When encoding a large number of sentences, growing memory usage might indicate a memory leak, but it could also stem from inefficient resource management. A memory leak occurs when objects are not released after they are no longer needed, often due to lingering references or unclosed resources. For example, if your code caches intermediate results unnecessarily, retains references to processed data, or fails to close file handles or network connections, memory can accumulate. However, high memory usage might also result from processing large datasets without batching, or from the encoding model itself holding onto resources (e.g., GPU memory in deep learning frameworks). To diagnose, monitor memory usage over time: if it grows indefinitely even after processing completes, a leak is likely. If it plateaus, the issue may be scaling inefficiencies.
To check for leaks, use profiling tools. In Python, the tracemalloc module can track allocations, while memory-profiler helps identify line-by-line memory growth. For example, if your code loads sentences into a list and never clears it, tracemalloc would show the list as a source of retained memory. Framework-specific tools like PyTorch’s torch.cuda.memory_summary() can reveal GPU memory issues. If you find objects persisting beyond their intended scope, refactor code to explicitly delete them (e.g., del large_list followed by gc.collect()). Also, ensure libraries like Hugging Face’s transformers release internal caches—some models store attention masks or tokenized outputs unless cleared.
To manage memory, adopt strategies like batching, streaming, and resource cleanup. Process sentences in smaller batches instead of loading all data at once. For example, read sentences from a file using a generator instead of a list, which avoids holding all data in memory. If using GPU-based models, reduce batch sizes and use torch.cuda.empty_cache() periodically. Free intermediate variables explicitly, especially in loops: after encoding a batch, delete tensors and clear model caches (e.g., model.zero_grad() or past_key_values=None in autoregressive models). Consider using memory-efficient libraries like datasets for streaming large datasets, or switch to lighter-weight models (e.g., distilbert instead of bert-large). Finally, enable mixed-precision training (fp16) to reduce memory footprint.