diff --git a/python/tvm/autotvm/testing/__init__.py b/python/tvm/autotvm/testing/__init__.py new file mode 100644 index 0000000000000..972d0cbaae5c0 --- /dev/null +++ b/python/tvm/autotvm/testing/__init__.py @@ -0,0 +1,17 @@ +# 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. +"""Testing utilities for autotvm""" diff --git a/python/tvm/autotvm/testing/tune_relay.py b/python/tvm/autotvm/testing/tune_relay.py new file mode 100644 index 0000000000000..d65d0d50eb500 --- /dev/null +++ b/python/tvm/autotvm/testing/tune_relay.py @@ -0,0 +1,245 @@ +# 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 +from distutils.util import strtobool + +import tvm +from tvm import autotvm +from tvm import meta_schedule as ms +from tvm import relay +from tvm.autotvm.graph_tuner import DPTuner +from tvm.autotvm.tuner import XGBTuner +from tvm.meta_schedule.testing.custom_builder_runner import run_module_via_rpc +from tvm.meta_schedule.testing.relay_workload import get_network +from tvm.meta_schedule.testing.tune_utils import create_timer, generate_input_data +from tvm.support import describe + + +def _parse_args(): + args = argparse.ArgumentParser() + args.add_argument( + "--workload", + type=str, + required=True, + ) + args.add_argument( + "--input-shape", + type=str, + required=True, + ) + 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( + "--work-dir", + type=str, + required=True, + ) + args.add_argument( + "--layout", + type=str, + default=None, + ) + args.add_argument( + "--cache-dir", + type=str, + default=None, + ) + args.add_argument( + "--number", + type=int, + default=3, + ) + args.add_argument( + "--repeat", + type=int, + default=1, + ) + args.add_argument( + "--min-repeat-ms", + type=int, + default=100, + ) + args.add_argument( + "--cpu-flush", + type=lambda x: bool(strtobool(x)), + help="example: True / False", + required=True, + ) + args.add_argument( + "--graph-tuner", + type=lambda x: bool(strtobool(x)), + help="example: True / False", + required=True, + ) + args.add_argument( + "--backend", + type=str, + choices=["graph", "vm"], + help="example: graph / vm", + 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=600, + ) + return parsed + + +ARGS = _parse_args() + + +def main(): + log_file = os.path.join(ARGS.work_dir, f"{ARGS.workload}.json") + graph_opt_sch_file = os.path.join(ARGS.work_dir, f"{ARGS.workload}_graph_opt.log") + measure_option = autotvm.measure_option( + builder=autotvm.LocalBuilder(), + runner=autotvm.RPCRunner( + key=ARGS.rpc_key, + host=ARGS.rpc_host, + port=ARGS.rpc_port, + number=ARGS.number, + repeat=ARGS.repeat, + min_repeat_ms=ARGS.min_repeat_ms, + enable_cpu_cache_flush=ARGS.cpu_flush, + ), + ) + describe() + print(f"Workload: {ARGS.workload}") + mod, params, (input_name, input_shape, input_dtype) = get_network( + ARGS.workload, + ARGS.input_shape, + layout=ARGS.layout, + cache_dir=ARGS.cache_dir, + ) + input_info = [ + { + "name": input_name, + "shape": input_shape, + "dtype": input_dtype, + }, + ] + input_data = { + item["name"]: generate_input_data(item["shape"], item["dtype"]) for item in input_info + } + for item in input_info: + print(f" input_name : {item['name']}") + print(f" input_shape: {item['shape']}") + print(f" input_dtype: {item['dtype']}") + + with ms.Profiler() as profiler: + with ms.Profiler.timeit("TaskExtraction"): + # extract workloads from relay program + tasks = autotvm.task.extract_from_program( + mod["main"], + target=ARGS.target, + params=params, + ops=( + relay.op.get("nn.conv2d"), + relay.op.get("nn.conv3d"), + relay.op.get("nn.conv2d_transpose"), + relay.op.get("nn.dense"), + relay.op.get("nn.batch_matmul"), + ), + ) + for i, task in enumerate(tasks): + print(f"Task {i} {task.name}: {task}") + + with ms.Profiler.timeit("Tuning"): + if ARGS.num_trials > 0: + for i, task in enumerate(tasks): + prefix = "[Task %2d/%2d] " % (i + 1, len(tasks)) + tuner_obj = XGBTuner(task, loss_type="rank") + n_trial = min(len(task.config_space), ARGS.num_trials) + tuner_obj.tune( + n_trial=n_trial, + early_stopping=800, + measure_option=measure_option, + callbacks=[ + autotvm.callback.progress_bar(n_trial, prefix=prefix), + autotvm.callback.log_to_file(log_file), + ], + ) + if ARGS.graph_tuner: + executor = DPTuner( + graph=mod["main"], + input_shapes={input_name: input_shape}, + records=log_file, + target_ops=[ + relay.op.get("nn.conv2d"), + ], + target=ARGS.target, + ) + executor.benchmark_layout_transform(min_exec_num=1000) + executor.run() + executor.write_opt_sch2record_file(graph_opt_sch_file) + + relay_build = {"graph": relay.build, "vm": relay.vm.compile}[ARGS.backend] + with ms.Profiler.timeit("PostTuningCompilation"): + if ARGS.graph_tuner: + ctx = autotvm.apply_graph_best(graph_opt_sch_file) + else: + ctx = autotvm.apply_history_best(log_file) + with ctx: + print("compile...") + with tvm.transform.PassContext(opt_level=3): + lib = relay_build(mod, target=ARGS.target, params=params) + print("Tuning Time:") + print(profiler.table()) + + run_module_via_rpc( + rpc_config=ARGS.rpc_config, + lib=lib, + dev_type=ARGS.target.kind.name, + args=input_data, + continuation=create_timer(ARGS.backend), + backend=ARGS.backend, + ) + + +if __name__ == "__main__": + main()