Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,783 Bytes
d711508 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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.
from dataclasses import dataclass, field
from typing import List, Literal, Optional, Union
from peft.config import PeftConfig
from peft.utils import PeftType
@dataclass
class PolyConfig(PeftConfig):
"""
This is the configuration class to store the configuration of a [`PolyModel`].
- [Polytropon (Poly)](https://arxiv.org/abs/2202.13914)
- [Multi-Head Routing (MHR)](https://arxiv.org/abs/2211.03831)
Args:
r (`int`): Attention dimension of each Lora in Poly.
target_modules (`Union[List[str],str]`): The names of the modules to apply Poly to.
modules_to_save (`List[str]`): List of modules apart from Poly layers to be set as trainable
and saved in the final checkpoint.
init_weights (bool): Whether to perform initialization of Poly weights.
poly_type (`Literal["poly"]`): The variant of the Poly module to use. Currently, only "poly"
is supported.
n_tasks (`int`): The number of tasks in a multitasking scenario.
n_skills (`int`): The number of skills (LoRA) in each Poly layer.
n_splits (`int`): The number of splits within each LoRA of a Poly layer. A value greater
than 1 indicates the use of Multi-Head Routing (MHR).
"""
r: int = field(default=8, metadata={"help": "Lora attention dimension"})
target_modules: Optional[Union[List[str], str]] = field(
default=None,
metadata={
"help": "List of module names or regex expression of the module names to replace with Poly."
"For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
},
)
modules_to_save: Optional[List[str]] = field(
default=None,
metadata={
"help": "List of modules apart from Poly layers to be set as trainable and saved in the final checkpoint. "
"For example, in Sequence Classification or Token Classification tasks, "
"the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
},
)
init_weights: bool = field(
default=True,
metadata={
"help": (
"Whether to initialize the weights of the Poly layers with their default initialization. Don't change "
"this setting, except if you know exactly what you're doing."
),
},
)
poly_type: Literal["poly"] = field(
default="poly",
metadata={"help": 'Type of Poly modules to be used. Currently only "poly" is supported.'},
)
n_tasks: int = field(
default=1,
metadata={"help": "Number of tasks in multitasking scenario."},
)
n_skills: int = field(
default=4,
metadata={"help": "Number of skills (LoRA) in each Poly layer."},
)
n_splits: int = field(
default=1,
metadata={"help": "Number of splits within each LoRA of a Poly layer."},
)
def __post_init__(self):
self.peft_type = PeftType.POLY
self.target_modules = (
set(self.target_modules) if isinstance(self.target_modules, list) else self.target_modules
)
|