146 lines
7.0 KiB
Python
146 lines
7.0 KiB
Python
# coding=utf-8
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# Copyright 2024 The GTE Team Authors and Alibaba Group.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" NEW model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class NewConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to
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instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the NEW
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[izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`].
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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position_embedding_type (`str`, *optional*, defaults to `"rope"`):
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Type of position embedding. Choose one of `"absolute"`, `"rope"`.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking API changes in future versions.
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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Examples:
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```python
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>>> from transformers import NewConfig, NewModel
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>>> # Initializing a NEW izhx/new-base-en style configuration
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>>> configuration = NewConfig()
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>>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration
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>>> model = NewModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "new"
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def __init__(
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self,
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vocab_size=30528,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.0,
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max_position_embeddings=2048,
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type_vocab_size=1,
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initializer_range=0.02,
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layer_norm_type='layer_norm',
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layer_norm_eps=1e-12,
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# pad_token_id=0,
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position_embedding_type="rope",
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rope_theta=10000.0,
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rope_scaling=None,
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classifier_dropout=None,
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pack_qkv=True,
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unpad_inputs=False,
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use_memory_efficient_attention=False,
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logn_attention_scale=False,
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logn_attention_clip1=False,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_type = layer_norm_type
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.classifier_dropout = classifier_dropout
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self.pack_qkv = pack_qkv
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self.unpad_inputs = unpad_inputs
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self.use_memory_efficient_attention = use_memory_efficient_attention
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self.logn_attention_scale = logn_attention_scale
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self.logn_attention_clip1 = logn_attention_clip1
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