def test_rpc_remote_module():
if not tvm.module.enabled("rpc"):
return
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
# graph
n = tvm.convert(1024)
A = tvm.placeholder((n, ), name='A')
B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
s = tvm.create_schedule(B.op)
def check_remote():
if not tvm.module.enabled("llvm"):
print("Skip because llvm is not enabled")
return
temp = util.tempdir()
ctx = remote.cpu(0)
f = tvm.build(s, [A, B], "llvm", name="myadd")
path_dso = temp.relpath("dev_lib.so")
f.export_library(path_dso)
remote.upload(path_dso)
f1 = remote.load_module("dev_lib.so")
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, remote.cpu(0), number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
check_remote()
def test_rpc_simple():
if not tvm.module.enabled("rpc"):
return
@tvm.register_func("rpc.test.addone")
def addone(x):
return x + 1
@tvm.register_func("rpc.test.strcat")
def strcat(name, x):
return "%s:%d" % (name, x)
@tvm.register_func("rpc.test.except")
def remotethrow(name):
raise ValueError("%s" % name)
server = rpc.Server("localhost")
client = rpc.connect(server.host, server.port, key="x1")
f1 = client.get_function("rpc.test.addone")
assert f1(10) == 11
f3 = client.get_function("rpc.test.except")
try:
f3("abc")
assert False
except tvm.TVMError as e:
assert "abc" in str(e)
f2 = client.get_function("rpc.test.strcat")
assert f2("abc", 11) == "abc:11"
def test_rpc_array():
if not tvm.module.enabled("rpc"):
return
# graph
n = tvm.convert(1024)
A = tvm.placeholder((n, ), name='A')
B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
s = tvm.create_schedule(B.op)
remote = rpc.connect(proxy_host, proxy_port, key="js")
target = "llvm -target=asmjs-unknown-emscripten -system-lib"
def check_remote():
if not tvm.module.enabled(target):
print("Skip because %s is not enabled" % target)
return
temp = util.tempdir()
ctx = remote.cpu(0)
f = tvm.build(s, [A, B], target, name="myadd")
path_obj = temp.relpath("dev_lib.bc")
path_dso = temp.relpath("dev_lib.js")
f.save(path_obj)
emscripten.create_js(path_dso, path_obj, side_module=True)
# Upload to suffix as dso so it can be loaded remotely
remote.upload(path_dso, "dev_lib.dso")
data = remote.download("dev_lib.dso")
f1 = remote.load_module("dev_lib.dso")
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, remote.cpu(0), number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
check_remote()
def test_rpc_array():
if not tvm.module.enabled("rpc"):
return
# graph
n = tvm.convert(1024)
A = tvm.placeholder((n,), name='A')
B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
s = tvm.create_schedule(B.op)
remote = rpc.connect(proxy_host, proxy_port, key="js")
target = "llvm -target=asmjs-unknown-emscripten -system-lib"
def check_remote():
if not tvm.module.enabled(target):
print("Skip because %s is not enabled" % target)
return
temp = util.tempdir()
ctx = remote.cpu(0)
f = tvm.build(s, [A, B], target, name="myadd")
path_obj = temp.relpath("dev_lib.bc")
path_dso = temp.relpath("dev_lib.js")
f.save(path_obj)
emscripten.create_js(path_dso, path_obj, side_module=True)
# Upload to suffix as dso so it can be loaded remotely
remote.upload(path_dso, "dev_lib.dso")
data = remote.download("dev_lib.dso")
f1 = remote.load_module("dev_lib.dso")
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, remote.cpu(0), number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
check_remote()
def build_arm():
target = "llvm -target=armv7-none-linux-gnueabihf"
if not tvm.module.enabled(target):
print("Skip because %s is not enabled.." % target)
return
temp = util.tempdir()
f = tvm.build(s, [A, B, C], target)
path = temp.relpath("myadd.o")
f.save(path)
verify_elf(path, 0x28)
asm_path = temp.relpath("myadd.asm")
f.save(asm_path)
# Do a RPC verification, launch kernel on Arm Board if available.
host = os.environ.get('TVM_RPC_ARM_HOST', None)
remote = None
if host:
port = int(os.environ['TVM_RPC_ARM_PORT'])
try:
remote = rpc.connect(host, port)
except tvm.TVMError as e:
pass
if remote:
remote.upload(path)
farm = remote.load_module("myadd.o")
ctx = remote.cpu(0)
n = nn
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
farm(a, b, c)
np.testing.assert_allclose(
c.asnumpy(), a.asnumpy() + b.asnumpy())
print("Verification finish on remote..")
def test_bigendian_rpc():
"""Test big endian rpc when there is a PowerPC RPC server available"""
host = os.environ.get("TVM_POWERPC_TEST_HOST", None)
port = os.environ.get("TVM_POWERPC_TEST_PORT", 9090)
if host is None:
return
def verify_rpc(remote, target, shape, dtype):
A = tvm.placeholder(shape, dtype=dtype)
B = tvm.compute(A.shape, lambda i: A[i] + tvm.const(1, A.dtype))
s = tvm.create_schedule(B.op)
f = tvm.build(s, [A, B], target, name="myadd")
ctx = remote.cpu(0)
a = tvm.nd.array(np.random.randint(0, 256, size=shape).astype(A.dtype),
ctx=ctx)
b = tvm.nd.array(np.zeros(shape).astype(A.dtype), ctx=ctx)
temp = util.tempdir()
path_dso = temp.relpath("dev_lib.o")
f.save(path_dso)
remote.upload(path_dso)
f = remote.load_module("dev_lib.o")
f(a, b)
np.testing.assert_allclose(a.asnumpy() + 1, b.asnumpy())
print("Test RPC connection to PowerPC...")
remote = rpc.connect(host, port)
target = "llvm -mtriple=powerpc-linux-gnu"
for dtype in ["float32", "float64", "int32", "int8"]:
verify_rpc(remote, target, (10, ), dtype)
def test_rpc_simple():
if not tvm.module.enabled("rpc"):
return
@tvm.register_func("rpc.test.addone")
def addone(x):
return x + 1
@tvm.register_func("rpc.test.strcat")
def strcat(name, x):
return "%s:%d" % (name, x)
@tvm.register_func("rpc.test.except")
def remotethrow(name):
raise ValueError("%s" % name)
server = rpc.Server("localhost")
client = rpc.connect(server.host, server.port, key="x1")
f1 = client.get_function("rpc.test.addone")
assert f1(10) == 11
f3 = client.get_function("rpc.test.except")
try:
f3("abc")
assert False
except tvm.TVMError as e:
assert "abc" in str(e)
f2 = client.get_function("rpc.test.strcat")
assert f2("abc", 11) == "abc:11"
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, required=True, choices=['resnet', 'mobilenet'],
help="The model type.")
parser.add_argument('--host', type=str, required=True, help="The host address of your Raspberry Pi.")
parser.add_argument('--port', type=int, required=True, help="The port number of your Raspberry Pi.")
parser.add_argument('--opt-level', type=int, default=1, help="Level of optimization.")
parser.add_argument('--num-iter', type=int, default=50, help="Number of iteration during benchmark.")
args = parser.parse_args()
opt_level = args.opt_level
num_iter = args.num_iter
batch_size = 1
num_classes = 1000
image_shape = (3, 224, 224)
data_shape = (batch_size,) + image_shape
out_shape = (batch_size, num_classes)
if args.model == 'resnet':
net, params = nnvm.testing.resnet.get_workload(
batch_size=1, image_shape=image_shape)
elif args.model == 'mobilenet':
net, params = nnvm.testing.mobilenet.get_workload(
batch_size=1, image_shape=image_shape)
else:
raise ValueError('no benchmark prepared for {}.'.format(args.model))
with nnvm.compiler.build_config(opt_level=opt_level):
graph, lib, params = nnvm.compiler.build(
net, tvm.target.rasp(), shape={"data": data_shape}, params=params)
tmp = util.tempdir()
lib_fname = tmp.relpath('net.o')
lib.save(lib_fname)
remote = rpc.connect(args.host, args.port)
remote.upload(lib_fname)
ctx = remote.cpu(0)
rlib = remote.load_module('net.o')
rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
module = runtime.create(graph, rlib, ctx)
module.set_input('data', tvm.nd.array(np.random.uniform(size=(data_shape)).astype("float32")))
module.set_input(**rparams)
module.run()
out = module.get_output(0, tvm.nd.empty(out_shape, ctx=ctx))
out.asnumpy()
print('benchmark args: {}'.format(args))
ftimer = module.module.time_evaluator("run", ctx, num_iter)
for i in range(3):
prof_res = ftimer()
print(prof_res)
# sleep for avoiding cpu overheat
time.sleep(45)
def test_rpc_file_exchange():
if not tvm.module.enabled("rpc"):
return
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
blob = bytearray(np.random.randint(0, 10, size=(10)))
remote.upload(blob, "dat.bin")
rev = remote.download("dat.bin")
assert(rev == blob)
def test_rpc_return_func():
@tvm.register_func("rpc.test.remote_func")
def addone(x):
return lambda y: x+y
server = rpc.Server("localhost")
client = rpc.connect(server.host, server.port, key="x1")
f1 = client.get_function("rpc.test.remote_func")
fadd = f1(10)
assert fadd(12) == 22
def try_remote_save_load():
if not tvm.module.enabled("rpc"):
return
if not tvm.module.enabled("opengl"):
return
if not tvm.module.enabled("llvm"):
return
# Build the module.
n = tvm.var("n")
A = tvm.placeholder((n, ), name='A')
B = tvm.placeholder((n, ), name='B')
C = tvm.compute(A.shape, lambda i: A[i] + B[i], name="C")
s = tvm.create_schedule(C.op)
s[C].opengl()
target_host = "llvm -target=asmjs-unknown-emscripten -system-lib"
f = tvm.build(s, [A, B, C],
"opengl",
target_host=target_host,
name="myadd")
remote = rpc.connect(proxy_host, proxy_port, key="js")
temp = util.tempdir()
ctx = remote.opengl(0)
path_obj = temp.relpath("myadd.bc")
path_dso = temp.relpath("myadd.js")
path_gl = temp.relpath("myadd.gl")
path_json = temp.relpath("myadd.tvm_meta.json")
f.save(path_obj)
emscripten.create_js(path_dso, path_obj, side_module=True)
f.imported_modules[0].save(path_gl)
remote.upload(path_dso, "myadd.dso")
remote.upload(path_gl)
remote.upload(path_json)
remote.download("myadd.dso")
remote.download("myadd.gl")
remote.download("myadd.tvm_meta.json")
print('Loading myadd.dso')
fhost = remote.load_module("myadd.dso")
print('Loading myadd.gl')
fdev = remote.load_module("myadd.gl")
print('import_module')
fhost.import_module(fdev)
print('running...')
a = tvm.nd.array(np.random.uniform(size=16).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(16, dtype=A.dtype), ctx)
c = tvm.nd.array(np.zeros(16, dtype=C.dtype), ctx)
fhost(a, b, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
def test_rpc_file_exchange():
if not tvm.module.enabled("rpc"):
return
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
blob = bytearray(np.random.randint(0, 10, size=(10)))
remote.upload(blob, "dat.bin")
rev = remote.download("dat.bin")
assert (rev == blob)
def test_rpc_module():
# graph
n = tvm.convert(1024)
A = tvm.placeholder((n, ), name='A')
B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
temp = util.tempdir()
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=64)
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
# Build the dynamic lib.
# If we don't want to do metal and only use cpu, just set target to be target
f = tvm.build(s, [A, B], "metal", target_host=target, name="myadd")
path_dso1 = temp.relpath("dev_lib.dylib")
f.export_library(path_dso1, xcode.create_dylib, arch=arch, sdk=sdk)
xcode.codesign(path_dso1)
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=64)
s[B].parallel(xi)
s[B].pragma(xo, "parallel_launch_point")
s[B].pragma(xi, "parallel_barrier_when_finish")
f = tvm.build(s, [A, B], target, name="myadd_cpu")
path_dso2 = temp.relpath("cpu_lib.dylib")
f.export_library(path_dso2, xcode.create_dylib, arch=arch, sdk=sdk)
xcode.codesign(path_dso2)
# Start RPC test server that contains the compiled library.
server = xcode.popen_test_rpc(proxy_host,
proxy_port,
key,
destination=destination,
options=['-quiet'],
libs=[path_dso1, path_dso2])
# connect to the proxy
remote = rpc.connect(proxy_host, proxy_port, key=key)
ctx = remote.metal(0)
f1 = remote.load_module("dev_lib.dylib")
a_np = np.random.uniform(size=1024).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
# CPU
ctx = remote.cpu(0)
f2 = remote.load_module("cpu_lib.dylib")
a_np = np.random.uniform(size=1024).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f2.time_evaluator(f1.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
def try_remote_save_load():
if not tvm.module.enabled("rpc"):
return
if not tvm.module.enabled("opengl"):
return
if not tvm.module.enabled("llvm"):
return
# Build the module.
n = tvm.var("n")
A = tvm.placeholder((n,), name='A')
B = tvm.placeholder((n,), name='B')
C = tvm.compute(A.shape, lambda i: A[i] + B[i], name="C")
s = tvm.create_schedule(C.op)
s[C].opengl()
target_host = "llvm -target=asmjs-unknown-emscripten -system-lib"
f = tvm.build(s, [A, B, C], "opengl", target_host=target_host, name="myadd")
remote = rpc.connect(proxy_host, proxy_port, key="js")
temp = util.tempdir()
ctx = remote.opengl(0)
path_obj = temp.relpath("myadd.bc")
path_dso = temp.relpath("myadd.js")
path_gl = temp.relpath("myadd.gl")
path_json = temp.relpath("myadd.tvm_meta.json")
f.save(path_obj)
emscripten.create_js(path_dso, path_obj, side_module=True)
f.imported_modules[0].save(path_gl)
remote.upload(path_dso, "myadd.dso")
remote.upload(path_gl)
remote.upload(path_json)
remote.download("myadd.dso")
remote.download("myadd.gl")
remote.download("myadd.tvm_meta.json")
print('Loading myadd.dso')
fhost = remote.load_module("myadd.dso")
print('Loading myadd.gl')
fdev = remote.load_module("myadd.gl")
print('import_module')
fhost.import_module(fdev)
print('running...')
a = tvm.nd.array(np.random.uniform(size=16).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(16, dtype=A.dtype), ctx)
c = tvm.nd.array(np.zeros(16, dtype=C.dtype), ctx)
fhost(a, b, c)
np.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
def test_rpc_module():
# graph
n = tvm.convert(1024)
A = tvm.placeholder((n, ), name='A')
B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
temp = util.tempdir()
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=64)
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
# Build the dynamic lib.
# If we don't want to do metal and only use cpu, just set target to be target
f = tvm.build(s, [A, B], "opencl", target_host=target, name="myadd")
path_dso1 = temp.relpath("dev_lib.so")
f.export_library(path_dso1, ndk.create_shared)
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=64)
s[B].parallel(xi)
s[B].pragma(xo, "parallel_launch_point")
s[B].pragma(xi, "parallel_barrier_when_finish")
f = tvm.build(s, [A, B], target, name="myadd_cpu")
path_dso2 = temp.relpath("cpu_lib.so")
f.export_library(path_dso2, ndk.create_shared)
# connect to the proxy
remote = rpc.connect(proxy_host, proxy_port, key=key)
print('Run GPU test ...')
ctx = remote.cl(0)
remote.upload(path_dso1)
f1 = remote.load_module("dev_lib.so")
a_np = np.random.uniform(size=1024).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
print('Run CPU test ...')
ctx = remote.cpu(0)
remote.upload(path_dso2)
f2 = remote.load_module("cpu_lib.so")
a_np = np.random.uniform(size=1024).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f2.time_evaluator(f2.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
def test_rpc_array():
if not tvm.module.enabled("rpc"):
return
x = np.random.randint(0, 10, size=(3, 4))
@tvm.register_func("rpc.test.remote_array_func")
def remote_array_func(y):
np.testing.assert_equal(y.asnumpy(), x)
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
r_cpu = tvm.nd.array(x, remote.cpu(0))
assert str(r_cpu.context).startswith("remote")
np.testing.assert_equal(r_cpu.asnumpy(), x)
fremote = remote.get_function("rpc.test.remote_array_func")
fremote(r_cpu)
def test_rpc_array():
if not tvm.module.enabled("rpc"):
return
x = np.random.randint(0, 10, size=(3, 4))
@tvm.register_func("rpc.test.remote_array_func")
def remote_array_func(y):
np.testing.assert_equal(y.asnumpy(), x)
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
print("second connect")
r_cpu = tvm.nd.array(x, remote.cpu(0))
assert str(r_cpu.context).startswith("remote")
np.testing.assert_equal(r_cpu.asnumpy(), x)
fremote = remote.get_function("rpc.test.remote_array_func")
fremote(r_cpu)
def mobileNet_rpc_module(dtype):
# load model
net, params = nnvm.testing.mobilenet.get_workload(batch_size=1,
image_shape=image_shape,
dtype=dtype)
# compile
opt_level = 2 if dtype == 'float32' else 1
with nnvm.compiler.build_config(opt_level=opt_level):
graph, lib, params = nnvm.compiler.build(net,
tvm.target.mali(),
shape={"data": data_shape},
params=params,
dtype=dtype,
target_host=target)
# upload model to remote device
tmp = util.tempdir()
lib_fname = tmp.relpath('net.so')
lib.export_library(lib_fname, ndk.create_shared)
remote = rpc.connect(proxy_host, proxy_port, key=key)
remote.upload(lib_fname)
ctx = remote.cl(0)
rlib = remote.load_module('net.so')
rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
print('Run GPU test ...')
# create graph runtime
module = runtime.create(graph, rlib, ctx)
module.set_input(
'data',
tvm.nd.array(np.random.uniform(size=(data_shape)).astype(dtype)))
module.set_input(**rparams)
# the num of runs for warm up and test
num_warmup = 50
num_test = 300
warm_up_timer = module.module.time_evaluator("run", ctx, num_warmup)
warm_up_timer()
ftimer = module.module.time_evaluator("run", ctx, num_test)
prof_res = ftimer()
print("backend: TVM-mali\tmodel: %s\tdtype: %s\tcost:%.4f" %
("mobileNet", dtype, prof_res.mean))
def check_remote():
if not tvm.module.enabled("llvm"):
print("Skip because llvm is not enabled")
return
mlib = tvm.build(s, [A, B], "llvm", name="myadd")
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
temp = util.tempdir()
ctx = remote.cpu(0)
path_dso = temp.relpath("dev_lib.so")
mlib.export_library(path_dso)
remote.upload(path_dso)
mlib = remote.load_module("dev_lib.so")
mod = graph_runtime.create(graph, mlib, remote.cpu(0))
a = np.random.uniform(size=(n,)).astype(A.dtype)
mod.run(x=tvm.nd.array(a, ctx))
out = tvm.nd.empty((n,), ctx=ctx)
out = mod.get_output(0, out)
np.testing.assert_equal(out.asnumpy(), a + 1)
def check_remote():
if not tvm.module.enabled("llvm"):
print("Skip because llvm is not enabled")
return
mlib = tvm.build(s, [A, B], "llvm", name="myadd")
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
temp = util.tempdir()
ctx = remote.cpu(0)
path_dso = temp.relpath("dev_lib.so")
mlib.export_library(path_dso)
remote.upload(path_dso)
mlib = remote.load_module("dev_lib.so")
mod = graph_runtime.create(graph, mlib, remote.cpu(0))
a = np.random.uniform(size=(n, )).astype(A.dtype)
mod.run(x=tvm.nd.array(a, ctx))
out = tvm.nd.empty((n, ), ctx=ctx)
out = mod.get_output(0, out)
np.testing.assert_equal(out.asnumpy(), a + 1)
def check():
if not tvm.module.enabled("rpc"):
return
@tvm.register_func("rpc.test2.addone")
def addone(x):
return x + 1
@tvm.register_func("rpc.test2.strcat")
def addone(name, x):
return "%s:%d" % (name, x)
server = multiprocessing.Process(
target=rpc_proxy.websocket_proxy_server,
args=("ws://localhost:%d/ws" % web_port,"x1"))
# Need to make sure that the connection start after proxy comes up
time.sleep(0.1)
server.deamon = True
server.start()
client = rpc.connect(prox.host, prox.port, key="x1")
f1 = client.get_function("rpc.test2.addone")
assert f1(10) == 11
f2 = client.get_function("rpc.test2.strcat")
assert f2("abc", 11) == "abc:11"
def test_rpc_executor():
host = "localhost"
port = 9100
server = rpc.Server(host, port, use_popen=True)
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.exp(y + x)
shape = (10, 128)
dtype = tvm.float32
shape_dict = {"x": shape, "y": shape}
tmp = util.tempdir()
lib_name = tmp.relpath("net.o")
graph, lib, _ = nnvm.compiler.build(z, "llvm", shape_dict)
# save module
lib.save(lib_name)
remote = rpc.connect(host, port)
remote.upload(lib_name)
ctx = remote.cpu(0)
# load remote
rlib = remote.load_module("net.o")
# Create remotemodule
m = graph_runtime.create(graph, rlib, remote.cpu(0))
# get member functions
set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
na = tvm.nd.array(np.ones(shape).astype(dtype), ctx)
nb = tvm.nd.array(np.ones(shape).astype(dtype), ctx)
# set inputs
set_input("x", na)
set_input("y", nb)
# execute
run()
# get outputs
out = tvm.nd.empty(shape, dtype, ctx)
get_output(0, out)
np.testing.assert_allclose(
out.asnumpy(), np.exp(na.asnumpy() + nb.asnumpy()))
server.terminate()
def test_rpc_executor():
host = "localhost"
port = 9091
server = rpc.Server(host, port)
x = sym.Variable("x")
y = sym.Variable("y")
z = sym.exp(y + x)
shape = (10, 128)
dtype = tvm.float32
shape_dict = {"x": shape, "y": shape}
tmp = util.tempdir()
lib_name = tmp.relpath("net.o")
graph, lib, _ = nnvm.compiler.build(z, "llvm", shape_dict)
# save module
lib.save(lib_name)
remote = rpc.connect(host, port)
remote.upload(lib_name)
ctx = remote.cpu(0)
# load remote
rlib = remote.load_module("net.o")
# Create remotemodule
m = graph_runtime.create(graph, rlib, remote.cpu(0))
# get member functions
set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
na = tvm.nd.array(np.ones(shape).astype(dtype), ctx)
nb = tvm.nd.array(np.ones(shape).astype(dtype), ctx)
# set inputs
set_input("x", na)
set_input("y", nb)
# execute
run()
# get outputs
out = tvm.nd.empty(shape, dtype, ctx)
get_output(0, out)
np.testing.assert_allclose(out.asnumpy(),
np.exp(na.asnumpy() + nb.asnumpy()))
server.terminate()
def measure_peak_all(target, target_host, host, port):
"""measure memory bandwidth and peak compute for gpu devices
Parameters
----------
target: str or :any:`tvm.target.Target`
target_host: str
host: str
port: int
"""
target = tvm.target.create(target)
remote = rpc.connect(host, port)
n_times = 20
bandwidth_total_item = 1 << 25
bandwidth_item_per_thread = 32
compute_total_item = 1 << 21
compute_item_per_thread = 4096
if str(target).startswith("opencl"):
ctx = remote.cl()
elif str(target).startswith("cuda"):
ctx = remote.gpu()
elif str(target).startswith("metal"):
ctx = remote.metal()
else:
raise RuntimeError("Unsupported target")
logging.info("========== measure memory bandwidth ==========")
measure_bandwidth_all_types(bandwidth_total_item, bandwidth_item_per_thread,
n_times, target, target_host, remote, ctx)
logging.info("========== measure peak compute ==========")
measure_compute_all_types(compute_total_item, compute_item_per_thread,
n_times, target, target_host, remote, ctx)
def check():
if not tvm.module.enabled("rpc"):
return
@tvm.register_func("rpc.test2.addone")
def addone(x):
return x + 1
@tvm.register_func("rpc.test2.strcat")
def addone(name, x):
return "%s:%d" % (name, x)
server = multiprocessing.Process(
target=proxy.websocket_proxy_server,
args=("ws://localhost:%d/ws" % web_port, "x1"))
# Need to make sure that the connection start after proxy comes up
time.sleep(0.1)
server.deamon = True
server.start()
client = rpc.connect(prox.host, prox.port, key="x1")
f1 = client.get_function("rpc.test2.addone")
assert f1(10) == 11
f2 = client.get_function("rpc.test2.strcat")
assert f2("abc", 11) == "abc:11"
def test_bigendian_rpc_param():
"""Test big endian rpc when there is a PowerPC RPC server available"""
host = os.environ.get("TVM_POWERPC_TEST_HOST", None)
port = os.environ.get("TVM_POWERPC_TEST_PORT", 9090)
if host is None:
return
def verify_nnvm(remote, target, shape, dtype):
x = nnvm.sym.Variable("x")
y = x + 1
graph, lib, _ = nnvm.compiler.build(y,
target,
shape={"x": shape},
dtype={"x": dtype})
temp = util.tempdir()
path_dso = temp.relpath("dev_lib.o")
lib.save(path_dso)
remote.upload(path_dso)
lib = remote.load_module("dev_lib.o")
a = np.random.randint(0, 256, size=shape).astype(dtype)
a[:] = 1
params = {"x": a}
ctx = remote.cpu(0)
m = graph_runtime.create(graph, lib, ctx)
# uses save param_dict
m.load_params(nnvm.compiler.save_param_dict(params))
m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype, ctx=ctx))
np.testing.assert_allclose(a + 1, out.asnumpy())
print("Test RPC connection to PowerPC...")
remote = rpc.connect(host, port)
target = "llvm -mtriple=powerpc-linux-gnu"
for dtype in ["float32", "float64", "int32", "int8"]:
verify_nnvm(remote, target, (10, ), dtype)
from tvm.contrib import rpc
REMOTE = rpc.connect("0.0.0.0", 9090, key="android")
input_name = 'input_0'
data_shape = img.shape
out_shape = (1,125,n//32,n//32)
# GET model from frameworks
# change xyz to supported framework name.
sym, params = nnvm.frontend.from_onnx(onnx_graph)
#print(sym.debug_str()
# connect to the proxy
# Set to be address of tvm proxy.
proxy_host = os.environ["TVM_ANDROID_RPC_PROXY_HOST"]
proxy_port = 9090
key = "android"
print('RPC Connecting...')
remote = rpc.connect(proxy_host, proxy_port, key=key)
print('RPC Connected')
arch = "arm64"
if exec_gpu:
# Mobile GPU
target = 'opencl'
target_host = "llvm -target=%s-linux-android" % arch
ctx = remote.cl(0)
else:
# Mobile CPU
target = "llvm -target=%s-linux-android" % arch
target_host = None
ctx = remote.cpu(0)
print('Build Graph...')
def test_gemm_gpu(N, times, bn, num_block, num_thread):
assert (bn <= N)
assert (num_thread * num_thread * 16 <= N)
assert (num_block * num_block * 2 <= N)
A = tvm.placeholder((N, N), name='A')
B = tvm.placeholder((N, N), name='Btmp')
k = tvm.reduce_axis((0, N), name='k')
packedB = tvm.compute((N, N / bn, bn),
lambda x, y, z: B[x, y * bn + z],
name='B')
C = tvm.compute((N, N),
lambda ii, jj: tvm.sum(
A[ii, k] * packedB[k, jj / bn, jj % bn], axis=k),
name='C')
s = tvm.create_schedule(C.op)
CC = s.cache_write(C, "local")
block_x = tvm.thread_axis("blockIdx.x")
block_y = tvm.thread_axis("blockIdx.y")
thread_x = tvm.thread_axis("threadIdx.x")
thread_y = tvm.thread_axis("threadIdx.y")
thread_xz = tvm.thread_axis((0, 2), "vthread", name="vx")
thread_yz = tvm.thread_axis((0, 2), "vthread", name="vy")
pby, pbi = s[packedB].split(packedB.op.axis[0], nparts=num_thread)
pbx, pbj = s[packedB].split(packedB.op.axis[1], nparts=num_thread)
s[packedB].bind(pby, thread_y)
s[packedB].bind(pbx, thread_x)
pbz, pbk = s[packedB].split(packedB.op.axis[2], factor=8)
s[packedB].vectorize(pbk)
by, yi = s[C].split(C.op.axis[0], nparts=num_block)
bx, xi = s[C].split(C.op.axis[1], nparts=num_thread)
s[C].bind(by, block_y)
s[C].bind(bx, thread_y)
s[C].reorder(by, bx, yi, xi)
tyz, yi = s[C].split(yi, nparts=2)
ty, yi = s[C].split(yi, nparts=num_block)
txz, xi = s[C].split(xi, nparts=2)
tx, xi = s[C].split(xi, nparts=num_thread)
s[C].reorder(tyz, txz, ty, tx, yi, xi)
s[C].bind(tyz, thread_yz)
s[C].bind(txz, thread_xz)
s[C].bind(ty, block_x)
s[C].bind(tx, thread_x)
xyi, xxi = s[C].split(xi, factor=8)
s[C].reorder(tyz, txz, ty, tx, yi, xyi, xxi)
s[C].vectorize(xxi)
s[CC].compute_at(s[C], yi)
yo, xo = CC.op.axis
s[CC].reorder(k, yo, xo)
xo, xi = s[CC].split(xo, factor=8)
s[CC].vectorize(xi)
ko, ki = s[CC].split(k, factor=2)
s[CC].unroll(ki)
print(tvm.lower(s, [A, B, C], simple_mode=True))
f = tvm.build(s, [A, B, C], "opencl", target_host=target, name="gemm_gpu")
temp = util.tempdir()
path_dso = temp.relpath("gemm_gpu.so")
f.export_library(path_dso, ndk.create_shared)
# connect to the proxy
remote = rpc.connect(proxy_host, proxy_port, key=key)
ctx = remote.cl(0)
remote.upload(path_dso)
f = remote.load_module("gemm_gpu.so")
evaluate(f, ctx, N, times)
# library and the new parameter, since we do some optimization that will
# change the parameters but keep the result of model as the same.
# Save the library at local temporary directory.
tmp = util.tempdir()
lib_fname = tmp.relpath('net.tar')
lib.export_library(lib_fname)
######################################################################
# Deploy the Model Remotely by RPC
# --------------------------------
# With RPC, you can deploy the model remotely from your host machine
# to the remote device.
# connect the server
remote = rpc.connect(host, port)
# upload the library to remote device and load it
remote.upload(lib_fname)
rlib = remote.load_module('net.tar')
ctx = remote.cl(0)
# upload the parameter
rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
# create the remote runtime module
module = runtime.create(graph, rlib, ctx)
# set parameter
module.set_input(**rparams)
# set input data
module.set_input('data', tvm.nd.array(x.astype('float32')))
graph, lib, params = nnvm.compiler.build(sym,
target,
shape_dict,
params=params,
target_host=target_host)
# Save the library at local temporary directory.
tmp = util.tempdir()
path_o = tmp.relpath('sym.o')
path_cl = tmp.relpath('sym.cl')
path_json = tmp.relpath('sym.tvm_meta.json')
lib.save(path_o)
lib.imported_modules[0].save(path_cl)
# connect the server
remote = rpc.connect('192.168.1.14', 9090)
# upload the library to remote device and load it
remote.upload(path_o)
remote.upload(path_cl)
remote.upload(path_json)
fhost = remote.load_module('sym.o')
fdev = remote.load_module('sym.cl')
fhost.import_module(fdev)
from tvm.contrib import graph_runtime
ctx = remote.cl(0)
# upload the parameter
rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
dtype = 'float32'
def run_case(model, dtype):
# load model
if model == 'vgg16':
net, params = nnvm.testing.vgg.get_workload(num_layers=16,
batch_size=1,
image_shape=image_shape,
dtype=dtype)
elif model == 'resnet18':
net, params = nnvm.testing.resnet.get_workload(num_layers=18,
batch_size=1,
image_shape=image_shape,
dtype=dtype)
elif model == 'mobilenet':
net, params = nnvm.testing.mobilenet.get_workload(
batch_size=1, image_shape=image_shape, dtype=dtype)
else:
raise ValueError('no benchmark prepared for {}.'.format(model))
# compile
opt_level = 2 if dtype == 'float32' else 1
with nnvm.compiler.build_config(opt_level=opt_level):
graph, lib, params = nnvm.compiler.build(net,
tvm.target.mali(),
shape={"data": data_shape},
params=params,
dtype=dtype,
target_host=args.target_host)
# upload model to remote device
tmp = util.tempdir()
lib_fname = tmp.relpath('net.tar')
lib.export_library(lib_fname)
if args.host is not None:
remote = rpc.connect(args.host, args.port)
remote.upload(lib_fname)
ctx = remote.cl(0)
rlib = remote.load_module('net.tar')
rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
else:
ctx = tvm.cl(0)
rlib = lib
rparams = params
# create graph runtime
module = runtime.create(graph, rlib, ctx)
module.set_input(
'data',
tvm.nd.array(np.random.uniform(size=(data_shape)).astype(dtype)))
module.set_input(**rparams)
# benchmark
# print("============================================================")
# print("model: %s, dtype: %s" % (model, dtype))
# the num of runs for warm up and test
num_warmup = 10
num_test = 60
if model == 'mobilenet': # mobilenet is fast, need more runs for stable measureament
num_warmup *= 5
num_test *= 5
# perform some warm up runs
# print("warm up..")
warm_up_timer = module.module.time_evaluator("run", ctx, num_warmup)
warm_up_timer()
# test
# print("test..")
ftimer = module.module.time_evaluator("run", ctx, num_test)
prof_res = ftimer()
# print("cost per image: %.4fs" % prof_res.mean)
print("backend: TVM-mali\tmodel: %s\tdtype: %s\tcost:%.4f" %
(model, dtype, prof_res.mean))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
type=str,
required=True,
choices=['resnet', 'mobilenet'],
help="The model type.")
parser.add_argument('--host',
type=str,
required=True,
help="The host address of your Raspberry Pi.")
parser.add_argument('--port',
type=int,
required=True,
help="The port number of your Raspberry Pi.")
parser.add_argument('--opt-level',
type=int,
default=1,
help="Level of optimization.")
parser.add_argument('--num-iter',
type=int,
default=50,
help="Number of iteration during benchmark.")
args = parser.parse_args()
opt_level = args.opt_level
target = "llvm -target=armv7l-none-linux-anueabihf -mcpu=cortex-a53 -mattr=+neon"
num_iter = args.num_iter
batch_size = 1
num_classes = 1000
image_shape = (3, 224, 224)
data_shape = (batch_size, ) + image_shape
out_shape = (batch_size, num_classes)
if args.model == 'resnet':
net, params = nnvm.testing.resnet.get_workload(batch_size=1,
image_shape=image_shape)
elif args.model == 'mobilenet':
net, params = nnvm.testing.mobilenet.get_workload(
batch_size=1, image_shape=image_shape)
else:
raise ValueError('no benchmark prepared for {}.'.format(args.model))
with nnvm.compiler.build_config(opt_level=opt_level):
with tvm.target.rasp():
graph, lib, params = nnvm.compiler.build(
net, target, shape={"data": data_shape}, params=params)
tmp = util.tempdir()
lib_fname = tmp.relpath('net.o')
lib.save(lib_fname)
remote = rpc.connect(args.host, args.port)
remote.upload(lib_fname)
ctx = remote.cpu(0)
rlib = remote.load_module('net.o')
rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
module = runtime.create(graph, rlib, ctx)
module.set_input(
'data',
tvm.nd.array(np.random.uniform(size=(data_shape)).astype("float32")))
module.set_input(**rparams)
module.run()
out = module.get_output(0, tvm.nd.empty(out_shape, ctx=ctx))
out.asnumpy()
print('benchmark args: {}'.format(args))
ftimer = module.module.time_evaluator("run", ctx, num_iter)
for i in range(3):
prof_res = ftimer()
print(prof_res)
# sleep for avoiding cpu overheat
time.sleep(45)
def deploy_rpc():
"""Runs the demo that deploys a model remotely through RPC.
"""
from tvm.contrib import rpc, util, emscripten
# As usual, load the resnet18 model.
net, params, data_shape, out_shape = load_mxnet_resnet()
# Compile the model.
# Note that this time we are changing the target.
# This is because we want to translate the host library into JavaScript
# through Emscripten.
graph, lib, params = compile_net(
net,
target_host="llvm -target=asmjs-unknown-emscripten -system-lib",
target="opengl",
data_shape=data_shape,
params=params)
# Now we want to deploy our model through RPC.
# First we ned to prepare the module files locally.
print("Saving the compiled module...")
temp = util.tempdir()
path_obj = temp.relpath("deploy.bc") # host LLVM part
path_dso = temp.relpath("deploy.js") # host JavaScript part
path_gl = temp.relpath("deploy.gl") # device GLSL part
path_json = temp.relpath("deploy.tvm_meta.json")
lib.save(path_obj)
emscripten.create_js(path_dso, path_obj, side_module=True)
lib.imported_modules[0].save(path_gl)
print("- Saved files:", temp.listdir())
# Connect to the RPC server.
print("Connecting to RPC server...")
proxy_host = 'localhost'
proxy_port = 9090
remote = rpc.connect(proxy_host, proxy_port, key="js")
print("- Connected to RPC server!")
# Upload module to RPC server.
print("Uploading module to RPC server...")
remote.upload(path_dso, "deploy.dso")
remote.upload(path_gl)
remote.upload(path_json)
print("- Upload completed!")
# Load remote library.
print("Loading remote library...")
fdev = remote.load_module("deploy.gl")
fhost = remote.load_module("deploy.dso")
fhost.import_module(fdev)
rlib = fhost
print("- Remote library loaded!")
ctx = remote.opengl(0)
# Upload the parameters.
print("Uploading parameters...")
rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
print("- Parameters uploaded!")
# Create the remote runtime module.
print("Running remote module...")
from tvm.contrib import graph_runtime
module = graph_runtime.create(graph, rlib, ctx)
# Set parameter.
module.set_input(**rparams)
# Set input data.
input_data = np.random.uniform(size=data_shape)
module.set_input('data', tvm.nd.array(input_data.astype('float32')))
# Run.
module.run()
print("- Remote module execution completed!")
out = module.get_output(0, out=tvm.nd.empty(out_shape, ctx=ctx))
# Print first 10 elements of output.
print(out.asnumpy()[0][0:10])
##---------------------------------
## Under debugging
##---------------------------------
import cv2
import time
inshape = (1,3,224,224)
outshape = (1,1000)
basename="models/alexnet/deploy_rasp"
loaded_params = bytearray(open(basename+".params", "rb").read())
# connect the server
remote = rpc.connect("raspberrypi.local", 9090)
# upload the library to remote device and load it
lib_fname='models/alexnet/deploy_rasp.o'
print("uploading")
remote.upload(lib_fname)
print("loading module")
rlib = remote.load_module("deploy_rasp.o")
print("loading graph")
graph = open(basename+".json").read()
ctx = remote.cpu(0)
# upload the parameter
print("loading paramdict")
params = nnvm.compiler.load_param_dict(loaded_params)
print("converting paramdict")
def test_gemm_gpu(N, times, bn, num_block, num_thread):
assert(bn <= N)
assert(num_thread * num_thread * 16 <= N)
assert(num_block * num_block * 2 <= N)
A = tvm.placeholder((N, N), name='A')
B = tvm.placeholder((N, N), name='Btmp')
k = tvm.reduce_axis((0, N), name='k')
packedB = tvm.compute((N, N / bn, bn),
lambda x, y, z: B[x, y * bn + z], name = 'B')
C = tvm.compute(
(N, N),
lambda ii, jj: tvm.sum(A[ii, k] * packedB[k, jj / bn, jj % bn], axis=k),
name='C')
s = tvm.create_schedule(C.op)
CC = s.cache_write(C, "local")
block_x = tvm.thread_axis("blockIdx.x")
block_y = tvm.thread_axis("blockIdx.y")
thread_x = tvm.thread_axis("threadIdx.x")
thread_y = tvm.thread_axis("threadIdx.y")
thread_xz = tvm.thread_axis((0, 2), "vthread", name="vx")
thread_yz = tvm.thread_axis((0, 2), "vthread", name="vy")
pby, pbi = s[packedB].split(packedB.op.axis[0], nparts=num_thread)
pbx, pbj = s[packedB].split(packedB.op.axis[1], nparts=num_thread)
s[packedB].bind(pby, thread_y)
s[packedB].bind(pbx, thread_x)
pbz, pbk = s[packedB].split(packedB.op.axis[2], factor=8)
s[packedB].vectorize(pbk)
by, yi = s[C].split(C.op.axis[0], nparts=num_block)
bx, xi = s[C].split(C.op.axis[1], nparts=num_thread)
s[C].bind(by, block_y)
s[C].bind(bx, thread_y)
s[C].reorder(by, bx, yi, xi)
tyz, yi = s[C].split(yi, nparts=2)
ty, yi = s[C].split(yi, nparts=num_block)
txz, xi = s[C].split(xi, nparts=2)
tx, xi = s[C].split(xi, nparts=num_thread)
s[C].reorder(tyz, txz, ty, tx, yi, xi)
s[C].bind(tyz, thread_yz)
s[C].bind(txz, thread_xz)
s[C].bind(ty, block_x)
s[C].bind(tx, thread_x)
xyi, xxi = s[C].split(xi, factor=8)
s[C].reorder(tyz, txz, ty, tx, yi, xyi, xxi)
s[C].vectorize(xxi)
s[CC].compute_at(s[C], yi)
yo, xo = CC.op.axis
s[CC].reorder(k, yo, xo)
xo, xi = s[CC].split(xo, factor=8)
s[CC].vectorize(xi)
ko, ki = s[CC].split(k, factor=2)
s[CC].unroll(ki)
print(tvm.lower(s, [A, B, C], simple_mode=True))
f = tvm.build(s, [A, B, C], "opencl", target_host=target, name="gemm_gpu")
temp = util.tempdir()
path_dso = temp.relpath("gemm_gpu.so")
f.export_library(path_dso, ndk.create_shared)
# connect to the proxy
remote = rpc.connect(proxy_host, proxy_port, key=key)
ctx = remote.cl(0)
remote.upload(path_dso)
f = remote.load_module("gemm_gpu.so")
evaluate(f, ctx, N, times)
# It is recommended to set target triple and feature set to contain specific
# feature available, so we can take full advantage of the features of the
# board.
# You can find more details about cross compilation attributes from
# `LLVM guide of cross compilation <https://clang.llvm.org/docs/CrossCompilation.html>`_.
######################################################################
# Run Kernel Remotely by RPC
# --------------------------
# Here we will show you how to run the kernel on the remote device:
# replace host with the ip address of your device
host = '0.0.0.0'
port = 9090
# connect the remote device
remote = rpc.connect(host, port)
######################################################################
# Here we upload the lib to the remote device, then invoke a device local
# compiler for shared lib and load it into device memory. now `f` is a
# remote module object.
remote.upload(path)
f = remote.load_module('mylib.o')
# create array on the remote device
ctx = remote.cpu(0)
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
# the function will run on the remote device
f(a, b)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
def test_rpc_remote_module():
if not tvm.module.enabled("rpc"):
return
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
# graph
n = tvm.convert(1024)
A = tvm.placeholder((n,), name='A')
B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
s = tvm.create_schedule(B.op)
def check_remote():
if not tvm.module.enabled("llvm"):
print("Skip because llvm is not enabled")
return
temp = util.tempdir()
ctx = remote.cpu(0)
f = tvm.build(s, [A, B], "llvm", name="myadd")
path_dso = temp.relpath("dev_lib.so")
f.export_library(path_dso)
remote.upload(path_dso)
f1 = remote.load_module("dev_lib.so")
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, remote.cpu(0), number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
def check_remote_link_cl():
"""Test function to run remote code such as cl
This is not enabled because there is forking issue
of TVM runtime when server launches after OpenCL
runtime initializes. We leave it as an example
on how to do rpc when we want to do linking on remote.
"""
if not tvm.module.enabled("llvm"):
print("Skip because llvm is not enabled")
return
if not tvm.module.enabled("opencl"):
print("Skip because opencl is not enabled")
return
temp = util.tempdir()
ctx = remote.cl(0)
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=32)
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
f = tvm.build(s, [A, B], "opencl", target_host="llvm", name="myadd")
# Option 1: save modules separately and rely on remote compiler
path_o = temp.relpath("myadd.o")
path_cl = temp.relpath("myadd.cl")
path_json = temp.relpath("myadd.tvm_meta.json")
f.save(path_o)
f.imported_modules[0].save(path_cl)
remote.upload(path_o)
remote.upload(path_cl)
# upload meta data
remote.upload(path_json)
fhost = remote.load_module("myadd.o")
fdev = remote.load_module("myadd.cl")
fhost.import_module(fdev)
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
fhost(a, b)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
# Option 2: export library as a tar ball then handled by remote compiler
path_tar = temp.relpath("myadd.tar")
f.export_library(path_tar)
remote.upload(path_tar)
fhost = remote.load_module("myadd.tar")
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
fhost(a, b)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
check_remote()
def test_rpc_remote_module():
if not tvm.module.enabled("rpc"):
return
server = rpc.Server("localhost")
remote = rpc.connect(server.host, server.port)
# graph
n = tvm.convert(1024)
A = tvm.placeholder((n, ), name='A')
B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
s = tvm.create_schedule(B.op)
def check_remote():
if not tvm.module.enabled("llvm"):
print("Skip because llvm is not enabled")
return
temp = util.tempdir()
ctx = remote.cpu(0)
f = tvm.build(s, [A, B], "llvm", name="myadd")
path_dso = temp.relpath("dev_lib.so")
f.export_library(path_dso)
remote.upload(path_dso)
f1 = remote.load_module("dev_lib.so")
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, remote.cpu(0), number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
def check_remote_link_cl():
"""Test function to run remote code such as cl
This is not enabled because there is forking issue
of TVM runtime when server launches after OpenCL
runtime initializes. We leave it as an example
on how to do rpc when we want to do linking on remote.
"""
if not tvm.module.enabled("llvm"):
print("Skip because llvm is not enabled")
return
if not tvm.module.enabled("opencl"):
print("Skip because opencl is not enabled")
return
temp = util.tempdir()
ctx = remote.cl(0)
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=32)
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
f = tvm.build(s, [A, B], "opencl", target_host="llvm", name="myadd")
# Option 1: save modules separately and rely on remote compiler
path_o = temp.relpath("myadd.o")
path_cl = temp.relpath("myadd.cl")
path_json = temp.relpath("myadd.tvm_meta.json")
f.save(path_o)
f.imported_modules[0].save(path_cl)
remote.upload(path_o)
remote.upload(path_cl)
# upload meta data
remote.upload(path_json)
fhost = remote.load_module("myadd.o")
fdev = remote.load_module("myadd.cl")
fhost.import_module(fdev)
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
fhost(a, b)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
# Option 2: export library as a tar ball then handled by remote compiler
path_tar = temp.relpath("myadd.tar")
f.export_library(path_tar)
remote.upload(path_tar)
fhost = remote.load_module("myadd.tar")
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
fhost(a, b)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
check_remote()