# 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. """Unit tests for annotating spans.""" import tvm import tvm.relay as relay from tvm.relay import testing import tvm.testing def test_annotate_spans_compatibility(): data = relay.var("data", relay.TensorType((1, 3, 64, 64), "float32")) weight = relay.var("weight") bn_gamma = relay.var("bn_gamma") bn_beta = relay.var("bn_beta") bn_mmean = relay.var("bn_mean") bn_mvar = relay.var("bn_var") simple_net = relay.nn.conv2d( data=data, weight=weight, kernel_size=(3, 3), channels=3, padding=(1, 1) ) simple_net = relay.nn.batch_norm(simple_net, bn_gamma, bn_beta, bn_mmean, bn_mvar)[0] simple_net = relay.Function(relay.analysis.free_vars(simple_net), simple_net) module, params = testing.create_workload(simple_net) # Apply some simple passes to legalize the IR. with tvm.transform.PassContext(opt_level=0): module, params = relay.optimize(module, tvm.testing.enabled_targets()[0][0], params) seq = tvm.transform.Sequential([relay.transform.AnnotateSpans(), relay.transform.DefuseOps()]) with tvm.transform.PassContext(opt_level=3): module = seq(module) if __name__ == "__main__": test_annotate_spans_compatibility()