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Upload configuration_RW.py with huggingface_hub

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  1. configuration_RW.py +75 -0
configuration_RW.py ADDED
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+ # coding=utf-8
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+ # Copyright 2022 the Big Science Workshop and HuggingFace Inc. team. All rights reserved.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """ Bloom configuration"""
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ class RWConfig(PretrainedConfig):
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+ model_type = "RefinedWeb"
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+ keys_to_ignore_at_inference = ["past_key_values"]
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+ attribute_map = {
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+ "num_hidden_layers": "n_layer",
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+ "num_attention_heads": "n_head",
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+ }
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+
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+ def __init__(
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+ self,
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+ vocab_size=250880,
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+ hidden_size=64,
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+ n_layer=2,
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+ n_head=8,
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+ layer_norm_epsilon=1e-5,
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+ initializer_range=0.02,
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+ use_cache=True,
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+ bos_token_id=1,
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+ eos_token_id=2,
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+ apply_residual_connection_post_layernorm=False,
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+ hidden_dropout=0.0,
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+ attention_dropout=0.0,
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+ n_head_kv=None,
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+ alibi=False,
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+ **kwargs,
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+ ):
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+ self.vocab_size = vocab_size
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+ # Backward compatibility with n_embed kwarg
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+ n_embed = kwargs.pop("n_embed", None)
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+ self.hidden_size = hidden_size if n_embed is None else n_embed
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+ self.n_layer = n_layer
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+ self.n_head = n_head
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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.use_cache = use_cache
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+ self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
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+ self.hidden_dropout = hidden_dropout
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+ self.attention_dropout = attention_dropout
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+
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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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+ self.n_head_kv = n_head if n_head_kv is None else n_head_kv
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+ self.alibi = alibi
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+
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+ super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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+
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+ @property
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+ def head_dim(self):
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+ return self.hidden_size // self.n_head
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+
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+ @property
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+ def rotary(self):
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+ return not self.alibi