# 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 pytest pytest.importorskip("ethosu.vela") import tvm from tvm import relay from tvm.relay.testing import run_opt_pass from tvm.relay.backend.contrib.ethosu.tir.compiler import extract_constants import numpy as np def test_extract_constants_single(): def _get_func(): var_input = relay.var("data", shape=(10, 10), dtype="uint8") const_data = np.random.uniform(0, 255, (10, 10)).astype("uint8") const_input = relay.const(const_data, dtype="uint8") out = relay.add(var_input, const_input) func = relay.Function(relay.analysis.free_vars(out), out) func = run_opt_pass(func, relay.transform.InferType()) return func, const_input def _expected(): var_input1 = relay.var("data", shape=(10, 10), dtype="uint8") var_input2 = relay.var("p1", shape=(10, 10), dtype="uint8") out = relay.add(var_input1, var_input2) func = relay.Function(relay.analysis.free_vars(out), out) func = run_opt_pass(func, relay.transform.InferType()) return func func, const = _get_func() new_func, const_dict = extract_constants(func) assert tvm.ir.structural_equal(new_func, _expected()) assert 1 in const_dict assert (const_dict[1] == const.data.asnumpy()).all() def test_extract_constants_multi(): def _get_func(): var_input1 = relay.var("data1", shape=(10, 10), dtype="uint8") var_input2 = relay.var("data2", shape=(10, 10), dtype="uint8") const_data_1 = np.random.uniform(0, 255, (10, 10)).astype("uint8") const_data_2 = np.random.uniform(0, 255, (10, 10)).astype("uint8") const_data_3 = np.random.uniform(0, 255, (10, 10)).astype("uint8") const_data_4 = np.random.uniform(0, 255, (10, 10)).astype("uint8") const_input_1 = relay.const(const_data_1, dtype="uint8") const_input_2 = relay.const(const_data_2, dtype="uint8") const_input_3 = relay.const(const_data_3, dtype="uint8") const_input_4 = relay.const(const_data_4, dtype="uint8") out = relay.add(var_input1, var_input2) out = relay.add(out, const_input_1) out = relay.add(out, const_input_2) out = relay.add(out, const_input_3) out = relay.add(out, const_input_4) func = relay.Function(relay.analysis.free_vars(out), out) func = run_opt_pass(func, relay.transform.InferType()) return func, [const_input_1, const_input_2, const_input_3, const_input_4] def _expected(): var_input1 = relay.var("data1", shape=(10, 10), dtype="uint8") var_input2 = relay.var("data2", shape=(10, 10), dtype="uint8") var_input3 = relay.var("p1", shape=(10, 10), dtype="uint8") var_input4 = relay.var("p2", shape=(10, 10), dtype="uint8") var_input5 = relay.var("p3", shape=(10, 10), dtype="uint8") var_input6 = relay.var("p4", shape=(10, 10), dtype="uint8") out = relay.add(var_input1, var_input2) out = relay.add(out, var_input3) out = relay.add(out, var_input4) out = relay.add(out, var_input5) out = relay.add(out, var_input6) func = relay.Function(relay.analysis.free_vars(out), out) func = run_opt_pass(func, relay.transform.InferType()) return func func, consts = _get_func() new_func, const_dict = extract_constants(func) assert tvm.ir.structural_equal(new_func, _expected()) for i, const in enumerate(consts): assert i + 2 in const_dict assert (const_dict[i + 2] == consts[i].data.asnumpy()).all() if __name__ == "__main__": pytest.main([__file__])