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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 either `su` or `yarn` 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_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_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 ["su", "yarn"]: |
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raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], 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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