zxybazh commented on a change in pull request #10079: URL: https://github.com/apache/tvm/pull/10079#discussion_r796106818
########## File path: tests/python/unittest/test_meta_schedule_tune_relay.py ########## @@ -0,0 +1,151 @@ +# 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 logging +import tempfile +import pytest +import numpy as np +from typing import Tuple, List + +import tvm +from tvm import relay +from tvm.ir import IRModule +from tvm.runtime.ndarray import cpu, cuda +from tvm.target.target import Target +from tvm.contrib import graph_executor +from tvm.meta_schedule import ReplayTraceConfig +from tvm.meta_schedule.database import PyDatabase, Workload, TuningRecord +from tvm.meta_schedule.testing import MODEL_TYPE, MODEL_TYPES, get_torch_model +from tvm.meta_schedule.tune import tune_relay + +logging.basicConfig() +logging.getLogger("tvm.meta_schedule").setLevel(logging.DEBUG) + + +class DummyDatabase(PyDatabase): + def __init__(self): + super().__init__() + self.records = [] + self.workload_reg = [] + + def has_workload(self, mod: IRModule) -> Workload: + for workload in self.workload_reg: + if tvm.ir.structural_equal(workload.mod, mod): + return True + return False + + def commit_tuning_record(self, record: TuningRecord) -> None: + self.records.append(record) + + def commit_workload(self, mod: IRModule) -> Workload: + for workload in self.workload_reg: + if tvm.ir.structural_equal(workload.mod, mod): + return workload + workload = Workload(mod) + self.workload_reg.append(workload) + return workload + + def get_top_k(self, workload: Workload, top_k: int) -> List[TuningRecord]: + return list( + filter( + lambda x: x.workload == workload, + sorted(self.records, key=lambda x: sum(x.run_secs) / len(x.run_secs)), + ) + )[: int(top_k)] + + def __len__(self) -> int: + return len(self.records) + + def print_results(self) -> None: + print("\n".join([str(r) for r in self.records])) + + [email protected]("Integration test") [email protected]("model_name", ["resnet18", "mobilenet_v2", "bert_base"]) [email protected]("batch_size", [1]) [email protected]("target", ["llvm --num-cores=16", "nvidia/geforce-rtx-3070"]) +def test_meta_schedule_tune_relay(model_name: str, batch_size: int, target: str): + if model_name == "inception_v3" and batch_size == 1: + pytest.skip("inception_v3 does not handle batch_size of 1") + + input_shape: Tuple[int, ...] + input_name = "input0" + dev = tvm.cpu() if str(target).startswith("llvm") else cuda() + if MODEL_TYPES[model_name] == MODEL_TYPE.TEXT_CLASSIFICATION: + seq_length = 128 + input_name = "input_ids" + input_shape = (batch_size, seq_length) + data = tvm.nd.array(np.random.randint(0, 30521, size=input_shape), dev) # embedding size + else: + if MODEL_TYPES[model_name] == MODEL_TYPE.IMAGE_CLASSIFICATION: + input_shape = (batch_size, 3, 299, 299) + elif MODEL_TYPES[model_name] == MODEL_TYPE.SEGMENTATION: + input_shape = (batch_size, 3, 299, 299) + elif MODEL_TYPES[model_name] == MODEL_TYPE.OBJECT_DETECTION: + input_shape = (1, 3, 300, 300) + elif MODEL_TYPES[model_name] == MODEL_TYPE.VIDEO_CLASSIFICATION: + input_shape = (batch_size, 3, 3, 299, 299) + else: + raise ValueError("Unsupported model: " + model_name) + data = tvm.nd.array(np.random.randn(*input_shape).astype("float32"), dev) + + output_shape: Tuple[int, int] = (batch_size, 1000) + + mod, params = get_torch_model( + model_name=model_name, + input_shape=input_shape, + output_shape=output_shape, + dtype="float32", + ) + + with tempfile.TemporaryDirectory() as work_dir: + target = Target(target) + database = DummyDatabase() + rt_mod: tvm.module = tune_relay( + mod=mod, + params=params, + target=target, + config=ReplayTraceConfig( + num_trials_per_iter=32, + num_trials_total=32, + ), + work_dir=work_dir, + database=database, + ) + # Compile without meta-scheduler for correctness check + with tvm.transform.PassContext(opt_level=0): + rt_mod2 = relay.build(mod, target=target, params=params) + + def get_output(data, lib): + module = graph_executor.GraphModule(lib["default"](dev)) + module.set_input(input_name, data) + module.run() + return module.get_output(0).numpy() + + # Check correctness + actual_output = get_output(data, rt_mod) + expected_output = get_output(data, rt_mod2) + assert np.allclose(actual_output, expected_output, rtol=1e-4, atol=2e-4) + + +if __name__ == """__main__""": + test_meta_schedule_tune_relay("resnet18", 1, "llvm --num-cores=16") + test_meta_schedule_tune_relay("resnet18", 1, "nvidia/geforce-rtx-3070") + test_meta_schedule_tune_relay("mobilenet_v2", 1, "llvm --num-cores=16") + test_meta_schedule_tune_relay("mobilenet_v2", 1, "nvidia/geforce-rtx-3070") + test_meta_schedule_tune_relay("bert_base", 1, "llvm --num-cores=16") + test_meta_schedule_tune_relay("bert_base", 1, "nvidia/geforce-rtx-3070") Review comment: Around 30 minutes. -- This is an automated message from the Apache Git Service. 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