Upload 14 files
Browse files- 1_Pooling/config.json +7 -0
- README.md +177 -0
- config.json +23 -0
- config_sentence_transformers.json +7 -0
- data_config.json +1452 -0
- model.safetensors +3 -0
- modules.json +20 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- train_script.py +344 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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---
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language: en
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license: apache-2.0
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library_name: sentence-transformers
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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datasets:
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- s2orc
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- flax-sentence-embeddings/stackexchange_xml
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- ms_marco
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- gooaq
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- yahoo_answers_topics
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- code_search_net
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- search_qa
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- eli5
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- snli
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- multi_nli
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- wikihow
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- natural_questions
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- trivia_qa
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- embedding-data/sentence-compression
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- embedding-data/flickr30k-captions
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- embedding-data/altlex
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- embedding-data/simple-wiki
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- embedding-data/QQP
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- embedding-data/SPECTER
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- embedding-data/PAQ_pairs
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- embedding-data/WikiAnswers
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pipeline_tag: sentence-similarity
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---
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# all-mpnet-base-v2
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Usage (HuggingFace Transformers)
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Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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import torch.nn.functional as F
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2')
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model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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# Normalize embeddings
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sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation Results
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-mpnet-base-v2)
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------
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## Background
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The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
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contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a
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1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
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We developped this model during the
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[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
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organized by Hugging Face. We developped this model as part of the project:
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[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
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## Intended uses
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Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
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the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
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By default, input text longer than 384 word pieces is truncated.
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## Training procedure
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### Pre-training
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We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
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### Fine-tuning
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We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
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We then apply the cross entropy loss by comparing with true pairs.
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#### Hyper parameters
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We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
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We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
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a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
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#### Training data
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We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
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We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
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| Dataset | Paper | Number of training tuples |
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|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
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| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
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| 146 |
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| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
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| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
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| 148 |
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| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
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| 149 |
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| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
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| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
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| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
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| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
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| 153 |
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| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
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| 154 |
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| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
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| 155 |
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| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
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| 156 |
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| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
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| 157 |
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| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
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| 158 |
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| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
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| 159 |
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| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
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| 160 |
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| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
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| 161 |
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| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
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| 162 |
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| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
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| 163 |
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| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
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| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
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| 165 |
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| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
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| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
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| 167 |
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| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
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| 168 |
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| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
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| 169 |
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| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
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| 170 |
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| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
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| 171 |
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| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
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| 172 |
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| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
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| 173 |
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| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
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| 174 |
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| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
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| 175 |
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| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
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| 176 |
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| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
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| **Total** | | **1,170,060,424** |
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{
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"_name_or_path": "microsoft/mpnet-base",
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"architectures": [
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"MPNetForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "mpnet",
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"num_attention_heads": 12,
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| 18 |
+
"num_hidden_layers": 12,
|
| 19 |
+
"pad_token_id": 1,
|
| 20 |
+
"relative_attention_num_buckets": 32,
|
| 21 |
+
"transformers_version": "4.8.2",
|
| 22 |
+
"vocab_size": 30527
|
| 23 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,7 @@
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|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "2.0.0",
|
| 4 |
+
"transformers": "4.6.1",
|
| 5 |
+
"pytorch": "1.8.1"
|
| 6 |
+
}
|
| 7 |
+
}
|
data_config.json
ADDED
|
@@ -0,0 +1,1452 @@
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|
| 1 |
+
"""
|
| 2 |
+
Train script for a single file
|
| 3 |
+
|
| 4 |
+
Need to set the TPU address first:
|
| 5 |
+
export XRT_TPU_CONFIG="localservice;0;localhost:51011"
|
| 6 |
+
"""
|
| 7 |
+
|
| 8 |
+
import torch.multiprocessing as mp
|
| 9 |
+
import threading
|
| 10 |
+
import time
|
| 11 |
+
import random
|
| 12 |
+
import sys
|
| 13 |
+
import argparse
|
| 14 |
+
import gzip
|
| 15 |
+
import json
|
| 16 |
+
import logging
|
| 17 |
+
import tqdm
|
| 18 |
+
import torch
|
| 19 |
+
from torch import nn
|
| 20 |
+
from torch.utils.data import DataLoader
|
| 21 |
+
import torch
|
| 22 |
+
import torch_xla
|
| 23 |
+
import torch_xla.core
|
| 24 |
+
import torch_xla.core.functions
|
| 25 |
+
import torch_xla.core.xla_model as xm
|
| 26 |
+
import torch_xla.distributed.xla_multiprocessing as xmp
|
| 27 |
+
import torch_xla.distributed.parallel_loader as pl
|
| 28 |
+
import os
|
| 29 |
+
from shutil import copyfile
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
from transformers import (
|
| 33 |
+
AdamW,
|
| 34 |
+
AutoModel,
|
| 35 |
+
AutoTokenizer,
|
| 36 |
+
get_linear_schedule_with_warmup,
|
| 37 |
+
set_seed,
|
| 38 |
+
)
|
| 39 |
+
|
| 40 |
+
class AutoModelForSentenceEmbedding(nn.Module):
|
| 41 |
+
def __init__(self, model_name, tokenizer, normalize=True):
|
| 42 |
+
super(AutoModelForSentenceEmbedding, self).__init__()
|
| 43 |
+
|
| 44 |
+
self.model = AutoModel.from_pretrained(model_name)
|
| 45 |
+
self.normalize = normalize
|
| 46 |
+
self.tokenizer = tokenizer
|
| 47 |
+
|
| 48 |
+
def forward(self, **kwargs):
|
| 49 |
+
model_output = self.model(**kwargs)
|
| 50 |
+
embeddings = self.mean_pooling(model_output, kwargs['attention_mask'])
|
| 51 |
+
if self.normalize:
|
| 52 |
+
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
|
| 53 |
+
|
| 54 |
+
return embeddings
|
| 55 |
+
|
| 56 |
+
def mean_pooling(self, model_output, attention_mask):
|
| 57 |
+
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
|
| 58 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
| 59 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
| 60 |
+
|
| 61 |
+
def save_pretrained(self, output_path):
|
| 62 |
+
if xm.is_master_ordinal():
|
| 63 |
+
self.tokenizer.save_pretrained(output_path)
|
| 64 |
+
self.model.config.save_pretrained(output_path)
|
| 65 |
+
|
| 66 |
+
xm.save(self.model.state_dict(), os.path.join(output_path, "pytorch_model.bin"))
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
def train_function(index, args, queue):
|
| 72 |
+
tokenizer = AutoTokenizer.from_pretrained(args.model)
|
| 73 |
+
model = AutoModelForSentenceEmbedding(args.model, tokenizer)
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
### Train Loop
|
| 77 |
+
device = xm.xla_device()
|
| 78 |
+
model = model.to(device)
|
| 79 |
+
|
| 80 |
+
# Instantiate optimizer
|
| 81 |
+
optimizer = AdamW(params=model.parameters(), lr=2e-5, correct_bias=True)
|
| 82 |
+
|
| 83 |
+
lr_scheduler = get_linear_schedule_with_warmup(
|
| 84 |
+
optimizer=optimizer,
|
| 85 |
+
num_warmup_steps=500,
|
| 86 |
+
num_training_steps=args.steps,
|
| 87 |
+
)
|
| 88 |
+
|
| 89 |
+
# Now we train the model
|
| 90 |
+
cross_entropy_loss = nn.CrossEntropyLoss()
|
| 91 |
+
max_grad_norm = 1
|
| 92 |
+
|
| 93 |
+
model.train()
|
| 94 |
+
|
| 95 |
+
for global_step in tqdm.trange(args.steps, disable=not xm.is_master_ordinal()):
|
| 96 |
+
#### Get the batch data
|
| 97 |
+
batch = queue.get()
|
| 98 |
+
#print(index, "batch {}x{}".format(len(batch), ",".join([str(len(b)) for b in batch])))
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
if len(batch[0]) == 2: #(anchor, positive)
|
| 102 |
+
text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
| 103 |
+
text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
| 104 |
+
|
| 105 |
+
### Compute embeddings
|
| 106 |
+
embeddings_a = model(**text1.to(device))
|
| 107 |
+
embeddings_b = model(**text2.to(device))
|
| 108 |
+
|
| 109 |
+
### Gather all embedings
|
| 110 |
+
embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
|
| 111 |
+
embeddings_b = torch_xla.core.functions.all_gather(embeddings_b)
|
| 112 |
+
|
| 113 |
+
### Compute similarity scores 512 x 512
|
| 114 |
+
scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
|
| 115 |
+
|
| 116 |
+
### Compute cross-entropy loss
|
| 117 |
+
labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
|
| 118 |
+
|
| 119 |
+
## Symmetric loss as in CLIP
|
| 120 |
+
loss = (cross_entropy_loss(scores, labels) + cross_entropy_loss(scores.transpose(0, 1), labels)) / 2
|
| 121 |
+
|
| 122 |
+
else: #(anchor, positive, negative)
|
| 123 |
+
text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
| 124 |
+
text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
| 125 |
+
text3 = tokenizer([b[2] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
| 126 |
+
|
| 127 |
+
embeddings_a = model(**text1.to(device))
|
| 128 |
+
embeddings_b1 = model(**text2.to(device))
|
| 129 |
+
embeddings_b2 = model(**text3.to(device))
|
| 130 |
+
|
| 131 |
+
embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
|
| 132 |
+
embeddings_b1 = torch_xla.core.functions.all_gather(embeddings_b1)
|
| 133 |
+
embeddings_b2 = torch_xla.core.functions.all_gather(embeddings_b2)
|
| 134 |
+
|
| 135 |
+
embeddings_b = torch.cat([embeddings_b1, embeddings_b2])
|
| 136 |
+
|
| 137 |
+
### Compute similarity scores 512 x 1024
|
| 138 |
+
scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
|
| 139 |
+
|
| 140 |
+
### Compute cross-entropy loss
|
| 141 |
+
labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
|
| 142 |
+
|
| 143 |
+
## One-way loss
|
| 144 |
+
loss = cross_entropy_loss(scores, labels)
|
| 145 |
+
|
| 146 |
+
|
| 147 |
+
# Backward pass
|
| 148 |
+
optimizer.zero_grad()
|
| 149 |
+
loss.backward()
|
| 150 |
+
torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
|
| 151 |
+
|
| 152 |
+
xm.optimizer_step(optimizer, barrier=True)
|
| 153 |
+
lr_scheduler.step()
|
| 154 |
+
|
| 155 |
+
|
| 156 |
+
#Save model
|
| 157 |
+
if (global_step+1) % args.save_steps == 0:
|
| 158 |
+
output_path = os.path.join(args.output, str(global_step+1))
|
| 159 |
+
xm.master_print("save model: "+output_path)
|
| 160 |
+
model.save_pretrained(output_path)
|
| 161 |
+
|
| 162 |
+
|
| 163 |
+
output_path = os.path.join(args.output, "final")
|
| 164 |
+
xm.master_print("save model final: "+ output_path)
|
| 165 |
+
model.save_pretrained(output_path)
|
| 166 |
+
|
| 167 |
+
|
| 168 |
+
def produce_data(args, queue, filepaths, dataset_indices):
|
| 169 |
+
global_batch_size = args.batch_size*args.nprocs #Global batch size
|
| 170 |
+
size_per_dataset = int(global_batch_size / args.datasets_per_batch) #How many datasets per batch
|
| 171 |
+
num_same_dataset = int(size_per_dataset / args.batch_size)
|
| 172 |
+
print("producer", "global_batch_size", global_batch_size)
|
| 173 |
+
print("producer", "size_per_dataset", size_per_dataset)
|
| 174 |
+
print("producer", "num_same_dataset", num_same_dataset)
|
| 175 |
+
|
| 176 |
+
datasets = []
|
| 177 |
+
for filepath in filepaths:
|
| 178 |
+
if "reddit_" in filepath: #Special dataset class for Reddit files
|
| 179 |
+
data_obj = RedditDataset(filepath)
|
| 180 |
+
else:
|
| 181 |
+
data_obj = Dataset(filepath)
|
| 182 |
+
datasets.append(iter(data_obj))
|
| 183 |
+
|
| 184 |
+
# Store if dataset is in a 2 col or 3 col format
|
| 185 |
+
num_cols = {idx: len(next(dataset)) for idx, dataset in enumerate(datasets)}
|
| 186 |
+
|
| 187 |
+
while True:
|
| 188 |
+
texts_in_batch = set()
|
| 189 |
+
batch_format = None #2 vs 3 col format for this batch
|
| 190 |
+
|
| 191 |
+
#Add data from several sub datasets
|
| 192 |
+
for _ in range(args.datasets_per_batch):
|
| 193 |
+
valid_dataset = False #Check that datasets have the same 2/3 col format
|
| 194 |
+
while not valid_dataset:
|
| 195 |
+
data_idx = random.choice(dataset_indices)
|
| 196 |
+
if batch_format is None:
|
| 197 |
+
batch_format = num_cols[data_idx]
|
| 198 |
+
valid_dataset = True
|
| 199 |
+
else: #Check that this dataset has the same format
|
| 200 |
+
valid_dataset = (batch_format == num_cols[data_idx])
|
| 201 |
+
|
| 202 |
+
#Get data from this dataset
|
| 203 |
+
dataset = datasets[data_idx]
|
| 204 |
+
for _ in range(num_same_dataset):
|
| 205 |
+
for _ in range(args.nprocs):
|
| 206 |
+
batch_device = [] #A batch for one device
|
| 207 |
+
while len(batch_device) < args.batch_size:
|
| 208 |
+
sample = next(dataset)
|
| 209 |
+
in_batch = False
|
| 210 |
+
for text in sample:
|
| 211 |
+
if text in texts_in_batch:
|
| 212 |
+
in_batch = True
|
| 213 |
+
break
|
| 214 |
+
|
| 215 |
+
if not in_batch:
|
| 216 |
+
for text in sample:
|
| 217 |
+
texts_in_batch.add(text)
|
| 218 |
+
batch_device.append(sample)
|
| 219 |
+
|
| 220 |
+
queue.put(batch_device)
|
| 221 |
+
|
| 222 |
+
|
| 223 |
+
class RedditDataset:
|
| 224 |
+
"""
|
| 225 |
+
A class that handles the reddit data files
|
| 226 |
+
"""
|
| 227 |
+
def __init__(self, filepath):
|
| 228 |
+
self.filepath = filepath
|
| 229 |
+
|
| 230 |
+
def __iter__(self):
|
| 231 |
+
while True:
|
| 232 |
+
with gzip.open(self.filepath, "rt") as fIn:
|
| 233 |
+
for line in fIn:
|
| 234 |
+
data = json.loads(line)
|
| 235 |
+
|
| 236 |
+
if "response" in data and "context" in data:
|
| 237 |
+
yield [data["response"], data["context"]]
|
| 238 |
+
|
| 239 |
+
class Dataset:
|
| 240 |
+
"""
|
| 241 |
+
A class that handles one dataset
|
| 242 |
+
"""
|
| 243 |
+
def __init__(self, filepath):
|
| 244 |
+
self.filepath = filepath
|
| 245 |
+
|
| 246 |
+
def __iter__(self):
|
| 247 |
+
max_dataset_size = 10*1000*1000 #Cache small datasets in memory
|
| 248 |
+
dataset = []
|
| 249 |
+
data_format = None
|
| 250 |
+
|
| 251 |
+
while dataset is None or len(dataset) == 0:
|
| 252 |
+
with gzip.open(self.filepath, "rt") as fIn:
|
| 253 |
+
for line in fIn:
|
| 254 |
+
data = json.loads(line)
|
| 255 |
+
if isinstance(data, dict):
|
| 256 |
+
data = data['texts']
|
| 257 |
+
|
| 258 |
+
if data_format is None:
|
| 259 |
+
data_format = len(data)
|
| 260 |
+
|
| 261 |
+
#Ensure that all entries are of the same 2/3 col format
|
| 262 |
+
assert len(data) == data_format
|
| 263 |
+
|
| 264 |
+
if dataset is not None:
|
| 265 |
+
dataset.append(data)
|
| 266 |
+
if len(dataset) >= max_dataset_size:
|
| 267 |
+
dataset = None
|
| 268 |
+
|
| 269 |
+
yield data
|
| 270 |
+
|
| 271 |
+
# Data loaded. Now stream to the queue
|
| 272 |
+
# Shuffle for each epoch
|
| 273 |
+
while True:
|
| 274 |
+
random.shuffle(dataset)
|
| 275 |
+
for data in dataset:
|
| 276 |
+
yield data
|
| 277 |
+
|
| 278 |
+
|
| 279 |
+
|
| 280 |
+
if __name__ == "__main__":
|
| 281 |
+
parser = argparse.ArgumentParser()
|
| 282 |
+
parser.add_argument('--model', default='nreimers/MiniLM-L6-H384-uncased')
|
| 283 |
+
parser.add_argument('--steps', type=int, default=2000)
|
| 284 |
+
parser.add_argument('--save_steps', type=int, default=10000)
|
| 285 |
+
parser.add_argument('--batch_size', type=int, default=64)
|
| 286 |
+
parser.add_argument('--max_length', type=int, default=128)
|
| 287 |
+
parser.add_argument('--nprocs', type=int, default=8)
|
| 288 |
+
parser.add_argument('--datasets_per_batch', type=int, default=2, help="Number of datasets per batch")
|
| 289 |
+
parser.add_argument('--scale', type=float, default=20, help="Use 20 for cossim, and 1 when you work with unnormalized embeddings with dot product")
|
| 290 |
+
parser.add_argument('--data_folder', default="/data", help="Folder with your dataset files")
|
| 291 |
+
parser.add_argument('data_config', help="A data_config.json file")
|
| 292 |
+
parser.add_argument('output')
|
| 293 |
+
args = parser.parse_args()
|
| 294 |
+
|
| 295 |
+
# Ensure global batch size is divisble by data_sample_size
|
| 296 |
+
assert (args.batch_size*args.nprocs) % args.datasets_per_batch == 0
|
| 297 |
+
|
| 298 |
+
logging.info("Output: "+args.output)
|
| 299 |
+
if os.path.exists(args.output):
|
| 300 |
+
print("Output folder already exists.")
|
| 301 |
+
input("Continue?")
|
| 302 |
+
|
| 303 |
+
# Write train script to output path
|
| 304 |
+
os.makedirs(args.output, exist_ok=True)
|
| 305 |
+
|
| 306 |
+
data_config_path = os.path.join(args.output, 'data_config.json')
|
| 307 |
+
copyfile(args.data_config, data_config_path)
|
| 308 |
+
|
| 309 |
+
train_script_path = os.path.join(args.output, 'train_script.py')
|
| 310 |
+
copyfile(__file__, train_script_path)
|
| 311 |
+
with open(train_script_path, 'a') as fOut:
|
| 312 |
+
fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))
|
| 313 |
+
|
| 314 |
+
|
| 315 |
+
|
| 316 |
+
#Load data config
|
| 317 |
+
with open(args.data_config) as fIn:
|
| 318 |
+
data_config = json.load(fIn)
|
| 319 |
+
|
| 320 |
+
queue = mp.Queue(maxsize=100*args.nprocs)
|
| 321 |
+
|
| 322 |
+
filepaths = []
|
| 323 |
+
dataset_indices = []
|
| 324 |
+
for idx, data in enumerate(data_config):
|
| 325 |
+
filepaths.append(os.path.join(os.path.expanduser(args.data_folder), data['name']))
|
| 326 |
+
dataset_indices.extend([idx]*data['weight'])
|
| 327 |
+
|
| 328 |
+
# Start producer
|
| 329 |
+
p = mp.Process(target=produce_data, args=(args, queue, filepaths, dataset_indices))
|
| 330 |
+
p.start()
|
| 331 |
+
|
| 332 |
+
# Run training
|
| 333 |
+
print("Start processes:", args.nprocs)
|
| 334 |
+
xmp.spawn(train_function, args=(args, queue), nprocs=args.nprocs, start_method='fork')
|
| 335 |
+
print("Training done")
|
| 336 |
+
print("It might be that not all processes exit automatically. In that case you must manually kill this process.")
|
| 337 |
+
print("With 'pkill python' you can kill all remaining python processes")
|
| 338 |
+
p.kill()
|
| 339 |
+
exit()
|
| 340 |
+
|
| 341 |
+
|
| 342 |
+
|
| 343 |
+
# Script was called via:
|
| 344 |
+
#python train_many_data_files_v2.py --steps 1000000 --batch_size 64 --model microsoft/mpnet-base train_data_configs/all_datasets_v4.json output/all_datasets_v4_mpnet-base
|
vocab.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|