[Benchmarking-Py] Huggingface Scripts run_all.sh cleanup and chmod +x executed on all scripts

Author: DEKHTIARJonathan

tftrt/benchmarking-python/huggingface/gpt2/download_all_models.sh

@@ -12,11 +12,7 @@ ELECTRA_MODELS=(
   "electra_large"
 )
 
-MODEL_DIR="/models/tf_hub/electra"
-DATA_DIR="/workspace/tensorflow_tensorrt/tftrt/benchmarking-python/tf_hub/electra/data"
-TOKENIZER_DIR="/models/tf_hub/electra/tokenizer"
-
-RUN_ARGS="--input_saved_model_dir=${MODEL_DIR} --data_dir=${DATA_DIR} --tokenizer_dir=${TOKENIZER_DIR} --debug --batch_size=32 --display_every=1 --use_synthetic_data --num_iterations=1000"
+RUN_ARGS="--input_saved_model_dir=/models/tf_hub/electra --data_dir=${BASE_DIR}/data --tokenizer_dir=/models/tf_hub/electra/tokenizer --debug --batch_size=32 --display_every=1 --use_synthetic_data --num_iterations=1000"
 TF_TRT_ARGS="--use_tftrt --use_dynamic_shape --num_calib_batches=10"
 TF_XLA_ARGS="--use_xla_auto_jit"
 

tftrt/benchmarking-python/tf_hub/electra/download_models.sh

@@ -17,11 +17,7 @@ MOVINET_MODELS=(
     "a5"
 )
 
-DATA_DIR="/tmp"
-INPUT_SAVED_MODEL_DIR="/models/tf_hub/movinet"
-BATCH_SIZE=32
-
-RUN_ARGS="--data_dir=${DATA_DIR} --batch_size=${BATCH_SIZE} --display_every=50"
+BASE_RUN_ARGS="--input_saved_model_dir=/models/tf_hub/movinet --data_dir=/tmp --batch_size=32 --display_every=50"
 TF_TRT_ARGS="--use_tftrt --use_dynamic_shape --num_calib_batches=10"
 TF_XLA_ARGS="--use_xla_auto_jit"
 
@@ -30,8 +26,6 @@ export TF_TRT_SHOW_DETAILED_REPORT=1
 for model_name in "${MOVINET_MODELS[@]}"; do
     echo "Processing Model: ${model_name} ..."
 
-    RUN_ARGS="${RUN_ARGS} --input_saved_model_dir=${INPUT_SAVED_MODEL_DIR}"
-
     MODEL_DATA_EXPORT_DIR="${BASE_BENCHMARK_DATA_EXPORT_DIR}/movinet_${model_name}"
     mkdir -p ${MODEL_DATA_EXPORT_DIR}
     echo "Running "${BASE_DIR}/models/${model_name}/run_inference.sh" for the set of following benchmarks. "

tftrt/benchmarking-python/tf_hub/electra/run_all.sh

@@ -6,19 +6,12 @@ BASE_BENCHMARK_DATA_EXPORT_DIR="${BASE_DIR}/benchmark_data"
 rm -rf ${BASE_BENCHMARK_DATA_EXPORT_DIR}
 mkdir -p ${BASE_BENCHMARK_DATA_EXPORT_DIR}
 
-# Default Argument Values
-SAMPLES_PER_INPUT="128"
-BYPASS_ARGUMENTS=""
-MODEL_DIR="/models/tf_hub/spice"
-DATA_DIR="/tmp/"
-NUM_ITERATIONS="1000"
-
 # List of models. spice is a single implementation
 MODELS=(
     "spice"
 )
 
-RUN_ARGS="--data_dir=${DATA_DIR} --input_saved_model_dir=${MODEL_DIR} --display_every=50 --samples_per_input=${SAMPLES_PER_INPUT} --num_iterations=${NUM_ITERATIONS}"
+RUN_ARGS="--data_dir=/tmp/ --input_saved_model_dir=/models/tf_hub/spice --display_every=50 --samples_per_input=128 --num_iterations=1000"
 TF_TRT_ARGS="--use_tftrt --use_dynamic_shape --num_calib_batches=10"
 TF_XLA_ARGS="--use_xla_auto_jit"
 
@@ -32,7 +25,7 @@ for model_name in "${MODELS[@]}"; do
 
     # ============================ TF NATIVE ============================ #
     # TF Native - FP32
-    echo "Running ${BASE_DIR}/run_inference.sh"  
+    echo "Running ${BASE_DIR}/run_inference.sh"
     script -q -c "${BASE_DIR}/run_inference.sh ${RUN_ARGS} --precision=FP32" /dev/null | tee ${MODEL_DATA_EXPORT_DIR}/inference_tf_native_fp32.log
 
     # TF Native - FP16

tftrt/benchmarking-python/tf_hub/movinet/base_run_inference.sh

@@ -18,10 +18,7 @@ SWIN_MODELS=(
   "swin_large_patch4_window12_384"
 )
 
-MODEL_DIR="/models/tf_hub/swin_transformers"
-DATA_DIR="/data/imagenet"
-
-RUN_ARGS="--input_saved_model_dir=${MODEL_DIR} --data_dir=${DATA_DIR} --debug --batch_size=32 --display_every=1 --use_synthetic_data --num_iterations=1000"
+RUN_ARGS="--input_saved_model_dir=/models/tf_hub/swin_transformers --data_dir=/data/imagenet --debug --batch_size=32 --display_every=1 --use_synthetic_data --num_iterations=1000"
 TF_TRT_ARGS="--use_tftrt --use_dynamic_shape --num_calib_batches=10"
 TF_XLA_ARGS="--use_xla_auto_jit"
 

tftrt/benchmarking-python/tf_hub/movinet/run_all.sh

@@ -17,10 +17,7 @@ vit_MODELS=(
   "vit_r50_l32_classification"
 )
 
-MODEL_DIR="/models/tf_hub/vision_transformers"
-DATA_DIR="/data/imagenet"
-
-RUN_ARGS="--input_saved_model_dir=${MODEL_DIR} --data_dir=${DATA_DIR} --debug --batch_size=32 --display_every=1 --use_synthetic_data --num_iterations=1000"
+RUN_ARGS="--input_saved_model_dir=/models/tf_hub/vision_transformers --data_dir=/data/imagenet --debug --batch_size=32 --display_every=1 --use_synthetic_data --num_iterations=1000"
 TF_TRT_ARGS="--use_tftrt --use_dynamic_shape --num_calib_batches=10"
 TF_XLA_ARGS="--use_xla_auto_jit"
 

tftrt/benchmarking-python/tf_hub/spice/run_all.sh

@@ -6,18 +6,12 @@ BASE_BENCHMARK_DATA_EXPORT_DIR="${BASE_DIR}/benchmark_data"
 rm -rf ${BASE_BENCHMARK_DATA_EXPORT_DIR}
 mkdir -p ${BASE_BENCHMARK_DATA_EXPORT_DIR}
 
-# Default Argument Values
-BYPASS_ARGUMENTS=""
-MODEL_DIR="/models/tf_hub/YAMNet"
-DATA_DIR="/tmp/"
-FRAME_LENGTH="1"
-
 # List of models. YAMNet is a single implementation
 MODELS=(
     "YAMNet"
 )
 
-RUN_ARGS="--data_dir=${DATA_DIR} --input_saved_model_dir=${MODEL_DIR} --display_every=50 --frame_length=${FRAME_LENGTH}"
+RUN_ARGS="--data_dir=/tmp/ --input_saved_model_dir=/models/tf_hub/YAMNet --display_every=50 --frame_length=1"
 TF_TRT_ARGS="--use_tftrt --use_dynamic_shape --num_calib_batches=10"
 TF_XLA_ARGS="--use_xla_auto_jit"