How to use PyTorch for half-precision, mixed (mixed) precision training

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Brief introduction of nvidia deep learning acceleration library apex

NVIDIA Deep Learning Acceleration Library Apex is an open-source mixed-precision training toolkit for PyTorch designed to speed up training and reduce memory usage. Apex provides many tools for mixed-precision training, including half-precision floating-point number (float16) support, dynamic precision scaling, distributed training and other functions.

The most commonly used feature in Apex is half precision floating point support. Half-precision floating point numbers are often used to speed up deep learning training and can significantly reduce GPU memory usage. Apex provides an easy way to implement half-precision training, requiring only a few lines of code to be added to the model definition and training loop.

In addition to half-precision training, Apex also provides some other functions, including:

1. Dynamic Precision Scaling: Apex provides the GradScaler class, which automatically scales gradients to fit within the range of half-precision floating point numbers and prevents underflow or overflow.

2. Distributed training: Apex supports distributed training using PyTorch's built-in distributed training tools, and provides some tools and optimizers for distributed training.

3. Deep learning optimizer: Apex provides some tools and optimizers for deep learning optimizer, including FusedAdam, FusedLAMB, etc.

4. Other Tools: Apex also provides some other useful tools like AMP, SyncBatchNorm, etc.

In summary, Apex is an open-source mixed-precision training toolkit for PyTorch that speeds up training and reduces memory usage. In addition to half-precision training, Apex also provides some other useful features, such as dynamic precision scaling, distributed training, deep learning optimizer, etc. If you want to speed up your PyTorch training and reduce memory usage, consider Apex.

How to use Apex

PyTorch supports half-precision training, and can use half-precision floating-point numbers (float16) to speed up training and reduce the memory usage of the model. Here are the steps for half-precision training with PyTorch:

1. Install the Apex library (optional): Apex is an open-source mixed-precision training library from NVIDIA, which can help users easily use PyTorch for half-precision training. It can be installed with the following command:

pip install apex

2. Define the model: define the PyTorch model, you can use modules such as nn.Module or nn.Sequential.

3. Convert the model to a half-precision model: use GradScaler and autocast in torch.cuda.amp to achieve half-precision training. First, the model needs to be converted to a half-precision model, which can be achieved with the following code:

from torch.cuda.amp import autocast, GradScaler

model = model.half()

4. Define the optimizer: Define the optimizer, you can use SGD, Adam and other optimizers in torch.optim.

5. Define GradScaler and amp autocast: To define GradScaler and autocast, you can use the following code to achieve:

scaler = GradScaler()

with autocast():
    ...

6. Write the training code: In the training loop, you need to use autocast() to convert the input to half-precision floating point numbers, use GradScaler() to scale the gradient, and then use the optimizer to update. This can be achieved with the following code:

for input, target in dataloader:
    input = input.to(device).half()
    target = target.to(device)

    with autocast():
        output = model(input)
        loss = criterion(output, target)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

7. Test model: When testing the model, it is necessary to convert the model back to a floating-point number model, which can be achieved by using the following code:

model.float()

In summary, half-precision training with PyTorch requires converting the model to a half-precision model, using GradScaler and autocast to scale the gradient and transform the input and output, and then use the optimizer to update. When testing the model, it is necessary to convert the model back to a floating point model. Half-precision training can be more conveniently implemented using the Apex library.

How to use GradScaler for scaling gradients to fit within the range of half-precision floating point numbers and prevent underflow or overflow. autocast is used to convert the input to half-precision floating point numbers, used during forward and back propagation. This speeds up computation and reduces memory usage.

Mixed-precision training with GradScaler and autocast can speed up computation and reduce memory usage. Here are the steps for mixed precision training with GradScaler and autocast:

1. Import GradScaler and autocast: Import GradScaler and autocast from the torch.cuda.amp module.

from torch.cuda.amp import GradScaler, autocast

2. Define GradScaler: Define the GradScaler object for scaling the gradient. This can be achieved with the following code:

scaler = GradScaler()

3. Using autocast() in the training loop: Use autocast() in the training loop to convert the input and model to half-precision floating point numbers. This can be achieved with the following code:

for inputs, labels in dataloader:
    inputs, labels = inputs.to(device), labels.to(device)

    with autocast():
        outputs = model(inputs)
        loss = criterion(outputs, labels)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

In the above code, autocast() is used to convert the input data and model to half-precision floating point numbers, which are used during forward and backpropagation. Then, the loss is scaled using GradScaler, backpropagation is performed, and the optimizer is updated using GradScaler. Finally, update the scaling factor using the update() method of GradScaler.

In summary, mixed precision training with GradScaler and autocast can speed up computation and reduce memory usage. In the training loop, use autocast() to convert the input and model to half-precision floating point numbers, use GradScaler to scale the loss, perform backpropagation, use GradScaler to update the optimizer, and use GradScaler's update() method to update the scaling factor.

The most commonly used method for pytoch to enable mixed precision

In PyTorch, enabling mixed precision training can be achieved using GradScaler and autocast in the torch.cuda.amp module. GradScaler is used to scale the gradient to fit the range of half-precision floating point numbers and prevent underflow or overflow. autocast is used to convert input to half-precision floating point numbers to speed up calculations and reduce memory usage.

The most commonly used methods to enable mixed precision are as follows:

1. Import GradScaler and autocast:

from torch.cuda.amp import GradScaler, autocast

2. Create a GradScaler object:

scaler = GradScaler()

3. Use autocast() in the training loop to convert the input and model to half-precision floating point numbers:

for inputs, labels in dataloader:
    inputs, labels = inputs.to(device), labels.to(device)

    with autocast():
        outputs = model(inputs)
        loss = criterion(outputs, labels)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

In the above code, autocast() is used to convert the input and model to half-precision floating point numbers, which are used during forward and backpropagation. Then, the loss is scaled using GradScaler, backpropagation is performed, and the optimizer is updated using GradScaler. Finally, update the scaling factor using the update() method of GradScaler.

4. When testing the model, you need to use torch.cuda.amp.autocast() to convert the input to a half-precision floating point number:

with torch.no_grad():
    with autocast():
        outputs = model(inputs)

In the above code, the gradient calculation is disabled using torch.no_grad(), and then autocast() is used to convert the input to half-precision floating point numbers.

In summary, using GradScaler and autocast enables mixed precision training in PyTorch, which speeds up computation and reduces memory usage. In the training loop, use autocast() to convert the input and model to half-precision floating point numbers, use GradScaler to scale the loss, perform backpropagation, use GradScaler to update the optimizer, and use GradScaler's update() method to update the scaling factor. When testing the model, you need to use torch.cuda.amp.autocast() to convert the input to half-precision floating point numbers.

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What is the internal implementation process of the GradScaler interface?

GradScaler is a tool in PyTorch for scaling gradients to fit within the range of half-precision floating point numbers and preventing underflow or overflow. The internal implementation process of GradScaler is as follows:

1. Initialize scale factor and context:

When the GradScaler object is created, the scaling factor is initialized to 1, and a context (scale_ctx) is created to store the current scaling factor and gradient scaling state.

2. Call the scale() method for gradient scaling:

When performing backpropagation calculations, each time the scale() method is called, GradScaler will multiply the loss by the current scaling factor to obtain the scaled loss. GradScaler will then store the scaled gradients in the context and return the scaled loss.

3. Call the step() method to update the model parameters:

Before calling the optimizer's step() method to update the model parameters, GradScaler will call the unscale_() method to divide the scaled gradient by the current scaling factor to obtain the real gradient. Then, GradScaler will call the step() method of the optimizer to update the model parameters.

4. Call the update() method to update the zoom factor:

At the end of each training iteration, GradScaler calls the update() method to update the scaling factor. GradScaler will dynamically adjust the scaling factor according to the size of the gradient, and store the new scaling factor in the context for the next use.

In summary, GradScaler scales gradients by dynamically adjusting the scaling factor to fit within the range of half-precision floating-point numbers and prevents underflow or overflow. When performing backpropagation calculations, GradScaler will multiply the loss by the current scaling factor to obtain the scaled loss. Before updating the model parameters, GradScaler divides the scaled gradient by the current scaling factor to get the true gradient. At the end of each training iteration, GradScaler dynamically adjusts the scaling factor according to the magnitude of the gradient, and stores the new scaling factor in the context for next use.

Explain the specific meaning of Loss scaling and intermediate result FP32 respectively

In mixed-precision training, using half-precision floating-point numbers (float16) can speed up calculations and reduce memory usage, but there may be problems with gradient underflow or overflow. To solve this problem, Loss scaling and intermediate result FP32 representation can be used.

1. Loss scaling:

When training with half-precision floating-point numbers, problems with gradient underflow or overflow may cause training to fail. To solve this problem, Loss scaling can be used. Specifically, the loss is multiplied by a scaling factor, and the gradient is then divided by the scaling factor during backpropagation. This prevents gradient underflow or overflow, and keeps gradients sized appropriately.

2. The intermediate result FP32 says:

In mixed-precision training, using half-precision floating-point numbers can speed up computation and reduce memory usage, but may result in a loss of precision. To solve this problem, the FP32 representation of the intermediate results can be used. Specifically, intermediate results (e.g., the output of a convolutional layer) can use half-precision floating-point numbers during computation, but these intermediate results need to be converted to single-precision floating-point numbers (float32) before computing the loss. This maintains high precision and avoids loss of precision.

In conclusion, Loss scaling and intermediate result FP32 representation are two techniques used to solve the problem of gradient underflow or overflow and precision loss in mixed precision training. Loss scaling multiplies the loss by a scaling factor and then divides the gradients by this scaling factor during backpropagation, preventing gradient underflow or overflow. The intermediate result FP32 means that half-precision floating-point numbers can be used in the calculation process, and these intermediate results are converted to single-precision floating-point numbers before calculating the loss to maintain high precision. This speeds up calculations and reduces memory usage while maintaining high precision.

Which video cards support mixed precision?

Currently, most NVIDIA graphics cards support mixed-precision training. Specifically, starting from the Pascal architecture, NVIDIA's graphics cards began to support mixed precision training. The following are some common graphics card models and their support for mixed precision training:

1. NVIDIA Tesla V100: Support for mixed precision training.

2. NVIDIA Tesla P100: Support for mixed precision training.

3. NVIDIA Tesla T4: Support for mixed precision training.

4. NVIDIA GeForce RTX 30 series (such as RTX 3090, RTX 3080, etc.): Support mixed precision training.

5. NVIDIA GeForce RTX 20 series (such as RTX 2080 Ti, RTX 2080, etc.): Supports mixed-precision training.

6. NVIDIA GeForce GTX 10 series (such as GTX 1080 Ti, GTX 1080, etc.): Supports mixed precision training, but requires some special tricks and tools (such as NVIDIA Apex ).

It should be noted that mixed-precision training needs to use hardware that supports FP16 (half-precision floating-point number) calculations, so not all graphics cards can support mixed-precision training. Additionally, different graphics cards will vary in how they support mixed-precision training and in how they perform. Therefore, when choosing a graphics card, you need to choose the most suitable graphics card according to your needs and budget.

In short, most NVIDIA graphics cards support mixed precision training, but different graphics cards will vary in the way and performance they support mixed precision training. When choosing a graphics card, you need to choose the most suitable graphics card according to your needs and budget.