| # 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() |