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# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on Yandex's YaLM-100B library and the LLaMA
# implementations in transformers library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to LLaMA used by the Yandex 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.
"""YaLM model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
YALM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class YalmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`YalmModel`]. It is used to instantiate an YaLM
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 YaLM-100B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
padded_vocab_size (`int`, *optional*, defaults to 128000):
Vocabulary size of the YaLM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`YalmModel`]
embedding_size (`int`, *optional*, defaults to 2048):
Token embeding dimension
hidden_size (`int`, *optional*, defaults to 10240):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 27308):
Dimension of the MLP representations.
num_layers (`int`, *optional*, defaults to 80):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 128):
Number of attention heads for each attention layer in the Transformer encoder.
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to True):
Whether to scale attention output by inverse layer depth
activation_type (`str` or `function`, *optional*, defaults to `"geglu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`):
If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layernorm_epsilon (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer 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`.
Example:
```python
>>> from transformers import YalmModel, YalmConfig
>>> # Initializing a YaLM yalm-100b style configuration
>>> configuration = YalmConfig()
>>> # Initializing a model from the yalm-100b style configuration
>>> model = YalmModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "yalm"
def __init__(
self,
padded_vocab_size=128000,
embedding_size=2048,
hidden_size=10240,
intermediate_size=27308,
num_layers=80,
num_attention_heads=128,
scale_attn_by_inverse_layer_idx=True,
activation_type="geglu",
max_position_embeddings=1024,
apply_residual_connection_post_layernorm=False,
initializer_range=0.02,
layernorm_epsilon=1e-5,
attention_dropout=0.1,
hidden_dropout=0.1,
use_cache=True,
bos_token_id=1,
eos_token_id=2,
**kwargs,
):
self.padded_vocab_size = padded_vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_layers = num_layers
self.num_attention_heads = num_attention_heads
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.activation_type = activation_type
self.max_position_embeddings = max_position_embeddings
self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
self.initializer_range = initializer_range
self.layernorm_epsilon = layernorm_epsilon
self.attention_dropout = attention_dropout
self.hidden_dropout = hidden_dropout
self.use_cache = use_cache
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
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