refactor evaluation

applications/ColossalChat/benchmarks/benchmark_sft.sh

@@ -17,18 +17,28 @@ set_n_least_used_CUDA_VISIBLE_DEVICES 4
 
 PROJECT_NAME="sft"
 PARENT_CONFIG_FILE="./benchmark_config" # Path to a folder to save training config logs
-PRETRAINED_MODEL_PATH="" # huggingface or local model path
-PRETRAINED_TOKENIZER_PATH="" # huggingface or local tokenizer path
+PRETRAINED_MODEL_PATH="/root/commonData/Llama-2-7b-hf" # huggingface or local model path
+PRETRAINED_TOKENIZER_PATH="/root/commonData/Llama-2-7b-hf" # huggingface or local tokenizer path
+BENCHMARK_DATA_DIR="./temp/sft" # Path to benchmark data
+DATASET_SIZE=640
 
 TIMESTAMP=$(date +%Y-%m-%d-%H-%M-%S)
 FULL_PROJECT_NAME="${PROJECT_NAME}-${TIMESTAMP}"
 CONFIG_FILE="${PARENT_CONFIG_FILE}-${FULL_PROJECT_NAME}.json"
+declare -a dataset=(
+    $BENCHMARK_DATA_DIR/arrow/part-0
+)
+
+
+# Generate dummy test data
+python prepare_dummy_test_dataset.py --data_dir $BENCHMARK_DATA_DIR --dataset_size $DATASET_SIZE --max_length 2048 --data_type sft
+
 
 # the real batch size for gradient descent is number_of_node_in_hostfile * nproc_per_node * train_batch_size
-colossalai run --nproc_per_node 1 --master_port 31312 benchmark_sft.py \
+colossalai run --nproc_per_node 1 --master_port 31312 ../examples/training_scripts/train_sft.py \
     --pretrain $PRETRAINED_MODEL_PATH \
     --tokenizer_dir $PRETRAINED_TOKENIZER_PATH \
-    --config_file $CONFIG_FILE \
+    --dataset ${dataset[@]} \
     --plugin zero2 \
     --batch_size 8 \
     --max_epochs 1 \
@@ -36,6 +46,5 @@ colossalai run --nproc_per_node 1 --master_port 31312 benchmark_sft.py \
     --lr 5e-5 \
     --lora_rank 32 \
     --max_len 2048 \
-    --dataset_size 640 \
     --grad_checkpoint \
     --use_flash_attn