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# Copyright 2022 The HuggingFace 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.
from huggingface_hub import PyTorchModelHubMixin
class BaseTrainer(PyTorchModelHubMixin):
r"""
Base class for all trainers - this base class implements the basic functions that we
need for a trainer.
The trainer needs to have the following functions:
- step: takes in a batch of data and performs a step of training
- loss: takes in a batch of data and returns the loss
- compute_rewards: takes in a batch of data and returns the rewards
- _build_models_and_tokenizer: builds the models and tokenizer
- _build_dataset: builds the dataset
Each user is expected to implement their own trainer class that inherits from this base
if they want to use a new training algorithm.
"""
def __init__(self, config):
self.config = config
def step(self, *args):
raise NotImplementedError("Not implemented")
def loss(self, *args):
raise NotImplementedError("Not implemented")
def compute_rewards(self, *args):
raise NotImplementedError("Not implemented")
def _save_pretrained(self, save_directory):
raise NotImplementedError("Not implemented")
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