.. meta::
   :description: hydra-zen can be used to design a boilerplate-free Hydra application for running PyTorch Lightning experiments.

.. _Lightning:

.. admonition:: Prerequisites

   Your must install `PyTorch <https://pytorch.org/>`_ and `PyTorch Lightning <https://
   www.pytorchlightning.ai/>`_ in your Python environment in order to follow this 
   tutorial.

.. tip::

   Using hydra-zen for your research project? `Cite us <https://zenodo.org/record/5584711>`_! 😊

======================================================================
Run Boilerplate-Free ML Experiments with PyTorch Lightning & hydra-zen
======================================================================

`PyTorch Lightning <https://www.pytorchlightning.ai/>`_ is a library designed to 
eliminate the boilerplate code that is associated with training and testing neural 
networks in PyTorch. This is a natural bedfellow of Hydra and hydra-zen, which eliminate the boilerplate associated with designing software that is configurable, repeatable, and scalable.

Let's use Hydra, hydra-zen, and PyTorch Lightning to **configure and train multiple 
single-layer neural networks without any boilerplate code**. For the sake of 
simplicity, we will train it to simply fit :math:`\cos{x}` on 
:math:`x \in [-2\pi, 2\pi]`.

In this tutorial we will do the following:

1. Define a simple neural network and `lightning module <https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html>`_.
2. Create configs for our lighting module, data loader, optimizer, and trainer.
3. Define a task-function for training and testing a model.
4. Train four different models using combinations of two batch-sizes and two model-sizes (i.e. the number of neurons).
5. Analyze our models' results.
6. Load our best model using the checkpoints saved by PyTorch Lightning and the job-config saved by Hydra.

Defining Our Model
==================

Create a script called ``zen_model.py`` (or, open a Jupyter notebook and include the 
following code. Here, we define our single-layer neural network and the `lightning module 
<https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html>`_ that describes how to train and evaluate our model.

.. code-block:: python
   :caption: Contents of ``zen_model.py``

   from typing import Callable, Type
   
   import pytorch_lightning as pl
   import torch as tr
   import torch.nn as nn
   import torch.nn.functional as F
   from torch.optim import Optimizer
   from torch.utils.data import DataLoader, TensorDataset
   
   from hydra_zen.typing import Partial
   
   __all__ = ["UniversalFuncModule", "single_layer_nn", "train_and_eval"]
   
   
   def single_layer_nn(num_neurons: int) -> nn.Module:
       """y = sum(V sigmoid(X W + b))"""
       return nn.Sequential(
           nn.Linear(1, num_neurons),
           nn.Sigmoid(),
           nn.Linear(num_neurons, 1, bias=False),
       )
   
   
   class UniversalFuncModule(pl.LightningModule):
       def __init__(
           self,
           model: nn.Module,
           optim: Partial[Optimizer],
           dataloader: Type[DataLoader],
           target_fn: Callable[[tr.Tensor], tr.Tensor],
           training_domain: tr.Tensor,
       ):
           super().__init__()
           self.optim = optim
           self.dataloader = dataloader
           self.training_domain = training_domain
           self.target_fn = target_fn
   
           self.model = model
   
       def forward(self, x):
           return self.model(x)
   
       def configure_optimizers(self):
           # provide optimizer with model parameters
           return self.optim(self.parameters())
   
       def training_step(self, batch, batch_idx):
           x, y = batch
           # compute |cos(x) - model(x)|^2
           return F.mse_loss(self.model(x), y)
   
       def train_dataloader(self):
           # generate dataset: x, cos(x)
           x = self.training_domain.reshape(-1, 1)
           y = self.target_fn(x)
           return self.dataloader(TensorDataset(x, y))


   def train_and_eval(
       model: tr.nn.Module,
       optim: Partial[Optimizer],
       dataloader: Type[DataLoader],
       target_fn: Callable[[tr.Tensor], tr.Tensor],
       training_domain: tr.Tensor,
       lit_module: Type[UniversalFuncModule],
       trainer: pl.Trainer,
   ):
   
       
       lit = lit_module(
           model=model,
           optim=optim,
           dataloader=dataloader,
           target_fn=target_fn,
           training_domain=training_domain,
       )
   
       # train the model
       trainer.fit(lit)
   
       # evaluate the model over the domain to assess the fit
       final_eval = lit(training_domain.reshape(-1, 1))
       final_eval = final_eval.detach().cpu().numpy().ravel()
   
       # return the final evaluation of our model:
       # a shape-(N,) numpy-array
       return final_eval


.. attention::

   :plymi:`Type-annotations <Module5_OddsAndEnds/Writing_Good_Code.html#Type-Hinting>` are **not** required by hydra-zen. However, they do enable :ref:`runtime type-checking of configured values <type-support>` for our app.


Creating Our Configs and Task Function
======================================

Create another script - named ``experiment.py`` - in the same directory as ``zen_model.py``.
Here, we will create the configs for our optimizer, model, data-loader, lightning module,
and trainer. We'll also define the task function that trains and tests our model.


.. code-block:: python
   :caption: Contents of ``experiment.py``

   from math import pi
   
   import pytorch_lightning as pl
   from hydra_zen import builds, make_config, make_custom_builds_fn, zen
   import torch as tr
   from torch.optim import Adam
   from torch.utils.data import DataLoader
   
   from zen_model import UniversalFuncModule, train_and_eval, single_layer_nn
   
   pbuilds = make_custom_builds_fn(zen_partial=True, populate_full_signature=True)
   
   
   ExperimentConfig = make_config(
       seed=1,
       lit_module=UniversalFuncModule,
       trainer=builds(pl.Trainer, max_epochs=100),
       model=builds(single_layer_nn, num_neurons=10),
       optim=pbuilds(Adam),
       dataloader=pbuilds(DataLoader, batch_size=25, shuffle=True, drop_last=True),
       target_fn=tr.cos,
       training_domain=builds(tr.linspace, start=-2 * pi, end=2 * pi, steps=1000),
   )
   
   # Wrapping `train_and_eval` with `zen` makes it compatible with Hydra as a task function
   #
   # We must specify `pre_call` to ensure that pytorch lightning seeds everything
   # *before* any of our configs are instantiated (which will initialize the pytorch
   # model whose weights depend on the seed)
   pre_seed = zen(lambda seed: pl.seed_everything(seed))
   task_function = zen(train_and_eval, pre_call=pre_seed)
   
   if __name__ == "__main__":
       # enables us to call 
       from hydra_zen import ZenStore
   
       store = ZenStore(deferred_hydra_store=False)
       store(ExperimentConfig, name="lit_app")
   
       task_function.hydra_main(
           config_name="lit_app",
           version_base="1.1",
           config_path=".",
       )

.. admonition:: Be Mindful of What Your Task Function Returns

   We *could* make this `train_and_eval` return our trained neural network, which would enable
   convenient access to it, in-memory, after our Hydra job completes. However, launching this
   task function in a multirun fashion will train multiple models and thus would keep *all* of
   those models in-memory (and perhaps on-GPU) simultaneously! 
   
   By not returning the model from our task function, we avoid the risk of hitting out-of-memory
   errors when training multiple large models.


Running Our Experiments
========================

We will use :func:`hydra_zen.launch` to run four jobs: training our model with all four combinations of:

- a batch-size of 20 and 200
- a model with 10 and 100 neurons

Open a Python console (or Jupyter notebook) in the same directory as ``experiment.py`` 
and run the following code.

.. code-block:: pycon
   :caption: Launching four jobs from a Python console.

   >>> from hydra_zen import launch
   >>> from experiment import ExperimentConfig, task_function
   >>> (jobs,) = launch(
   ...     ExperimentConfig,
   ...     task_function,
   ...     overrides=[
   ...         "dataloader.batch_size=20,200",
   ...         "model.num_neurons=10,100",
   ...     ],
   ...     multirun=True,
   ... )
   [2021-10-24 21:23:32,556][HYDRA] Launching 4 jobs locally
   [2021-10-24 21:23:32,558][HYDRA] 	#0 : dataloader.batch_size=20 model.num_neurons=10
   [2021-10-24 21:23:45,809][HYDRA] 	#1 : dataloader.batch_size=20 model.num_neurons=100
   [2021-10-24 21:23:58,656][HYDRA] 	#2 : dataloader.batch_size=200 model.num_neurons=10
   [2021-10-24 21:24:01,796][HYDRA] 	#3 : dataloader.batch_size=200 model.num_neurons=100

Keep this Python console open; we will be making use of ``jobs`` in order to inspect 
our results.

Note that this is equivalent to running the following from the CLI:

.. code-block:: console
   :caption: Launching four jobs from the CLI.

   $ python experiment.py dataloader.batch_size=20,200 model.num_neurons=10,100 -m
   [2021-10-24 21:23:32,556][HYDRA] Launching 4 jobs locally
   [2021-10-24 21:23:32,558][HYDRA] 	#0 : dataloader.batch_size=20 model.num_neurons=10
   [2021-10-24 21:23:45,809][HYDRA] 	#1 : dataloader.batch_size=20 model.num_neurons=100
   [2021-10-24 21:23:58,656][HYDRA] 	#2 : dataloader.batch_size=200 model.num_neurons=10
   [2021-10-24 21:24:01,796][HYDRA] 	#3 : dataloader.batch_size=200 model.num_neurons=100


Inspecting Our Results
=======================

Visualizing Our Results
-----------------------

Let's begin inspecting our results by plotting our four models on :math:`x \in [-2\pi, 2\pi]`, alongside the
target function: :math:`\cos{x}`. Continuing to work in our current Python console (or Jupyter notebook), run
the following code and verify that you see the plot shown below.

.. code-block:: pycon
   :caption: Plotting our models

   >>> from hydra_zen import instantiate
   >>> import matplotlib.pyplot as plt
   >>> from matplotlib.axes import Axes
   
   >>> x = instantiate(ExperimentConfig.training_domain)
   >>> target_fn = instantiate(ExperimentConfig.target_fn)
   
   >>> fig, ax = plt.subplots()
   >>> assert isinstance(ax, Axes)
   >>> ax.plot(x, target_fn(x), ls="--", label="Target")

   >>> for j in jobs:
   ...     out = j.return_value
   ...     ax.plot(x, out, label=",".join(s.split(".")[-1] for s in j.overrides))
   ... 
   >>> ax.grid(True)
   >>> ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
   >>> plt.show()

.. image:: https://user-images.githubusercontent.com/29104956/138622935-3a3a960f-301f-477e-b5ab-7f4c741b1f9e.png
   :width: 800
   :alt: Plot of four trained models vs the target function


Loading the Model of Best-Fit 
-----------------------------

The 100-neuron model trained with a batch-size of 20 best fits our target function. 
Let's load the model weights that were saved by PyTorch Lightning during training.

Continuing our work in the same Python console, let's verify that job-1 corresponds to 
our desired model. Verify that you see the following outputs.

.. code-block:: pycon
   :caption: Job 1 corresponds to the 100-neuron model trained with batch-size 20.
   
   >>> best = jobs[1]
   >>> best.cfg.dataloader.batch_size
   20
   >>> best.cfg.model.num_neurons
   100

Next, we'll load the config for this job. Recall that Hydra saves a ``.hydra/config.yaml`` file, which contains the complete configuration of this job -- we can reproduce 
all aspects of it from this YAML. 

.. code-block:: pycon
   :caption: Loading the complete config for this job
   
   >>> from hydra_zen import load_from_yaml, get_target, to_yaml
   >>> from pathlib import Path

   >>> outdir = Path(best.working_dir)
   >>> cfg = load_from_yaml(outdir / ".hydra" / "config.yaml")

It is worth printing our this config to appreciate all of the exhaustive details that 
it captures about this job.

.. code-block:: pycon
   
   >>> print(to_yaml(cfg))  # fully details this job's config
   seed: 1
   lit_module:
     path: zen_model.UniversalFuncModule
     _target_: hydra_zen.funcs.get_obj
   trainer:
     _target_: pytorch_lightning.trainer.trainer.Trainer
     max_epochs: 100
   model:
     _target_: zen_model.single_layer_nn
     num_neurons: 100
   optim:
     _target_: torch.optim.adam.Adam
     _partial_: true
     lr: 0.001
     betas:
     - 0.9
     - 0.999
     eps: 1.0e-08
     weight_decay: 0
     amsgrad: false
   dataloader:
     _target_: torch.utils.data.dataloader.DataLoader
     _partial_: true
     batch_size: 20
     shuffle: true
     sampler: null
     batch_sampler: null
     num_workers: 0
     collate_fn: null
     pin_memory: false
     drop_last: true
     timeout: 0.0
     worker_init_fn: null
     multiprocessing_context: null
     generator: null
     prefetch_factor: 2
     persistent_workers: false
   target_fn:
     path: torch.cos
     _target_: hydra_zen.funcs.get_obj
   training_domain:
     _target_: torch.linspace
     start: -6.283185307179586
     end: 6.283185307179586
     steps: 1000

PyTorch Lightning saved the model's trained weights as a ``.ckpt`` file in this job's 
working directory. Let's load these weights and use them to instantiate our lighting 
module.

.. code-block:: pycon
   :caption: Loading our lighting module with trained weights

   >>> from hydra_zen import zen
   >>> from functools import partial
   >>> *_, last_ckpt = sorted(outdir.glob("**/*.ckpt"))
   >>> LitModule = get_target(cfg.lit_module)

   >>> pload = partial(LitModule.load_from_checkpoint, last_ckpt)
   >>> # extract top-level fields from `cfg`, instantiate them, and pass to `load_from_checkpoint`
   >>> loaded = zen(pload, unpack_kwargs=True)(cfg)  # type: ignore

Finally, let's double check that this loaded model behaves as-expected. Evaluating it 
at :math:`-\pi/2`, :math:`0`, and :math:`\pi/2` should return, approximately, :math:`0`, :math:`1`, and :math:`0`, respectively.

.. code-block:: pycon
   :caption: Checkout our loaded model's behavior
   
   >>> import torch as tr
   >>> loaded(tr.tensor([-3.1415 / 2, 0.0, 3.1415 / 2]).reshape(-1, 1))
   tensor([[0.0110],
           [0.9633],
           [0.0364]], grad_fn=<MmBackward>)




.. admonition:: Math Details

   For the interested reader... In this toy-problem we are optimizing `arbitrary-width universal function approximators    <https://en.wikipedia.org/wiki/Universal_approximation_theorem#Arbitrary-width_case>`_ to fit :math:`\cos{x}`
   on :math:`x \in [-2\pi, 2\pi]`.
   In mathematical notation, we want to solve the following optimization problem:
   
   .. math::
   
      F(\vec{v}, \vec{w}, \vec{b}; x) &= \sum_{i=1}^{N}{v_{i}\sigma(x w_i + b_i)}
   
      \vec{v}^*, \vec{w}^*, \vec{b}^* &= \operatorname*{arg\,min}_{\vec{v}, \vec{w}, \vec   {b}\in\mathbb{R}^{N}} \;  \|F(\vec{v}, \vec{w}, \vec{b}; x)\ - \cos{x}\|_{2}
   
      x &\in [-2\pi, 2\pi]
   
   where :math:`N` – the number of "neurons" in our layer – is a hyperparameter.

.. attention:: **Cleaning Up**:
   To clean up after this tutorial, delete the ``multirun`` directory that Hydra 
   created upon launching our app. You can find this in the same directory as your 
   ``experiment.py`` file.

