def try_remote_save_load():
if not tvm.runtime.enabled("rpc"):
return
if not tvm.runtime.enabled("opengl"):
return
if not tvm.runtime.enabled("llvm"):
return
# Build the module.
n = te.var("n")
A = te.placeholder((n, ), name='A')
B = te.placeholder((n, ), name='B')
C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")
s = te.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)
tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
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 prepare_test_libs(base_path):
target = "llvm -target=asmjs-unknown-emscripten -system-lib"
if not tvm.module.enabled(target):
raise RuntimeError("Target %s is not enbaled" % target)
n = tvm.var("n")
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)
fadd1 = tvm.build(s, [A, B], target, name="add_one")
obj_path = os.path.join(base_path, "test_add_one.bc")
fadd1.save(obj_path)
emscripten.create_js(os.path.join(base_path, "test_module.js"), obj_path)
def prepare_test_libs(base_path):
target = "llvm -target=asmjs-unknown-emscripten -system-lib"
if not tvm.module.enabled(target):
raise RuntimeError("Target %s is not enbaled" % target)
n = tvm.var("n")
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)
fadd1 = tvm.build(s, [A, B], target, name="add_one")
obj_path = os.path.join(base_path, "test_add_one.bc")
fadd1.save(obj_path)
emscripten.create_js(os.path.join(base_path, "test_module.js"), obj_path)
def check_remote():
if not tvm.runtime.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)
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)
def deploy_rpc():
"""Runs the demo that deploys a model remotely through RPC.
"""
from tvm import rpc
from tvm.contrib import 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])
def deploy_rpc():
"""Runs the demo that deploys a model remotely through RPC.
"""
from tvm import rpc
from tvm.contrib import 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])
