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new makefile structure
This commit is contained in:
Joerg Tiedemann
parent
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commit
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186
Makefile
186
Makefile
@ -1,24 +1,101 @@
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# -*-makefile-*-
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#
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# train Opus-MT models using MarianNMT
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# train and test Opus-MT models using MarianNMT
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#
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#--------------------------------------------------------------------
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#
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# (1) train NMT model
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#
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# make train .............. train NMT model for current language pair
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#
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# (2) translate and evaluate
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# make all
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#
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# make data ............... create training data
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# make train .............. train NMT model
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# make translate .......... translate test set
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# make eval ............... evaluate
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#
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# make train-job .......... create data and submit training job
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#
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#--------------------------------------------------------------------
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# general parameters / variables (see lib/config.mk)
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#
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# Makefile.tasks ...... various common and specific tasks/experiments
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# Makefile.generic .... generic targets (in form of prefixes to be added to other targets)
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# * most essential parameters (language IDs used in OPUS):
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#
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# SRCLANGS ................ set of source languages
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# TRGLANGS ................ set of target languages
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#
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# * other important parameters (can leave defaults)
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#
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# MODELTYPE ............... transformer|transformer-align
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# TRAINSET ................ set of corpora used for training (default = all of OPUS)
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# TESTSET ................. test set corpus (default - subset of Tatoeba with some fallbacks)
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# DEVSET .................. validation corpus (default - another subset of TESTSET)
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#
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# DEVSIZE ................. nr of sentences in validation data
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# TESTSIZE ................ nr of sentences in test data
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# HELDOUTSIZE ............. nr of sentence in heldout data from each train corpus
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#
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# TESTSMALLSIZE ........... reduced size for low-resource settings
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# DEVSMALLSIZE ............ reduced size for low-resource settings
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# DEVMINSIZE .............. minimum size for validation data
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#--------------------------------------------------------------------
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# lib/generic.mk
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#
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# There are implicit targets that define certain frequent tasks
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# They typically modify certain settings and make another target
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# with those modifiction. They can be used by adding a suffix to
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# the actual target that needs to be done. For example,
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# adding -RL triggers right-to-left models:
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#
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# make train-RL
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# make eval-RL
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#
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# Another example would be to run something over a number of models,
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# for example, translate and evaluate with those models:
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#
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# make eval-allmodels.submit
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# make eval-allbilingual.submit # only bilingual models
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# make eval-allmultlingual.submit # only multilingual models
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#--------------------------------------------------------------------
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# lib/slurm.mk
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#
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# Defines generic targets for submitting jobs. They work in the
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# same way as the generic targets in lib/generic.mk but submit a
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# job using SLURM sbatch. This only works if the SLURM parameters
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# are correctly set. Check lib/env.mk, lib/config.mk and lib/slurm.mk
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#
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# %.submit ........ job on GPU nodes (for train and translate)
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# %.submitcpu ..... job on CPU nodes (for translate and eval)
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#
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# They can be combined with any other target, even with generic
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# extensions described above. For exaple, subkit a job to train
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# an en-ru right-to-left model for 24 hours you can run
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#
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# make WALLTIME=24 SRCLANGS=en TRGLANGS=ru train-RL.submit
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#
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# Other extensions can be added to modify the SLURM job, for example
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# to submit the same job to run on multiple GPUs on one node:
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#
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# make WALLTIME=24 SRCLANGS=en TRGLANGS=ru train-RL.submit-multigpu
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#
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# There can also be targets that submit jobs via SLURM, for exampl
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# the train-job target. This can be combined with starting a CPU
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# job to create the data sets, which will then submit the train
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# job on GPUs once the training data are ready. For example, to
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# submit a job with 4 threads (using make -j 4) that will run
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# the train-job target on a CPU node allocating 4 CPU cores you
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# can do:
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#
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# make HPC_CORES=4 SRCLANGS=en TRGLANGS=ru train-job-RL.submitcpu
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#
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#--------------------------------------------------------------------
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# lib/dist.mk
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#
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# Targets to create and upload packages of trained models
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#
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#--------------------------------------------------------------------
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# There are various special targets for specific and generic tasks.
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# Look into the makefiles in lib/generic.mk and lib/models/*.mk
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# Many of those targets can be further adjusted by setting certain variables
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# Some examples are below but all of those things are subject to change ....
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#
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# Examples from Makefile.tasks:
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#
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# * submit job to train a model in one specific translation direction
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# (make data on CPU and then start a job on a GPU node with 4 GPUs)
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@ -44,41 +121,14 @@
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# make LANGS="en de fr" multilingual.submitcpu
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#
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#--------------------------------------------------------------------
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# Some examples using generic extensions
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#
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# * submit job to train en-ru with backtranslation data from backtranslate/
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# make HPC_CORES=4 WALLTIME=24 SRCLANGS=en TRGLANGS=ru unidirectional-add-backtranslations.submitcpu
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#
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# * submit job that evaluates all currently trained models:
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# make eval-allmodels.submit
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# make eval-allbilingual.submit # only bilingual models
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# make eval-allbilingual.submit # only multilingual models
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#
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#--------------------------------------------------------------------
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#
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# general parameters / variables (see Makefile.config)
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# SRCLANGS ............ set source language(s) (en)
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# TRGLANGS ............ set target language(s) (de)
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#
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# Ensembles: One can train a number of models and ensemble them.
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# NOTE: make sure that the data files and vocabularies exist before
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# training models. Otherwise, thete could be a racing situation
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# when those jobs start simultaneously!!!
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#
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# submit jobs by adding suffix to make-target to be run
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# .submit ........ job on GPU nodes (for train and translate)
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# .submitcpu ..... job on CPU nodes (for translate and eval)
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#
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# for example:
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# make train.submit
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#
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# run a multigpu job, for example
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# make train-multigpu.submit
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# make train-twogpu.submit
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# make train-gpu01.submit
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# make train-gpu23.submit
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#
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#
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# typical procedure: train and evaluate en-de with 3 models in ensemble
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#
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# make data.submitcpu
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# make vocab.submit
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# make data
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# make vocab
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# make NR=1 train.submit
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# make NR=2 train.submit
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# make NR=3 train.submit
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@ -86,50 +136,20 @@
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# make NR=1 eval.submit
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# make NR=2 eval.submit
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# make NR=3 eval.submit
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#
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# make eval-ensemble.submit
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#
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#
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# include right-to-left models:
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#
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# make NR=1 train-RL.submit
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# make NR=2 train-RL.submit
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# make NR=3 train-RL.submit
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#
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#
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#--------------------------------------------------------------------
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# train several versions of the same model (for ensembling)
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#
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# make NR=1 ....
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# make NR=2 ....
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# make NR=3 ....
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#
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# DANGER: problem with vocabulary files if you start them simultaneously
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# --> racing situation for creating them between the processes
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#
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#--------------------------------------------------------------------
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# resume training
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#
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# make resume
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#
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#--------------------------------------------------------------------
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# translate with ensembles of models
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#
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# make translate-ensemble
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# make eval-ensemble
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#
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# this only makes sense if there are several models
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# (created with different NR)
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#--------------------------------------------------------------------
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# check and adjust lib/env.mk and lib/config.mk
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# add specific tasks in lib/tasks.mk
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include lib/env.mk
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include lib/config.mk
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## load model-specific configuration parameters
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# load model-specific configuration parameters
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# if they exist in the work directory
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ifneq ($(wildcard ${WORKDIR}/config.mk),)
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include ${WORKDIR}/config.mk
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endif
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@ -170,8 +190,20 @@ include lib/models/simplify.mk
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# include Makefile.slurm
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.PHONY: all
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all: ${WORKDIR}/config.mk
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${MAKE} data
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${MAKE} train
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${MAKE} eval
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${MAKE} compare
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.PHONY: train-job
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train-job: ${WORKDIR}/config.mk
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${MAKE} data
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${MAKE} HPC_CORES=1 HPC_MEM=${GPUJOB_HPC_MEM} train.submit${GPUJOB_SUBMIT}
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#------------------------------------------------------------------------
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# make various data sets
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# make various data sets (and word alignment)
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#------------------------------------------------------------------------
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21
README.md
21
README.md
@ -8,13 +8,20 @@ This package includes scripts for training NMT models using MarianNMT and OPUS d
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Essential files for making new models:
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* `Makefile`: top-level makefile
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* `Makefile.env`: system-specific environment (now based on CSC machines)
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* `Makefile.config`: essential model configuration
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* `Makefile.data`: data pre-processing tasks
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* `Makefile.doclevel`: experimental document-level models
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* `Makefile.tasks`: tasks for training specific models and other things (this frequently changes)
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* `Makefile.dist`: make packages for distributing models (CSC ObjectStorage based)
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* `Makefile.slurm`: submit jobs with SLURM
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* `lib/env.mk`: system-specific environment (now based on CSC machines)
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* `lib/config.mk`: essential model configuration
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* `lib/data.mk`: data pre-processing tasks
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* `lib/generic.mk`: generic implicit rules that can extend other tasks
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* `lib/dist.mk`: make packages for distributing models (CSC ObjectStorage based)
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* `lib/slurm.mk`: submit jobs with SLURM
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There are also make targets for specific models and tasks. Look into `lib/models/` to see what has been defined already.
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Note that this frequently changes! There is, for example:
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* `lib/models/multilingua.mk`: various multilingual models
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* `lib/models/celtic.mk`: data and models for Celtic languages
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* `lib/models/doclevel.mk`: experimental document-level models
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Run this if you want to train a model, for example for translating English to French:
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@ -10,13 +10,8 @@
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SRCLANGS = sv
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TRGLANGS = fi
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ifndef SRC
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SRC := ${firstword ${SRCLANGS}}
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endif
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ifndef TRG
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TRG := ${lastword ${TRGLANGS}}
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endif
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SRC ?= ${firstword ${SRCLANGS}}
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TRG ?= ${lastword ${TRGLANGS}}
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# sorted languages and langpair used to match resources in OPUS
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@ -29,15 +24,9 @@ LANGPAIRSTR = ${LANGSRCSTR}-${LANGTRGSTR}
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## for monolingual things
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ifndef LANGS
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LANGS := ${SRCLANGS}
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endif
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ifndef LANGID
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LANGID := ${firstword ${LANGS}}
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endif
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ifndef LANGSTR
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LANGSTR = ${subst ${SPACE},+,$(LANGS)}
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endif
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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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@ -103,6 +92,7 @@ HELDOUTSIZE = ${DEVSIZE}
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## - check that data exist
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## - check that there are at least 2 x DEVMINSIZE examples
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## TODO: this does not work well for multilingual models!
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## TODO: find a better solution than looking into *.info files (use OPUS API?)
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ifneq ($(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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@ -175,9 +165,7 @@ BPESIZE = 32000
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SRCBPESIZE = ${BPESIZE}
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TRGBPESIZE = ${BPESIZE}
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ifndef VOCABSIZE
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VOCABSIZE = $$((${SRCBPESIZE} + ${TRGBPESIZE} + 1000))
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endif
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VOCABSIZE ?= $$((${SRCBPESIZE} + ${TRGBPESIZE} + 1000))
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## for document-level models
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CONTEXT_SIZE = 100
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15
lib/env.mk
15
lib/env.mk
@ -33,18 +33,11 @@ WALLTIME = 72
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## set variables with HPC prefix
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ifndef HPC_TIME
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HPC_TIME = ${WALLTIME}:00
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endif
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ifndef HPC_CORES
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HPC_CORES = ${THREADS}
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endif
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ifndef HPC_MEM
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HPC_MEM = ${MEM}
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endif
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HPC_TIME ?= ${WALLTIME}:00
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HPC_CORES ?= ${THREADS}
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HPC_MEM ?= ${MEM}
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GPUJOB_HPC_MEM ?= 4g
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