# 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 collections import OrderedDict from typing import TYPE_CHECKING, Any, List, Mapping, Optional from packaging import version if TYPE_CHECKING: from transformers import PreTrainedTokenizer, TensorType from transformers.configuration_utils import PretrainedConfig from transformers.utils import is_torch_available, logging logger = logging.get_logger(__name__) class CCBloomConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`BloomModel`]. It is used to instantiate a Bloom model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to the Bloom architecture [bigscience/bloom](https://huggingface.co/bigscience/bloom). 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 250880): Vocabulary size of the Bloom model. Defines the maximum number of different tokens that can be represented by the `inputs_ids` passed when calling [`BloomModel`]. Check [this discussion](https://huggingface.co/bigscience/bloom/discussions/120#633d28389addb8530b406c2a) on how the `vocab_size` has been defined. hidden_size (`int`, *optional*, defaults to 64): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 2): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. 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. apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled, use the layer norm of the hidden states as the residual in the transformer blocks hidden_dropout (`float`, *optional*, defaults to 0.1): Dropout rate of the dropout function on the bias dropout. attention_dropout (`float`, *optional*, defaults to 0.1): Dropout rate applied to the attention probs use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). pretraining_tp (`int`, *optional*, defaults to `1`): Experimental feature. Tensor parallelism rank used during pretraining with Megatron. Please refer to [this document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). Note also that this is enabled only when `slow_but_exact=True`. slow_but_exact (`bool`, *optional*, defaults to `False`): Experimental feature. Whether to use slow but exact implementation of the attention mechanism. While merging the TP rank tensors, due to slicing operations the results may be slightly different between the model trained on Megatron and our model. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). A solution to obtain more accurate results is to enable this feature. Enabling this will hurt the computational time of the inference. Will be probably resolved in the future once the main model has been fine-tuned with TP_rank=1. """ model_type = "ccbloom" 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, pretraining_tp=1, # TP rank used when training with megatron slow_but_exact=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.pretraining_tp = pretraining_tp 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.slow_but_exact = slow_but_exact super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)