| | from transformers.configuration_utils import PretrainedConfig
|
| | class EConfig(PretrainedConfig):
|
| | r"""
|
| | This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
|
| | 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 LLaMA-7B.
|
| |
|
| | Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| | documentation from [`PretrainedConfig`] for more information.
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| |
|
| |
|
| | Args:
|
| | vocab_size (`int`, *optional*, defaults to 32000):
|
| | Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
|
| | `inputs_ids` passed when calling [`LlamaModel`]
|
| | 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.
|
| | num_key_value_heads (`int`, *optional*):
|
| | 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
|
| | 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`.
|
| | pretraining_tp (`int`, *optional*, defaults to `1`):
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| | Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
| | document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
| | necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
| | issue](https://github.com/pytorch/pytorch/issues/76232).
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| | hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
| | The non-linear activation function (function or string) in the decoder.
|
| | 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
|
| | just in case (e.g., 512 or 1024 or 2048).
|
| | initializer_range (`float`, *optional*, defaults to 0.02):
|
| | The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| | rms_norm_eps (`float`, *optional*, defaults to 1e-12):
|
| | The epsilon used by the rms normalization layers.
|
| | 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
|
| | relevant if `config.is_decoder=True`.
|
| | tie_word_embeddings(`bool`, *optional*, defaults to `False`):
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| | Whether to tie weight 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
|
| | strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
|
| | is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
| | `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
| | these scaling strategies behave:
|
| | https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
| | experimental feature, subject to breaking API changes in future versions.
|
| |
|
| | Example:
|
| |
|
| | ```python
|
| | >>> from transformers import LlamaModel, LlamaConfig
|
| |
|
| | >>> # Initializing a LLaMA llama-7b style configuration
|
| | >>> configuration = LlamaConfig()
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| |
|
| | >>> # Initializing a model from the llama-7b style configuration
|
| | >>> model = LlamaModel(configuration)
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| |
|
| | >>> # Accessing the model configuration
|
| | >>> configuration = model.config
|
| | ```"""
|
| | model_type = "llama"
|
| | keys_to_ignore_at_inference = ["past_key_values"]
|
| |
|
| | def __init__(
|
| | self,
|
| | vocab_size=32000,
|
| | hidden_size=4096,
|
| | intermediate_size=11008,
|
| | num_hidden_layers=32,
|
| | num_attention_heads=32,
|
| | num_key_value_heads=None,
|
| | hidden_act="silu",
|
| | max_position_embeddings=2048,
|
| | initializer_range=0.02,
|
| | rms_norm_eps=1e-6,
|
| | use_cache=True,
|
| | pad_token_id=None,
|
| | bos_token_id=1,
|
| | eos_token_id=2,
|
| | pretraining_tp=1,
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| | tie_word_embeddings=False,
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| | rope_scaling=None,
|
| | **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
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| |
|
| |
|
| | 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.rms_norm_eps = rms_norm_eps
|
| | self.pretraining_tp = pretraining_tp
|
| | self.use_cache = use_cache
|
| | self.rope_scaling = rope_scaling
|
| | self._rope_scaling_validation()
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| |
|
| | 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_scaling_validation(self):
|
| | """
|
| | Validate the `rope_scaling` configuration.
|
| | """
|
| | if self.rope_scaling is None:
|
| | return
|
| |
|
| | if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| | raise ValueError(
|
| | "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
|
| | f"got {self.rope_scaling}"
|
| | )
|
| | rope_scaling_type = self.rope_scaling.get("type", None)
|
| | rope_scaling_factor = self.rope_scaling.get("factor", None)
|
| | if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
| | raise ValueError(
|
| | f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| | )
|
| | if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
| | raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}") |
