初始化项目，由ModelHub XC社区提供模型
Model: flax-sentence-embeddings/all_datasets_v3_MiniLM-L6 Source: Original Platform
This commit is contained in:
ModelHub XC
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1_Pooling/config.json
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1_Pooling/config.json
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README.md
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README.md
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---
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pipeline_tag: sentence-similarity
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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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language: en
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---
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# Model description
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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 ['MiniLM-L6-H384-uncased'](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) 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
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as intervention from Google’s 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 encoder. Given an input sentence, it ouptuts a vector which captures
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the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence
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similarity tasks.
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## How to use
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Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_MiniLM-L6')
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text = "Replace me by any text you'd like."
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text_embbedding = model.encode(text)
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# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
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# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
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# dtype=float32)
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```
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# Training procedure
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## Pre-training
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We use the pretrained ['MiniLM-L6-H384-uncased'](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) which is a 6 layer version of
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['microsoft/MiniLM-L12-H384-uncased'](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) by keeping only every second layer.
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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 540k 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.
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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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| [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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| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
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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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| [COCO 2020](COCO 2020) | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
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| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
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| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
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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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| [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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| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
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| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
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| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
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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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| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
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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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| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
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| [SPECTER](https://github.com/allenai/specter) | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
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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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| [S2ORC](https://github.com/allenai/s2orc) Citation/Citation | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
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| [S2ORC](https://github.com/allenai/s2orc) Citation/Abstract | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
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| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
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| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
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| SearchQA | - | 582,261 |
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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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| [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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| [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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| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
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| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
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| total | | 1,097,953,922 |
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config.json
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{
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"_name_or_path": "nreimers/MiniLM-L6-H384-uncased",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.8.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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config_sentence_transformers.json
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config_sentence_transformers.json
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"__version__": {
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"sentence_transformers": "2.0.0",
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"transformers": "4.6.1",
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"pytorch": "1.8.1"
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}
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}
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data_config.json
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|
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|
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|
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20
modules.json
Executable file
20
modules.json
Executable file
@@ -0,0 +1,20 @@
|
||||
[
|
||||
{
|
||||
"idx": 0,
|
||||
"name": "0",
|
||||
"path": "",
|
||||
"type": "sentence_transformers.models.Transformer"
|
||||
},
|
||||
{
|
||||
"idx": 1,
|
||||
"name": "1",
|
||||
"path": "1_Pooling",
|
||||
"type": "sentence_transformers.models.Pooling"
|
||||
},
|
||||
{
|
||||
"idx": 2,
|
||||
"name": "2",
|
||||
"path": "2_Normalize",
|
||||
"type": "sentence_transformers.models.Normalize"
|
||||
}
|
||||
]
|
||||
3
pytorch_model.bin
Normal file
3
pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1c32ef85f7d91b0c792be77e8ba461b216ed9500f641f51415733b6b90fe4d05
|
||||
size 90888945
|
||||
4
sentence_bert_config.json
Executable file
4
sentence_bert_config.json
Executable file
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"max_seq_length": 128,
|
||||
"do_lower_case": false
|
||||
}
|
||||
1
special_tokens_map.json
Normal file
1
special_tokens_map.json
Normal file
@@ -0,0 +1 @@
|
||||
{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
|
||||
1
tokenizer.json
Normal file
1
tokenizer.json
Normal file
File diff suppressed because one or more lines are too long
1
tokenizer_config.json
Normal file
1
tokenizer_config.json
Normal file
@@ -0,0 +1 @@
|
||||
{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": "/home/ukp-reimers/.cache/huggingface/transformers/1e5909e4dfaa904617797ed35a6105a23daa56cbefca48fef329f772584699fb.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "nreimers/MiniLM-L6-H384-uncased", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}
|
||||
344
train_script.py
Executable file
344
train_script.py
Executable file
@@ -0,0 +1,344 @@
|
||||
"""
|
||||
Train script for a single file
|
||||
|
||||
Need to set the TPU address first:
|
||||
export XRT_TPU_CONFIG="localservice;0;localhost:51011"
|
||||
"""
|
||||
|
||||
import torch.multiprocessing as mp
|
||||
import threading
|
||||
import time
|
||||
import random
|
||||
import sys
|
||||
import argparse
|
||||
import gzip
|
||||
import json
|
||||
import logging
|
||||
import tqdm
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.utils.data import DataLoader
|
||||
import torch
|
||||
import torch_xla
|
||||
import torch_xla.core
|
||||
import torch_xla.core.functions
|
||||
import torch_xla.core.xla_model as xm
|
||||
import torch_xla.distributed.xla_multiprocessing as xmp
|
||||
import torch_xla.distributed.parallel_loader as pl
|
||||
import os
|
||||
from shutil import copyfile
|
||||
|
||||
|
||||
from transformers import (
|
||||
AdamW,
|
||||
AutoModel,
|
||||
AutoTokenizer,
|
||||
get_linear_schedule_with_warmup,
|
||||
set_seed,
|
||||
)
|
||||
|
||||
class AutoModelForSentenceEmbedding(nn.Module):
|
||||
def __init__(self, model_name, tokenizer, normalize=True):
|
||||
super(AutoModelForSentenceEmbedding, self).__init__()
|
||||
|
||||
self.model = AutoModel.from_pretrained(model_name)
|
||||
self.normalize = normalize
|
||||
self.tokenizer = tokenizer
|
||||
|
||||
def forward(self, **kwargs):
|
||||
model_output = self.model(**kwargs)
|
||||
embeddings = self.mean_pooling(model_output, kwargs['attention_mask'])
|
||||
if self.normalize:
|
||||
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
|
||||
|
||||
return embeddings
|
||||
|
||||
def mean_pooling(self, model_output, attention_mask):
|
||||
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
|
||||
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
||||
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
||||
|
||||
def save_pretrained(self, output_path):
|
||||
if xm.is_master_ordinal():
|
||||
self.tokenizer.save_pretrained(output_path)
|
||||
self.model.config.save_pretrained(output_path)
|
||||
|
||||
xm.save(self.model.state_dict(), os.path.join(output_path, "pytorch_model.bin"))
|
||||
|
||||
|
||||
|
||||
|
||||
def train_function(index, args, queue):
|
||||
tokenizer = AutoTokenizer.from_pretrained(args.model)
|
||||
model = AutoModelForSentenceEmbedding(args.model, tokenizer)
|
||||
|
||||
|
||||
### Train Loop
|
||||
device = xm.xla_device()
|
||||
model = model.to(device)
|
||||
|
||||
# Instantiate optimizer
|
||||
optimizer = AdamW(params=model.parameters(), lr=2e-5, correct_bias=True)
|
||||
|
||||
lr_scheduler = get_linear_schedule_with_warmup(
|
||||
optimizer=optimizer,
|
||||
num_warmup_steps=500,
|
||||
num_training_steps=args.steps,
|
||||
)
|
||||
|
||||
# Now we train the model
|
||||
cross_entropy_loss = nn.CrossEntropyLoss()
|
||||
max_grad_norm = 1
|
||||
|
||||
model.train()
|
||||
|
||||
for global_step in tqdm.trange(args.steps, disable=not xm.is_master_ordinal()):
|
||||
#### Get the batch data
|
||||
batch = queue.get()
|
||||
#print(index, "batch {}x{}".format(len(batch), ",".join([str(len(b)) for b in batch])))
|
||||
|
||||
|
||||
if len(batch[0]) == 2: #(anchor, positive)
|
||||
text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
||||
text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
||||
|
||||
### Compute embeddings
|
||||
embeddings_a = model(**text1.to(device))
|
||||
embeddings_b = model(**text2.to(device))
|
||||
|
||||
### Gather all embedings
|
||||
embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
|
||||
embeddings_b = torch_xla.core.functions.all_gather(embeddings_b)
|
||||
|
||||
### Compute similarity scores 512 x 512
|
||||
scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
|
||||
|
||||
### Compute cross-entropy loss
|
||||
labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
|
||||
|
||||
## Symmetric loss as in CLIP
|
||||
loss = (cross_entropy_loss(scores, labels) + cross_entropy_loss(scores.transpose(0, 1), labels)) / 2
|
||||
|
||||
else: #(anchor, positive, negative)
|
||||
text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
||||
text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
||||
text3 = tokenizer([b[2] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
||||
|
||||
embeddings_a = model(**text1.to(device))
|
||||
embeddings_b1 = model(**text2.to(device))
|
||||
embeddings_b2 = model(**text3.to(device))
|
||||
|
||||
embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
|
||||
embeddings_b1 = torch_xla.core.functions.all_gather(embeddings_b1)
|
||||
embeddings_b2 = torch_xla.core.functions.all_gather(embeddings_b2)
|
||||
|
||||
embeddings_b = torch.cat([embeddings_b1, embeddings_b2])
|
||||
|
||||
### Compute similarity scores 512 x 1024
|
||||
scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
|
||||
|
||||
### Compute cross-entropy loss
|
||||
labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
|
||||
|
||||
## One-way loss
|
||||
loss = cross_entropy_loss(scores, labels)
|
||||
|
||||
|
||||
# Backward pass
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
|
||||
|
||||
xm.optimizer_step(optimizer, barrier=True)
|
||||
lr_scheduler.step()
|
||||
|
||||
|
||||
#Save model
|
||||
if (global_step+1) % args.save_steps == 0:
|
||||
output_path = os.path.join(args.output, str(global_step+1))
|
||||
xm.master_print("save model: "+output_path)
|
||||
model.save_pretrained(output_path)
|
||||
|
||||
|
||||
output_path = os.path.join(args.output, "final")
|
||||
xm.master_print("save model final: "+ output_path)
|
||||
model.save_pretrained(output_path)
|
||||
|
||||
|
||||
def produce_data(args, queue, filepaths, dataset_indices):
|
||||
global_batch_size = args.batch_size*args.nprocs #Global batch size
|
||||
size_per_dataset = int(global_batch_size / args.datasets_per_batch) #How many datasets per batch
|
||||
num_same_dataset = int(size_per_dataset / args.batch_size)
|
||||
print("producer", "global_batch_size", global_batch_size)
|
||||
print("producer", "size_per_dataset", size_per_dataset)
|
||||
print("producer", "num_same_dataset", num_same_dataset)
|
||||
|
||||
datasets = []
|
||||
for filepath in filepaths:
|
||||
if "reddit_" in filepath: #Special dataset class for Reddit files
|
||||
data_obj = RedditDataset(filepath)
|
||||
else:
|
||||
data_obj = Dataset(filepath)
|
||||
datasets.append(iter(data_obj))
|
||||
|
||||
# Store if dataset is in a 2 col or 3 col format
|
||||
num_cols = {idx: len(next(dataset)) for idx, dataset in enumerate(datasets)}
|
||||
|
||||
while True:
|
||||
texts_in_batch = set()
|
||||
batch_format = None #2 vs 3 col format for this batch
|
||||
|
||||
#Add data from several sub datasets
|
||||
for _ in range(args.datasets_per_batch):
|
||||
valid_dataset = False #Check that datasets have the same 2/3 col format
|
||||
while not valid_dataset:
|
||||
data_idx = random.choice(dataset_indices)
|
||||
if batch_format is None:
|
||||
batch_format = num_cols[data_idx]
|
||||
valid_dataset = True
|
||||
else: #Check that this dataset has the same format
|
||||
valid_dataset = (batch_format == num_cols[data_idx])
|
||||
|
||||
#Get data from this dataset
|
||||
dataset = datasets[data_idx]
|
||||
for _ in range(num_same_dataset):
|
||||
for _ in range(args.nprocs):
|
||||
batch_device = [] #A batch for one device
|
||||
while len(batch_device) < args.batch_size:
|
||||
sample = next(dataset)
|
||||
in_batch = False
|
||||
for text in sample:
|
||||
if text in texts_in_batch:
|
||||
in_batch = True
|
||||
break
|
||||
|
||||
if not in_batch:
|
||||
for text in sample:
|
||||
texts_in_batch.add(text)
|
||||
batch_device.append(sample)
|
||||
|
||||
queue.put(batch_device)
|
||||
|
||||
|
||||
class RedditDataset:
|
||||
"""
|
||||
A class that handles the reddit data files
|
||||
"""
|
||||
def __init__(self, filepath):
|
||||
self.filepath = filepath
|
||||
|
||||
def __iter__(self):
|
||||
while True:
|
||||
with gzip.open(self.filepath, "rt") as fIn:
|
||||
for line in fIn:
|
||||
data = json.loads(line)
|
||||
|
||||
if "response" in data and "context" in data:
|
||||
yield [data["response"], data["context"]]
|
||||
|
||||
class Dataset:
|
||||
"""
|
||||
A class that handles one dataset
|
||||
"""
|
||||
def __init__(self, filepath):
|
||||
self.filepath = filepath
|
||||
|
||||
def __iter__(self):
|
||||
max_dataset_size = 10*1000*1000 #Cache small datasets in memory
|
||||
dataset = []
|
||||
data_format = None
|
||||
|
||||
while dataset is None or len(dataset) == 0:
|
||||
with gzip.open(self.filepath, "rt") as fIn:
|
||||
for line in fIn:
|
||||
data = json.loads(line)
|
||||
if isinstance(data, dict):
|
||||
data = data['texts']
|
||||
|
||||
if data_format is None:
|
||||
data_format = len(data)
|
||||
|
||||
#Ensure that all entries are of the same 2/3 col format
|
||||
assert len(data) == data_format
|
||||
|
||||
if dataset is not None:
|
||||
dataset.append(data)
|
||||
if len(dataset) >= max_dataset_size:
|
||||
dataset = None
|
||||
|
||||
yield data
|
||||
|
||||
# Data loaded. Now stream to the queue
|
||||
# Shuffle for each epoch
|
||||
while True:
|
||||
random.shuffle(dataset)
|
||||
for data in dataset:
|
||||
yield data
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--model', default='nreimers/MiniLM-L6-H384-uncased')
|
||||
parser.add_argument('--steps', type=int, default=2000)
|
||||
parser.add_argument('--save_steps', type=int, default=10000)
|
||||
parser.add_argument('--batch_size', type=int, default=64)
|
||||
parser.add_argument('--max_length', type=int, default=128)
|
||||
parser.add_argument('--nprocs', type=int, default=8)
|
||||
parser.add_argument('--datasets_per_batch', type=int, default=2, help="Number of datasets per batch")
|
||||
parser.add_argument('--scale', type=float, default=20, help="Use 20 for cossim, and 1 when you work with unnormalized embeddings with dot product")
|
||||
parser.add_argument('--data_folder', default="/data", help="Folder with your dataset files")
|
||||
parser.add_argument('data_config', help="A data_config.json file")
|
||||
parser.add_argument('output')
|
||||
args = parser.parse_args()
|
||||
|
||||
# Ensure global batch size is divisble by data_sample_size
|
||||
assert (args.batch_size*args.nprocs) % args.datasets_per_batch == 0
|
||||
|
||||
logging.info("Output: "+args.output)
|
||||
if os.path.exists(args.output):
|
||||
print("Output folder already exists.")
|
||||
input("Continue?")
|
||||
|
||||
# Write train script to output path
|
||||
os.makedirs(args.output, exist_ok=True)
|
||||
|
||||
data_config_path = os.path.join(args.output, 'data_config.json')
|
||||
copyfile(args.data_config, data_config_path)
|
||||
|
||||
train_script_path = os.path.join(args.output, 'train_script.py')
|
||||
copyfile(__file__, train_script_path)
|
||||
with open(train_script_path, 'a') as fOut:
|
||||
fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))
|
||||
|
||||
|
||||
|
||||
#Load data config
|
||||
with open(args.data_config) as fIn:
|
||||
data_config = json.load(fIn)
|
||||
|
||||
queue = mp.Queue(maxsize=100*args.nprocs)
|
||||
|
||||
filepaths = []
|
||||
dataset_indices = []
|
||||
for idx, data in enumerate(data_config):
|
||||
filepaths.append(os.path.join(os.path.expanduser(args.data_folder), data['name']))
|
||||
dataset_indices.extend([idx]*data['weight'])
|
||||
|
||||
# Start producer
|
||||
p = mp.Process(target=produce_data, args=(args, queue, filepaths, dataset_indices))
|
||||
p.start()
|
||||
|
||||
# Run training
|
||||
print("Start processes:", args.nprocs)
|
||||
xmp.spawn(train_function, args=(args, queue), nprocs=args.nprocs, start_method='fork')
|
||||
print("Training done")
|
||||
print("It might be that not all processes exit automatically. In that case you must manually kill this process.")
|
||||
print("With 'pkill python' you can kill all remaining python processes")
|
||||
p.kill()
|
||||
exit()
|
||||
|
||||
|
||||
|
||||
# Script was called via:
|
||||
#python train_many_data_files_v2.py --steps 1000000 --batch_size 128 --model nreimers/MiniLM-L6-H384-uncased train_data_configs/all_datasets_v3.json output/all_datasets_v3_MiniLM-L6-H384-uncased-batch128
|
||||
Reference in New Issue
Block a user