Using Producer-Consumer for batches #1450
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Fix explicit garbage collector in inputters and others
vince62s
changed the title
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Thanks guys, I am fully testing this on a real case run and merge if ok. |
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As a first statement: |
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for 5000 steps, wall time 3602 sec versus 4594 sec |
[queues] properly set random seed and fill queues sequentially
[queues] Fix several cases when using consumer-producer
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super neat. 26% of wall time gain on a full run. |
rishibommasani
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Aug 29, 2019
* advanced noam with decay and accum scheduler (OpenNMT#1367) * advanced noam with decay and accum scheduler * Add phrase_table translation argument (OpenNMT#1370) * Add phrase_table translation argument If phrase_table is provided (with replace_unk), it will look up the identified source token and give the corresponding target token. If it is not provided (or the identified source token does not exist in the table), then it will copy the source token. * Have EnsembleDecoder set attentional property. (OpenNMT#1381) * More efficient embeddings_to_torch.py (OpenNMT#1372) * Update embeddings_to_torch.py to be more memory efficient by only loading vectors which are present in the vocab into memory. * remove dead code and flake8 violations introduced with 57cefb7 * update docs of using Glove embeddings. Fix spelling error * write attention debug to log file (OpenNMT#1384) * Better handle Cuda OOM with overflow batches (OpenNMT#1385) * Added earlystopping mechanism (OpenNMT#1389) * Added earlystopping mechanism * Fixed earlystopping multi-gpu stoppage * check vocab files exist at start of preprocessing (OpenNMT#1396) * Avoid padding indices in MeanEncoder (OpenNMT#1398) * We avoid padding while mean pooling * placed batch dimension first for bmm * replaced accidentally deleted line * fix Runtime error in Library tutorial (OpenNMT#1399) * Check -gpu_ranks option to ensure saving a model (OpenNMT#1407) * Check -gpu_ranks option to ensure saving a model * split condition to check -gpu_ranks inconsistency * add src or tgt min frequency to counter value (OpenNMT#1414) * fix typo (OpenNMT#1416) * fix goldscore OpenNMT#1383 (OpenNMT#1423) * fix OpenNMT#1383 * fix gold score only * Upgrade Travis to Torch 1.1 (OpenNMT#1426) * Introduce dropout scheduler (OpenNMT#1421) * add update_dropout methods approx. everywhere, dropout scheduler * more meaningful log * forgot some layers in audio_encoder * Preprocessing: faster build vocab + multiple weighted datasets (OpenNMT#1413) * handle multiple training corpora and enable weighting * move fields vocab building logic in function * fix device handling MultipleDatasetIterator * fix multi/yield_raw_batch parameter DatasetLazyIter * update FAQ.md * add -pool_factor option * reduce pool_factor for travis runs * bump version (OpenNMT#1434) * make MultipleDatasetIterator only if necessary (OpenNMT#1436) * Update README.md (OpenNMT#1437) * small fix multi when common root in data_ids (OpenNMT#1444) * do not overwrite pt vocab when preprocessing again (OpenNMT#1447) * trim vocab(s) before saving checkpoint (OpenNMT#1453) * Using Producer-Consumer for batches (OpenNMT#1450) * Working queues on multi-GPU on text and audio * Working quite well, even with dynamic_dict * Remove explicit garbage collect making some queue hang and other fixes * fix process not ending * properly set random seed and fill queues sequentially * make queues work with distributed training * [fix] Make queue.put() blocking again (OpenNMT#1455) Fix OpenNMT#1454 . * Clarify mixed precision training support (OpenNMT#1458) Change the wording to avoid confusion. Mixed precision ensures both higher arithmetic throughput and numerical stability, not exactly synonymous to pure half-precision/FP16 training. Also add mentioning of tensor cores since older generation GPUs without tensor cores don't support true mixed precision training. * Update requirements.opt.txt * Update requirements.opt.txt * Change map_location to be 'cpu' (OpenNMT#1461) * Change map_location to be 'cpu' If you are on a CPU-only machine, it will give an error otherwise. Model averaging should not require a GPU; moreover, it may be faster to use CPU rather than move all models to the GPU to average them. * New apex amp API (OpenNMT#1465) * use new apex amp API * make apex opt_level as option * bump 0.9.1 (OpenNMT#1466) * Do not raise an error for missing validation data (OpenNMT#1467) * fix incorrect script path in CONTRIBUTING.md (OpenNMT#1470) (OpenNMT#1472) * Fix a potential IndexError when translating with replace_unk (OpenNMT#1469) * Fix IndexError which happens with replace_unk, when the argmax of the attention is on the padding instead of a real source token * add health endpoint to server.py (OpenNMT#1471) * fix typo * Minor change in MultiHeadedAttention documentation (OpenNMT#1479) * Minor change in documentation * Optimize AAN transformer and small fixes (OpenNMT#1482) * Optimize AAN transformer and small fixes * Make use of FFN layer in AAN an option * Implementing coverage loss of abisee (2017) (OpenNMT#1464) * Implementing coverage loss of abisee (2017) * fix lambda_coverage value * Video captioning (OpenNMT#1409) * Add feature extraction tool. * Update preprocess. * Add training and translation. * Adapt transformer for video. * Add tutorial to docs. * Add folded val files for easier 'early stop.' * Add and document transformer. * ignore batch if over allowed tokens batch, add warning (OpenNMT#1490) * allow implicit batch_size in translation_server (OpenNMT#1492) * ensure building sequence mask on same device as lengths (OpenNMT#1494) * add preprocess_opt in rest server (ZH) (OpenNMT#1493) * fix build_dataset_iter in train_single (OpenNMT#1499) * Use functions as preprocess / postprocess in REST server (OpenNMT#1505) * add preprocess_opt in rest server (ZH) * add preprocess and postprocess in rest server * simplify * fix function name * fix function name v2 * [fix] remove implicit check in preprocess (OpenNMT#1507) * [fix] remove implicit check in preprocess There were some implicit checks on `src_vocab` and `tgt_vocab` in preprocessing. This was creating some unwanted behavior when loading an existing vocab as a text file. * fix typo * add attention_dropout separate from dropout (OpenNMT#1512) * add attention_dropout separate from dropout * fix compatibility with models without attention_dropout (OpenNMT#1514) * pytorch 1.2 compatibility - mask & bool tensor (OpenNMT#1527) * Fix typo: traget -> target (OpenNMT#1537) * Tokens batch for translation (OpenNMT#1545) * wip translate batch tokens * move logic in translator
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Goal: load the dataset only once in RAM (instead of n_gpu times) which enables much bigger datasets or less sharding.
The idea is to have a producer that reads data (i.e. shards) and sends batch to consumers (i.e. process that actually trains the model).
It's quite working now, but we're still investigating not trivial cases with @francoishernandez (including Multiple dataset that show weird behavior)