PINA/tutorials/tutorial11/tutorial.py

#!/usr/bin/env python
# coding: utf-8

# # Tutorial: Introduction to `Trainer` class
# [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mathLab/PINA/blob/master/tutorials/tutorial11/tutorial.ipynb)
# 
# In this tutorial, we will delve deeper into the functionality of the `Trainer` class, which serves as the cornerstone for training **PINA** [Solvers](https://mathlab.github.io/PINA/_rst/_code.html#solvers). 
# 
# The `Trainer` class offers a plethora of features aimed at improving model accuracy, reducing training time and memory usage, facilitating logging visualization, and more thanks to the amazing job done by the PyTorch Lightning team!
# 
# Our leading example will revolve around solving a simple regression problem where we want to approximate the following function with a Neural Net model $\mathcal{M}_{\theta}$:
# $$y = x^3$$
# by having only a set of $20$ observations $\{x_i, y_i\}_{i=1}^{20}$, with  $x_i \sim\mathcal{U}[-3, 3]\;\;\forall i\in(1,\dots,20)$.
# 
# Let's start by importing useful modules!

# In[ ]:


try:
    import google.colab

    IN_COLAB = True
except:
    IN_COLAB = False
if IN_COLAB:
    get_ipython().system('pip install "pina-mathlab[tutorial]"')

import torch
import warnings

from pina import Trainer
from pina.solver import SupervisedSolver
from pina.model import FeedForward
from pina.problem.zoo import SupervisedProblem

warnings.filterwarnings("ignore")


# Define problem and solver.

# In[2]:


# defining the problem
x_train = torch.empty((20, 1)).uniform_(-3, 3)
y_train = x_train.pow(3) + 3 * torch.randn_like(x_train)

problem = SupervisedProblem(x_train, y_train)

# build the model
model = FeedForward(
    layers=[10, 10],
    func=torch.nn.Tanh,
    output_dimensions=1,
    input_dimensions=1,
)

# create the SupervisedSolver object
solver = SupervisedSolver(problem, model, use_lt=False)


# Till now we just followed the extact step of the previous tutorials. The `Trainer` object
# can be initialized by simiply passing the `SupervisedSolver` solver

# In[3]:


trainer = Trainer(solver=solver)


# ## Trainer Accelerator
# 
# When creating the `Trainer`, **by default** the most performing `accelerator` for training which is available in your system will be chosen, ranked as follows:
# 1. [TPU](https://cloud.google.com/tpu/docs/intro-to-tpu)
# 2. [IPU](https://www.graphcore.ai/products/ipu)
# 3. [HPU](https://habana.ai/)
# 4. [GPU](https://www.intel.com/content/www/us/en/products/docs/processors/what-is-a-gpu.html#:~:text=What%20does%20GPU%20stand%20for,video%20editing%2C%20and%20gaming%20applications) or [MPS](https://developer.apple.com/metal/pytorch/)
# 5. CPU
# 
# For setting manually the `accelerator` run:
# 
# * `accelerator = {'gpu', 'cpu', 'hpu', 'mps', 'cpu', 'ipu'}` sets the accelerator to a specific one

# In[15]:


trainer = Trainer(solver=solver, accelerator="cpu")


# As you can see, even if a `GPU` is available on the system, it is not used since we set `accelerator='cpu'`.

# ## Trainer Logging
# 
# In **PINA** you can log metrics in different ways. The simplest approach is to use the `MetricTracker` class from `pina.callbacks`, as seen in the [*Introduction to Physics Informed Neural Networks training*](https://github.com/mathLab/PINA/blob/master/tutorials/tutorial1/tutorial.ipynb) tutorial.
# 
# However, especially when we need to train multiple times to get an average of the loss across multiple runs, `lightning.pytorch.loggers` might be useful. Here we will use `TensorBoardLogger` (more on [logging](https://lightning.ai/docs/pytorch/stable/extensions/logging.html) here), but you can choose the one you prefer (or make your own one).
# 
# We will now import `TensorBoardLogger`, do three runs of training, and then visualize the results. Notice we set `enable_model_summary=False` to avoid model summary specifications (e.g. number of parameters); set it to `True` if needed.

# In[17]:


from lightning.pytorch.loggers import TensorBoardLogger

# three run of training, by default it trains for 1000 epochs, we set the max to 100
# we reinitialize the model each time otherwise the same parameters will be optimized
for _ in range(3):
    model = FeedForward(
        layers=[10, 10],
        func=torch.nn.Tanh,
        output_dimensions=1,
        input_dimensions=1,
    )
    solver = SupervisedSolver(problem, model, use_lt=False)
    trainer = Trainer(
        solver=solver,
        accelerator="cpu",
        logger=TensorBoardLogger(save_dir="training_log"),
        enable_model_summary=False,
        train_size=1.0,
        val_size=0.0,
        test_size=0.0,
        max_epochs=100,
    )
    trainer.train()


# We can now visualize the logs by simply running `tensorboard --logdir=training_log/` in the terminal. You should obtain a webpage similar to the one shown below if running for 1000 epochs:

# <p align=\"center\">
#     <img src="http://raw.githubusercontent.com/mathLab/PINA/master/tutorials/static/logging.png" alt=\"Logging API\" width=\"400\"/>
# </p>

# As you can see, by default, **PINA** logs the losses which are shown in the progress bar, as well as the number of epochs. You can always insert more loggings by either defining a **callback** ([more on callbacks](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html)), or inheriting the solver and modifying the programs with different **hooks** ([more on hooks](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#hooks)).
# 
# ## Trainer Callbacks
# 
# Whenever we need to access certain steps of the training for logging, perform static modifications (i.e. not changing the `Solver`), or update `Problem` hyperparameters (static variables), we can use **Callbacks**. Notice that **Callbacks** allow you to add arbitrary self-contained programs to your training. At specific points during the flow of execution (hooks), the Callback interface allows you to design programs that encapsulate a full set of functionality. It de-couples functionality that does not need to be in **PINA** `Solver`s.
# 
# Lightning has a callback system to execute them when needed. **Callbacks** should capture NON-ESSENTIAL logic that is NOT required for your lightning module to run.
# 
# The following are best practices when using/designing callbacks:
# 
# * Callbacks should be isolated in their functionality.
# * Your callback should not rely on the behavior of other callbacks in order to work properly.
# * Do not manually call methods from the callback.
# * Directly calling methods (e.g., on_validation_end) is strongly discouraged.
# * Whenever possible, your callbacks should not depend on the order in which they are executed.
# 
# We will try now to implement a naive version of `MetricTraker` to show how callbacks work. Notice that this is a very easy application of callbacks, fortunately in **PINA** we already provide more advanced callbacks in `pina.callbacks`.

# In[18]:


from lightning.pytorch.callbacks import Callback
from lightning.pytorch.callbacks import EarlyStopping
import torch


# define a simple callback
class NaiveMetricTracker(Callback):
    def __init__(self):
        self.saved_metrics = []

    def on_train_epoch_end(
        self, trainer, __
    ):  # function called at the end of each epoch
        self.saved_metrics.append(
            {key: value for key, value in trainer.logged_metrics.items()}
        )


# Let's see the results when applied to the problem. You can define **callbacks** when initializing the `Trainer` by using the `callbacks` argument, which expects a list of callbacks.
# 

# In[19]:


model = FeedForward(
    layers=[10, 10],
    func=torch.nn.Tanh,
    output_dimensions=1,
    input_dimensions=1,
)
solver = SupervisedSolver(problem, model, use_lt=False)
trainer = Trainer(
    solver=solver,
    accelerator="cpu",
    logger=True,
    callbacks=[NaiveMetricTracker()],  # adding a callbacks
    enable_model_summary=False,
    train_size=1.0,
    val_size=0.0,
    test_size=0.0,
    max_epochs=10,  # training only for 10 epochs
)
trainer.train()


# We can easily access the data by calling `trainer.callbacks[0].saved_metrics` (notice the zero representing the first callback in the list given at initialization).

# In[20]:


trainer.callbacks[0].saved_metrics[:3]  # only the first three epochs


# PyTorch Lightning also has some built-in `Callbacks` which can be used in **PINA**, [here is an extensive list](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html#built-in-callbacks). 
# 
# We can, for example, try the `EarlyStopping` routine, which automatically stops the training when a specific metric converges (here the `train_loss`). In order to let the training keep going forever, set `max_epochs=-1`.

# In[22]:


model = FeedForward(
    layers=[10, 10],
    func=torch.nn.Tanh,
    output_dimensions=1,
    input_dimensions=1,
)
solver = SupervisedSolver(problem, model, use_lt=False)
trainer = Trainer(
    solver=solver,
    accelerator="cpu",
    max_epochs=-1,
    enable_model_summary=False,
    enable_progress_bar=False,
    val_size=0.2,
    train_size=0.8,
    test_size=0.0,
    callbacks=[EarlyStopping("val_loss")],
)  # adding a callbacks
trainer.train()


# As we can see the model automatically stop when the logging metric stopped improving!

# ## Trainer Tips to Boost Accuracy, Save Memory and Speed Up Training
# 
# Until now we have seen how to choose the right `accelerator`, how to log and visualize the results, and how to interface with the program in order to add specific parts of code at specific points via `callbacks`.
# Now, we will focus on how to boost your training by saving memory and speeding it up, while maintaining the same or even better degree of accuracy!
# 
# There are several built-in methods developed in PyTorch Lightning which can be applied straightforward in **PINA**. Here we report some:
# 
# * [Stochastic Weight Averaging](https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/) to boost accuracy
# * [Gradient Clipping](https://deepgram.com/ai-glossary/gradient-clipping) to reduce computational time (and improve accuracy)
# * [Gradient Accumulation](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption
# * [Mixed Precision Training](https://lightning.ai/docs/pytorch/stable/common/optimization.html#id3) to save memory consumption
# 
# We will just demonstrate how to use the first two and see the results compared to standard training.
# We use the [`Timer`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.Timer.html#lightning.pytorch.callbacks.Timer) callback from `pytorch_lightning.callbacks` to track the times. Let's start by training a simple model without any optimization (train for 500 epochs).

# In[23]:


from lightning.pytorch.callbacks import Timer
from lightning.pytorch import seed_everything

# setting the seed for reproducibility
seed_everything(42, workers=True)

model = FeedForward(
    layers=[10, 10],
    func=torch.nn.Tanh,
    output_dimensions=1,
    input_dimensions=1,
)

solver = SupervisedSolver(problem, model, use_lt=False)
trainer = Trainer(
    solver=solver,
    accelerator="cpu",
    deterministic=True,  # setting deterministic=True ensure reproducibility when a seed is imposed
    max_epochs=500,
    enable_model_summary=False,
    callbacks=[Timer()],
)  # adding a callbacks
trainer.train()
print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s')


# Now we do the same but with `StochasticWeightAveraging` enabled

# In[24]:


from lightning.pytorch.callbacks import StochasticWeightAveraging

# setting the seed for reproducibility
seed_everything(42, workers=True)

model = FeedForward(
    layers=[10, 10],
    func=torch.nn.Tanh,
    output_dimensions=1,
    input_dimensions=1,
)
solver = SupervisedSolver(problem, model, use_lt=False)
trainer = Trainer(
    solver=solver,
    accelerator="cpu",
    deterministic=True,
    max_epochs=500,
    enable_model_summary=False,
    callbacks=[Timer(), StochasticWeightAveraging(swa_lrs=0.005)],
)  # adding StochasticWeightAveraging callbacks
trainer.train()
print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s')


# As you can see, the training time does not change at all! Notice that around epoch 350
# the scheduler is switched from the defalut one `ConstantLR` to the Stochastic Weight Average Learning Rate (`SWALR`).
# This is because by default `StochasticWeightAveraging` will be activated after `int(swa_epoch_start * max_epochs)` with `swa_epoch_start=0.7` by default. Finally, the final `train_loss` is lower when `StochasticWeightAveraging` is used.
# 
# We will now do the same but clippling the gradient to be relatively small.

# In[25]:


# setting the seed for reproducibility
seed_everything(42, workers=True)

model = FeedForward(
    layers=[10, 10],
    func=torch.nn.Tanh,
    output_dimensions=1,
    input_dimensions=1,
)
solver = SupervisedSolver(problem, model, use_lt=False)
trainer = Trainer(
    solver=solver,
    accelerator="cpu",
    max_epochs=500,
    enable_model_summary=False,
    gradient_clip_val=0.1,  # clipping the gradient
    callbacks=[Timer(), StochasticWeightAveraging(swa_lrs=0.005)],
)
trainer.train()
print(f'Total training time {trainer.callbacks[0].time_elapsed("train"):.5f} s')


# As we can see, by applying gradient clipping, we were able to achieve even lower error!
# 
# ## What's Next?
# 
# Now you know how to use the `Trainer` class efficiently in **PINA**! There are several directions you can explore next:
# 
# 1. **Explore Training on Different Devices**: Test training times on various devices (e.g., `TPU`) to compare performance.
# 
# 2. **Reduce Memory Costs**: Experiment with mixed precision training and gradient accumulation to optimize memory usage, especially when training Neural Operators.
# 
# 3. **Benchmark `Trainer` Speed**: Benchmark the training speed of the `Trainer` class for different precisions to identify potential optimizations.
# 
# 4. **...and many more!**: Consider expanding to **multi-GPU** setups or other advanced configurations for large-scale training.
# 
# For more resources and tutorials, check out the [PINA Documentation](https://mathlab.github.io/PINA/).
#