improve check-requirements.sh
This commit is contained in:
Jared Van Bortel
parent
cb58775719
commit
ce26f49208
3 changed files with 186 additions and 194 deletions
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@ -1,4 +1,5 @@
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#!/bin/bash
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set -euo pipefail
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#
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# check-requirements.sh checks all requirements files for each top-level
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# convert*.py script.
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@ -8,7 +9,7 @@
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# sized tmpfs /tmp or ramdisk is recommended if running this frequently.
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#
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# usage: ./check-requirements.sh [<working_dir>]
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# ./check-requirements.sh 'nocleanup' [<working_dir>]
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# ./check-requirements.sh nocleanup [<working_dir>]
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#
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# where:
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# - <working_dir> is a directory that can be used as the base for
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@ -20,135 +21,108 @@
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# - bash >= 3.2.57
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# - shellcheck
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#
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# For each script, it creates a fresh venv, `pip install -r` the
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# requirements, and finally executes the python script with no arguments to
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# check for a `ModuleNotFoundError`.
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# For each script, it creates a fresh venv, `pip install`s the requirements, and
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# finally imports the python script to check for `ImportError`.
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#
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log() {
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local level="$1"; shift
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local format="$1"; shift
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# shellcheck disable=SC2059
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>&2 printf "$level: $format\n" "$@"
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local level=$1 msg=$2
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printf >&2 '%s: %s\n' "$level" "$msg"
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}
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debug () {
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log 'DEBUG' "$@"
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debug() {
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log DEBUG "$@"
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}
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info() {
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log 'INFO' "$@"
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log INFO "$@"
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}
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fatal() {
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log 'FATAL' "$@"
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log FATAL "$@"
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exit 1
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}
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cleanup() {
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if [[ -n ${workdir+x} && -d $workdir && -w $workdir ]]; then
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info "Removing $workdir"
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(
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count=0
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rm -rfv "$workdir" | while read -r; do
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if (( count++ > 750 )); then
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printf '.'
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count=0
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fi
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done
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printf '\n'
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)&
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wait $!
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info "Removed '$workdir'"
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local count=0
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rm -rfv -- "$workdir" | while read -r; do
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if (( count++ > 750 )); then
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printf .
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count=0
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fi
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done
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printf '\n'
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info "Removed $workdir"
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fi
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}
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abort() {
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cleanup
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exit 1
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}
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if [[ $1 == nocleanup ]]; then
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shift # discard nocleanup arg
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if [[ ${1-} == nocleanup ]]; then
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shift # discard nocleanup arg
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else
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trap abort SIGINT SIGTERM SIGQUIT SIGABRT
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trap exit INT TERM
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trap cleanup EXIT
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fi
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set -eu -o pipefail
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this="$(realpath "$0")"; readonly this
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this=$(realpath -- "$0"); readonly this
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cd "$(dirname "$this")"
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shellcheck "$this"
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readonly reqs_dir='./requirements'
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readonly reqs_dir=requirements
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workdir=
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if [[ -n ${1+x} ]]; then
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arg_dir="$(realpath "$1")"
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if [[ ! ( -d $arg_dir && -w $arg_dir ) ]]; then
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fatal "$arg_dir is not a valid directory"
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if [[ ${1+x} ]]; then
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tmp_dir=$(realpath -- "$1")
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if [[ ! ( -d $tmp_dir && -w $tmp_dir ) ]]; then
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fatal "$tmp_dir is not a writable directory"
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fi
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workdir="$(mktemp -d "$arg_dir/check-requirements.XXXX")"
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else
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workdir="$(mktemp -d "/tmp/check-requirements.XXXX")"
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tmp_dir=/tmp
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fi
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readonly workdir
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workdir=$(mktemp -d "$tmp_dir/check-requirements.XXXX"); readonly workdir
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info "Working directory: $workdir"
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assert_arg_count() {
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local argcount="$1"; shift
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if (( $# != argcount )); then
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fatal "${FUNCNAME[1]}: incorrect number of args"
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fi
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}
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check_requirements() {
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assert_arg_count 2 "$@"
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local venv="$1"
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local reqs="$2"
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local reqs=$1
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info "$reqs: beginning check"
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(
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# shellcheck source=/dev/null
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source "$venv/bin/activate"
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pip --disable-pip-version-check install -q -r "$reqs"
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)
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pip --disable-pip-version-check install -qr "$reqs"
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info "$reqs: OK"
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}
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check_convert_script() {
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assert_arg_count 1 "$@"
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local py="$1"; shift # e.g. ./convert-hf-to-gguf.py
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local pyname; pyname="$(basename "$py")" # e.g. convert-hf-to-gguf.py
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pyname="${pyname%.py}" # e.g. convert-hf-to-gguf
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local py=$1 # e.g. ./convert-hf-to-gguf.py
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local pyname=${py##*/} # e.g. convert-hf-to-gguf.py
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pyname=${pyname%.py} # e.g. convert-hf-to-gguf
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info "$py: beginning check"
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local reqs="$reqs_dir/requirements-$pyname.txt"
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if [[ ! -r "$reqs" ]]; then
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if [[ ! -r $reqs ]]; then
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fatal "$py missing requirements. Expected: $reqs"
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fi
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local venv="$workdir/$pyname-venv"
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python3 -m venv "$venv"
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check_requirements "$venv" "$reqs"
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# Because we mask the return value of the subshell,
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# we don't need to use set +e/-e.
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# shellcheck disable=SC2155
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local py_err=$(
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(
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# shellcheck source=/dev/null
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source "$venv/bin/activate"
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python "$py" 2>&1
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check_requirements "$reqs"
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python - "$py" "$pyname" <<EOF
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import sys
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from importlib.machinery import SourceFileLoader
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py, pyname = sys.argv[1:]
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SourceFileLoader(pyname, py).load_module()
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EOF
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)
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# shellcheck disable=SC2181
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if grep -Fe 'ModuleNotFoundError' <<< "$py_err"; then
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fatal "$py: some imports not declared in $reqs"
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fi
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rm -rf -- "$venv"
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info "$py: imports OK"
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}
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@ -156,25 +130,37 @@ readonly ignore_eq_eq='check_requirements: ignore "=="'
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for req in "$reqs_dir"/*; do
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# Check that all sub-requirements are added to top-level requirements.txt
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if ! grep -qFe "$req" ./requirements.txt; then
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fatal "$req needs to be added to ./requirements.txt"
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if ! grep -qF "$req" requirements.txt; then
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fatal "$req needs to be added to requirements.txt"
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fi
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# Make sure exact release versions aren't being pinned in the requirements
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# Filters out the ignore string
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req_no_ignore_eq_eq="$(grep -vF "$ignore_eq_eq" "$req")"
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if grep -Fe '==' <<< "$req_no_ignore_eq_eq" ; then
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fatal "Avoid pinning exact package versions. Use '~=' instead.\nYou can suppress this error by appending the following to the line: \n\t# $ignore_eq_eq"
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if grep -vF "$ignore_eq_eq" "$req" | grep -q '=='; then
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tab=$'\t'
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cat >&2 <<EOF
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FATAL: Avoid pinning exact package versions. Use '~=' instead.
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You can suppress this error by appending the following to the line:
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$tab# $ignore_eq_eq
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EOF
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exit 1
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fi
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done
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all_venv="$workdir/all-venv"
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python3 -m venv "$all_venv"
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check_requirements "$all_venv" './requirements.txt'
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check_convert_script './convert.py'
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for py in ./convert-*.py;do
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(
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# shellcheck source=/dev/null
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source "$all_venv/bin/activate"
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check_requirements requirements.txt
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)
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rm -rf -- "$all_venv"
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check_convert_script convert.py
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for py in convert-*.py; do
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check_convert_script "$py"
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done
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info "Done! No issues found."
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info 'Done! No issues found.'
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@ -238,7 +238,7 @@ class Model:
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tokens: list[bytearray] = []
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toktypes: list[int] = []
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from transformers import AutoTokenizer # type: ignore[attr-defined]
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
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assert max(tokenizer.vocab.values()) < vocab_size
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@ -848,7 +848,7 @@ class StableLMModel(Model):
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hparams = self.hparams
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block_count = hparams["num_hidden_layers"]
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self.gguf_writer.add_name(dir_model.name)
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
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self.gguf_writer.add_embedding_length(hparams["hidden_size"])
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self.gguf_writer.add_block_count(block_count)
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@ -894,7 +894,7 @@ class QwenModel(Model):
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tokens: list[bytearray] = []
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toktypes: list[int] = []
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from transformers import AutoTokenizer # type: ignore[attr-defined]
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
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vocab_size = hparams["vocab_size"]
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assert max(tokenizer.get_vocab().values()) < vocab_size
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@ -1112,43 +1112,48 @@ def parse_args() -> argparse.Namespace:
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return parser.parse_args()
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args = parse_args()
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def main() -> None:
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args = parse_args()
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dir_model = args.model
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if not dir_model.is_dir():
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print(f'Error: {args.model} is not a directory', file=sys.stderr)
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sys.exit(1)
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dir_model = args.model
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if not dir_model.is_dir():
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print(f'Error: {args.model} is not a directory', file=sys.stderr)
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sys.exit(1)
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ftype_map = {
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"f32": gguf.GGMLQuantizationType.F32,
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"f16": gguf.GGMLQuantizationType.F16,
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}
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ftype_map = {
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"f32": gguf.GGMLQuantizationType.F32,
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"f16": gguf.GGMLQuantizationType.F16,
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}
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if args.outfile is not None:
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fname_out = args.outfile
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else:
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# output in the same directory as the model by default
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fname_out = dir_model / f'ggml-model-{args.outtype}.gguf'
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print(f"Loading model: {dir_model.name}")
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hparams = Model.load_hparams(dir_model)
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with torch.inference_mode():
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model_class = Model.from_model_architecture(hparams["architectures"][0])
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model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
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print("Set model parameters")
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model_instance.set_gguf_parameters()
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print("Set model tokenizer")
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model_instance.set_vocab()
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if args.vocab_only:
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print(f"Exporting model vocab to '{fname_out}'")
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model_instance.write_vocab()
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if args.outfile is not None:
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fname_out = args.outfile
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else:
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print(f"Exporting model to '{fname_out}'")
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model_instance.write()
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# output in the same directory as the model by default
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fname_out = dir_model / f'ggml-model-{args.outtype}.gguf'
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print(f"Model successfully exported to '{fname_out}'")
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print(f"Loading model: {dir_model.name}")
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hparams = Model.load_hparams(dir_model)
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with torch.inference_mode():
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model_class = Model.from_model_architecture(hparams["architectures"][0])
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model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
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print("Set model parameters")
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model_instance.set_gguf_parameters()
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print("Set model tokenizer")
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model_instance.set_vocab()
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if args.vocab_only:
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print(f"Exporting model vocab to '{fname_out}'")
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model_instance.write_vocab()
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else:
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print(f"Exporting model to '{fname_out}'")
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model_instance.write()
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print(f"Model successfully exported to '{fname_out}'")
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if __name__ == '__main__':
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main()
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@ -47,95 +47,96 @@ def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_ty
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fout.seek((fout.tell() + 31) & -32)
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if len(sys.argv) < 2:
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print(f"Usage: python {sys.argv[0]} <path> [arch]")
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print(
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"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
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)
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print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
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sys.exit(1)
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if __name__ == '__main__':
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if len(sys.argv) < 2:
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print(f"Usage: python {sys.argv[0]} <path> [arch]")
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print(
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"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
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)
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print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
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sys.exit(1)
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input_json = os.path.join(sys.argv[1], "adapter_config.json")
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input_model = os.path.join(sys.argv[1], "adapter_model.bin")
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output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
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input_json = os.path.join(sys.argv[1], "adapter_config.json")
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input_model = os.path.join(sys.argv[1], "adapter_model.bin")
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output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
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model = torch.load(input_model, map_location="cpu")
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arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
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model = torch.load(input_model, map_location="cpu")
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arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
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if arch_name not in gguf.MODEL_ARCH_NAMES.values():
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print(f"Error: unsupported architecture {arch_name}")
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sys.exit(1)
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if arch_name not in gguf.MODEL_ARCH_NAMES.values():
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print(f"Error: unsupported architecture {arch_name}")
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sys.exit(1)
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arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
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name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
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arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
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name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
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with open(input_json, "r") as f:
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params = json.load(f)
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with open(input_json, "r") as f:
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params = json.load(f)
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if params["peft_type"] != "LORA":
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print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
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sys.exit(1)
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if params["peft_type"] != "LORA":
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print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
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sys.exit(1)
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if params["fan_in_fan_out"] is True:
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print("Error: param fan_in_fan_out is not supported")
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sys.exit(1)
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if params["fan_in_fan_out"] is True:
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print("Error: param fan_in_fan_out is not supported")
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sys.exit(1)
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if params["bias"] is not None and params["bias"] != "none":
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print("Error: param bias is not supported")
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sys.exit(1)
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if params["bias"] is not None and params["bias"] != "none":
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print("Error: param bias is not supported")
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sys.exit(1)
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# TODO: these seem to be layers that have been trained but without lora.
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# doesn't seem widely used but eventually should be supported
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if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
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print("Error: param modules_to_save is not supported")
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sys.exit(1)
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# TODO: these seem to be layers that have been trained but without lora.
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# doesn't seem widely used but eventually should be supported
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if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
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print("Error: param modules_to_save is not supported")
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sys.exit(1)
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with open(output_path, "wb") as fout:
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fout.truncate()
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with open(output_path, "wb") as fout:
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fout.truncate()
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write_file_header(fout, params)
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for k, v in model.items():
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orig_k = k
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if k.endswith(".default.weight"):
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k = k.replace(".default.weight", ".weight")
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if k in ["llama_proj.weight", "llama_proj.bias"]:
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continue
|
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if k.endswith("lora_A.weight"):
|
||||
if v.dtype != torch.float16 and v.dtype != torch.float32:
|
||||
write_file_header(fout, params)
|
||||
for k, v in model.items():
|
||||
orig_k = k
|
||||
if k.endswith(".default.weight"):
|
||||
k = k.replace(".default.weight", ".weight")
|
||||
if k in ["llama_proj.weight", "llama_proj.bias"]:
|
||||
continue
|
||||
if k.endswith("lora_A.weight"):
|
||||
if v.dtype != torch.float16 and v.dtype != torch.float32:
|
||||
v = v.float()
|
||||
v = v.T
|
||||
else:
|
||||
v = v.float()
|
||||
v = v.T
|
||||
else:
|
||||
v = v.float()
|
||||
|
||||
t = v.detach().numpy()
|
||||
t = v.detach().numpy()
|
||||
|
||||
prefix = "base_model.model."
|
||||
if k.startswith(prefix):
|
||||
k = k[len(prefix) :]
|
||||
prefix = "base_model.model."
|
||||
if k.startswith(prefix):
|
||||
k = k[len(prefix) :]
|
||||
|
||||
lora_suffixes = (".lora_A.weight", ".lora_B.weight")
|
||||
if k.endswith(lora_suffixes):
|
||||
suffix = k[-len(lora_suffixes[0]):]
|
||||
k = k[: -len(lora_suffixes[0])]
|
||||
else:
|
||||
print(f"Error: unrecognized tensor name {orig_k}")
|
||||
sys.exit(1)
|
||||
lora_suffixes = (".lora_A.weight", ".lora_B.weight")
|
||||
if k.endswith(lora_suffixes):
|
||||
suffix = k[-len(lora_suffixes[0]):]
|
||||
k = k[: -len(lora_suffixes[0])]
|
||||
else:
|
||||
print(f"Error: unrecognized tensor name {orig_k}")
|
||||
sys.exit(1)
|
||||
|
||||
tname = name_map.get_name(k)
|
||||
if tname is None:
|
||||
print(f"Error: could not map tensor name {orig_k}")
|
||||
print(" Note: the arch parameter must be specified if the model is not llama")
|
||||
sys.exit(1)
|
||||
tname = name_map.get_name(k)
|
||||
if tname is None:
|
||||
print(f"Error: could not map tensor name {orig_k}")
|
||||
print(" Note: the arch parameter must be specified if the model is not llama")
|
||||
sys.exit(1)
|
||||
|
||||
if suffix == ".lora_A.weight":
|
||||
tname += ".weight.loraA"
|
||||
elif suffix == ".lora_B.weight":
|
||||
tname += ".weight.loraB"
|
||||
else:
|
||||
assert False
|
||||
if suffix == ".lora_A.weight":
|
||||
tname += ".weight.loraA"
|
||||
elif suffix == ".lora_B.weight":
|
||||
tname += ".weight.loraB"
|
||||
else:
|
||||
assert False
|
||||
|
||||
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
|
||||
write_tensor_header(fout, tname, t.shape, t.dtype)
|
||||
t.tofile(fout)
|
||||
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
|
||||
write_tensor_header(fout, tname, t.shape, t.dtype)
|
||||
t.tofile(fout)
|
||||
|
||||
print(f"Converted {input_json} and {input_model} to {output_path}")
|
||||
print(f"Converted {input_json} and {input_model} to {output_path}")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue