1bitLLM/bitnet_b1_58-large

1# coding=utf-82# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.3#4# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX5# and OPT implementations in this library. It has been modified from its6# original forms to accommodate minor architectural differences compared7# to GPT-NeoX and OPT used by the Meta AI team that trained the model.8#9# Licensed under the Apache License, Version 2.0 (the "License");10# you may not use this file except in compliance with the License.11# You may obtain a copy of the License at12#13#     http://www.apache.org/licenses/LICENSE-2.014#15# Unless required by applicable law or agreed to in writing, software16# distributed under the License is distributed on an "AS IS" BASIS,17# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.18# See the License for the specific language governing permissions and19# limitations under the License.20""" LLaMA model configuration"""21 22from transformers.configuration_utils import PretrainedConfig23from transformers.utils import logging24 25 26logger = logging.get_logger(__name__)27 28LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}29 30 31class BitnetConfig(PretrainedConfig):32    r"""33    This is the configuration class to store the configuration of a [`BitnetModel`]. It is used to instantiate an LLaMA34    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the35    defaults will yield a similar configuration to that of the LLaMA-7B.36 37    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the38    documentation from [`PretrainedConfig`] for more information.39 40 41    Args:42        vocab_size (`int`, *optional*, defaults to 32000):43            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the44            `inputs_ids` passed when calling [`BitnetModel`]45        hidden_size (`int`, *optional*, defaults to 4096):46            Dimension of the hidden representations.47        intermediate_size (`int`, *optional*, defaults to 11008):48            Dimension of the MLP representations.49        num_hidden_layers (`int`, *optional*, defaults to 32):50            Number of hidden layers in the Transformer decoder.51        num_attention_heads (`int`, *optional*, defaults to 32):52            Number of attention heads for each attention layer in the Transformer decoder.53        num_key_value_heads (`int`, *optional*):54            This is the number of key_value heads that should be used to implement Grouped Query Attention. If55            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if56            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When57            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed58            by meanpooling all the original heads within that group. For more details checkout [this59            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to60            `num_attention_heads`.61        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):62            The non-linear activation function (function or string) in the decoder.63        max_position_embeddings (`int`, *optional*, defaults to 2048):64            The maximum sequence length that this model might ever be used with. Bitnet 1 supports up to 2048 tokens,65            Bitnet 2 up to 4096, CodeBitnet up to 16384.66        initializer_range (`float`, *optional*, defaults to 0.02):67            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.68        rms_norm_eps (`float`, *optional*, defaults to 1e-06):69            The epsilon used by the rms normalization layers.70        use_cache (`bool`, *optional*, defaults to `True`):71            Whether or not the model should return the last key/values attentions (not used by all models). Only72            relevant if `config.is_decoder=True`.73        pad_token_id (`int`, *optional*):74            Padding token id.75        bos_token_id (`int`, *optional*, defaults to 1):76            Beginning of stream token id.77        eos_token_id (`int`, *optional*, defaults to 2):78            End of stream token id.79        pretraining_tp (`int`, *optional*, defaults to 1):80            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this81            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is82            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this83            issue](https://github.com/pytorch/pytorch/issues/76232).84        tie_word_embeddings (`bool`, *optional*, defaults to `False`):85            Whether to tie weight embeddings86        rope_theta (`float`, *optional*, defaults to 10000.0):87            The base period of the RoPE embeddings.88        rope_scaling (`Dict`, *optional*):89            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling90            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is91            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update92            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how93            these scaling strategies behave:94            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an95            experimental feature, subject to breaking API changes in future versions.96        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):97            Whether to use a bias in the query, key, value and output projection layers during self-attention.98        attention_dropout (`float`, *optional*, defaults to 0.0):99            The dropout ratio for the attention probabilities.100 101    ```python102    >>> from transformers import BitnetModel, BitnetConfig103 104    >>> # Initializing a LLaMA llama-7b style configuration105    >>> configuration = BitnetConfig()106 107    >>> # Initializing a model from the llama-7b style configuration108    >>> model = BitnetModel(configuration)109 110    >>> # Accessing the model configuration111    >>> configuration = model.config112    ```"""113 114    model_type = "llama"115    keys_to_ignore_at_inference = ["past_key_values"]116 117    def __init__(118        self,119        vocab_size=32000,120        hidden_size=4096,121        intermediate_size=11008,122        num_hidden_layers=32,123        num_attention_heads=32,124        num_key_value_heads=None,125        hidden_act="silu",126        max_position_embeddings=2048,127        initializer_range=0.02,128        rms_norm_eps=1e-6,129        use_cache=True,130        pad_token_id=None,131        bos_token_id=1,132        eos_token_id=2,133        pretraining_tp=1,134        tie_word_embeddings=False,135        rope_theta=10000.0,136        rope_scaling=None,137        attention_bias=False,138        attention_dropout=0.0,139        weight_bits=1,140        input_bits=8,141        **kwargs,142    ):143        self.vocab_size = vocab_size144        self.max_position_embeddings = max_position_embeddings145        self.hidden_size = hidden_size146        self.intermediate_size = intermediate_size147        self.num_hidden_layers = num_hidden_layers148        self.num_attention_heads = num_attention_heads149 150        # for backward compatibility151        if num_key_value_heads is None:152            num_key_value_heads = num_attention_heads153 154        self.num_key_value_heads = num_key_value_heads155        self.hidden_act = hidden_act156        self.initializer_range = initializer_range157        self.rms_norm_eps = rms_norm_eps158        self.pretraining_tp = pretraining_tp159        self.use_cache = use_cache160        self.rope_theta = rope_theta161        self.rope_scaling = rope_scaling162        self._rope_scaling_validation()163        self.attention_bias = attention_bias164        self.attention_dropout = attention_dropout165        self.weight_bits = weight_bits166        self.input_bits = input_bits167 168        super().__init__(169            pad_token_id=pad_token_id,170            bos_token_id=bos_token_id,171            eos_token_id=eos_token_id,172            tie_word_embeddings=tie_word_embeddings,173            **kwargs,174        )175 176    def _rope_scaling_validation(self):177        """178        Validate the `rope_scaling` configuration.179        """180        if self.rope_scaling is None:181            return182 183        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:184            raise ValueError(185                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "186                f"got {self.rope_scaling}"187            )188        rope_scaling_type = self.rope_scaling.get("type", None)189        rope_scaling_factor = self.rope_scaling.get("factor", None)190        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:191            raise ValueError(192                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"193            )194        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:195            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")