Replaced core.ops with _C_ops

python/paddle/distributed/utils.py

@@ -31,7 +31,7 @@
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.framework import in_dygraph_mode
 from paddle.fluid.data_feeder import check_variable_and_dtype
-
+from paddle import _C_ops
 
 __all__ = [ #noqa
  'get_host_name_ip',
@@ -146,7 +146,7 @@ def global_scatter(x,
 
  ring_id = 0 if group is None else group.id
  if in_dygraph_mode():
- return core.ops.global_scatter(x, local_count, \
+ return _C_ops.global_scatter(x, local_count, \
  global_count, \
  'use_calc_stream', use_calc_stream, \
  'ring_id', ring_id)
@@ -258,7 +258,7 @@ def global_gather(x,
 
  ring_id = 0 if group is None else group.id
  if in_dygraph_mode():
- return core.ops.global_gather(x, local_count, \
+ return _C_ops.global_gather(x, local_count, \
  global_count, \
  'use_calc_stream', use_calc_stream, \
  'ring_id', ring_id)

python/paddle/fluid/clip.py

@@ -29,6 +29,7 @@
 from .framework import in_dygraph_mode
 from .layer_helper import LayerHelper
 from .framework import default_main_program
+from paddle import _C_ops
 
 __all__ = [
  'set_gradient_clip', 'ErrorClipByValue', 'ClipGradByValue',
@@ -47,7 +48,7 @@ def _squared_l2_norm(x):
  return sum_square
 
  if in_dygraph_mode():
- return core.ops.squared_l2_norm(x)
+ return _C_ops.squared_l2_norm(x)
 
  op_type = 'squared_l2_norm'
  check_variable_and_dtype(x, 'x', ['float32', 'float64'], op_type)

python/paddle/fluid/tests/unittests/test_deprecated_decorator.py

@@ -140,7 +140,7 @@ def test_ops_elementwise_mul(self):
  b = np.random.uniform(0.1, 1, [51, 76]).astype(np.float32)
  x = paddle.to_tensor(a)
  y = paddle.to_tensor(b)
- res = core.ops.elementwise_mul(x, y)
+ res = _C_ops.elementwise_mul(x, y)
 
  # expected
  expected = LOWEST_WARNING_POSTION

python/paddle/fluid/tests/unittests/test_faster_tokenizer_op.py

@@ -24,6 +24,7 @@
 from paddle.dataset.common import DATA_HOME
 from paddle.fluid.framework import core, in_dygraph_mode
 from paddle.fluid.layer_helper import LayerHelper
+from paddle import _C_ops
 
 import sys
 sys.path.append("./tokenizer")
@@ -75,7 +76,7 @@ def forward(self,
  is_split_into_words=False,
  pad_to_max_seq_len=False):
  if in_dygraph_mode():
- input_ids, seg_ids = core.ops.faster_tokenizer(
+ input_ids, seg_ids = _C_ops.faster_tokenizer(
  self.vocab, text, text_pair, "do_lower_case", do_lower_case,
  "max_seq_len", max_seq_len, "pad_to_max_seq_len",
  pad_to_max_seq_len, "is_split_into_words", is_split_into_words)

python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py

@@ -16,6 +16,7 @@
 import paddle.fluid as fluid
 import numpy as np
 import unittest
+from paddle import _C_ops
 
 if fluid.is_compiled_with_cuda():
  fluid.core.globals()['FLAGS_cudnn_deterministic'] = True
@@ -112,8 +113,8 @@ def __init__(self, num_channels, epsilon=1e-5):
 
  def forward(self, input):
  if fluid.in_dygraph_mode():
- out, _, _ = fluid.core.ops.instance_norm(
- input, self.scale, self.bias, 'epsilon', self.epsilon)
+ out, _, _ = _C_ops.instance_norm(input, self.scale, self.bias,
+   'epsilon', self.epsilon)
  return out
  else:
  return fluid.layers.instance_norm(

python/paddle/fluid/tests/unittests/test_op_function_generator.py

@@ -21,14 +21,15 @@
 import paddle.fluid.core as core
 from paddle.fluid.dygraph.jit import TracedLayer
 import numpy as np
+from paddle import _C_ops
 
 
 class TestTracedLayer(fluid.dygraph.Layer):
  def __init__(self, name_scope):
  super(TestTracedLayer, self).__init__(name_scope)
 
  def forward(self, input):
- return core.ops.relu(input)
+ return _C_ops.relu(input)
 
 
 class TestVariable(unittest.TestCase):
@@ -46,7 +47,7 @@ def test_elementwise_add(self):
  x.stop_gradient = False
 
  res1 = layers.elementwise_add(x, y)
- res2 = core.ops.elementwise_add(x, y)
+ res2 = _C_ops.elementwise_add(x, y)
 
  self.assertTrue(np.array_equal(res1.numpy(), res2.numpy()))
 
@@ -58,7 +59,7 @@ def test_elementwise_mul(self):
  y = fluid.dygraph.to_variable(b)
 
  res1 = layers.elementwise_mul(x, y)
- res2 = core.ops.elementwise_mul(x, y)
+ res2 = _C_ops.elementwise_mul(x, y)
 
  self.assertTrue(np.array_equal(res1.numpy(), res2.numpy()))
 
@@ -68,7 +69,7 @@ def test_relu(self):
  x = fluid.dygraph.to_variable(a)
 
  res1 = layers.relu(x)
- res2 = core.ops.relu(x)
+ res2 = _C_ops.relu(x)
 
  self.assertTrue(np.array_equal(res1.numpy(), res2.numpy()))
 
@@ -81,7 +82,7 @@ def test_trace_backward(self):
  x.stop_gradient = False
  y.stop_gradient = False
 
- loss = core.ops.elementwise_mul(x, y)
+ loss = _C_ops.elementwise_mul(x, y)
 
  loss.backward()
  x_grad = x.gradient()

python/paddle/fluid/tests/unittests/test_sum_op.py

@@ -24,6 +24,7 @@
 from paddle.fluid.op import Operator
 from paddle.fluid.tests.unittests.op_test import (
  OpTest, convert_float_to_uint16, convert_uint16_to_float)
+from paddle import _C_ops
 
 
 class TestSumOp(OpTest):
@@ -382,11 +383,11 @@ class TestSumOpError(unittest.TestCase):
  def test_errors(self):
  def test_empty_list_input():
  with fluid.dygraph.guard():
- fluid.core.ops.sum([])
+ fluid._C_ops.sum([])
 
  def test_list_of_none_input():
  with fluid.dygraph.guard():
- fluid.core.ops.sum([None])
+ fluid._C_ops.sum([None])
 
  self.assertRaises(Exception, test_empty_list_input)
  self.assertRaises(Exception, test_list_of_none_input)

python/paddle/fluid/tests/unittests/xpu/test_sum_op_xpu.py

@@ -25,6 +25,7 @@
 from paddle.fluid.op import Operator
 from paddle.fluid.tests.unittests.op_test import (
  OpTest, convert_float_to_uint16, convert_uint16_to_float)
+from paddle import _C_ops
 
 paddle.enable_static()
 
@@ -171,11 +172,11 @@ class TestSumOpError(unittest.TestCase):
  def test_errors(self):
  def test_empty_list_input():
  with fluid.dygraph.guard():
- fluid.core.ops.sum([])
+ fluid._C_ops.sum([])
 
  def test_list_of_none_input():
  with fluid.dygraph.guard():
- fluid.core.ops.sum([None])
+ fluid._C_ops.sum([None])
 
  self.assertRaises(Exception, test_empty_list_input)
  self.assertRaises(Exception, test_list_of_none_input)

python/paddle/incubate/operators/graph_send_recv.py

@@ -16,6 +16,7 @@
 from paddle.fluid.framework import in_dygraph_mode
 from paddle.fluid.data_feeder import check_variable_and_dtype
 from paddle.fluid import core
+from paddle import _C_ops
 
 
 def graph_send_recv(x, src_index, dst_index, pool_type="sum", name=None):
@@ -82,8 +83,8 @@ def graph_send_recv(x, src_index, dst_index, pool_type="sum", name=None):
  % pool_type)
 
  if in_dygraph_mode():
- out, tmp = core.ops.graph_send_recv(x, src_index, dst_index,
-  'pool_type', pool_type.upper())
+ out, tmp = _C_ops.graph_send_recv(x, src_index, dst_index, 'pool_type',
+ pool_type.upper())
  return out
 
  check_variable_and_dtype(x, "X", ("float32", "float64", "int32", "int64"),

python/paddle/incubate/operators/softmax_mask_fuse.py

@@ -17,6 +17,7 @@
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.framework import in_dygraph_mode
 from paddle.fluid import core
+from paddle import _C_ops
 
 
 def softmax_mask_fuse(x, mask, name=None):
@@ -58,7 +59,7 @@ def softmax_mask_fuse(x, mask, name=None):
  # [[[[0.02404429, 0.04658398, 0.02746007, ..., 0.01489375, 0.02397441, 0.02851614] ... ]]]
  """
  if in_dygraph_mode():
- out = core.ops.fused_softmax_mask(x, mask)
+ out = _C_ops.fused_softmax_mask(x, mask)
  return out
  helper = LayerHelper('fused_softmax_mask', **locals())
  out = helper.create_variable_for_type_inference(dtype=x.dtype)

python/paddle/incubate/operators/softmax_mask_fuse_upper_triangle.py

@@ -17,6 +17,7 @@
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.framework import in_dygraph_mode
 from paddle.fluid import core
+from paddle import _C_ops
 
 
 def softmax_mask_fuse_upper_triangle(x):
@@ -58,7 +59,7 @@ def softmax_mask_fuse_upper_triangle(x):
  # ... ]]]
  """
  if in_dygraph_mode():
- out = core.ops.fused_softmax_mask_upper_triangle(x)
+ out = _C_ops.fused_softmax_mask_upper_triangle(x)
  return out
 
  helper = LayerHelper('fused_softmax_mask_upper_triangle', **locals())

python/paddle/nn/functional/common.py

@@ -1763,7 +1763,7 @@ class centers and the shape of sampled_class_center will be [num_positive_class_
  seed = default_main_program().random_seed
 
  if in_dygraph_mode():
- remapped_label, sampled_class_center = core.ops.class_center_sample(
+ remapped_label, sampled_class_center = _C_ops.class_center_sample(
  label, 'num_classes', num_classes, 'num_samples', num_samples,
  'ring_id', ring_id, 'nranks', nranks, 'rank', rank, 'fix_seed',
  seed is not None, 'seed', seed if seed is not None else 0)

python/paddle/nn/functional/loss.py

@@ -1320,7 +1320,7 @@ def margin_cross_entropy(logits,
  label = paddle.unsqueeze(label, axis=-1)
 
  if in_dygraph_mode():
- softmax, loss = core.ops.margin_cross_entropy(
+ softmax, loss = _C_ops.margin_cross_entropy(
  logits, label, 'ring_id', ring_id, 'rank', rank, 'nranks', nranks,
  'margin1', margin1, 'margin2', margin2, 'margin3', margin3, 'scale',
  scale, 'return_softmax', return_softmax)

python/paddle/tensor/linalg.py

@@ -1430,7 +1430,7 @@ def det(x, name=None):
 
  """
  if in_dygraph_mode():
- return core.ops.determinant(x)
+ return _C_ops.determinant(x)
 
  check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')
 
@@ -1485,7 +1485,7 @@ def slogdet(x, name=None):
 
  """
  if in_dygraph_mode():
- return core.ops.slogdeterminant(x)
+ return _C_ops.slogdeterminant(x)
 
  check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')
 
@@ -1633,7 +1633,7 @@ def matrix_power(x, n, name=None):
  # [ 1.80555556 , -1.91666667 , 0.44444444 ]]
  """
  if in_dygraph_mode():
- return core.ops.matrix_power(x, "n", n)
+ return _C_ops.matrix_power(x, "n", n)
 
  check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')
  check_type(n, 'n', int, 'matrix_power')

python/paddle/tensor/manipulation.py

@@ -70,8 +70,8 @@ def fill_(x, value):
  raise TypeError(
  "The type of 'value' must be int or float, but received %s." %
  (type(value)))
- return core.ops.fill_any_(x, "value_float",
-  float(value), "value_int", int(value))
+ return _C_ops.fill_any_(x, "value_float",
+ float(value), "value_int", int(value))
 
 
 setattr(core.VarBase, 'fill_', fill_)
@@ -102,7 +102,7 @@ def zero_(x):
  print(tensor.tolist()) #[0, 0, 0, 0, 0]
 
  """
- return core.ops.fill_any_(x, "value_float", 0., "value_int", int(0))
+ return _C_ops.fill_any_(x, "value_float", 0., "value_int", int(0))
 
 
 setattr(core.VarBase, 'zero_', zero_)
@@ -148,10 +148,10 @@ def fill_diagonal_(x, value, offset=0, wrap=False, name=None):
  'Tensor dims should be equal while input dims > 2 in fill_diagonal_ API'
  )
  if len(inshape) == 2:
- return core.ops.fill_diagonal_(x, 'value', value, 'offset', offset,
-  'wrap', wrap)
- return core.ops.fill_diagonal_(x, 'value', value, 'offset', offset, 'wrap',
-  True)
+ return _C_ops.fill_diagonal_(x, 'value', value, 'offset', offset,
+ 'wrap', wrap)
+ return _C_ops.fill_diagonal_(x, 'value', value, 'offset', offset, 'wrap',
+ True)
 
 
 setattr(core.VarBase, 'fill_diagonal_', fill_diagonal_)
@@ -182,10 +182,10 @@ def _fill_diagonal_tensor_impl(x, y, offset=0, dim1=0, dim2=1, inplace=False):
  y = y.reshape([1, -1])
 
  if inplace:
- return core.ops.fill_diagonal_tensor_(x, y, 'dim1', dim1, 'dim2', dim2,
-  'offset', offset)
- return core.ops.fill_diagonal_tensor(x, y, 'dim1', dim1, 'dim2', dim2,
-  'offset', offset)
+ return _C_ops.fill_diagonal_tensor_(x, y, 'dim1', dim1, 'dim2', dim2,
+ 'offset', offset)
+ return _C_ops.fill_diagonal_tensor(x, y, 'dim1', dim1, 'dim2', dim2,
+ 'offset', offset)
 
 
 def fill_diagonal_tensor_(x, y, offset=0, dim1=0, dim2=1, name=None):
@@ -475,7 +475,7 @@ def flip(x, axis, name=None):
  if isinstance(axis, int):
  axis = [axis]
  if in_dygraph_mode():
- return core.ops.flip(x, "axis", axis)
+ return _C_ops.flip(x, "axis", axis)
 
  helper = LayerHelper("flip", **locals())
  check_type(x, 'X', (Variable), 'flip')
@@ -1107,7 +1107,7 @@ def unique_consecutive(x,
  axis = [axis]
  attr_dtype = convert_np_dtype_to_dtype_(dtype)
  if in_dygraph_mode():
- out, inverse, counts = core.ops.unique_consecutive(
+ out, inverse, counts = _C_ops.unique_consecutive(
  x, 'dtype', attr_dtype, 'return_inverse', return_inverse,
  'return_counts', return_counts, 'axis', axis)
  outs = [out]

python/paddle/tensor/random.py

@@ -555,8 +555,8 @@ def uniform_(x, min=-1.0, max=1.0, seed=0, name=None):
  # [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # random
  # [ 0.433519, 0.39483607, -0.8660099, 0.83664286]] # random
  """
- return core.ops.uniform_random_inplace_(x, 'min', min, 'max', max, 'seed',
-  seed)
+ return _C_ops.uniform_random_inplace_(x, 'min', min, 'max', max, 'seed',
+ seed)
 
 
 def randint(low=0, high=None, shape=[1], dtype=None, name=None):

python/paddle/text/viterbi_decode.py

@@ -16,6 +16,7 @@
 from ..fluid.framework import core, in_dygraph_mode
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.data_feeder import check_variable_and_dtype, check_type
+from paddle import _C_ops
 
 __all__ = ['viterbi_decode', 'ViterbiDecoder']
 
@@ -58,9 +59,8 @@ def viterbi_decode(potentials,
  scores, path = paddle.text.viterbi_decode(emission, transition, length, False) # scores: [3.37089300, 1.56825531], path: [[1, 0, 0], [1, 1, 0]]
  """
  if in_dygraph_mode():
- return core.ops.viterbi_decode(potentials, transition_params, lengths,
- 'include_bos_eos_tag',
- include_bos_eos_tag)
+ return _C_ops.viterbi_decode(potentials, transition_params, lengths,
+ 'include_bos_eos_tag', include_bos_eos_tag)
  check_variable_and_dtype(potentials, 'input', ['float32', 'float64'],
  'viterbi_decode')
  check_variable_and_dtype(transition_params, 'transitions',