def _get_remote(cls):
"""Get a remote (or local) device to use for testing."""
if cls.connection_type == "tracker":
device = request_remote(cls.device_key, cls.host, cls.port, timeout=1000)
elif cls.connection_type == "remote":
device = rpc.connect(cls.host, cls.port)
elif cls.connection_type == "local":
device = rpc.LocalSession()
else:
raise ValueError(
"connection_type in test_config.json should be one of: " "local, tracker, remote."
)
return device
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 run_module(
module_file,
hostname,
port=9090,
rpc_key=None,
device=None,
inputs_file=None,
fill_mode="random",
repeat=1,
profile=False,
):
"""Run a compiled graph runtime 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
----------
module_file : str
The path to the module file (a .tar file).
hostname : str
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.
device: str, optional
the device (e.g. "cpu" or "gpu") to be targeted by the RPC
session, local or remote).
inputs_file : str, optional
Path to an .npz file containing the inputs.
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.
profile : bool
Whether to profile the run with the debug runtime.
Returns
-------
outputs : dict
a dictionary with output tensors, generated by the module
times : list of str
execution times generated by the time evaluator
"""
with tempfile.TemporaryDirectory() as tmp_dir:
logger.debug("extracting module file %s", module_file)
t = tarfile.open(module_file)
t.extractall(tmp_dir)
graph = open(os.path.join(tmp_dir, "mod.json")).read()
params = bytearray(
open(os.path.join(tmp_dir, "mod.params"), "rb").read())
if hostname:
# 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)
else:
# Local
logger.debug("running a local session")
session = rpc.LocalSession()
session.upload(os.path.join(tmp_dir, "mod.so"))
lib = session.load_module("mod.so")
# TODO expand to other supported devices, as listed in tvm.rpc.client (@leandron)
logger.debug("device is %s", device)
if device == "gpu":
ctx = session.gpu()
elif device == "cl":
ctx = session.cl()
else:
assert device == "cpu"
ctx = session.cpu()
if profile:
logger.debug("creating runtime with profiling enabled")
module = debug_runtime.create(graph, lib, ctx, dump_root="./prof")
else:
logger.debug("creating runtime with profiling disabled")
module = runtime.create(graph, lib, ctx)
logger.debug("load params into the runtime module")
module.load_params(params)
shape_dict, dtype_dict = get_input_info(graph, params)
inputs_dict = make_inputs_dict(inputs_file, shape_dict, dtype_dict,
fill_mode)
logger.debug("setting inputs to the module")
module.set_input(**inputs_dict)
# Run must be called explicitly if profiling
if profile:
logger.debug("running the module with profiling enabled")
module.run()
# create the module time evaluator (returns a function)
timer = module.module.time_evaluator("run", ctx, 1, repeat=repeat)
# call the evaluator function to invoke the module and save execution times
prof_result = timer()
# collect a list of execution times from the profiling results
times = prof_result.results
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).asnumpy()
return outputs, times
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,
):
"""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 runtime.
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.")
# Currently only two package formats are supported: "classic" and
# "mlf". The later can only be used for micro targets, i.e. with µTVM.
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 targets (µTVM).")
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)
else:
# Local
logger.debug("Running a local session.")
session = rpc.LocalSession()
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()
else:
assert device == "cpu"
dev = session.cpu()
if profile:
logger.debug("Creating runtime with profiling enabled.")
module = debug_executor.create(tvmc_package.graph,
lib,
dev,
dump_root="./prof")
else:
logger.debug("Creating runtime with profiling disabled.")
module = runtime.create(tvmc_package.graph, lib, dev)
logger.debug("Loading params into the runtime module.")
module.load_params(tvmc_package.params)
shape_dict, dtype_dict = get_input_info(tvmc_package.graph,
tvmc_package.params)
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.")
module.run()
# create the module time evaluator (returns a function)
timer = module.module.time_evaluator("run",
dev,
number=number,
repeat=repeat)
# call the evaluator function to invoke the module and save execution times
prof_result = timer()
# collect a list of execution times from the profiling results
times = prof_result.results
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 test_can_call_remote_function_with_rpc_tracker_via_proxy(host, port):
remote_session = request_remote(DEVICE_KEY, host, port)
f = remote_session.get_function("runtime.GetFFIString")
assert f("hello") == "hello"
