# 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. """Test eliminate common subexpr pass""" from tvm import relay from tvm.relay.op import register_alter_op_layout from tvm.relay import transform, analysis def run_opt_pass(expr, opt_pass): assert isinstance(opt_pass, transform.Pass) mod = relay.Module.from_expr(expr) mod = opt_pass(mod) entry = mod["main"] return entry if isinstance(expr, relay.Function) else entry.body def test_simple(): def before(): x = relay.var("x", shape=(1, 16)) y1 = relay.nn.relu(x) y2 = relay.nn.relu(x) y1 = relay.add(y1, relay.const(1.0, "float32")) y2 = relay.add(y2, relay.const(1.0, "float32")) y = relay.add(y1, y2) f = relay.Function([x], y) return f def expected(): x = relay.var("x", shape=(1, 16)) y = relay.nn.relu(x) y = relay.add(y, relay.const(1.0, "float32")) y = relay.add(y, y) f = relay.Function([x], y) return run_opt_pass(f, transform.InferType()) z = before() z = run_opt_pass(z, transform.EliminateCommonSubexpr()) assert analysis.alpha_equal(z, expected()) def test_callback(): def before(): x = relay.var("x", shape=(1, 16)) y1 = relay.nn.relu(x) y2 = relay.nn.relu(x) y1 = relay.add(y1, relay.const(1.0, "float32")) y2 = relay.add(y2, relay.const(1.0, "float32")) y = relay.add(y1, y2) f = relay.Function([x], y) return f def expected(): x = relay.var("x", shape=(1, 16)) y = relay.nn.relu(x) y1 = relay.add(y, relay.const(1.0, "float32")) y2 = relay.add(y, relay.const(1.0, "float32")) y = relay.add(y1, y2) f = relay.Function([x], y) return run_opt_pass(f, transform.InferType()) def fskip(expr): if isinstance(expr, relay.expr.Call) and expr.op.name == 'add': return True return False z = before() z = run_opt_pass(z, transform.EliminateCommonSubexpr(fskip)) assert analysis.alpha_equal(z, expected()) if __name__ == "__main__": test_simple() test_callback()