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lodestone-base-4096-v1

Sentence Similarity
sentence-transformers
PyTorch
English
bert
feature-extraction
mteb
custom_code
Eval Results
text-embeddings-inference
6 papers
Model card Files Community
4
dylanAtHum commited on
Commit
64ae4c7
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1 Parent(s): 55fdd33

Initial Commit

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  1. 1_Pooling/config.json +4 -0
  2. Data_Records.ipynb +92 -0
  3. Dataloading.ipynb +675 -0
  4. README.md +1937 -0
  5. Replication.txt +40 -0
  6. Training.py +465 -0
  7. bert_layers.py +1072 -0
  8. bert_padding.py +159 -0
  9. config.json +33 -0
  10. config_sentence_transformers.json +7 -0
  11. configuration_bert.py +25 -0
  12. data_records.json +1 -0
  13. flash_attn_triton.py +1112 -0
  14. modules.json +20 -0
  15. mteb_results/AmazonCounterfactualClassification.json +29 -0
  16. mteb_results/AmazonPolarityClassification.json +15 -0
  17. mteb_results/AmazonReviewsClassification.json +25 -0
  18. mteb_results/ArguAna.json +38 -0
  19. mteb_results/ArxivClusteringP2P.json +10 -0
  20. mteb_results/ArxivClusteringS2S.json +10 -0
  21. mteb_results/AskUbuntuDupQuestions.json +10 -0
  22. mteb_results/BIOSSES.json +20 -0
  23. mteb_results/Banking77Classification.json +13 -0
  24. mteb_results/BiorxivClusteringP2P.json +10 -0
  25. mteb_results/BiorxivClusteringS2S.json +10 -0
  26. mteb_results/CQADupstackEnglishRetrieval.json +38 -0
  27. mteb_results/ClimateFEVER.json +38 -0
  28. mteb_results/DBPedia.json +38 -0
  29. mteb_results/EmotionClassification.json +21 -0
  30. mteb_results/FEVER.json +38 -0
  31. mteb_results/FiQA2018.json +38 -0
  32. mteb_results/HotpotQA.json +38 -0
  33. mteb_results/ImdbClassification.json +15 -0
  34. mteb_results/MSMARCO.json +38 -0
  35. mteb_results/MTOPDomainClassification.json +25 -0
  36. mteb_results/MTOPIntentClassification.json +25 -0
  37. mteb_results/MassiveIntentClassification.json +25 -0
  38. mteb_results/MassiveScenarioClassification.json +25 -0
  39. mteb_results/MedrxivClusteringP2P.json +10 -0
  40. mteb_results/MedrxivClusteringS2S.json +10 -0
  41. mteb_results/MindSmallReranking.json +10 -0
  42. mteb_results/NFCorpus.json +38 -0
  43. mteb_results/NQ.json +38 -0
  44. mteb_results/QuoraRetrieval.json +38 -0
  45. mteb_results/RedditClustering.json +10 -0
  46. mteb_results/RedditClusteringP2P.json +10 -0
  47. mteb_results/SCIDOCS.json +38 -0
  48. mteb_results/SICK-R.json +20 -0
  49. mteb_results/STS12.json +20 -0
  50. mteb_results/STS13.json +20 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_mean_tokens": true
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+ }
Data_Records.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "id": "e66bbb77-71f5-4d80-b766-f67144ea7a93",
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+ "metadata": {},
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+ "source": [
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+ "# Data Records\n",
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+ "\n",
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+ "## This notebook generates the data_records.json file where each entry in the resulting dictionary follows the form {filename: num_records} for every dataset we will use during training"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 39,
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+ "id": "74ad6613-44ff-435e-8550-df993e915677",
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+ "metadata": {
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+ "tags": []
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+ },
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+ "outputs": [],
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+ "source": [
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+ "# import relevant libraries\n",
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+ "import os\n",
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+ "import boto3\n",
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+ "import json\n",
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+ "from smart_open import open"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "e2d53761-da0e-44f4-8a3e-1285bf810b03",
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+ "metadata": {
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+ "tags": []
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+ },
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+ "outputs": [],
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+ "source": [
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+ "s3 = boto3.resource('s3')\n",
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+ "my_bucket = s3.Bucket('lodestone-rnd')\n",
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+ "\n",
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+ "# collect all filenames from the data/ directory of the lodestone-rnd S3 bucket\n",
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+ "files = [\"\"]*((621+12+9+36)+1)\n",
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+ "for i, object_summary in enumerate(my_bucket.objects.filter(Prefix=\"data/\")):\n",
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+ " files[i] = object_summary.key[5:]\n",
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+ "files = files[1:]\n",
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+ "files = [file for file in files if file != 'cnn_dailymail_splitted.json.gz']\n",
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+ "\n",
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+ "s3_client = boto3.client(\"s3\")\n",
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+ "\n",
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+ "# for each training dataset, store the number of records in a dictionary with the following form {filename: num_records}\n",
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+ "data_lengths = {}\n",
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+ "for file in files:\n",
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+ " source_uri = f's3://lodestone-rnd/data/{file}'\n",
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+ " # S2ORC_citations_abstracts.json.gz and amazon-qa.json.gz must be handled differently since each line in their training\n",
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+ " # data is split into multiple records due to the fact that each query has multiple positive pair responses\n",
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+ " if file in ['S2ORC_citations_abstracts.json.gz','amazon-qa.json.gz']:\n",
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+ " length = 0\n",
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+ " for json_line in open(source_uri, transport_params={\"client\": s3_client}):\n",
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+ " data = json.loads(json_line.strip())\n",
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+ " length += len(data['pos'])\n",
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+ " else:\n",
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+ " length = int(os.popen(f'aws s3 cp {source_uri} - | zcat | wc -l').read().rstrip())\n",
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+ " data_lengths[f'{file}'] = length\n",
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+ " \n",
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+ "# write the resulting dictionary to a .json file for future use during training\n",
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+ "with open('data_records.json', 'w') as fileout:\n",
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+ " json.dump(data_lengths, fileout)"
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+ ]
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "conda_pytorch_p310",
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+ "language": "python",
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+ "name": "conda_pytorch_p310"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.10.10"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 5
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+ }
Dataloading.ipynb ADDED
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+ "cell_type": "markdown",
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+ "id": "3f9ce240-fd1a-4550-83c7-8cf9658b1d3a",
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+ "metadata": {},
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+ "source": [
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+ "# Dataloading (1B+ Training Pairs)\n",
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+ "\n",
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+ "## This notebook collects and uploads all 50 relevant sentence embedding datasets to S3 as .json.gz files where each line contains one training record"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 62,
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+ "id": "d7af8d0e-b3ed-4007-abdd-5952d775e119",
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+ "metadata": {
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+ "tags": []
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+ },
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+ "outputs": [],
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+ "source": [
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+ "import os\n",
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+ "import pandas as pd\n",
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+ "\n",
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+ "os.chdir('/home/ec2-user/SageMaker')"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 63,
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+ "id": "686699fb-d2c9-4653-afca-580aef343451",
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+ "metadata": {
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+ "collapsed": true,
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+ "jupyter": {
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+ "outputs_hidden": true
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+ "Loaded plugins: dkms-build-requires, extras_suggestions, langpacks, priorities,\n",
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+ "21 metadata files removed\n",
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+ "--> Running transaction check\n",
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+ "--> Finished Dependency Resolution\n",
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+ "Dependencies Resolved\n",
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+ " Package Arch Version Repository Size\n",
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+ "Trying other mirror.\n",
261
+ "http://mirror.es.its.nyu.edu/epel/7/x86_64/repodata/repomd.xml: [Errno 12] Timeout on http://mirror.es.its.nyu.edu/epel/7/x86_64/repodata/repomd.xml: (28, 'Failed to connect to mirror.es.its.nyu.edu port 80 after 5001 ms: Timeout was reached')\n",
262
+ "Trying other mirror.\n"
263
+ ]
264
+ },
265
+ {
266
+ "name": "stdout",
267
+ "output_type": "stream",
268
+ "text": [
269
+ "286 packages excluded due to repository priority protections\n",
270
+ "Resolving Dependencies\n",
271
+ "--> Running transaction check\n",
272
+ "---> Package git-lfs.x86_64 0:2.10.0-2.el7 will be installed\n",
273
+ "--> Finished Dependency Resolution\n",
274
+ "\n",
275
+ "Dependencies Resolved\n",
276
+ "\n",
277
+ "================================================================================\n",
278
+ " Package Arch Version Repository Size\n",
279
+ "================================================================================\n",
280
+ "Installing:\n",
281
+ " git-lfs x86_64 2.10.0-2.el7 epel 3.7 M\n",
282
+ "\n",
283
+ "Transaction Summary\n",
284
+ "================================================================================\n",
285
+ "Install 1 Package\n",
286
+ "\n",
287
+ "Total download size: 3.7 M\n",
288
+ "Installed size: 13 M\n",
289
+ "Downloading packages:\n"
290
+ ]
291
+ },
292
+ {
293
+ "name": "stderr",
294
+ "output_type": "stream",
295
+ "text": [
296
+ "warning: /var/cache/yum/x86_64/2/epel/packages/git-lfs-2.10.0-2.el7.x86_64.rpm: Header V4 RSA/SHA256 Signature, key ID 352c64e5: NOKEY\n",
297
+ "Importing GPG key 0x352C64E5:\n",
298
+ " Userid : \"Fedora EPEL (7) <epel@fedoraproject.org>\"\n",
299
+ " Fingerprint: 91e9 7d7c 4a5e 96f1 7f3e 888f 6a2f aea2 352c 64e5\n",
300
+ " Package : epel-release-7-11.noarch (@amzn2extra-epel)\n",
301
+ " From : /etc/pki/rpm-gpg/RPM-GPG-KEY-EPEL-7\n"
302
+ ]
303
+ },
304
+ {
305
+ "name": "stdout",
306
+ "output_type": "stream",
307
+ "text": [
308
+ "Public key for git-lfs-2.10.0-2.el7.x86_64.rpm is not installed\n",
309
+ "Retrieving key from file:///etc/pki/rpm-gpg/RPM-GPG-KEY-EPEL-7\n",
310
+ "Running transaction check\n",
311
+ "Running transaction test\n",
312
+ "Transaction test succeeded\n",
313
+ "Running transaction\n",
314
+ " Installing : git-lfs-2.10.0-2.el7.x86_64 1/1 \n",
315
+ " Verifying : git-lfs-2.10.0-2.el7.x86_64 1/1 \n",
316
+ "\n",
317
+ "Installed:\n",
318
+ " git-lfs.x86_64 0:2.10.0-2.el7 \n",
319
+ "\n",
320
+ "Complete!\n",
321
+ "Git LFS initialized.\n"
322
+ ]
323
+ },
324
+ {
325
+ "data": {
326
+ "text/plain": [
327
+ "0"
328
+ ]
329
+ },
330
+ "execution_count": 63,
331
+ "metadata": {},
332
+ "output_type": "execute_result"
333
+ }
334
+ ],
335
+ "source": [
336
+ "# install git-lfs\n",
337
+ "os.system('sudo amazon-linux-extras install epel -y')\n",
338
+ "os.system('sudo yum-config-manager --enable epel')\n",
339
+ "os.system('sudo yum install git-lfs -y')\n",
340
+ "os.system('git lfs install')"
341
+ ]
342
+ },
343
+ {
344
+ "cell_type": "code",
345
+ "execution_count": 3,
346
+ "id": "6595a6c5-9fae-4bd4-b096-53475ec98294",
347
+ "metadata": {
348
+ "tags": []
349
+ },
350
+ "outputs": [
351
+ {
352
+ "name": "stderr",
353
+ "output_type": "stream",
354
+ "text": [
355
+ "Cloning into 'stackexchange_title_body_jsonl'...\n",
356
+ "Cloning into 'stackexchange_titlebody_best_voted_answer_jsonl'...\n",
357
+ "Cloning into 'stackexchange_title_best_voted_answer_jsonl'...\n",
358
+ "Cloning into 'stackexchange_titlebody_best_and_down_voted_answer_jsonl'...\n",
359
+ "Cloning into 'reddit-title-body'...\n",
360
+ "Cloning into '1B_sentence_embeddings'...\n"
361
+ ]
362
+ },
363
+ {
364
+ "data": {
365
+ "text/plain": [
366
+ "0"
367
+ ]
368
+ },
369
+ "execution_count": 3,
370
+ "metadata": {},
371
+ "output_type": "execute_result"
372
+ }
373
+ ],
374
+ "source": [
375
+ "# clone relevant datasets' github repositories\n",
376
+ "stacks = ['stackexchange_title_body_jsonl', #25.3M \n",
377
+ " 'stackexchange_titlebody_best_voted_answer_jsonl', #4.75M\n",
378
+ " 'stackexchange_title_best_voted_answer_jsonl', #4.75M\n",
379
+ " 'stackexchange_titlebody_best_and_down_voted_answer_jsonl'] #210K\n",
380
+ "\n",
381
+ "os.environ['GIT_LFS_SKIP_SMUDGE'] = \"1\"\n",
382
+ "\n",
383
+ "# clone stackexchange repos\n",
384
+ "for stack in stacks:\n",
385
+ " os.system(f'git clone https://huggingface.co/datasets/flax-sentence-embeddings/{stack}')\n",
386
+ "# clone reddit repo\n",
387
+ "os.system('git clone https://huggingface.co/datasets/sentence-transformers/reddit-title-body')\n",
388
+ "# clone 1B+ sentence embeddings repo (this one is just for reference)\n",
389
+ "os.system('git clone https://github.com/AntoineSimoulin/1B_sentence_embeddings')"
390
+ ]
391
+ },
392
+ {
393
+ "cell_type": "code",
394
+ "execution_count": null,
395
+ "id": "56438029-0f26-491c-b405-f92fb9caeff7",
396
+ "metadata": {
397
+ "collapsed": true,
398
+ "jupyter": {
399
+ "outputs_hidden": true
400
+ },
401
+ "tags": []
402
+ },
403
+ "outputs": [
404
+ {
405
+ "name": "stdout",
406
+ "output_type": "stream",
407
+ "text": [
408
+ "Downloading 4 StackExchange GitHub datasets into s3://lodestone-rnd/data/\n",
409
+ "upload: ./networkengineering.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/networkengineering.stackexchange.com.json.gz\n",
410
+ "upload: ./emacs.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/emacs.stackexchange.com.json.gz\n",
411
+ "upload: ./christianity.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/christianity.stackexchange.com.json.gz\n",
412
+ "upload: ./bitcoin.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/bitcoin.stackexchange.com.json.gz\n",
413
+ "upload: ./academia.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/academia.stackexchange.com.json.gz\n",
414
+ "upload: ./music.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/music.stackexchange.com.json.gz\n",
415
+ "upload: ./biology.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/biology.stackexchange.com.json.gz\n",
416
+ "upload: ./history.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/history.stackexchange.com.json.gz\n",
417
+ "upload: ./skeptics.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/skeptics.stackexchange.com.json.gz\n",
418
+ "upload: ./anime.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/anime.stackexchange.com.json.gz\n",
419
+ "upload: ./quant.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/quant.stackexchange.com.json.gz\n",
420
+ "upload: ./boardgames.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/boardgames.stackexchange.com.json.gz\n",
421
+ "upload: ./judaism.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/judaism.stackexchange.com.json.gz\n",
422
+ "upload: ./travel.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/travel.stackexchange.com.json.gz\n",
423
+ "upload: ./gaming.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/gaming.stackexchange.com.json.gz\n",
424
+ "upload: ./webapps.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/webapps.stackexchange.com.json.gz\n",
425
+ "upload: ./stats.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/stats.stackexchange.com.json.gz\n",
426
+ "upload: ./law.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/law.stackexchange.com.json.gz\n",
427
+ "upload: ./scifi.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/scifi.stackexchange.com.json.gz\n",
428
+ "upload: ./bicycles.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/bicycles.stackexchange.com.json.gz\n",
429
+ "upload: ./datascience.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/datascience.stackexchange.com.json.gz\n",
430
+ "upload: ./softwareengineering.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/softwareengineering.stackexchange.com.json.gz\n",
431
+ "upload: ./islam.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/islam.stackexchange.com.json.gz\n",
432
+ "upload: ./craftcms.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/craftcms.stackexchange.com.json.gz\n",
433
+ "upload: ./diy.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/diy.stackexchange.com.json.gz\n",
434
+ "upload: ./arduino.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/arduino.stackexchange.com.json.gz\n",
435
+ "upload: ./raspberrypi.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/raspberrypi.stackexchange.com.json.gz\n",
436
+ "upload: ./wordpress.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/wordpress.stackexchange.com.json.gz\n",
437
+ "upload: ./dba.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/dba.stackexchange.com.json.gz\n",
438
+ "upload: ./apple.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/apple.stackexchange.com.json.gz\n",
439
+ "upload: ./hinduism.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/hinduism.stackexchange.com.json.gz\n",
440
+ "upload: ./mechanics.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/mechanics.stackexchange.com.json.gz\n",
441
+ "upload: ./gamedev.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/gamedev.stackexchange.com.json.gz\n",
442
+ "upload: ./writers.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/writers.stackexchange.com.json.gz\n",
443
+ "upload: ./mathematica.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/mathematica.stackexchange.com.json.gz\n",
444
+ "upload: ./unix.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/unix.stackexchange.com.json.gz\n",
445
+ "upload: ./magento.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/magento.stackexchange.com.json.gz\n",
446
+ "upload: ./ethereum.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/ethereum.stackexchange.com.json.gz\n",
447
+ "upload: ./electronics.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/electronics.stackexchange.com.json.gz\n",
448
+ "upload: ./cs.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/cs.stackexchange.com.json.gz\n",
449
+ "upload: ./blender.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/blender.stackexchange.com.json.gz\n",
450
+ "upload: ./drupal.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/drupal.stackexchange.com.json.gz\n",
451
+ "upload: ./small_stackexchanges.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/small_stackexchanges.json.gz\n",
452
+ "upload: ./photo.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/photo.stackexchange.com.json.gz\n",
453
+ "upload: ./engineering.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/engineering.stackexchange.com.json.gz\n",
454
+ "upload: ./ux.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/ux.stackexchange.com.json.gz\n",
455
+ "upload: ./german.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/german.stackexchange.com.json.gz\n",
456
+ "upload: ./japanese.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/japanese.stackexchange.com.json.gz\n",
457
+ "upload: ./civicrm.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/civicrm.stackexchange.com.json.gz\n",
458
+ "upload: ./sharepoint.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/sharepoint.stackexchange.com.json.gz\n",
459
+ "upload: ./mathoverflow.net.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/mathoverflow.net.json.gz\n",
460
+ "upload: ./meta.stackoverflow.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/meta.stackoverflow.com.json.gz\n",
461
+ "upload: ./rpg.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/rpg.stackexchange.com.json.gz\n",
462
+ "upload: ./crypto.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/crypto.stackexchange.com.json.gz\n",
463
+ "upload: ./vi.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/vi.stackexchange.com.json.gz\n",
464
+ "upload: ./graphicdesign.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/graphicdesign.stackexchange.com.json.gz\n",
465
+ "upload: ./cooking.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/cooking.stackexchange.com.json.gz\n",
466
+ "upload: ./math.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/math.stackexchange.com.json.gz\n",
467
+ "upload: ./expressionengine.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/expressionengine.stackexchange.com.json.gz\n",
468
+ "upload: ./movies.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/movies.stackexchange.com.json.gz\n",
469
+ "upload: ./salesforce.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/salesforce.stackexchange.com.json.gz\n",
470
+ "upload: ./physics.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/physics.stackexchange.com.json.gz\n",
471
+ "upload: ./aviation.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/aviation.stackexchange.com.json.gz\n",
472
+ "upload: ./gardening.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/gardening.stackexchange.com.json.gz\n",
473
+ "upload: ./english.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/english.stackexchange.com.json.gz\n",
474
+ "upload: ./askubuntu.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/askubuntu.com.json.gz\n",
475
+ "upload: ./french.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/french.stackexchange.com.json.gz\n",
476
+ "upload: ./codereview.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/codereview.stackexchange.com.json.gz\n",
477
+ "upload: ./softwarerecs.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/softwarerecs.stackexchange.com.json.gz\n",
478
+ "upload: ./rus.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/rus.stackexchange.com.json.gz\n",
479
+ "upload: ./money.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/money.stackexchange.com.json.gz\n",
480
+ "upload: ./philosophy.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/philosophy.stackexchange.com.json.gz\n",
481
+ "upload: ./chemistry.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/chemistry.stackexchange.com.json.gz\n",
482
+ "upload: ./meta.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/meta.stackexchange.com.json.gz\n",
483
+ "upload: ./cstheory.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/cstheory.stackexchange.com.json.gz\n",
484
+ "upload: ./space.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/space.stackexchange.com.json.gz\n",
485
+ "upload: ./politics.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/politics.stackexchange.com.json.gz\n",
486
+ "upload: ./ell.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/ell.stackexchange.com.json.gz\n",
487
+ "upload: ./puzzling.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/puzzling.stackexchange.com.json.gz\n",
488
+ "upload: ./astronomy.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/astronomy.stackexchange.com.json.gz\n",
489
+ "upload: ./worldbuilding.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/worldbuilding.stackexchange.com.json.gz\n",
490
+ "upload: ./economics.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/economics.stackexchange.com.json.gz\n",
491
+ "upload: ./workplace.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/workplace.stackexchange.com.json.gz\n",
492
+ "upload: ./tex.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/tex.stackexchange.com.json.gz\n",
493
+ "upload: ./android.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/android.stackexchange.com.json.gz\n",
494
+ "upload: ./gis.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/gis.stackexchange.com.json.gz\n",
495
+ "upload: ./dsp.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/dsp.stackexchange.com.json.gz\n",
496
+ "upload: ./superuser.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_body_jsonl/superuser.com.json.gz\n",
497
+ "upload: ./english.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/english.stackexchange.com.json.gz\n",
498
+ "upload: ./meta.serverfault.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/meta.serverfault.com.json.gz\n",
499
+ "upload: ./scicomp.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/scicomp.stackexchange.com.json.gz\n",
500
+ "upload: ./askubuntu.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/askubuntu.com.json.gz\n",
501
+ "upload: ./french.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/french.stackexchange.com.json.gz\n",
502
+ "upload: ./coffee.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/coffee.stackexchange.com.json.gz\n",
503
+ "upload: ./codereview.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/codereview.stackexchange.com.json.gz\n",
504
+ "upload: ./sound.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/sound.stackexchange.com.json.gz\n",
505
+ "upload: ./opensource.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/opensource.stackexchange.com.json.gz\n",
506
+ "upload: ./woodworking.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/woodworking.stackexchange.com.json.gz\n",
507
+ "upload: ./outdoors.stackexchange.com.jsonl.gz to s3://lodestone-rnd/data/stackexchange_title_best_voted_answer_jsonl/outdoors.stackexchange.com.json.gz\n",
508
+ "upload: ./reddit_title_text_2018.jsonl.gz to s3://lodestone-rnd/data/reddit-title-body/reddit_title_text_2018.json.gz\n",
509
+ "upload: ./reddit_title_text_2011.jsonl.gz to s3://lodestone-rnd/data/reddit-title-body/reddit_title_text_2011.json.gz\n",
510
+ "upload: ./reddit_title_text_2020.jsonl.gz to s3://lodestone-rnd/data/reddit-title-body/reddit_title_text_2020.json.gz\n",
511
+ "upload: ./reddit_title_text_2012.jsonl.gz to s3://lodestone-rnd/data/reddit-title-body/reddit_title_text_2012.json.gz\n",
512
+ "upload: ./reddit_title_text_2021.jsonl.gz to s3://lodestone-rnd/data/reddit-title-body/reddit_title_text_2021.json.gz\n",
513
+ "upload: ./reddit_title_text_2019.jsonl.gz to s3://lodestone-rnd/data/reddit-title-body/reddit_title_text_2019.json.gz\n",
514
+ "upload: ./reddit_title_text_2010.jsonl.gz to s3://lodestone-rnd/data/reddit-title-body/reddit_title_text_2010.json.gz\n",
515
+ "upload: ./reddit_title_text_2014.jsonl.gz to s3://lodestone-rnd/data/reddit-title-body/reddit_title_text_2014.json.gz\n",
516
+ "\u001b[32mDone\u001b[0m\n",
517
+ "Total files uploaded: 12\n",
518
+ "\n",
519
+ "\n",
520
+ "Downloading 9 HuggingFace datasets into s3://lodestone-rnd/data/\n",
521
+ "Downloading dataset S2ORC_citations_abstracts (39,567,485 pairs) ... "
522
+ ]
523
+ }
524
+ ],
525
+ "source": [
526
+ "# DOWNLOAD GITHUB DATASETS (STACKEXCHANGE (https://huggingface.co/flax-sentence-embeddings) & REDDIT (https://huggingface.co/datasets/sentence-transformers/reddit-title-body))\n",
527
+ "\n",
528
+ "# these are the files marked as unsafe by HuggingFace when viewing the each of the datasets' pages\n",
529
+ "unsafe = [[\"serverfault.com.jsonl.gz\", \"security.stackexchange.com.jsonl.gz\"],\n",
530
+ " [\"monero.stackexchange.com.jsonl.gz\", \"serverfault.com.jsonl.gz\", \"security.stackexchange.com.jsonl.gz\"],\n",
531
+ " [\"elementaryos.stackexchange.com.jsonl.gz\", \"monero.stackexchange.com.jsonl.gz\", \"security.stackexchange.com.jsonl.gz\"],\n",
532
+ " [\"\"]]\n",
533
+ "\n",
534
+ "file_counts = []\n",
535
+ "print('Downloading {:,} StackExchange GitHub datasets into s3://lodestone-rnd/data/'.format(len(stacks)))\n",
536
+ "for i, stack in enumerate(stacks):\n",
537
+ " # get the names of all the files in the repository that are not unsafe\n",
538
+ " files = [file for file in os.listdir(f'/home/ec2-user/SageMaker/{stack}') if file.endswith(\".jsonl.gz\")==True if file not in unsafe[i]]\n",
539
+ " file_counts.append(len(files))\n",
540
+ " os.chdir(f'/home/ec2-user/SageMaker/{stack}')\n",
541
+ " print('Downloading dataset {} ({} files) ... '.format(stack, len(files)), end='', flush=True)\n",
542
+ " # sequentially pull each dataset from git lfs, stream it to S3, and then delete the local copy to free up disk memory\n",
543
+ " for file_name in files:\n",
544
+ " os.system(f'git lfs pull --include={file_name}')\n",
545
+ " os.system(f'aws s3 cp {file_name} s3://lodestone-rnd/data/{stack}/{file_name[:-9] + \".json.gz\"}')\n",
546
+ " os.remove(file_name)\n",
547
+ " os.system('rm -r .git/lfs/objects/*')\n",
548
+ " if len(os.listdir('.git/objects/pack')) == 4:\n",
549
+ " os.system('ls -t .git/objects/pack/* | head -2 | xargs rm --')\n",
550
+ " print('\\033[32m' + 'Done' + '\\033[0m')\n",
551
+ "print(f'Total files uploaded: {sum(file_counts)}')\n",
552
+ "\n",
553
+ "print(\"\\n\")\n",
554
+ "\n",
555
+ "print('Downloading {:,} Reddit GitHub dataset into s3://lodestone-rnd/data/'.format(1))\n",
556
+ "# get the names of all the files in the repository\n",
557
+ "files = [file for file in os.listdir(f'/home/ec2-user/SageMaker/reddit-title-body') if file.endswith(\".jsonl.gz\")==True]\n",
558
+ "os.chdir(f'/home/ec2-user/SageMaker/reddit-title-body')\n",
559
+ "print('Downloading dataset {} ({} files) ... '.format(\"reddit-title-body\", len(files)), end='', flush=True)\n",
560
+ "# sequentially pull each dataset from git lfs, stream it to S3, and then delete the local copy to free up disk memory\n",
561
+ "for file_name in files:\n",
562
+ " os.system(f'git lfs pull --include={file_name}')\n",
563
+ " os.system(f'aws s3 cp {file_name} s3://lodestone-rnd/data/reddit-title-body/{file_name[:-9] + \".json.gz\"}')\n",
564
+ " os.remove(file_name)\n",
565
+ " os.system('rm -r .git/lfs/objects/*')\n",
566
+ " if len(os.listdir('.git/objects/pack')) == 4:\n",
567
+ " os.system('ls -t .git/objects/pack/* | head -2 | xargs rm --')\n",
568
+ "print('\\033[32m' + 'Done' + '\\033[0m')\n",
569
+ "print(f'Total files uploaded: {len(files)}')\n",
570
+ "\n",
571
+ "os.chdir('/home/ec2-user/SageMaker')\n",
572
+ "\n",
573
+ "print(\"\\n\")\n",
574
+ "\n",
575
+ "# DOWNLOAD HUGGINGFACE DATASETS (https://huggingface.co/datasets/sentence-transformers/embedding-training-data)\n",
576
+ "\n",
577
+ "# read dataset information from HuggingFace_datasets.tsv\n",
578
+ "datasets = pd.read_csv(\n",
579
+ " 'HuggingFace_datasets.tsv',\n",
580
+ " index_col=0,\n",
581
+ " sep='\\t',\n",
582
+ " dtype={\n",
583
+ " 'Description': str,\n",
584
+ " 'Size (#Pairs)': str,\n",
585
+ " 'Performance': float,\n",
586
+ " 'Download link': str,\n",
587
+ " 'Source': str})\n",
588
+ "datasets['Size (#Pairs)'] = datasets['Size (#Pairs)'].str.replace(',', '').astype(int)\n",
589
+ "datasets = datasets.to_dict(orient='index')\n",
590
+ "\n",
591
+ "print('Downloading {:,} HuggingFace datasets into s3://lodestone-rnd/data/'.format(len(datasets)))\n",
592
+ "\n",
593
+ "# stream each of the datasets from the URL provided into S3\n",
594
+ "# (note that S2ORC_citations_abstracts is larger than 50GB and therefore requires the expected size to be passed into the command line as well)\n",
595
+ "for d in datasets.keys():\n",
596
+ " print('Downloading dataset {} ({:,} pairs) ... '.format(d, datasets[d]['Size (#Pairs)']), end='', flush=True)\n",
597
+ " if d == \"S2ORC_citations_abstracts\":\n",
598
+ " os.system(f'wget -qO- {datasets[d][\"Download link\"]} | aws s3 cp - s3://lodestone-rnd/data/{d + \".json.gz\"} --expected-size 120259084288')\n",
599
+ " else:\n",
600
+ " os.system(f'wget -qO- {datasets[d][\"Download link\"]} | aws s3 cp - s3://lodestone-rnd/data/{d + \".json.gz\"}')\n",
601
+ " print('\\033[32m' + 'Done' + '\\033[0m')\n",
602
+ "\n",
603
+ "print(\"\\n\")\n",
604
+ "\n",
605
+ "# DOWNLOAD GOOGLE SHEETS DATASETS (https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0)\n",
606
+ "\n",
607
+ "# read dataset information from GoogleSheets_datasets.tsv\n",
608
+ "datasets = pd.read_csv(\n",
609
+ " 'GoogleSheets_datasets.tsv',\n",
610
+ " index_col=0,\n",
611
+ " sep='\\t',\n",
612
+ " dtype={\n",
613
+ " 'Description': str,\n",
614
+ " 'Size (#Pairs)': str,\n",
615
+ " 'Performance': float,\n",
616
+ " 'Download link': str,\n",
617
+ " 'Source': str})\n",
618
+ "datasets['Size (#Pairs)'] = datasets['Size (#Pairs)'].str.replace(',', '').astype(int)\n",
619
+ "datasets = datasets.to_dict(orient='index')\n",
620
+ "\n",
621
+ "print('Downloading {:,} 1B+ Google Sheets datasets into s3://lodestone-rnd/data/'.format(len(datasets)))\n",
622
+ "\n",
623
+ "# stream each of the datasets from the URL provided into S3\n",
624
+ "for d in datasets.keys():\n",
625
+ " print('Downloading dataset {} ({:,} pairs) ... '.format(d, datasets[d]['Size (#Pairs)']), end='', flush=True)\n",
626
+ " os.system(f'wget -qO- {datasets[d][\"Download link\"]} | aws s3 cp - s3://lodestone-rnd/data/{d + \".json.gz\"}')\n",
627
+ " print('\\033[32m' + 'Done' + '\\033[0m')\n",
628
+ "\n",
629
+ "print(\"\\n\")\n",
630
+ " \n",
631
+ "print(f'Successfully downloaded 50 datasets and {621+12+9+36} files into s3://lodestone-rnd/data/')"
632
+ ]
633
+ },
634
+ {
635
+ "cell_type": "code",
636
+ "execution_count": 1,
637
+ "id": "216d4564-6b69-4688-94ec-a67287134a2d",
638
+ "metadata": {
639
+ "tags": []
640
+ },
641
+ "outputs": [],
642
+ "source": [
643
+ "# clean up (remove the cloned repositories)\n",
644
+ "import shutil\n",
645
+ "shutil.rmtree(\"/home/ec2-user/SageMaker/stackexchange_title_body_jsonl\")\n",
646
+ "shutil.rmtree(\"/home/ec2-user/SageMaker/stackexchange_titlebody_best_voted_answer_jsonl\")\n",
647
+ "shutil.rmtree(\"/home/ec2-user/SageMaker/stackexchange_title_best_voted_answer_jsonl\")\n",
648
+ "shutil.rmtree(\"/home/ec2-user/SageMaker/stackexchange_titlebody_best_and_down_voted_answer_jsonl\")\n",
649
+ "shutil.rmtree(\"/home/ec2-user/SageMaker/reddit-title-body\")\n",
650
+ "shutil.rmtree(\"/home/ec2-user/SageMaker/1B_sentence_embeddings\")"
651
+ ]
652
+ }
653
+ ],
654
+ "metadata": {
655
+ "kernelspec": {
656
+ "display_name": "conda_pytorch_p310",
657
+ "language": "python",
658
+ "name": "conda_pytorch_p310"
659
+ },
660
+ "language_info": {
661
+ "codemirror_mode": {
662
+ "name": "ipython",
663
+ "version": 3
664
+ },
665
+ "file_extension": ".py",
666
+ "mimetype": "text/x-python",
667
+ "name": "python",
668
+ "nbconvert_exporter": "python",
669
+ "pygments_lexer": "ipython3",
670
+ "version": "3.10.10"
671
+ }
672
+ },
673
+ "nbformat": 4,
674
+ "nbformat_minor": 5
675
+ }
README.md CHANGED
@@ -1,3 +1,1940 @@
1
  ---
2
  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  license: apache-2.0
3
+ pipeline_tag: sentence-similarity
4
+ tags:
5
+ - sentence-transformers
6
+ - feature-extraction
7
+ - sentence-similarity
8
+ - mteb
9
+ language: en
10
+ datasets:
11
+ - s2orc
12
+ - flax-sentence-embeddings/stackexchange_title_body_jsonl
13
+ - flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl
14
+ - flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl
15
+ - flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl
16
+ - sentence-transformers/reddit-title-body
17
+ - msmarco
18
+ - gooaq
19
+ - yahoo_answers_topics
20
+ - code_search_net
21
+ - search_qa
22
+ - eli5
23
+ - snli
24
+ - multi_nli
25
+ - wikihow
26
+ - natural_questions
27
+ - trivia_qa
28
+ - embedding-data/sentence-compression
29
+ - embedding-data/flickr30k-captions
30
+ - embedding-data/altlex
31
+ - embedding-data/simple-wiki
32
+ - embedding-data/QQP
33
+ - embedding-data/SPECTER
34
+ - embedding-data/PAQ_pairs
35
+ - embedding-data/WikiAnswers
36
+ - sentence-transformers/embedding-training-data
37
+ model-index:
38
+ - name: lodestone-base-4096-v1
39
+ results:
40
+ - task:
41
+ type: Classification
42
+ dataset:
43
+ type: mteb/amazon_counterfactual
44
+ name: MTEB AmazonCounterfactualClassification (en)
45
+ config: en
46
+ split: test
47
+ revision: e8379541af4e31359cca9fbcf4b00f2671dba205
48
+ metrics:
49
+ - type: accuracy
50
+ value: 69.7313432835821
51
+ - type: ap
52
+ value: 31.618259511417733
53
+ - type: f1
54
+ value: 63.30313825394228
55
+ - task:
56
+ type: Classification
57
+ dataset:
58
+ type: mteb/amazon_polarity
59
+ name: MTEB AmazonPolarityClassification
60
+ config: default
61
+ split: test
62
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
63
+ metrics:
64
+ - type: accuracy
65
+ value: 86.89837499999999
66
+ - type: ap
67
+ value: 82.39500885672128
68
+ - type: f1
69
+ value: 86.87317947399657
70
+ - task:
71
+ type: Classification
72
+ dataset:
73
+ type: mteb/amazon_reviews_multi
74
+ name: MTEB AmazonReviewsClassification (en)
75
+ config: en
76
+ split: test
77
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
78
+ metrics:
79
+ - type: accuracy
80
+ value: 44.05
81
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1312
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1313
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1314
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1316
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1331
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1332
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1333
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1334
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1343
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1345
+ name: MTEB SciFact
1346
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1347
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1348
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1349
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1350
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1351
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1352
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1400
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1402
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1404
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1405
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1406
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1408
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1411
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1412
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1413
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1414
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1415
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1416
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1417
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1419
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1432
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1449
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1451
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1467
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1469
+ name: MTEB StackExchangeClustering
1470
+ config: default
1471
+ split: test
1472
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1473
+ metrics:
1474
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1475
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1478
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1479
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1480
+ name: MTEB StackExchangeClusteringP2P
1481
+ config: default
1482
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1483
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1484
+ metrics:
1485
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+ - task:
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+ type: Reranking
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+ dataset:
1490
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1491
+ name: MTEB StackOverflowDupQuestions
1492
+ config: default
1493
+ split: test
1494
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1495
+ metrics:
1496
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+ - task:
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1502
+ dataset:
1503
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1504
+ name: MTEB SummEval
1505
+ config: default
1506
+ split: test
1507
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
1508
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1509
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1516
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+ - task:
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1519
+ dataset:
1520
+ type: trec-covid
1521
+ name: MTEB TRECCOVID
1522
+ config: default
1523
+ split: test
1524
+ revision: None
1525
+ metrics:
1526
+ - type: map_at_1
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - task:
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1588
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1589
+ type: webis-touche2020
1590
+ name: MTEB Touche2020
1591
+ config: default
1592
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1593
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1594
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1595
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1596
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1651
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1652
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1653
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1654
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1655
+ - task:
1656
+ type: Classification
1657
+ dataset:
1658
+ type: mteb/toxic_conversations_50k
1659
+ name: MTEB ToxicConversationsClassification
1660
+ config: default
1661
+ split: test
1662
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1663
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1664
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1665
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1666
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+ - type: f1
1669
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1672
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1673
+ type: mteb/tweet_sentiment_extraction
1674
+ name: MTEB TweetSentimentExtractionClassification
1675
+ config: default
1676
+ split: test
1677
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
1678
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1679
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1680
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1681
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1682
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1683
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+ type: Clustering
1685
+ dataset:
1686
+ type: mteb/twentynewsgroups-clustering
1687
+ name: MTEB TwentyNewsgroupsClustering
1688
+ config: default
1689
+ split: test
1690
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1691
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1692
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1693
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1694
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1695
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1696
+ dataset:
1697
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1698
+ name: MTEB TwitterSemEval2015
1699
+ config: default
1700
+ split: test
1701
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
1702
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1703
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1704
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1751
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1752
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1753
+ name: MTEB TwitterURLCorpus
1754
+ config: default
1755
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1756
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1757
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1799
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1800
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+ - type: max_f1
1803
+ value: 75.41118114347518
1804
  ---
1805
+
1806
+ # lodestone-base-4096-v1
1807
+
1808
+ This new [sentence-transformers](https://www.SBERT.net) model from [Hum](https://www.hum.works/) maps long sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
1809
+
1810
+ ## Abstract
1811
+
1812
+ In the hopes of furthering Hum's overarching mission of increasing the accessibility and interconnectivity of human knowledge, this model was developed as part of a project intending to boost the maximum input sequence length of sentence embedding models by leveraging recent architectural advances in the design of transformer models such as the incorporation of FlashAttention, Attention with Linear Biases (ALiBi), and Gated Linear Units (GLU). These modifications and enhancements were implemented by the team at MosaicML who designed and constructed the pre-trained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model, and more information regarding the details of their development and testing specifications can be found on the model card.
1813
+
1814
+ While the fine-tuning procedure followed during the course of this project loosely mirrors that of the of the original [Flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) team responsible for the creation of many other popular sentence-transformers models (e.g. [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1), and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)), our methodology includes novel techniques for data loading, batch sampling, and model checkpointing intended to improve training efficiency with regards to memory allocation and data storage.
1815
+
1816
+ Through combining these well-established and proven fine-tuning practices with novel advances in transformer architectural elements, our `lodestone-base-4096-v1` model is able to achieve comparable performance metrics on standard text embedding evaluation benchmarks while also supporting a longer and more robust input sequence length of 4096 while retaining a smaller, more manageable size capable of being run on either a GPU or CPU.
1817
+
1818
+ ## Usage
1819
+
1820
+ Using this model becomes relatively easy when you have [sentence-transformers](https://www.SBERT.net) installed.
1821
+ *At the time of publishing, sentence-transformers does not support remote code which is required for flash-attention used by the model. A fork of the sentence-transformers repository that allows remote code execution is provided for convenience. It can be installed using the following command:*
1822
+ ```
1823
+ pip install git+https://github.com/Hum-Works/sentence-transformers.git
1824
+ ```
1825
+
1826
+ Then you can use the model like this:
1827
+ ```python
1828
+ from sentence_transformers import SentenceTransformer
1829
+
1830
+ model = SentenceTransformer('lodestone-base-4096-v1', trust_remote_code=True, revision='v1.0.0')
1831
+ sentences = ["This is an example sentence", "Each sentence is converted"]
1832
+ embeddings = model.encode(sentences)
1833
+ print(embeddings)
1834
+ ```
1835
+ *Note: The model will use the openAI/Triton implementation of FlashAttention if installed. This is more performant than the fallback, torch implementation. Some platforms and GPUs may not be supported by Triton - up to date compatibility can be found on [Triton’s github page](https://github.com/openai/triton#compatibility).*
1836
+
1837
+ ------
1838
+
1839
+ ## Background
1840
+
1841
+ The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model and fine-tuned it on a nearly 1.5B 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.
1842
+
1843
+ ## Intended uses
1844
+
1845
+ Our model is intended to be used as a long sentence and paragraph encoder. Given an input text, it outputs a vector containing the semantic information. The sentence vector may be used for information retrieval, clustering, or sentence similarity tasks.
1846
+
1847
+ ## Training procedure
1848
+
1849
+ ### Pre-training
1850
+
1851
+ We use the pretrained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048). Please refer to the model card for more detailed information about the pre-training procedure.
1852
+
1853
+ ### Fine-tuning
1854
+
1855
+ We fine-tune the model using a contrastive objective. Formally, we compute the dot product of each possible sentence pairing in the batch. We then apply the cross entropy loss by comparing with true pairs.
1856
+
1857
+ #### Hyperparameters
1858
+
1859
+ We trained our model on an ml.g5.4xlarge EC2 instance with 1 NVIDIA A10G Tensor Core GPU. We train the model during 1.4 million steps using a batch size of 16. We use a learning rate warm up of 500. The sequence length during training was limited to 2048 tokens. We used the AdamW optimizer with a 2e-5 learning rate and weight decay of 0.01 (i.e. the default parameter values for SentenceTransformer.fit()). The full training script is accessible in this current repository: `Training.py`.
1860
+
1861
+ ## Model Architecture
1862
+ By incorporating FlashAttention, [Attention with Linear Biases (ALiBi)](https://arxiv.org/abs/2108.12409), and Gated Linear Units (GLU), this model is able to handle input sequences of 4096, 8x longer than that supported by most comparable sentence embedding models.
1863
+ The model was trained using a sequence length maximum of 2048, but the final model has a maximum sequence length of 4096. This is accomplished by taking advantage of ALiBi’s positional attention extrapolation which has been shown to allow sequence lengths of 2x the initial trained length.
1864
+
1865
+ ## Full Model Architecture
1866
+ ```
1867
+ SentenceTransformer(
1868
+ (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: BertModel
1869
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
1870
+ (2): Normalize()
1871
+ )
1872
+ ```
1873
+
1874
+ #### Training data
1875
+
1876
+ We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is nearly 1.5 billion sentences. We sampled each dataset given a weighted probability proportional to its relative contribution to the entire dataset.
1877
+ The breakdown of the dataset can be seen below, and the entire dataset can be publicly accessed and uploaded via the `Dataloading.ipynb` located within this repository.
1878
+
1879
+ | Dataset | Paper | Number of training tuples |
1880
+ |--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
1881
+ | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
1882
+ | **[S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts)** | [paper](https://aclanthology.org/2020.acl-main.447/) | 252,102,397 |
1883
+ | **[Reddit posts](https://huggingface.co/datasets/sentence-transformers/reddit-title-body) (Title, Body) pairs** | - | 127,445,911 |
1884
+ | **[Amazon reviews (2018)](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, Review) pairs** | - | 87,877,725 |
1885
+ | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
1886
+ | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
1887
+ | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
1888
+ | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
1889
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) (Title, Body) pairs | - | 25,368,423 |
1890
+ | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
1891
+ | **[Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) (Title, Most Upvoted Answer) pairs** | - | 4,784,250 |
1892
+ | **[Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl) (Title+Body, Most Upvoted Answer) pairs** | - | 4,551,660 |
1893
+ | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
1894
+ | **[Amazon QA](https://huggingface.co/datasets/sentence-transformers/embedding-training-data)** | - | 2,507,114 |
1895
+ | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,375,067 |
1896
+ | [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 |
1897
+ | **[AG News]((Title, Description) pairs of news articles from the AG News dataset)** | - | 1,157,745 |
1898
+ | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
1899
+ | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
1900
+ | [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 |
1901
+ | [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 |
1902
+ | **[CC News](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, article) pairs** | - | 614,664 |
1903
+ | **[NPR](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, Body) pairs** | - | 594,384 |
1904
+ | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
1905
+ | **[MS Marco](https://microsoft.github.io/msmarco/) (Query, Answer Passage) pairs** | [paper](https://doi.org/10.1145/3404835.3462804) | 532,751 |
1906
+ | [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) (Title, Body) pairs | - | 364,000 |
1907
+ | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
1908
+ | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
1909
+ | **[CNN & DailyMail](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (highlight sentences, article) pairs** | - | 311,971 |
1910
+ | [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (titles) | - | 304,524 |
1911
+ | 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 |
1912
+ | [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (bodies) | - | 250,518 |
1913
+ | [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (titles+bodies) | - | 250,459 |
1914
+ | **[XSUM](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Summary, News Article) pairs** | - | 226,711 |
1915
+ | **[Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl) (Title+Body, Most Upvoted Answer, Most Downvoted Answer) triplets** | - | 216,454 |
1916
+ | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
1917
+ | **[FEVER](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) training data** | - | 139,051 |
1918
+ | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
1919
+ | **[SearchQA](https://huggingface.co/datasets/search_qa) (Question, Top-Snippet)** | [paper](https://arxiv.org/abs/1704.05179) | 117,384 |
1920
+ | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
1921
+ | **[Quora Question Duplicates](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)** | - | 103,663 |
1922
+ | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
1923
+ | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
1924
+ | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
1925
+ | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
1926
+ | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
1927
+ | **Total** | | **1,492,453,113** |
1928
+
1929
+ #### Replication
1930
+
1931
+ The entire fine-tuning process for this model can be replicated by following the steps outlined in the `Replication.txt` file within this repository. This document explains how to modify the [sentence-transformers](https://www.SBERT.net) library, configure the pre-trained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model, load all of the training data, and execute the training script.
1932
+
1933
+ #### Limitations
1934
+
1935
+ Due to technical constraints (e.g. limited GPU memory capacity), this model was trained with a smaller batch size of 16, making it so that each step during training was less well-informed than it would have been on a higher performance system. This smaller than ideal hyperparameter value will generally cause the model to be more likely to get stuck in a local minimum and for the parameter configuration to take a longer time to converge to the optimum. In order to counteract this potential risk, we trained the model for a larger number of steps than many of its contemporaries to ensure a greater chance of achieving strong performance, but this is an area which could be improved if further fine-tuning was performed.
1936
+
1937
+ It is also worth noting that, while this model is able to handle longer input sequences of up to 4096 word pieces, the training dataset used consists of sentence and paragraph pairs and triplets which do not necessarily reach that maximum sequence length. Since the data was not tailored specifically for this larger input size, further fine-tuning may be required to ensure highly accurate embeddings for longer texts of that magnitude.
1938
+
1939
+ Finally, as stated on https://huggingface.co/datasets/sentence-transformers/reddit-title-body, an additional reminder and warning regarding the Reddit posts data is that one should "Be aware that this dataset is not filtered for biases, hate-speech, spam, racial slurs etc. It depicts the content as it is posted on Reddit." Thus, while we believe this has not induced any pathological behaviors in the model's performance due to its relatively low prevalence of records in the whole dataset of nearly 1.5B sentence pairs and the fact that this model was trained to produce semantic embeddings rather than generative text outputs, it is always important to be aware of vulnerabilities to bias.
1940
+
Replication.txt ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Lodestone Replication
2
+
3
+ The dataloading, library modification, model preparation, and training process can be replicated in a straightforward manner by simply running a few Jupyter notebooks and Python files.
4
+
5
+ Data Wrangling and Loading
6
+
7
+ Dataloading.ipynb utilizes the contents of the GoogleSheets_datasets.tsv and HuggingFace_datasets.tsv to fetch data from various URLs provided by the original distilroberta team to their curated datasets in cloud storage. The data is then streamed directly into the data folder of the lodestone-rnd S3 bucket in us-east-1. In addition to the data used by the distilroberta team and provided at https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0, data was also collected from https://huggingface.co/datasets/sentence-transformers/embedding-training-data and the following HuggingFace dataset repositories:
8
+ Stack Exchange
9
+ https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl
10
+ https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl
11
+ https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl
12
+ https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl
13
+ Reddit
14
+ https://huggingface.co/datasets/sentence-transformers/reddit-title-body
15
+ All of the HuggingFace data is handled remotely or pulled via the script in Dataloading.ipynb, so the only files required for this entire process are Dataloading.ipynb, GoogleSheets_datasets.tsv, and HuggingFace_datasets.tsv. Running this notebook results in 679 objects and 310.5GB of data being loaded into S3.
16
+
17
+ Once the data is in S3, run Data_Records.ipynb to generate the data_records.json file which contains a dictionary of {filename: record count} pairs and is used throughout the Training.py script.
18
+
19
+ Library and Model Preparation
20
+
21
+ In order to run the training process with our specific model, we need to make a few custom modifications to the sentence-transformers library and to the config.json file of the mosaic-bert-base-seqlen-2048 base model.
22
+
23
+ To alter the sentence-transformers library, clone the repository from https://github.com/UKPLab/sentence-transformers locally and replace the SentenceTransformer.py and Transformer.py files located within the sentence-transformers/sentence_transformers/ and sentence-transformers/sentence_transformers/models/ directories of the cloned repository, respectively, with those located inside dev/ folder. (This has already been done in this notebook instance, but this will have to be completed if training on another system.)
24
+
25
+ Before conducting actual training, we also need to clone the mosaic-bert-base-seqlen-2048 model locally and make a few small changes to its config.json file. Running Mosaic_Model.ipynb will execute this process and get our model ready to begin training. (Again, this has already been done in this notebook instance, but this will have to be completed if training on another system.)
26
+
27
+ Training
28
+
29
+ To perform the final training run, open a SageMaker Terminal window and execute the following:
30
+ cd SageMaker
31
+ screen -S training
32
+ python Training.py
33
+ ^a d (that is, Ctrl + a, then d)
34
+
35
+ To reattach to the screen and observe how training is progressing, run `screen -r training` in the Terminal. Occasionally epochs may stall and require manual intervention to kickstart the process again. Pressing ^c (that is, Ctrl+c) inside the screen should suffice the get things going again, but this action will automatically cause the currently stalled epoch to fail and for the training to proceed to the next epoch or data chunk without updating the existing model parameterization. Epoch successes and failures and the cumulative number of successfully completed steps can be monitored via the train_logs.txt file which is updated automatically throughout the course of training.
36
+
37
+ The Training.py file can be reconfigured such that training hyperparameters could be passed in through the command line, but, at present, hyperparameters should be set within the file before running it.
38
+
39
+ This concludes the steps required for replication of the Lodestone training process.
40
+
Training.py ADDED
@@ -0,0 +1,465 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # This training script is a duplicate of the Training.ipynb notebook but can be invoked from the terminal
2
+
3
+ import os
4
+ print(os.getcwd())
5
+ os.environ["PATH"]="/usr/local/cuda-11.7/bin:"+os.getenv("PATH")
6
+
7
+ os.system('pip uninstall -y torch')
8
+ os.system('pip uninstall -y einops')
9
+ os.system('pip uninstall -y transformers')
10
+ os.system('pip uninstall -y sentence_transformers')
11
+ os.system('pip uninstall -y datasets')
12
+ os.system('pip uninstall -y sagemaker')
13
+ os.system('pip uninstall -y smart_open')
14
+ os.system('pip uninstall -y pynvml')
15
+
16
+ os.system('pip install -r lodestone-reqs.txt')
17
+
18
+ os.system('pip install -e ./sentence-transformers')
19
+
20
+ os.system('pip uninstall -y triton')
21
+ os.system('pip install --no-deps triton==2.0.0.dev20221202')
22
+
23
+ #####
24
+
25
+ from pynvml import *
26
+ import math
27
+ from sentence_transformers import models, losses
28
+ from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
29
+ import logging
30
+ import os
31
+ import json
32
+ import torch
33
+ import boto3
34
+ from smart_open import open
35
+ import random
36
+ import time
37
+ import gc
38
+
39
+ os.environ["PATH"]="/usr/local/cuda-11.7/bin:"+os.getenv("PATH")
40
+ os.environ["TOKENIZERS_PARALLELISM"] = "false"
41
+
42
+ #####
43
+
44
+
45
+ def print_gpu_utilization():
46
+ "This helper function outputs the current GPU memory usage."
47
+ nvmlInit()
48
+ handle = nvmlDeviceGetHandleByIndex(0)
49
+ info = nvmlDeviceGetMemoryInfo(handle)
50
+ return f"GPU memory occupied: {info.used/1024**3} GB."
51
+
52
+ #####
53
+
54
+
55
+ class MultiDatasetDataLoader:
56
+ """
57
+ This custom dataloader class consumes a list of datasets and a batch size and produces batches randomly sampled
58
+ from the datasets provided where each batch consists of records from a single dataset and datasets are chosen
59
+ for batches in proportion to their total number of records.
60
+ """
61
+ def __init__(self, datasets, batch_size_pairs, batch_size_triplets=None, dataset_size_temp=-1, allow_swap=True):
62
+ self.allow_swap = allow_swap
63
+ self.batch_size_pairs = batch_size_pairs
64
+ self.batch_size_triplets = batch_size_pairs if batch_size_triplets is None else batch_size_triplets
65
+
66
+ # Compute dataset weights
67
+ self.dataset_lengths = list(map(len, datasets))
68
+ self.dataset_lengths_sum = sum(self.dataset_lengths)
69
+
70
+ weights = []
71
+ # if dataset_size_temp > 0: # Scale probability with dataset size
72
+ # for dataset in datasets:
73
+ # prob = len(dataset) / self.dataset_lengths_sum
74
+ # weights.append(max(1, int(math.pow(prob, 1 / dataset_size_temp) * 1000)))
75
+ # else: # Equal weighting of all datasets
76
+ # weights = [100] * len(datasets)
77
+ for dataset in datasets:
78
+ weights.append(len(dataset))
79
+
80
+ # logging.info("Dataset lengths and weights: {}".format(list(zip(self.dataset_lengths, weights))))
81
+
82
+ self.dataset_idx = []
83
+ self.dataset_idx_pointer = 0
84
+
85
+ for idx, weight in enumerate(weights):
86
+ self.dataset_idx.extend([idx] * weight)
87
+ random.shuffle(self.dataset_idx)
88
+
89
+ self.datasets = []
90
+ for dataset in datasets:
91
+ random.shuffle(dataset)
92
+ self.datasets.append({
93
+ 'elements': dataset,
94
+ 'pointer': 0,
95
+ })
96
+
97
+ def __iter__(self):
98
+ for _ in range(int(self.__len__())):
99
+ # Select dataset
100
+ if self.dataset_idx_pointer >= len(self.dataset_idx):
101
+ self.dataset_idx_pointer = 0
102
+ random.shuffle(self.dataset_idx)
103
+
104
+ dataset_idx = self.dataset_idx[self.dataset_idx_pointer]
105
+ self.dataset_idx_pointer += 1
106
+
107
+ # Select batch from this dataset
108
+ dataset = self.datasets[dataset_idx]
109
+ batch_size = self.batch_size_pairs if len(dataset['elements'][0].texts) == 2 else self.batch_size_triplets
110
+
111
+ batch = []
112
+ texts_in_batch = set()
113
+ guid_in_batch = set()
114
+ while len(batch) < batch_size:
115
+ example = dataset['elements'][dataset['pointer']]
116
+
117
+ valid_example = True
118
+ # First check if one of the texts in already in the batch
119
+ for text in example.texts:
120
+ text_norm = text.strip().lower()
121
+ if text_norm in texts_in_batch:
122
+ valid_example = False
123
+
124
+ texts_in_batch.add(text_norm)
125
+
126
+ # If the example has a label, check if label is in batch
127
+ if example.guid is not None:
128
+ valid_example = valid_example and example.guid not in guid_in_batch
129
+ guid_in_batch.add(example.guid)
130
+
131
+ if valid_example:
132
+ if self.allow_swap and random.random() > 0.5:
133
+ example.texts[0], example.texts[1] = example.texts[1], example.texts[0]
134
+
135
+ batch.append(example)
136
+
137
+ dataset['pointer'] += 1
138
+ if dataset['pointer'] >= len(dataset['elements']):
139
+ dataset['pointer'] = 0
140
+ random.shuffle(dataset['elements'])
141
+
142
+ yield self.collate_fn(batch) if self.collate_fn is not None else batch
143
+
144
+ def __len__(self):
145
+ return int(self.dataset_lengths_sum / self.batch_size_pairs)
146
+
147
+ #####
148
+
149
+
150
+ # These four classes of custom generators parse the raw data from the files in S3 and format it into InputExamples which can be properly interpreted by a SentenceTransformer model.
151
+
152
+ class RedditTitleBodyDataset:
153
+ def __init__(self, source_uri, max_seq_length):
154
+ self.source_uri = source_uri
155
+ self.s3_client = boto3.client("s3")
156
+ self.max_seq_length = max_seq_length
157
+
158
+ def __iter__(self):
159
+ while True:
160
+ for json_line in open(self.source_uri, transport_params={"client": self.s3_client}):
161
+ data_line = json.loads(json_line.strip())
162
+
163
+ if "title" in data_line and "body" in data_line:
164
+ data = {'guid': None, 'texts': [" ".join(data_line['title'].split(" ")[:self.max_seq_length]), " ".join(data_line['body'].split(" ")[:self.max_seq_length])]}
165
+ record = InputExample(guid=data.get('guid', None), texts=data['texts'])
166
+
167
+ yield record
168
+
169
+
170
+ class RedditYearDataset:
171
+ def __init__(self, source_uri, max_seq_length):
172
+ self.source_uri = source_uri
173
+ self.s3_client = boto3.client("s3")
174
+ self.max_seq_length = max_seq_length
175
+
176
+ def __iter__(self):
177
+ while True:
178
+ for json_line in open(self.source_uri, transport_params={"client": self.s3_client}):
179
+ data_line = json.loads(json_line.strip())
180
+
181
+ if "response" in data_line and "context" in data_line:
182
+ data = {'guid': None, 'texts': [" ".join(data_line['response'].split(" ")[:self.max_seq_length]), " ".join(data_line['context'].split(" ")[:self.max_seq_length])]}
183
+ record = InputExample(guid=data.get('guid', None), texts=data['texts'])
184
+
185
+ yield record
186
+
187
+
188
+ class HuggingFaceQueryPosDataset:
189
+ def __init__(self, source_uri, max_seq_length):
190
+ self.source_uri = source_uri
191
+ self.s3_client = boto3.client("s3")
192
+ self.max_seq_length = max_seq_length
193
+
194
+ def __iter__(self):
195
+ while True:
196
+ for json_line in open(self.source_uri, transport_params={"client": self.s3_client}):
197
+ data_line = json.loads(json_line.strip())
198
+
199
+ if "query" in data_line and "pos" in data_line:
200
+ for i in range(len(data_line['pos'])):
201
+ data = {'guid': None, 'texts': [" ".join(data_line['query'].split(" ")[:self.max_seq_length]), " ".join(data_line['pos'][i].split(" ")[:self.max_seq_length])]}
202
+ record = InputExample(guid=data.get('guid', None), texts=data['texts'])
203
+
204
+ yield record
205
+
206
+
207
+ class Dataset:
208
+ def __init__(self, source_uri, max_seq_length):
209
+ self.source_uri = source_uri
210
+ self.s3_client = boto3.client("s3")
211
+ self.max_seq_length = max_seq_length
212
+
213
+ def __iter__(self):
214
+ while True:
215
+ for json_line in open(self.source_uri, transport_params={"client": self.s3_client}):
216
+ data_line = json.loads(json_line.strip())
217
+
218
+ if not isinstance(data_line, dict):
219
+ data = {'guid': None, 'texts': data_line}
220
+ for text_idx in range(len(data['texts'])):
221
+ data['texts'][text_idx] = " ".join(data['texts'][text_idx].split(" ")[:self.max_seq_length])
222
+ record = InputExample(guid=data.get('guid', None), texts=data['texts'])
223
+ else:
224
+ for text_idx in range(len(data_line['texts'])):
225
+ data_line['texts'][text_idx] = " ".join(data_line['texts'][text_idx].split(" ")[:self.max_seq_length])
226
+ record = InputExample(guid=data_line.get('guid', None), texts=data_line['texts'])
227
+
228
+ yield record
229
+
230
+ #####
231
+
232
+
233
+ def build_generators(data_records, max_seq_length=512, testing=False):
234
+ """
235
+ This function consumes the data_records dictionary and creates a new dictionary of data generators where each entry is
236
+ of the form {filename: data generator object}.
237
+ """
238
+ if testing:
239
+ # filepaths = [file for file in list(data_records.keys()) if file.startswith('S2ORC') or file.startswith('reddit_')]
240
+ filepaths = [file for file in list(data_records.keys())][:3]
241
+ else:
242
+ filepaths = list(data_records.keys())
243
+ generators = {}
244
+ for filepath in filepaths:
245
+ filepath = filepath.strip()
246
+ source_uri = 's3://lodestone-rnd/data/'+filepath
247
+ if filepath in ['S2ORC_citations_abstracts.json.gz', 'amazon-qa.json.gz'] or 'reddit' in filepath:
248
+ if "title" in filepath:
249
+ generators[f'{filepath.split(".")[0]}'] = iter(RedditTitleBodyDataset(source_uri, max_seq_length))
250
+ elif "reddit" in filepath:
251
+ generators[f'{filepath.split(".")[0]}'] = iter(RedditYearDataset(source_uri, max_seq_length))
252
+ else:
253
+ generators[f'{filepath.split(".")[0]}'] = iter(HuggingFaceQueryPosDataset(source_uri, max_seq_length))
254
+ else:
255
+ generators[f'{filepath.split(".")[0]}'] = iter(Dataset(source_uri, max_seq_length))
256
+
257
+ return generators
258
+
259
+ #####
260
+
261
+
262
+ def produce_data(data_records, num_chunks, generators, batch_size, failed_on=None, first_iter=False, testing=False, temp=-1):
263
+ """
264
+ This function consumes the data_records dictionary, the number of chunks to break the datasets into, the dictionary of
265
+ data generators, and a batch size and returns a MultiDatasetDataloader which can be fed into the .fit method of a
266
+ SentenceTransformer model.
267
+ """
268
+ if testing:
269
+ # filepaths = [file for file in list(data_records.keys()) if file.startswith('S2ORC') or file.startswith('reddit_')]
270
+ filepaths = [file for file in list(data_records.keys())][:3]
271
+ else:
272
+ filepaths = list(data_records.keys())
273
+ datasets = []
274
+ for file_idx, filepath in enumerate(filepaths):
275
+ filepath = filepath.strip()
276
+ dataset = []
277
+
278
+ if failed_on is not None and failed_on != 1 and first_iter:
279
+ for k in range((failed_on-1)*max(1, data_records[filepath]//num_chunks)):
280
+ next(generators[f'{filepath.split(".")[0]}'])
281
+ for m in range(max(1, data_records[filepath]//num_chunks)):
282
+ dataset.append(next(generators[f'{filepath.split(".")[0]}']))
283
+ else:
284
+ for n in range(max(1, data_records[filepath]//num_chunks)):
285
+ dataset.append(next(generators[f'{filepath.split(".")[0]}']))
286
+
287
+ datasets.append(dataset)
288
+ logging.info("{}. {}: {}".format(file_idx+1, filepath, len(dataset)))
289
+
290
+ dataset_lengths_sum = sum(list(map(len, datasets)))
291
+
292
+ batch_size_pairs = batch_size_triplets = batch_size
293
+ # Special data loader to load from multiple datasets
294
+ train_dataloader = MultiDatasetDataLoader(datasets=datasets,
295
+ batch_size_pairs=batch_size_pairs,
296
+ batch_size_triplets=batch_size_triplets,
297
+ dataset_size_temp=temp)
298
+
299
+ return train_dataloader, dataset_lengths_sum
300
+
301
+ #####
302
+
303
+
304
+ def construct_model(model_name, max_seq_length=512):
305
+ """
306
+ This function constructs a SentenceTransformer model from a HuggingFace transformer model name
307
+ or from a local path to a transformer model repository.
308
+ """
309
+ word_embedding_model = models.Transformer(model_name_or_path=model_name,
310
+ max_seq_length=max_seq_length,
311
+ tokenizer_name_or_path='bert-base-uncased',
312
+ trust_remote_code=True,
313
+ model_args={'torch_dtype': torch.bfloat16})
314
+ pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
315
+ norm = models.Normalize()
316
+ model = SentenceTransformer(modules=[word_embedding_model, pooling_model, norm], device='cuda')
317
+ model[0].tokenizer.model_max_length = max_seq_length
318
+
319
+ return model
320
+
321
+ #####
322
+
323
+
324
+ # Just some code to print debug information to stdout
325
+ logging.basicConfig(format='%(asctime)s - %(message)s',
326
+ datefmt='%Y-%m-%d %H:%M:%S',
327
+ level=logging.INFO,
328
+ handlers=[LoggingHandler()])
329
+ # /print debug information to stdout
330
+
331
+ #####
332
+
333
+
334
+ # Set Hyperparameters
335
+ model_name = 'mosaic-bert-base-seqlen-2048'
336
+ # model_name = 'hum-lodestone-v1'
337
+ batch_size = 16
338
+ batch_size_pairs = batch_size_triplets = batch_size
339
+ max_seq_length = 2048
340
+ use_amp = False
341
+
342
+ num_cycles = 2
343
+ num_chunks = 50
344
+ num_epochs = 2
345
+ steps_per_epoch = 10000
346
+ # Total training steps = num_cycles * num_chunks * num_epochs * steps_per_epoch = 2 * 50 * 2 * 10,000 = 2,000,000 steps
347
+ warmup_steps = 500
348
+
349
+ testing = False
350
+ temp = -1
351
+
352
+ #####
353
+
354
+
355
+ output_path = 'hum-lodestone-v1'
356
+ logging.info("Output: "+output_path)
357
+
358
+ # Instantiate SentenceTransformer Model
359
+ model = construct_model(model_name=model_name, max_seq_length=max_seq_length)
360
+
361
+ # Load File Names and Record Volumes
362
+ with open('data_records.json') as fIn:
363
+ data_records = json.load(fIn)
364
+
365
+ total_pairs = sum(data_records.values())
366
+
367
+ logging.info("Total Training Pairs: {}".format(total_pairs))
368
+
369
+ # Initialize Data Generators
370
+ generators = build_generators(data_records=data_records,
371
+ max_seq_length=max_seq_length,
372
+ testing=testing)
373
+
374
+ logging.info("Data Generators Initialized")
375
+
376
+ # Define Training Loss Function
377
+ train_loss = losses.MultipleNegativesRankingLoss(model,
378
+ scale=20,
379
+ similarity_fct=util.dot_score)
380
+
381
+ logging.info(print_gpu_utilization())
382
+
383
+ #####
384
+
385
+
386
+ # Configure Training Cycles
387
+ failed_on = None # chunk that the process failed on
388
+ random.seed(42)
389
+ steps = 0
390
+ first_iter = True
391
+ for cycle_num in range(num_cycles):
392
+ logging.info("Starting Cycle {}".format(cycle_num+1))
393
+ for chunk_num in range(num_chunks):
394
+ if failed_on is not None and (chunk_num+1) < failed_on and (cycle_num+1) == 1:
395
+ pass
396
+ else:
397
+ logging.info("Chunk {}/{}".format(chunk_num+1, num_chunks))
398
+ logging.info("Loading {} Datasets".format(len([file for file in list(data_records.keys()) if file.startswith('S2ORC') or file.startswith('reddit_')]) if testing else len(data_records)))
399
+ # t_dataload0 = time.time()
400
+ # Create the training dataloader for the given chunk of data
401
+ train_dataloader, dataset_lengths_sum = produce_data(data_records,
402
+ num_chunks,
403
+ generators,
404
+ batch_size,
405
+ failed_on=failed_on,
406
+ first_iter=first_iter,
407
+ testing=testing,
408
+ temp=temp)
409
+ first_iter = False
410
+ # t_dataload1 = time.time()
411
+ # print(t_dataload1-t_dataload0)
412
+
413
+ logging.info(print_gpu_utilization())
414
+
415
+ # steps_per_epoch = dataset_lengths_sum // batch_size_pairs
416
+
417
+ for epoch_num in range(num_epochs):
418
+ logging.info("Performing Cycle {}, Chunk {}, Epoch {}".format(cycle_num+1, chunk_num+1, epoch_num+1))
419
+ try:
420
+ # t_fit0 = time.time()
421
+ # Train the model
422
+ model.fit(train_objectives=[(train_dataloader, train_loss)],
423
+ evaluator=None,
424
+ epochs=1,
425
+ warmup_steps=warmup_steps,
426
+ steps_per_epoch=steps_per_epoch,
427
+ use_amp=use_amp,
428
+ output_path=output_path)
429
+ # t_fit1 = time.time()
430
+ # print(t_fit1-t_fit0)
431
+
432
+ steps += steps_per_epoch
433
+
434
+ logging.info(print_gpu_utilization())
435
+ logging.info("Succeeded on Cycle {}, Chunk {}, Epoch {}".format(cycle_num+1, chunk_num+1, epoch_num+1))
436
+ logging.info("{} Steps Completed in Total".format(steps))
437
+
438
+ with open('train_logs.txt', 'a') as log:
439
+ log.write("Succeeded on Cycle {}, Chunk {}, Epoch {}: {} Steps Completed in Total\n".format(cycle_num+1, chunk_num+1, epoch_num+1, steps))
440
+
441
+ except:
442
+ logging.info("Failed on Cycle {}, Chunk {}, Epoch {}".format(cycle_num+1, chunk_num+1, epoch_num+1))
443
+
444
+ with open('train_logs.txt', 'a') as log:
445
+ log.write("Failed on Cycle {}, Chunk {}, Epoch {}: {} Steps Completed in Total\n".format(cycle_num+1, chunk_num+1, epoch_num+1, steps))
446
+
447
+ finally:
448
+ warmup_steps = 0
449
+
450
+ # Clear GPU/CUDA memory cache between data chunks
451
+ train_dataloader = None
452
+ model = None
453
+ train_loss = None
454
+
455
+ gc.collect()
456
+ torch.cuda.empty_cache()
457
+
458
+ # Reload the model and reinitialize the loss function
459
+ model = construct_model(model_name='hum-lodestone-v1', max_seq_length=max_seq_length)
460
+
461
+ train_loss = losses.MultipleNegativesRankingLoss(model,
462
+ scale=20,
463
+ similarity_fct=util.dot_score)
464
+
465
+ logging.info(print_gpu_utilization())
bert_layers.py ADDED
@@ -0,0 +1,1072 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 MosaicML Examples authors
2
+ # SPDX-License-Identifier: Apache-2.0
3
+
4
+ # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
5
+ # Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
6
+ # Copyright (c) 2022, Tri Dao.
7
+
8
+ """Implements Mosaic BERT, with an eye towards the Hugging Face API.
9
+
10
+ Mosaic BERT improves performance over Hugging Face BERT through the following:
11
+
12
+ 1. ALiBi. This architectural change removes positional embeddings and instead encodes positional
13
+ information through attention biases based on query-key position distance. It improves the effectiveness
14
+ of training with shorter sequence lengths by enabling extrapolation to longer sequences.
15
+
16
+ 2. Gated Linear Units (GLU). This architectural change replaces the FFN component of the BERT layer
17
+ to improve overall expressiveness, providing better convergence properties.
18
+
19
+ 3. Flash Attention. The Mosaic BERT's self-attention layer makes use of Flash Attention, which dramatically
20
+ improves the speed of self-attention. Our implementation utilizes a bleeding edge implementation that
21
+ supports attention biases, which allows us to use Flash Attention with ALiBi.
22
+
23
+ 4. Unpadding. Padding is often used to simplify batching across sequences of different lengths. Standard BERT
24
+ implementations waste computation on padded tokens. Mosaic BERT internally unpads to reduce unnecessary computation
25
+ and improve speed. It does this without changing how the user interfaces with the model, thereby
26
+ preserving the simple API of standard implementations.
27
+
28
+
29
+ Currently, Mosaic BERT is available for masked language modeling :class:`BertForMaskedLM` and sequence
30
+ classification :class:`BertForSequenceClassification`. We aim to expand this catalogue in future releases.
31
+
32
+ See :file:`./mosaic_bert.py` for utilities to simplify working with Mosaic BERT in Composer, and for example usage
33
+ of the core Mosaic BERT classes.
34
+ """
35
+
36
+ import copy
37
+ import logging
38
+ import math
39
+ import warnings
40
+ from typing import List, Optional, Tuple, Union
41
+
42
+ import torch
43
+ import torch.nn as nn
44
+ from einops import rearrange
45
+ from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
46
+ from transformers.activations import ACT2FN
47
+ from transformers.modeling_outputs import (MaskedLMOutput,
48
+ SequenceClassifierOutput)
49
+ from transformers.models.bert.modeling_bert import BertPreTrainedModel
50
+
51
+ from .bert_padding import (index_first_axis,
52
+ index_put_first_axis, pad_input,
53
+ unpad_input, unpad_input_only)
54
+
55
+ try:
56
+ from .flash_attn_triton import flash_attn_qkvpacked_func
57
+ except ImportError as e:
58
+ flash_attn_qkvpacked_func = None
59
+
60
+ logger = logging.getLogger(__name__)
61
+
62
+
63
+ class BertEmbeddings(nn.Module):
64
+ """Construct the embeddings for words, ignoring position.
65
+
66
+ There are no positional embeddings since we use ALiBi and token_type
67
+ embeddings.
68
+
69
+ This module is modeled after the Hugging Face BERT's
70
+ :class:`~transformers.model.bert.modeling_bert.BertEmbeddings`, but is
71
+ modified as part of Mosaic BERT's ALiBi implementation. The key change is
72
+ that position embeddings are removed. Position information instead comes
73
+ from attention biases that scale linearly with the position distance
74
+ between query and key tokens.
75
+
76
+ This module ignores the `position_ids` input to the `forward` method.
77
+ """
78
+
79
+ def __init__(self, config):
80
+ super().__init__()
81
+ self.word_embeddings = nn.Embedding(config.vocab_size,
82
+ config.hidden_size,
83
+ padding_idx=config.pad_token_id)
84
+ # ALiBi doesn't use position embeddings
85
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
86
+ config.hidden_size)
87
+
88
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model
89
+ # variable name and be able to load any TensorFlow checkpoint file
90
+ self.LayerNorm = nn.LayerNorm(config.hidden_size,
91
+ eps=config.layer_norm_eps)
92
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
93
+ self.register_buffer('token_type_ids',
94
+ torch.zeros(config.max_position_embeddings,
95
+ dtype=torch.long),
96
+ persistent=False)
97
+
98
+ def forward(
99
+ self,
100
+ input_ids: Optional[torch.LongTensor] = None,
101
+ token_type_ids: Optional[torch.LongTensor] = None,
102
+ position_ids: Optional[torch.LongTensor] = None,
103
+ inputs_embeds: Optional[torch.FloatTensor] = None,
104
+ past_key_values_length: int = 0,
105
+ ) -> torch.Tensor:
106
+ if (input_ids is not None) == (inputs_embeds is not None):
107
+ raise ValueError('Must specify either input_ids or input_embeds!')
108
+ if input_ids is not None:
109
+ input_shape = input_ids.size()
110
+ else:
111
+ assert inputs_embeds is not None # just for type checking
112
+ input_shape = inputs_embeds.size()[:-1]
113
+
114
+ seq_length = input_shape[1]
115
+
116
+ if position_ids is None:
117
+ # great! ALiBi
118
+ pass
119
+
120
+ # Setting the token_type_ids to the registered buffer in constructor
121
+ # where it is all zeros, which usually occurs when it's auto-generated;
122
+ # registered buffer helps users when tracing the model without passing
123
+ # token_type_ids, solves issue #5664
124
+ if token_type_ids is None:
125
+ if hasattr(self, 'token_type_ids'):
126
+ assert isinstance(self.token_type_ids, torch.LongTensor)
127
+ buffered_token_type_ids = self.token_type_ids[:, :seq_length]
128
+ buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
129
+ input_shape[0], seq_length)
130
+ token_type_ids = buffered_token_type_ids_expanded # type: ignore
131
+ else:
132
+ token_type_ids = torch.zeros(input_shape, # type: ignore
133
+ dtype=torch.long,
134
+ device=self.word_embeddings.device) # type: ignore # yapf: disable
135
+
136
+ if inputs_embeds is None:
137
+ inputs_embeds = self.word_embeddings(input_ids)
138
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
139
+
140
+ embeddings = inputs_embeds + token_type_embeddings
141
+ # no position embeddings! ALiBi
142
+ embeddings = self.LayerNorm(embeddings)
143
+ embeddings = self.dropout(embeddings)
144
+ return embeddings
145
+
146
+
147
+ class BertUnpadSelfAttention(nn.Module):
148
+ """Performs multi-headed self attention on a batch of unpadded sequences.
149
+
150
+ If Triton is installed, this module uses Flash Attention to greatly improve throughput.
151
+ The Flash Attention implementation used in Mosaic BERT supports arbitrary attention biases (which
152
+ we use to implement ALiBi), but does not support attention dropout. If either Triton is not installed
153
+ or `config.attention_probs_dropout_prob > 0`, the implementation will default to a
154
+ math-equivalent pytorch version, which is much slower.
155
+
156
+ See `forward` method for additional detail.
157
+ """
158
+
159
+ def __init__(self, config):
160
+ super().__init__()
161
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
162
+ config, 'embedding_size'):
163
+ raise ValueError(
164
+ f'The hidden size ({config.hidden_size}) is not a multiple of the number of attention '
165
+ f'heads ({config.num_attention_heads})')
166
+
167
+ self.num_attention_heads = config.num_attention_heads
168
+ self.attention_head_size = int(config.hidden_size /
169
+ config.num_attention_heads)
170
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
171
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
172
+ self.p_dropout = config.attention_probs_dropout_prob
173
+ self.Wqkv = nn.Linear(self.all_head_size, 3 * config.hidden_size)
174
+
175
+ # Warn if defaulting to pytorch because of import issues
176
+ if flash_attn_qkvpacked_func is None:
177
+ warnings.warn(
178
+ 'Unable to import Triton; defaulting MosaicBERT attention implementation to pytorch (this will reduce throughput when using this model).'
179
+ )
180
+
181
+ def forward(self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor,
182
+ max_seqlen_in_batch: int, indices: torch.Tensor,
183
+ attn_mask: torch.Tensor, bias: torch.Tensor) -> torch.Tensor:
184
+ """Perform self-attention.
185
+
186
+ If dropout is zero, then we can use the Triton kernel, so we do that. However, if not, we send through a standard PyTorch
187
+ implementation of self-attention.
188
+
189
+ The arguments are unpadded, and our implementations of attention require padded arguments,
190
+ so we first call `pad_input`. Once we compute attention, we re-unpad our outputs for the other layers.
191
+ The pad/unpad operations add overhead, but not sending pad tokens through ffs saves compute.
192
+ It is possible to write an unpadded implementation of attention (in Triton and PyTorch), which we will eventually do.
193
+
194
+ Args:
195
+ hidden_states: (total_nnz, dim)
196
+ cu_seqlens: (batch + 1,)
197
+ max_seqlen_in_batch: int
198
+ indices: (total_nnz,)
199
+ attn_mask: (batch, max_seqlen_in_batch)
200
+ bias: (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
201
+
202
+ Returns:
203
+ attention: (total_nnz, dim)
204
+ """
205
+ qkv = self.Wqkv(hidden_states)
206
+ qkv = pad_input(qkv, indices, cu_seqlens.shape[0] - 1,
207
+ max_seqlen_in_batch) # batch, max_seqlen_in_batch, thd
208
+ qkv = rearrange(qkv,
209
+ 'b s (t h d) -> b s t h d',
210
+ t=3,
211
+ h=self.num_attention_heads)
212
+ if self.p_dropout or flash_attn_qkvpacked_func is None:
213
+ # if we have nonzero attention dropout (e.g. during fine-tuning) or no Triton, compute attention in PyTorch
214
+ q = qkv[:, :, 0, :, :].permute(0, 2, 1, 3) # b h s d
215
+ k = qkv[:, :, 1, :, :].permute(0, 2, 3, 1) # b h d s
216
+ v = qkv[:, :, 2, :, :].permute(0, 2, 1, 3) # b h s d
217
+ attention_scores = torch.matmul(q, k) / math.sqrt(
218
+ self.attention_head_size)
219
+ attention_scores = attention_scores + bias
220
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
221
+ attention_probs = self.dropout(attention_probs)
222
+ attention = torch.matmul(attention_probs, v).permute(0, 2, 1,
223
+ 3) # b s h d
224
+ else:
225
+ # Triton implementation only supports 0 attention dropout
226
+ convert_dtype = qkv.dtype not in [torch.float16, torch.bfloat16]
227
+ if convert_dtype:
228
+ # Triton implementation only supports fp16 and bf16
229
+ orig_dtype = qkv.dtype
230
+ qkv = qkv.to(torch.float16)
231
+ bias_dtype = bias.dtype
232
+ bias = bias.to(torch.float16)
233
+ attention = flash_attn_qkvpacked_func(qkv, bias)
234
+ attention = attention.to(orig_dtype)
235
+ bias = bias.to(bias_dtype)
236
+ else:
237
+ attention = flash_attn_qkvpacked_func(qkv, bias)
238
+
239
+ # attn_mask is 1 for attend and 0 for don't
240
+ attention = unpad_input_only(attention, torch.squeeze(attn_mask) == 1)
241
+ return rearrange(attention, 'nnz h d -> nnz (h d)')
242
+
243
+
244
+ # Copy of transformer's library BertSelfOutput that will not be caught by surgery methods looking for HF BERT modules.
245
+ class BertSelfOutput(nn.Module):
246
+ """Computes the output of the attention layer.
247
+
248
+ This module is modeled after the Hugging Face BERT's
249
+ :class:`~transformers.model.bert.modeling_bert.BertSelfOutput`.
250
+ The implementation is identical. Rather than use the original module
251
+ directly, we re-implement it here so that Mosaic BERT's modules will not
252
+ be affected by any Composer surgery algorithm that modifies Hugging Face
253
+ BERT modules.
254
+ """
255
+
256
+ def __init__(self, config):
257
+ super().__init__()
258
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
259
+ self.LayerNorm = nn.LayerNorm(config.hidden_size,
260
+ eps=config.layer_norm_eps)
261
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
262
+
263
+ def forward(self, hidden_states: torch.Tensor,
264
+ input_tensor: torch.Tensor) -> torch.Tensor:
265
+ hidden_states = self.dense(hidden_states)
266
+ hidden_states = self.dropout(hidden_states)
267
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
268
+ return hidden_states
269
+
270
+
271
+ class BertUnpadAttention(nn.Module):
272
+ """Chains attention, Dropout, and LayerNorm for Mosaic BERT."""
273
+
274
+ def __init__(self, config):
275
+ super().__init__()
276
+ self.self = BertUnpadSelfAttention(config)
277
+ self.output = BertSelfOutput(config)
278
+
279
+ def forward(
280
+ self,
281
+ input_tensor: torch.Tensor,
282
+ cu_seqlens: torch.Tensor,
283
+ max_s: int,
284
+ subset_idx: Optional[torch.Tensor] = None,
285
+ indices: Optional[torch.Tensor] = None,
286
+ attn_mask: Optional[torch.Tensor] = None,
287
+ bias: Optional[torch.Tensor] = None,
288
+ ) -> torch.Tensor:
289
+ """Forward pass for scaled self-attention without padding.
290
+
291
+ Arguments:
292
+ input_tensor: (total_nnz, dim)
293
+ cu_seqlens: (batch + 1,)
294
+ max_s: int
295
+ subset_idx: () set of indices whose values we care about at the end of the layer
296
+ (e.g., the masked tokens, if this is the final layer).
297
+ indices: None or (total_nnz,)
298
+ attn_mask: None or (batch, max_seqlen_in_batch)
299
+ bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
300
+ """
301
+ self_output = self.self(input_tensor, cu_seqlens, max_s, indices,
302
+ attn_mask, bias)
303
+ if subset_idx is not None:
304
+ return self.output(index_first_axis(self_output, subset_idx),
305
+ index_first_axis(input_tensor, subset_idx))
306
+ else:
307
+ return self.output(self_output, input_tensor)
308
+
309
+
310
+ class BertGatedLinearUnitMLP(nn.Module):
311
+ """Applies the FFN at the end of each Mosaic BERT layer.
312
+
313
+ Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
314
+ and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality, but
315
+ introduces Gated Linear Units.
316
+
317
+ Note: Mosaic BERT adds parameters in order to implement Gated Linear Units. To keep parameter count consistent with that of a
318
+ standard Hugging Face BERT, scale down `config.intermediate_size` by 2/3. For example, a Mosaic BERT constructed with
319
+ `config.intermediate_size=2048` will have the same parameter footprint as its Hugging Face BERT counterpart constructed
320
+ with the `config.intermediate_size=3072`.
321
+ However, in most cases it will not be necessary to adjust `config.intermediate_size` since, despite the increased
322
+ parameter size, Mosaic BERT typically offers a net higher throughput than a Hugging Face BERT built from the same `config`.
323
+ """
324
+
325
+ def __init__(self, config):
326
+ super().__init__()
327
+ self.config = config
328
+ self.gated_layers = nn.Linear(config.hidden_size,
329
+ config.intermediate_size * 2,
330
+ bias=False)
331
+ self.act = nn.GELU(approximate='none')
332
+ self.wo = nn.Linear(config.intermediate_size, config.hidden_size)
333
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
334
+ self.layernorm = nn.LayerNorm(config.hidden_size,
335
+ eps=config.layer_norm_eps)
336
+
337
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
338
+ """Compute new hidden states from current hidden states.
339
+
340
+ Args:
341
+ hidden_states (torch.Tensor): The (unpadded) hidden states from
342
+ the attention layer [nnz, dim].
343
+ """
344
+ residual_connection = hidden_states
345
+ # compute the activation
346
+ hidden_states = self.gated_layers(hidden_states)
347
+ gated = hidden_states[:, :self.config.intermediate_size]
348
+ non_gated = hidden_states[:, self.config.intermediate_size:]
349
+ hidden_states = self.act(gated) * non_gated
350
+ hidden_states = self.dropout(hidden_states)
351
+ # multiply by the second matrix
352
+ hidden_states = self.wo(hidden_states)
353
+ # add the residual connection and post-LN
354
+ hidden_states = self.layernorm(hidden_states + residual_connection)
355
+ return hidden_states
356
+
357
+
358
+ class BertLayer(nn.Module):
359
+ """Composes the Mosaic BERT attention and FFN blocks into a single layer."""
360
+
361
+ def __init__(self, config):
362
+ super(BertLayer, self).__init__()
363
+ self.attention = BertUnpadAttention(config)
364
+ self.mlp = BertGatedLinearUnitMLP(config)
365
+
366
+ def forward(
367
+ self,
368
+ hidden_states: torch.Tensor,
369
+ cu_seqlens: torch.Tensor,
370
+ seqlen: int,
371
+ subset_idx: Optional[torch.Tensor] = None,
372
+ indices: Optional[torch.Tensor] = None,
373
+ attn_mask: Optional[torch.Tensor] = None,
374
+ bias: Optional[torch.Tensor] = None,
375
+ ) -> torch.Tensor:
376
+ """Forward pass for a BERT layer, including both attention and MLP.
377
+
378
+ Args:
379
+ hidden_states: (total_nnz, dim)
380
+ cu_seqlens: (batch + 1,)
381
+ seqlen: int
382
+ subset_idx: () set of indices whose values we care about at the end of the layer
383
+ (e.g., the masked tokens, if this is the final layer).
384
+ indices: None or (total_nnz,)
385
+ attn_mask: None or (batch, max_seqlen_in_batch)
386
+ bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
387
+ """
388
+ attention_output = self.attention(hidden_states, cu_seqlens, seqlen,
389
+ subset_idx, indices, attn_mask, bias)
390
+ layer_output = self.mlp(attention_output)
391
+ return layer_output
392
+
393
+
394
+ class BertEncoder(nn.Module):
395
+ """A stack of BERT layers providing the backbone of Mosaic BERT.
396
+
397
+ This module is modeled after the Hugging Face BERT's :class:`~transformers.model.bert.modeling_bert.BertEncoder`,
398
+ but with substantial modifications to implement unpadding and ALiBi.
399
+
400
+ Compared to the analogous Hugging Face BERT module, this module handles unpadding to reduce unnecessary computation
401
+ at padded tokens, and pre-computes attention biases to implement ALiBi.
402
+ """
403
+
404
+ def __init__(self, config):
405
+ super().__init__()
406
+ layer = BertLayer(config)
407
+ self.layer = nn.ModuleList(
408
+ [copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
409
+
410
+ self.num_attention_heads = config.num_attention_heads
411
+
412
+ # The alibi mask will be dynamically expanded if it is too small for
413
+ # the input the model receives. But it generally helps to initialize it
414
+ # to a reasonably large size to help pre-allocate CUDA memory.
415
+ # The default `alibi_starting_size` is 512.
416
+ self._current_alibi_size = int(config.alibi_starting_size)
417
+ self.alibi = torch.zeros(
418
+ (1, self.num_attention_heads, self._current_alibi_size,
419
+ self._current_alibi_size))
420
+ self.rebuild_alibi_tensor(size=config.alibi_starting_size)
421
+
422
+ def rebuild_alibi_tensor(self,
423
+ size: int,
424
+ device: Optional[Union[torch.device, str]] = None):
425
+ # Alibi
426
+ # Following https://github.com/ofirpress/attention_with_linear_biases/issues/5 (Implementation 1)
427
+ # In the causal case, you can exploit the fact that softmax is invariant to a uniform translation
428
+ # of the logits, which makes the math work out *after* applying causal masking. If no causal masking
429
+ # will be applied, it is necessary to construct the diagonal mask.
430
+ n_heads = self.num_attention_heads
431
+
432
+ def _get_alibi_head_slopes(n_heads: int) -> List[float]:
433
+
434
+ def get_slopes_power_of_2(n_heads: int) -> List[float]:
435
+ start = (2**(-2**-(math.log2(n_heads) - 3)))
436
+ ratio = start
437
+ return [start * ratio**i for i in range(n_heads)]
438
+
439
+ # In the paper, they only train models that have 2^a heads for some a. This function
440
+ # has some good properties that only occur when the input is a power of 2. To
441
+ # maintain that even when the number of heads is not a power of 2, we use a
442
+ # workaround.
443
+ if math.log2(n_heads).is_integer():
444
+ return get_slopes_power_of_2(n_heads)
445
+
446
+ closest_power_of_2 = 2**math.floor(math.log2(n_heads))
447
+ slopes_a = get_slopes_power_of_2(closest_power_of_2)
448
+ slopes_b = _get_alibi_head_slopes(2 * closest_power_of_2)
449
+ slopes_b = slopes_b[0::2][:n_heads - closest_power_of_2]
450
+ return slopes_a + slopes_b
451
+
452
+ context_position = torch.arange(size, device=device)[:, None]
453
+ memory_position = torch.arange(size, device=device)[None, :]
454
+ relative_position = torch.abs(memory_position - context_position)
455
+ # [n_heads, max_token_length, max_token_length]
456
+ relative_position = relative_position.unsqueeze(0).expand(
457
+ n_heads, -1, -1)
458
+ slopes = torch.Tensor(_get_alibi_head_slopes(n_heads)).to(device)
459
+ alibi = slopes.unsqueeze(1).unsqueeze(1) * -relative_position
460
+ # [1, n_heads, max_token_length, max_token_length]
461
+ alibi = alibi.unsqueeze(0)
462
+ assert alibi.shape == torch.Size([1, n_heads, size, size])
463
+
464
+ self._current_alibi_size = size
465
+ self.alibi = alibi
466
+
467
+ def forward(
468
+ self,
469
+ hidden_states: torch.Tensor,
470
+ attention_mask: torch.Tensor,
471
+ output_all_encoded_layers: Optional[bool] = True,
472
+ subset_mask: Optional[torch.Tensor] = None,
473
+ ) -> List[torch.Tensor]:
474
+
475
+ extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
476
+ extended_attention_mask = extended_attention_mask.to(
477
+ dtype=next(self.parameters()).dtype) # fp16 compatibility
478
+ extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
479
+
480
+ attention_mask_bool = attention_mask.bool()
481
+ batch, seqlen = hidden_states.shape[:2]
482
+ # Unpad inputs and mask. It will remove tokens that are padded.
483
+ # Assume ntokens is total number of tokens (padded and non-padded)
484
+ # and ntokens_unpad is total number of non-padded tokens.
485
+ # Then unpadding performs the following compression of the inputs:
486
+ # hidden_states[ntokens,hidden] -> hidden_states[ntokens_unpad,hidden]
487
+ hidden_states, indices, cu_seqlens, _ = unpad_input(
488
+ hidden_states, attention_mask_bool)
489
+
490
+ # Add alibi matrix to extended_attention_mask
491
+ if self._current_alibi_size < seqlen:
492
+ # Rebuild the alibi tensor when needed
493
+ warnings.warn(
494
+ f'Increasing alibi size from {self._current_alibi_size} to {seqlen}'
495
+ )
496
+ self.rebuild_alibi_tensor(size=seqlen, device=hidden_states.device)
497
+ elif self.alibi.device != hidden_states.device:
498
+ # Device catch-up
499
+ self.alibi = self.alibi.to(hidden_states.device)
500
+ alibi_bias = self.alibi[:, :, :seqlen, :seqlen]
501
+ attn_bias = extended_attention_mask[:, :, :seqlen, :seqlen]
502
+ alibi_attn_mask = attn_bias + alibi_bias
503
+
504
+ all_encoder_layers = []
505
+ if subset_mask is None:
506
+ for layer_module in self.layer:
507
+ hidden_states = layer_module(hidden_states,
508
+ cu_seqlens,
509
+ seqlen,
510
+ None,
511
+ indices,
512
+ attn_mask=attention_mask,
513
+ bias=alibi_attn_mask)
514
+ if output_all_encoded_layers:
515
+ all_encoder_layers.append(hidden_states)
516
+ # Pad inputs and mask. It will insert back zero-padded tokens.
517
+ # Assume ntokens is total number of tokens (padded and non-padded)
518
+ # and ntokens_unpad is total number of non-padded tokens.
519
+ # Then padding performs the following de-compression:
520
+ # hidden_states[ntokens_unpad,hidden] -> hidden_states[ntokens,hidden]
521
+ hidden_states = pad_input(hidden_states, indices, batch, seqlen)
522
+ else:
523
+ for i in range(len(self.layer) - 1):
524
+ layer_module = self.layer[i]
525
+ hidden_states = layer_module(hidden_states,
526
+ cu_seqlens,
527
+ seqlen,
528
+ None,
529
+ indices,
530
+ attn_mask=attention_mask,
531
+ bias=alibi_attn_mask)
532
+ if output_all_encoded_layers:
533
+ all_encoder_layers.append(hidden_states)
534
+ subset_idx = torch.nonzero(subset_mask[attention_mask_bool],
535
+ as_tuple=False).flatten()
536
+ hidden_states = self.layer[-1](hidden_states,
537
+ cu_seqlens,
538
+ seqlen,
539
+ subset_idx=subset_idx,
540
+ indices=indices,
541
+ attn_mask=attention_mask,
542
+ bias=alibi_attn_mask)
543
+
544
+ if not output_all_encoded_layers:
545
+ all_encoder_layers.append(hidden_states)
546
+ return all_encoder_layers
547
+
548
+
549
+ class BertPooler(nn.Module):
550
+
551
+ def __init__(self, config):
552
+ super(BertPooler, self).__init__()
553
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
554
+ self.activation = nn.Tanh()
555
+
556
+ def forward(self,
557
+ hidden_states: torch.Tensor,
558
+ pool: Optional[bool] = True) -> torch.Tensor:
559
+ # We "pool" the model by simply taking the hidden state corresponding
560
+ # to the first token.
561
+ first_token_tensor = hidden_states[:, 0] if pool else hidden_states
562
+ pooled_output = self.dense(first_token_tensor)
563
+ pooled_output = self.activation(pooled_output)
564
+ return pooled_output
565
+
566
+
567
+ class BertPredictionHeadTransform(nn.Module):
568
+
569
+ def __init__(self, config):
570
+ super().__init__()
571
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
572
+ if isinstance(config.hidden_act, str):
573
+ self.transform_act_fn = ACT2FN[config.hidden_act]
574
+ else:
575
+ self.transform_act_fn = config.hidden_act
576
+ self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)
577
+
578
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
579
+ hidden_states = self.dense(hidden_states)
580
+ hidden_states = self.transform_act_fn(hidden_states)
581
+ hidden_states = self.LayerNorm(hidden_states)
582
+ return hidden_states
583
+
584
+
585
+ class BertModel(BertPreTrainedModel):
586
+ """Overall BERT model.
587
+
588
+ Args:
589
+ config: a BertConfig class instance with the configuration to build a new model
590
+
591
+ Inputs:
592
+ `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
593
+ with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
594
+ `extract_features.py`, `run_classifier.py` and `run_squad.py`)
595
+ `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
596
+ types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
597
+ a `sentence B` token (see BERT paper for more details).
598
+ `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
599
+ selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
600
+ input sequence length in the current batch. It's the mask that we typically use for attention when
601
+ a batch has varying length sentences.
602
+ `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
603
+
604
+ Outputs: Tuple of (encoded_layers, pooled_output)
605
+ `encoded_layers`: controlled by `output_all_encoded_layers` argument:
606
+ - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
607
+ of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
608
+ encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
609
+ - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
610
+ to the last attention block of shape [batch_size, sequence_length, hidden_size],
611
+ `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
612
+ classifier pretrained on top of the hidden state associated to the first character of the
613
+ input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
614
+
615
+ Example usage:
616
+ ```python
617
+ # Already been converted into WordPiece token ids
618
+ input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
619
+ input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
620
+ token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
621
+ config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
622
+ num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
623
+ model = BertModel(config=config)
624
+ all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
625
+ ```
626
+ """
627
+
628
+ def __init__(self, config, add_pooling_layer=True):
629
+ super(BertModel, self).__init__(config)
630
+ self.embeddings = BertEmbeddings(config)
631
+ self.encoder = BertEncoder(config)
632
+ self.pooler = BertPooler(config) if add_pooling_layer else None
633
+ self.post_init()
634
+
635
+ def get_input_embeddings(self):
636
+ return self.embeddings.word_embeddings
637
+
638
+ def set_input_embeddings(self, value):
639
+ self.embeddings.word_embeddings = value
640
+
641
+ def forward(
642
+ self,
643
+ input_ids: torch.Tensor,
644
+ token_type_ids: Optional[torch.Tensor] = None,
645
+ attention_mask: Optional[torch.Tensor] = None,
646
+ position_ids: Optional[torch.Tensor] = None,
647
+ output_all_encoded_layers: Optional[bool] = False,
648
+ masked_tokens_mask: Optional[torch.Tensor] = None,
649
+ **kwargs
650
+ ) -> Tuple[Union[List[torch.Tensor], torch.Tensor], Optional[torch.Tensor]]:
651
+ if attention_mask is None:
652
+ attention_mask = torch.ones_like(input_ids)
653
+ if token_type_ids is None:
654
+ token_type_ids = torch.zeros_like(input_ids)
655
+
656
+ embedding_output = self.embeddings(input_ids, token_type_ids,
657
+ position_ids)
658
+
659
+ subset_mask = []
660
+ first_col_mask = []
661
+
662
+ if masked_tokens_mask is None:
663
+ subset_mask = None
664
+ else:
665
+ first_col_mask = torch.zeros_like(masked_tokens_mask)
666
+ first_col_mask[:, 0] = True
667
+ subset_mask = masked_tokens_mask | first_col_mask
668
+
669
+ encoder_outputs = self.encoder(
670
+ embedding_output,
671
+ attention_mask,
672
+ output_all_encoded_layers=output_all_encoded_layers,
673
+ subset_mask=subset_mask)
674
+
675
+ if masked_tokens_mask is None:
676
+ sequence_output = encoder_outputs[-1]
677
+ pooled_output = self.pooler(
678
+ sequence_output) if self.pooler is not None else None
679
+ else:
680
+ # TD [2022-03-01]: the indexing here is very tricky.
681
+ attention_mask_bool = attention_mask.bool()
682
+ subset_idx = subset_mask[attention_mask_bool] # type: ignore
683
+ sequence_output = encoder_outputs[-1][
684
+ masked_tokens_mask[attention_mask_bool][subset_idx]]
685
+ if self.pooler is not None:
686
+ pool_input = encoder_outputs[-1][
687
+ first_col_mask[attention_mask_bool][subset_idx]]
688
+ pooled_output = self.pooler(pool_input, pool=False)
689
+ else:
690
+ pooled_output = None
691
+
692
+ if not output_all_encoded_layers:
693
+ encoder_outputs = sequence_output
694
+
695
+ if self.pooler is not None:
696
+ return encoder_outputs, pooled_output
697
+
698
+ return encoder_outputs, None
699
+
700
+
701
+ ###################
702
+ # Bert Heads
703
+ ###################
704
+ class BertLMPredictionHead(nn.Module):
705
+
706
+ def __init__(self, config, bert_model_embedding_weights):
707
+ super().__init__()
708
+ self.transform = BertPredictionHeadTransform(config)
709
+ # The output weights are the same as the input embeddings, but there is
710
+ # an output-only bias for each token.
711
+ self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
712
+ bert_model_embedding_weights.size(0))
713
+ self.decoder.weight = bert_model_embedding_weights
714
+
715
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
716
+ hidden_states = self.transform(hidden_states)
717
+ hidden_states = self.decoder(hidden_states)
718
+ return hidden_states
719
+
720
+
721
+ class BertOnlyMLMHead(nn.Module):
722
+
723
+ def __init__(self, config, bert_model_embedding_weights):
724
+ super().__init__()
725
+ self.predictions = BertLMPredictionHead(config,
726
+ bert_model_embedding_weights)
727
+
728
+ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
729
+ prediction_scores = self.predictions(sequence_output)
730
+ return prediction_scores
731
+
732
+
733
+ class BertOnlyNSPHead(nn.Module):
734
+
735
+ def __init__(self, config):
736
+ super().__init__()
737
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
738
+
739
+ def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
740
+ seq_relationship_score = self.seq_relationship(pooled_output)
741
+ return seq_relationship_score
742
+
743
+
744
+ #####################
745
+ # Various Bert models
746
+ #####################
747
+
748
+
749
+ class BertForPreTraining(BertPreTrainedModel):
750
+ #TBD: Coming in Future Commit
751
+ pass
752
+
753
+
754
+ class BertLMHeadModel(BertPreTrainedModel):
755
+ #TBD: Coming in Future Commit
756
+ pass
757
+
758
+
759
+ class BertForMaskedLM(BertPreTrainedModel):
760
+
761
+ def __init__(self, config):
762
+ super().__init__(config)
763
+
764
+ if config.is_decoder:
765
+ warnings.warn(
766
+ 'If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for '
767
+ 'bi-directional self-attention.')
768
+
769
+ self.bert = BertModel(config, add_pooling_layer=False)
770
+ self.cls = BertOnlyMLMHead(config,
771
+ self.bert.embeddings.word_embeddings.weight)
772
+
773
+ # Initialize weights and apply final processing
774
+ self.post_init()
775
+
776
+ @classmethod
777
+ def from_composer(cls,
778
+ pretrained_checkpoint,
779
+ state_dict=None,
780
+ cache_dir=None,
781
+ from_tf=False,
782
+ config=None,
783
+ *inputs,
784
+ **kwargs):
785
+ """Load from pre-trained."""
786
+ model = cls(config, *inputs, **kwargs)
787
+ if from_tf:
788
+ raise ValueError(
789
+ 'Mosaic BERT does not support loading TensorFlow weights.')
790
+
791
+ state_dict = torch.load(pretrained_checkpoint)
792
+ # If the state_dict was saved after wrapping with `composer.HuggingFaceModel`, it takes on the `model` prefix
793
+ consume_prefix_in_state_dict_if_present(state_dict, prefix='model.')
794
+ missing_keys, unexpected_keys = model.load_state_dict(state_dict,
795
+ strict=False)
796
+
797
+ if len(missing_keys) > 0:
798
+ logger.warning(
799
+ f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}"
800
+ )
801
+ if len(unexpected_keys) > 0:
802
+ logger.warning(
803
+ f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}"
804
+ )
805
+
806
+ return model
807
+
808
+ def get_output_embeddings(self):
809
+ return self.cls.predictions.decoder
810
+
811
+ def set_output_embeddings(self, new_embeddings):
812
+ self.cls.predictions.decoder = new_embeddings
813
+
814
+ def forward(
815
+ self,
816
+ input_ids: Optional[torch.Tensor] = None,
817
+ attention_mask: Optional[torch.Tensor] = None,
818
+ token_type_ids: Optional[torch.Tensor] = None,
819
+ position_ids: Optional[torch.Tensor] = None,
820
+ head_mask: Optional[torch.Tensor] = None,
821
+ inputs_embeds: Optional[torch.Tensor] = None,
822
+ encoder_hidden_states: Optional[torch.Tensor] = None,
823
+ encoder_attention_mask: Optional[torch.Tensor] = None,
824
+ labels: Optional[torch.Tensor] = None,
825
+ output_attentions: Optional[bool] = None,
826
+ output_hidden_states: Optional[bool] = None,
827
+ return_dict: Optional[bool] = None,
828
+ ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
829
+ # labels should be a `torch.LongTensor` of shape
830
+ # `(batch_size, sequence_length)`. These are used for computing the
831
+ # masked language modeling loss.
832
+ #
833
+ # Indices should be in `[-100, 0, ..., config.vocab_size]` (see
834
+ # `input_ids` docstring) Tokens with indices set to `-100` are ignored
835
+ # (masked), the loss is only computed for the tokens with labels in `[0,
836
+ # ..., config.vocab_size]`
837
+ #
838
+ # Prediction scores are only computed for masked tokens and the (bs,
839
+ # seqlen) dimensions are flattened
840
+ if (input_ids is not None) == (inputs_embeds is not None):
841
+ raise ValueError('Must specify either input_ids or input_embeds!')
842
+
843
+ if labels is None:
844
+ masked_tokens_mask = None
845
+ else:
846
+ masked_tokens_mask = labels > 0
847
+
848
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
849
+
850
+ outputs = self.bert(
851
+ input_ids,
852
+ attention_mask=attention_mask,
853
+ token_type_ids=token_type_ids,
854
+ position_ids=position_ids,
855
+ head_mask=head_mask,
856
+ inputs_embeds=inputs_embeds,
857
+ encoder_hidden_states=encoder_hidden_states,
858
+ encoder_attention_mask=encoder_attention_mask,
859
+ output_attentions=output_attentions,
860
+ output_hidden_states=output_hidden_states,
861
+ return_dict=return_dict,
862
+ masked_tokens_mask=masked_tokens_mask,
863
+ )
864
+
865
+ sequence_output = outputs[0]
866
+ prediction_scores = self.cls(sequence_output)
867
+
868
+ loss = None
869
+ if labels is not None:
870
+ # Compute loss
871
+ loss_fct = nn.CrossEntropyLoss()
872
+ masked_token_idx = torch.nonzero(labels.flatten() > 0,
873
+ as_tuple=False).flatten()
874
+ loss = loss_fct(prediction_scores,
875
+ labels.flatten()[masked_token_idx])
876
+
877
+ assert input_ids is not None, 'Coding error; please open an issue'
878
+ batch, seqlen = input_ids.shape[:2]
879
+ prediction_scores = rearrange(index_put_first_axis(
880
+ prediction_scores, masked_token_idx, batch * seqlen),
881
+ '(b s) d -> b s d',
882
+ b=batch)
883
+
884
+ if not return_dict:
885
+ output = (prediction_scores,) + outputs[2:]
886
+ return ((loss,) + output) if loss is not None else output
887
+
888
+ return MaskedLMOutput(
889
+ loss=loss,
890
+ logits=prediction_scores,
891
+ hidden_states=None,
892
+ attentions=None,
893
+ )
894
+
895
+ def prepare_inputs_for_generation(self, input_ids: torch.Tensor,
896
+ attention_mask: torch.Tensor,
897
+ **model_kwargs):
898
+ input_shape = input_ids.shape
899
+ effective_batch_size = input_shape[0]
900
+
901
+ # add a dummy token
902
+ if self.config.pad_token_id is None:
903
+ raise ValueError('The PAD token should be defined for generation')
904
+
905
+ attention_mask = torch.cat([
906
+ attention_mask,
907
+ attention_mask.new_zeros((attention_mask.shape[0], 1))
908
+ ],
909
+ dim=-1)
910
+ dummy_token = torch.full((effective_batch_size, 1),
911
+ self.config.pad_token_id,
912
+ dtype=torch.long,
913
+ device=input_ids.device)
914
+ input_ids = torch.cat([input_ids, dummy_token], dim=1)
915
+
916
+ return {'input_ids': input_ids, 'attention_mask': attention_mask}
917
+
918
+
919
+ class BertForNextSentencePrediction(BertPreTrainedModel):
920
+ #TBD: Push in future commit
921
+ pass
922
+
923
+
924
+ class BertForSequenceClassification(BertPreTrainedModel):
925
+ """Bert Model transformer with a sequence classification/regression head.
926
+
927
+ This head is just a linear layer on top of the pooled output. Used for,
928
+ e.g., GLUE tasks.
929
+ """
930
+
931
+ def __init__(self, config):
932
+ super().__init__(config)
933
+ self.num_labels = config.num_labels
934
+ self.config = config
935
+
936
+ self.bert = BertModel(config)
937
+ classifier_dropout = (config.classifier_dropout
938
+ if config.classifier_dropout is not None else
939
+ config.hidden_dropout_prob)
940
+ self.dropout = nn.Dropout(classifier_dropout)
941
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
942
+
943
+ # Initialize weights and apply final processing
944
+ self.post_init()
945
+
946
+ @classmethod
947
+ def from_composer(cls,
948
+ pretrained_checkpoint,
949
+ state_dict=None,
950
+ cache_dir=None,
951
+ from_tf=False,
952
+ config=None,
953
+ *inputs,
954
+ **kwargs):
955
+ """Load from pre-trained."""
956
+ model = cls(config, *inputs, **kwargs)
957
+ if from_tf:
958
+ raise ValueError(
959
+ 'Mosaic BERT does not support loading TensorFlow weights.')
960
+
961
+ state_dict = torch.load(pretrained_checkpoint)
962
+ # If the state_dict was saved after wrapping with `composer.HuggingFaceModel`, it takes on the `model` prefix
963
+ consume_prefix_in_state_dict_if_present(state_dict, prefix='model.')
964
+ missing_keys, unexpected_keys = model.load_state_dict(state_dict,
965
+ strict=False)
966
+
967
+ if len(missing_keys) > 0:
968
+ logger.warning(
969
+ f"Found these missing keys in the checkpoint: {', '.join(missing_keys)}"
970
+ )
971
+ if len(unexpected_keys) > 0:
972
+ logger.warning(
973
+ f"Found these unexpected keys in the checkpoint: {', '.join(unexpected_keys)}"
974
+ )
975
+
976
+ return model
977
+
978
+ def forward(
979
+ self,
980
+ input_ids: Optional[torch.Tensor] = None,
981
+ attention_mask: Optional[torch.Tensor] = None,
982
+ token_type_ids: Optional[torch.Tensor] = None,
983
+ position_ids: Optional[torch.Tensor] = None,
984
+ head_mask: Optional[torch.Tensor] = None,
985
+ inputs_embeds: Optional[torch.Tensor] = None,
986
+ labels: Optional[torch.Tensor] = None,
987
+ output_attentions: Optional[bool] = None,
988
+ output_hidden_states: Optional[bool] = None,
989
+ return_dict: Optional[bool] = None,
990
+ ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
991
+ # labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
992
+ # Labels for computing the sequence classification/regression loss.
993
+ # Indices should be in `[0, ..., config.num_labels - 1]`.
994
+ # If `config.num_labels == 1` a regression loss is computed
995
+ # (mean-square loss). If `config.num_labels > 1` a classification loss
996
+ # is computed (cross-entropy).
997
+
998
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
999
+
1000
+ outputs = self.bert(
1001
+ input_ids,
1002
+ attention_mask=attention_mask,
1003
+ token_type_ids=token_type_ids,
1004
+ position_ids=position_ids,
1005
+ head_mask=head_mask,
1006
+ inputs_embeds=inputs_embeds,
1007
+ output_attentions=output_attentions,
1008
+ output_hidden_states=output_hidden_states,
1009
+ return_dict=return_dict,
1010
+ )
1011
+
1012
+ pooled_output = outputs[1]
1013
+
1014
+ pooled_output = self.dropout(pooled_output)
1015
+ logits = self.classifier(pooled_output)
1016
+
1017
+ loss = None
1018
+ if labels is not None:
1019
+ # Compute loss
1020
+ if self.config.problem_type is None:
1021
+ if self.num_labels == 1:
1022
+ self.config.problem_type = 'regression'
1023
+ elif self.num_labels > 1 and (labels.dtype == torch.long or
1024
+ labels.dtype == torch.int):
1025
+ self.config.problem_type = 'single_label_classification'
1026
+ else:
1027
+ self.config.problem_type = 'multi_label_classification'
1028
+
1029
+ if self.config.problem_type == 'regression':
1030
+ loss_fct = nn.MSELoss()
1031
+ if self.num_labels == 1:
1032
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
1033
+ else:
1034
+ loss = loss_fct(logits, labels)
1035
+ elif self.config.problem_type == 'single_label_classification':
1036
+ loss_fct = nn.CrossEntropyLoss()
1037
+ loss = loss_fct(logits.view(-1, self.num_labels),
1038
+ labels.view(-1))
1039
+ elif self.config.problem_type == 'multi_label_classification':
1040
+ loss_fct = nn.BCEWithLogitsLoss()
1041
+ loss = loss_fct(logits, labels)
1042
+
1043
+ if not return_dict:
1044
+ output = (logits,) + outputs[2:]
1045
+ return ((loss,) + output) if loss is not None else output
1046
+
1047
+ return SequenceClassifierOutput(
1048
+ loss=loss,
1049
+ logits=logits,
1050
+ hidden_states=None,
1051
+ attentions=None,
1052
+ )
1053
+
1054
+
1055
+ class BertForMultipleChoice(BertPreTrainedModel):
1056
+ #TBD: Push in future commit
1057
+ pass
1058
+
1059
+
1060
+ class BertForTokenClassification(BertPreTrainedModel):
1061
+ #TBD: Push in future commit
1062
+ pass
1063
+
1064
+
1065
+ class BertForQuestionAnswering(BertPreTrainedModel):
1066
+ """Bert Model with a span classification head.
1067
+
1068
+ This is used for extractive question-answering tasks like SQuAD (a linear
1069
+ layers on top of the hidden states' output to compute `span start logits`
1070
+ and `span end logits`).
1071
+ """
1072
+ #TBD: Push in future commit
bert_padding.py ADDED
@@ -0,0 +1,159 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 MosaicML Examples authors
2
+ # SPDX-License-Identifier: Apache-2.0
3
+
4
+ # Adapted from https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py
5
+ # Which was adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
6
+
7
+ """Helper functions for padding and unpadding batches.
8
+
9
+ These functions are used extensively throughout the Mosaic BERT implementation
10
+ in `bert_layers.py`.
11
+ """
12
+
13
+ from typing import Tuple, cast
14
+
15
+ import torch
16
+ import torch.nn.functional as F
17
+ from einops import rearrange, repeat
18
+
19
+
20
+ class IndexFirstAxis(torch.autograd.Function):
21
+
22
+ @staticmethod
23
+ def forward(ctx, input: torch.Tensor,
24
+ indices: torch.Tensor) -> torch.Tensor:
25
+ """Get just the values of `input` which are at `indices`.
26
+
27
+ Arguments:
28
+ ctx: the autograd context object
29
+ input: (b, ...) 2+ dimensional tensor
30
+ indices: (num_idx) 1D tensor
31
+ """
32
+ ctx.save_for_backward(indices)
33
+ assert input.ndim >= 2
34
+ ctx.first_axis_dim, other_shape = input.shape[0], input.shape[
35
+ 1:] # type: ignore
36
+ second_dim = other_shape.numel(
37
+ ) # product of sizes of all but first dimension
38
+ # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
39
+ return torch.gather(
40
+ rearrange(input, 'b ... -> b (...)'), # (b, ...) -> (b, second_dim)
41
+ 0,
42
+ repeat(indices, 'z -> z d',
43
+ d=second_dim) # (indices,) -> (indices, second_dim)
44
+ ).reshape(-1, *other_shape) # (num_idx, ...)
45
+
46
+ @staticmethod
47
+ def backward(ctx, grad_output: torch.Tensor) -> Tuple[torch.Tensor, None]:
48
+ indices, = ctx.saved_tensors
49
+ assert grad_output.ndim >= 2
50
+ other_shape = grad_output.shape[1:]
51
+ grad_output = rearrange(grad_output, 'b ... -> b (...)')
52
+ grad_input = torch.zeros([ctx.first_axis_dim, grad_output.shape[1]],
53
+ device=grad_output.device,
54
+ dtype=grad_output.dtype)
55
+ # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
56
+ # grad_input[indices] = grad_output
57
+ grad_input.scatter_(0,
58
+ repeat(indices, 'z -> z d', d=grad_output.shape[1]),
59
+ grad_output)
60
+ return grad_input.reshape(ctx.first_axis_dim, *other_shape), None
61
+
62
+
63
+ index_first_axis = IndexFirstAxis.apply
64
+
65
+
66
+ class IndexPutFirstAxis(torch.autograd.Function):
67
+
68
+ @staticmethod
69
+ def forward(ctx, values: torch.Tensor, indices: torch.Tensor,
70
+ first_axis_dim) -> torch.Tensor:
71
+ ctx.save_for_backward(indices)
72
+ assert indices.ndim == 1
73
+ assert values.ndim >= 2
74
+ output = torch.zeros(first_axis_dim,
75
+ *values.shape[1:],
76
+ device=values.device,
77
+ dtype=values.dtype)
78
+ output[indices] = values
79
+ return output
80
+
81
+ @staticmethod
82
+ def backward(ctx,
83
+ grad_output: torch.Tensor) -> Tuple[torch.Tensor, None, None]:
84
+ indices, = ctx.saved_tensors
85
+ grad_values = grad_output[indices]
86
+ return grad_values, None, None
87
+
88
+
89
+ index_put_first_axis = IndexPutFirstAxis.apply
90
+
91
+
92
+ def unpad_input(
93
+ hidden_states: torch.Tensor,
94
+ attention_mask: torch.Tensor,
95
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]:
96
+ """Remove padding from input sequences.
97
+
98
+ Arguments:
99
+ hidden_states: (batch, seqlen, ...)
100
+ attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
101
+
102
+ Returns:
103
+ hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
104
+ indices: (total_nnz)
105
+ cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
106
+ max_seqlen_in_batch: int ()
107
+ """
108
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
109
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
110
+ max_seqlen_in_batch = int(seqlens_in_batch.max().item())
111
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32),
112
+ (1, 0))
113
+ # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
114
+ # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
115
+ # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
116
+ # index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
117
+ # so we write custom forward and backward to make it a bit faster.
118
+ hidden_states = cast(
119
+ torch.Tensor,
120
+ index_first_axis(rearrange(hidden_states, 'b s ... -> (b s) ...'),
121
+ indices))
122
+ return hidden_states, indices, cu_seqlens, max_seqlen_in_batch
123
+
124
+
125
+ def unpad_input_only(
126
+ hidden_states: torch.Tensor,
127
+ attention_mask: torch.Tensor,
128
+ ) -> torch.Tensor:
129
+ """Like unpad_input, but only return the unpadded first tensor.
130
+
131
+ Save a small amount of overhead.
132
+
133
+ Arguments:
134
+ hidden_states: (batch, seqlen, ...)
135
+ attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
136
+
137
+ Returns:
138
+ hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
139
+ """
140
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
141
+ return index_first_axis(rearrange(hidden_states, 'b s ... -> (b s) ...'),
142
+ indices)
143
+
144
+
145
+ def pad_input(hidden_states: torch.Tensor, indices: torch.Tensor, batch: int,
146
+ seqlen: int) -> torch.Tensor:
147
+ """Add padding to sequences.
148
+
149
+ Arguments:
150
+ hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
151
+ indices: (total_nnz)
152
+ batch: int batch_size
153
+ seqlen: int max sequence length
154
+
155
+ Returns:
156
+ hidden_states: (batch, seqlen, ...)
157
+ """
158
+ output = index_put_first_axis(hidden_states, indices, batch * seqlen)
159
+ return rearrange(output, '(b s) ... -> b s ...', b=batch)
config.json ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "hum-lodestone-v1",
3
+ "alibi_starting_size": 4096,
4
+ "architectures": [
5
+ "BertModel"
6
+ ],
7
+ "attention_probs_dropout_prob": 0.0,
8
+ "auto_map": {
9
+ "AutoConfig": "configuration_bert.BertConfig",
10
+ "AutoModel": "bert_layers.BertModel",
11
+ "AutoModelForMaskedLM": "bert_layers.BertForMaskedLM"
12
+ },
13
+ "classifier_dropout": null,
14
+ "gradient_checkpointing": false,
15
+ "hidden_act": "gelu",
16
+ "hidden_dropout_prob": 0.1,
17
+ "hidden_size": 768,
18
+ "initializer_range": 0.02,
19
+ "intermediate_size": 3072,
20
+ "layer_norm_eps": 1e-12,
21
+ "max_position_embeddings": 512,
22
+ "model_type": "bert",
23
+ "num_attention_heads": 12,
24
+ "num_hidden_layers": 12,
25
+ "pad_token_id": 0,
26
+ "position_embedding_type": "absolute",
27
+ "tokenizer_class": "BertTokenizerFast",
28
+ "torch_dtype": "bfloat16",
29
+ "transformers_version": "4.28.1",
30
+ "type_vocab_size": 2,
31
+ "use_cache": true,
32
+ "vocab_size": 30528
33
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.2.2",
4
+ "transformers": "4.28.1",
5
+ "pytorch": "2.0.1+cu117"
6
+ }
7
+ }
configuration_bert.py ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 MosaicML Examples authors
2
+ # SPDX-License-Identifier: Apache-2.0
3
+
4
+ from transformers import BertConfig as TransformersBertConfig
5
+
6
+
7
+ class BertConfig(TransformersBertConfig):
8
+
9
+ def __init__(
10
+ self,
11
+ alibi_starting_size: int = 512,
12
+ attention_probs_dropout_prob: float = 0.0,
13
+ **kwargs,
14
+ ):
15
+ """Configuration class for MosaicBert.
16
+
17
+ Args:
18
+ alibi_starting_size (int): Use `alibi_starting_size` to determine how large of an alibi tensor to
19
+ create when initializing the model. You should be able to ignore this parameter in most cases.
20
+ Defaults to 512.
21
+ attention_probs_dropout_prob (float): By default, turn off attention dropout in Mosaic BERT
22
+ (otherwise, Flash Attention will be off by default). Defaults to 0.0.
23
+ """
24
+ super().__init__(attention_probs_dropout_prob=attention_probs_dropout_prob, **kwargs)
25
+ self.alibi_starting_size = alibi_starting_size
data_records.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"AllNLI.json.gz": 277230, "CodeSearchNet.json.gz": 1375067, "NQ-train_pairs.json.gz": 100231, "PAQ_pairs.json.gz": 64371441, "S2ORC_citation_pairs.json.gz": 52603982, "S2ORC_citations_abstracts.json.gz": 252102397, "S2ORC_title_abstract.json.gz": 41769185, "SimpleWiki.json.gz": 102225, "TriviaQA_pairs.json.gz": 73346, "WikiAnswers_pairs.json.gz": 77427422, "agnews.json.gz": 1157745, "altlex.json.gz": 112696, "amazon-qa.json.gz": 2507114, "amazon_review_2018.json.gz": 87877725, "ccnews_title_text.json.gz": 614664, "cnn_dailymail.json.gz": 311971, "coco_captions.json.gz": 828395, "eli5_question_answer.json.gz": 325475, "fever_train.json.gz": 139051, "flickr30k_captions.json.gz": 317695, "gooaq_pairs.json.gz": 3012496, "msmarco-query_passage.json.gz": 532751, "msmarco-query_passage_negative.json.gz": 9144553, "npr.json.gz": 594384, "quora_duplicates.json.gz": 103663, "quora_duplicates_triplets.json.gz": 103663, "reddit-title-body/reddit_title_text_2010.json.gz": 431782, 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flash_attn_triton.py ADDED
@@ -0,0 +1,1112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright 2022 MosaicML Examples authors
2
+ # SPDX-License-Identifier: Apache-2.0
3
+
4
+ """Triton implementation of Flash Attention.
5
+
6
+ # Copyright (c) 2022, Tri Dao.
7
+ #
8
+ # Licensed under the Apache License, Version 2.0 (the "License");
9
+ # you may not use this file except in compliance with the License.
10
+ # You may obtain a copy of the License at
11
+ #
12
+ # http://www.apache.org/licenses/LICENSE-2.0
13
+ #
14
+ # Unless required by applicable law or agreed to in writing, software
15
+ # distributed under the License is distributed on an "AS IS" BASIS,
16
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
17
+ # See the License for the specific language governing permissions and
18
+ # limitations under the License.
19
+
20
+ *Experimental* implementation of FlashAttention in Triton.
21
+ We use the FlashAttention implementation from Phil Tillet a starting point.
22
+ https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py
23
+
24
+ Changes:
25
+ - Implement both causal and non-causal attention.
26
+ - Implement both self-attention and cross-attention.
27
+ - Support arbitrary seqlens (not just multiples of 128), for both forward and backward.
28
+ - Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward.
29
+ - Support attention bias.
30
+ - Speed up the forward pass a bit, and only store the LSE instead of m and l.
31
+ - Make the backward for d=128 much faster by reducing register spilling.
32
+ - Optionally parallelize the backward pass across seqlen_k, to deal with the case of
33
+ small batch size * nheads.
34
+
35
+ Caution:
36
+ - If you plan to use headdim other than 64 and 128, you should test for race conditions
37
+ (due to the Triton compiler), as done in tests/test_flash_attn.py
38
+ "test_flash_attn_triton_race_condition". I've tested and fixed many race conditions
39
+ for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident
40
+ that there are none left for other head dimensions.
41
+ Differences between this Triton version and the CUDA version:
42
+ - Triton version doesn't support dropout.
43
+ - Triton forward is generally faster than CUDA forward.
44
+ - Triton backward is faster than CUDA backward when batch * nheads is small, and when headdim=64.
45
+ It is slightly slower when headdim=128 and batch * nheads is large.
46
+ - Triton version doesn't yet support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor).
47
+ """
48
+
49
+ import math
50
+
51
+ import torch
52
+ import triton # type: ignore (reportMissingImports)
53
+ import triton.language as tl # type: ignore (reportMissingImports)
54
+ from einops import repeat
55
+
56
+
57
+ @triton.autotune(
58
+ configs=[
59
+ triton.Config({
60
+ 'BLOCK_M': 128,
61
+ 'BLOCK_N': 128
62
+ },
63
+ num_warps=8,
64
+ num_stages=1),
65
+ # This config has a race condition when EVEN_M == False, disabling it for now.
66
+ # triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=1),
67
+ ],
68
+ key=[
69
+ 'CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL',
70
+ 'BLOCK_HEADDIM'
71
+ ])
72
+ @triton.heuristics({
73
+ 'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0,
74
+ 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0,
75
+ 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM'],
76
+ })
77
+ @triton.jit
78
+ def _fwd_kernel(
79
+ Q,
80
+ K,
81
+ V,
82
+ Bias,
83
+ Out,
84
+ Lse,
85
+ TMP, # NOTE: TMP is a scratchpad buffer to workaround a compiler bug
86
+ softmax_scale,
87
+ stride_qb,
88
+ stride_qh,
89
+ stride_qm,
90
+ stride_kb,
91
+ stride_kh,
92
+ stride_kn,
93
+ stride_vb,
94
+ stride_vh,
95
+ stride_vn,
96
+ stride_bb,
97
+ stride_bh,
98
+ stride_bm,
99
+ stride_ob,
100
+ stride_oh,
101
+ stride_om,
102
+ nheads,
103
+ seqlen_q,
104
+ seqlen_k,
105
+ seqlen_q_rounded,
106
+ headdim,
107
+ CACHE_KEY_SEQLEN_Q,
108
+ CACHE_KEY_SEQLEN_K,
109
+ BIAS_TYPE: tl.constexpr,
110
+ IS_CAUSAL: tl.constexpr,
111
+ BLOCK_HEADDIM: tl.constexpr,
112
+ EVEN_M: tl.constexpr,
113
+ EVEN_N: tl.constexpr,
114
+ EVEN_HEADDIM: tl.constexpr,
115
+ BLOCK_M: tl.constexpr,
116
+ BLOCK_N: tl.constexpr,
117
+ ):
118
+ start_m = tl.program_id(0)
119
+ off_hb = tl.program_id(1)
120
+ off_b = off_hb // nheads
121
+ off_h = off_hb % nheads
122
+ # off_b = tl.program_id(1)
123
+ # off_h = tl.program_id(2)
124
+ # off_hb = off_b * nheads + off_h
125
+ # initialize offsets
126
+ offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
127
+ offs_n = tl.arange(0, BLOCK_N)
128
+ offs_d = tl.arange(0, BLOCK_HEADDIM)
129
+ # Initialize pointers to Q, K, V
130
+ # Adding parenthesis around indexing might use int32 math instead of int64 math?
131
+ # https://github.com/openai/triton/issues/741
132
+ # I'm seeing a tiny bit of difference (5-7us)
133
+ q_ptrs = Q + off_b * stride_qb + off_h * stride_qh + (
134
+ offs_m[:, None] * stride_qm + offs_d[None, :])
135
+ k_ptrs = K + off_b * stride_kb + off_h * stride_kh + (
136
+ offs_n[:, None] * stride_kn + offs_d[None, :])
137
+ v_ptrs = V + off_b * stride_vb + off_h * stride_vh + (
138
+ offs_n[:, None] * stride_vn + offs_d[None, :])
139
+ if BIAS_TYPE == 'vector':
140
+ b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n
141
+ elif BIAS_TYPE == 'matrix':
142
+ b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + (
143
+ offs_m[:, None] * stride_bm + offs_n[None, :])
144
+ else:
145
+ raise ValueError("BIAS_TYPE must be one of {'vector', 'matrix'}")
146
+ # initialize pointer to m and l
147
+ t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m
148
+ lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
149
+ m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
150
+ acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32)
151
+ # load q: it will stay in SRAM throughout
152
+ # [2022-10-30] TD: Triton bug - in the case of EVEN_M=True and EVEN_N=False, if we just call
153
+ # tl.load(q_ptrs), we get the wrong output!
154
+ if EVEN_M & EVEN_N:
155
+ if EVEN_HEADDIM:
156
+ q = tl.load(q_ptrs)
157
+ else:
158
+ q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
159
+ else:
160
+ if EVEN_HEADDIM:
161
+ q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0)
162
+ else:
163
+ q = tl.load(q_ptrs,
164
+ mask=(offs_m[:, None] < seqlen_q) &
165
+ (offs_d[None, :] < headdim),
166
+ other=0.0)
167
+ # loop over k, v and update accumulator
168
+ end_n = seqlen_k if not IS_CAUSAL else tl.minimum(
169
+ (start_m + 1) * BLOCK_M, seqlen_k)
170
+ for start_n in range(0, end_n, BLOCK_N):
171
+ start_n = tl.multiple_of(start_n, BLOCK_N)
172
+ # -- compute qk ----
173
+ if EVEN_N & EVEN_M: # If we just do "if EVEN_N", there seems to be some race condition
174
+ if EVEN_HEADDIM:
175
+ k = tl.load(k_ptrs + start_n * stride_kn)
176
+ else:
177
+ k = tl.load(k_ptrs + start_n * stride_kn,
178
+ mask=offs_d[None, :] < headdim,
179
+ other=0.0)
180
+ else:
181
+ if EVEN_HEADDIM:
182
+ k = tl.load(k_ptrs + start_n * stride_kn,
183
+ mask=(start_n + offs_n)[:, None] < seqlen_k,
184
+ other=0.0)
185
+ else:
186
+ k = tl.load(k_ptrs + start_n * stride_kn,
187
+ mask=((start_n + offs_n)[:, None] < seqlen_k) &
188
+ (offs_d[None, :] < headdim),
189
+ other=0.0)
190
+ qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
191
+ qk += tl.dot(q, k, trans_b=True)
192
+ # Trying to combine the two masks seem to make the result wrong
193
+ if not EVEN_N: # Need to mask out otherwise the softmax is wrong
194
+ qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0,
195
+ float('-inf'))
196
+ if IS_CAUSAL:
197
+ qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0,
198
+ float('-inf'))
199
+ if BIAS_TYPE != 'none':
200
+ if BIAS_TYPE == 'vector':
201
+ if EVEN_N:
202
+ bias = tl.load(b_ptrs + start_n).to(tl.float32)
203
+ else:
204
+ bias = tl.load(b_ptrs + start_n,
205
+ mask=(start_n + offs_n) < seqlen_k,
206
+ other=0.0).to(tl.float32)
207
+ bias = bias[None, :]
208
+ elif BIAS_TYPE == 'matrix':
209
+ if EVEN_M & EVEN_N:
210
+ bias = tl.load(b_ptrs + start_n).to(tl.float32)
211
+ else:
212
+ bias = tl.load(b_ptrs + start_n,
213
+ mask=(offs_m[:, None] < seqlen_q) &
214
+ ((start_n + offs_n)[None, :] < seqlen_k),
215
+ other=0.0).to(tl.float32)
216
+ else:
217
+ raise ValueError(
218
+ "BIAS_TYPE must be one of {'vector', 'matrix'}")
219
+ # Slightly faster to multiply the softmax_scale in the tl.exp below since the compiler
220
+ # can then fuse the mult and add into an fma instruction. But if we have bias we need to
221
+ # to multiply with softmax_scale here.
222
+ qk = qk * softmax_scale + bias
223
+ m_ij = tl.maximum(tl.max(qk, 1), lse_i)
224
+ p = tl.exp(qk - m_ij[:, None])
225
+ else:
226
+ m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i)
227
+ p = tl.exp(qk * softmax_scale - m_ij[:, None])
228
+ l_ij = tl.sum(p, 1)
229
+
230
+ # scale acc_o
231
+ acc_o_scale = tl.exp(m_i - m_ij)
232
+
233
+ # # -- update output accumulator --
234
+ # BUG: have to store and immediately load
235
+ tl.store(t_ptrs, acc_o_scale)
236
+ acc_o_scale = tl.load(t_ptrs)
237
+ acc_o = acc_o * acc_o_scale[:, None]
238
+ # update acc_o
239
+ if EVEN_N & EVEN_M: # If we just do "if EVEN_N", there seems to be some race condition
240
+ if EVEN_HEADDIM:
241
+ v = tl.load(v_ptrs + start_n * stride_vn)
242
+ else:
243
+ v = tl.load(v_ptrs + start_n * stride_vn,
244
+ mask=offs_d[None, :] < headdim,
245
+ other=0.0)
246
+ else:
247
+ if EVEN_HEADDIM:
248
+ v = tl.load(v_ptrs + start_n * stride_vn,
249
+ mask=(start_n + offs_n)[:, None] < seqlen_k,
250
+ other=0.0)
251
+ else:
252
+ v = tl.load(v_ptrs + start_n * stride_vn,
253
+ mask=((start_n + offs_n)[:, None] < seqlen_k) &
254
+ (offs_d[None, :] < headdim),
255
+ other=0.0)
256
+ p = p.to(v.dtype)
257
+ acc_o += tl.dot(p, v)
258
+
259
+ # -- update statistics
260
+ m_i = m_ij
261
+ l_i_new = tl.exp(lse_i - m_ij) + l_ij
262
+ lse_i = m_ij + tl.log(l_i_new)
263
+
264
+ o_scale = tl.exp(m_i - lse_i)
265
+ # BUG: have to store and immediately load
266
+ tl.store(t_ptrs, o_scale)
267
+ o_scale = tl.load(t_ptrs)
268
+ acc_o = acc_o * o_scale[:, None]
269
+ # rematerialize offsets to save registers
270
+ start_m = tl.program_id(0)
271
+ offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
272
+ # write back l and m
273
+ lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m
274
+ tl.store(lse_ptrs, lse_i)
275
+ # initialize pointers to output
276
+ offs_n = tl.arange(0, BLOCK_HEADDIM)
277
+ out_ptrs = Out + off_b * stride_ob + off_h * stride_oh + (
278
+ offs_m[:, None] * stride_om + offs_n[None, :])
279
+ if EVEN_M:
280
+ if EVEN_HEADDIM:
281
+ tl.store(out_ptrs, acc_o)
282
+ else:
283
+ tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim)
284
+ else:
285
+ if EVEN_HEADDIM:
286
+ tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q)
287
+ else:
288
+ tl.store(out_ptrs,
289
+ acc_o,
290
+ mask=(offs_m[:, None] < seqlen_q) &
291
+ (offs_d[None, :] < headdim))
292
+
293
+
294
+ @triton.jit
295
+ def _bwd_preprocess_do_o_dot(
296
+ Out,
297
+ DO,
298
+ Delta,
299
+ stride_ob,
300
+ stride_oh,
301
+ stride_om,
302
+ stride_dob,
303
+ stride_doh,
304
+ stride_dom,
305
+ nheads,
306
+ seqlen_q,
307
+ seqlen_q_rounded,
308
+ headdim,
309
+ BLOCK_M: tl.constexpr,
310
+ BLOCK_HEADDIM: tl.constexpr,
311
+ ):
312
+ start_m = tl.program_id(0)
313
+ off_hb = tl.program_id(1)
314
+ off_b = off_hb // nheads
315
+ off_h = off_hb % nheads
316
+ # initialize offsets
317
+ offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
318
+ offs_d = tl.arange(0, BLOCK_HEADDIM)
319
+ # load
320
+ o = tl.load(Out + off_b * stride_ob + off_h * stride_oh +
321
+ offs_m[:, None] * stride_om + offs_d[None, :],
322
+ mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim),
323
+ other=0.0).to(tl.float32)
324
+ do = tl.load(DO + off_b * stride_dob + off_h * stride_doh +
325
+ offs_m[:, None] * stride_dom + offs_d[None, :],
326
+ mask=(offs_m[:, None] < seqlen_q) &
327
+ (offs_d[None, :] < headdim),
328
+ other=0.0).to(tl.float32)
329
+ delta = tl.sum(o * do, axis=1)
330
+ # write-back
331
+ tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta)
332
+
333
+
334
+ @triton.jit
335
+ def _bwd_kernel_one_col_block(
336
+ start_n,
337
+ Q,
338
+ K,
339
+ V,
340
+ Bias,
341
+ DO,
342
+ DQ,
343
+ DK,
344
+ DV,
345
+ LSE,
346
+ D,
347
+ softmax_scale,
348
+ stride_qm,
349
+ stride_kn,
350
+ stride_vn,
351
+ stride_bm,
352
+ stride_dom,
353
+ stride_dqm,
354
+ stride_dkn,
355
+ stride_dvn,
356
+ seqlen_q,
357
+ seqlen_k,
358
+ headdim,
359
+ ATOMIC_ADD: tl.constexpr,
360
+ BIAS_TYPE: tl.constexpr,
361
+ IS_CAUSAL: tl.constexpr,
362
+ BLOCK_HEADDIM: tl.constexpr,
363
+ EVEN_M: tl.constexpr,
364
+ EVEN_N: tl.constexpr,
365
+ EVEN_HEADDIM: tl.constexpr,
366
+ BLOCK_M: tl.constexpr,
367
+ BLOCK_N: tl.constexpr,
368
+ ):
369
+ # We need to make sure begin_m is a multiple of BLOCK_M (not BLOCK_N)
370
+ begin_m = 0 if not IS_CAUSAL else ((start_n * BLOCK_N) // BLOCK_M) * BLOCK_M
371
+ # initialize row/col offsets
372
+ offs_qm = begin_m + tl.arange(0, BLOCK_M)
373
+ offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
374
+ offs_m = tl.arange(0, BLOCK_M)
375
+ offs_d = tl.arange(0, BLOCK_HEADDIM)
376
+ # initialize pointers to value-like data
377
+ q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :])
378
+ k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :])
379
+ v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :])
380
+ do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :])
381
+ dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :])
382
+ if BIAS_TYPE == 'vector':
383
+ b_ptrs = Bias + offs_n
384
+ elif BIAS_TYPE == 'matrix':
385
+ b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :])
386
+ else:
387
+ raise ValueError("BIAS_TYPE must be one of {'vector', 'matrix'}")
388
+ # initialize dv and dk
389
+ dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
390
+ dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
391
+ # k and v stay in SRAM throughout
392
+ # [2022-10-30] TD: Same bug as the fwd. In the case of EVEN_N=True and EVEN_M=False,
393
+ # if we just call tl.load(k_ptrs), we get the wrong output!
394
+ if EVEN_N & EVEN_M:
395
+ if EVEN_HEADDIM:
396
+ k = tl.load(k_ptrs)
397
+ v = tl.load(v_ptrs)
398
+ else:
399
+ k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
400
+ v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
401
+ else:
402
+ if EVEN_HEADDIM:
403
+ k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
404
+ v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
405
+ else:
406
+ k = tl.load(k_ptrs,
407
+ mask=(offs_n[:, None] < seqlen_k) &
408
+ (offs_d[None, :] < headdim),
409
+ other=0.0)
410
+ v = tl.load(v_ptrs,
411
+ mask=(offs_n[:, None] < seqlen_k) &
412
+ (offs_d[None, :] < headdim),
413
+ other=0.0)
414
+ # loop over rows
415
+ num_block_m = tl.cdiv(seqlen_q, BLOCK_M)
416
+ for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M):
417
+ start_m = tl.multiple_of(start_m, BLOCK_M)
418
+ offs_m_curr = start_m + offs_m
419
+ # load q, k, v, do on-chip
420
+ # Same bug as below. Otherwise gives wrong result for headdim=40, seqlen=(128, 117)
421
+ if EVEN_M & EVEN_HEADDIM:
422
+ q = tl.load(q_ptrs)
423
+ else:
424
+ if EVEN_HEADDIM:
425
+ q = tl.load(q_ptrs,
426
+ mask=offs_m_curr[:, None] < seqlen_q,
427
+ other=0.0)
428
+ else:
429
+ q = tl.load(q_ptrs,
430
+ mask=(offs_m_curr[:, None] < seqlen_q) &
431
+ (offs_d[None, :] < headdim),
432
+ other=0.0)
433
+ # recompute p = softmax(qk, dim=-1).T
434
+ qk = tl.dot(q, k, trans_b=True)
435
+ # Trying to combine the two masks seem to make the result wrong
436
+ if not EVEN_N: # Need to mask out otherwise the softmax is wrong
437
+ qk = tl.where(offs_n[None, :] < seqlen_k, qk, float('-inf'))
438
+ if IS_CAUSAL:
439
+ qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), qk,
440
+ float('-inf'))
441
+ if BIAS_TYPE != 'none':
442
+ if BIAS_TYPE == 'vector':
443
+ if EVEN_N:
444
+ bias = tl.load(b_ptrs).to(tl.float32)
445
+ else:
446
+ bias = tl.load(b_ptrs, mask=offs_n < seqlen_k,
447
+ other=0.0).to(tl.float32)
448
+ bias = bias[None, :]
449
+ elif BIAS_TYPE == 'matrix':
450
+ if EVEN_M & EVEN_N:
451
+ bias = tl.load(b_ptrs).to(tl.float32)
452
+ else:
453
+ bias = tl.load(b_ptrs,
454
+ mask=(offs_m_curr[:, None] < seqlen_q) &
455
+ (offs_n[None, :] < seqlen_k),
456
+ other=0.0).to(tl.float32)
457
+ else:
458
+ raise ValueError(
459
+ "BIAS_TYPE must be one of {'vector', 'matrix'}")
460
+ qk = qk * softmax_scale + bias
461
+ # There seems to be a race condition when headdim=48/96, and dq, dk, dv are wrong.
462
+ # Also wrong for headdim=64.
463
+ if not (EVEN_M & EVEN_HEADDIM):
464
+ tl.debug_barrier()
465
+ lse_i = tl.load(LSE + offs_m_curr)
466
+ if BIAS_TYPE == 'none':
467
+ p = tl.exp(qk * softmax_scale - lse_i[:, None])
468
+ else:
469
+ p = tl.exp(qk - lse_i[:, None])
470
+ # compute dv
471
+ # [2022-10-30] TD: A Triton bug: if EVEN_M=True and EVEN_HEADDIM=False, if we call
472
+ # do = tl.load(do_ptrs, mask=offs_d[None, :] < headdim, other=0.0), we get wrong outputs
473
+ # in the case of headdim=48/96, seqlen_q & seqlen_k >= 512. If headdim=40 or seqlen < 512,
474
+ # the output is correct.
475
+ if EVEN_M & EVEN_HEADDIM:
476
+ do = tl.load(do_ptrs)
477
+ else:
478
+ # [2022-11-01] TD: Triton bug, there's a race condition if we just use m_mask and not d_mask.
479
+ do = tl.load(do_ptrs,
480
+ mask=(offs_m_curr[:, None] < seqlen_q) &
481
+ (offs_d[None, :] < headdim),
482
+ other=0.0)
483
+ # if EVEN_M:
484
+ # if EVEN_HEADDIM:
485
+ # do = tl.load(do_ptrs)
486
+ # else:
487
+ # do = tl.load(do_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
488
+ # else:
489
+ # if EVEN_HEADDIM:
490
+ # do = tl.load(do_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0)
491
+ # else:
492
+ # do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q)
493
+ # & (offs_d[None, :] < headdim), other=0.0)
494
+ dv += tl.dot(p.to(do.dtype), do, trans_a=True)
495
+ # compute dp = dot(v, do)
496
+ # There seems to be a race condition when headdim=48/96, and dq, dk are wrong.
497
+ # Also wrong for headdim=128, seqlen=(108, 256), and ATOMIC_ADD=True
498
+ # Also wrong for headdim=64, seqlen=(1023, 1024), and ATOMIC_ADD=False
499
+ if not (EVEN_M & EVEN_HEADDIM):
500
+ tl.debug_barrier()
501
+ dp = tl.dot(do, v, trans_b=True)
502
+ # There's a race condition for headdim=48
503
+ if not EVEN_HEADDIM:
504
+ tl.debug_barrier()
505
+ # compute ds = p * (dp - delta[:, None])
506
+ # Putting the subtraction after the dp matmul (instead of before) is slightly faster
507
+ Di = tl.load(D + offs_m_curr)
508
+ # Converting ds to q.dtype here reduces register pressure and makes it much faster
509
+ # for BLOCK_HEADDIM=128
510
+ ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype)
511
+ # compute dk = dot(ds.T, q)
512
+ dk += tl.dot(ds, q, trans_a=True)
513
+ # compute dq
514
+ if not ATOMIC_ADD:
515
+ if EVEN_M & EVEN_HEADDIM: # Race condition if we just do EVEN_M
516
+ dq = tl.load(dq_ptrs, eviction_policy='evict_last')
517
+ dq += tl.dot(ds, k)
518
+ tl.store(dq_ptrs, dq, eviction_policy='evict_last')
519
+ else:
520
+ if EVEN_HEADDIM:
521
+ dq = tl.load(dq_ptrs,
522
+ mask=offs_m_curr[:, None] < seqlen_q,
523
+ other=0.0,
524
+ eviction_policy='evict_last')
525
+ dq += tl.dot(ds, k)
526
+ tl.store(dq_ptrs,
527
+ dq,
528
+ mask=offs_m_curr[:, None] < seqlen_q,
529
+ eviction_policy='evict_last')
530
+ else:
531
+ dq = tl.load(dq_ptrs,
532
+ mask=(offs_m_curr[:, None] < seqlen_q) &
533
+ (offs_d[None, :] < headdim),
534
+ other=0.0,
535
+ eviction_policy='evict_last')
536
+ dq += tl.dot(ds, k)
537
+ tl.store(dq_ptrs,
538
+ dq,
539
+ mask=(offs_m_curr[:, None] < seqlen_q) &
540
+ (offs_d[None, :] < headdim),
541
+ eviction_policy='evict_last')
542
+ else: # If we're parallelizing across the seqlen_k dimension
543
+ dq = tl.dot(ds, k)
544
+ if EVEN_M & EVEN_HEADDIM: # Race condition if we just do EVEN_M
545
+ tl.atomic_add(dq_ptrs, dq)
546
+ else:
547
+ if EVEN_HEADDIM:
548
+ tl.atomic_add(dq_ptrs,
549
+ dq,
550
+ mask=offs_m_curr[:, None] < seqlen_q)
551
+ else:
552
+ tl.atomic_add(dq_ptrs,
553
+ dq,
554
+ mask=(offs_m_curr[:, None] < seqlen_q) &
555
+ (offs_d[None, :] < headdim))
556
+ # increment pointers
557
+ dq_ptrs += BLOCK_M * stride_dqm
558
+ q_ptrs += BLOCK_M * stride_qm
559
+ do_ptrs += BLOCK_M * stride_dom
560
+ if BIAS_TYPE == 'matrix':
561
+ b_ptrs += BLOCK_M * stride_bm
562
+ # write-back
563
+ dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
564
+ dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
565
+ # [2022-11-01] TD: Same bug. In the case of EVEN_N=True and EVEN_M=False,
566
+ # if we just call tl.store(dv_ptrs), there's a race condition
567
+ if EVEN_N & EVEN_M:
568
+ if EVEN_HEADDIM:
569
+ tl.store(dv_ptrs, dv)
570
+ tl.store(dk_ptrs, dk)
571
+ else:
572
+ tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim)
573
+ tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim)
574
+ else:
575
+ if EVEN_HEADDIM:
576
+ tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k)
577
+ tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k)
578
+ else:
579
+ tl.store(dv_ptrs,
580
+ dv,
581
+ mask=(offs_n[:, None] < seqlen_k) &
582
+ (offs_d[None, :] < headdim))
583
+ tl.store(dk_ptrs,
584
+ dk,
585
+ mask=(offs_n[:, None] < seqlen_k) &
586
+ (offs_d[None, :] < headdim))
587
+
588
+
589
+ def init_to_zero(name):
590
+ return lambda nargs: nargs[name].zero_()
591
+
592
+
593
+ @triton.autotune(
594
+ configs=[
595
+ triton.Config(
596
+ {
597
+ 'BLOCK_M': 128,
598
+ 'BLOCK_N': 128,
599
+ 'SEQUENCE_PARALLEL': False
600
+ },
601
+ num_warps=8,
602
+ num_stages=1,
603
+ pre_hook=init_to_zero('DQ')),
604
+ triton.Config(
605
+ {
606
+ 'BLOCK_M': 128,
607
+ 'BLOCK_N': 128,
608
+ 'SEQUENCE_PARALLEL': True
609
+ },
610
+ num_warps=8,
611
+ num_stages=1,
612
+ pre_hook=init_to_zero('DQ')),
613
+ # Other configs seem to give wrong results when seqlen_q % 128 != 0, disabling them for now
614
+ # # Kernel is buggy (give wrong result) if we set BLOCK_m=128, BLOCK_n=64, num_warps=*4*
615
+ # triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "SEQUENCE_PARALLEL": False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')),
616
+ # triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "SEQUENCE_PARALLEL": True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')),
617
+ # triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "SEQUENCE_PARALLEL": False}, num_warps=4, num_stages=1, pre_hook=init_to_zero('DQ')),
618
+ # triton.Config({"BLOCK_M": 64, "BLOCK_N": 64, "SEQUENCE_PARALLEL": True}, num_warps=4, num_stages=1, pre_hook=init_to_zero('DQ')),
619
+ ],
620
+ key=[
621
+ 'CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL',
622
+ 'BLOCK_HEADDIM'
623
+ ],
624
+ )
625
+ @triton.heuristics({
626
+ 'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0,
627
+ 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0,
628
+ 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM'],
629
+ })
630
+ @triton.jit
631
+ def _bwd_kernel(
632
+ Q,
633
+ K,
634
+ V,
635
+ Bias,
636
+ DO,
637
+ DQ,
638
+ DK,
639
+ DV,
640
+ LSE,
641
+ D,
642
+ softmax_scale,
643
+ stride_qb,
644
+ stride_qh,
645
+ stride_qm,
646
+ stride_kb,
647
+ stride_kh,
648
+ stride_kn,
649
+ stride_vb,
650
+ stride_vh,
651
+ stride_vn,
652
+ stride_bb,
653
+ stride_bh,
654
+ stride_bm,
655
+ stride_dob,
656
+ stride_doh,
657
+ stride_dom,
658
+ stride_dqb,
659
+ stride_dqh,
660
+ stride_dqm,
661
+ stride_dkb,
662
+ stride_dkh,
663
+ stride_dkn,
664
+ stride_dvb,
665
+ stride_dvh,
666
+ stride_dvn,
667
+ nheads,
668
+ seqlen_q,
669
+ seqlen_k,
670
+ seqlen_q_rounded,
671
+ headdim,
672
+ CACHE_KEY_SEQLEN_Q,
673
+ CACHE_KEY_SEQLEN_K,
674
+ BIAS_TYPE: tl.constexpr,
675
+ IS_CAUSAL: tl.constexpr,
676
+ BLOCK_HEADDIM: tl.constexpr,
677
+ SEQUENCE_PARALLEL: tl.constexpr,
678
+ EVEN_M: tl.constexpr,
679
+ EVEN_N: tl.constexpr,
680
+ EVEN_HEADDIM: tl.constexpr,
681
+ BLOCK_M: tl.constexpr,
682
+ BLOCK_N: tl.constexpr,
683
+ ):
684
+ off_hb = tl.program_id(1)
685
+ off_b = off_hb // nheads
686
+ off_h = off_hb % nheads
687
+ # offset pointers for batch/head
688
+ Q += off_b * stride_qb + off_h * stride_qh
689
+ K += off_b * stride_kb + off_h * stride_kh
690
+ V += off_b * stride_vb + off_h * stride_vh
691
+ DO += off_b * stride_dob + off_h * stride_doh
692
+ DQ += off_b * stride_dqb + off_h * stride_dqh
693
+ DK += off_b * stride_dkb + off_h * stride_dkh
694
+ DV += off_b * stride_dvb + off_h * stride_dvh
695
+ if BIAS_TYPE != 'none':
696
+ Bias += off_b * stride_bb + off_h * stride_bh
697
+ # pointer to row-wise quantities in value-like data
698
+ D += off_hb * seqlen_q_rounded
699
+ LSE += off_hb * seqlen_q_rounded
700
+ if not SEQUENCE_PARALLEL:
701
+ num_block_n = tl.cdiv(seqlen_k, BLOCK_N)
702
+ for start_n in range(0, num_block_n):
703
+ _bwd_kernel_one_col_block(start_n,
704
+ Q,
705
+ K,
706
+ V,
707
+ Bias,
708
+ DO,
709
+ DQ,
710
+ DK,
711
+ DV,
712
+ LSE,
713
+ D,
714
+ softmax_scale,
715
+ stride_qm,
716
+ stride_kn,
717
+ stride_vn,
718
+ stride_bm,
719
+ stride_dom,
720
+ stride_dqm,
721
+ stride_dkn,
722
+ stride_dvn,
723
+ seqlen_q,
724
+ seqlen_k,
725
+ headdim,
726
+ ATOMIC_ADD=False,
727
+ BIAS_TYPE=BIAS_TYPE,
728
+ IS_CAUSAL=IS_CAUSAL,
729
+ BLOCK_HEADDIM=BLOCK_HEADDIM,
730
+ EVEN_M=EVEN_M,
731
+ EVEN_N=EVEN_N,
732
+ EVEN_HEADDIM=EVEN_HEADDIM,
733
+ BLOCK_M=BLOCK_M,
734
+ BLOCK_N=BLOCK_N)
735
+ else:
736
+ start_n = tl.program_id(0)
737
+ _bwd_kernel_one_col_block(start_n,
738
+ Q,
739
+ K,
740
+ V,
741
+ Bias,
742
+ DO,
743
+ DQ,
744
+ DK,
745
+ DV,
746
+ LSE,
747
+ D,
748
+ softmax_scale,
749
+ stride_qm,
750
+ stride_kn,
751
+ stride_vn,
752
+ stride_bm,
753
+ stride_dom,
754
+ stride_dqm,
755
+ stride_dkn,
756
+ stride_dvn,
757
+ seqlen_q,
758
+ seqlen_k,
759
+ headdim,
760
+ ATOMIC_ADD=True,
761
+ BIAS_TYPE=BIAS_TYPE,
762
+ IS_CAUSAL=IS_CAUSAL,
763
+ BLOCK_HEADDIM=BLOCK_HEADDIM,
764
+ EVEN_M=EVEN_M,
765
+ EVEN_N=EVEN_N,
766
+ EVEN_HEADDIM=EVEN_HEADDIM,
767
+ BLOCK_M=BLOCK_M,
768
+ BLOCK_N=BLOCK_N)
769
+
770
+
771
+ def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None):
772
+ # shape constraints
773
+ batch, seqlen_q, nheads, d = q.shape
774
+ _, seqlen_k, _, _ = k.shape
775
+ assert k.shape == (batch, seqlen_k, nheads, d)
776
+ assert v.shape == (batch, seqlen_k, nheads, d)
777
+ assert d <= 128, 'FlashAttention only support head dimensions up to 128'
778
+ assert q.dtype == k.dtype == v.dtype, 'All tensors must have the same type'
779
+ assert q.dtype in [torch.float16,
780
+ torch.bfloat16], 'Only support fp16 and bf16'
781
+ assert q.is_cuda and k.is_cuda and v.is_cuda
782
+ softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
783
+
784
+ has_bias = bias is not None
785
+ bias_type = 'none'
786
+ if has_bias:
787
+ assert bias.dtype in [q.dtype, torch.float]
788
+ assert bias.is_cuda
789
+ assert bias.dim() == 4
790
+ if bias.stride(-1) != 1:
791
+ bias = bias.contiguous()
792
+ if bias.shape[2:] == (1, seqlen_k):
793
+ bias_type = 'vector'
794
+ elif bias.shape[2:] == (seqlen_q, seqlen_k):
795
+ bias_type = 'matrix'
796
+ else:
797
+ raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k)'
798
+ ' or (seqlen_q, seqlen_k)')
799
+ if bias.shape[:2] == (1, nheads):
800
+ bias = repeat(bias, '1 h ... -> b h ...', b=batch)
801
+ elif bias.shape[:2] == (batch, 1):
802
+ bias = repeat(bias, 'b 1 ... -> b h ...', h=nheads)
803
+ elif bias.shape[:2] == (1, 1):
804
+ bias = repeat(bias, '1 h ... -> b h ...', b=batch)
805
+ bias = repeat(bias, 'b 1 ... -> b h ...', h=nheads)
806
+ assert bias.shape[:2] == (
807
+ batch, nheads
808
+ ), f'First 2 dimensions of bias must be broadcastible to (batch, nheads) = ({batch, nheads}). Bias has shape: {bias.shape}'
809
+ assert bias is not None # for type checking
810
+ bias_strides = (bias.stride(0), bias.stride(1),
811
+ bias.stride(2)) if has_bias else (0, 0, 0)
812
+
813
+ seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
814
+ lse = torch.empty((batch, nheads, seqlen_q_rounded),
815
+ device=q.device,
816
+ dtype=torch.float32)
817
+ tmp = torch.empty((batch, nheads, seqlen_q_rounded),
818
+ device=q.device,
819
+ dtype=torch.float32)
820
+ o = torch.empty_like(q)
821
+
822
+ BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
823
+ # BLOCK = 128
824
+ # num_warps = 4 if d <= 64 else 8
825
+ grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
826
+ _fwd_kernel[grid]( # type: ignore
827
+ q,
828
+ k,
829
+ v,
830
+ bias,
831
+ o,
832
+ lse,
833
+ tmp,
834
+ softmax_scale,
835
+ q.stride(0),
836
+ q.stride(2),
837
+ q.stride(1),
838
+ k.stride(0),
839
+ k.stride(2),
840
+ k.stride(1),
841
+ v.stride(0),
842
+ v.stride(2),
843
+ v.stride(1),
844
+ *bias_strides,
845
+ o.stride(0),
846
+ o.stride(2),
847
+ o.stride(1),
848
+ nheads,
849
+ seqlen_q,
850
+ seqlen_k,
851
+ seqlen_q_rounded,
852
+ d,
853
+ seqlen_q // 32,
854
+ seqlen_k // 32, # key for triton cache (limit number of compilations)
855
+ # Can't use kwargs here because triton autotune expects key to be args, not kwargs
856
+ # IS_CAUSAL=causal, BLOCK_HEADDIM=d,
857
+ bias_type,
858
+ causal,
859
+ BLOCK_HEADDIM,
860
+ # BLOCK_M=BLOCK, BLOCK_N=BLOCK,
861
+ # num_warps=num_warps,
862
+ # num_stages=1,
863
+ )
864
+ return o, lse, softmax_scale # softmax_scale could have been updated
865
+
866
+
867
+ def _flash_attn_backward(do,
868
+ q,
869
+ k,
870
+ v,
871
+ o,
872
+ lse,
873
+ dq,
874
+ dk,
875
+ dv,
876
+ bias=None,
877
+ causal=False,
878
+ softmax_scale=None):
879
+ # Make sure that the last dimension is contiguous
880
+ if do.stride(-1) != 1:
881
+ do = do.contiguous()
882
+ batch, seqlen_q, nheads, d = q.shape
883
+ _, seqlen_k, _, _ = k.shape
884
+ # assert d in {16, 32, 64, 128}
885
+ assert d <= 128
886
+ seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
887
+ assert lse.shape == (batch, nheads, seqlen_q_rounded)
888
+ assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1
889
+ assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1
890
+ softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
891
+ # dq_accum = torch.zeros_like(q, dtype=torch.float32)
892
+ dq_accum = torch.empty_like(q, dtype=torch.float32)
893
+ delta = torch.empty_like(lse)
894
+ # delta = torch.zeros_like(lse)
895
+
896
+ BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
897
+ grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
898
+ _bwd_preprocess_do_o_dot[grid]( # type: ignore
899
+ o,
900
+ do,
901
+ delta,
902
+ o.stride(0),
903
+ o.stride(2),
904
+ o.stride(1),
905
+ do.stride(0),
906
+ do.stride(2),
907
+ do.stride(1),
908
+ nheads,
909
+ seqlen_q,
910
+ seqlen_q_rounded,
911
+ d,
912
+ BLOCK_M=128,
913
+ BLOCK_HEADDIM=BLOCK_HEADDIM,
914
+ )
915
+
916
+ has_bias = bias is not None
917
+ bias_type = 'none'
918
+ if has_bias:
919
+ assert bias.dtype in [q.dtype, torch.float]
920
+ assert bias.is_cuda
921
+ assert bias.dim() == 4
922
+ assert bias.stride(-1) == 1
923
+ if bias.shape[2:] == (1, seqlen_k):
924
+ bias_type = 'vector'
925
+ elif bias.shape[2:] == (seqlen_q, seqlen_k):
926
+ bias_type = 'matrix'
927
+ else:
928
+ raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k)'
929
+ ' or (seqlen_q, seqlen_k)')
930
+ if bias.shape[:2] == (1, nheads):
931
+ bias = repeat(bias, '1 h ... -> b h ...', b=batch)
932
+ elif bias.shape[:2] == (batch, 1):
933
+ bias = repeat(bias, 'b 1 ... -> b h ...', h=nheads)
934
+ elif bias.shape[:2] == (1, 1):
935
+ bias = repeat(bias, '1 h ... -> b h ...', b=batch)
936
+ bias = repeat(bias, 'b 1 ... -> b h ...', h=nheads)
937
+ assert bias.shape[:2] == (
938
+ batch, nheads
939
+ ), f'First 2 dimensions of bias must be broadcastible to (batch, nheads) = ({batch, nheads}). Bias has shape: {bias.shape}'
940
+ assert bias is not None # type checking
941
+ bias_strides = (bias.stride(0), bias.stride(1),
942
+ bias.stride(2)) if has_bias else (0, 0, 0)
943
+
944
+ # BLOCK_M = 128
945
+ # BLOCK_N = 64
946
+ # num_warps = 4
947
+ grid = lambda META: (triton.cdiv(seqlen_k, META['BLOCK_N'])
948
+ if META['SEQUENCE_PARALLEL'] else 1, batch * nheads)
949
+ _bwd_kernel[grid]( # type: ignore
950
+ q,
951
+ k,
952
+ v,
953
+ bias,
954
+ do,
955
+ dq_accum,
956
+ dk,
957
+ dv,
958
+ lse,
959
+ delta,
960
+ softmax_scale,
961
+ q.stride(0),
962
+ q.stride(2),
963
+ q.stride(1),
964
+ k.stride(0),
965
+ k.stride(2),
966
+ k.stride(1),
967
+ v.stride(0),
968
+ v.stride(2),
969
+ v.stride(1),
970
+ *bias_strides,
971
+ do.stride(0),
972
+ do.stride(2),
973
+ do.stride(1),
974
+ dq_accum.stride(0),
975
+ dq_accum.stride(2),
976
+ dq_accum.stride(1),
977
+ dk.stride(0),
978
+ dk.stride(2),
979
+ dk.stride(1),
980
+ dv.stride(0),
981
+ dv.stride(2),
982
+ dv.stride(1),
983
+ nheads,
984
+ seqlen_q,
985
+ seqlen_k,
986
+ seqlen_q_rounded,
987
+ d,
988
+ seqlen_q // 32,
989
+ seqlen_k // 32, # key for triton cache (limit number of compilations)
990
+ # Can't use kwargs here because triton autotune expects key to be args, not kwargs
991
+ # IS_CAUSAL=causal, BLOCK_HEADDIM=d,
992
+ bias_type,
993
+ causal,
994
+ BLOCK_HEADDIM,
995
+ # SEQUENCE_PARALLEL=False,
996
+ # BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N,
997
+ # num_warps=num_warps,
998
+ # num_stages=1,
999
+ )
1000
+ dq.copy_(dq_accum)
1001
+
1002
+
1003
+ class _FlashAttnQKVPackedFunc(torch.autograd.Function):
1004
+
1005
+ @staticmethod
1006
+ def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None):
1007
+ """Forward pass for packed FlashAttention.
1008
+
1009
+ Args:
1010
+ ctx: autograd context
1011
+ qkv: (batch, seqlen, 3, nheads, headdim)
1012
+ bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen).
1013
+ For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen).
1014
+ ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen)
1015
+ causal (bool): whether to incorporate causal attention masking
1016
+ softmax_scale (float, optional): scale factor for softmax
1017
+ """
1018
+ # Make sure that the last dimension is contiguous
1019
+ if qkv.stride(-1) != 1:
1020
+ qkv = qkv.contiguous()
1021
+ o, lse, ctx.softmax_scale = _flash_attn_forward(
1022
+ qkv[:, :, 0],
1023
+ qkv[:, :, 1],
1024
+ qkv[:, :, 2],
1025
+ bias=bias,
1026
+ causal=causal,
1027
+ softmax_scale=softmax_scale)
1028
+ ctx.save_for_backward(qkv, o, lse, bias)
1029
+ ctx.causal = causal
1030
+ return o
1031
+
1032
+ @staticmethod
1033
+ def backward(ctx, do):
1034
+ qkv, o, lse, bias = ctx.saved_tensors
1035
+ assert not ctx.needs_input_grad[
1036
+ 1], 'FlashAttention does not support bias gradient yet'
1037
+ # Triton's autotune causes the Tensor._version to change, and so Pytorch autograd
1038
+ # does a memcpy. To avoid this we run in inference_mode, which doesn't track the version.
1039
+ with torch.inference_mode():
1040
+ dqkv = torch.empty_like(qkv)
1041
+ _flash_attn_backward(do,
1042
+ qkv[:, :, 0],
1043
+ qkv[:, :, 1],
1044
+ qkv[:, :, 2],
1045
+ o,
1046
+ lse,
1047
+ dqkv[:, :, 0],
1048
+ dqkv[:, :, 1],
1049
+ dqkv[:, :, 2],
1050
+ bias=bias,
1051
+ causal=ctx.causal,
1052
+ softmax_scale=ctx.softmax_scale)
1053
+ return dqkv, None, None, None
1054
+
1055
+
1056
+ flash_attn_qkvpacked_func = _FlashAttnQKVPackedFunc.apply
1057
+
1058
+
1059
+ class _FlashAttnFunc(torch.autograd.Function):
1060
+
1061
+ @staticmethod
1062
+ def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None):
1063
+ """Forward pass for FlashAttention.
1064
+
1065
+ Args:
1066
+ ctx: autograd context
1067
+ q: (batch_size, seqlen_q, nheads, headdim)
1068
+ k: (batch_size, seqlen_k, nheads, headdim)
1069
+ v: (batch_size, seqlen_k, nheads, headdim)
1070
+ bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
1071
+ For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
1072
+ ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
1073
+ causal (bool): whether to incorporate causal attention masking
1074
+ softmax_scale (float, optional): scale factor for softmax
1075
+ """
1076
+ # Make sure that the last dimension is contiguous
1077
+ q, k, v = [
1078
+ x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]
1079
+ ]
1080
+ o, lse, ctx.softmax_scale = _flash_attn_forward(
1081
+ q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale)
1082
+ ctx.save_for_backward(q, k, v, o, lse, bias)
1083
+ ctx.causal = causal
1084
+ return o
1085
+
1086
+ @staticmethod
1087
+ def backward(ctx, do):
1088
+ q, k, v, o, lse, bias = ctx.saved_tensors
1089
+ assert not ctx.needs_input_grad[
1090
+ 3], 'FlashAttention does not support bias gradient yet'
1091
+ # Triton's autotune causes the Tensor._version to change, and so Pytorch autograd
1092
+ # does a memcpy. To avoid this we run in inference_mode, which doesn't track the version.
1093
+ with torch.inference_mode():
1094
+ dq = torch.empty_like(q)
1095
+ dk = torch.empty_like(k)
1096
+ dv = torch.empty_like(v)
1097
+ _flash_attn_backward(do,
1098
+ q,
1099
+ k,
1100
+ v,
1101
+ o,
1102
+ lse,
1103
+ dq,
1104
+ dk,
1105
+ dv,
1106
+ bias=bias,
1107
+ causal=ctx.causal,
1108
+ softmax_scale=ctx.softmax_scale)
1109
+ return dq, dk, dv, None, None, None
1110
+
1111
+
1112
+ flash_attn_func = _FlashAttnFunc.apply
modules.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ },
14
+ {
15
+ "idx": 2,
16
+ "name": "2",
17
+ "path": "2_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
+ ]
mteb_results/AmazonCounterfactualClassification.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_revision": "e8379541af4e31359cca9fbcf4b00f2671dba205",
3
+ "mteb_dataset_name": "AmazonCounterfactualClassification",
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+ "mteb_version": "1.1.0",
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+ "test": {
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+ },
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+ "validation": {
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+ "main_score": 0.7074626865671642
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+ },
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+ "evaluation_time": 7.2
28
+ }
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+ }
mteb_results/AmazonPolarityClassification.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_revision": "e2d317d38cd51312af73b3d32a06d1a08b442046",
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+ "mteb_dataset_name": "AmazonPolarityClassification",
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+ "mteb_version": "1.1.0",
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+ "test": {
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+ "main_score": 0.8689837499999999
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+ }
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+ }
mteb_results/AmazonReviewsClassification.json ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_revision": "1399c76144fd37290681b995c656ef9b2e06e26d",
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+ "mteb_dataset_name": "AmazonReviewsClassification",
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+ "mteb_version": "1.1.0",
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+ "test": {
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+ },
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+ "validation": {
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+ "main_score": 0.43798000000000004
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+ },
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+ "evaluation_time": 13.69
24
+ }
25
+ }
mteb_results/ArguAna.json ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_revision": null,
3
+ "mteb_dataset_name": "ArguAna",
4
+ "mteb_version": "1.1.0",
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+ "test": {
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+ "recall_at_3": 0.48862,
36
+ "recall_at_5": 0.60953
37
+ }
38
+ }
mteb_results/ArxivClusteringP2P.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_revision": "a122ad7f3f0291bf49cc6f4d32aa80929df69d5d",
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+ "mteb_dataset_name": "ArxivClusteringP2P",
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+ "mteb_version": "1.1.0",
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+ }
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+ }
mteb_results/ArxivClusteringS2S.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "dataset_revision": "f910caf1a6075f7329cdf8c1a6135696f37dbd53",
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+ "mteb_dataset_name": "ArxivClusteringS2S",
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+ "mteb_version": "1.1.0",
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+ }
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+ }
mteb_results/AskUbuntuDupQuestions.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_revision": "2000358ca161889fa9c082cb41daa8dcfb161a54",
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+ "mteb_dataset_name": "AskUbuntuDupQuestions",
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+ "mteb_version": "1.1.0",
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+ "test": {
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+ "map": 0.5879109720839415,
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+ "mrr": 0.7179615705931495
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+ }
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+ }
mteb_results/BIOSSES.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_revision": "d3fb88f8f02e40887cd149695127462bbcf29b4a",
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+ "mteb_dataset_name": "BIOSSES",
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+ "mteb_version": "1.1.0",
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+ "test": {
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+ "cos_sim": {
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+ "spearman": 0.7068353828321327
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+ }
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+ }
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+ }
mteb_results/Banking77Classification.json ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "dataset_revision": "0fd18e25b25c072e09e0d92ab615fda904d66300",
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+ "mteb_dataset_name": "Banking77Classification",
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+ "mteb_version": "1.1.0",
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+ "test": {
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+ "main_score": 0.7540584415584415
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+ }
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+ }
mteb_results/BiorxivClusteringP2P.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "dataset_revision": "65b79d1d13f80053f67aca9498d9402c2d9f1f40",
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+ "mteb_dataset_name": "BiorxivClusteringP2P",
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+ "mteb_version": "1.1.0",
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+ }
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+ }
mteb_results/BiorxivClusteringS2S.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "dataset_revision": "258694dd0231531bc1fd9de6ceb52a0853c6d908",
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+ "mteb_dataset_name": "BiorxivClusteringS2S",
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+ "mteb_version": "1.1.0",
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+ "v_measure": 0.29319217023090705,
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+ "v_measure_std": 0.010219281239166302
9
+ }
10
+ }
mteb_results/CQADupstackEnglishRetrieval.json ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "dataset_revision": null,
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+ "mteb_dataset_name": "CQADupstackEnglishRetrieval",
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+ "mteb_version": "1.1.0",
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+ "recall_at_5": 0.38234
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+ }
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+ }
mteb_results/ClimateFEVER.json ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "recall_at_3": 0.16672,
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+ "recall_at_5": 0.21302
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+ }
38
+ }
mteb_results/DBPedia.json ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "dataset_revision": null,
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+ "mteb_dataset_name": "DBPedia",
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+ "mteb_version": "1.1.0",
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+ "test": {
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