12440895e4
This PR introduces 2 tuning script for meta schedule and auto scheduler tuning support with onnx files. Now we can easily introduce onnx models benchmarking with command line scripts. Sample tuning call looks similar to the following script
For Meta Schedule ONNX tuning:
```
python3 -m tvm.meta_schedule.testing.tune_onnx_meta_schedule \
--model-name "$MODEL_NAME" \
--onnx-path "$ONNX_PATH" \
--input-shape "$INPUT_SHAPE" \
--target "$TARGET" \
--num-trials $NUM_TRIALS \
--rpc-host $RPC_HOST \
--rpc-port $RPC_PORT \
--rpc-key $RPC_KEY \
--rpc-workers $RPC_WORKERS \
--work-dir $WORK_DIR \
|& tee "$WORK_DIR/$MODEL_NAME.log"
```
For AutoScheduler ONNX tuning:
```
python3 -m tvm.meta_schedule.testing.tune_onnx_auto_scheduler \
--model-name "$MODEL_NAME" \
--onnx-path "$ONNX_PATH" \
--input-shape "$INPUT_SHAPE" \
--target "$TARGET" \
--num-trials $NUM_TRIALS \
--rpc-host $RPC_HOST \
--rpc-port $RPC_PORT \
--rpc-key $RPC_KEY \
--rpc-workers $RPC_WORKERS \
--log-dir $WORK_DIR \
|& tee "$WORK_DIR/$MODEL_NAME.log"
```