KnowledgeDistillation 1.0.4

A general knowledge distillation framework

Update

July, 2020

Knowledge Distillation has been used in Deep Learning for about two years. It is still at an early stage of development. So far, many distillation methods have been proposed, due to complexity and diversity of these methods, it is hard to integrate all of them into a framework. Hence, I think this package is more suitable for the beginners.

This package mainly contain two parts:

1. Distillation of MultiLayerBasedModel

2. Other distillation methods

This is the last update for distillation of MultiLayerBasedModel. Other distillation methods will be added in succession. When Knowledge Distillation is mature enough, I will integrate them into a framework.

March, 2020

• Now, users could define their own loss functions. The requirement of loss function can be found in API document.

• Add more built-in loss functions (mse_with_mask and attention_mse_with_mask).

• Unify hidden loss and predict loss, the key “type” is removed from distill_config.

• Now, the device information is removed from loss value.

Introduction

What is knowledge distillation?

Knowledge Distillation is model compression method in which a small model is trained to mimic a pre-trained, larger model (or ensemble of models). Recently, many models have achieved SOTA performance. However, their billions of parameters make it computationally expensive and inefficient considering both memory consumption and high latency. Hence, it is necessary to get a small model from a large model by using knowledge distillation.

KnowledgeDistillation’s training setting is sometimes referred to as “teacher-student”, where the large model is the teacher and the small model is the student. The method was first proposed by Bucila and generalized by Hinton.

Introduction of KnowledgeDistillation Package

KnowledgeDistillation is a knowledge distillation framework. You can distill your own model by using this toolkit. Our framework is highly abstract and you can achieve many distillation methods by using this framework. Besides, we also provide a distillation of MultiLayerBasedModel considering many models are multi layers.

Usage

To use the package, you should define these objects:

• Teacher Model (large model, trained)

• Student Model (small model, untrained)

• Data loader, a generator or iterator to get training data or dev data. For example, torch.utils.data.DataLoader

• Train data adaptor, a function that turn batch_data (from train_dataloader) to the inputs of teacher_model and student_model

• Distill config, a list-object, each item indicates how to calculate loss. It also defines which output of which layer to calculate loss.

• Output adaptor, a function that turn your model’s output to dict-object output which meet distiller’s requirements

• Evaluator, a class with evaluate function, it define when and how to save your student model

Installation

Requirements

• Python >= 3.6

• PyTorch >= 1.1.0

• NumPy

• Transformers >= 2.0 (optional, used by some examples)

Install from PyPI

KnowledgeDistillation is currently available on the PyPi’s repository and you can install it via pip:

pip install -U KnowledgeDistillation

Install from the Github

If you prefer, you can clone it and run the setup.py file. Use the following command to get a copy from GitHub:

git clone https://github.com/DunZhang/KnowledgeDistillation.git

How to Contribute

Welcome to add examples for latest knowledge distillation methods. There is no need to add an example if the author has provided an official implementation. The example should be simple and easy to be executed. Hence, I suggest to make some fake data for your example.

A simple example

A simple example:

# import packages
import torch
import logging
import numpy as np
from transformers import BertModel, BertConfig
from torch.utils.data import DataLoader, RandomSampler, TensorDataset

from knowledge_distillation import KnowledgeDistiller, MultiLayerBasedDistillationLoss
from knowledge_distillation import MultiLayerBasedDistillationEvaluator

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# Some global variables
train_batch_size = 40
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
learning_rate = 1e-5
num_epoch = 10

# define student and teacher model
# Teacher Model
bert_config = BertConfig(num_hidden_layers=12, hidden_size=60, intermediate_size=60, output_hidden_states=True,
                         output_attentions=True)
teacher_model = BertModel(bert_config)
# Student Model
bert_config = BertConfig(num_hidden_layers=3, hidden_size=60, intermediate_size=60, output_hidden_states=True,
                         output_attentions=True)
student_model = BertModel(bert_config)

### Train data loader
input_ids = torch.LongTensor(np.random.randint(100, 1000, (100000, 50)))
attention_mask = torch.LongTensor(np.ones((100000, 50)))
token_type_ids = torch.LongTensor(np.zeros((100000, 50)))
train_data = TensorDataset(input_ids, attention_mask, token_type_ids)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=train_batch_size)


### Train data adaptor
### It is a function that turn batch_data (from train_dataloader) to the inputs of teacher_model and student_model
### You can define your own train_data_adaptor. Remember the input must be device and batch_data.
###  The output is either dict or tuple, but must be consistent with you model's input
def train_data_adaptor(device, batch_data):
    batch_data = tuple(t.to(device) for t in batch_data)
    batch_data_dict = {"input_ids": batch_data[0],
                       "attention_mask": batch_data[1],
                       "token_type_ids": batch_data[2], }
    # In this case, the teacher and student use the same input
    return batch_data_dict, batch_data_dict


### The loss model is the key for this generation.
### We have already provided a general loss model for distilling multi bert layer
### In most cases, you can directly use this model.
#### First, we should define a distill_config which indicates how to compute ths loss between teacher and student.
#### distill_config is a list-object, each item indicates how to calculate loss.
#### It also defines which output of which layer to calculate loss.
#### It shoulde be consistent with your output_adaptor
distill_config = [
    # means that compute a loss by their embedding_layer's embedding
    {"teacher_layer_name": "embedding_layer", "teacher_layer_output_name": "embedding",
     "student_layer_name": "embedding_layer", "student_layer_output_name": "embedding",
     "loss": {"loss_function": "mse_with_mask", "args": {}}, "weight": 1.0
     },
    # means that compute a loss between teacher's bert_layer12's hidden_states and student's bert_layer3's hidden_states
    {"teacher_layer_name": "bert_layer12", "teacher_layer_output_name": "hidden_states",
     "student_layer_name": "bert_layer3", "student_layer_output_name": "hidden_states",
     "loss": {"loss_function": "mse_with_mask", "args": {}}, "weight": 1.0
     },
    {"teacher_layer_name": "bert_layer12", "teacher_layer_output_name": "attention",
     "student_layer_name": "bert_layer3", "student_layer_output_name": "attention",
     "loss": {"loss_function": "attention_mse_with_mask", "args": {}}, "weight": 1.0
     },
    {"teacher_layer_name": "pred_layer", "teacher_layer_output_name": "pooler_output",
     "student_layer_name": "pred_layer", "student_layer_output_name": "pooler_output",
     "loss": {"loss_function": "mse", "args": {}}, "weight": 1.0
     },
]


### teacher_output_adaptor and student_output_adaptor
### In most cases, model's output is tuple-object, However, in our package, we need the output is dict-object,
### like: { "layer_name":{"output_name":value} .... }
### Hence, the output adaptor is to turn your model's output to dict-object output
### In my case, teacher and student can use one adaptor
def output_adaptor(model_output):
    last_hidden_state, pooler_output, hidden_states, attentions = model_output
    output = {"embedding_layer": {"embedding": hidden_states[0]}}
    for idx in range(len(attentions)):
        output["bert_layer" + str(idx + 1)] = {"hidden_states": hidden_states[idx + 1],
                                               "attention": attentions[idx]}
    output["pred_layer"] = {"pooler_output": pooler_output}
    return output


# loss_model
loss_model = MultiLayerBasedDistillationLoss(distill_config=distill_config,
                                             teacher_output_adaptor=output_adaptor,
                                             student_output_adaptor=output_adaptor)
# optimizer
param_optimizer = list(student_model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
    {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = torch.optim.Adam(params=optimizer_grouped_parameters, lr=learning_rate)
# evaluator
# this is a basic evalator, it can output loss value and save models
# You can define you own evaluator class that implements the interface IEvaluator

evaluator = MultiLayerBasedDistillationEvaluator(save_dir="save_model", save_step=1000, print_loss_step=20)
# Get a KnowledgeDistiller
distiller = KnowledgeDistiller(teacher_model=teacher_model, student_model=student_model,
                               train_dataloader=train_dataloader, dev_dataloader=None,
                               train_data_adaptor=train_data_adaptor, dev_data_adaptor=None,
                               device=device, loss_model=loss_model, optimizer=optimizer,
                               evaluator=evaluator, num_epoch=num_epoch)
# start distillate
distiller.distillate()