pyserini/2cr/miracl.py

#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import argparse
import math
import os
import sys
import time
import subprocess
import pkg_resources
from collections import defaultdict, OrderedDict
from string import Template

import yaml

from ._base import run_eval_and_return_metric, ok_str, okish_str, fail_str

languages = [
 ['ar', 'arabic'],
 ['bn', 'bengali'],
 ['en', 'english'],
 ['es', 'spanish'],
 ['fa', 'persian'],
 ['fi', 'finnish'],
 ['fr', 'french'],
 ['hi', 'hindi'],
 ['id', 'indonesian'],
 ['ja', 'japanese'],
 ['ko', 'korean'],
 ['ru', 'russian'],
 ['sw', 'swahili'],
 ['te', 'telugu'],
 ['th', 'thai'],
 ['zh', 'chinese'],
 ['de', 'german'],
 ['yo', 'yoruba']
]

html_display = OrderedDict()
html_display['bm25'] = 'BM25'
html_display['mdpr-tied-pft-msmarco'] = 'mDPR (tied encoders), pre-FT w/ MS MARCO'
html_display['mdpr-tied-pft-msmarco-ft-all'] = 'mDPR (tied encoders), pre-FT w/ MS MARCO then FT w/ all Mr. TyDi'
html_display['bm25-mdpr-tied-pft-msmarco-hybrid'] = 'Hybrid of `bm25` and `mdpr-tied-pft-msmarco`'
html_display['mdpr-tied-pft-msmarco-ft-miracl'] = 'mDPR (tied encoders), pre-FT w/ MS MARCO then in-lang FT w/ MIRACL'
html_display['mcontriever-tied-pft-msmarco'] = 'mContriever (tied encoders), pre-FT w/ MS MARCO'

models = list(html_display)

trec_eval_metric_definitions = {
 'nDCG@10': '-c -M 100 -m ndcg_cut.10',
 'R@100': '-c -m recall.100',
}


def format_run_command(raw):
 return raw.replace('--lang', '\\\n --lang') \
 .replace('--encoder', '\\\n --encoder') \
 .replace('--topics', '\\\n --topics') \
 .replace('--index', '\\\n --index') \
 .replace('--output ', '\\\n --output ') \
 .replace('--runs', '\\\n --runs ') \
 .replace('--batch ', '\\\n --batch ') \
 .replace('--threads 12', '--threads 12 \\\n ')


def format_eval_command(raw):
 return raw.replace('-c ', '\\\n -c ') \
 .replace(raw.split()[-1], f'\\\n {raw.split()[-1]}')


def read_file(f):
 fin = open(f, 'r')
 text = fin.read()
 fin.close()

 return text


def list_conditions():
 print('Conditions:\n-----------')
 for condition, _ in html_display.items():
 print(condition)
 print('\nLanguages\n---------')
 for language in languages:
 print(language[0])


def generate_table_rows(table, row_template, commands, eval_commands, table_id, split, metric):
 row_cnt = 1
 html_rows = []

 for model in models:
 s = Template(row_template)

 keys = {}
 used_langs = 0
 for lang in languages:
 keys[lang[0]] = f'{model}.{lang[0]}'
 used_langs += 1 if table[keys[lang[0]]][split][metric] != 0 else 0

 sum = table[keys["ar"]][split][metric] + \
 table[keys["bn"]][split][metric] + \
 table[keys["en"]][split][metric] + \
 table[keys["es"]][split][metric] + \
 table[keys["fa"]][split][metric] + \
 table[keys["fi"]][split][metric] + \
 table[keys["fr"]][split][metric] + \
 table[keys["hi"]][split][metric] + \
 table[keys["id"]][split][metric] + \
 table[keys["ja"]][split][metric] + \
 table[keys["ko"]][split][metric] + \
 table[keys["ru"]][split][metric] + \
 table[keys["sw"]][split][metric] + \
 table[keys["te"]][split][metric] + \
 table[keys["th"]][split][metric] + \
 table[keys["zh"]][split][metric] + \
 table[keys["de"]][split][metric] + \
 table[keys["yo"]][split][metric]
 avg = sum / used_langs

 s = s.substitute(table_cnt=table_id,
 row_cnt=row_cnt,
 model=html_display[model],
 ar=f'{table[keys["ar"]][split][metric]:.3f}',
 bn=f'{table[keys["bn"]][split][metric]:.3f}',
 en=f'{table[keys["en"]][split][metric]:.3f}',
 es=f'{table[keys["es"]][split][metric]:.3f}',
 fa=f'{table[keys["fa"]][split][metric]:.3f}',
 fi=f'{table[keys["fi"]][split][metric]:.3f}',
 fr=f'{table[keys["fr"]][split][metric]:.3f}',
 hi=f'{table[keys["hi"]][split][metric]:.3f}',
 id=f'{table[keys["id"]][split][metric]:.3f}',
 ja=f'{table[keys["ja"]][split][metric]:.3f}',
 ko=f'{table[keys["ko"]][split][metric]:.3f}',
 ru=f'{table[keys["ru"]][split][metric]:.3f}',
 sw=f'{table[keys["sw"]][split][metric]:.3f}',
 te=f'{table[keys["te"]][split][metric]:.3f}',
 th=f'{table[keys["th"]][split][metric]:.3f}',
 zh=f'{table[keys["zh"]][split][metric]:.3f}',
 de=f'{table[keys["de"]][split][metric]:.3f}',
 yo=f'{table[keys["yo"]][split][metric]:.3f}',
 avg=f'{avg:.3f}',
 cmd1=f'{commands[keys["ar"]]}',
 cmd2=f'{commands[keys["bn"]]}',
 cmd3=f'{commands[keys["en"]]}',
 cmd4=f'{commands[keys["es"]]}',
 cmd5=f'{commands[keys["fa"]]}',
 cmd6=f'{commands[keys["fi"]]}',
 cmd7=f'{commands[keys["fr"]]}',
 cmd8=f'{commands[keys["hi"]]}',
 cmd9=f'{commands[keys["id"]]}',
 cmd10=f'{commands[keys["ja"]]}',
 cmd11=f'{commands[keys["ko"]]}',
 cmd12=f'{commands[keys["ru"]]}',
 cmd13=f'{commands[keys["sw"]]}',
 cmd14=f'{commands[keys["te"]]}',
 cmd15=f'{commands[keys["th"]]}',
 cmd16=f'{commands[keys["zh"]]}',
 cmd17=f'{commands[keys["de"]]}',
 cmd18=f'{commands[keys["yo"]]}',
 eval_cmd1=f'{eval_commands[keys["ar"]][metric]}',
 eval_cmd2=f'{eval_commands[keys["bn"]][metric]}',
 eval_cmd3=f'{eval_commands[keys["en"]][metric]}',
 eval_cmd4=f'{eval_commands[keys["es"]][metric]}',
 eval_cmd5=f'{eval_commands[keys["fa"]][metric]}',
 eval_cmd6=f'{eval_commands[keys["fi"]][metric]}',
 eval_cmd7=f'{eval_commands[keys["fr"]][metric]}',
 eval_cmd8=f'{eval_commands[keys["hi"]][metric]}',
 eval_cmd9=f'{eval_commands[keys["id"]][metric]}',
 eval_cmd10=f'{eval_commands[keys["ja"]][metric]}',
 eval_cmd11=f'{eval_commands[keys["ko"]][metric]}',
 eval_cmd12=f'{eval_commands[keys["ru"]][metric]}',
 eval_cmd13=f'{eval_commands[keys["sw"]][metric]}',
 eval_cmd14=f'{eval_commands[keys["te"]][metric]}',
 eval_cmd15=f'{eval_commands[keys["th"]][metric]}',
 eval_cmd16=f'{eval_commands[keys["zh"]][metric]}',
 eval_cmd17=f'{eval_commands[keys["de"]][metric]}',
 eval_cmd18=f'{eval_commands[keys["yo"]][metric]}'
 )

 s = s.replace("0.000", "--")
 html_rows.append(s)
 row_cnt += 1

 return html_rows


def print_results(table, metric, split):
 print(f'Metric = {metric}, Split = {split}')
 print(' ' * 35, end='')
 for lang in languages:
 print(f'{lang[0]:3} ', end='')
 print('')
 for model in models:
 print(f'{model:33}', end='')
 for lang in languages:
 key = f'{model}.{lang[0]}'
 print(f'{table[key][split][metric]:7.3f}', end='')
 print('')
 print('')


def extract_topic_fn_from_cmd(cmd):
 cmd = cmd.split()
 topic_idx = cmd.index('--topics')
 return cmd[topic_idx + 1]


def generate_report(args):
 table = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: 0.0)))
 commands = defaultdict(lambda: '')
 eval_commands = defaultdict(lambda: defaultdict(lambda: ''))

 html_template = read_file(pkg_resources.resource_filename(__name__, 'miracl_html.template'))
 table_template = read_file(pkg_resources.resource_filename(__name__, 'miracl_html_table.template'))
 row_template = read_file(pkg_resources.resource_filename(__name__, 'miracl_html_table_row.template'))

 with open(pkg_resources.resource_filename(__name__, 'miracl.yaml')) as f:
 yaml_data = yaml.safe_load(f)
 for condition in yaml_data['conditions']:
 name = condition['name']
 eval_key = condition['eval_key']
 cmd_template = condition['command']
 cmd_lst = cmd_template.split()
 lang = name.split('.')[-1]
 is_hybrid_run = 'hybrid' in name

 for splits in condition['splits']:
 split = splits['split']
 if is_hybrid_run:
 hits = int(cmd_lst[cmd_lst.index('--k') + 1])
 else:
 hits = int(cmd_lst[cmd_lst.index('--hits') + 1])

 runfile = os.path.join(args.directory, f'run.miracl.{name}.{split}.txt')
 if is_hybrid_run:
 bm25_output = os.path.join(args.directory,
 f'run.miracl.bm25.{lang}.{split}.top{hits}.txt')
 mdpr_output = os.path.join(args.directory,
 f'run.miracl.mdpr-tied-pft-msmarco.{lang}.{split}.top{hits}.txt')
 expected_args = dict(output=runfile, bm25_output=bm25_output, mdpr_output=mdpr_output)
 else:
 expected_args = dict(split=split, output=runfile)

 if not all([f"${k}" in cmd_template or f"${{{k}}}" in cmd_template for k in expected_args]):
 raise ValueError(f"Not all arguements {list(expected_args)} detected from inputs: {cmd_template}.")
 cmd = Template(cmd_template).substitute(**expected_args)
 commands[name] = format_run_command(cmd)

 for expected in splits['scores']:
 for metric in expected:
 if str(expected[metric])[-1] == "5":
 # without adding espilon, there is a chance that f-string would round 0.5 to 0 rather than 1
 # e.g., 0.8885 -> 0.888 rather than 0.889
 # add a espilon to the expected score to avoid rounding error
 expected[metric] += 1e-5
 table[name][split][metric] = expected[metric]

 eval_cmd = f'python -m pyserini.eval.trec_eval ' + \
 f'{trec_eval_metric_definitions[metric]} {eval_key}-{split} {runfile}'
 eval_commands[name][metric] = format_eval_command(eval_cmd)

 tables_html = []

 split = 'dev'

 # Build the table for MRR@100, test queries
 html_rows = generate_table_rows(table, row_template, commands, eval_commands, 1, split, 'nDCG@10')
 all_rows = '\n'.join(html_rows)
 tables_html.append(Template(table_template).substitute(desc=f'nDCG@10, {split} queries', rows=all_rows))

 # Build the table for R@100, test queries
 html_rows = generate_table_rows(table, row_template, commands, eval_commands, 2, split, 'R@100')
 all_rows = '\n'.join(html_rows)
 tables_html.append(Template(table_template).substitute(desc=f'Recall@100, {split} queries', rows=all_rows))

 with open(args.output, 'w') as out:
 out.write(Template(html_template).substitute(title='MIRACL', tables=' '.join(tables_html)))


def run_conditions(args):
 if args.condition == 'mdpr-tied-pft-msmarco-ft-miracl' and args.language in ['de', 'yo']:
 print('MIRACL de and yo datasets do not have train splits to finetune with')
 return

 start = time.time()

 table = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: 0.0)))

 with open(pkg_resources.resource_filename(__name__, 'miracl.yaml')) as f:
 yaml_data = yaml.safe_load(f)
 for condition in yaml_data['conditions']:
 name = condition['name']
 encoder = name.split('.')[0]
 lang = name.split('.')[-1]
 if args.all:
 pass
 elif args.condition != encoder:
 continue
 elif args.language and args.language != lang:
 continue
 eval_key = condition['eval_key']
 cmd_template = condition['command']
 cmd_lst = cmd_template.split()

 print(f'condition {name}:')
 is_hybrid_run = 'hybrid' in name

 for splits in condition['splits']:
 split = splits['split']
 if is_hybrid_run:
 hits = int(cmd_lst[cmd_lst.index('--k') + 1])
 else:
 hits = int(cmd_lst[cmd_lst.index('--hits') + 1])

 print(f' - split: {split}')

 runfile = os.path.join(args.directory, f'run.miracl.{name}.{split}.top{hits}.txt')
 if is_hybrid_run:
 bm25_output = os.path.join(args.directory,
 f'run.miracl.bm25.{lang}.{split}.top{hits}.txt')
 mdpr_output = os.path.join(args.directory,
 f'run.miracl.mdpr-tied-pft-msmarco.{lang}.{split}.top{hits}.txt')
 if not os.path.exists(bm25_output):
 print(f'Missing BM25 file: {bm25_output}')
 continue
 if not os.path.exists(mdpr_output):
 print(f'Missing mDPR file: {mdpr_output}')
 continue
 cmd = Template(cmd_template).substitute(split=split, output=runfile, bm25_output=bm25_output,
 mdpr_output=mdpr_output)
 else:
 cmd = Template(cmd_template).substitute(split=split, output=runfile)

 # In the yaml file, the topics are written as something like '--topics miracl-v1.0-ar-${split}'
 # This works for the dev split because the topics are directly included in Anserini/Pyserini.
 # For this training split, we have to map the symbol into a file in tools/topics-and-qrels/
 # Here, we assume that the developer has cloned the miracl repo and placed the topics there.
 if split == 'train':
 cmd = cmd.replace(f'--topics miracl-v1.0-{lang}-{split}',
 f'--topics tools/topics-and-qrels/topics.miracl-v1.0-{lang}-{split}.tsv')

 if args.display_commands:
 print(f'\n```bash\n{format_run_command(cmd)}\n```\n')

 if not os.path.exists(runfile):
 if not args.dry_run:
 rtn = subprocess.run(cmd.split(), capture_output=True)
 stderr = rtn.stderr.decode()
 if '--topics' in cmd:
 topic_fn = extract_topic_fn_from_cmd(cmd)
 if f'ValueError: Topic {topic_fn} Not Found' in stderr:
 print(f'Skipping {topic_fn}: file not found.')
 continue

 for expected in splits['scores']:
 for metric in expected:
 if not args.skip_eval:
 # We have the translate the training qrels into a file located in tools/topics-and-qrels/
 # because they are not included with Anserini/Pyserini by default.
 # Here, we assume that the developer has cloned the miracl repo and placed the qrels there.
 if split == 'train':
 qrels = f'tools/topics-and-qrels/qrels.{eval_key}-train.tsv'
 else:
 qrels = f'{eval_key}-{split}'
 score = float(run_eval_and_return_metric(metric, qrels,
 trec_eval_metric_definitions[metric], runfile))
 if math.isclose(score, float(expected[metric])):
 result_str = ok_str
 # Flaky tests
 elif (name == 'mdpr-tied-pft-msmarco.hi' and split == 'train'
 and math.isclose(score, float(expected[metric]), abs_tol=2e-4)) or \
 (name == 'mdpr-tied-pft-msmarco-ft-all.ru'
 and split == 'dev' and metric == 'nDCG@10'
 and math.isclose(score, float(expected[metric]), abs_tol=2e-4)) or \
 (name == 'bm25-mdpr-tied-pft-msmarco-hybrid.te'
 and split == 'train' and metric == 'nDCG@10'
 and math.isclose(score, float(expected[metric]), abs_tol=2e-4)) or \
 (name == 'bm25-mdpr-tied-pft-msmarco-hybrid.zh'
 and split == 'dev' and metric == 'nDCG@10'
 and math.isclose(score, float(expected[metric]), abs_tol=2e-4)):
 result_str = okish_str
 else:
 result_str = fail_str + f' expected {expected[metric]:.4f}'
 print(f' {metric:7}: {score:.4f} {result_str}')
 table[name][split][metric] = score
 else:
 table[name][split][metric] = expected[metric]

 print('')

 for metric in ['nDCG@10', 'R@100']:
 for split in ['dev', 'train']:
 print_results(table, metric, split)

 end = time.time()
 print(f'Total elapsed time: {end - start:.0f}s')


if __name__ == '__main__':
 parser = argparse.ArgumentParser(description='Generate regression matrix for MIRACL.')
 parser.add_argument('--condition', type=str,
 help='Condition to run', required=False)
 # To list all conditions
 parser.add_argument('--list-conditions', action='store_true', default=False, help='List available conditions.')
 # For generating reports
 parser.add_argument('--generate-report', action='store_true', default=False, help='Generate report.')
 parser.add_argument('--output', type=str, help='File to store report.', required=False)
 # For actually running the experimental conditions
 parser.add_argument('--all', action='store_true', default=False, help='Run using all languages.')
 parser.add_argument('--language', type=str, help='Language to run.', required=False)
 parser.add_argument('--directory', type=str, help='Base directory.', default='', required=False)
 parser.add_argument('--dry-run', action='store_true', default=False, help='Print out commands but do not execute.')
 parser.add_argument('--skip-eval', action='store_true', default=False, help='Skip running trec_eval.')
 parser.add_argument('--display-commands', action='store_true', default=False, help='Display command.')
 args = parser.parse_args()

 if args.list_conditions:
 list_conditions()
 sys.exit()

 if args.generate_report:
 if not args.output:
 print(f'Must specify report filename with --output.')
 sys.exit()

 generate_report(args)
 sys.exit()

 if args.all and (args.condition or args.language):
 print('Specifying --all will run all conditions and languages')
 sys.exit()

 run_conditions(args)