def tearDownClass(cls):
"""Restore random seeds"""
np.random.set_state(cls._np_rand_state)
random.setstate(cls._py_rand_state)
_set_use_system_allocator(cls._use_system_allocator)
# unittest will to trigger IPUCompiledProgram.__del__ automatically
global ipu_compiler_ref
ipu_compiler_ref is not None and ipu_compiler_ref.clean()
def setUpClass(cls):
cls._np_rand_state = np.random.get_state()
cls._py_rand_state = random.getstate()
cls.SEED = SEED
np.random.seed(cls.SEED)
random.seed(cls.SEED)
cls._use_system_allocator = _set_use_system_allocator(True)
import subprocess
import traceback
import functools
import pickle
from contextlib import closing
import paddle.fluid as fluid
import paddle.fluid.unique_name as nameGen
from paddle.fluid import core
from six import string_types
import paddle
from paddle.fluid.tests.unittests.op_test import OpTest, _set_use_system_allocator
from paddle.fluid.tests.unittests.test_sync_batch_norm_op import create_or_get_tensor
_set_use_system_allocator(False)
paddle.enable_static()
SEED = 10
class TestSyncBatchNormRunnerBase(object):
def get_model(self,
main,
startup,
place,
layout,
seed,
sync_bn=False,
only_forward=False):
raise NotImplementedError(
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from paddle.fluid.tests.unittests.op_test import OpTest, _set_use_system_allocator
from paddle.fluid.framework import grad_var_name
from paddle.fluid.tests.unittests.test_batch_norm_op import TestBatchNormOpInference, TestBatchNormOpTraining, _reference_training, _reference_grad
from mkldnn_op_test import check_if_mkldnn_batchnorm_primitives_exist_in_bwd
_set_use_system_allocator(True)
class TestMKLDNNBatchNormOpTraining(TestBatchNormOpTraining):
def init_kernel_type(self):
self.use_mkldnn = True
self.data_formats = ["NCHW"]
def ref_forward_backward(self, x, y_grad, scale, bias, mean, variance,
epsilon, momentum, shape, data_layout):
if data_layout != "NCHW" and data_layout != "NHWC":
raise ValueError("Unknown data order.")
# run forward
y, saved_mean, saved_variance = _reference_training(