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| """ InternLM2 model configuration"""
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|
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| from transformers.configuration_utils import PretrainedConfig
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| from transformers.utils import logging
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|
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| logger = logging.get_logger(__name__)
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|
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| INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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| class InternLM2Config(PretrainedConfig):
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| r"""
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| This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
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| an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
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| configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
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|
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| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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| documentation from [`PretrainedConfig`] for more information.
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| Args:
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| vocab_size (`int`, *optional*, defaults to 32000):
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| Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
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| `inputs_ids` passed when calling [`InternLM2Model`]
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| hidden_size (`int`, *optional*, defaults to 4096):
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| Dimension of the hidden representations.
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| intermediate_size (`int`, *optional*, defaults to 11008):
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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 encoder.
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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*):
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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) in the decoder.
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| max_position_embeddings (`int`, *optional*, defaults to 2048):
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| The maximum sequence length that this model might ever be used with. Typically set this to something large
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| 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 all weight matrices.
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| rms_norm_eps (`float`, *optional*, defaults to 1e-12):
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| The epsilon used by the rms 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 (not used by all models). Only
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| relevant if `config.is_decoder=True`.
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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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| Example:
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|
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| """
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| model_type = 'internlm2'
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| _auto_class = 'AutoConfig'
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|
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| def __init__(
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| self,
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| vocab_size=103168,
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| hidden_size=4096,
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| intermediate_size=11008,
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| num_hidden_layers=32,
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| num_attention_heads=32,
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| num_key_value_heads=None,
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| hidden_act='silu',
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| max_position_embeddings=2048,
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| initializer_range=0.02,
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| rms_norm_eps=1e-6,
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| use_cache=True,
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| pad_token_id=0,
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| bos_token_id=1,
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| eos_token_id=2,
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| tie_word_embeddings=False,
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| bias=True,
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| rope_theta=10000,
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| rope_scaling=None,
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| attn_implementation='eager',
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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.bias = bias
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|
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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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|
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| self.hidden_act = hidden_act
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| self.initializer_range = initializer_range
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| self.rms_norm_eps = rms_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._rope_scaling_validation()
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|
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| self.attn_implementation = attn_implementation
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| if self.attn_implementation is None:
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| self.attn_implementation = 'eager'
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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 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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|
|
