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ColossalAI/applications/ColossalChat/tests/test_train.sh

398 lines
14 KiB

#!/usr/bin/env bash
set_n_least_used_CUDA_VISIBLE_DEVICES() {
local n=${1:-"9999"}
echo "GPU Memory Usage:"
local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv |
tail -n +2 |
nl -v 0 |
tee /dev/tty |
sort -g -k 2 |
awk '{print $1}' |
head -n $n)
export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g')
echo "Now CUDA_VISIBLE_DEVICES is set to:"
echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"
}
set_n_least_used_CUDA_VISIBLE_DEVICES 4
set -xu
NUM_RETRY=3
BASE_DIR=$(dirname $(dirname $(realpath $BASH_SOURCE)))
EXAMPLES_DIR=$BASE_DIR/examples
CONFIG_DIR=$BASE_DIR/config
TEMP_DIR=$BASE_DIR/temp
TEST_DIR=$BASE_DIR/tests
MODEL_SAVE_PATH=$TEMP_DIR/rlhf_models
MODELS_DIR=$TEMP_DIR/models_config
# Skip those tests due to CI tests timeout
MODELS=('llama')
PLUGINS=('gemini' 'gemini_auto' 'zero2' 'zero2_cpu' '3d')
LORA_RANK=('0') # skip to reduce CI execution time, can pass all locally
export OMP_NUM_THREADS=8
get_pretrain() {
local model=$1
if [[ $model == "llama" ]]; then
echo "nickypro/tinyllama-110M"
elif [[ $model == "opt" ]]; then
echo "facebook/opt-125m"
else
echo "Unknown model $model"
exit 1
fi
}
get_tokenizer_dirs() {
local model=$1
if [[ $model == "llama" ]]; then
echo "hf-internal-testing/llama-tokenizer"
elif [[ $model == "opt" ]]; then
echo "facebook/opt-125m"
else
echo "Unknown model $model"
exit 1
fi
}
get_conversation_template_config() {
local model=$1
if [[ $model == "llama" ]]; then
echo "$TEST_DIR/llama.json"
elif [[ $model == "opt" ]]; then
echo "$TEST_DIR/opt.json"
else
echo "Unknown model $model"
exit 1
fi
}
random_choice() {
local arr=("$@")
local len=${#arr[@]}
local idx=$((RANDOM % len))
echo ${arr[$idx]}
}
echo "[Test]: testing sft ..."
SKIPPED_TESTS=(
llama-3d-20 # 3d plugin doesn't support lora
llama-gemini_auto-20 # gemini_auto plugin doesn't support lora
llama-gemini-20 # gemini doesn't support lora
)
GRAD_CKPTS=('--grad_checkpoint')
for lora_rank in ${LORA_RANK[@]}; do
for model in ${MODELS[@]}; do
for plugin in ${PLUGINS[@]}; do
if [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$plugin-$lora_rank " ]]; then
echo "[Test]: Skipped $model-$plugin-$lora_rank"
continue
elif [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$plugin " ]]; then
echo "[Test]: Skipped $model-$plugin"
continue
fi
pretrain=$(get_pretrain $model)
tokenizer_dir=$(get_tokenizer_dirs $model)
grad_ckpt=$(random_choice "${GRAD_CKPTS[@]}")
tp='1'
bs='2'
if [[ $plugin == "3d" ]]; then
tp='4'
bs='8'
fi
grad_accu='2'
# Check if the plugin is either "gemini_auto" or "gemini" and set grad_accu to '1'
if [[ $plugin == "gemini_auto" ]]; then
grad_accu='1'
fi
for i in $(seq $NUM_RETRY); do
echo "[Test]: $model-$plugin-$lora_rank, attempt $i"
declare -a dataset=()
for split in $(seq -f "%05g" 0 0); do
dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_sft/arrow/part-$split")
done
colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_sft.py \
--pretrain $pretrain \
--tokenizer_dir $tokenizer_dir \
--dataset ${dataset[@]} \
--save_path $MODEL_SAVE_PATH \
--config_file $MODELS_DIR/config.jsonl \
--lora_rank $lora_rank \
--plugin $plugin \
--batch_size $bs \
--max_epochs 1 \
--accumulation_steps $grad_accu \
--tp $tp \
--lr 2e-5 \
$grad_ckpt \
--max_len 400 \
--use_flash_attn
passed=$?
if [ $passed -eq 0 ]; then
rm -rf $MODEL_SAVE_PATH/*
rm -rf $MODELS_DIR/*
break
fi
done
if [ $passed -ne 0 ]; then
echo "[Test]: Failed $model-$plugin-$lora_rank"
exit 1
fi
done
done
done
echo "[Test]: testing reward model ..."
SKIPPED_TESTS=(
llama-3d-20 # 3d plugin doesn't support lora
llama-gemini_auto-20 # gemini_auto plugin doesn't support lora
llama-gemini-20 # gemini doesn't support lora
)
GRAD_CKPTS=('--grad_checkpoint')
for lora_rank in ${LORA_RANK[@]}; do
for model in ${MODELS[@]}; do
for plugin in ${PLUGINS[@]}; do
if [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$plugin-$lora_rank " ]]; then
echo "[Test]: Skipped $model-$plugin-$lora_rank"
continue
elif [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$plugin " ]]; then
echo "[Test]: Skipped $model-$plugin"
continue
fi
pretrain=$(get_pretrain $model)
tokenizer_dir=$(get_tokenizer_dirs $model)
grad_ckpt=$(random_choice "${GRAD_CKPTS[@]}")
tp='1'
bs='2'
if [[ $plugin == "3d" ]]; then
tp='4'
bs='8'
fi
grad_accu='2'
# gemini_auto and gemini doesn't support gradient accumulation
if [[ $plugin == "gemini_auto" ]]; then
grad_accu='1'
fi
for i in $(seq $NUM_RETRY); do
echo "[Test]: $model-$plugin-$lora_rank, attempt $i"
declare -a dataset=()
for split in $(seq -f "%05g" 0 0); do
dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_preference/arrow/part-$split")
done
colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_rm.py \
--pretrain $pretrain \
--tokenizer_dir $tokenizer_dir \
--dataset ${dataset[@]} \
--save_dir $MODEL_SAVE_PATH \
--config_file $MODELS_DIR/config.jsonl \
--lora_rank $lora_rank \
--plugin $plugin \
--batch_size $bs \
--max_epochs 1 \
--accumulation_steps $grad_accu \
--tp $tp \
--lr 2e-5 \
$grad_ckpt \
--max_len 400 \
--use_flash_attn
passed=$?
if [ $passed -eq 0 ]; then
rm -rf $MODEL_SAVE_PATH/*
rm -rf $MODELS_DIR/*
break
fi
done
if [ $passed -ne 0 ]; then
echo "[Test]: Failed $model-$plugin-$lora_rank"
exit 1
fi
done
done
done
echo "[Test]: testing ppo ..."
SKIPPED_TESTS=(
llama-3d-20 # 3d plugin doesn't support lora
llama-gemini-20 # gemini doesn't support lora
)
GRAD_CKPTS=('--grad_checkpoint')
for lora_rank in ${LORA_RANK[@]}; do
for model in ${MODELS[@]}; do
for plugin in ${PLUGINS[@]}; do
if [[ $plugin == "gemini_auto" ]]; then
echo "[Test]: Skipped $model-$plugin"
continue # gemini_auto plugin doesn't support generation
fi
if [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$plugin-$lora_rank " ]]; then
echo "[Test]: Skipped $model-$plugin-$lora_rank"
continue
elif [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$plugin " ]]; then
echo "[Test]: Skipped $model-$plugin"
continue
fi
pretrain=$(get_pretrain $model)
tokenizer_dir=$(get_tokenizer_dirs $model)
grad_ckpt=$(random_choice "${GRAD_CKPTS[@]}")
tp='1'
bs='4'
ebs='8'
conversation_template=$(get_conversation_template_config $model)
if [[ $plugin == "3d" ]]; then
tp='4'
bs='16'
ebs='32'
fi
grad_accu='2'
# gemini_auto and gemini doesn't support gradient accumulation
if [[ $plugin == "gemini_auto" ]]; then
grad_accu='1'
fi
# gemini_auto and gemini doesn't support generation
if [[ $plugin == "gemini_auto" ]]; then
# gemini-auto doesn't support generation
echo "[Test]: Skipped $model-$plugin"
continue
fi
for i in $(seq $NUM_RETRY); do
echo "[Test]: $model-$plugin-$lora_rank, attempt $i"
declare -a prompt_dataset=()
for split in $(seq -f "%05g" 0 0); do
prompt_dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_prompt/arrow/part-$split")
done
declare -a ptx_dataset=()
for split in $(seq -f "%05g" 0 0); do
ptx_dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_sft/arrow/part-$split")
done
colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_ppo.py \
--pretrain $pretrain \
--rm_pretrain $pretrain \
--tokenizer_dir $tokenizer_dir \
--conversation_template_config $conversation_template \
--prompt_dataset ${prompt_dataset[@]} \
--ptx_dataset ${ptx_dataset[@]} \
--ptx_batch_size 1 \
--ptx_coef 0.2 \
--save_path $MODEL_SAVE_PATH \
--lora_rank $lora_rank \
--plugin $plugin \
--num_episodes 5 \
--num_collect_steps 1 \
--num_update_steps 1 \
--experience_batch_size $ebs \
--train_batch_size $bs \
--accumulation_steps $grad_accu \
--lr 9e-6 \
--mixed_precision "bf16" \
--grad_clip 1.0 \
--tp $tp \
--lr 2e-5 \
$grad_ckpt \
--max_len 400 \
--max_seq_len 10 \
--use_flash_attn
passed=$?
if [ $passed -eq 0 ]; then
rm -rf $MODEL_SAVE_PATH/*
rm -rf $MODELS_DIR/*
break
fi
done
if [ $passed -ne 0 ]; then
echo "[Test]: Failed $model-$plugin-$lora_rank"
exit 1
fi
done
done
done
echo "[Test]: testing DPO ..."
SKIPPED_TESTS=(
llama-3d-20 # 3d plugin doesn't support lora
llama-gemini_auto-20 # gemini_auto plugin doesn't support lora
llama-gemini-20 # gemini doesn't support lora
)
GRAD_CKPTS=('--grad_checkpoint')
for lora_rank in ${LORA_RANK[@]}; do
for model in ${MODELS[@]}; do
for plugin in ${PLUGINS[@]}; do
if [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$plugin-$lora_rank " ]]; then
echo "[Test]: Skipped $model-$plugin-$lora_rank"
continue
elif [[ " ${SKIPPED_TESTS[*]} " =~ " $model-$plugin " ]]; then
echo "[Test]: Skipped $model-$plugin"
continue
fi
pretrain=$(get_pretrain $model)
tokenizer_dir=$(get_tokenizer_dirs $model)
grad_ckpt=$(random_choice "${GRAD_CKPTS[@]}")
tp='1'
bs='2'
if [[ $plugin == "3d" ]]; then
tp='4'
bs='8'
fi
grad_accu='2'
# gemini_auto and gemini doesn't support gradient accumulation
if [[ $plugin == "gemini_auto" ]]; then
grad_accu='1'
fi
# gemini_auto doesn't support generation
# (need to calculate ref_model logits through forwarding in inference mode)
if [[ $plugin == "gemini_auto" ]]; then
echo "[Test]: Skipped $model-$plugin"
continue
fi
for i in $(seq $NUM_RETRY); do
echo "[Test]: $model-$plugin-$lora_rank, attempt $i"
declare -a dataset=()
for split in $(seq -f "%05g" 0 0); do
dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_preference/arrow/part-$split")
done
colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_dpo.py \
--pretrain $pretrain \
--tokenizer_dir $tokenizer_dir \
--dataset ${dataset[@]} \
--save_dir $MODEL_SAVE_PATH \
--config_file $MODELS_DIR/config.jsonl \
--lora_rank $lora_rank \
--plugin $plugin \
--batch_size $bs \
--max_epochs 1 \
--accumulation_steps $grad_accu \
--tp $tp \
--lr 2e-5 \
$grad_ckpt \
--max_len 400 \
--use_flash_attn
passed=$?
if [ $passed -eq 0 ]; then
rm -rf $MODEL_SAVE_PATH/*
rm -rf $MODELS_DIR/*
break
fi
done
if [ $passed -ne 0 ]; then
echo "[Test]: Failed $model-$plugin-$lora_rank"
exit 1
fi
done
done
done