# 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. """ MBart model configuration""" from __future__ import annotations from paddlenlp.transformers.configuration_utils import PretrainedConfig __all__ = [ "MegatronBert_PRETRAINED_INIT_CONFIGURATION", "MegatronBert_PRETRAINED_RESOURCE_FILES_MAP", "MegatronBertConfig", ] MegatronBert_PRETRAINED_INIT_CONFIGURATION = { "megatronbert-cased": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 24, "type_vocab_size": 2, "vocab_size": 29056, "pad_token_id": 0, }, "megatronbert-uncased": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 24, "type_vocab_size": 2, "vocab_size": 30592, "pad_token_id": 0, }, } MegatronBert_PRETRAINED_RESOURCE_FILES_MAP = { "model_state": { "megatronbert-cased": "http://bj.bcebos.com/paddlenlp/models/transformers/megatron-bert/megatronbert-cased/model_state.pdparams", "megatronbert-uncased": "http://bj.bcebos.com/paddlenlp/models/transformers/megatron-bert/megatronbert-uncased/model_state.pdparams", } } class MegatronBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MegatronBertModel`]. It is used to instantiate a MEGATRON_BERT 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 MEGATRON_BERT [nvidia/megatron-bert-uncased-345m](https://huggingface.co/nvidia/megatron-bert-uncased-345m) 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): Vocabulary size of `inputs_ids` in `MegatronBertModel`. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `MegatronBert`. hidden_size (int, optional): Dimensionality of the encoder layer and pooler layer. Defaults to `1024`. pad_token_id (int, optional): The index of padding token in the token vocabulary. Defaults to `0`. type_vocab_size (int, optional): The vocabulary size of `token_type_ids`. Defaults to `2`. hidden_act (str, optional): The non-linear activation function in the feed-forward layer. ``"gelu"``, ``"relu"`` and any other paddle supported activation functions are supported. Defaults to `"gelu"`. attention_probs_dropout_prob (float, optional): The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. Defaults to `0.1`. num_attention_heads (int, optional): Number of attention heads for each attention layer in the Transformer encoder. Defaults to `16`. num_hidden_layers (int, optional): Number of hidden layers in the Transformer encoder. Defaults to `24`. max_position_embeddings (int, optional): The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input sequence. Defaults to `512`. hidden_dropout_prob (float, optional): The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to `0.1`. intermediate_size (int, optional): Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors to ff layers are firstly projected from `hidden_size` to `intermediate_size`, and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`. Defaults to `4096`. position_embedding_type (str, optional): Type of position embedding. Defaults to "absolute" initializer_range (float, optional): The standard deviation of the normal initializer. Defaults to 0.02. .. note:: A normal_initializer initializes weight matrices as normal distributions. See :meth:`MegatronBertPretrainedModel.init_weights()` for how weights are initialized in `MegatronBertModel`. """ model_type = "megatronbert" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=29056, 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, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", # use_cache=True, **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.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.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type # self.use_cache = use_cache