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Fix random test_recurrent_op failure (#18718)

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The change includes 3 things:

1. Set CPU_NUM to 1 in the tests because the ParallelExecutor will print warning that CPU_NUM is not set and use default 1.

2. Old tests compare two RNNs, hand written simple RNN and same RNN built by Paddle, but initialized RNN weights in numpy random and Paddle random separately. Fixed it by setting weights and bias values.

3. Also set numpy random seed in the tests. Now the two RNNs diff can be smaller (rtol from 0.1, 0.2 to. 0.01) in the tests.

test=develop
DDDivano-patch-1
Huihuang Zheng 6 years ago committed by GitHub
parent bd22453f20
commit a3028bb729
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 51 additions and 31 deletions
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3
python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py
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@ -22,12 +22,15 @@ import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.layers as layers
import time
import os
from paddle.fluid import ParamAttr
from paddle.fluid.contrib.layers import basic_lstm
from paddle.fluid.executor import Executor
from paddle.fluid.layers.control_flow import StaticRNN as PaddingRNN
os.environ["CPU_NUM"] = "1"
class RnnConfig(object):
def __init__(self, model_type, rnn_model):

41
python/paddle/fluid/tests/unittests/test_eager_deletion_recurrent_op.py
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@ -14,6 +14,7 @@
from __future__ import print_function
import os
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.compiler as compiler
@ -21,10 +22,13 @@ import paddle.fluid.core as core
import paddle.fluid.layers as layers
import unittest
from paddle.fluid import ParamAttr
from paddle.fluid.framework import Program, grad_var_name
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward
np.random.seed(123)
os.environ["CPU_NUM"] = "1"
fluid.core._set_eager_deletion_mode(0.0, 1.0, True)
@ -71,8 +75,8 @@ class PySimpleRNN2(PyRNNBase):
super(PySimpleRNN2, self).__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32")
self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32")
self.W = np.ones(shape=(input_dim, input_dim)).astype("float32")
self.U = np.zeros(shape=(input_dim, input_dim)).astype("float32")
self.h_boot = np.ones(shape=(batch_size, input_dim)).astype("float32")
men_dim = (seq_len, batch_size, input_dim)
@ -186,7 +190,7 @@ class EagerDeletionRecurrentOpTest1(unittest.TestCase):
fetch_list=fetch_list,
return_numpy=False)
def test_backward(self, rtol=0.1):
def test_backward(self, rtol=0.01):
self.check_forward()
with fluid.program_guard(self.main_program, self.startup_program):
@ -208,7 +212,7 @@ class EagerDeletionRecurrentOpTest1(unittest.TestCase):
pd_output = self.forward()
py_output = self.py_rnn.forward()
self.assertEqual(pd_output.shape, py_output.shape)
self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all())
self.assertTrue(np.isclose(pd_output, py_output, rtol=0.01).all())
def get_numerical_gradient(self, delta=0.005):
dloss_dout = 1.0
@ -278,14 +282,20 @@ class EagerDeletionRecurrentOpTest2(EagerDeletionRecurrentOpTest1):
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
temp_l = layers.fc(input=x_t,
size=self.input_dim,
param_attr='W',
bias_attr=False)
temp_r = layers.fc(input=h_pre,
size=self.input_dim,
param_attr='U',
bias_attr=False)
temp_l = layers.fc(
input=x_t,
size=self.input_dim,
param_attr=ParamAttr(
name='W',
initializer=fluid.initializer.ConstantInitializer(1.0)),
bias_attr=False)
temp_r = layers.fc(
input=h_pre,
size=self.input_dim,
param_attr=ParamAttr(
name='U',
initializer=fluid.initializer.ConstantInitializer(0.0)),
bias_attr=False)
h = layers.sigmoid(x=layers.elementwise_add(x=temp_l, y=temp_r))
@ -295,7 +305,7 @@ class EagerDeletionRecurrentOpTest2(EagerDeletionRecurrentOpTest1):
return rnn()
def test_backward(self):
super(EagerDeletionRecurrentOpTest2, self).test_backward(rtol=0.2)
super(EagerDeletionRecurrentOpTest2, self).test_backward(rtol=0.01)
class EagerDeletionRecurrentOpMultipleMemoryTest(EagerDeletionRecurrentOpTest1):
@ -496,7 +506,6 @@ class EagerDeletionTwoRecurrentOpsTest(EagerDeletionRecurrentOpTest1):
# Second RNN
pre_mem = np.zeros_like(x) if step_id == 0 else self.mem_1[step_id -
1]
# print(np.sum(self.rnn_0_output))
self.mem_1[step_id] = x + np.sum(self.rnn_0_output)
self.y[step_id] = self.mem_1[step_id] + pre_mem
@ -675,8 +684,8 @@ class EagerDeletionFarwardOnlyRnnAndBackwardRnnTest(
self.assertEqual(pd_output.shape, py_output.shape)
self.assertTrue(
np.isclose(
forward_only_output, py_output, rtol=0.1).all)
self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all())
forward_only_output, py_output, rtol=0.01).all)
self.assertTrue(np.isclose(pd_output, py_output, rtol=0.01).all())
if __name__ == '__main__':

38
python/paddle/fluid/tests/unittests/test_recurrent_op.py
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@ -17,11 +17,13 @@ from __future__ import print_function
import unittest
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import numpy as np
import paddle.fluid.core as core
from paddle.fluid import ParamAttr
from paddle.fluid.framework import Program, grad_var_name
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward
import numpy as np
import paddle.fluid.core as core
np.random.seed(123)
@ -69,8 +71,8 @@ class PySimpleRNN2(PyRNNBase):
super(PySimpleRNN2, self).__init__(input_shape, output_shape)
seq_len, batch_size, input_dim = input_shape
self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32")
self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32")
self.W = np.ones(shape=(input_dim, input_dim)).astype("float32")
self.U = np.zeros(shape=(input_dim, input_dim)).astype("float32")
self.h_boot = np.ones(shape=(batch_size, input_dim)).astype("float32")
men_dim = (seq_len, batch_size, input_dim)
@ -184,7 +186,7 @@ class RecurrentOpTest1(unittest.TestCase):
fetch_list=fetch_list,
return_numpy=False)
def test_backward(self, rtol=0.1):
def test_backward(self, rtol=0.01):
self.check_forward()
with fluid.program_guard(self.main_program, self.startup_program):
@ -206,7 +208,7 @@ class RecurrentOpTest1(unittest.TestCase):
pd_output = self.forward()
py_output = self.py_rnn.forward()
self.assertEqual(pd_output.shape, py_output.shape)
self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all())
self.assertTrue(np.isclose(pd_output, py_output, rtol=0.01).all())
def get_numerical_gradient(self, delta=0.005):
dloss_dout = 1.0
@ -276,14 +278,20 @@ class RecurrentOpTest2(RecurrentOpTest1):
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
temp_l = layers.fc(input=x_t,
size=self.input_dim,
param_attr='W',
bias_attr=False)
temp_r = layers.fc(input=h_pre,
size=self.input_dim,
param_attr='U',
bias_attr=False)
temp_l = layers.fc(
input=x_t,
size=self.input_dim,
param_attr=ParamAttr(
name='W',
initializer=fluid.initializer.ConstantInitializer(1.0)),
bias_attr=False)
temp_r = layers.fc(
input=h_pre,
size=self.input_dim,
param_attr=ParamAttr(
name='U',
initializer=fluid.initializer.ConstantInitializer(0.0)),
bias_attr=False)
h = layers.sigmoid(x=layers.elementwise_add(x=temp_l, y=temp_r))
@ -293,7 +301,7 @@ class RecurrentOpTest2(RecurrentOpTest1):
return rnn()
def test_backward(self):
super(RecurrentOpTest2, self).test_backward(rtol=0.2)
super(RecurrentOpTest2, self).test_backward(rtol=0.01)
class RecurrentOpMultipleMemoryTest(RecurrentOpTest1):

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