# coding=utf-8 # Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # Copyright 2023 Cerebras Systems. # # 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. """ BTLM configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) BTLM_PRETRAINED_CONFIG_ARCHIVE_MAP = { "cerebras/btlm-3b-8k-base": "https://huggingface.co/cerebras/btlm-3b-8k-base/resolve/main/config.json", } class BTLMConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`BTLMModel`]. It is used to instantiate a BTLM model according to the specified arguments, defining the model 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 50257): Vocabulary size of the BTLM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BTLMModel`]. n_positions (`int`, *optional*, defaults to 1024): 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 768): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. 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"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new", "swiglu"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *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. scale_attn_weights (`bool`, *optional*, defaults to `True`): Scale attention weights by dividing by sqrt(hidden_size).. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): Whether to additionally scale attention weights by `1 / layer_idx + 1`. reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention dot-product/softmax to float() when training with mixed precision. position_embedding_type (`str`, *optional*, defaults to `"learned"`): Positional embedding can be either `"alibi"` or `"learned"`. mup_width_scale (`float`, *optional*, defaults to 1.0): muP parameter to scale learning rate and initializers. Calculated as (`d_model,0 / d_model`), where `d_model` is the model's width and `d_model,0` is the proxy model's width. mup_embeddings_scale (`float`, *optional*, defaults to 1.0): muP parameter to scale token and position embeddings. mup_output_alpha (`float`, *optional*, defaults to 1.0): muP parameter to scale output logits (`output_logits_scale = mup_output_alpha * mup_width_scale`). mup_scale_qk_dot_by_d (`bool`, *optional*, defaults to `False`): Scale attention weights by dividing by hidden_size instead of sqrt(hidden_size). Need to set scale_attn_weights to `True` as well. alibi_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for ALiBi embeddings. Currently only supports linear scaling strategy. Can specify either the scaling `factor` (must be a float greater than 1) for fixed scaling or `train_seq_len` for dynamic scaling on input samples with sequence length > `train_seq_len`. The expected formats are `{"type": strategy name, "factor": scaling factor}` or `{"type": strategy name, "train_seq_len": training sequence length}`. Example: ```python >>> from transformers import BTLMConfig, BTLMModel >>> # Initializing a BTLM configuration >>> configuration = BTLMConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = BTLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "btlm" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "hidden_size": "n_embd", "max_position_embeddings": "n_positions", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=50257, n_positions=1024, n_embd=768, n_layer=12, n_head=12, n_inner=None, activation_function="gelu_new", resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, scale_attn_weights=True, use_cache=True, bos_token_id=50256, eos_token_id=50256, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False, position_embedding_type="learned", mup_width_scale=1.0, mup_embeddings_scale=1.0, mup_output_alpha=1.0, mup_scale_qk_dot_by_d=False, alibi_scaling=None, **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.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.scale_attn_weights = scale_attn_weights self.use_cache = use_cache self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx self.reorder_and_upcast_attn = reorder_and_upcast_attn self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.position_embedding_type = position_embedding_type self.mup_width_scale = mup_width_scale self.mup_embeddings_scale = mup_embeddings_scale self.mup_output_alpha = mup_output_alpha self.mup_scale_qk_dot_by_d = mup_scale_qk_dot_by_d self.alibi_scaling = alibi_scaling self._alibi_scaling_validation() super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) def _alibi_scaling_validation(self): """ Validate the `alibi_scaling` configuration. """ if self.alibi_scaling is None: return if not isinstance(self.alibi_scaling, dict) or len(self.alibi_scaling) != 2: raise ValueError( "`alibi_scaling` must be a dictionary with two fields, `type` and `factor` or `type` and `train_seq_len`, " f"got {self.alibi_scaling}" ) alibi_scaling_type = self.alibi_scaling.get("type", None) alibi_scaling_factor = self.alibi_scaling.get("factor", None) alibi_dynamic_scaling = self.alibi_scaling.get("train_seq_len", None) if alibi_scaling_type is None or alibi_scaling_type != "linear": raise ValueError( f"`alibi_scaling`'s type field must be 'linear', got {alibi_scaling_type}" ) if alibi_scaling_factor is not None: if not isinstance(alibi_scaling_factor, float) or alibi_scaling_factor <= 1.0: raise ValueError(f"`alibi_scaling`'s factor field must be a float > 1.0, got {alibi_scaling_factor}") if alibi_dynamic_scaling is not None: if not isinstance(alibi_dynamic_scaling, int) or alibi_dynamic_scaling <= 1: raise ValueError(f"`alibi_scaling`'s `train_seq_len` field must be an integer > 1, got {alibi_dynamic_scaling}")