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| # coding=utf-8 | |
| # Copyright 2022 the Big Science Workshop and HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ Bloom configuration""" | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class RWConfig(PretrainedConfig): | |
| model_type = "RefinedWebModel" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| attribute_map = { | |
| "num_hidden_layers": "n_layer", | |
| "num_attention_heads": "n_head", | |
| } | |
| def __init__( | |
| self, | |
| vocab_size=250880, | |
| hidden_size=64, | |
| n_layer=2, | |
| n_head=8, | |
| layer_norm_epsilon=1e-5, | |
| initializer_range=0.02, | |
| use_cache=True, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| apply_residual_connection_post_layernorm=False, | |
| hidden_dropout=0.0, | |
| attention_dropout=0.0, | |
| multi_query=False, | |
| alibi=False, | |
| bias=False, | |
| parallel_attn=False, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| # Backward compatibility with n_embed kwarg | |
| n_embed = kwargs.pop("n_embed", None) | |
| self.hidden_size = hidden_size if n_embed is None else n_embed | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.initializer_range = initializer_range | |
| self.use_cache = use_cache | |
| self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm | |
| self.hidden_dropout = hidden_dropout | |
| self.attention_dropout = attention_dropout | |
| self.bos_token_id = bos_token_id | |
| self.eos_token_id = eos_token_id | |
| self.multi_query = multi_query | |
| self.alibi = alibi | |
| self.bias = bias | |
| self.parallel_attn = parallel_attn | |
| super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |
| def head_dim(self): | |
| return self.hidden_size // self.n_head | |
| def rotary(self): | |
| return not self.alibi | |