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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 GPT2Config |
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logger = logging.get_logger(__name__) |
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class LOLAConfig(PretrainedConfig): |
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""" |
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This is the configuration class is a modified copy of https://huggingface.co/openai-community/gpt2 with MoE support. |
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""" |
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model_type = "lola_v1" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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attribute_map = { |
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"hidden_size": "n_embd", |
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"max_position_embeddings": "n_positions", |
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"num_attention_heads": "n_head", |
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"num_hidden_layers": "n_layer", |
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} |
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def __init__( |
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self, |
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vocab_size=100096, |
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n_positions=2048, |
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n_embd=2048, |
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n_layer=24, |
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n_head=16, |
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n_inner=8192, |
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activation_function="gelu_new", |
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resid_pdrop=0.1, |
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embd_pdrop=0.1, |
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attn_pdrop=0.1, |
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layer_norm_epsilon=1e-5, |
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initializer_range=0.02, |
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summary_type="cls_index", |
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summary_use_proj=True, |
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summary_activation=None, |
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summary_proj_to_labels=True, |
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summary_first_dropout=0.1, |
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scale_attn_weights=True, |
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use_cache=True, |
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bos_token_id=100095, |
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eos_token_id=100095, |
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scale_attn_by_inverse_layer_idx=False, |
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reorder_and_upcast_attn=False, |
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num_experts=16, |
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topk=1, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.n_positions = n_positions |
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self.n_embd = n_embd |
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self.n_layer = n_layer |
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self.n_head = n_head |
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self.n_inner = n_inner |
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self.activation_function = activation_function |
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self.resid_pdrop = resid_pdrop |
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self.embd_pdrop = embd_pdrop |
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self.attn_pdrop = attn_pdrop |
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self.layer_norm_epsilon = layer_norm_epsilon |
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self.initializer_range = initializer_range |
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self.summary_type = summary_type |
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self.summary_use_proj = summary_use_proj |
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self.summary_activation = summary_activation |
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self.summary_first_dropout = summary_first_dropout |
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self.summary_proj_to_labels = summary_proj_to_labels |
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self.scale_attn_weights = scale_attn_weights |
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self.use_cache = use_cache |
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self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx |
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self.reorder_and_upcast_attn = reorder_and_upcast_attn |
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self.num_experts = num_experts |
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self.topk = topk |
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self.bos_token_id = bos_token_id |
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self.eos_token_id = eos_token_id |
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
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