add unit tests and config (#4575)

paddlenlp/transformers/__init__.py

@@ -110,6 +110,7 @@
 from .mobilebert.tokenizer import *
 from .mpnet.modeling import *
 from .mpnet.tokenizer import *
+from .nezha.configuration import *
 from .nezha.modeling import *
 from .nezha.tokenizer import *
 from .ppminilm.modeling import *

paddlenlp/transformers/nezha/configuration.py

@@ -0,0 +1,190 @@
+# 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.
+""" NeZha model configuration"""
+from __future__ import annotations
+
+from typing import Dict
+
+from ..configuration_utils import PretrainedConfig
+
+__all__ = ["NEZHA_PRETRAINED_INIT_CONFIGURATION", "NeZhaConfig", "NEZHA_PRETRAINED_RESOURCE_FILES_MAP"]
+
+NEZHA_PRETRAINED_INIT_CONFIGURATION = {
+    "nezha-base-chinese": {
+        "vocab_size": 21128,
+        "hidden_size": 768,
+        "num_hidden_layers": 12,
+        "num_attention_heads": 12,
+        "intermediate_size": 3072,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "max_relative_position": 64,
+        "type_vocab_size": 2,
+        "initializer_range": 0.02,
+        "use_relative_position": True,
+    },
+    "nezha-large-chinese": {
+        "vocab_size": 21128,
+        "hidden_size": 1024,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+        "intermediate_size": 4096,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "max_relative_position": 64,
+        "type_vocab_size": 2,
+        "initializer_range": 0.02,
+        "use_relative_position": True,
+    },
+    "nezha-base-wwm-chinese": {
+        "vocab_size": 21128,
+        "hidden_size": 768,
+        "num_hidden_layers": 12,
+        "num_attention_heads": 12,
+        "intermediate_size": 3072,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "max_relative_position": 64,
+        "type_vocab_size": 2,
+        "initializer_range": 0.02,
+        "use_relative_position": True,
+    },
+    "nezha-large-wwm-chinese": {
+        "vocab_size": 21128,
+        "hidden_size": 1024,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+        "intermediate_size": 4096,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "max_relative_position": 64,
+        "type_vocab_size": 2,
+        "initializer_range": 0.02,
+        "use_relative_position": True,
+    },
+}
+NEZHA_PRETRAINED_RESOURCE_FILES_MAP = {
+    "model_state": {
+        "nezha-base-chinese": "https://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-base-chinese.pdparams",
+        "nezha-large-chinese": "https://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-large-chinese.pdparams",
+        "nezha-base-wwm-chinese": "https://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-base-wwm-chinese.pdparams",
+        "nezha-large-wwm-chinese": "https://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-large-wwm-chinese.pdparams",
+    }
+}
+
+
+class NeZhaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of an [`NezhaModel`]. It is used to instantiate an Nezha
+    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 Nezha
+    [sijunhe/nezha-cn-base](https://huggingface.co/sijunhe/nezha-cn-base) 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 21128):
+            Vocabulary size of the NEZHA model. Defines the different tokens that can be represented by the
+            *inputs_ids* passed to the forward method of [`NezhaModel`].
+        embedding_size (`int`, optional, defaults to 128):
+            Dimensionality of vocabulary embeddings.
+        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):
+            The dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, optional, defaults to "gelu"):
+            The non-linear activation function (function or string) in the encoder and pooler.
+        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 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            (e.g., 512 or 1024 or 2048).
+        type_vocab_size (`int`, optional, defaults to 2):
+            The vocabulary size of the *token_type_ids* passed into [`NezhaModel`].
+        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.
+        classifier_dropout (`float`, optional, defaults to 0.1):
+            The dropout ratio for attached classifiers.
+        is_decoder (`bool`, *optional*, defaults to `False`):
+            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
+    Example:
+    ```python
+    >>> from paddlenlp.transformers import NeZhaConfig, NeZhaModel
+    >>> # Initializing an Nezha configuration
+    >>> configuration = NeZhaConfig()
+    >>> # Initializing a model (with random weights) from the Nezha-base style configuration model
+    >>> model = NeZhaModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    attribute_map: Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
+    pretrained_init_configuration = NEZHA_PRETRAINED_INIT_CONFIGURATION
+    model_type = "nezha"
+
+    def __init__(
+        self,
+        vocab_size=21128,
+        embedding_size=128,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        max_relative_position=64,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        classifier_dropout=0.1,
+        pad_token_id=0,
+        bos_token_id=2,
+        eos_token_id=3,
+        use_cache=True,
+        **kwargs
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.embedding_size = embedding_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.max_relative_position = max_relative_position
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.classifier_dropout = classifier_dropout
+        self.use_cache = use_cache