configuration.py

# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.

# This code has been adapted from Meta and Huggingface and inherits the above lisence.
# The original code can be found here:
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/configuration_llama.py

"""Extended Mind LLaMA model configuration"""

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class ExtendedLlamaConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ExtendedLlamaModel`].
    It is used to instantiate an Extended Mind LLaMA model according to the specified arguments,
    defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Extended Mind LLaMA-7B.

    Configuration objects inherit from [`PretrainedConfig`]
    and can be used to control the model outputs. 
    Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 32000):
            Vocabulary size of the LLaMA model. Defines the number of different tokens
            that can be represented by the `inputs_ids` passed when calling [`LlamaModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement
            Grouped Query Attention. If `num_key_value_heads=num_attention_heads`,
            the model will use Multi Head Attention (MHA), if `num_key_value_heads=1
            the model will use Multi Query Attention (MQA) otherwise GQA is used. 
            When converting a multi-head checkpoint to a GQA checkpoint,
            each group key and value head should be constructed by meanpooling 
            all the original heads within that group. For more details checkout
            [this paper](https://arxiv.org/pdf/2305.13245.pdf).
            If it is not specified, will default to
            `num_attention_heads`.
        pretraining_tp (`int`, *optional*, defaults to `1`):
            Experimental feature. Tensor parallelism rank used during pretraining.
            Please refer to [this document]
            (https://huggingface.co/docs/transformers/parallelism)
            to understand more about it. This value is
            necessary to ensure exact reproducibility of the pretraining results.
            Please refer to [this issue]
            (https://github.com/pytorch/pytorch/issues/76232).
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with.
            Llama 1 supports up to 2048 tokens,
            Llama 2 up to 4096, CodeLlama up to 16384.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer 
            for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the rms normalization layers.
        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`.
        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings.
            Currently supports two scaling strategies: linear and dynamic. 
            Their scaling factor must be an float greater than 1. The expected format
            is `{"type": strategy name, "factor": scaling factor}`.
            When using this flag, don't update `max_position_embeddings`
            to the expected new maximum. See the following thread for more information
            on how these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/
            14mrgpr/dynamically_scaled_rope_further_increases/. 
            This is an experimental feature, subject to breaking API changes in future versions.
        
        #### Memory Configuration ####
        use_external_mind (`bool`, *optional*, defaults to `True`):
            Whether to attend to external memories.
        use_external_mind_by_layer (`List[bool]`, *optional*,
            defaults to List[`True`, ..., `True`]):
            Whether to attend to external memories, on each decoder layer.
        topk (`int`, *optional*, defaults to `10`):
            Number of external memories for each query token to retrieve and attend to.
        memory_type (`string`, *optional*, defaults to `manual`):
            Whether to store external memories manually or in a vector database.
        memory_device (`string`, *optional*, defaults to `cpu`):
            Specify device to store memory.
        mask_by_sim (`bool`, *optional*, defaults to `True`):
            Whether or not to mask retrieved memories by similarity.
        sim_threshold (`float`, *optional*, defaults to `0.25`):
            Threshold for masking retrieved memories.
        tokenizer_all_special_ids (`list`, *optional*, defaults to `[0,1,2]`):
            Ids for special tokens to remove from memories.
        remove_special_tokens (`bool`, *optional*, defaults to `True`):
            Remove memories that correspond to tokenizer special ids.
        #### Memory Configuration ####

        Example:

    ```python
    >>> from transformers import LlamaModel, LlamaConfig

    >>> # Initializing a LLaMA llama-7b style configuration
    >>> configuration = LlamaConfig()

    >>> # Initializing a model from the llama-7b style configuration
    >>> model = LlamaModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "extended-llama"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.02,
        rms_norm_eps=1e-5,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        pretraining_tp=1,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        memory_config=None,
        **kwargs,
    ):
        if memory_config is None:
            memory_config = {
                "mask_by_sim": False,
                "sim_threshold": 0.25,
                "topk": 10,
                "use_external_mind": True,
                "memory_type": "manual",
                "memory_device": "cpu",
                "tokenizer_all_special_ids": [0, bos_token_id, eos_token_id],
                "use_external_mind_by_layer": [
                    True for _ in range(num_hidden_layers)
                ],
                "remove_special_ids": True,
            }
        for key, value in memory_config.items():
            setattr(self, key, value)

        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.pretraining_tp = pretraining_tp
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
            raise ValueError(
                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
                f"got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_factor = self.rope_scaling.get("factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
            raise ValueError(
                f"""`rope_scaling`'s type field must be one of ['linear', 'dynamic'],
                got {rope_scaling_type}"""
            )
        if (
            rope_scaling_factor is None
            or not isinstance(rope_scaling_factor, float)
            or rope_scaling_factor <= 1.0
        ):
            raise ValueError(
                f"""`rope_scaling`'s factor field must be an float > 1,
                got {rope_scaling_factor}"""
            )
        
        # Faiss memory not compatible with Grouped Query Attention
        if self.memory_type=='faiss' and self.num_key_value_heads != self.num_attention_heads:
            raise NotImplementedError(
                'Faiss memory not compatible with Grouped Query Attention.'
            )