# 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. # ruff: noqa: F401 """Tests to validate relax fast math tranform pass.""" import pytest import tvm.testing from tvm import relax, topi from tvm.ir.base import assert_structural_equal from tvm.relax.transform import FastMathTransform from tvm.script import ir as I from tvm.script import relax as R def _run_pass_compare_output(Before, Expected): fast_mod = FastMathTransform()(Before) if not relax.analysis.check_well_formed(fast_mod): print("IRModule is not well-formed") assert_structural_equal(Expected, fast_mod) def test_optimize_transform_layout_pass_one_arg(): @I.ir_module class Before: @R.function def main(x: R.Tensor((16,), dtype="float32")) -> R.Tensor((16,), dtype="float32"): lv1: R.Tensor((16,), dtype="float32") = R.nn.softmax(x) lv2: R.Tensor((16,), dtype="float32") = R.exp(lv1) lv3: R.Tensor((16,), dtype="float32") = R.erf(lv2) lv4: R.Tensor((16,), dtype="float32") = R.tanh(lv3) return lv4 bb = relax.BlockBuilder() x = relax.Var("x", R.Tensor((16,), "float32")) with bb.function("main", [x]): lv1 = bb.emit_te(topi.nn.fast_softmax, x) lv2 = bb.emit_te(topi.fast_exp, lv1) lv3 = bb.emit_te(topi.fast_erf, lv2) lv4 = bb.emit_te(topi.fast_tanh, lv3) bb.emit_func_output(lv4) Expected = bb.get() _run_pass_compare_output(Before, Expected) if __name__ == "__main__": tvm.testing.main()