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# coding=utf-8
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# 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.
"""Nemotron model configuration"""
from ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import rope_config_validation
from ...utils import logging
logger = logging.get_logger(__name__)
class NemotronConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NemotronModel`]. It is used to instantiate an Nemotron
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 Nemotron-8B.
e.g. [nvidia/nemotron-3-8b-base-4k-hf](https://huggingface.co/nvidia/nemotron-3-8b-base-4k-hf).
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 256000):
Vocabulary size of the Nemotron model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`NemotronModel`]
hidden_size (`int`, *optional*, defaults to 6144):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 24576):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 48):
Number of attention heads for each attention layer in the Transformer decoder.
head_dim (`int`, *optional*):
Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if None
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`.
hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.0134):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the 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`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 2):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 3):
End of stream token id.
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.
partial_rotary_factor (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj and down_proj layers in the MLP layers.
```python
>>> from transformers import NemotronModel, NemotronConfig
>>> # Initializing a Nemotron nemotron-15b style configuration
>>> configuration = NemotronConfig()
>>> # Initializing a model from the nemotron-15b style configuration
>>> model = NemotronModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "nemotron"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=256000,
hidden_size=6144,
intermediate_size=24576,
num_hidden_layers=32,
num_attention_heads=48,
head_dim=None,
num_key_value_heads=None,
hidden_act="relu2",
max_position_embeddings=4096,
initializer_range=0.0134,
norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=2,
eos_token_id=3,
tie_word_embeddings=False,
rope_theta=10000.0,
partial_rotary_factor=0.5,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
**kwargs,
):
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
self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.norm_eps = norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.partial_rotary_factor = partial_rotary_factor
rope_config_validation(self)
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
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,
)
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