Replaced core.ops with _C_ops (#38337)

python/paddle/distributed/utils.py

@@ -31,7 +31,7 @@ from distutils.util import strtobool
 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 .data_feeder import check_variable_and_dtype
 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 @@ class TestDeprecatedDocorator(unittest.TestCase):
         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 @@ import paddle.nn as nn
 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 @@ class FasterTokenizer(nn.Layer):
                 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
 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 @@ class InstanceNorm(fluid.dygraph.Layer):
 
     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

@@ -21,6 +21,7 @@ import paddle.fluid.layers as layers
 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):
@@ -28,7 +29,7 @@ class TestTracedLayer(fluid.dygraph.Layer):
         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 @@ class TestVariable(unittest.TestCase):
             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 @@ class TestVariable(unittest.TestCase):
             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 @@ class TestVariable(unittest.TestCase):
             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 @@ class TestVariable(unittest.TestCase):
             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 @@ import paddle.fluid.core as core
 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 @@ import paddle.fluid.core as core
 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.layer_helper import LayerHelper
 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 __future__ import print_function
 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 __future__ import print_function
 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 @@ def class_center_sample(label, num_classes, num_samples, group=None):
         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

@@ -70,7 +70,7 @@ 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",
+    return _C_ops.fill_any_(x, "value_float",
                             float(value), "value_int", int(value))
 
 
@@ -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_)
@@ -148,9 +148,9 @@ 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,
+        return _C_ops.fill_diagonal_(x, 'value', value, 'offset', offset,
                                      'wrap', wrap)
-    return core.ops.fill_diagonal_(x, 'value', value, 'offset', offset, 'wrap',
+    return _C_ops.fill_diagonal_(x, 'value', value, 'offset', offset, 'wrap',
                                  True)
 
 
@@ -182,9 +182,9 @@ 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,
+        return _C_ops.fill_diagonal_tensor_(x, y, 'dim1', dim1, 'dim2', dim2,
                                             'offset', offset)
-    return core.ops.fill_diagonal_tensor(x, y, 'dim1', dim1, 'dim2', dim2,
+    return _C_ops.fill_diagonal_tensor(x, y, 'dim1', dim1, 'dim2', dim2,
                                        'offset', offset)
 
 
@@ -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

@@ -555,7 +555,7 @@ 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',
+    return _C_ops.uniform_random_inplace_(x, 'min', min, 'max', max, 'seed',
                                           seed)
 
 

python/paddle/text/viterbi_decode.py

@@ -16,6 +16,7 @@ from ..nn import Layer
 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',

python/paddle/vision/ops.py

@@ -953,10 +953,10 @@ def psroi_pool(x, boxes, boxes_num, output_size, spatial_scale=1.0, name=None):
             "Input features with shape should be (N, C, H, W)")
     output_channels = int(x.shape[1] / (pooled_height * pooled_width))
     if in_dygraph_mode():
-        return core.ops.psroi_pool(x, boxes, boxes_num, "output_channels",
+        return _C_ops.psroi_pool(x, boxes, boxes_num, "output_channels",
                                  output_channels, "spatial_scale",
-                                   spatial_scale, "pooled_height",
-                                   pooled_height, "pooled_width", pooled_width)
+                                 spatial_scale, "pooled_height", pooled_height,
+                                 "pooled_width", pooled_width)
 
     helper = LayerHelper('psroi_pool', **locals())
     dtype = helper.input_dtype()
@@ -1064,7 +1064,7 @@ def roi_pool(x, boxes, boxes_num, output_size, spatial_scale=1.0, name=None):
     pooled_height, pooled_width = output_size
     if in_dygraph_mode():
         assert boxes_num is not None, "boxes_num should not be None in dygraph mode."
-        pool_out, argmaxes = core.ops.roi_pool(
+        pool_out, argmaxes = _C_ops.roi_pool(
             x, boxes, boxes_num, "pooled_height", pooled_height, "pooled_width",
             pooled_width, "spatial_scale", spatial_scale)
         return pool_out
@@ -1219,7 +1219,7 @@ def roi_align(x,
     pooled_height, pooled_width = output_size
     if in_dygraph_mode():
         assert boxes_num is not None, "boxes_num should not be None in dygraph mode."
-        align_out = core.ops.roi_align(
+        align_out = _C_ops.roi_align(
             x, boxes, boxes_num, "pooled_height", pooled_height, "pooled_width",
             pooled_width, "spatial_scale", spatial_scale, "sampling_ratio",
             sampling_ratio, "aligned", aligned)

tools/count_api_without_core_ops.py

@@ -22,6 +22,7 @@ import pydoc
 import hashlib
 import functools
 import platform
+from paddle import _C_ops
 
 __all__ = ['get_apis_with_and_without_core_ops', ]
 
@@ -207,7 +208,7 @@ if __name__ == "__main__":
 
     else:
         print("""Usage: 
-            1. Count and list all operator-raleated APIs that contains append_op but not core.ops.xx. 
+            1. Count and list all operator-raleated APIs that contains append_op but not _C_ops.xx. 
                 python ./count_api_without_core_ops.py -c paddle
             2. Print api and the md5 of source code of the api.
                 python ./count_api_without_core_ops.py -p paddle