "attention_probs_dropout_prob": 0.1,
+        "bos_token_id": 0,
+        "coordinate_size": 128,
+        "eos_token_id": 2,
+        "gradient_checkpointing": False,
+        "has_relative_attention_bias": True,
+        "has_spatial_attention_bias": True,
+        "has_visual_segment_embedding": False,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 768,
+        "image_feature_pool_shape": [7, 7, 256],
+        "initializer_range": 0.02,
+        "intermediate_size": 3072,
+        "layer_norm_eps": 1e-12,
+        "max_2d_position_embeddings": 1024,
+        "max_position_embeddings": 514,
+        "max_rel_2d_pos": 256,
+        "max_rel_pos": 128,
+        "model_type": "ernie_layout",
+        "num_attention_heads": 12,
+        "num_hidden_layers": 12,
+        "output_past": True,
+        "pad_token_id": 1,
+        "shape_size": 128,
+        "rel_2d_pos_bins": 64,
+        "rel_pos_bins": 32,
+        "type_vocab_size": 100,
+        "vocab_size": 250002,
+    },
+    "uie-x-base": {
+        "attention_probs_dropout_prob": 0.1,
+        "bos_token_id": 0,
+        "coordinate_size": 128,
+        "eos_token_id": 2,
+        "gradient_checkpointing": False,
+        "has_relative_attention_bias": True,
+        "has_spatial_attention_bias": True,
+        "has_visual_segment_embedding": False,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 768,
+        "image_feature_pool_shape": [7, 7, 256],
+        "initializer_range": 0.02,
+        "intermediate_size": 3072,
+        "layer_norm_eps": 1e-12,
+        "max_2d_position_embeddings": 1024,
+        "max_position_embeddings": 514,
+        "max_rel_2d_pos": 256,
+        "max_rel_pos": 128,
+        "model_type": "ernie_layout",
+        "num_attention_heads": 12,
+        "num_hidden_layers": 12,
+        "output_past": True,
+        "pad_token_id": 1,
+        "shape_size": 128,
+        "rel_2d_pos_bins": 64,
+        "rel_pos_bins": 32,
+        "type_vocab_size": 100,
+        "vocab_size": 250002,
+    },
+}
+
+ERNIE_LAYOUT_PRETRAINED_RESOURCE_FILES_MAP = {
+    "model_state": {
+        "ernie-layoutx-base-uncased": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_layout/ernie_layoutx_base_uncased.pdparams",
+        "uie-x-base": "https://bj.bcebos.com/paddlenlp/models/transformers/uie_x/uie_x_base.pdparams",
+    },
+}
+
+
+class ErnieLayoutConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ErnieLayoutModel`]. It is used to
+    instantiate a ErnieLayout model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the ErnieLayout
+    ernie-layoutx-base-uncased architecture.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 250002):
+            Vocabulary size of the ErnieLayout model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ErnieLayoutModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 514):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 514 or 1028 or 2056).
+        type_vocab_size (`int`, *optional*, defaults to 100):
+            The vocabulary size of the `token_type_ids` passed when calling [`ErnieModel`].
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for classifier.
+        has_visual_segment_embedding (`bool`, *optional*, defaults to `False`):
+            Whether or not the model has visual segment embedding.
+    Examples:
+    ```python
+    >>> from paddlenlp.transformers import ErnieLayoutModel, ErnieLayoutConfig
+    >>> # Initializing a ErnieLayout ernie-layoutx-base-uncased configuration
+    >>> configuration = ErnieLayoutConfig()
+    >>> # Initializing a model from the  style configuration
+    >>> model = ErnieLayoutModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "ernie_layout"
+    attribute_map: Dict[str, str] = {"num_classes": "num_labels", "dropout": "classifier_dropout"}
+    pretrained_init_configuration = ERNIE_LAYOUT_PRETRAINED_INIT_CONFIGURATION
+
+    def __init__(
+        self,
+        vocab_size: int = 30522,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        task_id=0,
+        intermediate_size: int = 3072,
+        hidden_act: str = "gelu",
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        max_position_embeddings: int = 512,
+        max_2d_position_embeddings: int = 1024,
+        task_type_vocab_size: int = 3,
+        type_vocab_size: int = 16,
+        initializer_range: float = 0.02,
+        pad_token_id: int = 0,
+        pool_act: str = "tanh",
+        fuse: bool = False,
+        image_feature_pool_shape=[7, 7, 256],
+        layer_norm_eps=1e-12,
+        use_cache=False,
+        use_task_id=True,
+        enable_recompute=False,
+        classifier_dropout=None,
+        has_visual_segment_embedding=False,
+        **kwargs
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+        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.task_id = task_id
+        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.max_2d_position_embeddings = max_2d_position_embeddings
+        self.task_type_vocab_size = task_type_vocab_size
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.pool_act = pool_act
+        self.fuse = fuse
+        self.image_feature_pool_shape = image_feature_pool_shape
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.use_task_id = use_task_id
+        self.classifier_dropout = classifier_dropout
+        self.has_visual_segment_embedding = has_visual_segment_embedding
+        self.enable_recompute = enable_recompute