btlm-3b-8k-chat / configuration_btlm.py

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	# 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}")