m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

add ut about nce

Browse Source
revert-15207-remove_op_handle_lock_and_fix_var
tangwei12 6 years ago
parent e196fa367b
commit 41790f1366
1 changed files with 33 additions and 119 deletions
Show Stats Download Patch File Download Diff File

152
python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py
Unescape View File

@ -88,158 +88,73 @@ class TestListenAndServOp(unittest.TestCase):
port = int(f.read().strip())
return port
def _run_nce_op_one_pserver(self, place, port):
def _run_nce_op_two_pserver(self, place, port0, port1):
scope = fluid.core.Scope()
program = Program()
with fluid.scope_guard(scope):
with program_guard(program, startup_program=Program()):
x = scope.var('X').get_tensor()
x = scope.var('Input').get_tensor()
x_array = np.random.random((4, 8)).astype("float32") * 2
x.set(x_array, place)
# create and initialize Param Variable
param = scope.var('W').get_tensor()
param = scope.var('Weight').get_tensor()
param_array = np.zeros((5, 8)).astype("float32") * 2
param.set(param_array, place)
path_table = scope.var('PathTable').get_tensor()
path_table_array = np.array(
[(0, 2, -1, -1, -1), (0, 1, 2, -1, -1), (0, 1, 4, -1, -1),
(0, 2, -1, -1, -1)]).astype(
"int64"
) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
path_table.set(path_table_array, place)
path_code = scope.var('PathCode').get_tensor()
path_code_array = np.array(
[(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
(0, 1, -1, -1, -1)]).astype("int64") #np.array to store
path_code.set(path_code_array, place)
label = scope.var('Label').get_tensor()
label_array = np.array([0, 1, 4, 5])
label.set(label_array, place)
bias = scope.var('Bias').get_tensor()
bias_array = np.random.random((5, 1)).astype("float32")
bias.set(bias_array, place)
out = scope.var('Out').get_tensor()
pre_out = scope.var('PreOut').get_tensor
w_out = scope.var('W_Out').get_tensor()
w_out.set(param_array, place)
emaps = ['127.0.0.1:' + str(port)]
table_names = ['table']
height_sections = [2]
# create and run sgd operator
hsigmoid_op = Operator(
"hierarchical_sigmoid",
X='X',
W='W',
PathTable='PathTable',
PathCode='PathCode',
Label='Label',
Bias='Bias',
Out='Out',
PreOut='PreOut',
W_Out='W_Out',
remote_prefetch=True,
epmap=emaps,
table_names=table_names,
height_sections=height_sections)
hsigmoid_op.run(scope, place)
# get and compare result
result_array = np.array(w_out)
self.assertEqual(list(result_array.shape), [5, 8])
correct = None
for i in range(5):
if i != 3:
correct = np.full((1, 8), i + 1).astype("float32")
self.assertTrue((result_array[i] == correct).all())
else:
correct = np.full((1, 8), 0).astype("float32")
self.assertTrue((result_array[i] == correct).all())
def _run_nce_op_two_pserver(self, place, port0, port1):
scope = fluid.core.Scope()
program = Program()
with fluid.scope_guard(scope):
with program_guard(program, startup_program=Program()):
x = scope.var('X').get_tensor()
x_array = np.random.random((4, 8)).astype("float32") * 2
x.set(x_array, place)
# create and initialize Param Variable
param = scope.var('W').get_tensor()
param_array = np.zeros((5, 8)).astype("float32") * 2
param.set(param_array, place)
path_table = scope.var('PathTable').get_tensor()
path_table_array = np.array(
[(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
(0, 2, -1, -1, -1)]).astype(
"int64"
) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
path_table.set(path_table_array, place)
path_code = scope.var('PathCode').get_tensor()
path_code_array = np.array(
[(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
(0, 1, -1, -1, -1)]).astype("int64") #np.array to store
path_code.set(path_code_array, place)
sample_w = scope.var('SampleWeight').get_tensor()
sample_weight = np.random.random((4, 1)).astype("float32")
sample_w.set(sample_weight, place)
label = scope.var('Label').get_tensor()
label_array = np.array([0, 1, 4, 5])
label.set(label_array, place)
bias = scope.var('Bias').get_tensor()
bias_array = np.random.random((5, 1)).astype("float32")
bias.set(bias_array, place)
cost = scope.var('Cost').get_tensor()
cost_w = np.zeros((4, 1)).astype("float32")
cost.set(cost_w, place)
out = scope.var('Out').get_tensor()
sample_l = scope.var('SampleLogits').get_tensor()
sample_l_w = np.zeros((4, 3)).astype("float32")
sample_l.set(sample_l_w, place)
pre_out = scope.var('PreOut').get_tensor
w_out = scope.var('W_Out').get_tensor()
w_out.set(param_array, place)
sample_la = scope.var('SampleLabels').get_tensor()
sample_la_w = np.zeros((4, 3)).astype("float32")
sample_la.set(sample_la_w, place)
emaps = ['127.0.0.1:' + str(port0), '127.0.0.1:' + str(port1)]
table_names = ['table', 'table']
height_sections = [2, 3]
# create and run sgd operator
hsigmoid_op = Operator(
"hierarchical_sigmoid",
X='X',
W='W',
PathTable='PathTable',
PathCode='PathCode',
# create and run nce operator
nce_op = Operator(
"nce",
Input='Input',
Weight='Weight',
Label='Label',
Bias='Bias',
Out='Out',
PreOut='PreOut',
W_Out='W_Out',
Cost='Cost',
SampleLogits='SampleLogits',
SampleLabels='SampleLabels',
num_total_classes=5,
num_neg_samples=2,
sampler=0,
seed=1,
is_sparse=True,
remote_prefetch=True,
epmap=emaps,
table_names=table_names,
height_sections=height_sections)
hsigmoid_op.run(scope, place)
nce_op.run(scope, place)
# get and compare result
result_array = np.array(w_out)
self.assertEqual(list(result_array.shape), [5, 8])
correct = None
for i in range(5):
if i < 2:
correct = np.full((1, 8), i + 1).astype("float32")
self.assertTrue((result_array[i] == correct).all())
else:
correct = np.full((1, 8), i + 9).astype("float32")
self.assertTrue((result_array[i] == correct).all())
o_cost = np.array(cost_w)
o_logits = np.array(sample_l)
o_labels = np.array(sample_la)
def test_nce_op_remote(self):
os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
@ -257,7 +172,6 @@ class TestListenAndServOp(unittest.TestCase):
places.append(core.CUDAPlace(0))
for place in places:
self._run_nce_op_one_pserver(place, port0)
self._run_nce_op_two_pserver(place, port0, port1)
# raise SIGTERM to pserver

Write Preview
Loading…
Cancel
Save