def nnvm_run(opt_level: int = 2,
nthreads: int = None,
iname=MODEL_INPUT,
oname=MODEL_OUTPUT,
init_method='std',
nwarmup: int = 10,
nloops: int = 100) -> Result:
""" Compile Model using TVM and run it multiple times """
r = Result()
r.desc = 'tvm running time'
try:
graph, lib, params, i, o = tvm_import(opt_level, iname, oname)
if nthreads is None:
nthread = 20
get_global_func('runtime.config_threadpool')(1, nthreads)
m = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
print('compiled')
perfs: List[float] = []
for it in range(nwarmup + nloops):
i_data = common_init(init_method,
shape=i.shape.as_list(),
dtype=i.dtype.as_numpy_dtype())
m.set_input(iname, tvm.nd.array(i_data))
m.set_input(**params)
b = perf_counter()
m.run()
e = perf_counter()
o_data = m.get_output(
0, tvm.nd.empty(o.shape.as_list(), o.dtype.name)).asnumpy()
print('tvm', e - b)
if it >= nwarmup:
perfs.append(e - b)
r.set_perfs(perfs)
r.last_data = o_data
except KeyboardInterrupt:
raise
except Exception as e:
r.err = e
return r
def create(graph_json_str, libmod, ctx, dump_root=None):
"""Create a runtime executor module given a graph and module.
Parameters
----------
graph_json_str : str or graph class
The graph to be deployed in json format output by nnvm graph.
The graph can only contain one operator(tvm_op) that
points to the name of PackedFunc in the libmod.
libmod : tvm.Module
The module of the corresponding function.
ctx : TVMContext
The context to deploy the module, can be local or remote.
dump_root : str
To select which folder the outputs should be kept.
None will make a temp folder in /tmp/tvmdbg<rand_string> and does the dumping
Returns
-------
graph_module : GraphModuleDebug
Debug Runtime graph module that can be used to execute the graph.
"""
if not isinstance(graph_json_str, string_types):
try:
graph_json_str = graph_json_str._tvm_graph_json()
except AttributeError:
raise ValueError("Type %s is not supported" % type(graph_json_str))
try:
fcreate = get_global_func("tvm.graph_runtime_debug.create")
except ValueError:
raise ValueError(
"Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in "
"config.cmake and rebuild TVM to enable debug mode"
)
ctx, num_rpc_ctx, device_type_id = graph_runtime.get_device_ctx(libmod, ctx)
if num_rpc_ctx == len(ctx):
libmod = rpc_base._ModuleHandle(libmod)
try:
fcreate = ctx[0]._rpc_sess.get_function(
"tvm.graph_runtime_debug.remote_create"
)
except ValueError:
raise ValueError(
"Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in "
"config.cmake and rebuild TVM to enable debug mode"
)
func_obj = fcreate(graph_json_str, libmod, *device_type_id)
return GraphModuleDebug(func_obj, ctx, graph_json_str, dump_root)
def create(graph_json_str, libmod, ctx, dump_root=None):
"""Create a runtime executor module given a graph and module.
Parameters
----------
graph_json_str : str or graph class
The graph to be deployed in json format output by nnvm graph.
The graph can only contain one operator(tvm_op) that
points to the name of PackedFunc in the libmod.
libmod : tvm.Module
The module of the corresponding function.
ctx : TVMContext
The context to deploy the module, can be local or remote.
dump_root : str
To select which folder the outputs should be kept.
None will make a temp folder in /tmp/tvmdbg<rand_string> and does the dumping
Returns
-------
graph_module : GraphModuleDebug
Debug Runtime graph module that can be used to execute the graph.
"""
if not isinstance(graph_json_str, string_types):
try:
graph_json_str = graph_json_str._tvm_graph_json()
except AttributeError:
raise ValueError("Type %s is not supported" % type(graph_json_str))
try:
fcreate = get_global_func("tvm.graph_runtime_debug.create")
except ValueError:
raise ValueError("Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in " \
"config.cmake and rebuild TVM to enable debug mode")
ctx, num_rpc_ctx, device_type_id = graph_runtime.get_device_ctx(libmod, ctx)
if num_rpc_ctx == len(ctx):
libmod = rpc_base._ModuleHandle(libmod)
try:
fcreate = ctx[0]._rpc_sess.get_function("tvm.graph_runtime_debug.remote_create")
except ValueError:
raise ValueError("Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in " \
"config.cmake and rebuild TVM to enable debug mode")
func_obj = fcreate(graph_json_str, libmod, *device_type_id)
return GraphModuleDebug(func_obj, ctx, graph_json_str, dump_root)
def create(graph_json_str, libmod, ctx, dbg_ux=FRONTEND_NONE, dump_root=None):
"""Create a runtime executor module given a graph and module.
Parameters
----------
graph_json_str : str or graph class
The graph to be deployed in json format output by nnvm graph.
The graph can only contain one operator(tvm_op) that
points to the name of PackedFunc in the libmod.
libmod : tvm.Module
The module of the corresponding function.
ctx : TVMContext
The context to deploy the module, can be local or remote.
dbg_ux : str
To select which ux user needs, Example, curses/tensorboard/None.
None will just do the dumping
dump_root : str
To select which folder the outputs should be kept.
None will make a temp folder in /tmp/tvmdbg<rand_string> and does the dumping
Returns
-------
graph_module : GraphModuleDebug
Debug Runtime graph module that can be used to execute the graph.
"""
if not isinstance(graph_json_str, string_types):
try:
graph_json_str = graph_json_str._tvm_graph_json()
except AttributeError:
raise ValueError("Type %s is not supported" % type(graph_json_str))
device_type = ctx.device_type
device_id = ctx.device_id
try:
fcreate = get_global_func("tvm.graph_runtime_debug.create")
except ValueError:
raise ValueError("Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in " \
"config.cmake and rebuild TVM to enable debug mode")
func_obj = fcreate(graph_json_str, libmod, device_type, device_id)
return GraphModuleDebug(func_obj, ctx, graph_json_str, dbg_ux, dump_root)
def create(graph_json_str, libmod, ctx, frontend=FRONTEND_CURSES):
"""Create a runtime executor module given a graph and module.
Parameters
----------
graph_json_str : str or graph class
The graph to be deployed in json format output by nnvm graph.
The graph can only contain one operator(tvm_op) that
points to the name of PackedFunc in the libmod.
libmod : tvm.Module
The module of the corresponding function.
ctx : TVMContext
The context to deploy the module, can be local or remote.
frontend : str
To select which ui user needs, by default its curses ui.
Returns
-------
graph_module : GraphModuleDebug
Debug Runtime graph module that can be used to execute the graph.
"""
if not isinstance(graph_json_str, string_types):
try:
graph_json_str = graph_json_str._tvm_graph_json()
except AttributeError:
raise ValueError("Type %s is not supported" % type(graph_json_str))
device_type = ctx.device_type
device_id = ctx.device_id
try:
fcreate = get_global_func("tvm.graph_runtime_debug.create")
except ValueError:
raise ValueError("Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in " \
"config.cmake and rebuild TVM to enable debug mode")
func_obj = fcreate(graph_json_str, libmod, device_type, device_id)
return GraphModuleDebug(func_obj, ctx, graph_json_str, frontend)
def test2():
f = get_global_func("test2")
print(f)
x = relay.Var('x')
y = relay.exp(x)
f(y)
def nnvmS_run(opt_level: int = 2,
nthreads: int = None,
iname: str = MODEL_INPUT,
oname: str = MODEL_OUTPUT,
init_method='std',
nwarmup: int = 0,
nloops: int = 1,
**kwargs) -> Result:
""" Run staged NNVM model """
r = Result()
print("Warning: unused args:", kwargs) if kwargs != {} else None
try:
g, gd = fropen()
_, params = from_tensorflow(
gd) # We still need from_tensorflow to get the parameters
mo, savepoints = staged_model()
nnvm_graph = nnvm.graph.create(savepoints[oname])
print('synthesized')
i = g.get_tensor_by_name(iname + ':0')
o = g.get_tensor_by_name(oname + ':0')
i_shape_dict = {iname + ':0': i.shape.as_list()}
i_dtype_dict = {iname + ':0': i.dtype.as_numpy_dtype()}
with nnvm.compiler.build_config(opt_level=opt_level):
graph, lib, params = nnvm.compiler.build(graph=nnvm_graph,
target='llvm',
shape=i_shape_dict,
dtype=i_dtype_dict,
params=params)
# print(graph.ir())
if nthreads is None:
nthreads = 20
get_global_func('runtime.config_threadpool')(1, nthreads)
m = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
print('compiled')
perfs: List[float] = []
for it in range(nwarmup + nloops):
i_data = common_init(init_method,
shape=i.shape.as_list(),
dtype=i.dtype.as_numpy_dtype())
m.set_input(iname, tvm.nd.array(i_data))
m.set_input(**params)
b = perf_counter()
m.run()
e = perf_counter()
o_data = m.get_output(
0, tvm.nd.empty(o.shape.as_list(), o.dtype.name)).asnumpy()
print('tvms', e - b)
if it >= nwarmup:
perfs.append(e - b)
r.set_perfs(perfs)
r.last_data = o_data
except KeyboardInterrupt:
raise
except Exception as e:
warn('exception: ' + str(e))
r.err = e
return r