internlm-7b / configuration_internlm.py

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	# coding=utf-8
	# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
	#
	# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
	#
	# 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.
	""" InternLM model configuration"""

	from transformers.configuration_utils import PretrainedConfig
	from transformers.utils import logging

	logger = logging.get_logger(__name__)

	INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}


	# Modified from transformers.model.llama.configuration_llama.LlamaConfig
	class InternLMConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate
	an InternLM model according to the specified arguments, defining the model architecture. Instantiating a
	configuration with the defaults will yield a similar configuration to that of the InternLM-7B.
	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 32000):
	Vocabulary size of the InternLM model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`InternLMModel`]
	hidden_size (`int`, optional, defaults to 4096):
	Dimension of the hidden representations.
	intermediate_size (`int`, optional, defaults to 11008):
	Dimension of the MLP representations.
	num_hidden_layers (`int`, optional, defaults to 32):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 32):
	Number of attention heads for each attention layer in the Transformer encoder.
	hidden_act (`str` or `function`, optional, defaults to `"silu"`):
	The non-linear activation function (function or string) in the decoder.
	max_position_embeddings (`int`, optional, defaults to 2048):
	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).
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	rms_norm_eps (`float`, optional, defaults to 1e-12):
	The epsilon used by the rms normalization layers.
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models). Only
	relevant if `config.is_decoder=True`.
	tie_word_embeddings(`bool`, optional, defaults to `False`):
	Whether to tie weight embeddings
	Example:
	```python
	>>> from transformers import InternLMModel, InternLMConfig
	>>> # Initializing a InternLM internlm-7b style configuration
	>>> configuration = InternLMConfig()
	>>> # Initializing a model from the internlm-7b style configuration
	>>> model = InternLMModel(configuration)
	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "internlm"
	_auto_class = "AutoConfig"

	def __init__( # pylint: disable=W0102
	self,
	vocab_size=103168,
	hidden_size=4096,
	intermediate_size=11008,
	num_hidden_layers=32,
	num_attention_heads=32,
	hidden_act="silu",
	max_position_embeddings=2048,
	initializer_range=0.02,
	rms_norm_eps=1e-6,
	use_cache=True,
	pad_token_id=0,
	bos_token_id=1,
	eos_token_id=2,
	tie_word_embeddings=False,
	bias=True,
	rotary={"base": 10000, "type": "dynamic"}, # pylint: disable=W0102
	attn_implementation="eager",
	**kwargs,
	):
	self.vocab_size = vocab_size
	self.max_position_embeddings = max_position_embeddings
	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.hidden_act = hidden_act
	self.initializer_range = initializer_range
	self.rms_norm_eps = rms_norm_eps
	self.use_cache = use_cache
	self.bias = bias
	self.rotary = rotary
	self.attn_implementation = attn_implementation
	if self.attn_implementation is None:
	self.attn_implementation = "eager"
	super().__init__(
	pad_token_id=pad_token_id,
	bos_token_id=bos_token_id,
	eos_token_id=eos_token_id,
	tie_word_embeddings=tie_word_embeddings,
	**kwargs,
	)