213 lines
8.9 KiB
Python
213 lines
8.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2024 HuggingFace Inc. team. All rights reserved.
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# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Nemotron model configuration"""
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from transformers import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class NemotronConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a
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[`NemotronModel`]. It is used to instantiate a Nemotron model
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according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar
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configuration to that of the Nemotron-8B.
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Configuration objects inherit from [`PretrainedConfig`] and can be
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used to control the model outputs. Read the documentation from
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[`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 256000):
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Vocabulary size of the Nemotron model. Defines the number of
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different tokens that can be represented by the
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`inputs_ids` passed when calling [`NemotronModel`]
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hidden_size (`int`, *optional*, defaults to 6144):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 24576):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 48):
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Number of attention heads for each attention layer in the
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Transformer decoder.
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head_dim (`int`, *optional*):
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Projection weights dimension in multi-head attention. Set to
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hidden_size // num_attention_heads if None
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to
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implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use
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Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention
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(MQA) otherwise GQA is used. When converting a multi-head
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checkpoint to a GQA checkpoint, each group key and value
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head should be constructed by meanpooling all the original
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heads within that group. For more details checkout
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[this paper](https://arxiv.org/pdf/2305.13245.pdf). If it
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is not specified, will default to `num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
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The non-linear activation function (function or string) in the
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decoder.
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used
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with.
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initializer_range (`float`, *optional*, defaults to 0.0134):
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The standard deviation of the truncated_normal_initializer for
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initializing all weight matrices.
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norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values
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attentions (not used by all models). Only relevant if
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`config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 2):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 3):
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End of stream token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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partial_rotary_factor (`float`, *optional*, defaults to 0.5):
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Percentage of the query and keys which will have rotary embedding.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output
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projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj and down_proj layers in the MLP
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layers.
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```python
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>>> from transformers import NemotronModel, NemotronConfig
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>>> # Initializing a Nemotron nemotron-15b style configuration
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>>> configuration = NemotronConfig()
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>>> # Initializing a model from the nemotron-15b style configuration
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>>> model = NemotronModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "nemotron"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=256000,
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hidden_size=6144,
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intermediate_size=24576,
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num_hidden_layers=32,
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num_attention_heads=48,
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head_dim=None,
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num_key_value_heads=None,
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hidden_act="relu2",
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max_position_embeddings=4096,
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initializer_range=0.0134,
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norm_eps=1e-5,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=2,
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eos_token_id=3,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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partial_rotary_factor=0.5,
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attention_bias=False,
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attention_dropout=0.0,
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mlp_bias=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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head_dim = head_dim or kwargs.get("kv_channels")
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self.head_dim = (
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head_dim if head_dim is not None else (hidden_size // num_attention_heads)
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)
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.norm_eps = norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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# for backward compatibility
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partial_rotary_factor = (
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kwargs.get("rope_percent")
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or kwargs.get("rope_percentage")
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or partial_rotary_factor
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)
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self.partial_rotary_factor = partial_rotary_factor
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self._rope_scaling_validation()
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with two fields, "
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f"`type` and `factor`, got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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"`rope_scaling`'s type field must be one of ['linear', "
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f"'dynamic'], got {rope_scaling_type}"
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)
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if (
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rope_scaling_factor is None
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or not isinstance(rope_scaling_factor, float)
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or rope_scaling_factor <= 1.0
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):
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raise ValueError(
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"`rope_scaling`'s factor field must be a float > 1, got "
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f"{rope_scaling_factor}"
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)
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