Making large AI models cheaper, faster and more accessible
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BASE_DIR=$(dirname $(dirname $(realpath $BASH_SOURCE)))
BASE_TEMP_DIR=$BASE_DIR/temp
EXAMPLES_DIR=$BASE_DIR/examples
TEST_DATA_DIR=$BASE_DIR/tests/test_data
DATA_SAVE_PATH=$BASE_TEMP_DIR/tests
CONFIG_DIR=$BASE_DIR/config
# MODELS=("colossal-llama2" "llama2" "mistral" "chatGLM2" "chatGLM3" "deepseek" "Yi" "baichuan") # for local test
MODELS=("colossal-llama2" "llama2" "chatGLM2" "chatGLM3" "deepseek" "Yi")
get_pretrain() {
local model=$1
if [[ $model == "colossal-llama2" ]]; then
echo "hpcai-tech/Colossal-LLaMA-2-7b-base"
elif [[ $model == "llama2" ]]; then
echo "hf-internal-testing/llama-tokenizer"
elif [[ $model == "phi" ]]; then
echo "microsoft/phi-2"
elif [[ $model == "mistral" ]]; then
echo "mistralai/Mistral-7B-Instruct-v0.3"
elif [[ $model == "chatGLM2" ]]; then
echo "THUDM/chatglm2-6b"
elif [[ $model == "chatGLM3" ]]; then
echo "THUDM/chatglm3-6b"
elif [[ $model == "deepseek" ]]; then
echo "deepseek-ai/DeepSeek-V2-Lite"
elif [[ $model == "Yi" ]]; then
echo "01-ai/Yi-1.5-9B-Chat"
elif [[ $model == "baichuan" ]]; then
echo "baichuan-inc/Baichuan2-13B-Chat"
else
echo "Unknown model $model"
exit 1
fi
}
get_conversation_template_config() {
local model=$1
if [[ $model == "colossal-llama2" ]]; then
echo "$CONFIG_DIR/conversation_template/colossal-llama2.json"
elif [[ $model == "llama2" ]]; then
echo "$CONFIG_DIR/conversation_template/llama2.json"
elif [[ $model == "deepseek" ]]; then
echo "$CONFIG_DIR/conversation_template/deepseek-ai_DeepSeek-V2-Lite.json"
elif [[ $model == "mistral" ]]; then
echo "$CONFIG_DIR/conversation_template/mistralai_Mixtral-8x7B-Instruct-v0.1.json"
elif [[ $model == "chatGLM2" ]]; then
echo "$CONFIG_DIR/conversation_template/THUDM_chatglm2-6b.json"
elif [[ $model == "chatGLM3" ]]; then
echo "$CONFIG_DIR/conversation_template/THUDM_chatglm3-6b.json"
elif [[ $model == "phi" ]]; then
echo "$CONFIG_DIR/conversation_template/microsoft_phi-2.json"
elif [[ $model == "Yi" ]]; then
echo "$CONFIG_DIR/conversation_template/01-ai_Yi-1.5-9B-Chat.json"
elif [[ $model == "baichuan" ]]; then
echo "$CONFIG_DIR/conversation_template/baichuan-inc_Baichuan2-13B-Chat.json"
else
echo "Unknown model $model"
exit 1
fi
}
# Test SFT data Preparation
for model in ${MODELS[@]}; do
echo "Testing SFT data templating for $model"
SAVE_DIR=$DATA_SAVE_PATH/sft/$model
rm -rf $SAVE_DIR/cache
rm -rf $SAVE_DIR/jsonl
rm -rf $SAVE_DIR/arrow
pretrain=$(get_pretrain $model)
conversation_template_config=$(get_conversation_template_config $model)
python $EXAMPLES_DIR/data_preparation_scripts/prepare_dataset.py --type sft --data_input_dirs $TEST_DATA_DIR/sft \
--tokenizer_dir $pretrain \
--conversation_template_config $conversation_template_config \
--data_cache_dir $SAVE_DIR/cache \
--data_jsonl_output_dir $SAVE_DIR/jsonl \
--data_arrow_output_dir $SAVE_DIR/arrow
passed=$?
if [ $passed -ne 0 ]; then
echo "[Test]: Failed in the SFT data templating for $model"
exit 1
fi
python $BASE_DIR/tests/verify_chat_data.py --data_source $TEST_DATA_DIR/sft/test_sft_data.jsonl \
--to_verify_file $SAVE_DIR/jsonl/part-00005.jsonl --data_type sft
passed=$?
if [ $passed -ne 0 ]; then
echo "[Test]: Failed in the SFT data templating test for $model"
exit 1
fi
done
# Test DPO/PPO data Preparation
for model in ${MODELS[@]}; do
echo "Testing DPO/RM data templating for $model"
SAVE_DIR=$DATA_SAVE_PATH/dpo/$model
rm -rf $SAVE_DIR/cache
rm -rf $SAVE_DIR/jsonl
rm -rf $SAVE_DIR/arrow
pretrain=$(get_pretrain $model)
conversation_template_config=$(get_conversation_template_config $model)
python $EXAMPLES_DIR/data_preparation_scripts/prepare_dataset.py --type preference --data_input_dirs $TEST_DATA_DIR/dpo \
--tokenizer_dir $pretrain \
--conversation_template_config $conversation_template_config \
--data_cache_dir $SAVE_DIR/cache \
--data_jsonl_output_dir $SAVE_DIR/jsonl \
--data_arrow_output_dir $SAVE_DIR/arrow
passed=$?
if [ $passed -ne 0 ]; then
echo "[Test]: Failed in the DPO/RM data templating for $model"
exit 1
fi
python $BASE_DIR/tests/verify_chat_data.py --data_source $TEST_DATA_DIR/dpo/test_dpo_data.jsonl \
--to_verify_file $SAVE_DIR/jsonl/part-00005.jsonl --data_type dpo
passed=$?
if [ $passed -ne 0 ]; then
echo "[Test]: Failed in the DPO/RM data templating test for $model"
exit 1
fi
done
# Test KTO data Preparation
for model in ${MODELS[@]}; do
echo "Testing KTO data templating for $model"
SAVE_DIR=$DATA_SAVE_PATH/kto/$model
rm -rf $SAVE_DIR/cache
rm -rf $SAVE_DIR/jsonl
rm -rf $SAVE_DIR/arrow
pretrain=$(get_pretrain $model)
conversation_template_config=$(get_conversation_template_config $model)
python $EXAMPLES_DIR/data_preparation_scripts/prepare_dataset.py --type kto --data_input_dirs $TEST_DATA_DIR/kto \
--tokenizer_dir $pretrain \
--conversation_template_config $conversation_template_config \
--data_cache_dir $SAVE_DIR/cache \
--data_jsonl_output_dir $SAVE_DIR/jsonl \
--data_arrow_output_dir $SAVE_DIR/arrow
passed=$?
if [ $passed -ne 0 ]; then
echo "[Test]: Failed in the KTO data templating for $model"
exit 1
fi
python $BASE_DIR/tests/verify_chat_data.py --data_source $TEST_DATA_DIR/kto/test_kto_data.jsonl \
--to_verify_file $SAVE_DIR/jsonl/part-00005.jsonl --data_type kto
passed=$?
if [ $passed -ne 0 ]; then
echo "[Test]: Failed in the KTO data templating test for $model"
exit 1
fi
done