# 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. """Common utilities for testing autotvm""" import time import numpy as np import tvm from tvm import te from tvm import autotvm from tvm.autotvm import MeasureInput, MeasureResult from tvm.autotvm.measure.measure import Runner class DummyRunner(Runner): def __init__(self): super(DummyRunner, self).__init__(1, 1) def run(self, measure_inputs, build_results): return [MeasureResult((np.random.random(),), 0, 0.2, time.time()) for _ in range(len(measure_inputs))] def get_build_kwargs(self): return {} @autotvm.template("testing/matmul") def matmul(N, L, M, dtype): A = te.placeholder((N, L), name='A', dtype=dtype) B = te.placeholder((L, M), name='B', dtype=dtype) k = te.reduce_axis((0, L), name='k') C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name='C') s = te.create_schedule(C.op) # schedule y, x = s[C].op.axis k = s[C].op.reduce_axis[0] ##### define space begin ##### cfg = autotvm.get_config() cfg.define_split("tile_y", y, num_outputs=2) cfg.define_split("tile_x", x, num_outputs=2) ##### define space end ##### # schedule according to config yo, yi = cfg["tile_y"].apply(s, C, y) xo, xi = cfg["tile_x"].apply(s, C, x) s[C].reorder(yo, xo, k, yi, xi) return s, [A, B, C] @autotvm.template("testing/bad_matmul") def bad_matmul(N, L, M, dtype): if 'bad_device' in tvm.target.Target.current().keys: A = te.placeholder((N, L), name='A', dtype=dtype) B = te.placeholder((L, M), name='B', dtype=dtype) k = te.reduce_axis((0, L-1), name='k') C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name='C') s = te.create_schedule(C.op) # schedule y, x = s[C].op.axis cfg = autotvm.get_config() cfg.define_split("tile_y", y, num_outputs=2) cfg.define_split("tile_x", x, num_outputs=2) return s, [A, B, C] return matmul(N, L, M, dtype) def get_sample_task(n=128): """return a sample task for testing""" target = tvm.target.create("llvm") task = autotvm.task.create("testing/matmul", args=(n, n, n, 'float32'), target=target) return task, target def get_sample_records(n): """get sample records for testing""" tsk, target = get_sample_task() inps, ress = [], [] for i in range(n): inps.append(MeasureInput(target, tsk, tsk.config_space.get(i))) ress.append(MeasureResult((i+1,), 0, i, time.time())) return list(zip(inps, ress))