how to accelerate bge m3 sparse embeding module when inference?
Feature request
how to accelerate bge m3 sparse embeding module when inference?
Motivation
the sparse embeding process is too slow during infer bge-m3 after accelerate the dense emb inference
Your contribution
you can give a idea,I will learn how to make it work
To accelerate the bge-m3 sparse embedding module during inference, consider the following steps:
-
Optimize Model Loading:
- Ensure the model is loaded efficiently in
select_model.py. - Use optimized libraries like ONNX or TensorRT if supported.
- Ensure the model is loaded efficiently in
-
Batch Processing:
- Increase the batch size in
BatchHandlerinitialization inengine.py. - Adjust
batch_delayto balance between latency and throughput.
- Increase the batch size in
-
Caching:
- Implement or enhance caching mechanisms in
batch_handler.pyto reuse embeddings.
- Implement or enhance caching mechanisms in
-
Hardware Acceleration:
- Utilize GPU or specialized hardware (e.g., AWS Neuron) for inference.
- Check
transformer/embedder/neuron.pyfor Neuron-specific optimizations.
-
Parallel Processing:
- Use multi-threading or asynchronous processing in
BatchHandlerandModelWorkerclasses.
- Use multi-threading or asynchronous processing in
Example Code Adjustments:
# engine.py
self._batch_handler = BatchHandler(
max_batch_size=self._engine_args.batch_size * 2, # Increase batch size
model=self._model,
batch_delay=self._min_inference_t / 4, # Adjust batch delay
vector_disk_cache_path=self._engine_args.vector_disk_cache_path,
verbose=logger.level <= 10,
lengths_via_tokenize=self._engine_args.lengths_via_tokenize,
)
# batch_handler.py
class BatchHandler:
def __init__(self, ...):
...
self._threadpool = ThreadPoolExecutor(max_workers=8) # Increase thread pool size
...
These changes should help in accelerating the sparse embedding process for the bge-m3 model.
References
/libs/infinity_emb/infinity_emb/engine.py /libs/infinity_emb/infinity_emb/inference/select_model.py /libs/infinity_emb/infinity_emb/inference/batch_handler.py /libs/infinity_emb/infinity_emb/inference /libs/infinity_emb/infinity_emb/transformer/embedder
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These methods are difficult to work.