mehrdadh commented on code in PR #13514: URL: https://github.com/apache/tvm/pull/13514#discussion_r1065044105
########## tests/python/unittest/test_micro_ms_tuning.py: ########## @@ -0,0 +1,126 @@ +# 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. +import numpy as np +import pytest +from types import MappingProxyType +import pathlib +import json +from tests.micro.zephyr.test_ms_tuning import create_relay_module +import tvm +from tvm import relay +from tvm.relay.backend import Executor +from tvm.contrib import graph_executor, utils +from tvm import meta_schedule as ms +from tvm.contrib.micro.meta_schedule.local_builder_micro import get_local_builder_micro +from tvm.contrib.micro.meta_schedule.rpc_runner_micro import get_rpc_runner_micro + + +def test_micro_tuning_with_meta_schedule(): + platform = "crt" + target = tvm.target.target.micro(model="host") + options = {} + + work_dir = utils.tempdir() + mod, params, model_info = create_relay_module() + input_name = model_info["in_tensor"] + input_shape = model_info["in_shape"] + input_dtype = model_info["in_dtype"] + data_sample = np.random.rand(*input_shape).astype(input_dtype) + + runtime = relay.backend.Runtime("crt", {"system-lib": True}) + executor = Executor("aot", {"link-params": True}) + # This line is necessary for link-params to take effect during + # task extraction and relay.build(...). + mod = mod.with_attr("executor", executor) + + builder = get_local_builder_micro() + + with ms.Profiler() as profiler: + with get_rpc_runner_micro( + platform=platform, options=options, session_timeout_sec=120 + ) as runner: + db: ms.Database = ms.relay_integration.tune_relay( + mod=mod, + params=params, + target=target, + builder=builder, + runner=runner, + strategy="evolutionary", + num_trials_per_iter=2, + max_trials_per_task=10, + max_trials_global=100, + work_dir=str(work_dir), + module_equality="ignore-ndarray", + ) + + # Build model using meta_schedule logs + ms_mod: tvm.runtime.Module = ms.relay_integration.compile_relay( + database=db, + mod=mod, + target=target, + params=params, + pass_config=MappingProxyType( + { + "relay.backend.use_meta_schedule": True, + "relay.backend.tir_converter": "default", + "tir.disable_vectorize": True, + } + ), + executor=executor, + runtime=runtime, + ) + print(profiler.table()) + + project = tvm.micro.generate_project( + str(tvm.micro.get_microtvm_template_projects(platform)), + ms_mod, + str(work_dir / "project"), + options=options, + ) + project.build() + project.flash() + with tvm.micro.Session(project.transport()) as session: + aot_executor = tvm.runtime.executor.aot_executor.AotModule(session.create_aot_executor()) + aot_executor.get_input(0).copyfrom(data_sample) + result = aot_executor.module.time_evaluator("run", session.device, number=3)() + output = aot_executor.get_output(0).numpy() + + # Build reference model (without tuning) + dev = tvm.cpu() + target = tvm.target.target.micro(model="host") + with tvm.transform.PassContext( + opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["AlterOpLayout"] + ): + ref_mod = relay.build( + mod, + target=target, + params=params, + runtime=runtime, + ) + ref_mod.export_library(work_dir / "compiled_lib2.so") + mod2: tvm.runtime.Module = tvm.runtime.load_module(work_dir / "compiled_lib2.so") + graph_mod = graph_executor.GraphModule(mod2["default"](dev)) + graph_mod.set_input(input_name, data_sample) + graph_mod.run() + ref_output = graph_mod.get_output(0).numpy() + + assert np.allclose(output, ref_output, rtol=1e-4, atol=2e-4), "FAILED" + work_dir.remove() + + +if __name__ == "__main__": + test_micro_tuning_with_meta_schedule() Review Comment: nit: `tvm.testing.main()` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
