def _get_gradient(self,
input_to_check,
place,
output_names,
no_grad_set,
parallel=False):
prog = Program()
block = prog.global_block()
self._append_ops(block)
loss = append_loss_ops(block, output_names)
param_grad_list = append_backward(loss=loss,
parameter_list=input_to_check,
no_grad_set=no_grad_set)
inputs = self._get_inputs(block)
feed_dict = self.feed_var(inputs, place)
fetch_list = [g for p, g in param_grad_list]
if parallel:
use_cuda = False
if isinstance(place, fluid.CUDAPlace(0)):
use_cuda = True
executor = fluid.ParallelExecutor(use_cuda=use_cuda,
loss_name=loss.name,
main_program=prog)
else:
executor = Executor(place)
return list(
map(np.array,
executor.run(prog, feed_dict, fetch_list, return_numpy=False)))
def closure(**kwargs):
role = kwargs['role']
pfl_mpc.init("aby3", role, "localhost", self.server,
int(self.port))
loss = append_loss_ops(block, output_names)
param_grad_list = append_backward(loss=loss,
parameter_list=input_to_check,
no_grad_set=no_grad_set)
inputs = self._get_inputs(block)
feed_dict = self.feed_var(inputs, place)
fetch_list = [g for p, g in param_grad_list]
executor = Executor(place)
executor.run()
outs = executor.run(prog,
feed=feed_dict,
fetch_list=fetch_list,
return_numpy=False)
# append lod information in last position
lod = []
for idx in range(fetch_list_len):
return_results[idx].append(np.array(outs[idx]))
lod_i = outs[idx].lod()
lod_concat = []
for i in lod_i:
lod_concat.append(i)
lod.append(lod_concat)
return_results[fetch_list_len].append(lod)
def _get_gradient(self,
input_to_check,
place,
output_names,
no_grad_set,
parallel=False):
prog = Program()
block = prog.global_block()
self._append_ops(block)
loss = append_loss_ops(block, output_names)
param_grad_list = append_backward(loss=loss,
parameter_list=input_to_check,
no_grad_set=no_grad_set)
inputs = self._get_inputs(block)
feed_dict = self.feed_var(inputs, place)
fetch_list = [g for p, g in param_grad_list]
return_results = [Manager().list() for _ in range(len(fetch_list))]
def closure(**kwargs):
role = kwargs['role']
pfl_mpc.init("privc", role, "localhost", self.server,
int(self.port))
#init_op = fluid.default_main_program().global_block().ops[0]
#_insert_init_op(program, init_op)
executor = Executor(place)
executor.run()
outs = executor.run(prog, feed=feed_dict, fetch_list=fetch_list)
for idx in range(len(fetch_list)):
return_results[idx].append(outs[idx])
ret = self.multi_party_run(target=closure)
self.assertEqual(ret[0], True)
outs = []
for idx in range(len(fetch_list)):
outs.append(self.reconstruct(np.array(return_results[idx])))
return outs
def _get_gradient(self,
input_to_check,
place,
output_names,
no_grad_set,
parallel=False):
prog = Program()
block = prog.global_block()
self._append_ops(block)
loss = append_loss_ops(block, output_names)
param_grad_list = append_backward(loss=loss,
parameter_list=input_to_check,
no_grad_set=no_grad_set)
inputs = self._get_inputs(block)
feed_dict = self.feed_var(inputs, place)
fetch_list = [g for p, g in param_grad_list]
# Manager() can not store LoDTensor directly
# So, use one additional element to store output lod
return_results = [Manager().list() for _ in range(len(fetch_list) + 1)]
def closure(**kwargs):
role = kwargs['role']
pfl_mpc.init("aby3", role, "localhost", self.server,
int(self.port))
#init_op = fluid.default_main_program().global_block().ops[0]
#_insert_init_op(program, init_op)
executor = Executor(place)
executor.run()
outs = executor.run(prog,
feed=feed_dict,
fetch_list=fetch_list,
return_numpy=False)
# append lod information in last position
lod = []
for idx in range(len(fetch_list)):
return_results[idx].append(np.array(outs[idx]))
lod_i = outs[idx].lod()
lod_concat = []
for i in lod_i:
lod_concat.append(i)
lod.append(lod_concat)
return_results[len(fetch_list)].append(lod)
ret = self.multi_party_run(target=closure)
self.assertEqual(ret[0], True)
outs = []
lod = np.array(return_results[len(fetch_list)])
# from numpy array to LoDTensor
for idx in range(len(fetch_list)):
t = fluid.LoDTensor()
reveal_data = aby3.reconstruct(np.array(return_results[idx]))
t.set(reveal_data, place)
lod_idx = lod[0][idx]
# TODO: fix: exception throw because some output lod error in gru op
# out.set_lod(out.lod()) will throw exception
try:
t.set_lod(lod_idx)
except Exception as e:
pass
outs.append(t)
return outs