# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # 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 transformers import PretrainedConfig from transformers.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 a 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. 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_parameters (`dict`, *optional*): The parameters of the RoPE embeddings. Expected contents: `rope_theta` (`float`): The base period of the RoPE embeddings. `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `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_parameters=None, 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 head_dim = head_dim or kwargs.get("kv_channels") self.head_dim = ( head_dim if head_dim is not None else (hidden_size // 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.norm_eps = norm_eps self.use_cache = use_cache # Try to set `rope_scaling` if available, otherwise use `rope_parameters` rope_scaling = kwargs.pop("rope_scaling", None) rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"} rope_theta = kwargs.pop("rope_theta", 10000.0) if "rope_theta" not in rope_parameters: rope_parameters["rope_theta"] = rope_theta # for backward compatibility partial_rotary_factor = ( kwargs.get("rope_percent") or kwargs.get("rope_percentage") or kwargs.get("partial_rotary_factor") or 0.5 ) if "partial_rotary_factor" not in rope_parameters: rope_parameters["partial_rotary_factor"] = partial_rotary_factor self.rope_parameters = rope_parameters self._rope_parameters_validation() 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, ) def _rope_parameters_validation(self): """ Validate the `rope_parameters` configuration. """ if self.rope_parameters is None: return rope_type: str | None = self.rope_parameters.get("rope_type", None) factor: float | None = self.rope_parameters.get("factor", None) if rope_type not in {"default", "linear", "dynamic"}: raise ValueError( "`rope_type` must be one of ['default', 'linear', 'dynamic'], " f"got {rope_type}" ) if rope_type != "default": if factor is None: raise ValueError( "If `rope_type` is not 'default', `rope_parameters` " "must include a `factor` field. Got `None`." ) if not isinstance(factor, float) or factor <= 1.0: raise ValueError( "`rope_parameters`'s factor field must be a float > 1, got " f"{factor}" )