def test_subgraph_exe2(sym, subgraph_backend, op_names):
def get_executor(sym,
subgraph_backend=None,
op_names=None,
original_exec=None):
exe = sym._simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(shape=exe.arg_dict[name].shape)\
if original_exec is None else original_exec.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(shape=exe.aux_dict[name].shape)\
if original_exec is None else original_exec.aux_dict[name]
exe.forward()
return exe
sym, _, _ = sym
original_exec = get_executor(sym)
check_call(
_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
partitioned_exec = get_executor(sym, subgraph_backend, op_names,
original_exec)
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
outputs1 = original_exec.outputs
outputs2 = partitioned_exec.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
onp.zeros(shape=(1, )))
def check_subgraph_exe9(sym, subgraph_backend, op_names):
"""Call hybridize() to partition the graph, and then compare results of the partitioned
sym and the original sym. Here do an inference before hybridizing with the subgraph_backend
which means we'll pass shapes/types"""
# create Gluon block for given symbol
inputs = [mx.sym.var(i, dtype=mx_real_t) for i in sym[1]]
sym_block = nn.SymbolBlock(sym[0], inputs)
sym_block.initialize(ctx=mx.current_context())
x = [
mx.nd.random.uniform(shape=s, ctx=mx.current_context()) for s in sym[2]
]
# hybridize and export to get baseline
sym_block.hybridize()
outputs1 = sym_block(*x)
sym_block.export('check_subgraph_exe9')
# load model and partition
sym_block = nn.SymbolBlock.imports('check_subgraph_exe9-symbol.json',
sym[1],
'check_subgraph_exe9-0000.params',
ctx=mx.current_context())
check_call(
_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
sym_block.hybridize(backend=subgraph_backend)
outputs2 = sym_block(*x)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
# compare outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1, )))
def _check_subgraph_exe3(sym, subgraph_backend, op_names):
"""Use the partitioned sym to bind an executor and compare the outputs
with those of the original executor"""
out = SymbolHandle()
check_call(_LIB.MXBuildSubgraphByOpNames(sym.handle, c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names), ctypes.byref(out)))
partitioned_sym = Symbol(out)
input_names = sym.list_inputs()
arg_names = sym.list_arguments()
aux_names = sym.list_auxiliary_states()
assert partitioned_sym.list_inputs() == input_names
assert partitioned_sym.list_arguments() == arg_names
assert partitioned_sym.list_auxiliary_states() == aux_names
arg_shapes, _, aux_shapes = sym.infer_shape()
arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
exe = sym.bind(ctx=mx.current_context(), args=arg_array, aux_states=aux_array, grad_req='null')
partitioned_exe = partitioned_sym.bind(ctx=mx.current_context(), args=arg_array,
aux_states=aux_array, grad_req='null')
exe.forward()
partitioned_exe.forward()
assert len(exe.outputs) == len(partitioned_exe.outputs)
for i in range(len(exe.outputs)):
assert_almost_equal((exe.outputs[i] - partitioned_exe.outputs[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
def _check_subgraph_exe1(sym, subgraph_backend, op_names):
"""Use the partitioned sym to simple_bind an executor and compare the outputs
with those of the original executor"""
out = SymbolHandle()
check_call(_LIB.MXBuildSubgraphByOpNames(sym.handle, c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names), ctypes.byref(out)))
partitioned_sym = Symbol(out)
assert partitioned_sym.list_inputs() == sym.list_inputs()
assert partitioned_sym.list_arguments() == sym.list_arguments()
assert partitioned_sym.list_auxiliary_states() == sym.list_auxiliary_states()
exe = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
partitioned_exe = partitioned_sym.simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(shape=exe.arg_dict[name].shape)
partitioned_exe.arg_dict[name][:] = exe.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(shape=exe.aux_dict[name].shape)
partitioned_exe.aux_dict[name][:] = exe.aux_dict[name]
exe.forward()
partitioned_exe.forward()
assert len(exe.outputs) == len(partitioned_exe.outputs)
for i in range(len(exe.outputs)):
assert_almost_equal((exe.outputs[i] - partitioned_exe.outputs[i]).abs().sum().asnumpy(),
np.zeros(shape=(1,)))
def test_subgraph_exe8(sym, subgraph_backend, op_names):
"""Call optimize_for to infer shapes, types and dtypes followed by graph partitioning,
then bind and compare results of the partitioned sym and the original sym."""
# bind
sym, _, _ = sym
arg_shapes, _, aux_shapes = sym.infer_shape()
arg_names = sym.list_arguments()
aux_names = sym.list_auxiliary_states()
arg_dict = {name:mx.nd.random.uniform(shape=shape) for name,shape in zip(arg_names,arg_shapes)}
aux_dict = {name:mx.nd.random.uniform(shape=shape) for name,shape in zip(aux_names,aux_shapes)}
exe1 = sym.bind(ctx=mx.current_context(), args=arg_dict, aux_states=aux_dict, grad_req='null')
exe1.forward()
# infer shape/type before partition before bind
check_call(_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names)))
part_sym = sym.optimize_for(subgraph_backend, arg_dict, aux_dict)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
exe2 = part_sym.bind(ctx=mx.current_context(), args=arg_dict, aux_states=aux_dict, grad_req='null')
exe2.forward()
# compare outputs
outputs1 = exe1.outputs
outputs2 = exe2.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(), np.zeros(shape=(1,)))
def get_executor(sym,
subgraph_backend=None,
op_names=None,
original_exec=None):
if subgraph_backend is not None:
os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
check_call(
_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
exe = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(shape=exe.arg_dict[name].shape)\
if original_exec is None else original_exec.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(shape=exe.aux_dict[name].shape)\
if original_exec is None else original_exec.aux_dict[name]
exe.forward()
if subgraph_backend is not None:
check_call(
_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
del os.environ['MXNET_SUBGRAPH_BACKEND']
return exe
def get_executor(sym,
subgraph_backend=None,
op_names=None,
original_exec=None):
if subgraph_backend is not None:
os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
check_call(
_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
arg_shapes, _, aux_shapes = sym.infer_shape()
if subgraph_backend is None:
arg_array = [
mx.nd.random.uniform(shape=shape) for shape in arg_shapes
]
aux_array = [
mx.nd.random.uniform(shape=shape) for shape in aux_shapes
]
else:
arg_array = None
aux_array = None
exe = sym.bind(ctx=mx.current_context(),
args=arg_array if subgraph_backend is None else
original_exec.arg_arrays,
aux_states=aux_array if subgraph_backend is None else
original_exec.aux_arrays,
grad_req='null')
exe.forward()
if subgraph_backend is not None:
check_call(
_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
del os.environ['MXNET_SUBGRAPH_BACKEND']
return exe
def test_subgraph_backend_gluon_ext1(tmpdir):
def get_net():
net = nn.HybridSequential() # Here we use the class HybridSequential.
net.add(nn.Dense(256, activation='relu'),
nn.Dense(128, activation='relu'), nn.Dense(2))
return net
# regular inference
x = mx.np.random.normal(size=(1, 512), ctx=mx.current_context())
net = get_net()
net.initialize(ctx=mx.current_context())
outputs1 = net(x)
param_path = os.path.join(str(tmpdir),
'test_subgraph_backend_gluon_ext1.params')
net.save_parameters(param_path)
# after partitioning
net = get_net()
net.load_parameters(param_path, ctx=mx.current_context())
subgraph_backend = 'default'
op_names = ['FullyConnected']
check_call(
_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
net.optimize_for(x, backend=subgraph_backend)
outputs2 = net(x)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
# compare outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal(
mx.np.abs((outputs1[i] - outputs2[i])).sum().asnumpy(),
onp.zeros(shape=(1, )))
def test_subgraph_exe7(sym, subgraph_backend, op_names):
"""Call optimize_for to trigger graph partitioning without infer shapes/types before,
then bind and compare results of the partitioned sym and the original sym."""
# bind
sym, _, _ = sym
arg_shapes, _, aux_shapes = sym.infer_shape()
arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
exe1 = sym._bind(ctx=mx.current_context(),
args=arg_array,
aux_states=aux_array,
grad_req='null')
exe1.forward()
# partition before bind
check_call(
_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
part_sym = sym.optimize_for(subgraph_backend)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
exe2 = part_sym._bind(ctx=mx.current_context(),
args=arg_array,
aux_states=aux_array,
grad_req='null')
exe2.forward()
# compare outputs
outputs1 = exe1.outputs
outputs2 = exe2.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
onp.zeros(shape=(1, )))
def test_subgraph_exe6(sym, subgraph_backend, op_names):
"""Call optimize_for to trigger graph partitioning with shapes/types, then _simple_bind
and compare results of the partitioned sym and the original sym."""
# _simple_bind
sym, _, _ = sym
exe1 = sym._simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
set_random_inputs(exe1, input_names)
exe1.forward()
# infer shape/type before partition before _simple_bind
check_call(
_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
part_sym = sym.optimize_for(subgraph_backend, exe1.arg_dict, exe1.aux_dict)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
exe2 = part_sym._simple_bind(ctx=mx.current_context(), grad_req='null')
copy_inputs_between_executors(exe1, exe2, input_names)
exe2.forward()
# compare outputs
outputs1 = exe1.outputs
outputs2 = exe2.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
onp.zeros(shape=(1, )))
def test_subgraph_exe4(sym, subgraph_backend, op_names):
"""Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph partitioning in bind
and compare results of the partitioned sym and the original sym."""
def get_executor(sym,
subgraph_backend=None,
op_names=None,
original_exec=None):
arg_shapes, _, aux_shapes = sym.infer_shape()
if subgraph_backend is None:
arg_array = [
mx.nd.random.uniform(shape=shape) for shape in arg_shapes
]
aux_array = [
mx.nd.random.uniform(shape=shape) for shape in aux_shapes
]
else:
arg_array = None
aux_array = None
exe = sym._bind(ctx=mx.current_context(),
args=arg_array if subgraph_backend is None else
original_exec.arg_arrays,
aux_states=aux_array if subgraph_backend is None else
original_exec.aux_arrays,
grad_req='null')
exe.forward()
return exe
sym, _, _ = sym
original_exec = get_executor(sym)
with environment('MXNET_SUBGRAPH_BACKEND', subgraph_backend):
check_call(
_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
partitioned_exec = get_executor(sym, subgraph_backend, op_names,
original_exec)
check_call(
_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
outputs1 = original_exec.outputs
outputs2 = partitioned_exec.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
onp.zeros(shape=(1, )))
def _quantize_symbol(sym,
excluded_symbols=None,
offline_params=None,
quantized_dtype='int8'):
"""Given a symbol object representing a neural network of data type FP32,
quantize it into a INT8 network.
Parameters
----------
sym : Symbol
FP32 neural network symbol.
excluded_sym_names : list of strings
A list of strings representing the names of the symbols that users want to excluding
from being quantized.
offline_params : list of strs
Names of the parameters that users want to quantize offline. It's always recommended to
quantize parameters offline so that quantizing parameters during the inference can be
avoided.
quantized_dtype: str
The quantized destination type for input data.
"""
num_excluded_symbols = 0
if excluded_symbols is not None:
assert isinstance(excluded_symbols, list)
num_excluded_symbols = len(excluded_symbols)
else:
excluded_symbols = []
num_offline = 0
offline = []
if offline_params is not None:
num_offline = len(offline_params)
for k in offline_params:
offline.append(c_str(k))
out = SymbolHandle()
check_call(
_LIB.MXQuantizeSymbol(sym.handle, ctypes.byref(out),
mx_uint(num_excluded_symbols),
c_str_array(excluded_symbols),
mx_uint(num_offline),
c_array(ctypes.c_char_p, offline),
c_str(quantized_dtype), ctypes.c_bool(True)))
return Symbol(out)
def calibrate_quantized_sym(qsym, th_dict):
if th_dict is None or len(th_dict) == 0:
return qsym
num_layer_outputs = len(th_dict)
layer_output_names = []
min_vals = []
max_vals = []
for k, v in th_dict.items():
layer_output_names.append(k)
min_vals.append(v[0])
max_vals.append(v[1])
calibrated_sym = SymbolHandle()
check_call(
_LIB.MXSetCalibTableToQuantizedSymbol(
qsym.handle, mx_uint(num_layer_outputs),
c_str_array(layer_output_names), c_array(ctypes.c_float, min_vals),
c_array(ctypes.c_float, max_vals), ctypes.byref(calibrated_sym)))
return Symbol(calibrated_sym)
def get_executor(sym, subgraph_backend=None, op_names=None, original_exec=None):
if subgraph_backend is not None:
os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names)))
exe = sym.simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(shape=exe.arg_dict[name].shape)\
if original_exec is None else original_exec.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(shape=exe.aux_dict[name].shape)\
if original_exec is None else original_exec.aux_dict[name]
exe.forward()
if subgraph_backend is not None:
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
del os.environ['MXNET_SUBGRAPH_BACKEND']
return exe
def test_subgraph_backend_gluon_ext2(tmpdir):
class Net(gluon.HybridBlock):
def __init__(self, **kwargs):
super(Net, self).__init__(**kwargs)
self.fc1 = nn.Dense(256)
self.fc2 = nn.Dense(128)
self.fc3 = nn.Dense(2)
def forward(self, x):
x = npx.relu(self.fc1(x))
x = npx.relu(self.fc2(x))
return self.fc3(x)
# regular inference
x = mx.np.random.normal(size=(1, 512), ctx=mx.current_context())
net = Net()
net.initialize(ctx=mx.current_context())
outputs1 = net(x)
param_path = os.path.join(str(tmpdir),
'test_subgraph_backend_gluon_ext2.params')
net.save_parameters(param_path)
# after partitioning
net = Net()
net.load_parameters(param_path, ctx=mx.current_context())
subgraph_backend = 'default'
op_names = ['FullyConnected']
check_call(
_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
net.optimize_for(x, backend=subgraph_backend)
outputs2 = net(x)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
# compare outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal(
mx.np.abs(outputs1[i] - outputs2[i]).sum().asnumpy(),
onp.zeros(shape=(1, )))
def get_executor(sym, subgraph_backend=None, op_names=None, original_exec=None):
if subgraph_backend is not None:
os.environ['MXNET_SUBGRAPH_BACKEND'] = subgraph_backend
check_call(_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend), mx_uint(len(op_names)),
c_str_array(op_names)))
arg_shapes, _, aux_shapes = sym.infer_shape()
if subgraph_backend is None:
arg_array = [mx.nd.random.uniform(shape=shape) for shape in arg_shapes]
aux_array = [mx.nd.random.uniform(shape=shape) for shape in aux_shapes]
else:
arg_array = None
aux_array = None
exe = sym.bind(ctx=mx.current_context(),
args=arg_array if subgraph_backend is None else original_exec.arg_arrays,
aux_states=aux_array if subgraph_backend is None else original_exec.aux_arrays,
grad_req='null')
exe.forward()
if subgraph_backend is not None:
check_call(_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
del os.environ['MXNET_SUBGRAPH_BACKEND']
return exe
def test_subgraph_backend_gluon_ext2():
class Net(gluon.HybridBlock):
def __init__(self, **kwargs):
super(Net, self).__init__(**kwargs)
with self.name_scope():
self.fc1 = nn.Dense(256)
self.fc2 = nn.Dense(128)
self.fc3 = nn.Dense(2)
def hybrid_forward(self, F, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
return self.fc3(x)
# regular inference
x = nd.random.normal(shape=(1, 512), ctx=mx.current_context())
net = Net()
net.collect_params().initialize(ctx=mx.current_context())
outputs1 = net(x)
net.save_parameters('test_subgraph_backend_gluon_ext2.params')
# after partitioning
net = Net()
net.load_parameters('test_subgraph_backend_gluon_ext2.params',
ctx=mx.current_context())
subgraph_backend = 'default'
op_names = ['FullyConnected']
check_call(
_LIB.MXSetSubgraphPropertyOpNamesV2(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
net.hybridize(backend=subgraph_backend)
outputs2 = net(x)
check_call(_LIB.MXRemoveSubgraphPropertyOpNamesV2(c_str(subgraph_backend)))
# compare outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1, )))
def _calibrate_quantized_sym(qsym, th_dict):
"""Given a dictionary containing the thresholds for quantizing the layers,
set the thresholds into the quantized symbol as the params of requantize operators.
"""
if th_dict is None or len(th_dict) == 0:
return qsym
num_layer_outputs = len(th_dict)
layer_output_names = []
min_vals = []
max_vals = []
for k, v in th_dict.items():
layer_output_names.append(k)
min_vals.append(v[0])
max_vals.append(v[1])
calibrated_sym = SymbolHandle()
check_call(
_LIB.MXSetCalibTableToQuantizedSymbol(
qsym.handle, mx_uint(num_layer_outputs),
c_str_array(layer_output_names), c_array(ctypes.c_float, min_vals),
c_array(ctypes.c_float, max_vals), ctypes.byref(calibrated_sym)))
return Symbol(calibrated_sym)
def test_subgraph_exe2(sym, subgraph_backend, op_names):
"""Use env var MXNET_SUBGRAPH_BACKEND=default to trigger graph partitioning in _simple_bind
and compare results of the partitioned sym and the original sym."""
def get_executor(sym,
subgraph_backend=None,
op_names=None,
original_exec=None):
exe = sym._simple_bind(ctx=mx.current_context(), grad_req='null')
input_names = sym.list_inputs()
for name in input_names:
if name in exe.arg_dict:
exe.arg_dict[name][:] = mx.nd.random.uniform(shape=exe.arg_dict[name].shape)\
if original_exec is None else original_exec.arg_dict[name]
else:
assert name in exe.aux_dict
exe.aux_dict[name][:] = mx.nd.random.uniform(shape=exe.aux_dict[name].shape)\
if original_exec is None else original_exec.aux_dict[name]
exe.forward()
return exe
sym, _, _ = sym
original_exec = get_executor(sym)
with environment('MXNET_SUBGRAPH_BACKEND', subgraph_backend):
check_call(
_LIB.MXSetSubgraphPropertyOpNames(c_str(subgraph_backend),
mx_uint(len(op_names)),
c_str_array(op_names)))
partitioned_exec = get_executor(sym, subgraph_backend, op_names,
original_exec)
check_call(
_LIB.MXRemoveSubgraphPropertyOpNames(c_str(subgraph_backend)))
outputs1 = original_exec.outputs
outputs2 = partitioned_exec.outputs
assert len(outputs1) == len(outputs2)
for i in range(len(outputs1)):
assert_almost_equal((outputs1[i] - outputs2[i]).abs().sum().asnumpy(),
np.zeros(shape=(1, )))
def quantize_symbol(sym,
excluded_symbols=[],
offline_params=[],
quantized_dtype='uint8',
calib_quantize_op=False):
"""
Quantize symbol.
:param sym: mxnet.symbol.Symbol
The symbol to quantize.
:param excluded_symbols: list of str
The names of symbols to exclude.
:param offline_params: list of str
The names of parameters to quantize offline.
:param quantized_dtype: {"int8", "uint8"}
The data type that you will quantize to.
:param calib_quantize_op: bool
Calibrate or not.(Only for quantization online.
:return: mxnet.symbol.Symbol
The symbol that has been quantized.
"""
assert isinstance(excluded_symbols, list)
num_excluded_symbols = len(excluded_symbols)
# exclude = [s.handle for s in excluded_symbols]
assert isinstance(offline_params, list)
offline = [c_str(k) for k in offline_params]
num_offline = len(offline)
out = SymbolHandle()
check_call(
_LIB.MXQuantizeSymbol(sym.handle, ctypes.byref(out),
mx_uint(num_excluded_symbols),
c_str_array(excluded_symbols),
mx_uint(num_offline),
c_array(ctypes.c_char_p, offline),
c_str(quantized_dtype),
ctypes.c_bool(calib_quantize_op)))
return Symbol(out)
