CofeAI/Tele-FLM

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""" Tele-FLM model configuration"""21 22from transformers.configuration_utils import PretrainedConfig23from transformers.utils import logging24 25 26logger = logging.get_logger(__name__)27 28TeleFLM_PRETRAINED_CONFIG_ARCHIVE_MAP={}29 30 31class TeleFLMConfig(PretrainedConfig):32    r"""33    This is the configuration class to store the configuration of a [`TeleFLM`]. It is used to instantiate an TeleFLM34    model according to the specified arguments, defining the model architecture. 35 36    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the37    documentation from [`PretrainedConfig`] for more information.38 39 40    Args:41        vocab_size (`int`, *optional*, defaults to 32000):42            Vocabulary size of the TeleFLM model. Defines the number of different tokens that can be represented by the43            `inputs_ids` passed when calling [`TeleFLM`]44        hidden_size (`int`, *optional*, defaults to 4096):45            Dimension of the hidden representations.46        intermediate_size (`int`, *optional*, defaults to 11008):47            Dimension of the MLP representations.48        num_hidden_layers (`int`, *optional*, defaults to 32):49            Number of hidden layers in the Transformer decoder.50        num_attention_heads (`int`, *optional*, defaults to 32):51            Number of attention heads for each attention layer in the Transformer decoder.52        num_key_value_heads (`int`, *optional*):53            This is the number of key_value heads that should be used to implement Grouped Query Attention. If54            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if55            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When56            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed57            by meanpooling all the original heads within that group. For more details checkout [this58            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to59            `num_attention_heads`.60        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):61            The non-linear activation function (function or string) in the decoder.62        max_position_embeddings (`int`, *optional*, defaults to 2048):63            The maximum sequence length that this model might ever be used with. TeleFLM supports up to 4096 tokens.64        initializer_range (`float`, *optional*, defaults to 0.02):65            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.66        rms_norm_eps (`float`, *optional*, defaults to 1e-06):67            The epsilon used by the rms normalization layers.68        use_cache (`bool`, *optional*, defaults to `True`):69            Whether or not the model should return the last key/values attentions (not used by all models). Only70            relevant if `config.is_decoder=True`.71        pad_token_id (`int`, *optional*):72            Padding token id.73        bos_token_id (`int`, *optional*, defaults to 1):74            Beginning of stream token id.75        eos_token_id (`int`, *optional*, defaults to 2):76            End of stream token id.77        pretraining_tp (`int`, *optional*, defaults to 1):78            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this79            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is80            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this81            issue](https://github.com/pytorch/pytorch/issues/76232).82        tie_word_embeddings (`bool`, *optional*, defaults to `False`):83            Whether to tie weight embeddings84        rope_theta (`float`, *optional*, defaults to 10000.0):85            The base period of the RoPE embeddings.86        rope_scaling (`Dict`, *optional*):87            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling88            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is89            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update90            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how91            these scaling strategies behave:92            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an93            experimental feature, subject to breaking API changes in future versions.94        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):95            Whether to use a bias in the query, key, value and output projection layers during self-attention.96        attention_dropout (`float`, *optional*, defaults to 0.0):97            The dropout ratio for the attention probabilities.98 99    ```python100    >>> from transformers import TeleFLMModel, TeleFLMConfig101 102    >>> # Initializing a TeleFLM configuration103    >>> configuration = TeleFLMConfig()104 105    >>> # Initializing a model from TeleFLM configuration106    >>> model = TeleFLMModel(configuration)107 108    >>> # Accessing the model configuration109    >>> configuration = model.config110    ```"""111 112    model_type = "TeleFLM"113    keys_to_ignore_at_inference = ["past_key_values"]114 115    def __init__(116        self,117        vocab_size=32000,118        hidden_size=4096,119        intermediate_size=11008,120        num_hidden_layers=32,121        num_attention_heads=32,122        num_key_value_heads=None,123        hidden_act="silu",124        max_position_embeddings=2048,125        initializer_range=0.02,126        rms_norm_eps=1e-6,127        use_cache=True,128        pad_token_id=None,129        bos_token_id=1,130        eos_token_id=2,131        pretraining_tp=1,132        tie_word_embeddings=False,133        rope_theta=10000.0,134        rope_scaling=None,135        attention_bias=False,136        attention_dropout=0.0,137        use_mup=False,138        mup_scale_factor=1.0,139        output_mult=1.0,140        input_mult=1.0,141        **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.use_mup=use_mup166        self.mup_scale_factor=mup_scale_factor167        self.output_mult=output_mult168        self.input_mult=input_mult169 170        super().__init__(171            pad_token_id=pad_token_id,172            bos_token_id=bos_token_id,173            eos_token_id=eos_token_id,174            tie_word_embeddings=tie_word_embeddings,175            **kwargs,176        )177 178    def _rope_scaling_validation(self):179        """180        Validate the `rope_scaling` configuration.181        """182        if self.rope_scaling is None:183            return184 185        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:186            raise ValueError(187                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"188            )189        rope_scaling_type = self.rope_scaling.get("type", None)190        rope_scaling_factor = self.rope_scaling.get("factor", None)191        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:192            raise ValueError(193                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"194            )195        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:196            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")197