192 lines
9.0 KiB
Python
192 lines
9.0 KiB
Python
|
# coding=utf-8
|
||
|
|
# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
|
||
|
|
""" Phi model configuration"""
|
||
|
|
|
||
|
|
|
||
|
|
from ...configuration_utils import PretrainedConfig
|
||
|
|
from ...utils import logging
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.get_logger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
from ..deprecated._archive_maps import PHI_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
||
|
|
|
||
|
|
|
||
|
|
class PhiConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
|
||
|
|
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 Phi
|
||
|
|
[microsoft/phi-1](https://huggingface.co/microsoft/phi-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 51200):
|
||
|
|
Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
|
||
|
|
`inputs_ids` passed when calling [`PhiModel`].
|
||
|
|
hidden_size (`int`, *optional*, defaults to 2048):
|
||
|
|
Dimension of the hidden representations.
|
||
|
|
intermediate_size (`int`, *optional*, defaults to 8192):
|
||
|
|
Dimension of the MLP representations.
|
||
|
|
num_hidden_layers (`int`, *optional*, defaults to 24):
|
||
|
|
Number of hidden layers in the Transformer decoder.
|
||
|
|
num_attention_heads (`int`, *optional*, defaults to 32):
|
||
|
|
Number of attention heads for each attention layer in the Transformer decoder.
|
||
|
|
num_key_value_heads (`int`, *optional*):
|
||
|
|
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
|
||
|
|
`num_attention_heads`.
|
||
|
|
resid_pdrop (`float`, *optional*, defaults to 0.0):
|
||
|
|
Dropout probability for mlp outputs.
|
||
|
|
embd_pdrop (`int`, *optional*, defaults to 0.0):
|
||
|
|
The dropout ratio for the embeddings.
|
||
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||
|
|
The dropout ratio after computing the attention scores.
|
||
|
|
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
|
||
|
|
The non-linear activation function (function or string) in the decoder.
|
||
|
|
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
||
|
|
The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
|
||
|
|
tokens.
|
||
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
||
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||
|
|
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||
|
|
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`. Whether to tie weight embeddings or not.
|
||
|
|
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether to tie weight embeddings
|
||
|
|
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||
|
|
The base period of the RoPE embeddings.
|
||
|
|
rope_scaling (`Dict`, *optional*):
|
||
|
|
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
||
|
|
strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
|
||
|
|
is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
||
|
|
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
||
|
|
these scaling strategies behave:
|
||
|
|
https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
|
||
|
|
is an experimental feature, subject to breaking API changes in future versions.
|
||
|
|
partial_rotary_factor (`float`, *optional*, defaults to 0.5):
|
||
|
|
Percentage of the query and keys which will have rotary embedding.
|
||
|
|
qk_layernorm (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not to normalize the Queries and Keys after projecting the hidden states.
|
||
|
|
bos_token_id (`int`, *optional*, defaults to 1):
|
||
|
|
Denotes beginning of sequences token id.
|
||
|
|
eos_token_id (`int`, *optional*, defaults to 2):
|
||
|
|
Denotes end of sequences token id.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import PhiModel, PhiConfig
|
||
|
|
|
||
|
|
>>> # Initializing a Phi-1 style configuration
|
||
|
|
>>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
|
||
|
|
|
||
|
|
>>> # Initializing a model from the configuration
|
||
|
|
>>> model = PhiModel(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```"""
|
||
|
|
|
||
|
|
model_type = "phi"
|
||
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_size=51200,
|
||
|
|
hidden_size=2048,
|
||
|
|
intermediate_size=8192,
|
||
|
|
num_hidden_layers=24,
|
||
|
|
num_attention_heads=32,
|
||
|
|
num_key_value_heads=None,
|
||
|
|
resid_pdrop=0.0,
|
||
|
|
embd_pdrop=0.0,
|
||
|
|
attention_dropout=0.0,
|
||
|
|
hidden_act="gelu_new",
|
||
|
|
max_position_embeddings=2048,
|
||
|
|
initializer_range=0.02,
|
||
|
|
layer_norm_eps=1e-5,
|
||
|
|
use_cache=True,
|
||
|
|
tie_word_embeddings=False,
|
||
|
|
rope_theta=10000.0,
|
||
|
|
rope_scaling=None,
|
||
|
|
partial_rotary_factor=0.5,
|
||
|
|
qk_layernorm=False,
|
||
|
|
bos_token_id=1,
|
||
|
|
eos_token_id=2,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
self.vocab_size = vocab_size
|
||
|
|
self.hidden_size = hidden_size
|
||
|
|
self.intermediate_size = intermediate_size
|
||
|
|
self.num_hidden_layers = num_hidden_layers
|
||
|
|
self.num_attention_heads = num_attention_heads
|
||
|
|
|
||
|
|
if num_key_value_heads is None:
|
||
|
|
num_key_value_heads = num_attention_heads
|
||
|
|
|
||
|
|
self.num_key_value_heads = num_key_value_heads
|
||
|
|
self.resid_pdrop = resid_pdrop
|
||
|
|
self.embd_pdrop = embd_pdrop
|
||
|
|
self.attention_dropout = attention_dropout
|
||
|
|
self.hidden_act = hidden_act
|
||
|
|
self.max_position_embeddings = max_position_embeddings
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
self.layer_norm_eps = layer_norm_eps
|
||
|
|
self.use_cache = use_cache
|
||
|
|
self.rope_theta = rope_theta
|
||
|
|
self.rope_scaling = rope_scaling
|
||
|
|
self.partial_rotary_factor = partial_rotary_factor
|
||
|
|
self.qk_layernorm = qk_layernorm
|
||
|
|
self._rope_scaling_validation()
|
||
|
|
|
||
|
|
super().__init__(
|
||
|
|
bos_token_id=bos_token_id,
|
||
|
|
eos_token_id=eos_token_id,
|
||
|
|
tie_word_embeddings=tie_word_embeddings,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
|
||
|
|
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 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}")
|