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configuration_stablelm.py
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183
configuration_stablelm.py
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# coding=utf-8
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# Copyright 2024 Stability AI and The HuggingFace Inc. team. 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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""" StableLM model configuration """
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"stabilityai/stablelm-3b-4e1t": "https://huggingface.co/stabilityai/stablelm-3b-4e1t/resolve/main/config.json",
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# See all StableLM models at https://huggingface.co/models?filter=stablelm
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}
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class StableLmConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`~StableLmModel`].
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It is used to instantiate an StableLM model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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the StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used
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to control the model outputs. Read the documentation from [`PretrainedConfig`]
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for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50304):
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Vocabulary size of the StableLM model. Defines the number of different tokens that
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can be represented by the `inputs_ids` passed when calling [`StableLmModel`].
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intermediate_size (`int`, *optional*, defaults to 6912):
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Dimension of the MLP representations.
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hidden_size (`int`, *optional*, defaults to 2560):
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Number of hidden layers in the Transformer decoder.
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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 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 32):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string).
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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 with.
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Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing
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all weight matrices.
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layer_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 attentions
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(not used by all models). Only relevant if `config.is_decoder=True`.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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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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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
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is an experimental feature, subject to breaking API changes in future versions.
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use_qkv_bias (`bool`, *optional*, defaults to `False`):
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Whether or not the model should use bias for qkv layers.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after applying the MLP to the hidden states.
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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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partial_rotary_factor (`float`, *optional*, defaults to 0.25):
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Percentage of the query and keys which will have rotary embedding.
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bos_token_id (int, *optional*, defaults to 0):
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The id of the `BOS` token in the vocabulary.
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eos_token_id (int, *optional*, defaults to 0):
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The id of the `EOS` token in the vocabulary.
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Example:
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```python
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>>> from transformers import StableLmModel, StableLmConfig
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>>> # Initializing a StableLM stablelm-3b style configuration
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>>> configuration = StableLmConfig()
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```"""
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model_type = "stablelm"
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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=50304,
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intermediate_size=6912,
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hidden_size=2560,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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max_position_embeddings=4096,
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initializer_range=0.02,
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layer_norm_eps=1.0e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10_000,
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rope_scaling=None,
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use_qkv_bias=False,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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partial_rotary_factor=0.25,
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bos_token_id=0,
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eos_token_id=0,
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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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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.layer_norm_eps = layer_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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self.use_qkv_bias = use_qkv_bias
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.partial_rotary_factor = partial_rotary_factor
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self._rope_scaling_validation()
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super().__init__(
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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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# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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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 with two fields, `type` and `factor`, "
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f"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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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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