mirror of
https://github.com/Helsinki-NLP/OPUS-MT-train.git
synced 2024-09-11 20:27:19 +03:00
418 lines
15 KiB
Makefile
418 lines
15 KiB
Makefile
# -*-makefile-*-
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#
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# make distribution packages
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# and upload them to cPouta ObjectStorage
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#
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MODELSHOME = ${WORKHOME}/models
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DIST_PACKAGE = ${MODELSHOME}/${LANGPAIRSTR}/${DATASET}.zip
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## minimum BLEU score for models to be accepted as distribution package
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MIN_BLEU_SCORE = 20
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.PHONY: dist
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dist: ${DIST_PACKAGE}
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.PHONY: scores
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scores:
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${MAKE} FIND_EVAL_FILES=1 ${WORKHOME}/eval/scores.txt
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## get the best model from all kind of alternative setups
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## in the following sub directories (add prefix work-)
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# ALT_MODEL_DIR = bpe-old bpe-memad bpe spm-noalign bpe-align spm
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ALT_MODEL_DIR = spm langid
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best_dist_all:
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for l in $(sort ${shell ls work* | grep -- '-' | grep -v old | grep -v work}); do \
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if [ `find work*/$$l -name '*.npz' | wc -l` -gt 0 ]; then \
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d=`find work-spm/$$l -name '*.best-perplexity.npz' -exec basename {} \; | cut -f1 -d.`; \
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${MAKE} SRCLANGS="`echo $$l | cut -f1 -d'-' | sed 's/\\+/ /g'`" \
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TRGLANGS="`echo $$l | cut -f2 -d'-' | sed 's/\\+/ /g'`" \
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DATASET=$$d best_dist; \
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fi \
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done
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# best_dist_all:
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# for l in $(sort ${shell ls work* | grep -- '-' | grep -v old | grep -v work}); do \
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# if [ `find work*/$$l -name '${DATASET}${TRAINSIZE}.*.npz' | wc -l` -gt 0 ]; then \
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# ${MAKE} SRCLANGS="`echo $$l | cut -f1 -d'-' | sed 's/\\+/ /g'`" \
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# TRGLANGS="`echo $$l | cut -f2 -d'-' | sed 's/\\+/ /g'`" best_dist; \
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# fi \
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# done
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## find the best model according to test set scores
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## and make a distribution package from that model
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## (BLEU needs to be above MIN_BLEU_SCORE)
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## NEW: don't trust models tested with GNOME test sets!
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best_dist:
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@m=0;\
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s=''; \
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echo "------------------------------------------------"; \
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echo "search best model for ${LANGPAIRSTR}"; \
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for d in ${ALT_MODEL_DIR}; do \
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e=`ls work-$$d/${LANGPAIRSTR}/val/*.trg | xargs basename | sed 's/\.trg//'`; \
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echo "evaldata = $$e"; \
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if [ "$$e" != "GNOME" ]; then \
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if ls work-$$d/${LANGPAIRSTR}/$$e*.eval 1> /dev/null 2>&1; then \
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b=`grep 'BLEU+' work-$$d/${LANGPAIRSTR}/$$e*.eval | cut -f3 -d' '`; \
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if (( $$(echo "$$m-$$b < 1" |bc -l) )); then \
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echo "$$d ($$b) is better or not much worse than $$s ($$m)!"; \
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m=$$b; \
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s=$$d; \
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else \
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echo "$$d ($$b) is worse than $$s ($$m)!"; \
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fi \
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fi \
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fi \
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done; \
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echo "------------------------------------------------"; \
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if [ "$$s" != "" ]; then \
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if (( $$(echo "$$m > ${MIN_BLEU_SCORE}" |bc -l) )); then \
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${MAKE} MODELSHOME=${PWD}/models \
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MODELS_URL=https://object.pouta.csc.fi/OPUS-MT-models dist-$$s; \
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fi; \
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fi
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## make a package for distribution
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## old: only accept models with a certain evaluation score:
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# if [ `grep BLEU $(TEST_EVALUATION) | cut -f3 -d ' ' | cut -f1 -d '.'` -ge ${MIN_BLEU_SCORE} ]; then \
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DATE = ${shell date +%F}
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MODELS_URL = https://object.pouta.csc.fi/OPUS-MT-dev
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SKIP_DIST_EVAL = 0
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## determine pre-processing type
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ifneq ("$(wildcard ${BPESRCMODEL})","")
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PREPROCESS_TYPE = bpe
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PREPROCESS_SRCMODEL = ${BPESRCMODEL}
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PREPROCESS_TRGMODEL = ${BPETRGMODEL}
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PREPROCESS_DESCRIPTION = normalization + tokenization + BPE
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else
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PREPROCESS_TYPE = spm
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PREPROCESS_SRCMODEL = ${SPMSRCMODEL}
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PREPROCESS_TRGMODEL = ${SPMTRGMODEL}
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PREPROCESS_DESCRIPTION = normalization + SentencePiece
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endif
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ifneq (${words ${TRGLANGS}},1)
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PREPROCESS_SCRIPT = preprocess-${PREPROCESS_TYPE}-multi-target.sh
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else
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PREPROCESS_SCRIPT = preprocess-${PREPROCESS_TYPE}.sh
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endif
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POSTPROCESS_SCRIPT = postprocess-${PREPROCESS_TYPE}.sh
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${DIST_PACKAGE}: ${MODEL_FINAL}
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ifneq (${SKIP_DIST_EVAL},1)
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@${MAKE} $(TEST_EVALUATION)
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@${MAKE} $(TEST_COMPARISON)
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endif
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@mkdir -p ${dir $@}
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@touch ${WORKDIR}/source.tcmodel
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@echo "# $(notdir ${@:.zip=})-${DATE}.zip" > ${WORKDIR}/README.md
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@echo '' >> ${WORKDIR}/README.md
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@echo "* dataset: ${DATASET}" >> ${WORKDIR}/README.md
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@echo "* model: ${MODELTYPE}" >> ${WORKDIR}/README.md
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@echo "* pre-processing: ${PREPROCESS_DESCRIPTION}" >> ${WORKDIR}/README.md
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@cp ${PREPROCESS_SRCMODEL} ${WORKDIR}/source.${PREPROCESS_TYPE}
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@cp ${PREPROCESS_TRGMODEL} ${WORKDIR}/target.${PREPROCESS_TYPE}
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@cp ${PREPROCESS_SCRIPT} ${WORKDIR}/preprocess.sh
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@cp ${POSTPROCESS_SCRIPT} ${WORKDIR}/postprocess.sh
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@if [ ${words ${TRGLANGS}} -gt 1 ]; then \
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echo '* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)' \
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>> ${WORKDIR}/README.md; \
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fi
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@echo "* download: [$(notdir ${@:.zip=})-${DATE}.zip](${MODELS_URL}/${LANGPAIRSTR}/$(notdir ${@:.zip=})-${DATE}.zip)" >> ${WORKDIR}/README.md
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if [ -e $(TEST_EVALUATION) ]; then \
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echo "* test set translations: [$(notdir ${@:.zip=})-${DATE}.test.txt](${MODELS_URL}/${LANGPAIRSTR}/$(notdir ${@:.zip=})-${DATE}.test.txt)" >> ${WORKDIR}/README.md; \
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echo "* test set scores: [$(notdir ${@:.zip=})-${DATE}.eval.txt](${MODELS_URL}/${LANGPAIRSTR}/$(notdir ${@:.zip=})-${DATE}.eval.txt)" >> ${WORKDIR}/README.md; \
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echo '' >> ${WORKDIR}/README.md; \
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echo '## Benchmarks' >> ${WORKDIR}/README.md; \
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echo '' >> ${WORKDIR}/README.md; \
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cd ${WORKDIR}; \
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grep -H BLEU *k${NR}.*eval | \
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tr '.' '/' | cut -f1,5,6 -d '/' | tr '/' "." > $@.1; \
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grep BLEU *k${NR}.*eval | cut -f3 -d ' ' > $@.2; \
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grep chrF *k${NR}.*eval | cut -f3 -d ' ' > $@.3; \
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echo '| testset | BLEU | chr-F |' >> README.md; \
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echo '|-----------------------|-------|-------|' >> README.md; \
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paste $@.1 $@.2 $@.3 | sed "s/\t/ | /g;s/^/| /;s/$$/ |/" >> README.md; \
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rm -f $@.1 $@.2 $@.3; \
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fi
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@cat ${WORKDIR}/README.md >> ${dir $@}README.md
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@echo '' >> ${dir $@}README.md
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@cp models/LICENSE ${WORKDIR}/
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@chmod +x ${WORKDIR}/preprocess.sh
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@sed -e 's# - /.*/\([^/]*\)$$# - \1#' \
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-e 's/beam-size: [0-9]*$$/beam-size: 6/' \
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-e 's/mini-batch: [0-9]*$$/mini-batch: 1/' \
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-e 's/maxi-batch: [0-9]*$$/maxi-batch: 1/' \
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-e 's/relative-paths: false/relative-paths: true/' \
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< ${MODEL_DECODER} > ${WORKDIR}/decoder.yml
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@cd ${WORKDIR} && zip ${notdir $@} \
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README.md LICENSE \
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${notdir ${MODEL_FINAL}} \
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${notdir ${MODEL_VOCAB}} \
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${notdir ${MODEL_VALIDLOG}} \
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${notdir ${MODEL_TRAINLOG}} \
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source.* target.* decoder.yml preprocess.sh postprocess.sh
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@mkdir -p ${dir $@}
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@mv -f ${WORKDIR}/${notdir $@} ${@:.zip=}-${DATE}.zip
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if [ -e $(TEST_EVALUATION) ]; then \
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cp $(TEST_EVALUATION) ${@:.zip=}-${DATE}.eval.txt; \
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cp $(TEST_COMPARISON) ${@:.zip=}-${DATE}.test.txt; \
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fi
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@rm -f $@
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@cd ${dir $@} && ln -s $(notdir ${@:.zip=})-${DATE}.zip ${notdir $@}
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@rm -f ${WORKDIR}/decoder.yml ${WORKDIR}/source.* ${WORKDIR}/target.*
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@rm -f ${WORKDIR}/preprocess.sh ${WORKDIR}/postprocess.sh
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## do this only if the flag is set
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## --> avoid expensive wildcard searches each time make is called
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ifeq (${FIND_EVAL_FILES},1)
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EVALSCORES = ${patsubst ${WORKHOME}/%.eval,${WORKHOME}/eval/%.eval.txt,${wildcard ${WORKHOME}/*/*.eval}}
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EVALTRANSL = ${patsubst ${WORKHOME}/%.compare,${WORKHOME}/eval/%.test.txt,${wildcard ${WORKHOME}/*/*.compare}}
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endif
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## upload to Object Storage
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## Don't forget to run this before uploading!
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# source project_2000661-openrc.sh
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#
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# - make upload ......... released models = all sub-dirs in models/
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# - make upload-models .. trained models in current WORKHOME to OPUS-MT-dev
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# - make upload-scores .. score file with benchmark results to OPUS-MT-eval
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# - make upload-eval .... benchmark tests from models in WORKHOME
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# - make upload-images .. images of VMs that run OPUS-MT
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upload:
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find models/ -type l | tar -cf models-links.tar -T -
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find models/ -type l -delete
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cd models && swift upload OPUS-MT-models --changed --skip-identical *
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tar -xf models-links.tar
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rm -f models-links.tar
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swift post OPUS-MT-models --read-acl ".r:*"
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swift list OPUS-MT-models > index.txt
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swift upload OPUS-MT-models index.txt
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rm -f index.txt
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upload-models:
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find ${WORKHOME}/models -type l | tar -cf dev-models-links.tar -T -
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find ${WORKHOME}/models -type l -delete
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cd ${WORKHOME} && swift upload OPUS-MT-dev --changed --skip-identical models
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tar -xf dev-models-links.tar
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rm -f dev-models-links.tar
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swift post OPUS-MT-dev --read-acl ".r:*"
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swift list OPUS-MT-dev > index.txt
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swift upload OPUS-MT-dev index.txt
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rm -f index.txt
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upload-scores: scores
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cd ${WORKHOME} && swift upload OPUS-MT-eval --changed --skip-identical eval/scores.txt
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swift post OPUS-MT-eval --read-acl ".r:*"
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upload-eval: scores
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cd ${WORKHOME} && swift upload OPUS-MT-eval --changed --skip-identical eval
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swift post OPUS-MT-eval --read-acl ".r:*"
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upload-images:
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cd ${WORKHOME} && swift upload OPUS-MT --changed --skip-identical \
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--use-slo --segment-size 5G opusMT-images
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swift post OPUS-MT-images --read-acl ".r:*"
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## this is for the multeval scores
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# ${WORKHOME}/eval/scores.txt: ${EVALSCORES}
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# cd ${WORKHOME} && \
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# grep base */*eval | cut -f1,2- -d '/' | cut -f1,6- -d '.' | \
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# sed 's/-/ /' | sed 's/\// /' | sed 's/ ([^)]*)//g' |\
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# sed 's/.eval:baseline//' | sed "s/ */\t/g" | sort > $@
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${WORKHOME}/eval/scores.txt: ${EVALSCORES} ${EVALTRANSL}
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cd ${WORKHOME} && grep BLEU */*k${NR}.*eval | cut -f1 -d '/' | tr '-' "\t" > $@.1
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cd ${WORKHOME} && grep BLEU */*k${NR}.*eval | tr '.' '/' | cut -f2,6,7 -d '/' | tr '/' "." > $@.2
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cd ${WORKHOME} && grep BLEU */*k${NR}.*eval | cut -f3 -d ' ' > $@.3
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cd ${WORKHOME} && grep chrF */*k${NR}.*eval | cut -f3 -d ' ' > $@.4
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paste $@.1 $@.2 $@.3 $@.4 > $@
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rm -f $@.1 $@.2 $@.3 $@.4
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${EVALSCORES}: # ${WORKHOME}/eval/%.eval.txt: ${WORKHOME}/models/%.eval
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mkdir -p ${dir $@}
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cp ${patsubst ${WORKHOME}/eval/%.eval.txt,${WORKHOME}/%.eval,$@} $@
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# cp $< $@
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${EVALTRANSL}: # ${WORKHOME}/eval/%.test.txt: ${WORKHOME}/models/%.compare
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mkdir -p ${dir $@}
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cp ${patsubst ${WORKHOME}/eval/%.test.txt,${WORKHOME}/%.compare,$@} $@
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# cp $< $@
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# ## dangerous area ....
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# delete-eval:
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# swift delete OPUS-MT eval
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######################################################################
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## handle old models in previous work directories
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## obsolete now?
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######################################################################
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##-----------------------------------
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## make packages from trained models
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## check old-models as well!
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TRAINED_NEW_MODELS = ${patsubst ${WORKHOME}/%/,%,${dir ${wildcard ${WORKHOME}/*/*.best-perplexity.npz}}}
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# TRAINED_OLD_MODELS = ${patsubst ${WORKHOME}/old-models/%/,%,${dir ${wildcard ${WORKHOME}/old-models/*/*.best-perplexity.npz}}}
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TRAINED_OLD_MODELS = ${patsubst ${WORKHOME}/old-models/%/,%,${dir ${wildcard ${WORKHOME}/old-models/??-??/*.best-perplexity.npz}}}
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TRAINED_OLD_ONLY_MODELS = ${filter-out ${TRAINED_NEW_MODELS},${TRAINED_OLD_MODELS}}
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TRAINED_NEW_ONLY_MODELS = ${filter-out ${TRAINED_OLD_MODELS},${TRAINED_NEW_MODELS}}
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TRAINED_DOUBLE_MODELS = ${filter ${TRAINED_NEW_MODELS},${TRAINED_OLD_MODELS}}
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## make packages of all new models
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## unless there are better models in old-models
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new-models-dist:
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@echo "nr of extra models: ${words ${TRAINED_NEW_ONLY_MODELS}}"
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for l in ${TRAINED_NEW_ONLY_MODELS}; do \
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${MAKE} SRCLANGS="`echo $$l | cut -f1 -d'-' | sed 's/\\+/ /g'`" \
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TRGLANGS="`echo $$l | cut -f2 -d'-' | sed 's/\\+/ /g'`" dist; \
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done
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@echo "trained double ${words ${TRAINED_DOUBLE_MODELS}}"
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for l in ${TRAINED_DOUBLE_MODELS}; do \
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n=`grep 'new best' work/$$l/*.valid1.log | tail -1 | cut -f12 -d ' '`; \
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o=`grep 'new best' work/old-models/$$l/*.valid1.log | tail -1 | cut -f12 -d ' '`; \
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if (( $$(echo "$$n < $$o" |bc -l) )); then \
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${MAKE} SRCLANGS="`echo $$l | cut -f1 -d'-' | sed 's/\\+/ /g'`" \
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TRGLANGS="`echo $$l | cut -f2 -d'-' | sed 's/\\+/ /g'`" dist; \
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fi \
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done
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## fix decoder path in old-models (to run evaluations
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fix-decoder-path:
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for l in ${wildcard ${WORKHOME}/old-models/*/*.best-perplexity.npz.decoder.yml}; do \
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sed --in-place=.backup 's#/\(..-..\)/opus#/old-models/\1/opus#' $$l; \
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sed --in-place=.backup2 's#/old-models/old-models/#/old-models/#' $$l; \
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sed --in-place=.backup2 's#/old-models/old-models/#/old-models/#' $$l; \
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done
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## make packages of all old models from old-models
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## unless there are better models in work (new models)
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old-models-dist:
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@echo "nr of extra models: ${words ${TRAINED_OLD_ONLY_MODELS}}"
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for l in ${TRAINED_OLD_ONLY_MODELS}; do \
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${MAKE} SRCLANGS="`echo $$l | cut -f1 -d'-' | sed 's/\\+/ /g'`" \
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TRGLANGS="`echo $$l | cut -f2 -d'-' | sed 's/\\+/ /g'`" \
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WORKHOME=${WORKHOME}/old-models \
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MODELSHOME=${WORKHOME}/models dist; \
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done
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@echo "trained double ${words ${TRAINED_DOUBLE_MODELS}}"
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for l in ${TRAINED_DOUBLE_MODELS}; do \
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n=`grep 'new best' work/$$l/*.valid1.log | tail -1 | cut -f12 -d ' '`; \
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o=`grep 'new best' work/old-models/$$l/*.valid1.log | tail -1 | cut -f12 -d ' '`; \
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if (( $$(echo "$$o < $$n" |bc -l) )); then \
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${MAKE} SRCLANGS="`echo $$l | cut -f1 -d'-' | sed 's/\\+/ /g'`" \
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TRGLANGS="`echo $$l | cut -f2 -d'-' | sed 's/\\+/ /g'`" \
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WORKHOME=${WORKHOME}/old-models \
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MODELSHOME=${WORKHOME}/models dist; \
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else \
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echo "$$l: new better than old"; \
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fi \
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done
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## old models had slightly different naming conventions
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LASTSRC = ${lastword ${SRCLANGS}}
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LASTTRG = ${lastword ${TRGLANGS}}
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MODEL_OLD = ${MODEL_SUBDIR}${DATASET}${TRAINSIZE}.${PRE_SRC}-${PRE_TRG}.${LASTSRC}${LASTTRG}
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MODEL_OLD_BASENAME = ${MODEL_OLD}.${MODELTYPE}.model${NR}
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MODEL_OLD_FINAL = ${WORKDIR}/${MODEL_OLD_BASENAME}.npz.best-perplexity.npz
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MODEL_OLD_VOCAB = ${WORKDIR}/${MODEL_OLD}.vocab.${MODEL_VOCABTYPE}
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MODEL_OLD_DECODER = ${MODEL_OLD_FINAL}.decoder.yml
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MODEL_TRANSLATE = ${WORKDIR}/${TESTSET}.${MODEL}${NR}.${MODELTYPE}.${SRC}.${TRG}
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MODEL_OLD_TRANSLATE = ${WORKDIR}/${TESTSET}.${MODEL_OLD}${NR}.${MODELTYPE}.${SRC}.${TRG}
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MODEL_OLD_VALIDLOG = ${MODEL_OLD}.${MODELTYPE}.valid${NR}.log
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MODEL_OLD_TRAINLOG = ${MODEL_OLD}.${MODELTYPE}.train${NR}.log
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link-old-models:
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if [ ! -e ${MODEL_FINAL} ]; then \
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if [ -e ${MODEL_OLD_FINAL} ]; then \
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ln -s ${MODEL_OLD_FINAL} ${MODEL_FINAL}; \
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ln -s ${MODEL_OLD_VOCAB} ${MODEL_VOCAB}; \
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ln -s ${MODEL_OLD_DECODER} ${MODEL_DECODER}; \
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fi \
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fi
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if [ ! -e ${MODEL_TRANSLATE} ]; then \
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if [ -e ${MODEL_OLD_TRANSLATE} ]; then \
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ln -s ${MODEL_OLD_TRANSLATE} ${MODEL_TRANSLATE}; \
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fi \
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fi
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if [ ! -e ${WORKDIR}/${MODEL_VALIDLOG} ]; then \
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if [ -e ${WORKDIR}/${MODEL_OLD_VALIDLOG} ]; then \
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ln -s ${WORKDIR}/${MODEL_OLD_VALIDLOG} ${WORKDIR}/${MODEL_VALIDLOG}; \
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ln -s ${WORKDIR}/${MODEL_OLD_TRAINLOG} ${WORKDIR}/${MODEL_TRAINLOG}; \
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|
fi \
|
|
fi
|
|
rm -f ${MODEL_TRANSLATE}.eval
|
|
rm -f ${MODEL_TRANSLATE}.compare
|
|
|
|
|
|
ifneq (${DATASET},${OLDDATASET})
|
|
TRAINFILES = ${wildcard ${WORKDIR}/train/*${OLDDATASET}*.*}
|
|
MODELFILES = ${wildcard ${WORKDIR}/*${OLDDATASET}*.*}
|
|
DECODERFILES = ${wildcard ${WORKDIR}/*${OLDDATASET}*.decoder.yml}
|
|
endif
|
|
|
|
|
|
## fix model names from old style
|
|
## where models trained on a single corpus got the name
|
|
## of that corpus
|
|
## Now: always use 'opus' as the name of the default dataset
|
|
|
|
fix-model-names:
|
|
ifneq (${DATASET},${OLDDATASET})
|
|
for f in ${DECODERFILES}; do \
|
|
perl -i.bak -pe 's/${OLDDATASET}/${DATASET}/' $$f; \
|
|
done
|
|
for f in ${TRAINFILES}; do \
|
|
mv -f $$f `echo $$f | sed 's/${OLDDATASET}/${DATASET}/'`; \
|
|
ln -s `echo $$f | sed 's/${OLDDATASET}/${DATASET}/'` $$f; \
|
|
done
|
|
for f in ${MODELFILES}; do \
|
|
mv -f $$f `echo $$f | sed 's/${OLDDATASET}/${DATASET}/'`; \
|
|
done
|
|
endif
|