# coding=utf-8 # Copyright 2023 The HuggingFace Inc. 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. """ Molformer model configuration""" from collections import OrderedDict from typing import Mapping from transformers.configuration_utils import PretrainedConfig from transformers.onnx import OnnxConfig from transformers.utils import logging logger = logging.get_logger(__name__) MOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "ibm/MoLFormer-XL-both-10pct": "https://huggingface.co/ibm/MoLFormer-XL-both-10pct/resolve/main/config.json", } class MolformerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MolformerModel`]. It is used to instantiate an Molformer 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 Molformer [ibm/MoLFormer-XL-both-10pct](https://huggingface.co/ibm/MoLFormer-XL-both-10pct) 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 2362): Vocabulary size of the Molformer model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MolformerModel`] or [`TFMolformerModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimension 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 768): Dimension 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. If string, `"gelu"`, `"relu"`, `"selu"` 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. embedding_dropout_prob (`float`, *optional*, defaults to 0.2): The dropout probability for the word embeddings. max_position_embeddings (`int`, *optional*, defaults to 202): 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 1536). 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. linear_attention_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the linear attention layers normalization step. num_random_features (`int`, *optional*, defaults to 32): Random feature map dimension used in linear attention. feature_map_kernel (`str` or `function`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the generalized random features. If string, `"gelu"`, `"relu"`, `"selu"`, and `"gelu_new"` ar supported. deterministic_eval (`bool`, *optional*, defaults to `False`): Whether the random features should only be redrawn when training or not. If `True` and `model.training` is `False`, linear attention random feature weights will be constant, i.e., deterministic. classifier_dropout_prob (`float`, *optional*): The dropout probability for the classification head. If `None`, use `hidden_dropout_prob`. classifier_skip_connection (`bool`, *optional*, defaults to `True`): Whether a skip connection should be made between the layers of the classification head or not. pad_token_id (`int`, *optional*, defaults to 2): The id of the _padding_ token. Example: ```python >>> from transformers import MolformerModel, MolformerConfig >>> # Initializing a Molformer ibm/MoLFormer-XL-both-10pct style configuration >>> configuration = MolformerConfig() >>> # Initializing a model from the ibm/MoLFormer-XL-both-10pct style configuration >>> model = MolformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "molformer" def __init__( self, vocab_size=2362, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=768, hidden_act="gelu", hidden_dropout_prob=0.1, embedding_dropout_prob=0.2, max_position_embeddings=202, initializer_range=0.02, layer_norm_eps=1e-12, linear_attention_eps=1e-6, num_random_features=32, feature_map_kernel="relu", deterministic_eval=False, classifier_dropout_prob=None, classifier_skip_connection=True, pad_token_id=2, **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.embedding_dropout_prob = embedding_dropout_prob self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.linear_attention_eps = linear_attention_eps self.num_random_features = num_random_features self.feature_map_kernel = feature_map_kernel self.deterministic_eval = deterministic_eval self.classifier_dropout_prob = classifier_dropout_prob self.classifier_skip_connection = classifier_skip_connection # Copied from transformers.models.roberta.configuration_roberta.RobertaOnnxConfig with Roberta->Molformer class MolformerOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} else: dynamic_axis = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ] )