Xonotic/phi-3-mini-sql-assistant-full

1# coding=utf-8
2# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
3#
4# Licensed under the Apache License, Version 2.0 (the "License");
5# you may not use this file except in compliance with the License.
6# You may obtain a copy of the License at
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8#     http://www.apache.org/licenses/LICENSE-2.0
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14# limitations under the License.
15
16""" Phi-3 model configuration"""
17
18
19from transformers.configuration_utils import PretrainedConfig
20from transformers.utils import logging
21
22
23logger = logging.get_logger(__name__)
24
25PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
26    "microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
27    "microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
28}
29
30
31class Phi3Config(PretrainedConfig):
32    r"""
33    This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
34    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35    defaults will yield a similar configuration to that of the
36    [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
37
38    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
39    documentation from [`PretrainedConfig`] for more information.
40
41    Args:
42        vocab_size (`int`, *optional*, defaults to 32064):
43            Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
44            `inputs_ids` passed when calling [`Phi3Model`].
45        hidden_size (`int`, *optional*, defaults to 3072):
46            Dimension of the hidden representations.
47        intermediate_size (`int`, *optional*, defaults to 8192):
48            Dimension of the MLP representations.
49        num_hidden_layers (`int`, *optional*, defaults to 32):
50            Number of hidden layers in the Transformer decoder.
51        num_attention_heads (`int`, *optional*, defaults to 32):
52            Number of attention heads for each attention layer in the Transformer decoder.
53        num_key_value_heads (`int`, *optional*):
54            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
55            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
56            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
57            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
58            by meanpooling all the original heads within that group. For more details checkout [this
59            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
60            `num_attention_heads`.
61        resid_pdrop (`float`, *optional*, defaults to 0.0):
62            Dropout probability for mlp outputs.
63        embd_pdrop (`int`, *optional*, defaults to 0.0):
64            The dropout ratio for the embeddings.
65        attention_dropout (`float`, *optional*, defaults to 0.0):
66            The dropout ratio after computing the attention scores.
67        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
68            The non-linear activation function (function or string) in the decoder.
69        max_position_embeddings (`int`, *optional*, defaults to 4096):
70            The maximum sequence length that this model might ever be used with.
71        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
72            The maximum sequence length that this model was trained with. This is used to determine the size of the
73            original RoPE embeddings when using long scaling.
74        initializer_range (`float`, *optional*, defaults to 0.02):
75            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
76        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
77            The epsilon value used for the RMSNorm.
78        use_cache (`bool`, *optional*, defaults to `True`):
79            Whether or not the model should return the last key/values attentions (not used by all models). Only
80            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
81        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
82            Whether to tie weight embeddings
83        rope_theta (`float`, *optional*, defaults to 10000.0):
84            The base period of the RoPE embeddings.
85        rope_scaling (`dict`, *optional*):
86            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
87            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and 
88            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size 
89            divided by the number of attention heads divided by 2.
90        bos_token_id (`int`, *optional*, defaults to 1):
91            The id of the "beginning-of-sequence" token.
92        eos_token_id (`int`, *optional*, defaults to 32000):
93            The id of the "end-of-sequence" token.
94        pad_token_id (`int`, *optional*, defaults to 32000):
95            The id of the padding token.
96        sliding_window (`int`, *optional*):
97            Sliding window attention window size. If `None`, no sliding window is applied.
98
99    Example:
100
101    ```python
102    >>> from transformers import Phi3Model, Phi3Config
103
104    >>> # Initializing a Phi-3 style configuration
105    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
106
107    >>> # Initializing a model from the configuration
108    >>> model = Phi3Model(configuration)
109
110    >>> # Accessing the model configuration
111    >>> configuration = model.config
112    ```"""
113
114    model_type = "phi3"
115    keys_to_ignore_at_inference = ["past_key_values"]
116
117    def __init__(
118        self,
119        vocab_size=32064,
120        hidden_size=3072,
121        intermediate_size=8192,
122        num_hidden_layers=32,
123        num_attention_heads=32,
124        num_key_value_heads=None,
125        resid_pdrop=0.0,
126        embd_pdrop=0.0,
127        attention_dropout=0.0,
128        hidden_act="silu",
129        max_position_embeddings=4096,
130        original_max_position_embeddings=4096,
131        initializer_range=0.02,
132        rms_norm_eps=1e-5,
133        use_cache=True,
134        tie_word_embeddings=False,
135        rope_theta=10000.0,
136        rope_scaling=None,
137        bos_token_id=1,
138        eos_token_id=32000,
139        pad_token_id=32000,
140        sliding_window=None,
141        **kwargs,
142    ):
143        self.vocab_size = vocab_size
144        self.hidden_size = hidden_size
145        self.intermediate_size = intermediate_size
146        self.num_hidden_layers = num_hidden_layers
147        self.num_attention_heads = num_attention_heads
148
149        if num_key_value_heads is None:
150            num_key_value_heads = num_attention_heads
151
152        self.num_key_value_heads = num_key_value_heads
153        self.resid_pdrop = resid_pdrop
154        self.embd_pdrop = embd_pdrop
155        self.attention_dropout = attention_dropout
156        self.hidden_act = hidden_act
157        self.max_position_embeddings = max_position_embeddings
158        self.original_max_position_embeddings = original_max_position_embeddings
159        self.initializer_range = initializer_range
160        self.rms_norm_eps = rms_norm_eps
161        self.use_cache = use_cache
162        self.rope_theta = rope_theta
163        self.rope_scaling = rope_scaling
164        self._rope_scaling_adjustment()
165        self._rope_scaling_validation()
166        self.sliding_window = sliding_window
167
168        super().__init__(
169            bos_token_id=bos_token_id,
170            eos_token_id=eos_token_id,
171            pad_token_id=pad_token_id,
172            tie_word_embeddings=tie_word_embeddings,
173            **kwargs,
174        )
175
176    def _rope_scaling_adjustment(self):
177        """
178        Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
179        """
180        if self.rope_scaling is None:
181            return
182
183        rope_scaling_type = self.rope_scaling.get("type", None)
184
185        # For backward compatibility if previous version used "su" or "yarn"
186        if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
187            self.rope_scaling["type"] = "longrope"
188
189    def _rope_scaling_validation(self):
190        """
191        Validate the `rope_scaling` configuration.
192        """
193        if self.rope_scaling is None:
194            return
195
196        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
197            raise ValueError(
198                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
199                f"got {self.rope_scaling}"
200            )
201        rope_scaling_type = self.rope_scaling.get("type", None)
202        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
203        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
204        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
205            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
206        if not (
207            isinstance(rope_scaling_short_factor, list)
208            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
209        ):
210            raise ValueError(
211                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
212            )
213        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
214            raise ValueError(
215                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
216            )
217        if not (
218            isinstance(rope_scaling_long_factor, list)
219            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
220        ):
221            raise ValueError(
222                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
223            )
224        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
225            raise ValueError(
226                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
227            )
228