PyTorch explained: tensors, automatic differentiation and your model
Measuring PyTorch: distinguish setup, computation and useful throughput
Design a benchmark that keeps model quality and device conditions visible.
What you will learn
- Define comparable work
- Measure the intended boundary
- Keep cost tied to accepted results
Before you start
- Basic Python and calculus
- An isolated environment for later exercises
A learning exercise compares analytical derivatives and framework outputs without hiding failed checks.
Key takeaways
- Training and inference are different workloads.
- Timing must include completion.
- Speed without accepted quality is insufficient.
Define comparable work
Fix model, shape, dtype, device and acceptance tolerance before timing. Report whether the task is inference, training or gradient computation; they retain different state and perform different work.
Separate model loading and warmup from steady-state execution. An initial run may include work absent from later calls, so one elapsed number can conceal the operating point.
Measure the intended boundary
Accelerator operations can be asynchronous, so a host-side timer must account for completion before claiming kernel or end-to-end latency. Use the timing method documented for the installed backend.
Report memory and batch size alongside throughput. A larger batch may improve aggregate work per second while making one user’s response slower or exceeding the service budget.
Keep cost tied to accepted results
Include failed runs, output tolerances and hardware configuration. A faster reduced-precision experiment is not equivalent if it fails the task’s quality requirements.
No CPU, GPU or cost benchmark was executed here. The worksheet deliberately contains no speedup numbers; the source study of gradient accumulation cannot justify a performance claim.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Fix workload and correctness tolerance.
- 2
Separate setup and completed execution timing.
- 3
Report memory, failures and quality.
Copy-ready example
trial,torch_build,device,dtype,shape,batch,latency,memory,quality_pass
example,,,,,,,,Frequently asked questions
Can a tiny tensor benchmark predict a full model?
No. Shapes, memory movement and operators differ.
Are speedups measured here?
No performance benchmark was run.
Sources
- PyTorch / README.mdSource checked 2026-09-23
- PyTorch / torch/autograd/__init__.pySource checked 2026-09-23