mirror of
https://github.com/Helsinki-NLP/OPUS-MT-train.git
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551 lines
17 KiB
Makefile
551 lines
17 KiB
Makefile
# -*-makefile-*-
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#
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# model configurations
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#
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## various ways of setting the model languages
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## (1) explicitly set source and target languages, for example:
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## SRCLANGS="da no sv" TRGLANGS="fi da"
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##
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## (2) specify language pairs, for example:
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## LANGPAIRS="de-en fi-sv da-es"
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## this will set SRCLANGS="de fi da" TRGLANGS="en sv es"
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##
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## if LANGPAIRS are set and the model is not supposed to be SYMMETRIC
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## then set SRCLANGS and TRGLANGS to the languages in LANGPAIRS
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ifdef LANGPAIRS
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SRCLANGS ?= ${sort ${shell echo "${LANGPAIRS}" | tr ' ' "\n" | cut -f1 -d '-'}}
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TRGLANGS ?= ${sort ${shell echo "${LANGPAIRS}" | tr ' ' "\n" | cut -f2 -d '-'}}
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endif
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## final default is sv-fi
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SRCLANGS ?= sv
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TRGLANGS ?= fi
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## set SRC and TRG unless they are specified already
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ifneq (${words ${SRCLANGS}},1)
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SRC ?= multi
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else
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SRC = ${SRCLANGS}
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endif
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ifneq (${words ${TRGLANGS}},1)
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TRG ?= multi
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else
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TRG = ${TRGLANGS}
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endif
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## OLD: set to first and last lang
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## --> this makes the evaluation look like it is one lang-pair
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##
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# SRC ?= ${firstword ${SRCLANGS}}
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# TRG ?= ${lastword ${TRGLANGS}}
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## SKIP_LANGPAIRS can be used to skip certain language pairs
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## in data preparation for multilingual models
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## ---> this can be good to skip BIG language pairs
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## that would very much dominate all the data
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## must be a pattern that can be matched by egrep
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## e.g. en-de|en-fr
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SKIP_LANGPAIRS ?= "nothing"
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## set SHUFFLE_DATA if you want to shuffle data for
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## each language pair to be added to the training data
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## --> especially useful in connection with FIT_DATA_SIZE
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##
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# SHUFFLE_DATA = 1
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## set FIT_DATA_SIZE to a specific value to fit the training data
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## to a certain number of lines for each language pair in the collection
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## --> especially useful for multilingual models for balancing the
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## the size for each language pair
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## the script does both, over- and undersampling
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##
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# FIT_DATA_SIZE = 100000
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## maximum number of repeating the same data set
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## in oversampling
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MAX_OVER_SAMPLING ?= 50
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## set CHECK_TRAINDATA_SIZE if you want to check that each
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## bitext has equal number of lines in source and target
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## ---> this only prints a warning if not
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##
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# CHECK_TRAINDATA_SIZE
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# sorted languages and langpair used to match resources in OPUS
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SORTLANGS = $(sort ${SRC} ${TRG})
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SPACE = $(empty) $(empty)
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LANGPAIR = ${firstword ${SORTLANGS}}-${lastword ${SORTLANGS}}
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LANGSRCSTR = ${subst ${SPACE},+,$(SRCLANGS)}
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LANGTRGSTR = ${subst ${SPACE},+,$(TRGLANGS)}
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LANGPAIRSTR = ${LANGSRCSTR}-${LANGTRGSTR}
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## for monolingual things
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LANGS ?= ${SRCLANGS}
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LANGID ?= ${firstword ${LANGS}}
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LANGSTR ?= ${subst ${SPACE},+,$(LANGS)}
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## for same language pairs: add numeric extension
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## (this is neccessary to keep source and target files separate)
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ifeq (${SRC},$(TRG))
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SRCEXT = ${SRC}1
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TRGEXT = ${SRC}2
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else
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SRCEXT = ${SRC}
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TRGEXT = ${TRG}
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endif
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## set a flag to use target language labels
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## in multi-target models
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ifneq (${words ${TRGLANGS}},1)
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USE_TARGET_LABELS = 1
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endif
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## set additional argument options for opus_read (if it is used)
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## e.g. OPUSREAD_ARGS = -a certainty -tr 0.3
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OPUSREAD_ARGS =
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## ELRA corpora
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ELRA_CORPORA = ${patsubst %/latest/xml/${LANGPAIR}.xml.gz,%,\
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${patsubst ${OPUSHOME}/%,%,\
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${shell ls ${OPUSHOME}/ELRA-*/latest/xml/${LANGPAIR}.xml.gz 2>/dev/null}}}
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## exclude certain data sets
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## TODO: include ELRA corpora
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EXCLUDE_CORPORA ?= WMT-News MPC1 ${ELRA_CORPORA}
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## all of OPUS (NEW: don't require MOSES format)
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OPUSCORPORA = $(filter-out ${EXCLUDE_CORPORA} ,${patsubst %/latest/xml/${LANGPAIR}.xml.gz,%,\
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${patsubst ${OPUSHOME}/%,%,\
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${shell ls ${OPUSHOME}/*/latest/xml/${LANGPAIR}.xml.gz 2>/dev/null}}})
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## monolingual data
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OPUSMONOCORPORA = $(filter-out ${EXCLUDE_CORPORA} ,${patsubst %/latest/mono/${LANGID}.txt.gz,%,\
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${patsubst ${OPUSHOME}/%,%,\
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${shell ls ${OPUSHOME}/*/latest/mono/${LANGID}.txt.gz}}})
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## all languages in OPUS (requires the opus-langs.txt file)
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ifneq (${wildcard opus-langs.txt},)
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OPUSLANGS = ${filter-out simple,${shell head -1 opus-langs.txt}}
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endif
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ALL_LANG_PAIRS = ${shell ls ${WORKHOME} | grep -- '-' | grep -v old}
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ALL_BILINGUAL_MODELS = ${shell echo '${ALL_LANG_PAIRS}' | tr ' ' "\n" | grep -v -- '\+'}
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ALL_MULTILINGUAL_MODELS = ${shell echo '${ALL_LANG_PAIRS}' | tr ' ' "\n" | grep -- '\+'}
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## size of dev data, test data and BPE merge operations
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## NEW default size = 2500 (keep more for training for small languages)
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DEVSIZE = 2500
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TESTSIZE = 2500
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## NEW: significantly reduce devminsize
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## (= absolute minimum we need as devdata)
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## NEW: define an alternative small size for DEV and TEST
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## OLD DEVMINSIZE:
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# DEVMINSIZE = 1000
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DEVSMALLSIZE = 1000
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TESTSMALLSIZE = 1000
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DEVMINSIZE = 250
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##----------------------------------------------------------------------------
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## train/dev/test data
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##----------------------------------------------------------------------------
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## dev/test data: default = Tatoeba otherwise, GlobalVoices, JW300, GNOME or bibl-uedin
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## - check that data exist
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## - check that there are at least 2 x DEVMINSIZE examples
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## TODO: this does not work well for multilingual models!
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## TODO: find a better solution than looking into *.info files (use OPUS API?)
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## ---> query for corpora bigger than a certain size and look for a suitable test/dev corpus
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ifneq ($(wildcard ${OPUSHOME}/Tatoeba/latest/moses/${LANGPAIR}.txt.zip),)
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ifeq ($(shell if (( `head -1 ${OPUSHOME}/Tatoeba/latest/info/${LANGPAIR}.txt.info` \
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> $$((${DEVMINSIZE} + ${DEVMINSIZE})) )); then echo "ok"; fi),ok)
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DEVSET = Tatoeba
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endif
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endif
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## backoff to GlobalVoices
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ifndef DEVSET
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ifneq ($(wildcard ${OPUSHOME}/GlobalVoices/latest/moses/${LANGPAIR}.txt.zip),)
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ifeq ($(shell if (( `head -1 ${OPUSHOME}/GlobalVoices/latest/info/${LANGPAIR}.txt.info` \
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> $$((${DEVMINSIZE} + ${DEVMINSIZE})) )); then echo "ok"; fi),ok)
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DEVSET = GlobalVoices
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endif
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endif
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endif
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## backoff to infopankki
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ifndef DEVSET
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ifneq ($(wildcard ${OPUSHOME}/infopankki/latest/moses/${LANGPAIR}.txt.zip),)
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ifeq ($(shell if (( `head -1 ${OPUSHOME}/infopankki/latest/info/${LANGPAIR}.txt.info` \
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> $$((${DEVMINSIZE} + ${DEVMINSIZE})) )); then echo "ok"; fi),ok)
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DEVSET = infopankki
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endif
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endif
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endif
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## backoff to JW300
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ifndef DEVSET
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ifneq ($(wildcard ${OPUSHOME}/JW300/latest/xml/${LANGPAIR}.xml.gz),)
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ifeq ($(shell if (( `sed -n 2p ${OPUSHOME}/JW300/latest/info/${LANGPAIR}.info` \
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> $$((${DEVMINSIZE} + ${DEVMINSIZE})) )); then echo "ok"; fi),ok)
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DEVSET = JW300
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endif
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endif
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endif
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## otherwise: bible-uedin
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ifndef DEVSET
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DEVSET = bible-uedin
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endif
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## increase dev/test sets for Tatoeba (very short sentences!)
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ifeq (${DEVSET},Tatoeba)
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DEVSIZE = 5000
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TESTSIZE = 5000
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endif
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## in case we want to use some additional data sets
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# EXTRA_TRAINSET =
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## TESTSET= DEVSET, TRAINSET = OPUS - WMT-News,DEVSET.TESTSET
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TESTSET ?= ${DEVSET}
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TRAINSET ?= $(filter-out ${EXCLUDE_CORPORA} ${DEVSET} ${TESTSET},${OPUSCORPORA} ${EXTRA_TRAINSET})
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MONOSET ?= $(filter-out ${EXCLUDE_CORPORA} ${DEVSET} ${TESTSET},${OPUSMONOCORPORA} ${EXTRA_TRAINSET})
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## 1 = use remaining data from dev/test data for training
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USE_REST_DEVDATA ?= 1
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##----------------------------------------------------------------------------
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## pre-processing and vocabulary
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##----------------------------------------------------------------------------
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BPESIZE ?= 32000
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SRCBPESIZE ?= ${BPESIZE}
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TRGBPESIZE ?= ${BPESIZE}
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VOCABSIZE ?= $$((${SRCBPESIZE} + ${TRGBPESIZE} + 1000))
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## for document-level models
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CONTEXT_SIZE = 100
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## pre-processing type
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# PRE = norm
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PRE = simple
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PRE_SRC = spm${SRCBPESIZE:000=}k
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PRE_TRG = spm${TRGBPESIZE:000=}k
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##-------------------------------------
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## default name of the data set (and the model)
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##-------------------------------------
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ifndef DATASET
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DATASET = opus
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endif
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ifndef BPEMODELNAME
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BPEMODELNAME = opus
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endif
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##-------------------------------------
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## OLD OLD OLD
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## name of the data set (and the model)
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## - single corpus = use that name
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## - multiple corpora = opus
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## add also vocab size to the name
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##-------------------------------------
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ifndef OLDDATASET
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ifeq (${words ${TRAINSET}},1)
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OLDDATASET = ${TRAINSET}
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else
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OLDDATASET = opus
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endif
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endif
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## DATADIR = directory where the train/dev/test data are
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## WORKDIR = directory used for training
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DATADIR = ${WORKHOME}/data
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WORKDIR = ${WORKHOME}/${LANGPAIRSTR}
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MODELDIR = ${WORKHOME}/models/${LANGPAIRSTR}
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SPMDIR = ${WORKHOME}/SentencePieceModels
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## data sets
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TRAIN_BASE = ${WORKDIR}/train/${DATASET}
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TRAIN_SRC = ${TRAIN_BASE}.src
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TRAIN_TRG = ${TRAIN_BASE}.trg
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TRAIN_ALG = ${TRAIN_BASE}${TRAINSIZE}.${PRE_SRC}-${PRE_TRG}.src-trg.alg.gz
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## training data in local space
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LOCAL_TRAIN_SRC = ${TMPDIR}/${LANGPAIRSTR}/train/${DATASET}.src
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LOCAL_TRAIN_TRG = ${TMPDIR}/${LANGPAIRSTR}/train/${DATASET}.trg
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LOCAL_MONO_DATA = ${TMPDIR}/${LANGSTR}/train/${DATASET}.mono
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## dev and test data come from one specific data set
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## if we have a bilingual model
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ifeq (${words ${SRCLANGS}},1)
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ifeq (${words ${TRGLANGS}},1)
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DEV_SRC = ${WORKDIR}/val/${DEVSET}.src
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DEV_TRG = ${WORKDIR}/val/${DEVSET}.trg
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TEST_SRC = ${WORKDIR}/test/${TESTSET}.src
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TEST_TRG = ${WORKDIR}/test/${TESTSET}.trg
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TESTSET_NAME = ${TESTSET}
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endif
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endif
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## otherwise we give them a generic name
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DEVSET_NAME ?= opus-dev
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TESTSET_NAME ?= opus-test
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DEV_SRC ?= ${WORKDIR}/val/${DEVSET_NAME}.src
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DEV_TRG ?= ${WORKDIR}/val/${DEVSET_NAME}.trg
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TEST_SRC ?= ${WORKDIR}/test/${TESTSET_NAME}.src
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TEST_TRG ?= ${WORKDIR}/test/${TESTSET_NAME}.trg
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MODEL_SUBDIR =
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MODEL = ${MODEL_SUBDIR}${DATASET}${TRAINSIZE}.${PRE_SRC}-${PRE_TRG}
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MODELTYPE = transformer-align
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NR = 1
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MODEL_BASENAME = ${MODEL}.${MODELTYPE}.model${NR}
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MODEL_VALIDLOG = ${MODEL}.${MODELTYPE}.valid${NR}.log
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MODEL_TRAINLOG = ${MODEL}.${MODELTYPE}.train${NR}.log
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MODEL_START = ${WORKDIR}/${MODEL_BASENAME}.npz
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MODEL_FINAL = ${WORKDIR}/${MODEL_BASENAME}.npz.best-perplexity.npz
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MODEL_VOCABTYPE = yml
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MODEL_VOCAB = ${WORKDIR}/${MODEL}.vocab.${MODEL_VOCABTYPE}
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MODEL_DECODER = ${MODEL_FINAL}.decoder.yml
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## test set translation and scores
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TEST_TRANSLATION = ${WORKDIR}/${TESTSET_NAME}.${MODEL}${NR}.${MODELTYPE}.${SRC}.${TRG}
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TEST_EVALUATION = ${TEST_TRANSLATION}.eval
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TEST_COMPARISON = ${TEST_TRANSLATION}.compare
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## parameters for running Marian NMT
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MARIAN_GPUS = 0
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MARIAN_EXTRA =
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MARIAN_VALID_FREQ = 10000
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MARIAN_SAVE_FREQ = ${MARIAN_VALID_FREQ}
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MARIAN_DISP_FREQ = ${MARIAN_VALID_FREQ}
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MARIAN_EARLY_STOPPING = 10
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MARIAN_VALID_MINI_BATCH = 16
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MARIAN_MAXI_BATCH = 500
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MARIAN_DROPOUT = 0.1
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MARIAN_MAX_LENGTH = 500
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MARIAN_DECODER_GPU = -b 12 -n1 -d ${MARIAN_GPUS} --mini-batch 8 --maxi-batch 32 --maxi-batch-sort src \
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--max-length ${MARIAN_MAX_LENGTH} --max-length-crop
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MARIAN_DECODER_CPU = -b 12 -n1 --cpu-threads ${HPC_CORES} --mini-batch 8 --maxi-batch 32 --maxi-batch-sort src \
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--max-length ${MARIAN_MAX_LENGTH} --max-length-crop
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MARIAN_DECODER_FLAGS = ${MARIAN_DECODER_GPU}
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## TODO: currently marianNMT crashes with workspace > 26000
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ifeq (${GPU},p100)
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MARIAN_WORKSPACE = 13000
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else ifeq (${GPU},v100)
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# MARIAN_WORKSPACE = 30000
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# MARIAN_WORKSPACE = 26000
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MARIAN_WORKSPACE = 24000
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# MARIAN_WORKSPACE = 18000
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# MARIAN_WORKSPACE = 16000
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else
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MARIAN_WORKSPACE = 10000
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endif
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## check whether we have GPUs available
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## if not: use CPU mode for decoding
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NVIDIA_SMI = ${shell which nvidia-smi 2>dev/null}
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ifdef NVIDIA_SMI
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ifeq (${shell nvidia-smi | grep failed | wc -l},1)
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MARIAN = ${MARIANCPU}
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MARIAN_DECODER_FLAGS = ${MARIAN_DECODER_CPU}
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MARIAN_EXTRA = --cpu-threads ${HPC_CORES}
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endif
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else
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MARIAN = ${MARIANCPU}
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MARIAN_DECODER_FLAGS = ${MARIAN_DECODER_CPU}
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MARIAN_EXTRA = --cpu-threads ${HPC_CORES}
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endif
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## weights associated with training examples
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ifneq ("$(wildcard ${TRAIN_WEIGHTS})","")
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MARIAN_TRAIN_WEIGHTS = --data-weighting ${TRAIN_WEIGHTS}
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endif
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### training a model with Marian NMT
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##
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## NR allows to train several models for proper ensembling
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## (with shared vocab)
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##
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## DANGER: if several models are started at the same time
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## then there is some racing issue with creating the vocab!
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ifdef NR
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SEED=${NR}${NR}${NR}${NR}
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else
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SEED=1234
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endif
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## list of all languages in OPUS
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opus-langs.txt:
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wget -O $@.tmp http://opus.nlpl.eu/opusapi/?languages=true
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grep '",' $@.tmp | tr '",' ' ' | sort | tr "\n" ' ' | sed 's/ */ /g' > $@
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rm -f $@.tmp
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## make some data size-specific configuration parameters
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## TODO: is it OK to delete LOCAL_TRAIN data?
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.PHONY: local-config
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local-config: ${WORKDIR}/config.mk
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SMALLEST_TRAINSIZE = 10000
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SMALL_TRAINSIZE = 100000
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MEDIUM_TRAINSIZE = 500000
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LARGE_TRAINSIZE = 1000000
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LARGEST_TRAINSIZE = 10000000
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${WORKDIR}/config.mk:
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mkdir -p ${dir $@}
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if [ -e ${TRAIN_SRC}.clean.${PRE_SRC}${TRAINSIZE}.gz ]; then \
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${MAKE} ${TRAIN_SRC}.clean.${PRE_SRC}${TRAINSIZE}.charfreq \
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${TRAIN_TRG}.clean.${PRE_TRG}${TRAINSIZE}.charfreq; \
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s=`zcat ${TRAIN_SRC}.clean.${PRE_SRC}${TRAINSIZE}.gz | head -10000001 | wc -l`; \
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S=`cat ${TRAIN_SRC}.clean.${PRE_SRC}${TRAINSIZE}.charfreq | wc -l`; \
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T=`cat ${TRAIN_TRG}.clean.${PRE_TRG}${TRAINSIZE}.charfreq | wc -l`; \
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else \
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${MAKE} ${LOCAL_TRAIN_SRC}; \
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${MAKE} ${LOCAL_TRAIN_SRC}.charfreq ${LOCAL_TRAIN_TRG}.charfreq; \
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s=`head -10000001 ${LOCAL_TRAIN_SRC} | wc -l`; \
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S=`cat ${LOCAL_TRAIN_SRC}.charfreq | wc -l`; \
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T=`cat ${LOCAL_TRAIN_TRG}.charfreq | wc -l`; \
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fi; \
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if [ $$s -gt ${LARGEST_TRAINSIZE} ]; then \
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echo "# ${LANGPAIRSTR} training data bigger than 10 million" > $@; \
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echo "GPUJOB_HPC_MEM = 8g" >> $@; \
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echo "GPUJOB_SUBMIT = -multigpu" >> $@; \
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echo "BPESIZE = ${BPESIZE}" >> $@; \
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echo "DEVSIZE = ${DEVSIZE}" >> $@; \
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echo "TESTSIZE = ${TESTSIZE}" >> $@; \
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echo "DEVMINSIZE = ${DEVMINSIZE}" >> $@; \
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elif [ $$s -gt ${LARGE_TRAINSIZE} ]; then \
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echo "# ${LANGPAIRSTR} training data bigger than 1 million" > $@; \
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echo "GPUJOB_HPC_MEM = 8g" >> $@; \
|
|
echo "GPUJOB_SUBMIT = " >> $@; \
|
|
echo "MARIAN_VALID_FREQ = 2500" >> $@; \
|
|
echo "BPESIZE = ${BPESIZE}" >> $@; \
|
|
echo "DEVSIZE = ${DEVSIZE}" >> $@; \
|
|
echo "TESTSIZE = ${TESTSIZE}" >> $@; \
|
|
echo "DEVMINSIZE = ${DEVMINSIZE}" >> $@; \
|
|
elif [ $$s -gt ${MEDIUM_TRAINSIZE} ]; then \
|
|
echo "# ${LANGPAIRSTR} training data bigger than 500k" > $@; \
|
|
echo "GPUJOB_HPC_MEM = 4g" >> $@; \
|
|
echo "GPUJOB_SUBMIT = " >> $@; \
|
|
echo "MARIAN_VALID_FREQ = 2500" >> $@; \
|
|
echo "MARIAN_WORKSPACE = 10000" >> $@; \
|
|
echo "BPESIZE = 12000" >> $@; \
|
|
echo "DEVSIZE = ${DEVSIZE}" >> $@; \
|
|
echo "TESTSIZE = ${TESTSIZE}" >> $@; \
|
|
echo "DEVMINSIZE = ${DEVMINSIZE}" >> $@; \
|
|
elif [ $$s -gt ${SMALL_TRAINSIZE} ]; then \
|
|
echo "# ${LANGPAIRSTR} training data bigger than 100k" > $@; \
|
|
echo "GPUJOB_HPC_MEM = 4g" >> $@; \
|
|
echo "GPUJOB_SUBMIT = " >> $@; \
|
|
echo "MARIAN_VALID_FREQ = 1000" >> $@; \
|
|
echo "MARIAN_WORKSPACE = 5000" >> $@; \
|
|
echo "MARIAN_VALID_MINI_BATCH = 8" >> $@; \
|
|
echo "BPESIZE = 4000" >> $@; \
|
|
echo "DEVSIZE = 1000" >> $@; \
|
|
echo "TESTSIZE = 1000" >> $@; \
|
|
echo "DEVMINSIZE = 250" >> $@; \
|
|
elif [ $$s -gt ${SMALLEST_TRAINSIZE} ]; then \
|
|
echo "# ${LANGPAIRSTR} training data less than 100k" > $@; \
|
|
echo "GPUJOB_HPC_MEM = 4g" >> $@; \
|
|
echo "GPUJOB_SUBMIT = " >> $@; \
|
|
echo "MARIAN_VALID_FREQ = 1000" >> $@; \
|
|
echo "MARIAN_WORKSPACE = 3500" >> $@; \
|
|
echo "MARIAN_DROPOUT = 0.5" >> $@; \
|
|
echo "MARIAN_VALID_MINI_BATCH = 4" >> $@; \
|
|
echo "BPESIZE = 1000" >> $@; \
|
|
echo "DEVSIZE = 500" >> $@; \
|
|
echo "TESTSIZE = 1000" >> $@; \
|
|
echo "DEVMINSIZE = 100" >> $@; \
|
|
else \
|
|
echo "${LANGPAIRSTR} too small"; \
|
|
fi; \
|
|
if [ -e $@ ]; then \
|
|
if [ $$S -gt 1000 ]; then \
|
|
echo "SRCBPESIZE = 32000" >> $@; \
|
|
fi; \
|
|
if [ $$T -gt 1000 ]; then \
|
|
echo "TRGBPESIZE = 32000" >> $@; \
|
|
fi; \
|
|
fi
|
|
echo "SRCLANGS = ${SRCLANGS}" >> $@
|
|
echo "TRGLANGS = ${TRGLANGS}" >> $@
|
|
echo "SKIPLANGS = ${SKIPLANGS}" >> $@
|
|
echo "LANGPAIRSTR = ${LANGPAIRSTR}" >> $@
|
|
echo "DATASET = ${DATASET}" >> $@
|
|
echo "TRAINSET = ${TRAINSET}" >> $@
|
|
echo "DEVSET = ${DEVSET}" >> $@
|
|
echo "TESTSET = ${TESTSET}" >> $@
|
|
echo "PRE = ${PRE}" >> $@
|
|
echo "PRE_SRC = ${PRE_SRC}" >> $@
|
|
echo "PRE_TRG = ${PRE_TRG}" >> $@
|
|
ifdef SHUFFLE_DATA
|
|
echo "SHUFFLE_DATA = ${SHUFFLE_DATA}" >> $@
|
|
endif
|
|
ifdef FIT_DATA_SIZE
|
|
echo "FIT_DATA_SIZE = ${FIT_DATA_SIZE}" >> $@
|
|
endif
|
|
echo "MAX_OVER_SAMPLING = ${MAX_OVER_SAMPLING}" >> $@
|
|
echo "USE_REST_DEVDATA = ${USE_REST_DEVDATA}" >> $@
|
|
ifdef USE_TARGET_LABELS
|
|
echo "USE_TARGET_LABELS = ${USE_TARGET_LABELS}" >> $@
|
|
endif
|