Adding a Backend to PyTorch - Why It Matters and How It Works

When developing models in PyTorch, we usually work with one of the two well-known computation engines: CPU or GPU. But PyTorch is built modularly - which means it allows adding a new backend: a computation layer that performs all tensor operations on different hardware or systems.

To understand why this is important, let’s explore the concept behind it - without diving into code.

What Does a Backend Actually Do?

Every time a model runs an operation like addition, matrix multiplication, or convolution, PyTorch needs to decide where and how to execute it. The backend is what answers two questions:

• Where is the computation performed - on a CPU, GPU, or dedicated accelerator.
• How is the computation performed - which optimizations, libraries, and memory management are used.

In other words: The backend is “the engine under the hood.”

Why Add a New Backend?

1. Running on Dedicated Hardware

If there’s hardware that accelerates models better than a CPU or GPU, there needs to be a way to connect it to PyTorch. The backend is that bridge.

2. Unique Optimizations

Sometimes, certain operations are much faster on an external computation system: for example, an engine that fuses multiple operations into one, leverages faster memory connections, or performs smarter scheduling.

3. Transparent Support for Model Developers

The developer continues working as usual with PyTorch, but in reality - the computations are performed on another system, without changing the model code.

What Does It Look Like for a Beginner User?

From the user’s perspective: They simply write something like:

x = tensor.to("my_backend")

From that point on, every operation the tensor performs runs on the new backend.

The beauty is that the model code doesn’t change at all. Switching the backend is just a matter of changing the device.

Conceptual Advantages

1. Tremendous Flexibility for Developers and Hardware Manufacturers

Anyone producing an AI accelerator can “connect” to the PyTorch ecosystem without requiring model developers to learn a new API.

2. Shorter Development Time

There’s no need to build everything from scratch. Existing models simply run on the customized backend.

3. Uniform Work Across Different Systems

You can switch backends for testing, production deployment, or optimization - without rewriting the model.

Why Is This Especially Important in Inference?

In inference, performance is determined at the hardware and optimization level: memory, scheduling, IO systems, multi-core processing, NUMA - all of these have a dramatic impact.

A customized backend enables:

• Maximizing hardware capabilities
• Precisely managing memory and processes
• Reducing latency
• Increasing throughput
• Integrating large-scale models

All of this happens “behind the scenes,” while the user sees a system that looks like PyTorch - but behaves much more efficiently.

Conclusion

Adding a backend to PyTorch isn’t just a feature for system experts - it’s a concept that bridges worlds: the convenience of PyTorch with the performance of dedicated hardware.