Latency/Energy Calculation

[krishnateja95]

Is there is a method to calculate performance metrics (Latency, Energy) of a Transformer model (BERT, ViT) on the H100 Transformer Engine using Pytorch?

[Advait251206]

Transformer Engine itself does not provide a built-in API to directly calculate end-to-end latency or energy consumption for Transformer models such as BERT or ViT. Its primary purpose is to accelerate training and inference using FP8/BF16 mixed precision on supported NVIDIA GPUs (such as the H100), rather than serving as a performance analysis framework.

Measuring Latency

The recommended approach is to benchmark your model using PyTorch with proper GPU synchronization to obtain accurate timings.

import time
import torch

model.eval()
x = ...

# Warm-up
for _ in range(20):
    with torch.no_grad():
        _ = model(x)

torch.cuda.synchronize()
start = time.perf_counter()

with torch.no_grad():
    _ = model(x)

torch.cuda.synchronize()
end = time.perf_counter()

print(f"Latency: {(end - start) * 1000:.3f} ms")

For reliable measurements:

• Perform multiple warm-up iterations before timing.
• Benchmark over 100–1000 iterations.
• Report the average, median, and optionally the standard deviation.

Measuring Energy Consumption

Transformer Engine does not expose energy measurements directly. Instead, NVIDIA recommends using GPU telemetry tools such as:

• nvidia-smi (power sampling)
• NVML (NVIDIA Management Library) via the pynvml Python package
• NVIDIA DCGM
• NVIDIA Nsight Systems / Nsight Compute for detailed profiling

Energy is computed by integrating power over time:

[
\text{Energy (J)} = \int P(t),dt
]

If the power draw is approximately constant, you can estimate it as:

[
\text{Energy} \approx \text{Average Power (W)} \times \text{Latency (s)}
]

Typical Workflow on H100

A common evaluation pipeline is:

1. Enable FP8/BF16 execution using Transformer Engine.
2. Measure inference or training latency with PyTorch (torch.cuda.synchronize() before and after timing).
3. Collect GPU power measurements using NVML or nvidia-smi.
4. Compute energy using the recorded power and execution time.

This is the standard methodology used in many research papers evaluating Transformer Engine and H100 performance.

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