| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class GPTJMoEConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J | |
| 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 GPT-J | |
| [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) architecture. Configuration objects inherit from | |
| [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] | |
| for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 50400): | |
| Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`GPTJModel`]. | |
| n_positions (`int`, *optional*, defaults to 2048): | |
| 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). | |
| n_embd (`int`, *optional*, defaults to 4096): | |
| Dimensionality of the embeddings and hidden states. | |
| n_layer (`int`, *optional*, defaults to 28): | |
| Number of hidden layers in the Transformer encoder. | |
| n_head (`int`, *optional*, defaults to 16): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| rotary_dim (`int`, *optional*, defaults to 64): | |
| Number of dimensions in the embedding that Rotary Position Embedding is applied to. | |
| n_inner (`int`, *optional*, defaults to None): | |
| Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd | |
| activation_function (`str`, *optional*, defaults to `"gelu_new"`): | |
| Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. | |
| resid_pdrop (`float`, *optional*, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| embd_pdrop (`int`, *optional*, defaults to 0.1): | |
| The dropout ratio for the embeddings. | |
| attn_pdrop (`float`, *optional*, defaults to 0.1): | |
| The dropout ratio for the attention. | |
| layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): | |
| The epsilon to use in the layer normalization layers. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| use_cache (`bool`, *optional*, defaults to `True`): | |
| Whether or not the model should return the last key/values attentions (not used by all models). | |
| num_experts_per_tok (`int`, *optional*, defaults to 2): | |
| The number of experts to root per-token, can be also interpreted as the `top-p` routing | |
| parameter | |
| num_local_experts (`int`, *optional*, defaults to 4): | |
| Number of experts per Sparse MLP layer. | |
| output_router_logits (`bool`, *optional*, defaults to `False`): | |
| Whether or not the router logits should be returned by the model. Enabeling this will also | |
| allow the model to output the auxiliary loss. See [here]() for more details | |
| router_aux_loss_coef (`float`, *optional*, defaults to 0.001): | |
| The aux loss factor for the total loss. | |
| router_jitter_noise (`float`, *optional*, defaults to 0.0): | |
| Amount of noise to add to the router. | |
| """ | |
| model_type = "gptj_moe" | |
| attribute_map = { | |
| "max_position_embeddings": "n_positions", | |
| "hidden_size": "n_embd", | |
| "num_attention_heads": "n_head", | |
| "num_hidden_layers": "n_layer", | |
| } | |
| def __init__( | |
| self, | |
| vocab_size=50400, | |
| n_positions=2048, | |
| n_embd=4096, | |
| n_layer=28, | |
| n_head=16, | |
| rotary_dim=64, | |
| n_inner=None, | |
| activation_function="gelu_new", | |
| resid_pdrop=0.0, | |
| embd_pdrop=0.0, | |
| attn_pdrop=0.0, | |
| layer_norm_epsilon=1e-5, | |
| initializer_range=0.02, | |
| use_cache=True, | |
| bos_token_id=50256, | |
| eos_token_id=50256, | |
| tie_word_embeddings=False, | |
| n_experts_per_tok=2, | |
| n_local_experts=4, | |
| output_router_logits=False, | |
| router_aux_loss_coef=0.001, | |
| router_jitter_noise=0.0, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.n_positions = n_positions | |
| self.n_embd = n_embd | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.n_inner = n_inner | |
| self.rotary_dim = rotary_dim | |
| self.activation_function = activation_function | |
| self.resid_pdrop = resid_pdrop | |
| self.embd_pdrop = embd_pdrop | |
| self.attn_pdrop = attn_pdrop | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.initializer_range = initializer_range | |
| self.use_cache = use_cache | |
| self.bos_token_id = bos_token_id | |
| self.eos_token_id = eos_token_id | |
| self.num_experts_per_tok = n_experts_per_tok | |
| self.num_local_experts = n_local_experts | |
| self.output_router_logits = output_router_logits | |
| self.router_aux_loss_coef = router_aux_loss_coef | |
| self.router_jitter_noise = router_jitter_noise | |
| super().__init__( | |
| bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs | |
| ) | |