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revert div #27122

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2 changes: 0 additions & 2 deletions paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -49,8 +49,6 @@ REGISTER_OP_WITHOUT_GRADIENT(elementwise_floordiv, ops::ElementwiseOp,

REGISTER_OP_CPU_KERNEL(
elementwise_floordiv,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext, float>,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext, double>,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext,
int64_t>);
2 changes: 0 additions & 2 deletions paddle/fluid/operators/elementwise/elementwise_floordiv_op.cu
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Expand Up @@ -19,7 +19,5 @@ namespace plat = paddle::platform;

REGISTER_OP_CUDA_KERNEL(
elementwise_floordiv,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, float>,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, double>,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int64_t>);
12 changes: 2 additions & 10 deletions paddle/fluid/operators/elementwise/elementwise_floordiv_op.h
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Expand Up @@ -14,7 +14,6 @@ limitations under the License. */

#pragma once

#include <math.h>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
Expand Down Expand Up @@ -62,15 +61,8 @@ void elementwise_floor_div(const framework::ExecutionContext &ctx,
const framework::Tensor *x,
const framework::Tensor *y, framework::Tensor *z) {
int axis = ctx.Attr<int>("axis");
auto x_dims = x->dims();
auto y_dims = y->dims();
if (x_dims.size() >= y_dims.size()) {
ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, FloorDivFunctor<T>(), z);
} else {
ElementwiseComputeEx<InverseFloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, InverseFloorDivFunctor<T>(), z);
}
ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, FloorDivFunctor<T>(), z);
}

template <typename DeviceContext, typename T>
Expand Down
39 changes: 10 additions & 29 deletions python/paddle/fluid/dygraph/math_op_patch.py
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Expand Up @@ -19,7 +19,6 @@
from ..layers.layer_function_generator import OpProtoHolder
from ..layers import common_methods
from . import to_variable, no_grad
import paddle

import numpy as np
import six
Expand Down Expand Up @@ -163,26 +162,6 @@ def _scalar_mul_(var, value):
def _scalar_div_(var, value):
return _scalar_elementwise_op_(var, 1.0 / value, 0.0)

# TODO(shenliang03): currently, it supports divide, floor_divide, remainder
# for binary operator by using the api to achieve the type promotion
def _binary_method_creator_(op_type, reverse=False):
import paddle

def __impl__(self, other_var):
import paddle
op = getattr(paddle, op_type)
if reverse:
return op(other_var, self)
else:
return op(self, other_var)

__impl__.__doc__ = """

See paddle.{}""".format(op_type)
__impl__.__name__ = op_type

return __impl__

# for binary operator such as elementwise, compare
def _binary_creator_(method_name,
op_type,
Expand Down Expand Up @@ -281,20 +260,22 @@ def __impl__(self):
## a*b == b*a. Do not need to reverse explicitly
('__rmul__',
_binary_creator_('__rmul__', 'elementwise_mul', False, _scalar_mul_)),
('__div__', _binary_creator_('__div__', 'elementwise_div', False,
_scalar_div_)),
('__truediv__', _binary_creator_('__truediv__', 'elementwise_div',
False, _scalar_div_)),
('__rdiv__', _binary_creator_('__rdiv__', 'elementwise_div', True,
None)),
('__rtruediv__', _binary_creator_('rtruediv__', 'elementwise_div', True,
None)),
('__pow__', _binary_creator_('__pow__', 'elementwise_pow', False,
None)),
('__rpow__', _binary_creator_('__rpow__', 'elementwise_pow', True,
None)),
# These binary use paddle.optype
('__div__', _binary_method_creator_('divide', False)),
('__truediv__', _binary_method_creator_('divide', False)),
('__rtruediv__', _binary_method_creator_('divide', True)),
('__rdiv__', _binary_method_creator_('divide', True)),
('__floordiv__', _binary_method_creator_('floor_divide', False)),
('__rfloordiv__', _binary_method_creator_('floor_divide', True)),
('__mod__', _binary_method_creator_('remainder', False)),
('__floordiv__', _binary_creator_('__floordiv__',
'elementwise_floordiv', False, None)),
('__mod__', _binary_creator_('__mod__', 'elementwise_mod', False,
None)),
## for logical compare
('__eq__', _binary_creator_('__eq__', 'equal', False, None)),
('__ne__', _binary_creator_('__ne__', 'not_equal', False, None)),
Expand Down
41 changes: 12 additions & 29 deletions python/paddle/fluid/layers/math_op_patch.py
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Expand Up @@ -16,7 +16,6 @@

import warnings
import inspect
import paddle

from .. import core
from ..framework import Variable, unique_name
Expand Down Expand Up @@ -46,7 +45,6 @@
"__pow__": "A ** B",
"__rpow__": "A **= B",
"__floordiv__": "A //B",
"__rfloordiv__": "A //= B",
"__mod__": "A % B",
"__eq__": "A == B",
"__ne__": "A != B",
Expand Down Expand Up @@ -235,25 +233,6 @@ def _scalar_mul_(var, value):
def _scalar_div_(var, value):
return _scalar_op_(var, 1.0 / value, 0.0)

# TODO(shenliang03): currently, it supports divide, floor_divide, remainder
# for binary operator by using the api to achieve the type promotion
def _binary_method_creator_(op_type, reverse=False):
import paddle

def __impl__(self, other_var):
op = getattr(paddle, op_type)
if reverse:
return op(other_var, self)
else:
return op(self, other_var)

__impl__.__doc__ = """

See paddle.{}""".format(op_type)
__impl__.__name__ = op_type

return __impl__

def _binary_creator_(method_name,
op_type,
reverse=False,
Expand Down Expand Up @@ -360,18 +339,22 @@ def __impl__(self, other_var):
# a*b == b*a. Do not need to reverse explicitly
('__rmul__',
_binary_creator_('__rmul__', 'elementwise_mul', False, _scalar_mul_)),
('__div__', _binary_creator_('__div__', 'elementwise_div', False,
_scalar_div_)),
('__truediv__', _binary_creator_('__truediv__', 'elementwise_div',
False, _scalar_div_)),
('__rdiv__', _binary_creator_('__rdiv__', 'elementwise_div', True,
None)),
('__rtruediv__', _binary_creator_('__rtruediv__', 'elementwise_div',
True, None)),
('__pow__', _binary_creator_('__pow__', 'elementwise_pow', False,
None)),
('__rpow__', _binary_creator_('__rpow__', 'elementwise_pow', True,
None)),
# These binary use paddle.optype
('__div__', _binary_method_creator_('divide', False)),
('__rdiv__', _binary_method_creator_('divide', True)),
('__truediv__', _binary_method_creator_('divide', False)),
('__rtruediv__', _binary_method_creator_('divide', True)),
('__floordiv__', _binary_method_creator_('floor_divide', False)),
('__rfloordiv__', _binary_method_creator_('floor_divide', True)),
('__mod__', _binary_method_creator_('remainder', False)),
('__floordiv__', _binary_creator_('__floordiv__',
'elementwise_floordiv', False, None)),
('__mod__', _binary_creator_('__mod__', 'elementwise_mod', False,
None)),
# for logical compare
('__eq__', _binary_creator_('__eq__', 'equal', False, None)),
('__ne__', _binary_creator_('__ne__', 'not_equal', False, None)),
Expand Down
4 changes: 2 additions & 2 deletions python/paddle/fluid/tests/unittests/test_dist_transpiler_async_decay.py
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Expand Up @@ -113,8 +113,8 @@ def transpiler_test_impl(self):
["listen_and_serv"])
# block1: sum,cast,scale,floor,fill_constant,elementwise_pow,scale
self.assertEqual([op.type for op in pserver.blocks[1].ops], [
"sum", "cast", "fill_constant", "elementwise_div", "floor",
"fill_constant", "elementwise_pow", "scale"
"sum", "cast", "scale", "floor", "fill_constant", "elementwise_pow",
"scale"
])

# block1~2: optimize pass
Expand Down
131 changes: 16 additions & 115 deletions python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
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Expand Up @@ -240,124 +240,25 @@ def test_shape_with_batch_sizes(self):
self.assertEqual((out_result == (2 / x)).all(), True)


class TestDivideAPI(unittest.TestCase):
def setUp(self):
paddle.set_default_dtype("float64")
self.places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
self.places.append(fluid.CUDAPlace(0))

def check_static_result(self, place):
# rule 1
with fluid.program_guard(fluid.Program(), fluid.Program()):
x = fluid.data(name="x", shape=[3], dtype="float64")
y = np.array([1, 2, 3])
self.assertRaises(TypeError, paddle.divide, x=x, y=y)

# rule 2: both the inputs are not Tensor
with fluid.program_guard(fluid.Program(), fluid.Program()):
x = 2
y = 4
res = paddle.divide(x, y)
exe = fluid.Executor(place)
np_z = exe.run(fluid.default_main_program(),
feed={},
fetch_list=[res])
self.assertEqual(np_z[0] == 0.5, True)

# rule 3:
with fluid.program_guard(fluid.Program(), fluid.Program()):
x = fluid.data(name="x", shape=[3], dtype="float64")
y = fluid.data(name="y", shape=[3], dtype="float32")
self.assertRaises(TypeError, paddle.divide, x=x, y=y)

# rule 4: x is Tensor, y is scalar
with fluid.program_guard(fluid.Program(), fluid.Program()):
x = fluid.data(name="x", shape=[3], dtype="float64")
y = 2
exe = fluid.Executor(place)
res = x / y
np_z = exe.run(fluid.default_main_program(),
feed={"x": np.array([2, 3, 4]).astype('float64')},
fetch_list=[res])
z_expected = np.array([1., 1.5, 2.])
self.assertEqual((np_z[0] == z_expected).all(), True)

# rule 5: y is Tensor, x is scalar
with fluid.program_guard(fluid.Program(), fluid.Program()):
x = fluid.data(name="x", shape=[3], dtype="float64")
y = 2
exe = fluid.Executor(place)
res = y / x
np_z = exe.run(fluid.default_main_program(),
feed={"x": np.array([2, 8, 4]).astype('float64')},
fetch_list=[res])
z_expected = np.array([1., 0.25, 0.5])
self.assertEqual((np_z[0] == z_expected).all(), True)

# rule 6: y is Tensor, x is Tensor
with fluid.program_guard(fluid.Program(), fluid.Program()):
x = fluid.data(name="x", shape=[3], dtype="float64")
y = fluid.data(name="y", shape=[3], dtype="float64")
exe = fluid.Executor(place)
res = x / y
np_z = exe.run(fluid.default_main_program(),
feed={
"x": np.array([2, 3, 4]).astype('float64'),
"y": np.array([1, 5, 2]).astype('float64')
},
fetch_list=[res])
z_expected = np.array([2., 0.6, 2.])
self.assertEqual((np_z[0] == z_expected).all(), True)
class TestDivideOp(unittest.TestCase):
def test_name(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data(name="x", shape=[2, 3], dtype="float32")
y = fluid.data(name='y', shape=[2, 3], dtype='float32')

def test_static(self):
for place in self.places:
self.check_static_result(place=place)
y_1 = paddle.divide(x, y, name='div_res')
self.assertEqual(('div_res' in y_1.name), True)

def test_dygraph(self):
for place in self.places:
with fluid.dygraph.guard(place):
# rule 1 : avoid numpy.ndarray
np_x = np.array([2, 3, 4])
np_y = np.array([1, 5, 2])
x = paddle.to_tensor(np_x)
self.assertRaises(TypeError, paddle.divide, x=x, y=np_y)

# rule 2: both the inputs are not Tensor
z = paddle.divide(3, 2)
self.assertEqual(z.numpy()[0] == 1.5, True)

# rule 3: both the inputs are Tensor
np_x = np.array([2, 3, 4])
np_y = np.array([1, 5, 2])
x = paddle.to_tensor(np_x, dtype="float32")
y = paddle.to_tensor(np_y, dtype="float64")
self.assertRaises(TypeError, paddle.divide, x=x, y=y)

# rule 4: x is Tensor, y is scalar
np_x = np.array([2, 3, 4])
x = paddle.to_tensor(np_x, dtype="int32")
y = 2
z = x / y
z_expected = np.array([1., 1.5, 2.])
self.assertEqual((z_expected == z.numpy()).all(), True)

# rule 5: y is Tensor, x is scalar
np_x = np.array([2, 1, 4])
x = paddle.to_tensor(np_x, dtype="int32")
y = 2
z = y / x
z_expected = np.array([1., 2., 0.5])
self.assertEqual((z_expected == z.numpy()).all(), True)

# rule 6: y is Tensor, x is Tensor
np_x = np.array([2, 3, 4])
np_y = np.array([1, 5, 2])
x = paddle.to_tensor(np_x)
y = paddle.to_tensor(np_y)
z = x / y
z_expected = np.array([2., 0.6, 2.])
self.assertEqual((z_expected == z.numpy()).all(), True)
with fluid.dygraph.guard():
np_x = np.array([2, 3, 4]).astype('float64')
np_y = np.array([1, 5, 2]).astype('float64')
x = paddle.to_tensor(np_x)
y = paddle.to_tensor(np_y)
z = paddle.divide(x, y)
np_z = z.numpy()
z_expected = np.array([2., 0.6, 2.])
self.assertEqual((np_z == z_expected).all(), True)


if __name__ == '__main__':
Expand Down
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