# 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 tvm from tvm.ir import IRModule from tvm import relay from tvm.relay.transform import FastMath def test_exp(): x = relay.var("x", shape=(1, 16, 16, 16), dtype="float32") y = relay.exp(x) func = relay.Function([x], y) mod = tvm.IRModule.from_expr(func) fast_mod = FastMath()(mod) assert "fast_exp" in fast_mod.astext() # Check that FastMath option works for relay.build. with tvm.transform.PassContext(opt_level=3, required_pass=["FastMath"]): fast_mod = relay.optimize(mod, target="llvm", params=None) assert "fast_exp" in fast_mod[0].astext() def test_tanh(): x = relay.var("x", shape=(1, 16, 16, 16), dtype="float32") y = relay.tanh(x) func = relay.Function([x], y) mod = tvm.IRModule.from_expr(func) fast_mod = FastMath()(mod) assert "fast_tanh" in fast_mod.astext() # Check that FastMath option works for relay.build. with tvm.transform.PassContext(opt_level=3, required_pass=["FastMath"]): fast_mod = relay.optimize(mod, target="llvm", params=None) assert "fast_tanh" in fast_mod[0].astext() def test_erf(): x = relay.var("x", shape=(1, 16, 16, 16), dtype="float32") y = relay.erf(x) func = relay.Function([x], y) mod = tvm.IRModule.from_expr(func) fast_mod = FastMath()(mod) assert "fast_erf" in fast_mod.astext() # Check that FastMath option works for relay.build. with tvm.transform.PassContext(opt_level=3, required_pass=["FastMath"]): fast_mod = relay.optimize(mod, target="llvm", params=None) assert "fast_erf" in fast_mod[0].astext() def test_softmax(): x = relay.var("x", shape=(1, 16), dtype="float32") y = relay.nn.softmax(x) func = relay.Function([x], y) mod = tvm.IRModule.from_expr(func) with tvm.transform.PassContext(opt_level=3, required_pass=["FastMath"]): fast_mod = relay.optimize(mod, target="llvm") assert "nn.fast_softmax" in fast_mod[0].astext() if __name__ == "__main__": test_exp() test_tanh() test_erf() test_softmax()