Add 'run_batch' mode for GPU encoding and decoding with batch_size >= 1 #1534
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…ame() deprecated in 1.13.1
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libtorch-gpu代码中，没有显式的释放显存。在调用量增加的时候，是否会存在out of memory的问题？ |
veelion
commented
Nov 21, 2022
| r_hyps_pad_sos_eos, ctc_scores_tensor).toTuple()->elements(); | ||
| auto rescores = outputs[1].toTensor().to(at::kCPU); | ||
| #ifdef USE_GPU | ||
| c10::cuda::CUDACachingAllocator::emptyCache(); |
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#1534 clear GPU memory cache here, so it could support much more concurrency.
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This mode improves the throughput of websocket server.
Test result:
hardware-1:
Platinum 8358P CPU @ 2.60GHz 15 cores + 80G memory, A5000 * 1 + 24G memory
hardware-2:
Platinum 8369B CPU @ 2.90GHz 32 cores + 120GB memory, A100-SXM4-80GB * 1 + 80GB memory
data:
3000 wavs with different durations in range [0.6, 15] seconds.
With same CPU, GPU is 2~3 times faster than CPU, run_batch is 2.x times faster than non run_batch mode, but the CER has a little bigger.