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""" Mistral 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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from transformers import MixtralConfig |
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logger = logging.get_logger(__name__) |
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class CLEXMixtralConfig(MixtralConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA |
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
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defaults will yield a similar configuration to that of the LLaMA-7B. |
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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 LLaMA model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`LlamaModel`] |
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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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pretraining_tp (`int`, *optional*, defaults to `1`): |
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this |
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document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is |
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necessary to ensure exact reproducibility of the pretraining results. Please refer to [this |
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issue](https://github.com/pytorch/pytorch/issues/76232). |
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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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rope_scaling (`Dict`, *optional*): |
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling |
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strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format |
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is `{"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 is an |
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experimental feature, subject to breaking API changes in future versions. |
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Example: |
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```python |
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>>> from transformers import LlamaModel, LlamaConfig |
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>>> # Initializing a LLaMA llama-7b style configuration |
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>>> configuration = LlamaConfig() |
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>>> # Initializing a model from the llama-7b style configuration |
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>>> model = LlamaModel(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 = "mixtral" |
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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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rope_scaling=None, |
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use_flashattn=True, |
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log_scale=True, |
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pretraining_tp=1, |
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**kwargs, |
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): |
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super().__init__( |
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**kwargs, |
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) |
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self.pretraining_tp = pretraining_tp |
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self.use_flashattn = use_flashattn |
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self.log_scale = log_scale |
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self.rope_scaling = rope_scaling |
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self._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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rope_scaling_type = self.rope_scaling.get("type", None) |
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rope_scaling_max_factor = self.rope_scaling.get("max_factor", None) |
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rope_scaling_param_factor = self.rope_scaling.get("param_factor", None) |
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "clex"]: |
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raise ValueError( |
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f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" |
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) |
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