[ConvBert P0 P1 P2] Add PretrainedConfig, unit tests and input_embs

paddlenlp/transformers/__init__.py

@@ -73,6 +73,7 @@
 from .chinesebert.configuration import *
 from .chinesebert.modeling import *
 from .chinesebert.tokenizer import *
+from .convbert.configuration import *
 from .convbert.modeling import *
 from .convbert.tokenizer import *
 from .ctrl.modeling import *

paddlenlp/transformers/convbert/__init__.py

@@ -12,5 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .modeling import *
-from .tokenizer import *
+# from .modeling import *
+# from .tokenizer import *

paddlenlp/transformers/convbert/configuration.py

@@ -0,0 +1,307 @@
+# 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.
+""" ConvBERT model configuration"""
+from __future__ import annotations
+
+from typing import Dict
+
+from paddlenlp.transformers.configuration_utils import PretrainedConfig
+
+__all__ = ["CONVBERT_PRETRAINED_INIT_CONFIGURATION", "ConvBertConfig", "CONVBERT_PRETRAINED_RESOURCE_FILES_MAP"]
+
+CONVBERT_PRETRAINED_INIT_CONFIGURATION = {
+    "convbert-base": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 768,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 768,
+        "initializer_range": 0.02,
+        "intermediate_size": 3072,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 12,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 1,
+    },
+    "convbert-medium-small": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 128,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 384,
+        "initializer_range": 0.02,
+        "intermediate_size": 1536,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 8,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 2,
+    },
+    "convbert-small": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 128,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 256,
+        "initializer_range": 0.02,
+        "intermediate_size": 1024,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 4,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 1,
+    },
+    "convbert-base-generator": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 768,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 256,
+        "initializer_range": 0.02,
+        "intermediate_size": 1024,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 4,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 1,
+    },
+    "convbert-medium-small-generator": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 128,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 96,
+        "initializer_range": 0.02,
+        "intermediate_size": 384,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 2,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 2,
+    },
+    "convbert-small-generator": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 128,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 64,
+        "initializer_range": 0.02,
+        "intermediate_size": 256,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 1,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 1,
+    },
+    "convbert-base-discriminator": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 768,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 768,
+        "initializer_range": 0.02,
+        "intermediate_size": 3072,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 12,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 1,
+    },
+    "convbert-medium-small-discriminator": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 128,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 384,
+        "initializer_range": 0.02,
+        "intermediate_size": 1536,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 8,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 2,
+    },
+    "convbert-small-discriminator": {
+        "attention_probs_dropout_prob": 0.1,
+        "embedding_size": 128,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "hidden_size": 256,
+        "initializer_range": 0.02,
+        "intermediate_size": 1024,
+        "max_position_embeddings": 512,
+        "num_attention_heads": 4,
+        "num_hidden_layers": 12,
+        "pad_token_id": 0,
+        "type_vocab_size": 2,
+        "vocab_size": 30522,
+        "conv_kernel_size": 9,
+        "head_ratio": 2,
+        "num_groups": 1,
+    },
+}
+
+CONVBERT_PRETRAINED_RESOURCE_FILES_MAP = {
+    "model_state": {
+        "convbert-base": "http://bj.bcebos.com/paddlenlp/models/transformers/convbert/convbert-base/model_state.pdparams",
+        "convbert-medium-small": "http://bj.bcebos.com/paddlenlp/models/transformers/convbert/convbert-medium-small/model_state.pdparams",
+        "convbert-small": "http://bj.bcebos.com/paddlenlp/models/transformers/convbert/convbert-small/model_state.pdparams",
+    }
+}
+
+
+class ConvBertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ConvBertModel`]. It is used to instantiate a
+    ConvBERT 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 ConvBert
+    conv-bert-base architecture. Configuration objects.
+
+    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 30522):
+            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`].
+        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 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`].
+        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.
+        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
+            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
+            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
+            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
+        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 the classification head.
+
+    Examples:
+
+    ```python
+    >>> from paddlenlp.transformers import BertModel, BertConfig
+
+    >>> # Initializing a BERT bert-base-uncased style configuration
+    >>> configuration = BertConfig()
+
+    >>> # Initializing a model from the bert-base-uncased style configuration
+    >>> model = BertModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ======================================================
+    ```"""
+    model_type = "convbert"
+    attribute_map: Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
+    pretrained_init_configuration = CONVBERT_PRETRAINED_INIT_CONFIGURATION
+
+    def __init__(
+        self,
+        vocab_size: int = 30522,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        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,
+        type_vocab_size: int = 2,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-12,
+        pad_token_id: int = 0,
+        pool_act: str = "tanh",
+        embedding_size: int = 768,
+        conv_kernel_size: int = 9,
+        head_ratio: int = 2,
+        num_groups: int = 1,
+        **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.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.pool_act = pool_act
+        self.layer_norm_eps = layer_norm_eps
+        self.embedding_size = embedding_size
+        self.conv_kernel_size = conv_kernel_size
+        self.head_ratio = head_ratio
+        self.num_groups = num_groups