# coding=utf-8 # This file has been modified from the configuration_roformer.py file in the transformers library. # Copyright 2021 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. """ E5Rope 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__) class E5RopeConfig(PretrainedConfig): r""" Args: vocab_size (`int`, *optional*, defaults to 50000): Vocabulary size of the E5Rope model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`E5RopeModel`] or [`TFE5RopeModel`]. embedding_size (`int`, *optional*, defaults to None): Dimensionality of the encoder layers and the pooler layer. Defaults to the `hidden_size` if not provided. 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 3072): 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 probabilitiy 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 1536): 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). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`E5RopeModel`] or [`TFE5RopeModel`]. 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. 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. 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`. rotary_value (`bool`, *optional*, defaults to `False`): Whether or not apply rotary position embeddings on value layer. rope_theta (`float`, *optional*, defaults to 10000): Frequency base for RoPE. use_pose (`bool`, *optional*, defaults to `False`): Whether or not to use positional skip-wise training for long context. https://arxiv.org/abs/2309.10400 pose_target_len (`int`, *optional*, defaults to None): target context length if use_pose is True """ model_type = "e5rope" def __init__( self, vocab_size=50000, embedding_size=None, 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=1536, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, rotary_value=False, use_cache=True, rope_theta=10000, use_pose=False, pose_target_len=None, **kwargs, ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.embedding_size = hidden_size if embedding_size is None else 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.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.rotary_value = rotary_value self.use_cache = use_cache self.rope_theta = rope_theta self.use_pose = use_pose self.pose_target_len = pose_target_len class E5RopeOnnxConfig(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"} dynamic_axis = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis), ] )