def test_adt_list():
mod = relay.Module()
p = Prelude(mod)
l1 = p.cons(relay.const(1), p.nil())
l21 = p.cons(relay.const(2), l1)
l321 = p.cons(relay.const(3), l21)
f = relay.Function([], l321)
mod["main"] = f
exe = create_exec(mod)
code, lib = exe.save()
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec)
des_vm.init(tvm.cpu())
result = veval(des_vm)
assert len(result) == 2
assert len(result[1]) == 2
assert len(result[1][1]) == 2
res = []
res.append(result[0].asnumpy().tolist())
res.append(result[1][0].asnumpy().tolist())
res.append(result[1][1][0].asnumpy().tolist())
tvm.testing.assert_allclose(res, np.array([3, 2, 1]))
def test_loop():
mod = relay.module.Module({})
sum_up = relay.GlobalVar('sum_up')
i = relay.var('i', shape=[], dtype='int32')
accum = relay.var('accum', shape=[], dtype='int32')
sb = ScopeBuilder()
with sb.if_scope(relay.equal(i, relay.const(0, 'int32'))):
sb.ret(accum)
with sb.else_scope():
one_less = relay.subtract(i, relay.const(1, 'int32'))
new_accum = relay.add(accum, i)
sb.ret(relay.Call(sum_up, [one_less, new_accum]))
func = relay.Function([i, accum], sb.get())
mod[sum_up] = func
loop_bound = 0
i_data = np.array(loop_bound, dtype='int32')
accum_data = np.array(0, dtype='int32')
iarg = relay.var('i', shape=[], dtype='int32')
aarg = relay.var('accum', shape=[], dtype='int32')
mod["main"] = relay.Function([iarg, aarg], sum_up(iarg, aarg))
exe = create_exec(mod)
code, lib = exe.save()
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec)
des_vm.init(tvm.cpu())
result = veval(des_vm, i_data, accum_data)
tvm.testing.assert_allclose(result.asnumpy(), sum(range(1, loop_bound + 1)))
def get_serialized_output(mod, *data, params=None, target="llvm", ctx=tvm.cpu()):
exe = create_exec(mod, target, params=params)
code, lib = exe.save()
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec, ctx)
result = des_vm.run(*data)
return result
def get_vm_output(mod,
data,
params,
target,
ctx,
dtype='float32',
number=2,
repeat=20):
with tvm.transform.PassContext(opt_level=3):
exe = vm.compile(mod, target, params=params)
rly_vm = vm_rt.VirtualMachine(exe)
rly_vm.init(ctx)
result = rly_vm.run(data)
if measure:
print("Evaluate vm inference cost of {} on {}".format(
model, repr(ctx)))
ftimer = rly_vm.mod.time_evaluator("invoke",
ctx,
number=number,
repeat=repeat)
# Measure in millisecond.
prof_res = np.array(ftimer("main", data).results) * 1000
print("Mean vm inference time (std dev): %.2f ms (%.2f ms)" %
(np.mean(prof_res), np.std(prof_res)))
return result.asnumpy().astype(dtype)
def test_save_load():
x = relay.var("x", shape=(10, 10))
f = relay.Function([x], x + x)
x_data = np.random.rand(10, 10).astype("float32")
# serialize.
vm = create_exec(f)
code, lib = vm.save()
assert isinstance(code, bytearray)
# save and load the code and lib file.
tmp = utils.tempdir()
path_lib = tmp.relpath("lib.so")
lib.export_library(path_lib)
with open(tmp.relpath("code.ro"), "wb") as fo:
fo.write(code)
loaded_lib = tvm.runtime.load_module(path_lib)
loaded_code = bytearray(open(tmp.relpath("code.ro"), "rb").read())
# deserialize.
des_exec = _vm.Executable.load_exec(loaded_code, loaded_lib)
des_vm = _vm.VirtualMachine(des_exec, tvm.cpu())
res = des_vm.run(x_data)
tvm.testing.assert_allclose(res.asnumpy(), x_data + x_data)
def __init__(self, mod, ctx, target):
if mod is None:
raise RuntimeError("Must provide module to get VM executor.")
self.mod = mod
self.ctx = ctx
self.target = target
self.executable = compile(mod, target)
self.vm = vm_rt.VirtualMachine(self.executable, ctx)
def get_serialized_output(mod, data, params, target, ctx, dtype='float32'):
exe = create_exec(mod, target, params=params)
code, lib = exe.save()
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec)
des_vm.init(ctx)
result = des_vm.run(data)
return result.asnumpy().astype(dtype)
def test_vm_onnx_process():
import onnx
onnx_model_path = "/data00/cuiqing.li/onnx_models/sr_dy.onnx"
onnx_model = onnx.load(onnx_model_path)
shape_dict = {"input.1": (1, 1, 640, 360)}
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
target = tvm.target.cuda()
ctx = tvm.context(str(target), 0)
with tvm.transform.PassContext(opt_level=3,
disabled_pass=["FoldScaleAxis"]):
exe = vm.compile(mod, target, params=params)
code, lib = exe.save()
saved_dir = "tmp"
if os.path.isdir("./tmp") == False:
os.system("mkdir {}".format(saved_dir))
path_lib = os.path.join(saved_dir, "lib.so")
lib.export_library(path_lib)
code_path = os.path.join(saved_dir, "code.ro")
with open(code_path, "wb") as fo:
fo.write(code)
loaded_lib = tvm.runtime.load_module(path_lib)
loaded_code = bytearray(open(code_path, "rb").read())
# deserialize.
des_exec = _vm.Executable.load_exec(loaded_code, loaded_lib)
des_vm = _vm.VirtualMachine(des_exec, ctx)
input_shape = [1, 1, 640, 360]
dtype = "float32"
data_tvm = tvm.nd.array(
(np.random.uniform(size=input_shape)).astype(dtype))
data = []
data.append(data_tvm)
data = tuple(data)
res = des_vm.run(*data)
print("Evaluate vm inference cost of {} on {}".format(
"your testing model", repr(ctx)))
ftimer_warmup = des_vm.module.time_evaluator("invoke",
ctx,
number=1,
repeat=50)
# Measure in millisecond.
print("finished warming up and start testing vm compile performance")
ftimer = des_vm.module.time_evaluator("invoke",
ctx,
number=1,
repeat=600)
# Measure in millisecond.
prof_res = np.array(ftimer("main", *data).results) * 1000
#prof_res = np.array(ftimer().results) * 1000
print("Mean vm inference time (std dev): %.2f ms (%.2f ms)" %
(np.mean(prof_res), np.std(prof_res)))
def _make_executor(self, expr=None):
if expr:
self.mod["main"] = expr
self.executable = compile(self.mod, self.target)
self.vm = vm_rt.VirtualMachine(self.executable, self.device)
def _vm_wrapper(*args, **kwargs):
args = self._convert_args(self.mod["main"], args, kwargs)
return self.vm.run(*args)
return _vm_wrapper
def test_const():
c = relay.const(1.0, "float32")
x = relay.var('x', shape=(10, 10), dtype='float32')
f = relay.Function([x], x + c)
exe = create_exec(f)
code, lib = exe.save()
assert isinstance(code, bytearray)
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec)
des_vm.init(tvm.cpu())
x_data = np.random.rand(10, 10).astype('float32')
res = veval(des_vm, x_data)
tvm.testing.assert_allclose(res.asnumpy(), x_data + 1)
def test_tuple():
ttype = relay.TupleType([relay.TensorType((1,)), relay.TensorType((10,))])
tup = relay.var('tup', type_annotation=ttype)
f = relay.Function([tup], relay.TupleGetItem(tup, 1))
i_data = np.random.rand(41).astype('float32')
j_data = np.random.rand(10).astype('float32')
exe = create_exec(f)
code, lib = exe.save()
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec)
des_vm.init(tvm.cpu())
result = veval(des_vm, (i_data, j_data))
tvm.testing.assert_allclose(result.asnumpy(), j_data)
def test_closure():
x = relay.var('x', shape=())
y = relay.var('y', shape=())
f = relay.Function([x], x + y)
ff = relay.Function([y], f)
clo = ff(relay.const(1.0))
main = clo(relay.const(2.0))
exe = create_exec(main)
code, lib = exe.save()
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec)
des_vm.init(tvm.cpu())
res = veval(des_vm)
tvm.testing.assert_allclose(res.asnumpy(), 3.0)
def test_adt_compose():
mod = relay.Module()
p = Prelude(mod)
compose = p.compose
# add_one = fun x -> x + 1
sb = relay.ScopeBuilder()
x = relay.var('x', 'float32')
x1 = sb.let('x1', x)
xplusone = x1 + relay.const(1.0, 'float32')
sb.ret(xplusone)
body = sb.get()
add_one = relay.GlobalVar("add_one")
add_one_func = relay.Function([x], body)
# add_two = compose(add_one, add_one)
sb = relay.ScopeBuilder()
y = relay.var('y', 'float32')
add_two_func = sb.let('add_two', compose(add_one_func, add_one_func))
add_two_res = add_two_func(y)
sb.ret(add_two_res)
add_two_body = sb.get()
mod[add_one] = add_one_func
f = relay.Function([y], add_two_body)
mod["main"] = f
exe = create_exec(mod)
code, lib = exe.save()
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec)
des_vm.init(tvm.cpu())
x_data = np.array(np.random.rand()).astype('float32')
result = veval(des_vm, x_data)
tvm.testing.assert_allclose(result.asnumpy(), x_data + 2.0)
def main():
params_dict = config
saved_dir = params_dict['saved_dir']
#target = "cuda -libs=cudnn,cublas"
target = params_dict['target']
ctx = tvm.context(str(target), 0)
path_lib = os.path.join(saved_dir, "lib.so")
code_path = os.path.join(saved_dir, "code.ro")
loaded_lib = tvm.runtime.load_module(path_lib)
loaded_code = bytearray(open(code_path, "rb").read())
# deserialize.
des_exec = _vm.Executable.load_exec(loaded_code, loaded_lib)
des_vm = _vm.VirtualMachine(des_exec, ctx)
data = []
dtype = params_dict['dtype']
for input_shape in params_dict['inference_input_shapes']:
data_tvm = tvm.nd.array(
(np.random.uniform(size=input_shape)).astype(dtype))
data.append(data_tvm)
data = tuple(data)
res = des_vm.run(*data)
print("Evaluate vm inference cost of {} on {}".format(
"your testing model", repr(ctx)))
ftimer_warmup = des_vm.module.time_evaluator("invoke",
ctx,
number=1,
repeat=50)
# Measure in millisecond.
print("finished warming up and start testing vm compile performance")
ftimer = des_vm.module.time_evaluator("invoke", ctx, number=1, repeat=600)
# Measure in millisecond.
prof_res = np.array(ftimer("main", *data).results) * 1000
#prof_res = np.array(ftimer().results) * 1000
print("Mean vm inference time (std dev): %.2f ms (%.2f ms)" %
(np.mean(prof_res), np.std(prof_res)))
def test_if():
x = relay.var('x', shape=(10, 10))
y = relay.var('y', shape=(10, 10))
equal = relay.op.equal(x, y)
equal = relay.op.nn.batch_flatten(equal)
f = relay.Function([x, y], relay.If(relay.op.min(equal, axis=[0, 1]), x,
y))
x_data = np.random.rand(10, 10).astype('float32')
y_data = np.random.rand(10, 10).astype('float32')
exe = create_exec(f)
code, lib = exe.save()
des_exec = _vm.Executable.load_exec(code, lib)
des_vm = _vm.VirtualMachine(des_exec)
des_vm.init(tvm.cpu())
# same
res = veval(des_vm, x_data, x_data)
tvm.testing.assert_allclose(res.asnumpy(), x_data)
# diff
res = veval(des_vm, x_data, y_data)
tvm.testing.assert_allclose(res.asnumpy(), y_data)
def run_module(
tvmc_package: TVMCPackage,
device: str,
hostname: Optional[str] = None,
port: Union[int, str] = 9090,
rpc_key: Optional[str] = None,
inputs: Optional[Dict[str, np.ndarray]] = None,
fill_mode: str = "random",
repeat: int = 10,
number: int = 10,
profile: bool = False,
end_to_end: bool = False,
options: dict = None,
):
"""Run a compiled graph executor module locally or remotely with
optional input values.
If input tensors are not specified explicitly, they can be filled
with zeroes, ones or random data.
Parameters
----------
tvmc_package: TVMCPackage
The compiled model package object that will be run.
device: str,
the device (e.g. "cpu" or "cuda") to be targeted by the RPC
session, local or remote).
hostname : str, optional
The hostname of the target device on which to run.
port : int, optional
The port of the target device on which to run.
rpc_key : str, optional
The tracker key of the target device. If this is set, it
will be assumed that remote points to a tracker.
inputs : dict, optional
A dictionary that maps input names to numpy values. If not provided,
inputs will be generated using the fill_mode argument.
fill_mode : str, optional
The fill-mode to use when generating data for input tensors.
Valid options are "zeros", "ones" and "random".
Defaults to "random".
repeat : int, optional
How many times to repeat the run.
number : int, optional
The number of runs to measure within each repeat.
profile : bool
Whether to profile the run with the debug executor.
end_to_end : bool
Whether to measure the time of memory copies as well as model
execution. Turning this on can provide a more realistic estimate
of how long running the model in production would take.
Returns
-------
outputs : dict
a dictionary with output tensors, generated by the module
times : list of str
execution times generated by the time evaluator
"""
if not isinstance(tvmc_package, TVMCPackage):
raise TVMCException(
"This model doesn't seem to have been compiled yet. "
"Try calling tvmc.compile on the model before running it.")
with ExitStack() as stack:
# Currently only two package formats are supported: "classic" and
# "mlf". The later can only be used for micro targets, i.e. with microTVM.
if device == "micro":
if tvmc_package.type != "mlf":
raise TVMCException(
f"Model {tvmc_package.package_path} is not a MLF archive.")
project_dir = get_project_dir(tvmc_package.project_dir)
# This is guaranteed to work since project_dir was already checked when
# building the dynamic parser to accommodate the project options, so no
# checks are in place when calling GeneratedProject.
project_ = project.GeneratedProject.from_directory(
project_dir, options)
else:
if tvmc_package.type == "mlf":
raise TVMCException(
"You're trying to run a model saved using the Model Library Format (MLF). "
"MLF can only be used to run micro device ('--device micro')."
)
if hostname:
if isinstance(port, str):
port = int(port)
# Remote RPC
if rpc_key:
logger.debug("Running on remote RPC tracker with key %s.",
rpc_key)
session = request_remote(rpc_key, hostname, port, timeout=1000)
else:
logger.debug("Running on remote RPC with no key.")
session = rpc.connect(hostname, port)
elif device == "micro":
# Remote RPC (running on a micro target)
logger.debug("Running on remote RPC (micro target).")
try:
session = tvm.micro.Session(project_.transport())
stack.enter_context(session)
except:
raise TVMCException(
"Could not open a session with the micro target.")
else:
# Local
logger.debug("Running a local session.")
session = rpc.LocalSession()
# Micro targets don't support uploading a model. The model to be run
# must be already flashed into the micro target before one tries
# to run it. Hence skip model upload for micro targets.
if device != "micro":
session.upload(tvmc_package.lib_path)
lib = session.load_module(tvmc_package.lib_name)
# TODO expand to other supported devices, as listed in tvm.rpc.client (@leandron)
logger.debug("Device is %s.", device)
if device == "cuda":
dev = session.cuda()
elif device == "cl":
dev = session.cl()
elif device == "metal":
dev = session.metal()
elif device == "vulkan":
dev = session.vulkan()
elif device == "rocm":
dev = session.rocm()
elif device == "micro":
dev = session.device
lib = session.get_system_lib()
else:
assert device == "cpu"
dev = session.cpu()
if tvmc_package.type == "vm":
assert inputs is not None, "vm runner requires inputs to be provided as a dict"
input_tensor = {}
for e, i in inputs.items():
input_tensor[e] = tvm.nd.array(i, dev)
if profile:
logger.debug("Creating vm with profile enabled.")
exe = profiler_vm.VirtualMachineProfiler(lib, dev)
res = exe.profile(**input_tensor, func_name="main")
# This print is intentional
print(res)
else:
exe = vm.VirtualMachine(lib, dev)
exe_outputs = exe.invoke("main", **input_tensor)
times = exe.benchmark(
dev,
**input_tensor,
func_name="main",
repeat=repeat,
number=number,
end_to_end=end_to_end,
)
# Special handling if the output only has a single value
if not isinstance(exe_outputs, list):
exe_outputs = [exe_outputs]
outputs = {}
for i, val in enumerate(exe_outputs):
output_name = "output_{}".format(i)
outputs[output_name] = val.numpy()
else:
# TODO(gromero): Adjust for micro targets.
if profile:
logger.debug("Creating runtime with profiling enabled.")
module = debug_executor.create(tvmc_package.graph,
lib,
dev,
dump_root="./prof")
else:
if device == "micro":
logger.debug(
"Creating runtime (micro) with profiling disabled.")
module = tvm.micro.create_local_graph_executor(
tvmc_package.graph, lib, dev)
else:
logger.debug("Creating runtime with profiling disabled.")
module = executor.create(tvmc_package.graph, lib, dev)
logger.debug("Loading params into the runtime module.")
module.load_params(tvmc_package.params)
logger.debug("Collecting graph input shape and type:")
shape_dict, dtype_dict = module.get_input_info()
logger.debug("Graph input shape: %s", shape_dict)
logger.debug("Graph input type: %s", dtype_dict)
inputs_dict = make_inputs_dict(shape_dict, dtype_dict, inputs,
fill_mode)
logger.debug("Setting inputs to the module.")
module.set_input(**inputs_dict)
# Run must be called explicitly if profiling
if profile:
logger.info("Running the module with profiling enabled.")
report = module.profile()
# This print is intentional
print(report)
if device == "micro":
# TODO(gromero): Fix time_evaluator() for micro targets. Once it's
# fixed module.benchmark() can be used instead and this if/else can
# be removed.
module.run()
times = []
else:
# Call the benchmarking function of the executor.
# Optionally measure e2e data transfers from the
# CPU to device memory overheads (e.g. PCIE
# overheads if the device is a discrete GPU).
if end_to_end:
dev = session.cpu()
times = module.benchmark(dev,
number=number,
repeat=repeat,
end_to_end=end_to_end)
logger.debug("Collecting the output tensors.")
num_outputs = module.get_num_outputs()
outputs = {}
for i in range(num_outputs):
output_name = "output_{}".format(i)
outputs[output_name] = module.get_output(i).numpy()
return TVMCResult(outputs, times)
def vm_tensorflow_model_process():
def normalize_node_name(nodes):
from tensorflow.compat import as_text
if isinstance(nodes, list):
ret = [as_text(node.split(':', 1)[0], 'ascii') for node in nodes]
else:
ret = as_text(nodes.split(':', 1)[0], 'ascii')
return ret
import tensorflow as tf
from tvm.relay.frontend.tensorflow_parser import TFParser
TF_pb_path = "/home/tiger/cuiqing.li/models/TF_checkpoint/latest"
graph_def = TFParser(TF_pb_path).parse()
input_names = ["input_ids_1:0", "input_mask_1:0", "segment_ids_1:0"]
output_names = ["loss/Softmax:0"]
input_shapes = [[1, 256], [1, 256], [1, 256]]
input_names = [normalize_node_name(i) for i in input_names]
output_names = [normalize_node_name(i) for i in output_names]
mod, params = relay.frontend.from_tensorflow(
graph_def,
shape={k: v
for k, v in zip(input_names, input_shapes)},
layout=None,
outputs=output_names)
desired_layouts = {'nn.conv2d': ['NCHW', 'default']}
seq = tvm.transform.Sequential([
relay.transform.RemoveUnusedFunctions(),
relay.transform.ConvertLayout(desired_layouts)
])
with tvm.ir.transform.PassContext(opt_level=3):
mod = seq(mod)
target = tvm.target.cuda()
ctx = tvm.context(str(target), 0)
with tvm.transform.PassContext(opt_level=3,
disabled_pass=["FoldScaleAxis"]):
exe = vm.compile(mod, target, params=params)
code, lib = exe.save()
saved_dir = "tmp"
if os.path.isdir("./tmp") == False:
os.system("mkdir {}".format(saved_dir))
path_lib = os.path.join(saved_dir, "lib.so")
lib.export_library(path_lib)
code_path = os.path.join(saved_dir, "code.ro")
with open(code_path, "wb") as fo:
fo.write(code)
loaded_lib = tvm.runtime.load_module(path_lib)
loaded_code = bytearray(open(code_path, "rb").read())
# deserialize.
des_exec = _vm.Executable.load_exec(loaded_code, loaded_lib)
des_vm = _vm.VirtualMachine(des_exec, ctx)
data = []
idx = 0
for input_shape in input_shapes:
dtype = "int32"
data_tvm = tvm.nd.array(
(np.random.uniform(size=input_shape)).astype(dtype), ctx)
data.append(data_tvm)
idx += 1
data = tuple(data)
res = des_vm.run(*data)
print("Evaluate vm inference cost of {} on {}".format(
"your testing model", repr(ctx)))
ftimer_warmup = des_vm.module.time_evaluator("invoke",
ctx,
number=1,
repeat=50)
# Measure in millisecond.
print("finished warming up and start testing vm compile performance")
ftimer = des_vm.module.time_evaluator("invoke",
ctx,
number=1,
repeat=100)
# Measure in millisecond.
prof_res = np.array(ftimer("main", *data).results) * 1000
#prof_res = np.array(ftimer().results) * 1000
print("Mean vm inference time (std dev): %.2f ms (%.2f ms)" %
(np.mean(prof_res), np.std(prof_res)))