blob: d57297ee6e2286ac7e316b81c7e23274be889afd [file] [log] [blame]
# 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 ctypes
import numpy as np
from tvm.contrib import cc, utils, popen_pool, tar
import tvm
import tvm.testing
from tvm.script import ir as I, tir as T
@tvm.testing.uses_gpu
def test_cuda_multi_lib():
# test combining two system lib together
# each contains a fatbin component in cuda
dev = tvm.cuda(0)
for device in ["llvm", "cuda"]:
if not tvm.testing.device_enabled(device):
print("skip because %s is not enabled..." % device)
return
@tvm.script.ir_module
class ModA:
I.module_attrs({"system_lib_prefix": "modA_"})
@T.prim_func
def my_inplace_update(x: T.Buffer((12), "float32")) -> None:
T.func_attr({"global_symbol": "modA_my_inplace_update"})
for bx in T.thread_binding(T.int64(1), thread="blockIdx.x"):
for tx in T.thread_binding(T.int64(12), thread="threadIdx.x"):
x[tx] = x[tx] + 1
@tvm.script.ir_module
class ModB:
I.module_attrs({"system_lib_prefix": "modB_"})
@T.prim_func
def my_inplace_update(x: T.Buffer((12), "float32")) -> None:
T.func_attr({"global_symbol": "modB_my_inplace_update"})
for bx in T.thread_binding(T.int64(1), thread="blockIdx.x"):
for tx in T.thread_binding(T.int64(12), thread="threadIdx.x"):
x[tx] = x[tx] + 2
temp = utils.tempdir()
target = tvm.target.Target("cuda", host="llvm")
libA = tvm.compile(ModA, target=target)
libB = tvm.compile(ModB, target=target)
pathA = temp.relpath("libA.tar")
pathB = temp.relpath("libB.tar")
pathAll = temp.relpath("libAll.a")
path_dso = temp.relpath("mylib.so")
libA.export_library(pathA, fcompile=tar.tar)
libB.export_library(pathB, fcompile=tar.tar)
cc.create_staticlib(pathAll, [pathA, pathB])
# package two static libs together
cc.create_shared(path_dso, ["-Wl,--whole-archive", pathAll, "-Wl,--no-whole-archive"])
def popen_check():
# Load dll, will trigger system library registration
ctypes.CDLL(path_dso)
# Load the system wide library
dev = tvm.cuda()
a_np = np.random.uniform(size=12).astype("float32")
a_nd = tvm.runtime.tensor(a_np, dev)
b_nd = tvm.runtime.tensor(a_np, dev)
syslibA = tvm.runtime.system_lib("modA_")
syslibB = tvm.runtime.system_lib("modB_")
# reload same lib twice
syslibA = tvm.runtime.system_lib("modA_")
syslibA["my_inplace_update"](a_nd)
syslibB["my_inplace_update"](b_nd)
np.testing.assert_equal(a_nd.numpy(), a_np + 1)
np.testing.assert_equal(b_nd.numpy(), a_np + 2)
# system lib should be loaded in different process
worker = popen_pool.PopenWorker()
worker.send(popen_check)
worker.recv()
if __name__ == "__main__":
test_synthetic()
test_cuda_multilib()