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PyTorch: Difference between revisions

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PyTorch is a popular machine learning library developed by Facebook
PyTorch is a popular machine learning library developed by Facebook


==Installation==
See [https://pytorch.org/get-started/locally/ PyTorch Getting Started] and [https://pytorch.org/get-started/previous-versions/ PyTorch Previous Versions]
I recommend using the conda installation method since it is paired with the correct version of cuda.
==Getting Started==
* [https://pytorch.org/tutorials/ PyTorch Tutorials]
{{hidden | Example |
<syntaxhighlight lang="python">
import torch
import torch.nn as nn
model = nn.Sequential(nn.Linear(5, 5),nn.ReLU(),nn.Linear(5, 1))
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# Training
for epoch in range(epochs):
    for i, data in enumerate(trainloader):
        # get the inputs; e.g. data is a list of [inputs, labels]
        inputs, labels = data
        # zero the parameter gradients
        optimizer.zero_grad()
        # forward
        outputs = model(inputs)
        loss = criterion(outputs, labels)
       
        # backward
        loss.backward()
        optimizer.step()
</syntaxhighlight>
}}
==Importing Data==
See [https://pytorch.org/tutorials/beginner/data_loading_tutorial.html Data Loading Tutorial]


==Usage==
==Usage==
Note that there are several useful functions under <code>torch.nn.functional</code> which is typically imported as <code>F</code>.
Most neural network layers are actually implemented in functional.
===torch.meshgrid===
Note that this is transposed compared to <code>np.meshgrid</code>.
===torch.multinomial===
[https://pytorch.org/docs/stable/generated/torch.multinomial.html torch.multinomial]<br>
If you need to sample with a lot of categories and with replacement, it may be faster to use `torch.cumsum` to build a CDF and `torch.searchsorted`.
{{hidden | torch.searchsorted example |
<syntaxhighlight lang="python">
# Create your weights variable.
weights_cdf = torch.cumsum(weights, dim=0)
weights_cdf_max = weights_cdf[0]
sample = torch.searchsorted(weights_cdf,
                            weights_cdf_max * torch.rand(num_samples))
</syntaxhighlight>
}}
===F.grid_sample===
[https://pytorch.org/docs/stable/nn.functional.html#grid-sample Doc]<br>
This function allows you to perform interpolation on your input tensor.<br>
It is very useful for resizing images or warping images.
==Building a Model==
To build a model, do the following:
* Create a class extending <code>nn.Module</code>.
* In your class include all other modules you need during init.
** If you have a list of modules, make sure to wrap them in <code>nn.ModuleList</code> or <code>nn.Sequential</code> so they are properly recognized.
* Wrap any parameters for you model in <code>nn.Parameter(weight, requires_grad=True)</code>.
* Write a forward pass for your model.
==Multi-GPU Training==
See [https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html Multi-GPU Examples].
===nn.DataParallel===
The basic idea is to wrap blocks in [https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html#torch.nn.DataParallel <code>nn.DataParallel</code>]. 
This will automatically duplicate the module across multiple GPUs and split the batch across GPUs during training.
However, doing so causes you to lose access to custom methods and attributes.
To save and load the model, just use <code>model.module.save_state_dict()</code> and <code>model.module.load_state_dict()</code>.
===nn.parallel.DistributedDataParallel===
[https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel nn.parallel.DistributedDataParallel] 
[https://pytorch.org/docs/stable/notes/cuda.html#cuda-nn-ddp-instead DistributedDataParallel vs DataParallel]
[https://pytorch.org/tutorials/intermediate/ddp_tutorial.html ddp tutorial]
The PyTorch documentation suggests using this instead of <code>nn.DataParallel</code>.
The main difference is this uses multiple processes instead of multithreading to work around the Python Interpreter. 
It also supports training on GPUs across multiple ''nodes'', or computers.
Using this is quite a bit more work than nn.DataParallel. 
You may want to consider using PyTorch Lightning which abstracts this away.
==Optimizations==
===Reducing GPU memory usage===
* Save loss using [https://pytorch.org/docs/stable/tensors.html#torch.Tensor.item <code>.item()</code>] which returns a standard Python number
* For non-scalar items, use <code>my_var.detach().cpu().numpy()</code>
* [https://pytorch.org/docs/stable/autograd.html#torch.Tensor.detach <code>detach()</code>] removes the item from the autograd edge.
* [https://pytorch.org/docs/stable/tensors.html?highlight=cpu#torch.Tensor.cpu <code>cpu()</code>] moves the tensor to the CPU.
* [https://pytorch.org/docs/stable/tensors.html#torch.Tensor.numpy <code>numpy()</code>] returns a numpy view of the tensor.
When possible, use functions which return new views of existing tensors rather than making duplicates of tensors:
* [https://pytorch.org/docs/stable/tensors.html#torch.Tensor.permute <code>permute</code>]
* [https://pytorch.org/docs/stable/generated/torch.Tensor.expand.html#torch.Tensor.expand <code>expand</code>] instead of [https://pytorch.org/docs/stable/tensors.html#torch.Tensor.repeat <code>repeat</code>]
* [https://pytorch.org/docs/stable/tensors.html#torch.Tensor.view <code>view</code>]
Note that <code>permute</code> does not change the underlying data. 
This can result in a minor performance hit which PyTorch will warn you about if you repeatedly use a contiguous tensor with a channels last tensor. 
To address this, call [https://pytorch.org/docs/stable/tensors.html#torch.Tensor.contiguous <code>contiguous</code>] on the tensor with the new memory format.
;During inference
* Use `model.eval()`
* Use `with torch.no_grad():`
===Float16===
Float16 uses half the memory of float32. 
New Nvidia GPUs also have dedicated hardware instructions called tensor cores to speed up float16 matrix multiplication. 
Typically it's best to train using float32 though for stability purposes. 
You can do truncate trained models and inference using float16.
Note that [https://en.wikipedia.org/wiki/Bfloat16_floating-point_format <code>bfloat16</code>] is different from IEEE float16. bfloat16 has fewer mantissa bits (8 exp, 7 mantissa) and is used by Google's TPUs. In contrast, float16 has 5 exp and 10 mantissa bits.
==Classification==
In classification, your model outputs a vector of ''logits''. 
These are relative scores for each potential output class. 
To compute the loss, pass the logits into a [https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html cross-entropy loss].
To compute the accuracy, you can use [https://pytorch.org/docs/stable/generated/torch.argmax.html <code>torch.argmax</code>] to get the top prediction or  [https://pytorch.org/docs/stable/generated/torch.topk.html <code>torch.topk</code>] to get the top-k prediction.
==Debugging==
{{see also | Debugging ML Models}}
If you get a cuda kernel error, you can rerun with the environment variable <code>CUDA_LAUNCH_BLOCKING=1</code> to get the correct line in the stack trace. 
<pre>
CUDA_LAUNCH_BLOCKING=1 python app.py
</pre>
For the following error:
<pre>
CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasGemmEx(...)`
</pre>
First check all your tensor types and shapes.<br>
If you've checked all your tensor shapes and types and you can try running with the environment variable:
<pre>
CUBLAS_WORKSPACE_CONFIG=:0:0
</pre>
References:
* [https://github.com/pytorch/pytorch/issues/54975 https://github.com/pytorch/pytorch/issues/54975]
==TensorBoard==
{{main | TensorBoard}}
See [https://pytorch.org/docs/stable/tensorboard.html PyTorch Docs: Tensorboard]
<syntaxhighlight lang="python">
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(log_dir="./runs")
# Calculate loss. Increment the step.
writer.add_scalar("train_loss", loss.item(), step)
# Optionally flush e.g. at checkpoints
writer.flush()
# Close the writer (will flush)
writer.close()
</syntaxhighlight>
==Libraries==
A list of useful libraries
===torchvision===
https://pytorch.org/vision/stable/index.html
Official tools for image manipulation such as blur, bounding boxes.
===torchmetrics===
https://torchmetrics.readthedocs.io/en/stable/
Various metrics such as PSNR, SSIM, LPIPS
===PyTorch3D===
{{main | PyTorch3D}}
[https://github.com/facebookresearch/pytorch3d PyTorch3D]
Facebook library with differentiable renderers for meshes and point clouds.
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