bespokelabs/Bespoke-MiniCheck-7B

1# coding=utf-82# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.3#4# This code is based on transformers/src/transformers/models/llama/configuration_llama.py5#6# Licensed under the Apache License, Version 2.0 (the "License");7# you may not use this file except in compliance with the License.8# You may obtain a copy of the License at9#10#     http://www.apache.org/licenses/LICENSE-2.011#12# Unless required by applicable law or agreed to in writing, software13# distributed under the License is distributed on an "AS IS" BASIS,14# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.15# See the License for the specific language governing permissions and16# limitations under the License.17""" InternLM2 model configuration"""18 19from transformers.configuration_utils import PretrainedConfig20from transformers.utils import logging21 22logger = logging.get_logger(__name__)23 24INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}25 26 27# Modified from transformers.model.llama.configuration_llama.LlamaConfig28class InternLM2Config(PretrainedConfig):29    r"""30    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate31    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a32    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.33 34    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the35    documentation from [`PretrainedConfig`] for more information.36 37 38    Args:39        vocab_size (`int`, *optional*, defaults to 32000):40            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the41            `inputs_ids` passed when calling [`InternLM2Model`]42        hidden_size (`int`, *optional*, defaults to 4096):43            Dimension of the hidden representations.44        intermediate_size (`int`, *optional*, defaults to 11008):45            Dimension of the MLP representations.46        num_hidden_layers (`int`, *optional*, defaults to 32):47            Number of hidden layers in the Transformer decoder.48        num_attention_heads (`int`, *optional*, defaults to 32):49            Number of attention heads for each attention layer in the Transformer decoder.50        num_key_value_heads (`int`, *optional*):51            This is the number of key_value heads that should be used to implement Grouped Query Attention. If52            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if53            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When54            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed55            by meanpooling all the original heads within that group. For more details checkout [this56            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to57            `num_attention_heads`.58        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):59            The non-linear activation function (function or string) in the decoder.60        max_position_embeddings (`int`, *optional*, defaults to 2048):61            The maximum sequence length that this model might ever be used with. InternLM2 supports up to 32768 tokens.62        initializer_range (`float`, *optional*, defaults to 0.02):63            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.64        rms_norm_eps (`float`, *optional*, defaults to 1e-06):65            The epsilon used by the rms normalization layers.66        use_cache (`bool`, *optional*, defaults to `True`):67            Whether or not the model should return the last key/values attentions (not used by all models). Only68            relevant if `config.is_decoder=True`.69        pad_token_id (`int`, *optional*):70            Padding token id.71        bos_token_id (`int`, *optional*, defaults to 1):72            Beginning of stream token id.73        eos_token_id (`int`, *optional*, defaults to 2):74            End of stream token id.75        pretraining_tp (`int`, *optional*, defaults to 1):76            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this77            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism)78            to understand more about it. This value is necessary to ensure exact reproducibility79            of the pretraining results. Please refer to [this80            issue](https://github.com/pytorch/pytorch/issues/76232).81        tie_word_embeddings (`bool`, *optional*, defaults to `False`):82            Whether to tie weight embeddings83        rope_theta (`float`, *optional*, defaults to 10000.0):84            The base period of the RoPE embeddings.85        rope_scaling (`Dict`, *optional*):86            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling87            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is88            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update89            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how90            these scaling strategies behave:91            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an92            experimental feature, subject to breaking API changes in future versions.93    """94    _auto_class = "AutoConfig"95    model_type = "internlm2"96    keys_to_ignore_at_inference = ["past_key_values"]97 98    def __init__(  # pylint: disable=W010299        self,100        vocab_size=103168,101        hidden_size=4096,102        intermediate_size=11008,103        num_hidden_layers=32,104        num_attention_heads=32,105        num_key_value_heads=None,106        hidden_act="silu",107        max_position_embeddings=2048,108        initializer_range=0.02,109        rms_norm_eps=1e-6,110        use_cache=True,111        pad_token_id=0,112        bos_token_id=1,113        eos_token_id=2,114        pretraining_tp=1,115        tie_word_embeddings=False,116        bias=True,117        rope_theta=10000,118        rope_scaling=None,119        attn_implementation=None,120        **kwargs,121    ):122        self.vocab_size = vocab_size123        self.max_position_embeddings = max_position_embeddings124        self.hidden_size = hidden_size125        self.intermediate_size = intermediate_size126        self.num_hidden_layers = num_hidden_layers127        self.num_attention_heads = num_attention_heads128        self.bias = bias129 130        if num_key_value_heads is None:131            num_key_value_heads = num_attention_heads132        self.num_key_value_heads = num_key_value_heads133 134        self.hidden_act = hidden_act135        self.initializer_range = initializer_range136        self.rms_norm_eps = rms_norm_eps137        self.pretraining_tp = pretraining_tp138        self.use_cache = use_cache139        self.rope_theta = rope_theta140        self.rope_scaling = rope_scaling141        self._rope_scaling_validation()142        self.attn_implementation = attn_implementation143        if self.attn_implementation is None:144            self.attn_implementation = "eager"145 146        super().__init__(147            pad_token_id=pad_token_id,148            bos_token_id=bos_token_id,149            eos_token_id=eos_token_id,150            tie_word_embeddings=tie_word_embeddings,151            **kwargs,152        )153 154    def _rope_scaling_validation(self):155        """156        Validate the `rope_scaling` configuration.157        """158        if self.rope_scaling is None:159            return160 161        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:162            raise ValueError(163                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "164                f"got {self.rope_scaling}"165            )166        rope_scaling_type = self.rope_scaling.get("type", None)167        rope_scaling_factor = self.rope_scaling.get("factor", None)168        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:169            raise ValueError(170                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"171            )172        if (173            rope_scaling_factor is None174            or not isinstance(rope_scaling_factor, (float, int))175            or rope_scaling_factor < 1.0176        ):177            raise ValueError(178                f"`rope_scaling`'s factor field must be a number >= 1, got {rope_scaling_factor} "179                f"of type {type(rope_scaling_factor)}"180            )181