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- # coding=utf-8
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- # Copyright 2023 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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-
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- """ Phi model configuration"""
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-
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-
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- from transformers.configuration_utils import PretrainedConfig
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- from transformers.utils import logging
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-
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-
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- logger = logging.get_logger(__name__)
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-
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- PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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- "microsoft/phi-1": "https://huggingface.co/microsoft/phi-1/resolve/main/config.json",
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- }
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-
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-
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- class PhiConfig(PretrainedConfig):
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- r"""
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- This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
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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 Phi
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- [microsoft/phi-1](https://huggingface.co/microsoft/phi-1).
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-
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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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-
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- Args:
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- vocab_size (`int`, *optional*, defaults to 51200):
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- Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
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- `inputs_ids` passed when calling [`PhiModel`].
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- hidden_size (`int`, *optional*, defaults to 2048):
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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 24):
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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 `"gelu_new"`):
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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 2048):
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- The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
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- tokens.
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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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- layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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- The epsilon used by the rms normalization layers.
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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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- Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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- strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
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- is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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- `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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- these scaling strategies behave:
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- https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
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- is an experimental feature, subject to breaking API changes in future versions.
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- partial_rotary_factor (`float`, *optional*, defaults to 0.5):
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- Percentage of the query and keys which will have rotary embedding.
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- qk_layernorm (`bool`, *optional*, defaults to `False`):
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- Whether or not to normalize the Queries and Keys after projecting the hidden states.
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- bos_token_id (`int`, *optional*, defaults to 1):
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- Denotes beginning of sequences token id.
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- eos_token_id (`int`, *optional*, defaults to 2):
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- Denotes end of sequences token id.
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-
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- Example:
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-
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- ```python
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- >>> from transformers import PhiModel, PhiConfig
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-
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- >>> # Initializing a Phi-1 style configuration
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- >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
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-
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- >>> # Initializing a model from the configuration
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- >>> model = PhiModel(configuration)
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-
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- >>> # Accessing the model configuration
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- >>> configuration = model.config
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- ```"""
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-
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- model_type = "phi"
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- keys_to_ignore_at_inference = ["past_key_values"]
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-
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- def __init__(
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- self,
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- vocab_size=51200,
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- hidden_size=2048,
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- intermediate_size=8192,
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- num_hidden_layers=24,
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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="gelu_new",
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- max_position_embeddings=2048,
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- initializer_range=0.02,
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- layer_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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- partial_rotary_factor=0.5,
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- qk_layernorm=False,
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- bos_token_id=1,
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- eos_token_id=2,
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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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-
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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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-
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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.initializer_range = initializer_range
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- self.layer_norm_eps = layer_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.partial_rotary_factor = partial_rotary_factor
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- self.qk_layernorm = qk_layernorm
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- self._rope_scaling_validation()
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-
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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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- tie_word_embeddings=tie_word_embeddings,
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- **kwargs,
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- )
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-
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- # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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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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-
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- if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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- raise ValueError(
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- "`rope_scaling` must be a dictionary with with two fields, `type` and `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_factor = self.rope_scaling.get("factor", None)
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- if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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- raise ValueError(
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- f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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- )
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- if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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- raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")