What is the effect of batch size on throughput and memory usage when encoding sentences with Sentence Transformers?
Batch size directly impacts both throughput and memory usage when encoding sentences with Sentence Transformers. Larger batches generally increase throughput (sentences processed per second) by leveraging parallel computation on GPUs more efficiently, but they also consume more memory. Smaller batches reduce memory usage but may underutilize hardware, leading to lower throughput. The relationship is not linear, as hardware limits (like GPU memory) cap the maximum usable batch size.
Throughput Considerations Sentence Transformers, like most neural models, process batches by performing matrix operations optimized for parallelism. GPUs excel at handling large matrices, so increasing batch size improves computational efficiency. For example, processing 64 sentences at once might take only marginally longer than processing 8, significantly boosting throughput. However, beyond a certain point, diminishing returns occur: the GPU’s compute units become saturated, and further increases in batch size yield minimal gains. For instance, moving from a batch size of 128 to 256 might only marginally improve throughput while risking memory exhaustion.
Memory Usage Trade-offs
Memory usage grows with batch size because the model must store intermediate activations and gradients for all sentences in the batch. For example, a batch size of 32 with a model like all-MiniLM-L6-v2 might use 2GB of GPU memory, while a batch size of 128 could require 8GB. Longer input sequences exacerbate this, as memory scales with batch size multiplied by sequence length. If the batch exceeds available GPU memory, the process crashes with an out-of-memory (OOM) error. Developers must balance these factors: a batch size of 64 might maximize throughput on a 16GB GPU, but the same setup could fail with a larger model like bert-large.
Practical Guidance
To optimize, start with a small batch size (e.g., 8) and incrementally increase it while monitoring memory usage and throughput. Tools like nvidia-smi can track GPU memory consumption. If OOM errors occur, reduce the batch size or shorten input sequences. Some Sentence Transformers pipelines offer auto-batching, which splits large batches into smaller chunks to avoid memory limits. For example, setting batch_size=64 with auto_batch_size=True might internally process 16 sentences at a time, trading some throughput for reliability. Always test configurations on representative hardware and data to find the optimal balance.