[MetaSchedule] Add Testing Script with ONNX Support (#11587)

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"
```

Author: zxybazh

python/tvm/meta_schedule/testing/tune_onnx_auto_scheduler.py

@@ -0,0 +1,238 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=missing-docstring
+import argparse
+import json
+import os
+
+import numpy as np  # type: ignore
+import onnx  # type: ignore
+import tvm
+from tvm.relay.frontend import from_onnx
+from tvm import auto_scheduler
+from tvm import meta_schedule as ms
+from tvm import relay
+from tvm.meta_schedule.testing.custom_builder_runner import run_module_via_rpc
+
+
+def _parse_args():
+    args = argparse.ArgumentParser()
+    args.add_argument(
+        "--model-name",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--onnx-path",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--input-shape",
+        type=str,
+        required=True,
+        help='example: `[{"name": "input1", "dtype": "int64", "shape": [1, 1, 8]}]',
+    )
+    args.add_argument(
+        "--target",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--num-trials",
+        type=int,
+        required=True,
+    )
+    args.add_argument(
+        "--rpc-host",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--rpc-port",
+        type=int,
+        required=True,
+    )
+    args.add_argument(
+        "--rpc-key",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--rpc-workers",
+        type=int,
+        required=True,
+    )
+    args.add_argument(
+        "--work-dir",
+        type=str,
+        required=True,
+    )
+    parsed = args.parse_args()
+    parsed.target = tvm.target.Target(parsed.target)
+    parsed.input_shape = json.loads(parsed.input_shape)
+    parsed.rpc_config = ms.runner.RPCConfig(
+        tracker_host=parsed.rpc_host,
+        tracker_port=parsed.rpc_port,
+        tracker_key=parsed.rpc_key,
+        session_timeout_sec=3600,
+    )
+    return parsed
+
+
+ARGS = _parse_args()
+
+
+def main():
+    log_file = os.path.join(ARGS.work_dir, f"{ARGS.model_name}.json")
+
+    runner = auto_scheduler.RPCRunner(
+        key=ARGS.rpc_key,
+        host=ARGS.rpc_host,
+        port=ARGS.rpc_port,
+        n_parallel=ARGS.rpc_workers,
+        number=3,
+        repeat=1,
+        min_repeat_ms=100,  # TODO
+        enable_cpu_cache_flush=False,  # TODO
+    )
+
+    if ARGS.target.kind.name == "llvm":
+        hardware_params = auto_scheduler.HardwareParams(
+            num_cores=int(ARGS.target.attrs["num-cores"]),
+            target=ARGS.target,
+        )
+    elif ARGS.target.kind.name == "cuda":
+        hardware_params = auto_scheduler.HardwareParams(
+            num_cores=-1,
+            vector_unit_bytes=16,
+            cache_line_bytes=64,
+            max_shared_memory_per_block=int(ARGS.target.attrs["max_shared_memory_per_block"]),
+            max_threads_per_block=int(ARGS.target.attrs["max_threads_per_block"]),
+            # The value `max_local_memory_per_block` is not used in AutoScheduler,
+            # but is required by the API.
+            max_local_memory_per_block=12345678,
+            max_vthread_extent=8,
+            warp_size=32,
+        )
+    else:
+        raise NotImplementedError(f"Unsupported target {ARGS.target}")
+
+    print(f"Workload: {ARGS.model_name}")
+    onnx_model = onnx.load(ARGS.onnx_path)
+    shape_dict = {}
+    for item in ARGS.input_shape:
+        print(f"  input_name: {item['name']}")
+        print(f"  input_shape: {item['shape']}")
+        print(f"  input_dtype: {item['dtype']}")
+        shape_dict[item["name"]] = item["shape"]
+    mod, params = from_onnx(onnx_model, shape_dict, freeze_params=True)
+    tasks, task_weights = auto_scheduler.extract_tasks(
+        mod["main"],
+        params,
+        target=ARGS.target,
+        hardware_params=hardware_params,
+    )
+    for idx, (task, task_weight) in enumerate(zip(tasks, task_weights)):
+        print(f"==== Task {idx}: {task.desc} (weight {task_weight} key: {task.workload_key}) =====")
+        print(task.compute_dag)
+
+    tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
+    tuner.tune(
+        auto_scheduler.TuningOptions(
+            num_measure_trials=ARGS.num_trials,
+            runner=runner,
+            measure_callbacks=[
+                auto_scheduler.RecordToFile(log_file),
+            ],
+        )
+    )
+
+    with auto_scheduler.ApplyHistoryBest(log_file):
+        with tvm.transform.PassContext(
+            opt_level=3,
+            config={"relay.backend.use_auto_scheduler": True},
+        ):
+            lib = relay.build(
+                mod,
+                target=ARGS.target,
+                params=params,
+            )
+    graph, rt_mod, params = lib.graph_json, lib.lib, lib.params
+    input_data = {}
+    for item in ARGS.input_shape:
+        input_name, input_shape, input_dtype = item["name"], item["shape"], item["dtype"]
+        if input_dtype.startswith("float"):
+            input_data[input_name] = np.random.uniform(size=input_shape).astype(input_dtype)
+        else:
+            input_data[input_name] = np.random.randint(
+                low=0, high=10000, size=input_shape, dtype=input_dtype
+            )
+
+    def f_timer(rt_mod, dev, input_data):
+        # pylint: disable=import-outside-toplevel
+        from tvm.contrib.graph_executor import GraphModule
+
+        # pylint: enable=import-outside-toplevel
+
+        mod = GraphModule(rt_mod["default"](dev))
+        for input_name, input_value in input_data.items():
+            mod.set_input(input_name, input_value)
+        ftimer = mod.module.time_evaluator(
+            "run",
+            dev,
+            min_repeat_ms=500,
+            repeat=3,
+        )
+        results = list(np.array(ftimer().results) * 1000.0)  # type: ignore
+        print("Running time in time_evaluator: ", results)
+
+    run_module_via_rpc(
+        rpc_config=ARGS.rpc_config,
+        lib=lib,
+        dev_type=ARGS.target.kind.name,
+        args=input_data,
+        continuation=f_timer,
+    )
+
+    def f_per_layer(rt_mod, dev, input_data):
+        # pylint: disable=import-outside-toplevel
+        from tvm.contrib.debugger.debug_executor import create
+
+        # pylint: enable=import-outside-toplevel
+        mod = create(graph, rt_mod, dev)
+        for input_name, input_value in input_data.items():
+            mod.set_input(input_name, input_value)
+        graph_nodes = [n["name"] for n in json.loads(graph)["nodes"]]
+        graph_time = mod.run_individual(number=10, repeat=1, min_repeat_ms=5000)
+        print("|graph_nodes| = ", len(graph_nodes))
+        print("|graph_time| = ", len(graph_time))
+        graph_nodes_time = {k: float(v) for k, v in zip(graph_nodes, graph_time)}
+        for k, v in graph_nodes_time.items():
+            print(f"{k} : {v:.3f}")
+
+    run_module_via_rpc(
+        rpc_config=ARGS.rpc_config,
+        lib=rt_mod,
+        dev_type=ARGS.target.kind.name,
+        args=input_data,
+        continuation=f_per_layer,
+    )
+
+
+if __name__ == "__main__":
+    main()