configuration_mistral.py

# coding=utf-8
# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" Mistral model configuration"""

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


logger = logging.get_logger(__name__)

MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 "mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
 "mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
}


class MistralConfig(PretrainedConfig):
 r"""
 This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
 Mistral 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 Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.

 [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)

 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 Mistral model. Defines the number of different tokens that can be represented by the
 `inputs_ids` passed when calling [`MistralModel`]
 hidden_size (`int`, *optional*, defaults to 4096):
 Dimension of the hidden representations.
 intermediate_size (`int`, *optional*, defaults to 14336):
 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.
 num_key_value_heads (`int`, *optional*, defaults to 8):
 This is the number of key_value heads that should be used to implement Grouped Query Attention. If
 `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
 `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
 converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
 by meanpooling all the original heads within that group. For more details checkout [this
 paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
 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 `4096*32`):
 The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
 allows sequence of up to 4096*32 tokens.
 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-06):
 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`.
 pad_token_id (`int`, *optional*):
 The id of the padding token.
 bos_token_id (`int`, *optional*, defaults to 1):
 The id of the "beginning-of-sequence" token.
 eos_token_id (`int`, *optional*, defaults to 2):
 The id of the "end-of-sequence" token.
 tie_word_embeddings (`bool`, *optional*, defaults to `False`):
 Whether the model's input and output word embeddings should be tied.
 rope_theta (`float`, *optional*, defaults to 10000.0):
 The base period of the RoPE embeddings.
 sliding_window (`int`, *optional*, defaults to 4096):
 Sliding window attention window size. If not specified, will default to `4096`.
 attention_dropout (`float`, *optional*, defaults to 0.0):
 The dropout ratio for the attention probabilities.

 ```python
 >>> from transformers import MistralModel, MistralConfig

 >>> # Initializing a Mistral 7B style configuration
 >>> configuration = MistralConfig()

 >>> # Initializing a model from the Mistral 7B style configuration
 >>> model = MistralModel(configuration)

 >>> # Accessing the model configuration
 >>> configuration = model.config
 ```"""

 model_type = "mistral"
 keys_to_ignore_at_inference = ["past_key_values"]

 def __init__(
 self,
 vocab_size=32000,
 hidden_size=4096,
 intermediate_size=14336,
 num_hidden_layers=32,
 num_attention_heads=32,
 num_key_value_heads=8,
 hidden_act="silu",
 max_position_embeddings=4096 * 32,
 initializer_range=0.02,
 rms_norm_eps=1e-6,
 use_cache=True,
 pad_token_id=None,
 bos_token_id=1,
 eos_token_id=2,
 tie_word_embeddings=False,
 rope_theta=10000.0,
 sliding_window=4096,
 rope_scaling=None,
 attention_dropout=0.0,
 beacon_window=1024,
 beacon_stride=1024,
 beacon_attn="full-coverage",
 beacon_ratio=[2,4,8,16,32],
 beacon_ratio_mix="step-random",
 beacon_param=[],
 beacon_embed_init="eos",
 beacon_sink_size=0,
 beacon_attend_prev=True,
 beacon_pos="interleave",
 beacon_parallel_window=1,
 beacon_accum=True,
 **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.sliding_window = sliding_window

 # for backward compatibility
 if num_key_value_heads is None:
 num_key_value_heads = num_attention_heads

 self.num_key_value_heads = num_key_value_heads
 self.hidden_act = hidden_act
 self.initializer_range = initializer_range
 self.rms_norm_eps = rms_norm_eps
 self.use_cache = use_cache
 self.rope_theta = rope_theta
 self.attention_dropout = attention_dropout

 self.rope_scaling = rope_scaling
 self._rope_scaling_validation()

 self.beacon_window = beacon_window
 self.beacon_stride = beacon_stride
 self.beacon_attn = beacon_attn
 self.beacon_ratio = beacon_ratio
 self.beacon_ratio_mix = beacon_ratio_mix
 self.beacon_param = beacon_param
 self.beacon_embed_init = beacon_embed_init
 self.beacon_sink_size = beacon_sink_size
 self.beacon_attend_prev = beacon_attend_prev
 self.beacon_pos = beacon_pos
 self.beacon_parallel_window = beacon_parallel_window
 self.beacon_accum = beacon_accum

 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,
 )

 def _rope_scaling_validation(self):
 """
 Validate the `rope_scaling` configuration.
 """
 if self.rope_scaling is None:
 return

 if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
 raise ValueError(
 "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
 f"got {self.rope_scaling}"
 )
 rope_scaling_type = self.rope_scaling.get("type", None)
 rope_scaling_factor = self.rope_scaling.get("factor", None)
 if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
 raise ValueError(
 f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
 )
 if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
 raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")