diff --git a/.github/actions/poetry_setup/action.yml b/.github/actions/poetry_setup/action.yml new file mode 100644 index 00000000..bf17a766 --- /dev/null +++ b/.github/actions/poetry_setup/action.yml @@ -0,0 +1,91 @@ +# An action for setting up poetry install with caching. +# Using a custom action since the default action does not +# take poetry install groups into account. +# Action code from: +# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236 +name: poetry-install-with-caching +description: Poetry install with support for caching of dependency groups. + +inputs: + python-version: + description: Python version, supporting MAJOR.MINOR only + required: true + + poetry-version: + description: Poetry version + required: true + + cache-key: + description: Cache key to use for manual handling of caching + required: true + + working-directory: + description: Directory whose poetry.lock file should be cached + required: true + +runs: + using: composite + steps: + - uses: actions/setup-python@v4 + name: Setup python ${{ inputs.python-version }} + with: + python-version: ${{ inputs.python-version }} + + - uses: actions/cache@v3 + id: cache-bin-poetry + name: Cache Poetry binary - Python ${{ inputs.python-version }} + env: + SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1" + with: + path: | + /opt/pipx/venvs/poetry + # This step caches the poetry installation, so make sure it's keyed on the poetry version as well. + key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }} + + - name: Refresh shell hashtable and fixup softlinks + if: steps.cache-bin-poetry.outputs.cache-hit == 'true' + shell: bash + env: + POETRY_VERSION: ${{ inputs.poetry-version }} + PYTHON_VERSION: ${{ inputs.python-version }} + run: | + set -eux + + # Refresh the shell hashtable, to ensure correct `which` output. + hash -r + + # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks. + # Delete and recreate the softlinks pipx expects to have. + rm /opt/pipx/venvs/poetry/bin/python + cd /opt/pipx/venvs/poetry/bin + ln -s "$(which "python$PYTHON_VERSION")" python + chmod +x python + cd /opt/pipx_bin/ + ln -s /opt/pipx/venvs/poetry/bin/poetry poetry + chmod +x poetry + + # Ensure everything got set up correctly. + /opt/pipx/venvs/poetry/bin/python --version + /opt/pipx_bin/poetry --version + + - name: Install poetry + if: steps.cache-bin-poetry.outputs.cache-hit != 'true' + shell: bash + env: + POETRY_VERSION: ${{ inputs.poetry-version }} + PYTHON_VERSION: ${{ inputs.python-version }} + run: pipx install "poetry==$POETRY_VERSION" --python "python$PYTHON_VERSION" --verbose + + - name: Restore pip and poetry cached dependencies + uses: actions/cache@v3 + env: + SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4" + WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }} + with: + path: | + ~/.cache/pip + ~/.cache/pypoetry/virtualenvs + ~/.cache/pypoetry/cache + ~/.cache/pypoetry/artifacts + ${{ env.WORKDIR }}/.venv + key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles(format('{0}/**/poetry.lock', env.WORKDIR)) }} \ No newline at end of file diff --git a/.github/tools/git-restore-mtime b/.github/tools/git-restore-mtime new file mode 100644 index 00000000..6d55eb10 --- /dev/null +++ b/.github/tools/git-restore-mtime @@ -0,0 +1,606 @@ +#!/usr/bin/env python3 +# +# git-restore-mtime - Change mtime of files based on commit date of last change +# +# Copyright (C) 2012 Rodrigo Silva (MestreLion) +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. See +# +# Source: https://github.com/MestreLion/git-tools +# Version: July 13, 2023 (commit hash 5f832e72453e035fccae9d63a5056918d64476a2) +""" +Change the modification time (mtime) of files in work tree, based on the +date of the most recent commit that modified the file, including renames. + +Ignores untracked files and uncommitted deletions, additions and renames, and +by default modifications too. +--- +Useful prior to generating release tarballs, so each file is archived with a +date that is similar to the date when the file was actually last modified, +assuming the actual modification date and its commit date are close. +""" + +# TODO: +# - Add -z on git whatchanged/ls-files, so we don't deal with filename decoding +# - When Python is bumped to 3.7, use text instead of universal_newlines on subprocess +# - Update "Statistics for some large projects" with modern hardware and repositories. +# - Create a README.md for git-restore-mtime alone. It deserves extensive documentation +# - Move Statistics there +# - See git-extras as a good example on project structure and documentation + +# FIXME: +# - When current dir is outside the worktree, e.g. using --work-tree, `git ls-files` +# assume any relative pathspecs are to worktree root, not the current dir. As such, +# relative pathspecs may not work. +# - Renames are tricky: +# - R100 should not change mtime, but original name is not on filelist. Should +# track renames until a valid (A, M) mtime found and then set on current name. +# - Should set mtime for both current and original directories. +# - Check mode changes with unchanged blobs? +# - Check file (A, D) for the directory mtime is not sufficient: +# - Renames also change dir mtime, unless rename was on a parent dir +# - If most recent change of all files in a dir was a Modification (M), +# dir might not be touched at all. +# - Dirs containing only subdirectories but no direct files will also +# not be touched. They're files' [grand]parent dir, but never their dirname(). +# - Some solutions: +# - After files done, perform some dir processing for missing dirs, finding latest +# file (A, D, R) +# - Simple approach: dir mtime is the most recent child (dir or file) mtime +# - Use a virtual concept of "created at most at" to fill missing info, bubble up +# to parents and grandparents +# - When handling [grand]parent dirs, stay inside +# - Better handling of merge commits. `-m` is plain *wrong*. `-c/--cc` is perfect, but +# painfully slow. First pass without merge commits is not accurate. Maybe add a new +# `--accurate` mode for `--cc`? + +if __name__ != "__main__": + raise ImportError("{} should not be used as a module.".format(__name__)) + +import argparse +import datetime +import logging +import os.path +import shlex +import signal +import subprocess +import sys +import time + +__version__ = "2022.12+dev" + +# Update symlinks only if the platform supports not following them +UPDATE_SYMLINKS = bool(os.utime in getattr(os, 'supports_follow_symlinks', [])) + +# Call os.path.normpath() only if not in a POSIX platform (Windows) +NORMALIZE_PATHS = (os.path.sep != '/') + +# How many files to process in each batch when re-trying merge commits +STEPMISSING = 100 + +# (Extra) keywords for the os.utime() call performed by touch() +UTIME_KWS = {} if not UPDATE_SYMLINKS else {'follow_symlinks': False} + + +# Command-line interface ###################################################### + +def parse_args(): + parser = argparse.ArgumentParser( + description=__doc__.split('\n---')[0]) + + group = parser.add_mutually_exclusive_group() + group.add_argument('--quiet', '-q', dest='loglevel', + action="store_const", const=logging.WARNING, default=logging.INFO, + help="Suppress informative messages and summary statistics.") + group.add_argument('--verbose', '-v', action="count", help=""" + Print additional information for each processed file. + Specify twice to further increase verbosity. + """) + + parser.add_argument('--cwd', '-C', metavar="DIRECTORY", help=""" + Run as if %(prog)s was started in directory %(metavar)s. + This affects how --work-tree, --git-dir and PATHSPEC arguments are handled. + See 'man 1 git' or 'git --help' for more information. + """) + + parser.add_argument('--git-dir', dest='gitdir', metavar="GITDIR", help=""" + Path to the git repository, by default auto-discovered by searching + the current directory and its parents for a .git/ subdirectory. + """) + + parser.add_argument('--work-tree', dest='workdir', metavar="WORKTREE", help=""" + Path to the work tree root, by default the parent of GITDIR if it's + automatically discovered, or the current directory if GITDIR is set. + """) + + parser.add_argument('--force', '-f', default=False, action="store_true", help=""" + Force updating files with uncommitted modifications. + Untracked files and uncommitted deletions, renames and additions are + always ignored. + """) + + parser.add_argument('--merge', '-m', default=False, action="store_true", help=""" + Include merge commits. + Leads to more recent times and more files per commit, thus with the same + time, which may or may not be what you want. + Including merge commits may lead to fewer commits being evaluated as files + are found sooner, which can improve performance, sometimes substantially. + But as merge commits are usually huge, processing them may also take longer. + By default, merge commits are only used for files missing from regular commits. + """) + + parser.add_argument('--first-parent', default=False, action="store_true", help=""" + Consider only the first parent, the "main branch", when evaluating merge commits. + Only effective when merge commits are processed, either when --merge is + used or when finding missing files after the first regular log search. + See --skip-missing. + """) + + parser.add_argument('--skip-missing', '-s', dest="missing", default=True, + action="store_false", help=""" + Do not try to find missing files. + If merge commits were not evaluated with --merge and some files were + not found in regular commits, by default %(prog)s searches for these + files again in the merge commits. + This option disables this retry, so files found only in merge commits + will not have their timestamp updated. + """) + + parser.add_argument('--no-directories', '-D', dest='dirs', default=True, + action="store_false", help=""" + Do not update directory timestamps. + By default, use the time of its most recently created, renamed or deleted file. + Note that just modifying a file will NOT update its directory time. + """) + + parser.add_argument('--test', '-t', default=False, action="store_true", + help="Test run: do not actually update any file timestamp.") + + parser.add_argument('--commit-time', '-c', dest='commit_time', default=False, + action='store_true', help="Use commit time instead of author time.") + + parser.add_argument('--oldest-time', '-o', dest='reverse_order', default=False, + action='store_true', help=""" + Update times based on the oldest, instead of the most recent commit of a file. + This reverses the order in which the git log is processed to emulate a + file "creation" date. Note this will be inaccurate for files deleted and + re-created at later dates. + """) + + parser.add_argument('--skip-older-than', metavar='SECONDS', type=int, help=""" + Ignore files that are currently older than %(metavar)s. + Useful in workflows that assume such files already have a correct timestamp, + as it may improve performance by processing fewer files. + """) + + parser.add_argument('--skip-older-than-commit', '-N', default=False, + action='store_true', help=""" + Ignore files older than the timestamp it would be updated to. + Such files may be considered "original", likely in the author's repository. + """) + + parser.add_argument('--unique-times', default=False, action="store_true", help=""" + Set the microseconds to a unique value per commit. + Allows telling apart changes that would otherwise have identical timestamps, + as git's time accuracy is in seconds. + """) + + parser.add_argument('pathspec', nargs='*', metavar='PATHSPEC', help=""" + Only modify paths matching %(metavar)s, relative to current directory. + By default, update all but untracked files and submodules. + """) + + parser.add_argument('--version', '-V', action='version', + version='%(prog)s version {version}'.format(version=get_version())) + + args_ = parser.parse_args() + if args_.verbose: + args_.loglevel = max(logging.TRACE, logging.DEBUG // args_.verbose) + args_.debug = args_.loglevel <= logging.DEBUG + return args_ + + +def get_version(version=__version__): + if not version.endswith('+dev'): + return version + try: + cwd = os.path.dirname(os.path.realpath(__file__)) + return Git(cwd=cwd, errors=False).describe().lstrip('v') + except Git.Error: + return '-'.join((version, "unknown")) + + +# Helper functions ############################################################ + +def setup_logging(): + """Add TRACE logging level and corresponding method, return the root logger""" + logging.TRACE = TRACE = logging.DEBUG // 2 + logging.Logger.trace = lambda _, m, *a, **k: _.log(TRACE, m, *a, **k) + return logging.getLogger() + + +def normalize(path): + r"""Normalize paths from git, handling non-ASCII characters. + + Git stores paths as UTF-8 normalization form C. + If path contains non-ASCII or non-printable characters, git outputs the UTF-8 + in octal-escaped notation, escaping double-quotes and backslashes, and then + double-quoting the whole path. + https://git-scm.com/docs/git-config#Documentation/git-config.txt-corequotePath + + This function reverts this encoding, so: + normalize(r'"Back\\slash_double\"quote_a\303\247a\303\255"') => + r'Back\slash_double"quote_açaí') + + Paths with invalid UTF-8 encoding, such as single 0x80-0xFF bytes (e.g, from + Latin1/Windows-1251 encoding) are decoded using surrogate escape, the same + method used by Python for filesystem paths. So 0xE6 ("æ" in Latin1, r'\\346' + from Git) is decoded as "\udce6". See https://peps.python.org/pep-0383/ and + https://vstinner.github.io/painful-history-python-filesystem-encoding.html + + Also see notes on `windows/non-ascii-paths.txt` about path encodings on + non-UTF-8 platforms and filesystems. + """ + if path and path[0] == '"': + # Python 2: path = path[1:-1].decode("string-escape") + # Python 3: https://stackoverflow.com/a/46650050/624066 + path = (path[1:-1] # Remove enclosing double quotes + .encode('latin1') # Convert to bytes, required by 'unicode-escape' + .decode('unicode-escape') # Perform the actual octal-escaping decode + .encode('latin1') # 1:1 mapping to bytes, UTF-8 encoded + .decode('utf8', 'surrogateescape')) # Decode from UTF-8 + if NORMALIZE_PATHS: + # Make sure the slash matches the OS; for Windows we need a backslash + path = os.path.normpath(path) + return path + + +def dummy(*_args, **_kwargs): + """No-op function used in dry-run tests""" + + +def touch(path, mtime): + """The actual mtime update""" + os.utime(path, (mtime, mtime), **UTIME_KWS) + + +def touch_ns(path, mtime_ns): + """The actual mtime update, using nanoseconds for unique timestamps""" + os.utime(path, None, ns=(mtime_ns, mtime_ns), **UTIME_KWS) + + +def isodate(secs: int): + # time.localtime() accepts floats, but discards fractional part + return time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(secs)) + + +def isodate_ns(ns: int): + # for integers fromtimestamp() is equivalent and ~16% slower than isodate() + return datetime.datetime.fromtimestamp(ns / 1000000000).isoformat(sep=' ') + + +def get_mtime_ns(secs: int, idx: int): + # Time resolution for filesystems and functions: + # ext-4 and other POSIX filesystems: 1 nanosecond + # NTFS (Windows default): 100 nanoseconds + # datetime.datetime() (due to 64-bit float epoch): 1 microsecond + us = idx % 1000000 # 10**6 + return 1000 * (1000000 * secs + us) + + +def get_mtime_path(path): + return os.path.getmtime(path) + + +# Git class and parse_log(), the heart of the script ########################## + +class Git: + def __init__(self, workdir=None, gitdir=None, cwd=None, errors=True): + self.gitcmd = ['git'] + self.errors = errors + self._proc = None + if workdir: self.gitcmd.extend(('--work-tree', workdir)) + if gitdir: self.gitcmd.extend(('--git-dir', gitdir)) + if cwd: self.gitcmd.extend(('-C', cwd)) + self.workdir, self.gitdir = self._get_repo_dirs() + + def ls_files(self, paths: list = None): + return (normalize(_) for _ in self._run('ls-files --full-name', paths)) + + def ls_dirty(self, force=False): + return (normalize(_[3:].split(' -> ', 1)[-1]) + for _ in self._run('status --porcelain') + if _[:2] != '??' and (not force or (_[0] in ('R', 'A') + or _[1] == 'D'))) + + def log(self, merge=False, first_parent=False, commit_time=False, + reverse_order=False, paths: list = None): + cmd = 'whatchanged --pretty={}'.format('%ct' if commit_time else '%at') + if merge: cmd += ' -m' + if first_parent: cmd += ' --first-parent' + if reverse_order: cmd += ' --reverse' + return self._run(cmd, paths) + + def describe(self): + return self._run('describe --tags', check=True)[0] + + def terminate(self): + if self._proc is None: + return + try: + self._proc.terminate() + except OSError: + # Avoid errors on OpenBSD + pass + + def _get_repo_dirs(self): + return (os.path.normpath(_) for _ in + self._run('rev-parse --show-toplevel --absolute-git-dir', check=True)) + + def _run(self, cmdstr: str, paths: list = None, output=True, check=False): + cmdlist = self.gitcmd + shlex.split(cmdstr) + if paths: + cmdlist.append('--') + cmdlist.extend(paths) + popen_args = dict(universal_newlines=True, encoding='utf8') + if not self.errors: + popen_args['stderr'] = subprocess.DEVNULL + log.trace("Executing: %s", ' '.join(cmdlist)) + if not output: + return subprocess.call(cmdlist, **popen_args) + if check: + try: + stdout: str = subprocess.check_output(cmdlist, **popen_args) + return stdout.splitlines() + except subprocess.CalledProcessError as e: + raise self.Error(e.returncode, e.cmd, e.output, e.stderr) + self._proc = subprocess.Popen(cmdlist, stdout=subprocess.PIPE, **popen_args) + return (_.rstrip() for _ in self._proc.stdout) + + def __del__(self): + self.terminate() + + class Error(subprocess.CalledProcessError): + """Error from git executable""" + + +def parse_log(filelist, dirlist, stats, git, merge=False, filterlist=None): + mtime = 0 + datestr = isodate(0) + for line in git.log( + merge, + args.first_parent, + args.commit_time, + args.reverse_order, + filterlist + ): + stats['loglines'] += 1 + + # Blank line between Date and list of files + if not line: + continue + + # Date line + if line[0] != ':': # Faster than `not line.startswith(':')` + stats['commits'] += 1 + mtime = int(line) + if args.unique_times: + mtime = get_mtime_ns(mtime, stats['commits']) + if args.debug: + datestr = isodate(mtime) + continue + + # File line: three tokens if it describes a renaming, otherwise two + tokens = line.split('\t') + + # Possible statuses: + # M: Modified (content changed) + # A: Added (created) + # D: Deleted + # T: Type changed: to/from regular file, symlinks, submodules + # R099: Renamed (moved), with % of unchanged content. 100 = pure rename + # Not possible in log: C=Copied, U=Unmerged, X=Unknown, B=pairing Broken + status = tokens[0].split(' ')[-1] + file = tokens[-1] + + # Handles non-ASCII chars and OS path separator + file = normalize(file) + + def do_file(): + if args.skip_older_than_commit and get_mtime_path(file) <= mtime: + stats['skip'] += 1 + return + if args.debug: + log.debug("%d\t%d\t%d\t%s\t%s", + stats['loglines'], stats['commits'], stats['files'], + datestr, file) + try: + touch(os.path.join(git.workdir, file), mtime) + stats['touches'] += 1 + except Exception as e: + log.error("ERROR: %s: %s", e, file) + stats['errors'] += 1 + + def do_dir(): + if args.debug: + log.debug("%d\t%d\t-\t%s\t%s", + stats['loglines'], stats['commits'], + datestr, "{}/".format(dirname or '.')) + try: + touch(os.path.join(git.workdir, dirname), mtime) + stats['dirtouches'] += 1 + except Exception as e: + log.error("ERROR: %s: %s", e, dirname) + stats['direrrors'] += 1 + + if file in filelist: + stats['files'] -= 1 + filelist.remove(file) + do_file() + + if args.dirs and status in ('A', 'D'): + dirname = os.path.dirname(file) + if dirname in dirlist: + dirlist.remove(dirname) + do_dir() + + # All files done? + if not stats['files']: + git.terminate() + return + + +# Main Logic ################################################################## + +def main(): + start = time.time() # yes, Wall time. CPU time is not realistic for users. + stats = {_: 0 for _ in ('loglines', 'commits', 'touches', 'skip', 'errors', + 'dirtouches', 'direrrors')} + + logging.basicConfig(level=args.loglevel, format='%(message)s') + log.trace("Arguments: %s", args) + + # First things first: Where and Who are we? + if args.cwd: + log.debug("Changing directory: %s", args.cwd) + try: + os.chdir(args.cwd) + except OSError as e: + log.critical(e) + return e.errno + # Using both os.chdir() and `git -C` is redundant, but might prevent side effects + # `git -C` alone could be enough if we make sure that: + # - all paths, including args.pathspec, are processed by git: ls-files, rev-parse + # - touch() / os.utime() path argument is always prepended with git.workdir + try: + git = Git(workdir=args.workdir, gitdir=args.gitdir, cwd=args.cwd) + except Git.Error as e: + # Not in a git repository, and git already informed user on stderr. So we just... + return e.returncode + + # Get the files managed by git and build file list to be processed + if UPDATE_SYMLINKS and not args.skip_older_than: + filelist = set(git.ls_files(args.pathspec)) + else: + filelist = set() + for path in git.ls_files(args.pathspec): + fullpath = os.path.join(git.workdir, path) + + # Symlink (to file, to dir or broken - git handles the same way) + if not UPDATE_SYMLINKS and os.path.islink(fullpath): + log.warning("WARNING: Skipping symlink, no OS support for updates: %s", + path) + continue + + # skip files which are older than given threshold + if (args.skip_older_than + and start - get_mtime_path(fullpath) > args.skip_older_than): + continue + + # Always add files relative to worktree root + filelist.add(path) + + # If --force, silently ignore uncommitted deletions (not in the filesystem) + # and renames / additions (will not be found in log anyway) + if args.force: + filelist -= set(git.ls_dirty(force=True)) + # Otherwise, ignore any dirty files + else: + dirty = set(git.ls_dirty()) + if dirty: + log.warning("WARNING: Modified files in the working directory were ignored." + "\nTo include such files, commit your changes or use --force.") + filelist -= dirty + + # Build dir list to be processed + dirlist = set(os.path.dirname(_) for _ in filelist) if args.dirs else set() + + stats['totalfiles'] = stats['files'] = len(filelist) + log.info("{0:,} files to be processed in work dir".format(stats['totalfiles'])) + + if not filelist: + # Nothing to do. Exit silently and without errors, just like git does + return + + # Process the log until all files are 'touched' + log.debug("Line #\tLog #\tF.Left\tModification Time\tFile Name") + parse_log(filelist, dirlist, stats, git, args.merge, args.pathspec) + + # Missing files + if filelist: + # Try to find them in merge logs, if not done already + # (usually HUGE, thus MUCH slower!) + if args.missing and not args.merge: + filterlist = list(filelist) + missing = len(filterlist) + log.info("{0:,} files not found in log, trying merge commits".format(missing)) + for i in range(0, missing, STEPMISSING): + parse_log(filelist, dirlist, stats, git, + merge=True, filterlist=filterlist[i:i + STEPMISSING]) + + # Still missing some? + for file in filelist: + log.warning("WARNING: not found in the log: %s", file) + + # Final statistics + # Suggestion: use git-log --before=mtime to brag about skipped log entries + def log_info(msg, *a, width=13): + ifmt = '{:%d,}' % (width,) # not using 'n' for consistency with ffmt + ffmt = '{:%d,.2f}' % (width,) + # %-formatting lacks a thousand separator, must pre-render with .format() + log.info(msg.replace('%d', ifmt).replace('%f', ffmt).format(*a)) + + log_info( + "Statistics:\n" + "%f seconds\n" + "%d log lines processed\n" + "%d commits evaluated", + time.time() - start, stats['loglines'], stats['commits']) + + if args.dirs: + if stats['direrrors']: log_info("%d directory update errors", stats['direrrors']) + log_info("%d directories updated", stats['dirtouches']) + + if stats['touches'] != stats['totalfiles']: + log_info("%d files", stats['totalfiles']) + if stats['skip']: log_info("%d files skipped", stats['skip']) + if stats['files']: log_info("%d files missing", stats['files']) + if stats['errors']: log_info("%d file update errors", stats['errors']) + + log_info("%d files updated", stats['touches']) + + if args.test: + log.info("TEST RUN - No files modified!") + + +# Keep only essential, global assignments here. Any other logic must be in main() +log = setup_logging() +args = parse_args() + +# Set the actual touch() and other functions based on command-line arguments +if args.unique_times: + touch = touch_ns + isodate = isodate_ns + +# Make sure this is always set last to ensure --test behaves as intended +if args.test: + touch = dummy + +# UI done, it's showtime! +try: + sys.exit(main()) +except KeyboardInterrupt: + log.info("\nAborting") + signal.signal(signal.SIGINT, signal.SIG_DFL) + os.kill(os.getpid(), signal.SIGINT) \ No newline at end of file diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 00000000..c52897a9 --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,45 @@ +--- +name: libs/infinity_emb CI + +on: + release: + types: [published] + push: + branches: + - main + - dev + pull_request: + branches: + - main + - dev + workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI + +# If another push to the same PR or branch happens while this workflow is still running, +# cancel the earlier run in favor of the next run. +# +# There's no point in testing an outdated version of the code. GitHub only allows +# a limited number of job runners to be active at the same time, so it's better to cancel +# pointless jobs early so that more useful jobs can run sooner. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + POETRY_VERSION: "1.6.1" + WORKDIR: "libs/infinity_emb" + +jobs: + lint: + uses: + ./.github/workflows/linting.yaml + with: + working-directory: libs/infinity_emb + secrets: inherit + + test: + uses: + ./.github/workflows/test.yaml + with: + working-directory: libs/infinity_emb + upload_coverage: true + secrets: inherit \ No newline at end of file diff --git a/.github/workflows/linting.yaml b/.github/workflows/linting.yaml new file mode 100644 index 00000000..5b5c6078 --- /dev/null +++ b/.github/workflows/linting.yaml @@ -0,0 +1,148 @@ +on: + workflow_call: + inputs: + working-directory: + required: true + type: string + description: "From which folder this pipeline executes" + +env: + POETRY_VERSION: "1.6.1" + WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }} + +jobs: + build: + runs-on: ubuntu-latest + env: + # This number is set "by eye": we want it to be big enough + # so that it's bigger than the number of commits in any reasonable PR, + # and also as small as possible since increasing the number makes + # the initial `git fetch` slower. + FETCH_DEPTH: 50 + strategy: + matrix: + # Only lint on the min and max supported Python versions. + # It's extremely unlikely that there's a lint issue on any version in between + # that doesn't show up on the min or max versions. + # + # GitHub rate-limits how many jobs can be running at any one time. + # Starting new jobs is also relatively slow, + # so linting on fewer versions makes CI faster. + python-version: + - "3.10" + - "3.11" + steps: + - uses: actions/checkout@v3 + with: + # Fetch the last FETCH_DEPTH commits, so the mtime-changing script + # can accurately set the mtimes of files modified in the last FETCH_DEPTH commits. + fetch-depth: ${{ env.FETCH_DEPTH }} + # - name: Restore workdir file mtimes to last-edited commit date + # id: restore-mtimes + # # This is needed to make black caching work. + # # Black's cache uses file (mtime, size) to check whether a lookup is a cache hit. + # # Without this command, files in the repo would have the current time as the modified time, + # # since the previous action step just created them. + # # This command resets the mtime to the last time the files were modified in git instead, + # # which is a high-quality and stable representation of the last modification date. + # run: | + # # Important considerations: + # # - These commands run at base of the repo, since we never `cd` to the `WORKDIR`. + # # - We only want to alter mtimes for Python files, since that's all black checks. + # # - We don't need to alter mtimes for directories, since black doesn't look at those. + # # - We also only alter mtimes inside the `WORKDIR` since that's all we'll lint. + # # - This should run before `poetry install`, because poetry's venv also contains + # # Python files, and we don't want to alter their mtimes since they aren't linted. + + # # Ensure we fail on non-zero exits and on undefined variables. + # # Also print executed commands, for easier debugging. + # set -eux + + # # Restore the mtimes of Python files in the workdir based on git history. + # .github/tools/git-restore-mtime --no-directories "$WORKDIR/**/*.py" + + # # Since CI only does a partial fetch (to `FETCH_DEPTH`) for efficiency, + # # the local git repo doesn't have full history. There are probably files + # # that were last modified in a commit *older than* the oldest fetched commit. + # # After `git-restore-mtime`, such files have a mtime set to the oldest fetched commit. + # # + # # As new commits get added, that timestamp will keep moving forward. + # # If left unchanged, this will make `black` think that the files were edited + # # more recently than its cache suggests. Instead, we can set their mtime + # # to a fixed date in the far past that won't change and won't cause cache misses in black. + # # + # # For all workdir Python files modified in or before the oldest few fetched commits, + # # make their mtime be 2000-01-01 00:00:00. + # OLDEST_COMMIT="$(git log --reverse '--pretty=format:%H' | head -1)" + # OLDEST_COMMIT_TIME="$(git show -s '--format=%ai' "$OLDEST_COMMIT")" + # find "$WORKDIR" -name '*.py' -type f -not -newermt "$OLDEST_COMMIT_TIME" -exec touch -c -m -t '200001010000' '{}' '+' + + # echo "oldest-commit=$OLDEST_COMMIT" >> "$GITHUB_OUTPUT" + + - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_setup" + with: + python-version: ${{ matrix.python-version }} + poetry-version: ${{ env.POETRY_VERSION }} + working-directory: ${{ inputs.working-directory }} + cache-key: lint-with-extras + + - name: Check Poetry File + shell: bash + working-directory: ${{ inputs.working-directory }} + run: | + poetry check + + - name: Check lock file + shell: bash + working-directory: ${{ inputs.working-directory }} + run: | + poetry lock --check + + - name: Install dependencies + # Also installs dev/lint/test/typing dependencies, to ensure we have + # type hints for as many of our libraries as possible. + # This helps catch errors that require dependencies to be spotted. + # + # If you change this configuration, make sure to change the `cache-key` + # in the `poetry_setup` action above to stop using the old cache. + # It doesn't matter how you change it, any change will cause a cache-bust. + working-directory: ${{ inputs.working-directory }} + run: | + poetry install --with test,lint,codespell + + - name: Install infinity_emb editable + working-directory: ${{ inputs.working-directory }} + if: ${{ inputs.working-directory != 'libs/infinity_emb' }} + run: | + pip install -e ../infinity_emb + + # - name: Restore black cache + # uses: actions/cache@v3 + # env: + # CACHE_BASE: black-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }} + # SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1" + # with: + # path: | + # ${{ env.WORKDIR }}/.black_cache + # key: ${{ env.CACHE_BASE }}-${{ steps.restore-mtimes.outputs.oldest-commit }} + # restore-keys: + # # If we can't find an exact match for our cache key, accept any with this prefix. + # ${{ env.CACHE_BASE }}- + + # - name: Get .mypy_cache to speed up mypy + # uses: actions/cache@v3 + # env: + # SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2" + # with: + # path: | + # ${{ env.WORKDIR }}/.mypy_cache + # key: mypy-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }} + + - name: Analysing the code with our lint + working-directory: ${{ inputs.working-directory }} + env: + BLACK_CACHE_DIR: .black_cache + run: | + # make lint + poetry run ruff . \ No newline at end of file diff --git a/.github/workflows/pypi_release.yaml b/.github/workflows/pypi_release.yaml new file mode 100644 index 00000000..b3005878 --- /dev/null +++ b/.github/workflows/pypi_release.yaml @@ -0,0 +1,64 @@ +name: release + +on: + workflow_call: + inputs: + working-directory: + required: true + type: string + description: "From which folder this pipeline executes" + +env: + POETRY_VERSION: "1.6.1" + +jobs: + if_release: + # Disallow publishing from branches that aren't `master`. + if: github.ref == 'refs/heads/master' + runs-on: ubuntu-latest + permissions: + # This permission is used for trusted publishing: + # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/ + # + # Trusted publishing has to also be configured on PyPI for each package: + # https://docs.pypi.org/trusted-publishers/adding-a-publisher/ + id-token: write + + # This permission is needed by `ncipollo/release-action` to create the GitHub release. + contents: write + defaults: + run: + working-directory: ${{ inputs.working-directory }} + steps: + - uses: actions/checkout@v3 + + - name: Set up Python + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_setup" + with: + python-version: "3.10" + poetry-version: ${{ env.POETRY_VERSION }} + working-directory: ${{ inputs.working-directory }} + cache-key: release + + - name: Build project for distribution + run: poetry build + - name: Check Version + id: check-version + run: | + echo version=$(poetry version --short) >> $GITHUB_OUTPUT + - name: Create Release + uses: ncipollo/release-action@v1 + if: ${{ inputs.working-directory == 'libs/infinity_emb' }} + with: + artifacts: "dist/*" + token: ${{ secrets.GITHUB_TOKEN }} + draft: false + generateReleaseNotes: true + tag: v${{ steps.check-version.outputs.version }} + commit: master + - name: Publish package distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: ${{ inputs.working-directory }}/dist/ + verbose: true + print-hash: true \ No newline at end of file diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 00000000..25fe27bd --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,27 @@ +--- +name: libs/infinity_emb Release + +on: + release: + types: [published] + +env: + POETRY_VERSION: "1.6.1" + WORKDIR: "libs/infinity_emb" + +jobs: + docker-container-push: + uses: + ./.github/workflows/release_docker_container.yaml + with: + # working-directory: libs/infinity_emb + dockerfile: libs/infinity_emb/Dockerfile + image: michaelf34/infinity + secrets: inherit + + # push-to-pypy: + # uses: + # ./.github/workflows/pypi_release.yaml + # with: + # working-directory: libs/infinity_emb + # secrets: inherit \ No newline at end of file diff --git a/.github/workflows/release_docker_container.yaml b/.github/workflows/release_docker_container.yaml new file mode 100644 index 00000000..73a590be --- /dev/null +++ b/.github/workflows/release_docker_container.yaml @@ -0,0 +1,73 @@ +name: release_docker + +on: + workflow_call: + inputs: + dockerfile: + required: true + type: string + description: "Path to the Dockerfile to build" + image: + required: true + type: string + description: "Name of the image to build" + context: + required: false + type: string + description: "Path to the build context" + default: "./libs/infinity_emb" + +env: + TEST_TAG: ${{ inputs.image }}:test + LATEST_TAG: ${{ inputs.image }}:latest + +jobs: + docker: + runs-on: ubuntu-latest + steps: + - name: Remove unnecessary files + run: | + sudo rm -rf /usr/share/dotnet + sudo rm -rf "$AGENT_TOOLSDIRECTORY" + - name: Checkout + uses: actions/checkout@v4 + - name: Get git tag + uses: actions-ecosystem/action-get-latest-tag@v1 + id: get-latest-tag + - name: Set docker tag + env: + VERSION: ${{ steps.get-latest-tag.outputs.tag }} + run: | + echo "VERSION_TAG=${{ inputs.image }}:${VERSION#v}" >> $GITHUB_ENV + echo $VERSION_TAG + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - name: Login to Docker Hub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + # - name: Build for Test + # uses: docker/build-push-action@v5 + # with: + # context: ${{ inputs.context }} + # file: ${{ inputs.dockerfile }} + # load: true + # tags: ${{ env.TEST_TAG }} + # - name: Test + # # show that at least --help command is running + # run: | + # docker run --rm ${{ env.TEST_TAG }} --help + - name: Build and Push to Docker Hub + uses: docker/build-push-action@v5 + with: + context: ${{ inputs.context }} + file: ${{ inputs.dockerfile }} + # We can only build for the intersection of platforms supported by + # QEMU and base python image, for now build only for + # linux/amd64 and linux/arm64 + platforms: linux/amd64,linux/arm64 + tags: ${{ env.LATEST_TAG }},${{ env.VERSION_TAG }} + push: true \ No newline at end of file diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 00000000..486fb67e --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,52 @@ +on: + workflow_call: + inputs: + working-directory: + required: true + type: string + description: "From which folder this pipeline executes" + upload_coverage: + description: 'Wheather to upload coverage report to codecov or not' + required: false + type: boolean + default: true + +env: + POETRY_VERSION: "1.6.1" + + +jobs: + build-linux: + defaults: + run: + working-directory: ${{ inputs.working-directory }} + runs-on: ubuntu-latest + strategy: + matrix: + python-version: + - "3.10" + + name: Python ${{ matrix.python-version }} + steps: + - uses: actions/checkout@v3 + + - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_setup" + with: + python-version: ${{ matrix.python-version }} + poetry-version: ${{ env.POETRY_VERSION }} + working-directory: ${{ inputs.working-directory }} + cache-key: core + + - name: Install dependencies + shell: bash + run: poetry install --extras all --with test + + - name: Run Pytest Coverage + run: | + poetry run coverage run -m --source ./infinity_emb pytest tests + poetry run coverage xml + + - name: Upload coverage Report to Codecov for python 3.10 + if: ${{ matrix.python-version == '3.10' && inputs.upload_coverage == true }} + uses: codecov/codecov-action@v2 \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..2129d58c --- /dev/null +++ b/.gitignore @@ -0,0 +1,162 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +.vscode \ No newline at end of file diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 00000000..56c32633 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,8 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +authors: +- family-names: "Feil" + given-names: "Michael" +title: "Embeddings - To Infinity and Beyond" +date-released: 2023-10-17 +url: "https://github.com/michaelfeil/infinity" diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..8e63b3a5 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Michael Feil + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 00000000..1842ef62 --- /dev/null +++ b/README.md @@ -0,0 +1,44 @@ +# Infinity ♾️ +Embedding Inference Server - finding TGI for embeddings + +## Why Infinity: +Infinity provides the following features: +- **Fast inference**: The inference server is built on top of [torch](https:) and [ctranslate2](https://github.com/OpenNMT/CTranslate2) under the hood, getting most out of your **CUDA** or **CPU** hardware. +- **Continous batching**: All new embedding requests are queued while GPU is busy with the previous ones. New requests are served as soon as GPU is ready. Adds only ~2% overhead for large datasets, over static batching. +- **Correct and tested implementation**: Unit and end-to-end tested. API embeddings are identical to [sentence-transformers](https://github.com/UKPLab/sentence-transformers/) (up to numerical precision). Lets API users create embeddings till infinity and beyond. +- **Easy to use**: The API is built on top of [FastAPI](https://fastapi.tiangolo.com/) and [Swagger](https://swagger.io/) and is fully documented. See below on how to get started. + +# Demo: +A quick demo of launching: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) with batch-size=2 and sending 3 requests via cURL. +![](docs/demo_v0_0_1.gif) + +# Getting started +Install via Poetry and Python +```bash +cd libs/infinity_emb +poetry install --extras all +``` + +### Launch via Python +```Python +from infinity_emb import create server +create_server() +``` + +### or launch the `create_server()` command via CLI +```bash +infinity_emb --help +``` + +### or launch the CLI using a pre-built docker container +Get the Python +```bash +model=sentence-transformers/all-MiniLM-L6-v2 +port=8080 +docker run -it --gpus all -p $port:$port michaelf34/infinity:latest --model-name-or-path $model --port $port --engine ctranslate2 +``` +The download path at runtime, can be controlled via the environment variable `SENTENCE_TRANSFORMERS_HOME`. + + +# Documentation +After startup, the Swagger Ui will be available under `{url}:{port}/docs`, in this case `http://localhost:8080/docs`. \ No newline at end of file diff --git a/docs/demo_v0_0_1.gif b/docs/demo_v0_0_1.gif new file mode 100644 index 00000000..770146c2 Binary files /dev/null and b/docs/demo_v0_0_1.gif differ diff --git a/libs/infinity_emb/Dockerfile b/libs/infinity_emb/Dockerfile new file mode 100644 index 00000000..fd6fb33e --- /dev/null +++ b/libs/infinity_emb/Dockerfile @@ -0,0 +1,62 @@ +# Use the Python base image +FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 AS builder + +ENV PYTHONUNBUFFERED=1 \ + # prevents python creating .pyc files + PYTHONDONTWRITEBYTECODE=1 \ + \ + # pip + PIP_NO_CACHE_DIR=off \ + PIP_DISABLE_PIP_VERSION_CHECK=on \ + PIP_DEFAULT_TIMEOUT=100 \ + \ + # make poetry create the virtual environment in the project's root + # it gets named `.venv` + POETRY_VIRTUALENVS_IN_PROJECT=true \ + # do not ask any interactive question + POETRY_NO_INTERACTION=1 + +RUN apt-get update && apt-get install python3.10 python3.10-venv -y + +# Define the version of Poetry to install (default is 1.4.2) +ARG POETRY_VERSION=1.6.1 + +# Define the directory to install Poetry to (default is /opt/poetry) +ARG POETRY_HOME=/opt/poetry + +# Create a Python virtual environment for Poetry and install it +RUN python3.10 -m venv ${POETRY_HOME} && \ + $POETRY_HOME/bin/pip install --upgrade pip && \ + $POETRY_HOME/bin/pip install poetry==${POETRY_VERSION} + +ENV PATH="${PATH}:${POETRY_VENV}/bin" + +# Test if Poetry is installed in the expected path +RUN echo "Poetry version:" && $POETRY_HOME/bin/poetry --version + +# Set the working directory for the app +WORKDIR /app + +# Use a multi-stage build to install dependencies +FROM builder AS dependencies + +ARG POETRY_HOME + +# Copy only the dependency files for installation +COPY pyproject.toml poetry.lock poetry.toml ./ + +RUN $POETRY_HOME/bin/poetry config installer.max-workers 10 + +# Install the Poetry dependencies (this layer will be cached as long as the dependencies don't change) +RUN $POETRY_HOME/bin/poetry install --no-root --no-interaction --no-ansi --extras all + +# Use a multi-stage build to run tests +FROM dependencies AS finisher + +# Copy the rest of the app source code (this layer will be invalidated and rebuilt whenever the source code changes) +COPY . . + +RUN $POETRY_HOME/bin/poetry install --no-interaction --no-ansi --extras all +ENV SENTENCE_TRANSFORMERS_HOME=/app/.cache/torch + +ENTRYPOINT ["/opt/poetry/bin/poetry","run","infinity_emb"] \ No newline at end of file diff --git a/libs/infinity_emb/Makefile b/libs/infinity_emb/Makefile new file mode 100644 index 00000000..e3e926c8 --- /dev/null +++ b/libs/infinity_emb/Makefile @@ -0,0 +1,91 @@ +.PHONY: all clean docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck format lint test tests test_watch integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +###################### +# TESTING AND COVERAGE +###################### + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ + +# Run unit tests and generate a coverage report. +coverage: + poetry run pytest --cov \ + --cov-config=.coveragerc \ + --cov-report xml \ + --cov-report term-missing:skip-covered \ + $(TEST_FILE) + +test tests: + poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) + +extended_tests: + poetry run pytest --disable-socket --allow-unix-socket --only-extended tests/unit_tests + +test_watch: + poetry run ptw --now . -- tests/unit_tests + +integration_tests: + poetry run pytest tests/integration_tests + +scheduled_tests: + poetry run pytest -m scheduled tests/integration_tests + +docker_tests: + docker build -t my-infinity_emb-image:test . + docker run --rm my-infinity_emb-image:test + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/infinity_emb --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') + +lint lint_diff: + poetry run ruff . + [ "$(PYTHON_FILES)" = "" ] || poetry run black $(PYTHON_FILES) --check + [ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES) + +format format_diff: + [ "$(PYTHON_FILES)" = "" ] || poetry run black $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +###################### +# HELP +###################### + +help: + @echo '====================' + @echo 'clean - run docs_clean and api_docs_clean' + @echo 'docs_build - build the documentation' + @echo 'docs_clean - clean the documentation build artifacts' + @echo 'docs_linkcheck - run linkchecker on the documentation' + @echo 'api_docs_build - build the API Reference documentation' + @echo 'api_docs_clean - clean the API Reference documentation build artifacts' + @echo 'api_docs_linkcheck - run linkchecker on the API Reference documentation' + @echo '-- LINTING --' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'spell_check - run codespell on the project' + @echo 'spell_fix - run codespell on the project and fix the errors' + @echo '-- TESTS --' + @echo 'coverage - run unit tests and generate coverage report' + @echo 'test - run unit tests' + @echo 'tests - run unit tests (alias for "make test")' + @echo 'test TEST_FILE= - run all tests in file' + @echo 'extended_tests - run only extended unit tests' + @echo 'test_watch - run unit tests in watch mode' + @echo 'integration_tests - run integration tests' + @echo 'docker_tests - run unit tests in docker' + @echo '-- DOCUMENTATION tasks are from the top-level Makefile --' \ No newline at end of file diff --git a/libs/infinity_emb/README.md b/libs/infinity_emb/README.md new file mode 100644 index 00000000..e69de29b diff --git a/libs/infinity_emb/infinity_emb/__init__.py b/libs/infinity_emb/infinity_emb/__init__.py new file mode 100644 index 00000000..ef1d3c25 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/__init__.py @@ -0,0 +1,8 @@ +__all__ = ["logger", "create_server", "inference", "fastapi_schemas", "__version__"] +import importlib.metadata + +from . import fastapi_schemas, inference +from .infinity_server import create_server +from .log_handler import logger + +__version__ = importlib.metadata.version("infinity_emb") diff --git a/libs/infinity_emb/infinity_emb/fastapi_schemas/__init__.py b/libs/infinity_emb/infinity_emb/fastapi_schemas/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/libs/infinity_emb/infinity_emb/fastapi_schemas/convert.py b/libs/infinity_emb/infinity_emb/fastapi_schemas/convert.py new file mode 100644 index 00000000..457553e7 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/fastapi_schemas/convert.py @@ -0,0 +1,32 @@ +from ..inference.primitives import NpEmbeddingType +from .pymodels import OpenAIEmbeddingResult, _EmbeddingObject, _Usage + + +def list_embeddings_to_response( + embeddings: NpEmbeddingType, model: str, usage: int +) -> OpenAIEmbeddingResult: + return OpenAIEmbeddingResult( + model=model, + data=[ + _EmbeddingObject( + object="embedding", + embedding=emb, + index=count, + ) + for count, emb in enumerate(embeddings) + ], + usage=_Usage(prompt_tokens=usage, total_tokens=usage), + ) + + # return { + # "model": model, + # "data": [ + # dict( + # object="embedding", + # embedding=emb, + # index=count, + # ) + # for count, emb in enumerate(embeddings) + # ], + # "usage": {"prompt_tokens": usage, "total_tokens": usage}, + # } diff --git a/libs/infinity_emb/infinity_emb/fastapi_schemas/errors.py b/libs/infinity_emb/infinity_emb/fastapi_schemas/errors.py new file mode 100644 index 00000000..5439e7e3 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/fastapi_schemas/errors.py @@ -0,0 +1,37 @@ +from typing import Optional + +from fastapi import Request +from fastapi.responses import JSONResponse + + +class OpenAIException(Exception): + """An exception in OpenAI Style""" + + def __init__( + self, + message: str, + code: int, + type: Optional[str] = None, + param: Optional[str] = None, + ): + self.message = message + self.type = type + self.param = param + self.code = code + + def json(self): + return { + "error": { + "message": self.message, + "type": self.type, + "param": self.param, + "code": self.code, + } + } + + +def openai_exception_handler(request: Request, exc: OpenAIException): + return JSONResponse( + status_code=exc.code, + content=exc.json(), + ) diff --git a/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py b/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py new file mode 100644 index 00000000..d3ca8702 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py @@ -0,0 +1,48 @@ +import time +from typing import Annotated, Any, Dict, List, Literal, Optional +from uuid import uuid4 + +from pydantic import BaseModel, Field, StringConstraints, conlist + + +class OpenAIEmbeddingInput(BaseModel): + input: conlist( + Annotated[str, StringConstraints(max_length=1024 * 10, strip_whitespace=True)], + min_length=1, + max_length=2048, + ) # type: ignore + model: Annotated[str, StringConstraints(min_length=2)] + user: Optional[str] = None + + +class _EmbeddingObject(BaseModel): + object: Literal["embedding"] = "embedding" + embedding: List[float] + index: int + + +class _Usage(BaseModel): + prompt_tokens: int + total_tokens: int + + +class OpenAIEmbeddingResult(BaseModel): + object: Literal["embedding"] = "embedding" + data: List[_EmbeddingObject] + model: str + usage: _Usage + id: str = Field(default_factory=lambda: f"infinity-{uuid4()}") + created: int = Field(default_factory=lambda: int(time.time())) + + +class ModelInfo(BaseModel): + id: str + stats: Dict[str, Any] + object: Literal["model"] = "model" + owned_by: Literal["infinity"] = "infinity" + created: int = int(time.time()) # no default factory + + +class OpenAIModelInfo(BaseModel): + data: ModelInfo + object: str = "list" diff --git a/libs/infinity_emb/infinity_emb/inference/__init__.py b/libs/infinity_emb/infinity_emb/inference/__init__.py new file mode 100644 index 00000000..3cbb7e28 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/inference/__init__.py @@ -0,0 +1,11 @@ +from .batch_handler import BatchHandler +from .primitives import EmbeddingResult, NpEmbeddingType, PrioritizedQueueItem +from .select_model import select_model_to_functional + +__all__ = [ + "EmbeddingResult", + "NpEmbeddingType", + "PrioritizedQueueItem", + "BatchHandler", + "select_model_to_functional", +] diff --git a/libs/infinity_emb/infinity_emb/inference/batch_handler.py b/libs/infinity_emb/infinity_emb/inference/batch_handler.py new file mode 100644 index 00000000..5d5029c9 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/inference/batch_handler.py @@ -0,0 +1,315 @@ +import asyncio +import bisect +import queue +import random +import threading +import time +from concurrent.futures import ThreadPoolExecutor +from typing import Dict, List, Union + +from ..log_handler import logger +from .models import BaseTransformer +from .primitives import ( + EmbeddingResult, + NpEmbeddingType, + OverloadStatus, + PrioritizedQueueItem, +) +from .threading_asyncio import EventTS + + +class CustomPrioQueue: + def __init__(self, sort_on_arrival=True) -> None: + self._lock_queue_event = threading.Lock() # lock queue and + # queue is a always sorted + self._queue: List[PrioritizedQueueItem] = [] + self._sync_event = threading.Event() + self._sort_on_arrival = sort_on_arrival + + def __len__(self): + return len(self._queue) + + async def extend(self, items: List[PrioritizedQueueItem]): + with self._lock_queue_event: + if self._sort_on_arrival: + for item in items: + bisect.insort(self._queue, item) + else: + self._queue.extend(items) + + self._sync_event.set() + + def pop_optimal_batch( + self, size: int, timeout=0.2 + ) -> Union[List[EmbeddingResult], None]: + """ + pop batch `up to size` + `continuous (sorted)` from queue + + returns: + None: if there is not a single item in self._queue + else: List[EmbeddingResult] with len(1<=size) + """ + if not self._queue: + if not self._sync_event.wait(timeout): + return None + + if len(self._queue) > size: + # pick a random continuous slice, at beginning or at the end. + start = random.randrange(-size, len(self._queue)) + start = max(0, min(len(self._queue) - size, start)) + else: + start = 0 + end = start + size + + if not self._sort_on_arrival: + self._queue.sort() + + with self._lock_queue_event: + new_items = self._queue[start:end] + self._queue = self._queue[:start] + self._queue[end:] + if not self._queue: + self._sync_event.clear() + + return list(n.item for n in new_items) + + +class ResultKVStore: + def __init__(self) -> None: + self._lock = threading.Lock() + self._kv: Dict[str, NpEmbeddingType] = {} + + def __len__(self): + return len(self._kv) + + async def wait_for_response(self, uuid: str, event: EventTS) -> NpEmbeddingType: + await event.wait() + # with self._lock: + response = self._kv[uuid] + del self._kv[uuid] + return response + + async def extend(self, batch: List[EmbeddingResult]) -> None: + """extend store with results""" + # # with self._lock: + # for item in batch: + # # first create (while _lock) + # self._kv[item.uuid] = item.embedding + # # all done, mark EmbeddingResult for collection + # item.event.set() + _update = {item.uuid: item.embedding for item in batch} + + self._kv.update(_update) + for item in batch: + # all done, mark EmbeddingResult for collection + item.event.set() + + +class BatchHandler: + def __init__( + self, + model: BaseTransformer, + max_batch_size: int, + threadpool: ThreadPoolExecutor, + max_queue_wait: int = 64_000, + verbose=False, + ) -> None: + self._shutdown = threading.Event() + self._queue_prio = CustomPrioQueue() + self._result_store = ResultKVStore() + self._feature_queue: queue.Queue = queue.Queue(4) + self._postprocess_queue: queue.Queue = queue.Queue(5) + self.max_batch_size = max_batch_size + self.model = model + self.max_queue_wait = max_queue_wait + self._threadpool = threadpool + self._ready = False + self._verbose = verbose + + def shutdown(self): + self._shutdown.set() + + async def schedule( + self, sentences: List[str], prios: List[int] + ) -> NpEmbeddingType | None: + """Schedule a sentence to be embedded. Awaits until embedded. + + Args: + sentences (List[str]): Sentences to be embedded + prio (List[int]): priority for this embedding + + Returns: + NpEmbeddingType: embedding as 1darray + """ + # add an unique identifier + uuid_event = [] + prioqueue = [] + for s, p in zip(sentences, prios): + inner = EmbeddingResult(sentence=s, event=EventTS(self._threadpool)) + item = PrioritizedQueueItem(item=inner, priority=p) + uuid_event.append((inner.uuid, inner.event)) + prioqueue.append(item) + await self._queue_prio.extend(prioqueue) + + gather_results = [ + self._result_store.wait_for_response(uuid, event) + for uuid, event in uuid_event + ] + return await asyncio.gather(*gather_results) + + def is_overloaded(self) -> bool: + # start consuming + return len(self._queue_prio) > self.max_queue_wait + + def ready(self) -> bool: + return self._ready + + def overload_status(self) -> OverloadStatus: + """ + returns info about the queue status + """ + return OverloadStatus( + queue_fraction=len(self._queue_prio) / self.max_queue_wait, + queue_absolute=len(self._queue_prio), + results_absolute=len(self._result_store), + ) + + def _preprocess_batch(self): + """loops and checks if the _core_batch has worked on all items""" + self._ready = True + logger.info("ready to receive requests.") + try: + while not self._shutdown.is_set(): + # patience: + # do not pop a batch if self._feature_queue still has an item left + # - until GPU / _core_batch starts processing the previous item + # - or if many items are queued anyhow, so that a good batch + # may be popped already. + if not self._feature_queue.empty() and ( + self._feature_queue.full() + or (len(self._queue_prio) < self.max_batch_size * 4) + ): + # add some stochastic delay + time.sleep(2e-3) + continue + # decision to attemp to pop a batch + # -> will happen if a single datapoint is available + batch = self._queue_prio.pop_optimal_batch(self.max_batch_size) + if not batch: + # not a single sentence available / len=0, wait for more + continue + # optimal batch has been selected -> + # lets tokenize it and move tensors to GPU. + sentences = [item.sentence for item in batch] + feat = self.model.encode_pre(sentences) + if self._verbose: + logger.debug( + "[📦] batched %s requests, queue remaining: %s", + len(sentences), + len(self._queue_prio), + ) + # while-loop just for shutdown + while not self._shutdown.is_set(): + try: + self._feature_queue.put((feat, batch), timeout=1) + break + except queue.Full: + continue + except Exception as ex: + logger.exception(ex) + exit("_preprocess_batch crashed") + + def _core_batch(self): + """waiting for preprocessed batches (on device) + and do the forward pass / `.encode` + """ + try: + while not self._shutdown.is_set(): + try: + core_batch = self._feature_queue.get(timeout=0.5) + except queue.Empty: + continue + (feat, batch) = core_batch + embed = self.model.encode_core(feat) + if self._verbose: + logger.debug("[🏃] Inference done on batch_size=%s", len(batch)) + + # while-loop just for shutdown + while not self._shutdown.is_set(): + try: + self._postprocess_queue.put((embed, batch), timeout=1) + break + except queue.Full: + continue + self._feature_queue.task_done() + except Exception as ex: + logger.exception(ex) + exit("_core_batch crashed.") + + async def _postprocess_batch(self): + """collecting forward(.encode) results and put them into the result store""" + # TODO: the ugly asyncio.sleep() could add to 3-8ms of latency worst case + # In constrast, at full batch size, sleep releases cruical CPU at time of + # the forward pass to GPU (after which there is crical time again) + # and not affecting the latency + try: + while not self._shutdown.is_set(): + try: + post_batch = self._postprocess_queue.get_nowait() + if not self._postprocess_queue.qsize(): + # queue is not in a hurry + # give the CPU some time to focus + # on moving the next batch to GPU on the forward pass + # before proceeding + await asyncio.sleep(1e-3) + except queue.Empty: + # 7 ms, assuming this is below + # 3-50ms for inference on avg. + await asyncio.sleep(7e-3) + continue + embed, batch = post_batch + embeddings = self.model.encode_post(embed).tolist() + for i, item in enumerate(batch): + item.embedding = embeddings[i] + await self._result_store.extend(batch) + self._postprocess_queue.task_done() + except Exception as ex: + logger.exception(ex) + exit("Postprocessor crashed") + + async def spawn(self): + """set up the resources in batch""" + logger.info("creating batching engine") + self._threadpool.submit(self._preprocess_batch) + self._threadpool.submit(self._core_batch) + asyncio.create_task(self._postprocess_batch()) + + async def spawn_one_thread(self): + """set up the resources in batch""" + logger.info("creating batching engine") + # self._threadpool.submit(self._preprocess_batch) + self._threadpool.submit(self._single_process) + asyncio.create_task(self._postprocess_batch()) + + def _single_process(self): + while not self._shutdown.is_set(): + # decision to pop a batch + batch = self._queue_prio.pop_optimal_batch(self.max_batch_size) + if not batch: + # not a single sentence available / len=0, wait for more + continue + # optimal batch has been selected -> + # lets tokenize it and move tensors to GPU. + sentences = [item.sentence for item in batch] + feat = self.model.encode_pre(sentences) + embed = self.model.encode_core(feat) + while not self._shutdown.is_set(): + try: + self._postprocess_queue.put((embed, batch), timeout=1) + break + except queue.Full: + continue + + # embeddings = self.model.encode_post(embed).tolist() + # for i, item in enumerate(batch): + # item.embedding = embeddings[i] + # self._result_store.extend(batch) diff --git a/libs/infinity_emb/infinity_emb/inference/models.py b/libs/infinity_emb/infinity_emb/inference/models.py new file mode 100644 index 00000000..2bdba0a6 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/inference/models.py @@ -0,0 +1,347 @@ +import os +from abc import ABC, abstractmethod +from enum import Enum +from typing import Any, Callable, Dict, List, Tuple, Union + +import numpy as np +import torch +from sentence_transformers import SentenceTransformer, util # type: ignore +from torch import Tensor + +from ..log_handler import logger +from .primitives import NpEmbeddingType + +__all__ = [ + "InferenceEngine", + "InferenceEngineTypeHint", + "DummyTransformer", + "CT2SentenceTransformer", + "BaseTransformer", +] + +INPUT_FEATURE = Any +OUT_FEATURES = Any + + +class BaseTransformer(ABC): # Inherit from ABC(Abstract base class) + @abstractmethod # Decorator to define an abstract method + def encode_pre(self, sentences: List[str]) -> INPUT_FEATURE: + pass + + @abstractmethod + def encode_core(self, features: INPUT_FEATURE) -> OUT_FEATURES: + pass + + @abstractmethod + def encode_post(self, embedding: OUT_FEATURES) -> NpEmbeddingType: + pass + + @abstractmethod + def tokenize_lengths(self, sentences: List[str]) -> List[int]: + pass + + +class DummyTransformer(BaseTransformer): + """fix-13 dimension embedding, filled with length of sentence""" + + def __init__(self, *args, **kwargs) -> None: + pass + + def encode_pre(self, sentences: List[str]) -> np.ndarray: + return np.asarray(sentences) + + def encode_core(self, features: np.ndarray) -> NpEmbeddingType: + lengths = np.array([[len(s) for s in features]]) + # embedding of size 13 + return np.ones([len(features), 13]) * lengths.T + + def encode_post(self, embedding: NpEmbeddingType): + return embedding + + def tokenize_lengths(self, sentences: List[str]) -> List[int]: + return [len(s) for s in sentences] + + +class SentenceTransformerPatched(SentenceTransformer, BaseTransformer): + """SentenceTransformer with .encode_core() and no microbatching""" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + device = self._target_device + self.eval() + self.to(device) + + def encode_pre(self, sentences) -> Dict[str, Tensor]: + # features = self._tokenize_actual(sentences) + features = self.tokenize(sentences) + + return features + + def encode_core(self, features: Dict[str, Tensor]) -> Tensor: + """ + Computes sentence embeddings + """ + # features = self._tokenize_actual(features) + device = self._target_device + features = util.batch_to_device(features, device) + # move forward + + with torch.no_grad(): + out_features = self.forward(features) + + return out_features["sentence_embedding"].detach().cpu() + + def encode_post( + self, out_features: Tensor, normalize_embeddings: bool = True + ) -> NpEmbeddingType: + with torch.no_grad(): + embeddings = out_features + if normalize_embeddings: + embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1) + embeddings_out: np.ndarray = embeddings.cpu().numpy() + + return embeddings_out + + def tokenize_lengths(self, sentences: List[str]) -> List[int]: + fm = self._first_module() + tks = fm.tokenizer.batch_encode_plus( + sentences, + add_special_tokens=False, + return_token_type_ids=False, + return_attention_mask=False, + return_length=False, + ).encodings + return [len(t.tokens) for t in tks] + + def _tokenize_actual(self, sentences: List[str]): + fm = self._first_module() + output = fm.tokenizer( + sentences, + padding=True, + truncation="longest_first", + return_tensors="pt", + max_length=fm.tokenizer.model_max_length, + # pad_to_multiple_of=16, + ) + + return dict(**output) + + +class CT2SentenceTransformer(SentenceTransformerPatched): + """ + Loads or create a SentenceTransformer model, that can be used to map sentences + / text to embeddings. + Extension of sentence_transformers.SentenceTransformer using a CTranslate2 + model for accelerated inference-only. + Adapted from https://gist.github.com/guillaumekln/fb125fc3eb108d1a304b7432486e712f + + :param model_name_or_path: If it is a filepath on disc, + it loads the model from that path. + If it is not a path, it first tries to download a + pre-trained SentenceTransformer model. + If that fails, tries to construct a model from Huggingface + models repository with that name. + :param modules: + This parameter can be used to create custom SentenceTransformer + models from scratch. + :param device: Device (like 'cuda' / 'cpu') that should be used + for computation. + If None, checks if a GPU can be used. + :param cache_folder: Path to store models. + Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable. + :param use_auth_token: + HuggingFace authentication token to download private models. + :param compute_type: weight quantization, scheme for computing, + (possible values are: int8, int8_float16, int16, float16). + :param force: force new conversion with CTranslate2, even if it already exists. + :param vmap: Optional path to a vocabulary mapping file that will be included + in the converted model directory. + """ + + def __init__( + self, + *args, + compute_type="default", + force=False, + vmap: Union[str, None] = None, + **kwargs + ): + super().__init__(*args, **kwargs) + self[0] = CT2Transformer( + self[0], + compute_type=compute_type, + force=force, + vmap=vmap, + ) + + +class CT2Transformer(torch.nn.Module): + """Wrapper around a sentence_transformers.models.Transformer + which routes the forward call to a CTranslate2 encoder model. + + :param compute_type: weight quantization, scheme for computing, + default uses same as quantization + (possible values are: int8, int8_float16, int16, float16). + :param force: force new conversion with CTranslate2, even if it already exists. + :param vmap: Optional path to a vocabulary mapping file that will be included + in the converted model directory. + """ + + def __init__( + self, + transformer, + compute_type="default", + force=False, + vmap: Union[str, None] = None, + ): + super().__init__() + try: + import ctranslate2 # type: ignore + except ImportError: + logger.exception( + "for running the CT2SentenceTransformer," + " it is required to install CTranslate2 by running " + " `pip install ctranslate2>=3.16.0`" + ) + + self.tokenizer = transformer.tokenizer + self._tokenize = transformer.tokenize + self.compute_type = compute_type + self.encoder = None + + # Convert to the CTranslate2 model format, if not already done. + model_dir = transformer.auto_model.config.name_or_path + self.ct2_model_dir = os.path.join( + model_dir, + "ctranslate2_" + ctranslate2.__version__, + ) + + if not os.path.exists(os.path.join(self.ct2_model_dir, "model.bin")) or force: + if os.path.exists(self.ct2_model_dir) and not os.listdir( + self.ct2_model_dir + ): + force = True + converter = ctranslate2.converters.TransformersConverter(model_dir) + converter.convert(self.ct2_model_dir, force=force, vmap=vmap) + self._ctranslate2_encoder_cls = ctranslate2.Encoder + self._storage_view = ctranslate2.StorageView + + def children(self): + # Do not consider the "transformer" attribute as a + # child module so that it will stay on the CPU. + return [] + + def half(self): + self.to(dtype="float16") + return self + + def to( + self, + device: int | torch.device | None = None, + dtype: torch.dtype | str | None = None, + non_blocking: bool = False, + ) -> "CT2Transformer": + if not isinstance(device, int): + raise ValueError("param `dtype` needs to be of type int") + if not isinstance(dtype, str) or dtype is not None: + raise ValueError("param `dtype` needs to be of type str") + + if dtype and not ("float" in dtype or "int" in dtype): + raise ValueError( + "dtype should be one of `int8`, `float16`, `int8_float16`, `float32`" + ) + elif dtype: + new_dtype = True + self.compute_type = new_dtype + else: + new_dtype = False + + if device and (device.startswith("cuda") or device.startswith("cpu")): + raise ValueError( + "for param `device`, f'cuda:{index}' or f'cpu:{index}' are supported" + ) + elif device: + if ":" in device: + new_device = device.split(":")[0] + new_index = device.split(":")[1] + else: + new_device = device + new_index = "0" + else: + new_device = "" + new_index = "" + + if new_device or new_dtype or new_index: + self.encoder = self._ctranslate2_encoder_cls( + self.ct2_model_dir, + device=new_device, + device_index=new_index, + intra_threads=torch.get_num_threads(), + compute_type=self.compute_type, + ) + return self + + def forward(self, features): + """overwrites torch forward method with CTranslate model""" + device = features["input_ids"].device + + if self.encoder is None: + # The encoder is lazy-loaded to correctly resolve the target device. + self.encoder = self._ctranslate2_encoder_cls( + self.ct2_model_dir, + device=device.type, + device_index=device.index or 0, + intra_threads=torch.get_num_threads(), + compute_type=self.compute_type, + ) + + input_ids = features["input_ids"].to(torch.int32) + length = features["attention_mask"].sum(1, dtype=torch.int32) + + if device.type == "cpu": + # PyTorch CPU tensors do not implement the Array interface + # so a roundtrip to Numpy + # is required for both the input and output. + input_ids = input_ids.numpy() + length = length.numpy() + + input_ids = self._storage_view.from_array(input_ids) + length = self._storage_view.from_array(length) + + outputs = self.encoder.forward_batch(input_ids, length) + + last_hidden_state = outputs.last_hidden_state + if device.type == "cpu": + last_hidden_state = np.array(last_hidden_state) + + features["token_embeddings"] = torch.as_tensor( + last_hidden_state, device=device + ).to(torch.float32) + + return features + + def tokenize(self, *args, **kwargs): + return self._tokenize(*args, **kwargs) + + +def length_tokenizer( + _sentences: List[str], +) -> List[int]: + return [len(i) for i in _sentences] + + +def get_lengths_with_tokenize( + _sentences: List[str], tokenize: Callable = length_tokenizer +) -> Tuple[List[int], int]: + _lengths = tokenize(_sentences) + return _lengths, sum(_lengths) + + +class InferenceEngine(Enum): + torch = SentenceTransformerPatched + debugengine = DummyTransformer + ctranslate2 = CT2SentenceTransformer + + +types: Dict[str, str] = {e.name: e.name for e in InferenceEngine} +InferenceEngineTypeHint = Enum("InferenceEngineTypeHint", types) # type: ignore diff --git a/libs/infinity_emb/infinity_emb/inference/primitives.py b/libs/infinity_emb/infinity_emb/inference/primitives.py new file mode 100644 index 00000000..698d4418 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/inference/primitives.py @@ -0,0 +1,32 @@ +import time +from dataclasses import dataclass, field +from typing import Optional +from uuid import uuid4 + +import numpy as np + +from .threading_asyncio import EventTS + +NpEmbeddingType = np.ndarray + + +@dataclass +class EmbeddingResult: + sentence: str + event: EventTS + uuid: str = field(default_factory=lambda: str(uuid4())) + created: float = field(default_factory=time.time) + embedding: Optional[NpEmbeddingType] = None + + +@dataclass(order=True) +class PrioritizedQueueItem: + priority: int + item: EmbeddingResult = field(compare=False) + + +@dataclass +class OverloadStatus: + queue_fraction: float + queue_absolute: int + results_absolute: int diff --git a/libs/infinity_emb/infinity_emb/inference/select_model.py b/libs/infinity_emb/infinity_emb/inference/select_model.py new file mode 100644 index 00000000..5608f54d --- /dev/null +++ b/libs/infinity_emb/infinity_emb/inference/select_model.py @@ -0,0 +1,76 @@ +from time import perf_counter + +from ..log_handler import logger +from .models import BaseTransformer, InferenceEngine +from .primitives import EmbeddingResult, NpEmbeddingType + + +def select_model_to_functional( + model_name_or_path: str, batch_size: int, engine: InferenceEngine, warmup=True +): + logger.info(f"model {model_name_or_path} selected, using engine={engine.value}") + init_engine = engine.value(model_name_or_path) + + if warmup: + # size one + runtime_check_callable(init_engine, log=False) + # size one + runtime_check_callable(init_engine, log=True) + # warm-up with max batch size + emb_per_sec_short = runtime_check_callable( + init_engine, sample=["up"] * batch_size + ) + # warm-up with max batch size + emb_per_sec = runtime_check_callable( + init_engine, + sample=["warming up with max batch size and 1K tokens per sentence " * 76] + * batch_size, + ) + logger.info( + f"model warmed up, between {emb_per_sec:.2f}-{emb_per_sec_short:.2f}" + f" embeddings/sec at batch_size={batch_size}" + ) + + return init_engine + + +def runtime_check_callable( + model: BaseTransformer, sample=["warmup"], log=True +) -> float: + inp = [EmbeddingResult(sentence=s, event=None) for s in sample] # type: ignore + start = perf_counter() + sentences = [item.sentence for item in inp] + feat = model.encode_pre(sentences) + tokenization_time = perf_counter() + embed = model.encode_core(feat) + inference_time = perf_counter() + embeddings = model.encode_post(embed) + for i, item in enumerate(inp): + item.embedding = embeddings[i] + post_time = perf_counter() + + if not len(inp) == len(sample): + raise ValueError( + "The output of the callable function is not of the same length as the input" + ) + + if not isinstance(inp[0].embedding, NpEmbeddingType): + raise ValueError( + "The output of the callable function is not of type EmbeddingResult" + ) + + if log: + logger.info( + ( + f"Getting timings for batch_size={len(sample)}" + f" and avg tokens per sentence={model.tokenize_lengths(sample)[0]}\n" + f"\t{(tokenization_time - start)*1000:.2f} \t ms tokenization\n" + f"\t{(inference_time-tokenization_time)*1000:.2f} \t ms inference\n" + f"\t{(post_time-inference_time)*1000:.2f} \t ms post-processing\n" + f"\t{(post_time - start)*1000:.2f} \t ms total\n" + f"embeddings/sec: {len(sample) / (post_time - start):.2f}" + ) + ) + + emb_per_sec = len(sample) / (post_time - start) + return emb_per_sec diff --git a/libs/infinity_emb/infinity_emb/inference/threading_asyncio.py b/libs/infinity_emb/infinity_emb/inference/threading_asyncio.py new file mode 100644 index 00000000..07ca8902 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/inference/threading_asyncio.py @@ -0,0 +1,94 @@ +"""High-level support for working with threads in asyncio""" + +import asyncio +import contextvars +import functools +from concurrent.futures import ThreadPoolExecutor + +__all__ = ["to_thread", "EventTS"] + + +# class EventTS(threading.Event): +# """making asyncio.Event threadsafe""" + +# def __init__(self, tp: ThreadPoolExecutor) -> None: +# super().__init__() +# self.tp = tp + +# async def wait(self): +# wait = partial(super().wait, None) +# await to_thread(wait, self.tp) + +# class EventTS(asyncio.Event): +# def __init__(self, tp=None, *args, **kwargs): +# super().__init__(*args, **kwargs) +# # if self._loop is None: +# # self._loop = asyncio.get_event_loop() + +# # def set(self): +# # self._loop.call_soon_threadsafe(super().set) + +# def clear(self): +# raise NotImplementedError +# # self._loop.call_soon_threadsafe(super().clear) + + +class EventTS: + """Throw-away async event. + wait and set once, and forget. + + Save only for one reading and one writing thread. + """ + + def __init__(self, tp=None): + self._waiter = None + self._value = False + + def is_set(self): + """Return True if and only if the internal flag is true.""" + return self._value + + def set(self): + """Set the internal flag to true. All coroutines waiting for it to + become true are awakened. Coroutine that call wait() once the flag is + true will not block at all. + """ + if not self._value: + self._value = True + + if self._waiter: + self._waiter.set_result(True) + + async def wait(self): + """Block until the internal flag is true. + + If the internal flag is true on entry, return True + immediately. Otherwise, block until another coroutine calls + set() to set the flag to true, then return True. + """ + if self._value: + return True + + fut = asyncio.events._get_running_loop().create_future() + self._waiter = fut + try: + await fut + return True + finally: + self._waiter = None + + +async def to_thread(func, tp: ThreadPoolExecutor, /, *args, **kwargs): + """Asynchronously run function *func* in a separate thread. + + Any *args and **kwargs supplied for this function are directly passed + to *func*. Also, the current :class:`contextvars.Context` is propagated, + allowing context variables from the main thread to be accessed in the + separate thread. + + Return a coroutine that can be awaited to get the eventual result of *func*. + """ + loop = asyncio.events.get_running_loop() + ctx = contextvars.copy_context() + func_call = functools.partial(ctx.run, func, *args, **kwargs) + return await loop.run_in_executor(tp, func_call) diff --git a/libs/infinity_emb/infinity_emb/infinity_server.py b/libs/infinity_emb/infinity_emb/infinity_server.py new file mode 100644 index 00000000..5ae10a61 --- /dev/null +++ b/libs/infinity_emb/infinity_emb/infinity_server.py @@ -0,0 +1,179 @@ +import concurrent.futures +import time + +import typer +import uvicorn +from fastapi import FastAPI, status +from prometheus_fastapi_instrumentator import Instrumentator + +# prometheus +import infinity_emb +from infinity_emb.fastapi_schemas import errors +from infinity_emb.fastapi_schemas.convert import list_embeddings_to_response +from infinity_emb.fastapi_schemas.pymodels import ( + ModelInfo, + OpenAIEmbeddingInput, + OpenAIEmbeddingResult, + OpenAIModelInfo, +) +from infinity_emb.inference import BatchHandler, models, select_model_to_functional +from infinity_emb.log_handler import UVICORN_LOG_LEVELS, logger + + +def create_server( + model_name_or_path: str = "sentence-transformers/all-MiniLM-L6-v2", + url_prefix: str = "/v1", + batch_size: int = 64, + engine: models.InferenceEngine = models.InferenceEngine.torch, + verbose: bool = False, +): + app = FastAPI( + title="♾️ Infinity - Embedding Inference Server", + summary="Embedding Inference Server - finding TGI for embeddings", + version=infinity_emb.__version__, + contact=dict(name="Michael Feil"), + docs_url="/docs", + license_info={ + "name": "MIT License", + "identifier": "MIT", + }, + ) + instrumentator = Instrumentator().instrument(app) + app.add_exception_handler(errors.OpenAIException, errors.openai_exception_handler) + + @app.on_event("startup") + async def _startup(): + instrumentator.expose(app) + + model = select_model_to_functional( + model_name_or_path=model_name_or_path, batch_size=batch_size, engine=engine + ) + + app.tp = concurrent.futures.ThreadPoolExecutor() + app.batch_handler = BatchHandler( + max_batch_size=batch_size, model=model, threadpool=app.tp, verbose=verbose + ) + app.tokenize_len = model.tokenize_lengths + # start in a threadpool + await app.batch_handler.spawn() + + @app.on_event("shutdown") + async def _shutdown(): + app.batch_handler.shutdown() + app.tp.shutdown() + + @app.get("/ready") + async def _ready() -> float: + if app.batch_handler.ready(): # type: ignore + return time.time() + else: + raise errors.OpenAIException( + "model not ready", code=status.HTTP_503_SERVICE_UNAVAILABLE + ) + + @app.get(f"{url_prefix}/models") + async def _models() -> OpenAIModelInfo: + """get models endpoint""" + s = app.batch_handler.overload_status() # type: ignore + return OpenAIModelInfo( + data=ModelInfo( + id=model_name_or_path, + stats=dict( + queue_fraction=s.queue_fraction, + queue_absolute=s.queue_absolute, + results_pending=s.results_absolute, + ), + ) + ) + + @app.post(f"{url_prefix}/embeddings") + async def _embeddings(data: OpenAIEmbeddingInput) -> OpenAIEmbeddingResult: + """Encode Embeddings + + ```python + import requests + requests.post("https://..:8000/v1/embeddings", + json={"model":"all-MiniLM-L6-v2","input":["A sentence to encode."]}) + """ + bh: BatchHandler = app.batch_handler + if bh.is_overloaded(): + raise errors.OpenAIException( + "model overloaded", code=status.HTTP_429_TOO_MANY_REQUESTS + ) + + try: + start = time.perf_counter() + + # lengths, usage = await to_thread( + # models.get_lengths_with_tokenize, app.tp, data.input, app.tokenize_len) + lengths, usage = models.get_lengths_with_tokenize( + data.input # , app.tokenize_len + ) + logger.debug("[📝] Received request with %s inputs ", len(lengths)) + + # emb = await asyncio.gather( + # *[(bh.schedule(s, prio=prio)) for s, prio in zip(data.input, lengths)] + # ) + emb = await bh.schedule(data.input, prios=lengths) + + duration = (time.perf_counter() - start) * 1000 + logger.debug("[✅] Done in %s ms", duration) + + res = list_embeddings_to_response( + embeddings=emb, model=data.model, usage=usage + ) + + return res + except Exception as ex: + raise errors.OpenAIException( + f"internal server error {ex}", + code=status.HTTP_500_INTERNAL_SERVER_ERROR, + ) + + return app + + +def start_uvicorn( + model_name_or_path: str = "sentence-transformers/all-MiniLM-L6-v2", + batch_size: int = 64, + url_prefix: str = "/v1", + host: str = "0.0.0.0", + port: int = 8001, + log_level: UVICORN_LOG_LEVELS = UVICORN_LOG_LEVELS.info.name, # type: ignore + engine: models.InferenceEngineTypeHint = models.InferenceEngineTypeHint.torch.name, # type: ignore # noqa +): + """Infinity Embedding API ♾️ cli to start a uvicorn-server instance; + MIT License; Copyright (c) 2023 Michael Feil + + Args: + model_name_or_path: str: Huggingface model, e.g. + "sentence-transformers/all-MiniLM-L6-v2". + batch_size: int: batch size for forward pass. + url_prefix str: prefix for api. typically "/v1". + host str: host-url, typically either "0.0.0.0" or "127.0.0.1". + port int: port that you want to expose. + log_level: logging level. + For high performance, use "info" or higher levels. Defaults to "info". + engine: framework that should perform inference. + """ + engine_load: models.InferenceEngine = models.InferenceEngine[engine.name] + logger.setLevel(log_level.to_int()) + + app = create_server( + model_name_or_path=model_name_or_path, + url_prefix=url_prefix, + batch_size=batch_size, + engine=engine_load, + verbose=log_level.to_int() <= 10, + ) + uvicorn.run(app, host=host, port=port, log_level=log_level.name) + + +def cli(): + """fires the command line using Python `typer.run()`""" + typer.run(start_uvicorn) + + +if __name__ == "__main__": + # for debugging + cli() diff --git a/libs/infinity_emb/infinity_emb/log_handler.py b/libs/infinity_emb/infinity_emb/log_handler.py new file mode 100644 index 00000000..5dacc15b --- /dev/null +++ b/libs/infinity_emb/infinity_emb/log_handler.py @@ -0,0 +1,30 @@ +import logging +from enum import Enum + +from rich.console import Console +from rich.logging import RichHandler +from uvicorn.config import LOG_LEVELS + +logging.getLogger().handlers.clear() + +FORMAT = "%(asctime)s %(name)s %(levelname)s: %(message)s" +logging.basicConfig( + level="INFO", + format=FORMAT, + datefmt="[%x %X]", + handlers=[RichHandler(console=Console(stderr=True))], +) + +logger = logging.getLogger("infinity_emb") + + +class UVICORN_LOG_LEVELS(Enum): + critical = "critical" + error = "error" + warning = "warning" + info = "info" + debug = "debug" + trace = "trace" + + def to_int(self) -> int: + return LOG_LEVELS[self.name] diff --git a/libs/infinity_emb/poetry.lock b/libs/infinity_emb/poetry.lock new file mode 100644 index 00000000..c0d16eb8 --- /dev/null +++ b/libs/infinity_emb/poetry.lock @@ -0,0 +1,2676 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = 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+[[package]] +name = "wheel" +version = "0.41.2" +description = "A built-package format for Python" +optional = false +python-versions = ">=3.7" +files = [ + {file = "wheel-0.41.2-py3-none-any.whl", hash = "sha256:75909db2664838d015e3d9139004ee16711748a52c8f336b52882266540215d8"}, + {file = "wheel-0.41.2.tar.gz", hash = "sha256:0c5ac5ff2afb79ac23ab82bab027a0be7b5dbcf2e54dc50efe4bf507de1f7985"}, +] + +[package.extras] +test = ["pytest (>=6.0.0)", "setuptools (>=65)"] + +[extras] +all = ["ctranslate2"] +ct2 = ["ctranslate2"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.10,<4.0" +content-hash = "238a1a2818488a318c73089d4a0b1281fab8fac29b22230b288736af40b0c462" diff --git a/libs/infinity_emb/poetry.toml b/libs/infinity_emb/poetry.toml new file mode 100644 index 00000000..42159b24 --- /dev/null +++ b/libs/infinity_emb/poetry.toml @@ -0,0 +1,5 @@ +[virtualenvs] +in-project = true + +[installer] +modern-installation = false diff --git a/libs/infinity_emb/pyproject.toml b/libs/infinity_emb/pyproject.toml new file mode 100644 index 00000000..1594f4ec --- /dev/null +++ b/libs/infinity_emb/pyproject.toml @@ -0,0 +1,59 @@ +[tool.poetry] +name = "infinity_emb" +version = "0.0.1" +description = "Embeddings til infinity" +authors = ["michaelfeil "] +license = "MIT" +readme = "README.md" +packages = [{include = "infinity_emb"}] + +[tool.poetry.dependencies] +python = ">=3.10,<4.0" +fastapi = "^0.103.2" +pydantic = ">=2.4.2,<3" +torch = ">=2.0.0, !=2.0.1, !=2.1.0" +sentence-transformers = "^2.2.2" +prometheus-fastapi-instrumentator = "^6.1.0" +uvicorn = "^0.23.2" +rich = "^13.6.0" +numpy = "^1" +ctranslate2 = {version = "^3.20.0", optional=true} +typer = {extras = ["all"], version = "^0.9.0"} + +[tool.poetry.scripts] +infinity_emb = "infinity_emb.infinity_server:cli" + +[tool.poetry.group.test.dependencies] +pytest = "^7.0.0" +pytest-mock = "*" +httpx = "*" +asgi_lifespan = "*" +anyio = "*" +trio = "*" +coverage = {extras = ["toml"], version = "^7.3.2"} +mypy = "^1.5.1" + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.lint.dependencies] +ruff = "^0.0.249" +types-toml = "^0.10.8.1" +types-redis = "^4.3.21.6" +types-pytz = "^2023.3.0.0" +black = "^23.1.0" +types-chardet = "^5.0.4.6" +mypy-protobuf = "^3.0.0" + +[tool.poetry.extras] +ct2=["ctranslate2"] +all=["ctranslate2"] + +[tool.pytest.ini_options] +markers = [ + "performance: tests that measure performance (deselect with '-m \"not performance\"')", +] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/infinity_emb/tests/__init__.py b/libs/infinity_emb/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/libs/infinity_emb/tests/conftest.py b/libs/infinity_emb/tests/conftest.py new file mode 100644 index 00000000..2d560a38 --- /dev/null +++ b/libs/infinity_emb/tests/conftest.py @@ -0,0 +1,47 @@ +import csv +import gzip +import os +from typing import List, Tuple + +import pytest +from sentence_transformers import InputExample, util # type: ignore + +pytest.DEFAULT_BERT_MODEL = "sentence-transformers/all-MiniLM-L6-v2" + + +@pytest.fixture +def anyio_backend(): + return "asyncio" + + +@pytest.fixture(scope="session") +def get_sts_bechmark_dataset() -> ( + Tuple[List[InputExample], List[InputExample], List[InputExample]] +): + sts_dataset_path = os.path.join( + os.path.dirname(__file__), "data", "datasets", "stsbenchmark.tsv.gz" + ) + + if not os.path.exists(sts_dataset_path): + util.http_get( + "https://sbert.net/datasets/stsbenchmark.tsv.gz", sts_dataset_path + ) + + train_samples = [] + dev_samples = [] + test_samples = [] + with gzip.open(sts_dataset_path, "rt", encoding="utf8") as fIn: + reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE) + for row in reader: + score = float(row["score"]) / 5.0 # Normalize score to range 0 ... 1 + inp_example = InputExample( + texts=[row["sentence1"], row["sentence2"]], label=score + ) + + if row["split"] == "dev": + dev_samples.append(inp_example) + elif row["split"] == "test": + test_samples.append(inp_example) + else: + train_samples.append(inp_example) + return train_samples, dev_samples, test_samples diff --git a/libs/infinity_emb/tests/data/datasets/stsbenchmark.tsv.gz b/libs/infinity_emb/tests/data/datasets/stsbenchmark.tsv.gz new file mode 100644 index 00000000..2dfc7eab Binary files /dev/null and b/libs/infinity_emb/tests/data/datasets/stsbenchmark.tsv.gz differ diff --git a/libs/infinity_emb/tests/end_to_end/__init__.py b/libs/infinity_emb/tests/end_to_end/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/libs/infinity_emb/tests/end_to_end/test_api_with_dummymodel.py b/libs/infinity_emb/tests/end_to_end/test_api_with_dummymodel.py new file mode 100644 index 00000000..a30d39a4 --- /dev/null +++ b/libs/infinity_emb/tests/end_to_end/test_api_with_dummymodel.py @@ -0,0 +1,95 @@ +import asyncio +import random +import time +from uuid import uuid4 + +import pytest +from asgi_lifespan import LifespanManager +from httpx import AsyncClient + +from infinity_emb import create_server +from infinity_emb.inference.models import InferenceEngine + +PREFIX = "/v2" +MODEL_NAME = str(uuid4()) +BATCH_SIZE = 16 +app = create_server( + model_name_or_path=MODEL_NAME, + batch_size=BATCH_SIZE, + url_prefix=PREFIX, + engine=InferenceEngine.debugengine, +) + + +@pytest.fixture() +async def client(): + async with AsyncClient(app=app, base_url="http://test") as client, LifespanManager( + app + ): + yield client + + +@pytest.mark.anyio +async def test_model_route(client): + response = await client.get(f"{PREFIX}/models") + assert response.status_code == 200 + rdata = response.json() + assert "data" in rdata + assert rdata["data"].get("id", "") == MODEL_NAME + assert isinstance(rdata["data"].get("stats"), dict) + + # ready test + response = await client.get("/ready") + assert response.status_code == 200 + + +@pytest.mark.anyio +async def test_embedding(client): + possible_inputs = [ + ["This is a test sentence."], + ["This is a test sentence.", "This is another test sentence."], + ] + for inp in possible_inputs: + response = await client.post( + f"{PREFIX}/embeddings", json=dict(input=inp, model=MODEL_NAME) + ) + assert response.status_code == 200, f"{response.status_code}, {response.text}" + rdata = response.json() + assert "data" in rdata and isinstance(rdata["data"], list) + assert all("embedding" in d for d in rdata["data"]) + assert len(rdata["data"]) == len(inp) + for embedding, sentence in zip(rdata["data"], inp): + assert len(sentence) == embedding["embedding"][0] + + +@pytest.mark.anyio +async def test_batch_embedding(client, get_sts_bechmark_dataset): + sentences = [] + for d in get_sts_bechmark_dataset: + for item in d: + sentences.append(item.texts[0]) + random.shuffle(sentences) + sentences = sentences + + async def _post_batch(inputs): + return await client.post( + f"{PREFIX}/embeddings", json=dict(input=inputs, model=MODEL_NAME) + ) + + _request_size = BATCH_SIZE // 2 + tasks = [ + _post_batch(inputs=sentences[sl : sl + _request_size]) + for sl in range(0, len(sentences), _request_size) + ] + start = time.perf_counter() + _responses = await asyncio.gather(*tasks) + end = time.perf_counter() + time_api = end - start + + responses = [] + for response in _responses: + responses.extend(response.json()["data"]) + for i in range(len(responses)): + responses[i] = responses[i]["embedding"] + + print(time_api) diff --git a/libs/infinity_emb/tests/end_to_end/test_ct2_sentence.py b/libs/infinity_emb/tests/end_to_end/test_ct2_sentence.py new file mode 100644 index 00000000..fb4c9860 --- /dev/null +++ b/libs/infinity_emb/tests/end_to_end/test_ct2_sentence.py @@ -0,0 +1,129 @@ +import asyncio +import random +import time + +import numpy as np +import pytest +import torch +from asgi_lifespan import LifespanManager +from httpx import AsyncClient +from sentence_transformers import SentenceTransformer # type: ignore + +from infinity_emb import create_server +from infinity_emb.inference.models import CT2SentenceTransformer, InferenceEngine + +PREFIX = "/v1_torch" +MODEL: str = pytest.DEFAULT_BERT_MODEL # type: ignore + +batch_size = 64 if torch.cuda.is_available() else 8 + +app = create_server( + model_name_or_path=MODEL, + batch_size=batch_size, + url_prefix=PREFIX, + engine=InferenceEngine.ctranslate2, +) + + +@pytest.fixture +def model_base() -> SentenceTransformer: + return SentenceTransformer(MODEL) + + +@pytest.fixture() +async def client(): + async with AsyncClient( + app=app, base_url="http://test", timeout=20 + ) as client, LifespanManager(app): + yield client + + +def test_load_model(model_base): + # this makes sure that the error below is not based on a slow download + # or internal pytorch errors + s = ["This is a test sentence."] + e1 = model_base.encode(s) + e2 = CT2SentenceTransformer(MODEL).encode(s) + np.testing.assert_almost_equal(e1, e2, decimal=6) + + +@pytest.mark.anyio +async def test_model_route(client): + response = await client.get(f"{PREFIX}/models") + assert response.status_code == 200 + rdata = response.json() + assert "data" in rdata + assert rdata["data"].get("id", "") == MODEL + assert isinstance(rdata["data"].get("stats"), dict) + + +@pytest.mark.anyio +async def test_embedding(client, model_base): + possible_inputs = [ + ["This is a test sentence."], + ["This is a test sentence.", "This is another test sentence."], + ] + + for inp in possible_inputs: + response = await client.post( + f"{PREFIX}/embeddings", json=dict(input=inp, model=MODEL) + ) + assert response.status_code == 200, f"{response.status_code}, {response.text}" + rdata = response.json() + assert "data" in rdata and isinstance(rdata["data"], list) + assert all("embedding" in d for d in rdata["data"]) + assert len(rdata["data"]) == len(inp) + + want_embeddings = model_base.encode(inp) + + for embedding, st_embedding in zip(rdata["data"], want_embeddings): + np.testing.assert_almost_equal(embedding["embedding"], st_embedding) + + +@pytest.mark.performance +@pytest.mark.anyio +async def test_batch_embedding(client, get_sts_bechmark_dataset, model_base): + sentences = [] + for d in get_sts_bechmark_dataset: + for item in d: + sentences.append(item.texts[0]) + random.shuffle(sentences) + sentences = sentences[::2] if torch.cuda.is_available() else sentences[::16] + # sentences = sentences[:batch_size*2] + dummy_sentences = ["test" * 512] * batch_size + + async def _post_batch(inputs): + return await client.post( + f"{PREFIX}/embeddings", json=dict(input=inputs, model=MODEL) + ) + + await _post_batch(inputs=dummy_sentences) + + _request_size = batch_size // 2 + tasks = [ + _post_batch(inputs=sentences[sl : sl + _request_size]) + for sl in range(0, len(sentences), _request_size) + ] + start = time.perf_counter() + _responses = await asyncio.gather(*tasks) + end = time.perf_counter() + time_api = end - start + + responses = [] + for response in _responses: + responses.extend(response.json()["data"]) + for i in range(len(responses)): + responses[i] = responses[i]["embedding"] + + model_base.encode( + dummy_sentences, + batch_size=batch_size, + normalize_embeddings=True, + convert_to_numpy=True, + ) + start = time.perf_counter() + encodings = model_base.encode(sentences, batch_size=batch_size).tolist() + end = time.perf_counter() + time_st = end - start + np.testing.assert_almost_equal(np.array(responses), np.array(encodings), decimal=6) + assert time_api / time_st < 2.5 diff --git a/libs/infinity_emb/tests/end_to_end/test_sentence_transformers.py b/libs/infinity_emb/tests/end_to_end/test_sentence_transformers.py new file mode 100644 index 00000000..63433e24 --- /dev/null +++ b/libs/infinity_emb/tests/end_to_end/test_sentence_transformers.py @@ -0,0 +1,124 @@ +import asyncio +import random +import time + +import numpy as np +import pytest +import torch +from asgi_lifespan import LifespanManager +from httpx import AsyncClient +from sentence_transformers import SentenceTransformer # type: ignore + +from infinity_emb import create_server +from infinity_emb.inference.models import InferenceEngine + +PREFIX = "/v1_ct2" +model = pytest.DEFAULT_BERT_MODEL +batch_size = 64 if torch.cuda.is_available() else 8 + +app = create_server( + model_name_or_path=model, + batch_size=batch_size, + url_prefix=PREFIX, + engine=InferenceEngine.torch, +) + + +@pytest.fixture +def model_base() -> SentenceTransformer: + return SentenceTransformer(model) + + +@pytest.fixture() +async def client(): + async with AsyncClient( + app=app, base_url="http://test", timeout=20 + ) as client, LifespanManager(app): + yield client + + +def test_load_model(model_base): + # this makes sure that the error below is not based on a slow download + # or internal pytorch errors + model_base.encode(["This is a test sentence."]) + + +@pytest.mark.anyio +async def test_model_route(client): + response = await client.get(f"{PREFIX}/models") + assert response.status_code == 200 + rdata = response.json() + assert "data" in rdata + assert rdata["data"].get("id", "") == model + assert isinstance(rdata["data"].get("stats"), dict) + + +@pytest.mark.anyio +async def test_embedding(client, model_base): + possible_inputs = [ + ["This is a test sentence."], + ["This is a test sentence.", "This is another test sentence."], + ] + + for inp in possible_inputs: + response = await client.post( + f"{PREFIX}/embeddings", json=dict(input=inp, model=model) + ) + assert response.status_code == 200, f"{response.status_code}, {response.text}" + rdata = response.json() + assert "data" in rdata and isinstance(rdata["data"], list) + assert all("embedding" in d for d in rdata["data"]) + assert len(rdata["data"]) == len(inp) + + want_embeddings = model_base.encode(inp) + + for embedding, st_embedding in zip(rdata["data"], want_embeddings): + np.testing.assert_almost_equal(embedding["embedding"], st_embedding) + + +@pytest.mark.performance +@pytest.mark.anyio +async def test_batch_embedding(client, get_sts_bechmark_dataset, model_base): + sentences = [] + for d in get_sts_bechmark_dataset: + for item in d: + sentences.append(item.texts[0]) + random.shuffle(sentences) + sentences = sentences if torch.cuda.is_available() else sentences[::16] + dummy_sentences = ["test" * 512] * batch_size + + async def _post_batch(inputs): + return await client.post( + f"{PREFIX}/embeddings", json=dict(input=inputs, model=model) + ) + + await _post_batch(inputs=dummy_sentences) + + _request_size = batch_size // 2 + tasks = [ + _post_batch(inputs=sentences[sl : sl + _request_size]) + for sl in range(0, len(sentences), _request_size) + ] + start = time.perf_counter() + _responses = await asyncio.gather(*tasks) + end = time.perf_counter() + time_api = end - start + + responses = [] + for response in _responses: + responses.extend(response.json()["data"]) + for i in range(len(responses)): + responses[i] = responses[i]["embedding"] + + model_base.encode( + dummy_sentences, + batch_size=batch_size, + normalize_embeddings=True, + convert_to_numpy=True, + ) + start = time.perf_counter() + encodings = model_base.encode(sentences, batch_size=batch_size).tolist() + end = time.perf_counter() + time_st = end - start + np.testing.assert_almost_equal(np.array(responses), np.array(encodings), decimal=6) + assert time_api / time_st < 3.0 # 3.80 diff --git a/libs/infinity_emb/tests/unit_test/inference/test_batch_handler.py b/libs/infinity_emb/tests/unit_test/inference/test_batch_handler.py new file mode 100644 index 00000000..9fabf5cd --- /dev/null +++ b/libs/infinity_emb/tests/unit_test/inference/test_batch_handler.py @@ -0,0 +1,128 @@ +import asyncio +import concurrent.futures +import copy +import random +import time + +import numpy as np +import pytest +import torch + +from infinity_emb.inference import BatchHandler +from infinity_emb.inference.models import ( + SentenceTransformerPatched, + get_lengths_with_tokenize, +) + +BATCH_SIZE = 32 +N_TIMINGS = 3 +LIMIT_SLOWDOWN = 1.15 if torch.cuda.is_available() else 1.3 + + +@pytest.fixture +def temp_threadpool(): + return concurrent.futures.ThreadPoolExecutor() + + +@pytest.fixture +@pytest.mark.anyio +async def load_patched_bh(temp_threadpool): + model = SentenceTransformerPatched(pytest.DEFAULT_BERT_MODEL) + model.encode(["hello " * 512] * BATCH_SIZE) + bh = BatchHandler( + model=model, max_batch_size=BATCH_SIZE, threadpool=temp_threadpool + ) + await bh.spawn() + return model, bh + + +@pytest.mark.performance +@pytest.mark.anyio +async def test_batch_performance_raw(get_sts_bechmark_dataset, load_patched_bh): + model, bh = load_patched_bh + try: + model: SentenceTransformerPatched = model + bh: BatchHandler = bh + sentences = [] + for d in get_sts_bechmark_dataset: + for item in d: + sentences.append(item.texts[0]) + random.shuffle(sentences) + sentences = sentences if torch.cuda.is_available() else sentences[::4] + + async def method_batch_handler(_sentences): + _sentences = copy.deepcopy(_sentences) + start = time.perf_counter() + lengths, _ = get_lengths_with_tokenize(_sentences, model.tokenize_lengths) + _request_size = BATCH_SIZE * 4 + tasks = [ + bh.schedule( + _sentences[sl : sl + _request_size], + prios=lengths[sl : sl + _request_size], + ) + for sl in range(0, len(_sentences), _request_size) + ] + _ = await asyncio.gather(*tasks) + end = time.perf_counter() + return round(end - start, 4) + + def method_patched(_sentences): + _sentences = copy.deepcopy(_sentences) + st_s = list(sorted(_sentences)) + start = time.perf_counter() + + emb = [] + for s in range(0, len(_sentences), BATCH_SIZE): + feat = model.encode_pre(st_s[s : s + BATCH_SIZE]) + embed = model.encode_core(feat) + emb.append(model.encode_post(embed)) + np.concatenate(emb).tolist() + end = time.perf_counter() + return round(end - start, 4) + + def method_st(_sentences): + _sentences = copy.deepcopy(_sentences) + start = time.perf_counter() + _ = model.encode(_sentences, batch_size=BATCH_SIZE).tolist() + end = time.perf_counter() + return round(end - start, 4) + + # yappi.get_func_stats().print_all() + # yappi.stop() + method_st(sentences[::10]) + time.sleep(0.2) + time_batch_handler = np.median( + [(await method_batch_handler(sentences)) for _ in range(N_TIMINGS)] + ) + time.sleep(0.2) + time_st_patched = np.median( + [method_patched(sentences) for _ in range(N_TIMINGS)] + ) + time.sleep(0.2) + time_st = np.median([method_st(sentences) for _ in range(N_TIMINGS)]) + + print( + f"times are sentence-transformers: {time_st}," + " patched-sentence-transformers: " + f" {time_st_patched}, batch-handler: {time_batch_handler}" + ) + + # WARNING: The timings may depend if you are on debugger. + # The Python calls may take a significant amount of time. + + assert time_st_patched / time_st < 1.1 if torch.cuda.is_available() else 1.6, ( + "patched-SentenceTransformers slower than" + f" SentenceTransformers.encode: {time_st_patched} > {time_st}" + ) + assert time_batch_handler / time_st < LIMIT_SLOWDOWN, ( + "batch_handler slower than Sentence Transformers" + f" {time_batch_handler}: > {time_st}" + ) + assert time_batch_handler / time_st_patched < LIMIT_SLOWDOWN, ( + "raw batch_handler threaded queueing looses significant " + f"time over non-queueing: {time_batch_handler} > {time_st_patched}" + ) + + finally: + bh.shutdown() + bh._threadpool.shutdown(wait=True) diff --git a/libs/infinity_emb/tests/unit_test/inference/test_models.py b/libs/infinity_emb/tests/unit_test/inference/test_models.py new file mode 100644 index 00000000..ff1084e2 --- /dev/null +++ b/libs/infinity_emb/tests/unit_test/inference/test_models.py @@ -0,0 +1,62 @@ +""" +Tests that the pretrained models produce the correct scores on the STSbenchmark dataset +""" +import copy +from typing import List + +import pytest +import torch +from sentence_transformers import InputExample # type: ignore +from sentence_transformers.evaluation import ( # type: ignore + EmbeddingSimilarityEvaluator, # type: ignore +) + +from infinity_emb.inference.models import ( + CT2SentenceTransformer, + SentenceTransformerPatched, +) + + +def _pretrained_model_score( + dataset: List[InputExample], + model_name, + expected_score, + ct2_compute_type: str = "", + device: str = "cuda", +): + test_samples = dataset[::3] + + if ct2_compute_type: + model = CT2SentenceTransformer(model_name, compute_type=ct2_compute_type) + if not torch.cuda.is_available() or device == "cpu": + model.to("cpu") + else: + model.to("cuda") + + else: + model = SentenceTransformerPatched(model_name) + evaluator = EmbeddingSimilarityEvaluator.from_input_examples( + test_samples, name="sts-test" + ) + + score = model.evaluate(evaluator) * 100 + print(model_name, "{:.2f} vs. exp: {:.2f}".format(score, expected_score)) + assert score > expected_score or abs(score - expected_score) < 0.1 + + +@pytest.mark.parametrize( + "model,score,compute_type,device", + [ + ("sentence-transformers/bert-base-nli-mean-tokens", 76.76, "int8", "cuda"), + ("sentence-transformers/bert-base-nli-mean-tokens", 76.86, None, "cuda"), + ("sentence-transformers/all-MiniLM-L6-v2", 82.03, None, "cuda"), + ("sentence-transformers/all-MiniLM-L6-v2", 82.03, "default", "cuda"), + ("sentence-transformers/all-MiniLM-L6-v2", 81.73, "int8", "cuda"), + ("sentence-transformers/all-MiniLM-L6-v2", 82.03, "default", "cpu"), + ], +) +def test_bert(get_sts_bechmark_dataset, model, score, compute_type, device): + samples = copy.deepcopy(get_sts_bechmark_dataset[2]) + _pretrained_model_score( + samples, model, score, ct2_compute_type=compute_type, device=device + ) diff --git a/libs/infinity_emb/tests/unit_test/inference/test_select_model.py b/libs/infinity_emb/tests/unit_test/inference/test_select_model.py new file mode 100644 index 00000000..e69f62d0 --- /dev/null +++ b/libs/infinity_emb/tests/unit_test/inference/test_select_model.py @@ -0,0 +1,13 @@ +import pytest + +from infinity_emb.inference.models import InferenceEngine +from infinity_emb.inference.select_model import select_model_to_functional + + +@pytest.mark.parametrize("engine", [e for e in InferenceEngine]) +def test_engine(engine): + select_model_to_functional( + engine=engine, + model_name_or_path="sentence-transformers/all-MiniLM-L6-v2", + batch_size=4, + ) diff --git a/libs/infinity_emb/tests/unit_test/test_infinity_server.py b/libs/infinity_emb/tests/unit_test/test_infinity_server.py new file mode 100644 index 00000000..486a1d13 --- /dev/null +++ b/libs/infinity_emb/tests/unit_test/test_infinity_server.py @@ -0,0 +1,21 @@ +import subprocess + +from fastapi import FastAPI + +from infinity_emb import create_server +from infinity_emb.inference.models import InferenceEngine + + +def test_cli_help(): + log = subprocess.run(["infinity_emb", "--help"]) + assert log.returncode == 0 + + +def test_cli_wrong_batch_size(): + log = subprocess.run(["infinity_emb", "--batch-size", "WrongArgument"]) + assert log.returncode == 2 + + +def test_create_server(): + app = create_server(engine=InferenceEngine.debugengine) + assert isinstance(app, FastAPI) diff --git a/libs/infinity_emb/tests/unit_test/test_log_handler.py b/libs/infinity_emb/tests/unit_test/test_log_handler.py new file mode 100644 index 00000000..aa44a0fd --- /dev/null +++ b/libs/infinity_emb/tests/unit_test/test_log_handler.py @@ -0,0 +1,12 @@ +from infinity_emb.log_handler import UVICORN_LOG_LEVELS, logger + + +def test_levels_to_int(): + prev_level = logger.level + try: + for level in UVICORN_LOG_LEVELS: + level_new = level.to_int() + assert level_new + logger.setLevel(level_new) + finally: + logger.setLevel(prev_level)