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UKPLab / gpl

Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval" https://arxiv.org/abs/2112.07577
Apache License 2.0
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RuntimeError: CUDA out of memory #18

Open Matthieu-Tinycoaching opened 1 year ago

Matthieu-Tinycoaching commented 1 year ago

Hi,

When trying to generate intermediate results with the following command:

dataset = 'tiny'
gpl.train(
    path_to_generated_data=f"generated/{dataset}",
    base_ckpt='sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2',  
    # base_ckpt='GPL/msmarco-distilbert-margin-mse',  # The starting checkpoint of the experiments in the paper
    gpl_score_function="dot",
    # Note that GPL uses MarginMSE loss, which works with dot-product
    batch_size_gpl=32,
    gpl_steps=140000,
    new_size=-1,
    # Resize the corpus to `new_size` (|corpus|) if needed. When set to None (by default), the |corpus| will be the full size. When set to -1, the |corpus| will be set automatically: If QPP * |corpus| <= 250K, |corpus| will be the full size; else QPP will be set 3 and |corpus| will be set to 250K / 3
    queries_per_passage=-1,
    # Number of Queries Per Passage (QPP) in the query generation step. When set to -1 (by default), the QPP will be chosen automatically: If QPP * |corpus| <= 250K, then QPP will be set to 250K / |corpus|; else QPP will be set 3 and |corpus| will be set to 250K / 3
    output_dir=f"output/{dataset}",
    evaluation_data=f"./{dataset}",
    evaluation_output=f"evaluation/{dataset}",
    generator="BeIR/query-gen-msmarco-t5-large-v1",
    retrievers=["msmarco-distilbert-base-tas-b", "msmarco-MiniLM-L6-cos-v5"],
    retriever_score_functions=["dot", "cos_sim"],
    # Note that these two retriever model work with cosine-similarity
    cross_encoder="cross-encoder/ms-marco-MiniLM-L-6-v2",
    qgen_prefix="qgen",
    # This prefix will appear as part of the (folder/file) names for query-generation results: For example, we will have "qgen-qrels/" and "qgen-queries.jsonl" by default.
    do_evaluation=True,
    use_amp=True   # One can use this flag for enabling the efficient float16 precision
)

I got the following error:

2022-08-26 11:55:08 - Loading faiss with AVX2 support.
2022-08-26 11:55:08 - Could not load library with AVX2 support due to:
ModuleNotFoundError("No module named 'faiss.swigfaiss_avx2'")
2022-08-26 11:55:08 - Loading faiss.
2022-08-26 11:55:08 - Successfully loaded faiss.
[2022-08-26 11:55:10] INFO [gpl.train.train:79] Corpus does not exist in generated/tiny. Now clone the one from the evaluation path ./tiny
[2022-08-26 11:55:10] INFO [gpl.train.train:84] Automatically set `new_size` to 83334
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:89] Loading Corpus...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4252/4252 [00:00<00:00, 277639.61it/s]
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:91] Loaded 4252 Documents.
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:92] Doc Example: {'text': 'Without a specific goal for your speech, your audience will be lost in understanding the message you are seeking to deliver, because you will not know yourself what you are seeking to deliver in that speech.', 'title': ''}
[2022-08-26 11:55:10] WARNING [gpl.toolkit.resize.resize:19] `new_size` should be smaller than the corpus size
[2022-08-26 11:55:10] INFO [gpl.toolkit.resize.resize:41] Resized the corpus in ./tiny to generated/tiny with new size 83334
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:89] Loading Corpus...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4252/4252 [00:00<00:00, 321974.74it/s]
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:91] Loaded 4252 Documents.
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:92] Doc Example: {'text': 'Without a specific goal for your speech, your audience will be lost in understanding the message you are seeking to deliver, because you will not know yourself what you are seeking to deliver in that speech.', 'title': ''}
[2022-08-26 11:55:10] INFO [gpl.train.train:99] Automatically set `queries_per_passage` to 59
[2022-08-26 11:55:10] INFO [gpl.train.train:125] No generated queries found. Now generating it
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:89] Loading Corpus...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4252/4252 [00:00<00:00, 308459.11it/s]
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:91] Loaded 4252 Documents.
[2022-08-26 11:55:10] INFO [beir.datasets.data_loader.load_corpus:92] Doc Example: {'text': 'Without a specific goal for your speech, your audience will be lost in understanding the message you are seeking to deliver, because you will not know yourself what you are seeking to deliver in that speech.', 'title': ''}
[2022-08-26 11:55:20] INFO [beir.generation.models.auto_model.__init__:16] Use pytorch device: cuda
[2022-08-26 11:55:21] INFO [beir.generation.generate.generate:40] Starting to Generate 59 Questions Per Passage using top-p (nucleus) sampling...
[2022-08-26 11:55:21] INFO [beir.generation.generate.generate:41] Params: top_p = 0.95
[2022-08-26 11:55:21] INFO [beir.generation.generate.generate:42] Params: top_k = 25
[2022-08-26 11:55:21] INFO [beir.generation.generate.generate:43] Params: max_length = 64
[2022-08-26 11:55:21] INFO [beir.generation.generate.generate:44] Params: ques_per_passage = 59
[2022-08-26 11:55:21] INFO [beir.generation.generate.generate:45] Params: batch size = 32
pas:   0%|                                                                                                                                                                                          | 0/133 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/matthieu/Tinycoaching/GPL/v.0.1.0/gpl_query_generation.py", line 316, in <module>
    gpl.train(
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/gpl/train.py", line 127, in train
    qgen(path_to_generated_data, path_to_generated_data, generator_name_or_path=generator, ques_per_passage=queries_per_passage, bsz=batch_size_generation, qgen_prefix=qgen_prefix)
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/gpl/toolkit/qgen.py", line 23, in qgen
    generator.generate(corpus, output_dir=output_dir, ques_per_passage=ques_per_passage, prefix=prefix, batch_size=bsz)
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/beir/generation/generate.py", line 54, in generate
    queries = self.model.generate(
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/beir/generation/models/auto_model.py", line 28, in generate
    outs = self.model.generate(
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/transformers/generation_utils.py", line 1326, in generate
    return self.sample(
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/transformers/generation_utils.py", line 1944, in sample
    outputs = self(
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/transformers/models/t5/modeling_t5.py", line 1639, in forward
    decoder_outputs = self.decoder(
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/transformers/models/t5/modeling_t5.py", line 1035, in forward
    layer_outputs = layer_module(
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/transformers/models/t5/modeling_t5.py", line 692, in forward
    cross_attention_outputs = self.layer[1](
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/transformers/models/t5/modeling_t5.py", line 606, in forward
    attention_output = self.EncDecAttention(
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/matthieu/anaconda3/envs/gpl_0.1.0/lib/python3.8/site-packages/transformers/models/t5/modeling_t5.py", line 509, in forward
    scores = torch.matmul(
RuntimeError: CUDA out of memory. Tried to allocate 584.00 MiB (GPU 0; 23.70 GiB total capacity; 20.69 GiB already allocated; 587.94 MiB free; 20.83 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

My corpus consists of small paragraphs of 3-4 lines and I used use_amp option. How could I deal with it?

mi1ebsco commented 1 year ago

Try lowering the batch size. Setting batch_size_gpl=16 might work but will take longer to run.

The reason for this is the amount of GPU RAM consumed by each batch. 32 examples at once might be too big, but 16 might fit.

ranjanravi7796 commented 1 year ago

How to use multiple GPUs in this training script?

mi1ebsco commented 1 year ago

Cross-encoder and sentence-transformer does not officially support multi-GPU training, though there are some (very old) forks that have been experimenting with this feature: https://github.com/UKPLab/sentence-transformers/pull/1215

IliasAarab commented 1 year ago

I'm getting the same error even when setting batch_size_gpl=1 and batch_size_generation = 1. I don't get the problem when I set gpl_steps=1 however, which seems odd? I'm using passages which average 500 tokens.