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train.mk
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train.mk
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JALAN:=0
SCUD2QUERY:=0
all: corpus_scud train_scud eval_scud \
corpus_scorer train_scorer eval_scorer
.PHONY: all
.DELETE_ON_ERROR:
SHELL=/bin/bash
ASDC_DIR:=~/data/asdc
SCUD_INTERNAL_ROOT_DIR:=~/data/scud_internal
SCUD_HOTEL_REVIEW_ROOT_DIR:=~/data/hotel_review_scud
SCUD_2_QUERY_ROOT_DIR:=~/data/scud2query
OUTPUT:=/please/designate
CONTEXT:=9999
EPOCH:=20
PERIOD:=$(EPOCH)
BATCH:=40
BATCH_DEV:=$(BATCH)
BATCH_PRED:=$(BATCH)
T5BASE:=megagonlabs/t5-base-japanese-web-8k
TRAIN_OPTION:=
OPTIMIZER:=fairseq.optim.adafactor.Adafactor
LR:=1e-3
IN_LEN:=128
OUT_LEN:=64
PREDICT_IN_LEN:=$(IN_LEN)
PREDICT_OUT_LEN:=$(OUT_LEN)
PREDICT_OPTION:=
DATA_DIR:=$(OUTPUT)/data
OUT_MODEL_DIR:=$(OUTPUT)/model
OUT_LOG:=$(OUTPUT)/logs
DATA_SCUD_DIR:=$(DATA_DIR)/scud
ifeq ($(JALAN),1)
DATA_SCUDS_MAIN_DIR:=$(DATA_SCUD_DIR)/jalan
DATA_SCUDS_SUP_DIR:=/dev/null
SCUD_DIR:=$(SCUD_HOTEL_REVIEW_ROOT_DIR)/data/scud/jalan
SCUDS_EXAMPLES:=$(shell find $(SCUD_DIR) -type f )
CORRECTNESS_LABELED_SCUD_DIR:=$(SCUD_HOTEL_REVIEW_ROOT_DIR)/data/correctness_labeled_scud/jalan
else ifeq ($(SCUD2QUERY),1)
DATA_SCUDS_MAIN_DIR:=$(DATA_SCUD_DIR)/scud2query
DATA_SCUDS_SUP_DIR:=/dev/null
SCUD_DIR:=$(SCUD_2_QUERY_ROOT_DIR)/data/scud/scud2query
SCUDS_EXAMPLES:=$(shell find $(SCUD_DIR) -type f )
CORRECTNESS_LABELED_SCUD_DIR:=$(SCUD_2_QUERY_ROOT_DIR)/data/correctness_labeled_scud/scud2query
CONTEXT:=0
else
DATA_SCUDS_MAIN_DIR:=$(DATA_SCUD_DIR)/main
DATA_SCUDS_SUP_DIR:=$(DATA_SCUD_DIR)/sup
ASDC_MAIN_SCUD_DIR:=$(ASDC_DIR)/data/main/scud_example
ASDC_SUP_SCUD_DIR:=$(ASDC_DIR)/data/supplemental/scud
SCUDS_EXAMPLES:=$(shell find $(ASDC_SUP_SCUD_DIR) $(ASDC_MAIN_SCUD_DIR) -type f )
CORRECTNESS_LABELED_SCUD_DIR:=$(ASDC_DIR)/data/supplemental/correctness_labeled_scud
INTERNAL_SCUD_DIR:=$(SCUD_INTERNAL_ROOT_DIR)/data/scud/for_tim
INTERNAL_SCUDS_EXAMPLES:=$(shell find $(INTERNAL_SCUD_DIR) -type f )
INTERNAL_CORRECTNESS_LABELED_SCUD_DIR:=$(SCUD_INTERNAL_ROOT_DIR)/data/correctness_labeled_scud/for_tim
INTERNAL_CORRECTNESS_LABELED_SCUDS:=$(shell find $(INTERNAL_CORRECTNESS_LABELED_SCUD_DIR) -type f)
endif
CORRECTNESS_LABELED_SCUDS:=$(shell find $(CORRECTNESS_LABELED_SCUD_DIR) -type f)
NEG:=0
ifeq ($(NEG),1)
DATA_SCUDS_SUP_DIR:=$(DATA_SCUD_DIR)/scud_negative
TRAIN_SCUDS_NEG_GOLD:=$(DATA_SCUDS_SUP_DIR)/train.tsv
endif
TRAIN_SCUDS_MAIN_GOLD_JSONL:=$(DATA_SCUDS_MAIN_DIR)/train.jsonl
TEST_SCUDS_MAIN_GOLD_JSONL:=$(DATA_SCUDS_MAIN_DIR)/test.jsonl
DEV_SCUDS_MAIN_GOLD_JSONL:=$(DATA_SCUDS_MAIN_DIR)/dev.jsonl
DEV_SCUDS_MAIN_GOLD:=$(DATA_SCUDS_MAIN_DIR)/dev.tsv
TRAIN_SCUDS_MAIN_GOLD:=$(DATA_SCUDS_MAIN_DIR)/train.tsv
TEST_SCUDS_MAIN_GOLD:=$(DATA_SCUDS_MAIN_DIR)/test.tsv
PILOTA_CONFIG:=$(DATA_SCUDS_MAIN_DIR)/pilota.config.json
TRAIN_SCUDS_SUP_GOLD_JSONL:=$(DATA_SCUDS_SUP_DIR)/train.jsonl
TEST_SCUDS_SUP_GOLD_JSONL:=$(DATA_SCUDS_SUP_DIR)/test.jsonl
DEV_SCUDS_SUP_GOLD_JSONL:=$(DATA_SCUDS_SUP_DIR)/dev.jsonl
DEV_SCUDS_SUP_GOLD:=$(DATA_SCUDS_SUP_DIR)/dev.tsv
TRAIN_SCUDS_SUP_GOLD:=$(DATA_SCUDS_SUP_DIR)/train.tsv
TEST_SCUDS_SUP_GOLD:=$(DATA_SCUDS_SUP_DIR)/test.tsv
ifeq ($(JALAN),1)
TRAIN_SCUDS_INPUT:=$(TRAIN_SCUDS_MAIN_GOLD)
DEV_SCUDS_INPUT:=$(DEV_SCUDS_MAIN_GOLD)
SCUD_TRAIN_ACCELERATOR_OPTION:=
else ifeq ($(SCUD2QUERY),1)
TRAIN_SCUDS_INPUT:=$(TRAIN_SCUDS_MAIN_GOLD)
DEV_SCUDS_INPUT:=$(DEV_SCUDS_MAIN_GOLD)
SCUD_TRAIN_ACCELERATOR_OPTION:=
else
TRAIN_SCUDS_INPUT:=$(TRAIN_SCUDS_MAIN_GOLD) $(TRAIN_SCUDS_SUP_GOLD)
DEV_SCUDS_INPUT:=$(DEV_SCUDS_MAIN_GOLD) $(DEV_SCUDS_SUP_GOLD)
SCUD_TRAIN_ACCELERATOR_OPTION:=
endif
SCORER_TRAIN_ACCELERATOR_OPTION:= --trainer.accelerator cuda --trainer.num_nodes 1
#CUDA_VISIBLE_DEVICES=0
ENV_PILOTA_EVAL:=
OUT_MODEL_SCUD:=$(OUT_MODEL_DIR)/scud
OUT_MODEL_WORK:=$(OUT_MODEL_SCUD).work
#------
OUT_MODEL_SCORER:=$(OUT_MODEL_DIR)/scorer
DATA_SCORER_DIR:=$(DATA_DIR)/scorer
TRAIN_SCORER_GOLD:=$(DATA_SCORER_DIR)/train.tsv
DEV_SCORER_GOLD:=$(DATA_SCORER_DIR)/dev.tsv
TEST_SCORER_GOLD:=$(DATA_SCORER_DIR)/test.tsv
LABELS_SCORER_GOLD:=$(DATA_SCORER_DIR)/labels.txt
SCORER_CONFIG:=$(DATA_SCORER_DIR)/scorer.config.json
OUT_EVAL_DIR:=$(OUTPUT)/eval
SCORER_DIR_EVAL:=$(OUT_EVAL_DIR)/scorer
SCORER_EVAL_PRED:= $(SCORER_DIR_EVAL)/prediction.jsonl
SCORER_EVAL_PRED_RESULT:= $(SCORER_DIR_EVAL)/result/done
SCORER_BATCH:=130
SCORER_EPOCH:=$(EPOCH)
SCORER_IN_LEN:=256
#------
$(OUT_MODEL_SCUD): $(TRAIN_SCUDS_INPUT) $(DEV_SCUDS_INPUT)
mkdir -p "$(OUT_LOG)"
mkdir -p "$(OUT_LOG)/scud"
git -C $(ASDC_DIR) rev-parse HEAD > "$(OUT_LOG)/version.asdc.txt"
git rev-parse HEAD > "$(OUT_LOG)/version.pilota.txt"
mkdir -p $(OUT_MODEL_WORK)
cp $(PILOTA_CONFIG) $(OUT_MODEL_WORK)
time python3 -m pilota.train \
fit \
--data.base $(T5BASE) \
$(addprefix --data.train+=,$(TRAIN_SCUDS_INPUT) ) \
$(addprefix --data.dev+=,$(DEV_SCUDS_INPUT) ) \
--data.il $(IN_LEN) \
--data.ol $(OUT_LEN) \
--output "$(OUT_MODEL_WORK)" \
--trainer.logger.init_args.save_dir "$(OUT_LOG)/scud" \
--trainer.max_epochs "$(EPOCH)" \
--data.bs $(BATCH) \
--data.bs_dev $(BATCH_DEV) \
--optimizer $(OPTIMIZER) \
--optimizer.init_args.lr $(LR) \
--optimizer.init_args.relative_step False \
--optimizer.init_args.scale_parameter False \
--optimizer.init_args.warmup_init 0 \
$(TRAIN_OPTION) \
$(SCUD_TRAIN_ACCELERATOR_OPTION) \
&& rm -rf $@ \
&& mv $(OUT_MODEL_WORK) $@
train_scud: $(OUT_MODEL_SCUD)
#------
ifeq ($(JALAN),1)
$(TRAIN_SCUDS_MAIN_GOLD_JSONL): $(SCUDS_EXAMPLES)
mkdir -p $(dir $@)
python3 -m pilota.convert.split_example \
$(addprefix -i ,$< ) \
--train $@ \
--dev $(dir $@)/dev.jsonl \
--test $(dir $@)/test.jsonl
$(TRAIN_SCUDS_MAIN_GOLD): $(TRAIN_SCUDS_MAIN_GOLD_JSONL)
python3 -m pilota.convert.example2request \
--tsv \
-i $(DATA_SCUDS_MAIN_DIR) \
-o $(DATA_SCUDS_MAIN_DIR) \
--output_config $(DATA_SCUDS_MAIN_DIR)/pilota.config.json \
--context $(CONTEXT) \
--name user
corpus_scud: $(TRAIN_SCUDS_MAIN_GOLD)
TEST_TARGET_FILES:= $(TEST_SCUDS_MAIN_GOLD)
else ifeq ($(SCUD2QUERY),1)
$(TRAIN_SCUDS_MAIN_GOLD_JSONL): $(SCUDS_EXAMPLES)
mkdir -p $(dir $@)
python3 -m pilota.convert.split_example \
$(addprefix -i ,$< ) \
--train $@ \
--dev $(dir $@)/dev.jsonl \
--test $(dir $@)/test.jsonl
$(TRAIN_SCUDS_MAIN_GOLD): $(TRAIN_SCUDS_MAIN_GOLD_JSONL)
python3 -m pilota.convert.example2request \
--tsv \
-i $(DATA_SCUDS_MAIN_DIR) \
-o $(DATA_SCUDS_MAIN_DIR) \
--output_config $(DATA_SCUDS_MAIN_DIR)/pilota.config.json \
--context $(CONTEXT) \
--name user
corpus_scud: $(TRAIN_SCUDS_MAIN_GOLD)
TEST_TARGET_FILES:= $(TEST_SCUDS_MAIN_GOLD)
else
$(TRAIN_SCUDS_MAIN_GOLD_JSONL): $(SCUDS_EXAMPLES) $(INTERNAL_SCUDS_EXAMPLES)
find $(SCUD_INTERNAL_ROOT_DIR)/data/scud/for_tim/*.Example.jsonl | xargs -n1 test -r
mkdir -p "$(OUT_LOG)"
git -C $(ASDC_DIR) rev-parse HEAD > "$(OUT_LOG)/version.scud_internal.txt"
$(MAKE) -C "$(ASDC_DIR)" -f ./mks/generate_example.mk \
ROOT_DIR=$(ASDC_DIR) \
generate_example_main \
generate_example_sup \
OUTPUT=$(DATA_SCUD_DIR) \
INPUT_SUP_EXTRA='$(SCUD_INTERNAL_ROOT_DIR)/data/scud/for_tim/*.Example.jsonl'
$(TRAIN_SCUDS_SUP_GOLD_JSONL): $(TRAIN_SCUDS_MAIN_GOLD_JSONL)
$(TRAIN_SCUDS_MAIN_GOLD): $(TRAIN_SCUDS_MAIN_GOLD_JSONL)
python3 -m pilota.convert.example2request \
--tsv \
-i $(DATA_SCUDS_MAIN_DIR) \
-o $(DATA_SCUDS_MAIN_DIR) \
--output_config $(DATA_SCUDS_MAIN_DIR)/pilota.config.json \
--context $(CONTEXT) \
--name agent --name user
$(TRAIN_SCUDS_SUP_GOLD): $(TRAIN_SCUDS_SUP_GOLD_JSONL)
python3 -m pilota.convert.example2request \
--tsv \
-i $(DATA_SCUDS_SUP_DIR) \
-o $(DATA_SCUDS_SUP_DIR) \
--context $(CONTEXT) \
--output_config $(DATA_SCUDS_SUP_DIR)/pilota.config.json \
--name agent --name user
corpus_scud: $(TRAIN_SCUDS_MAIN_GOLD) $(TRAIN_SCUDS_SUP_GOLD)
$(DEV_SCUDS_SUP_GOLD): $(TRAIN_SCUDS_SUP_GOLD)
$(TEST_SCUDS_SUP_GOLD): $(TRAIN_SCUDS_SUP_GOLD)
TEST_TARGET_FILES:= $(TEST_SCUDS_MAIN_GOLD) $(TEST_SCUDS_SUP_GOLD)
$(TEST_SCUDS_SUP_GOLD_JSONL) $(DEV_SCUDS_SUP_GOLD_JSONL): $(TRAIN_SCUDS_SUP_GOLD_JSONL)
endif
$(TEST_SCUDS_MAIN_GOLD_JSONL) $(DEV_SCUDS_MAIN_GOLD_JSONL): $(TRAIN_SCUDS_MAIN_GOLD_JSONL)
$(DEV_SCUDS_MAIN_GOLD): $(TRAIN_SCUDS_MAIN_GOLD)
$(TEST_SCUDS_MAIN_GOLD): $(TRAIN_SCUDS_MAIN_GOLD)
OUT_PRED_SCUD_DIR:=$(OUT_EVAL_DIR)/scud
SUFFIX_PREDICTION:=.prediction.jsonl
SUFFIX_EVAL:=.eval.jsonl
SUFFIX_STAT:=.eval.stat.tsv
SUFFIX_CSV:=.eval.csv
TEST_PREDICTION_FILES:=$(patsubst $(DATA_SCUD_DIR)%.tsv,$(OUT_PRED_SCUD_DIR)%$(SUFFIX_PREDICTION),$(TEST_TARGET_FILES))
TEST_EVAL_FILES:=$(patsubst $(DATA_SCUD_DIR)%.tsv,$(OUT_PRED_SCUD_DIR)%$(SUFFIX_EVAL),$(TEST_TARGET_FILES))
TEST_STAT_FILES:=$(patsubst $(DATA_SCUD_DIR)%.tsv,$(OUT_PRED_SCUD_DIR)%$(SUFFIX_STAT),$(TEST_TARGET_FILES))
TEST_CSV_FILES:=$(patsubst $(DATA_SCUD_DIR)%.tsv,$(OUT_PRED_SCUD_DIR)%$(SUFFIX_CSV),$(TEST_TARGET_FILES))
eval_scud: $(TEST_PREDICTION_FILES) $(TEST_EVAL_FILES) $(TEST_STAT_FILES) $(TEST_CSV_FILES)
IN_PRED_JOINT:=$(OUT_PRED_SCUD_DIR)/test.joint.txt
OUT_PRED_JOINT:=$(OUT_PRED_SCUD_DIR)/test.joint$(SUFFIX_PREDICTION)
$(IN_PRED_JOINT): $(TEST_TARGET_FILES)
mkdir -p $(dir $@) && \
python3 -m pilota.evaluate.joint $(addprefix --input ,$(TEST_TARGET_FILES) ) -o [email protected] \
&& rm -rf $@ \
&& mv [email protected] $@
$(OUT_PRED_JOINT): $(IN_PRED_JOINT) $(OUT_MODEL_SCUD) $(OUT_MODEL_SCORER)
mkdir -p $(dir $@) && \
cut -f2 $(IN_PRED_JOINT) \
| $(ENV_PILOTA_EVAL) python3 -m pilota.cli --raw_in \
--model $(OUT_MODEL_DIR) -o $@ \
$(NO_SCORER_OPTION) \
--il $(PREDICT_IN_LEN) --ol $(PREDICT_OUT_LEN) --bs $(BATCH_PRED) $(PREDICT_OPTION)
$(OUT_PRED_SCUD_DIR)/%$(SUFFIX_PREDICTION): $(DATA_SCUD_DIR)/%.tsv $(OUT_PRED_JOINT)
mkdir -p $(dir $@) && \
python3 -m pilota.evaluate.joint --ref $(IN_PRED_JOINT) -i $(OUT_PRED_JOINT) -s $(DATA_SCUD_DIR)/$*.jsonl -o $@
$(OUT_PRED_SCUD_DIR)/%$(SUFFIX_EVAL): $(OUT_PRED_SCUD_DIR)/%$(SUFFIX_PREDICTION) $(DATA_SCUD_DIR)/%.jsonl
mkdir -p $(dir $@) && \
python3 -m pilota.evaluate.cli \
-g $(DATA_SCUD_DIR)/$*.tsv \
-i $(OUT_PRED_SCUD_DIR)/$*$(SUFFIX_PREDICTION) \
--txt $(OUT_PRED_SCUD_DIR)/txt/$* \
--max_nbest_for_txt 4 \
-o $@
$(OUT_PRED_SCUD_DIR)/%$(SUFFIX_STAT): $(OUT_PRED_SCUD_DIR)/%$(SUFFIX_EVAL)
mkdir -p $(dir $@) && \
python3 -m pilota.evaluate.cli --stat -i $< -o $@
$(OUT_PRED_SCUD_DIR)/%$(SUFFIX_CSV): $(OUT_PRED_SCUD_DIR)/%$(SUFFIX_EVAL)
mkdir -p $(dir $@) && \
python3 -m pilota.evaluate.cli --csv -i $< -o $@
#--------
ifeq ($(JALAN),1)
$(DATA_SCORER_DIR): $(CORRECTNESS_LABELED_SCUDS) $(SCUDS_EXAMPLES)
python -m pilota.convert.scorer \
-i $(CORRECTNESS_LABELED_SCUD_DIR) \
--original $(SCUD_DIR) \
--context $(CONTEXT) \
--name user \
-o [email protected] \
--extra all \
&& rm -rf $@ \
&& mv [email protected] $@
else ifeq ($(SCUD2QUERY),1)
$(DATA_SCORER_DIR): $(CORRECTNESS_LABELED_SCUDS) $(SCUDS_EXAMPLES)
python -m pilota.convert.scorer \
-i $(CORRECTNESS_LABELED_SCUD_DIR) \
--original $(SCUD_DIR) \
--context $(CONTEXT) \
--name user \
-o [email protected] \
--extra all \
&& rm -rf $@ \
&& mv [email protected] $@
else
$(DATA_SCORER_DIR): $(CORRECTNESS_LABELED_SCUDS) $(SCUDS_EXAMPLES) $(INTERNAL_CORRECTNESS_LABELED_SCUDS) $(INTERNAL_SCUDS_EXAMPLES)
python -m pilota.convert.scorer \
-i $(CORRECTNESS_LABELED_SCUD_DIR) \
--original $(ASDC_SUP_SCUD_DIR) \
-i $(INTERNAL_CORRECTNESS_LABELED_SCUD_DIR) \
--original $(INTERNAL_SCUD_DIR) \
--context $(CONTEXT) \
--context_separator "<INPUT>" \
--name agent --name user \
-o [email protected] \
&& rm -rf $@ \
&& mv [email protected] $@
endif
$(TRAIN_SCORER_GOLD) $(DEV_SCORER_GOLD) $(TEST_SCORER_GOLD) $(LABELS_SCORER_GOLD): $(DATA_SCORER_DIR)
corpus_scorer: $(DATA_SCORER_DIR)
SCORER_BASE:=line-corporation/line-distilbert-base-japanese
SCORER_LR:=1e-4
$(OUT_MODEL_SCORER): $(TRAIN_SCORER_GOLD) $(DEV_SCORER_GOLD) $(LABELS_SCORER_GOLD)
mkdir -p "$(OUT_LOG)/scorer"
mkdir -p $(dir $@) && \
eval python -m pilota.mull.train \
fit \
--data.base $(SCORER_BASE) \
--data.train $(TRAIN_SCORER_GOLD)\
--data.dev $(DEV_SCORER_GOLD) \
--data.label $(LABELS_SCORER_GOLD) \
--data.il $(SCORER_IN_LEN) \
--output [email protected] \
--trainer.max_epochs "$(SCORER_EPOCH)" \
--data.bs $(SCORER_BATCH) \
--data.bs_dev $(SCORER_BATCH) \
--trainer.logger.init_args.save_dir "$(OUT_LOG)/scorer" \
--optimizer AdamW \
--optimizer.init_args.weight_decay 0.0001 \
--optimizer.init_args.lr $(SCORER_LR) \
$(SCORER_TRAIN_ACCELERATOR_OPTION) \
&& cp "$(SCORER_CONFIG)" [email protected] \
&& rm -rf $@ \
&& mv [email protected] $@
$(SCORER_EVAL_PRED) :$(OUT_MODEL_SCORER) $(TEST_SCORER_GOLD)
mkdir -p $(dir $@) && \
cut -f2 $(TEST_SCORER_GOLD) \
| python -m pilota.scorer -m $(OUT_MODEL_SCORER) \
&& rm -rf $@ \
&& mv [email protected] $@
$(SCORER_EVAL_PRED_RESULT): $(SCORER_EVAL_PRED) $(TEST_SCORER_GOLD) $(LABELS_SCORER_GOLD)
mkdir -p $(dir $@) \
&& python -m pilota.evaluate.scorer -i $(SCORER_EVAL_PRED) -g $(TEST_SCORER_GOLD) -o $(dir $@) --label $(LABELS_SCORER_GOLD) \
&& touch $@
train_scorer: $(OUT_MODEL_SCORER)
eval_scorer: $(SCORER_EVAL_PRED) $(SCORER_EVAL_PRED_RESULT)
scud: corpus_scud train_scud eval_scud
scorer: corpus_scorer train_scorer eval_scorer
#--------
.PHONY: all \
corpus_scud train_scud eval_scud \
corpus_scorer train_scorer eval_scorer