While the theoretical benefits of MoE inference optimizations are recognized, assessing their actual impact demands precise performance analysis. Inference performance for sparse models depends significantly on factors like batch size, sequence length, expert routing patterns, and hardware capabilities. This practical exercise guides you through profiling a basic MoE model's inference performance using standard tools, focusing on latency and throughput under varying conditions.
Before we begin, ensure you have a suitable environment. This typically involves:
pip install torch torchvision torchaudio). We'll use PyTorch for examples, but the principles apply to TensorFlow with its corresponding profiler.SimpleMoETransformer class exists.torch.profiler, which is sufficient for this exercise. For deeper hardware analysis, NVIDIA Nsight Systems or AMD µProf can be used but are outside the scope of this immediate practical.The primary goal is to measure how long inference takes (latency) and how many tokens or samples the model can process per second (throughput) under controlled conditions. We'll focus on the impact of batch size.
1. Prepare the Model and Input Data
First, load your MoE model and move it to the appropriate device (e.g., GPU). Prepare sample input data. Ensure the model is in evaluation mode to disable mechanisms like dropout.
import torch
import torch.profiler
from time import time
# Assume SimpleMoETransformer is defined elsewhere
# Load your pre-trained MoE model
model = SimpleMoETransformer(...)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Define parameters for profiling
sequence_length = 128
vocab_size = 10000 # Example vocab size
batch_sizes_to_profile = [1, 2, 4, 8, 16, 32]
results = []
# Warm-up run (important for accurate GPU timing)
print("Performing warm-up run...")
dummy_input = torch.randint(0, vocab_size, (1, sequence_length), device=device)
with torch.no_grad():
_ = model(dummy_input)
if torch.cuda.is_available():
torch.cuda.synchronize() # Wait for GPU operations to complete
print("Warm-up complete.")
2. Profile Inference Across Different Batch Sizes
Now, loop through the desired batch sizes, generate appropriate input tensors, and run inference within the torch.profiler.profile context manager. We'll record wall time for latency and calculate throughput.
for batch_size in batch_sizes_to_profile:
print(f"Profiling with batch size: {batch_size}")
input_data = torch.randint(0, vocab_size, (batch_size, sequence_length), device=device)
# Ensure gradients are not computed
with torch.no_grad():
# Use torch profiler to capture execution details
with torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
record_shapes=True, # Optional: record tensor shapes
profile_memory=True, # Optional: profile memory usage
with_stack=False # Optional: record source information (adds overhead)
) as prof:
# Run inference multiple times for stability (optional but recommended)
num_iterations = 5
start_time = time()
for _ in range(num_iterations):
_ = model(input_data)
if torch.cuda.is_available():
torch.cuda.synchronize() # Ensure GPU ops complete before timing next iter
end_time = time()
# Calculate average latency and throughput
avg_latency_ms = ((end_time - start_time) / num_iterations) * 1000 # in milliseconds
throughput_samples_sec = batch_size / (avg_latency_ms / 1000) if avg_latency_ms > 0 else float('inf')
throughput_tokens_sec = (batch_size * sequence_length) / (avg_latency_ms / 1000) if avg_latency_ms > 0 else float('inf')
results.append({
"batch_size": batch_size,
"avg_latency_ms": avg_latency_ms,
"throughput_samples_sec": throughput_samples_sec,
"throughput_tokens_sec": throughput_tokens_sec
})
# Print profiler summary for this batch size (optional)
# print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
# Optional: Save detailed trace
# prof.export_chrome_trace(f"moe_inference_trace_bs{batch_size}.json")
print("\nProfiling Results:")
for res in results:
print(f"Batch Size: {res['batch_size']}, Avg Latency: {res['avg_latency_ms']:.2f} ms, Throughput: {res['throughput_samples_sec']:.2f} samples/sec")
3. Analyzing the Results
The results list now contains latency and throughput metrics for each batch size. You can analyze this data directly or visualize it.
4. Visualizing Performance
A plot helps understand the relationship between batch size, latency, and throughput.
Example relationship between batch size, average inference latency, and throughput for a model. Note how throughput increases but eventually starts to plateau, while latency increases more significantly at larger batch sizes. Actual results depend heavily on the model, hardware, and implementation.
The torch.profiler provides much more detail than just wall time. By examining the profiler's output (e.g., using prof.key_averages().table(...) or exporting a Chrome trace via prof.export_chrome_trace(...)), you can investigate:
memcpy operations (transferring data between CPU and GPU), which can be a bottleneck if not managed carefully.Extend this practical exercise by profiling under different conditions:
k), profile the inference speed for k=1 versus k=2. Using more experts increases computation but might improve quality.By systematically profiling your MoE model under relevant conditions, you gain essential insights into its performance characteristics. This data is fundamental for choosing appropriate batching strategies, identifying optimization opportunities (like kernel fusion or quantization), selecting suitable hardware, and configuring efficient deployment environments.
Was this section helpful?
© 2026 ApX Machine LearningEngineered with