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# coding=utf-8
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# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Phi-3 model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
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"microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
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}
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class Phi3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the
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[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32064):
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Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Phi3Model`].
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hidden_size (`int`, *optional*, defaults to 3072):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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resid_pdrop (`float`, *optional*, defaults to 0.0):
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Dropout probability for mlp outputs.
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embd_pdrop (`int`, *optional*, defaults to 0.0):
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The dropout ratio for the embeddings.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after computing the attention scores.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used with.
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original_max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model was trained with. This is used to determine the size of the
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original RoPE embeddings when using long scaling.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon value used for the RMSNorm.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`dict`, *optional*):
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The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
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contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and
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the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
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divided by the number of attention heads divided by 2.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 32000):
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The id of the "end-of-sequence" token.
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pad_token_id (`int`, *optional*, defaults to 32000):
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The id of the padding token.
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sliding_window (`int`, *optional*):
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Sliding window attention window size. If `None`, no sliding window is applied.
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Example:
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```python
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>>> from transformers import Phi3Model, Phi3Config
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>>> # Initializing a Phi-3 style configuration
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>>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
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>>> # Initializing a model from the configuration
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>>> model = Phi3Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "phi3"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32064,
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hidden_size=3072,
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intermediate_size=8192,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attention_dropout=0.0,
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hidden_act="silu",
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max_position_embeddings=4096,
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original_max_position_embeddings=4096,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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bos_token_id=1,
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eos_token_id=32000,
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pad_token_id=32000,
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sliding_window=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attention_dropout = attention_dropout
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.original_max_position_embeddings = original_max_position_embeddings
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_adjustment()
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self._rope_scaling_validation()
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self.sliding_window = sliding_window
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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pad_token_id=pad_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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def _rope_scaling_adjustment(self):
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"""
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Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
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"""
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if self.rope_scaling is None:
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return
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rope_scaling_type = self.rope_scaling.get("type", None)
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# For backward compatibility if previous version used "su" or "yarn"
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if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
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self.rope_scaling["type"] = "longrope"
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
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raise ValueError(
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"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
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f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
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rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
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raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
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if not (
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isinstance(rope_scaling_short_factor, list)
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and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
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):
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raise ValueError(
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f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
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)
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if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
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raise ValueError(
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f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
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)
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if not (
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isinstance(rope_scaling_long_factor, list)
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and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
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):
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raise ValueError(
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f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
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)
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if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
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raise ValueError(
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f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
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)
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xxl