From 41790f13662a8a86fe5b6f4e3cee7a35703230a8 Mon Sep 17 00:00:00 2001
From: tangwei12 <tangwei12@baidu.com>
Date: Tue, 18 Dec 2018 14:04:40 +0800
Subject: [PATCH] add ut about nce
---
.../unittests/test_nce_remote_table_op.py | 152 ++++--------------
1 file changed, 33 insertions(+), 119 deletions(-)
diff --git a/python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py b/python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py
index f08b270d89..e87545cb9c 100644
--- a/python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py
+++ b/python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py
@@ -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