# 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 import tvm.testing from tvm import te from tvm.topi.cuda import stable_sort_by_key_thrust from tvm.topi.cuda.scan import exclusive_scan, scan_thrust, schedule_scan from tvm.contrib.thrust import can_use_thrust, can_use_rocthrust import numpy as np thrust_check_func = {"cuda": can_use_thrust, "rocm": can_use_rocthrust} def test_stable_sort_by_key(): size = 6 keys = te.placeholder((size,), name="keys", dtype="int32") values = te.placeholder((size,), name="values", dtype="int32") keys_out, values_out = stable_sort_by_key_thrust(keys, values) for target in ["cuda", "rocm"]: if not tvm.testing.device_enabled(target): print("Skip because %s is not enabled" % target) continue with tvm.target.Target(target + " -libs=thrust") as tgt: if not thrust_check_func[target](tgt, "tvm.contrib.thrust.stable_sort_by_key"): print("skip because thrust is not enabled...") return dev = tvm.device(target, 0) s = te.create_schedule([keys_out.op, values_out.op]) f = tvm.build(s, [keys, values, keys_out, values_out], target) keys_np = np.array([1, 4, 2, 8, 2, 7], np.int32) values_np = np.random.randint(0, 10, size=(size,)).astype(np.int32) keys_np_out = np.zeros(keys_np.shape, np.int32) values_np_out = np.zeros(values_np.shape, np.int32) keys_in = tvm.nd.array(keys_np, dev) values_in = tvm.nd.array(values_np, dev) keys_out = tvm.nd.array(keys_np_out, dev) values_out = tvm.nd.array(values_np_out, dev) f(keys_in, values_in, keys_out, values_out) ref_keys_out = np.sort(keys_np) ref_values_out = np.array([values_np[i] for i in np.argsort(keys_np)]) tvm.testing.assert_allclose(keys_out.numpy(), ref_keys_out, rtol=1e-5) tvm.testing.assert_allclose(values_out.numpy(), ref_values_out, rtol=1e-5) def test_exclusive_scan(): for target in ["cuda", "rocm"]: if not tvm.testing.device_enabled(target): print("Skip because %s is not enabled" % target) continue with tvm.target.Target(target + " -libs=thrust") as tgt: if not thrust_check_func[target](tgt, "tvm.contrib.thrust.sum_scan"): print("skip because thrust is not enabled...") return for ishape in [(10,), (10, 10), (10, 10, 10)]: values = te.placeholder(ishape, name="values", dtype="int32") scan, reduction = exclusive_scan(values, return_reduction=True) s = schedule_scan([scan, reduction]) dev = tvm.device(target, 0) f = tvm.build(s, [values, scan, reduction], target) values_np = np.random.randint(0, 10, size=ishape).astype(np.int32) values_np_out = np.zeros(values_np.shape, np.int32) if len(ishape) == 1: reduction_shape = () else: reduction_shape = ishape[:-1] reduction_np_out = np.zeros(reduction_shape, np.int32) values_in = tvm.nd.array(values_np, dev) values_out = tvm.nd.array(values_np_out, dev) reduction_out = tvm.nd.array(reduction_np_out, dev) f(values_in, values_out, reduction_out) ref_values_out = np.cumsum(values_np, axis=-1, dtype="int32") - values_np tvm.testing.assert_allclose(values_out.numpy(), ref_values_out, rtol=1e-5) ref_reduction_out = np.sum(values_np, axis=-1) tvm.testing.assert_allclose(reduction_out.numpy(), ref_reduction_out, rtol=1e-5) def test_inclusive_scan(): out_dtype = "int64" for target in ["cuda", "rocm"]: if not tvm.testing.device_enabled(target): print("Skip because %s is not enabled" % target) continue with tvm.target.Target(target + " -libs=thrust") as tgt: if not thrust_check_func[target](tgt, "tvm.contrib.thrust.sum_scan"): print("skip because thrust is not enabled...") return for ishape in [(10,), (10, 10)]: values = te.placeholder(ishape, name="values", dtype="int32") scan = scan_thrust(values, out_dtype, exclusive=False) s = tvm.te.create_schedule([scan.op]) dev = tvm.device(target, 0) f = tvm.build(s, [values, scan], target) values_np = np.random.randint(0, 10, size=ishape).astype(np.int32) values_np_out = np.zeros(values_np.shape, out_dtype) values_in = tvm.nd.array(values_np, dev) values_out = tvm.nd.array(values_np_out, dev) f(values_in, values_out) ref_values_out = np.cumsum(values_np, axis=-1, dtype=out_dtype) tvm.testing.assert_allclose(values_out.numpy(), ref_values_out, rtol=1e-5) if __name__ == "__main__": test_stable_sort_by_key() test_exclusive_scan() test_inclusive_scan()