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[MetaSchedule] Add Testing Script with ONNX Support (apache#11587)
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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"
```
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@zxybazh @Kathryn-cat
zxybazh authored and Kathryn-cat committed Jun 10, 2022
1 parent 0f1855b commit 0c3aaf7
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238 changes: 238 additions & 0 deletions python/tvm/meta_schedule/testing/tune_onnx_auto_scheduler.py
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# 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()
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