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# 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.'
)
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