提交 9f731a60 编写于 作者: Y Yang Yu

Add math operator patches

Users can use `a+b`, `a*10`.
上级 c73f00fe
......@@ -36,6 +36,7 @@ import clip
from memory_optimization_transpiler import memory_optimize
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + [
'io',
'initializer',
......@@ -93,4 +94,5 @@ def __bootstrap__():
core.init_devices()
layers.monkey_patch_variable()
__bootstrap__()
......@@ -23,6 +23,8 @@ import control_flow
from control_flow import *
import device
from device import *
import math_op_patch
from math_op_patch import *
__all__ = []
__all__ += nn.__all__
......@@ -31,3 +33,4 @@ __all__ += tensor.__all__
__all__ += control_flow.__all__
__all__ += ops.__all__
__all__ += device.__all__
__all__ += math_op_patch.__all__
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..framework import Variable, unique_name
from ..registry import OpProtoHolder
__all__ = ['monkey_patch_variable']
def monkey_patch_variable():
def new_name():
return unique_name("tmp")
def safe_get_dtype(var):
try:
dtype = var.dtype
except:
raise ValueError("Cannot get data type from %s", var.name)
return dtype
def create_scalar(block, value, dtype):
value = float(value)
tmp_name = new_name()
var = block.create_var(name=tmp_name, shape=[1], dtype=dtype)
block.append_op(
type="fill",
outputs={"Out": [var]},
attrs={"value": [value],
"shape": [1],
"dtype": dtype})
return var
def create_tensor(block, value, dtype, shape):
value = float(value)
tmp_name = new_name()
var = block.create_var(name=tmp_name, shape=shape, dtype=dtype)
block.append_op(
type="fill_constant",
outputs={'Out': [var]},
attrs={'dtype': var.dtype,
'shape': shape,
'value': value})
return var
def create_tensor_with_batchsize(ref_var, value, dtype):
assert isinstance(ref_var, Variable)
value = float(value)
tmp_name = new_name()
var = ref_var.block.create_var(name=tmp_name, dtype=dtype)
ref_var.block.append_op(
type='fill_constant_batch_size_like',
outputs={'Out': [var]},
inputs={'Input': [ref_var]},
attrs={'shape': ref_var.shape,
'value': value})
return var
def astype(self, dtype):
"""
Cast a variable to data type.
NOTE: The variable must be a Tensor
Args:
self(Variable): The source variable
dtype: The target dtype
Returns:
Variable with new dtype
"""
tmp_name = new_name()
out = self.block.create_var(name=tmp_name, dtype=dtype)
self.block.append_op(
type="cast",
inputs={"X": [self]},
outputs={"Out": [out]},
attrs={"in_dtype": self.dtype,
"out_dtype": out.dtype})
return out
def _elemwise_method_creator_(method_name, op_type, reverse=False):
def __impl__(self, other_var):
lhs_dtype = safe_get_dtype(self)
if not isinstance(other_var, Variable):
if reverse:
has_batch_size = False
for elem in self.shape:
if elem < 0:
has_batch_size = True
break
if not has_batch_size:
other_var = create_tensor(
self.block,
other_var,
dtype=lhs_dtype,
shape=self.shape)
else:
other_var = create_tensor_with_batchsize(
self, other_var, lhs_dtype)
else:
# add fill_op to self.block
other_var = create_scalar(
self.block, value=other_var, dtype=lhs_dtype)
rhs_dtype = safe_get_dtype(other_var)
if lhs_dtype != rhs_dtype:
other_var = astype(other_var, lhs_dtype)
if reverse:
tmp = self
self = other_var
other_var = tmp
tmp_name = new_name()
out = self.block.create_var(name=tmp_name, dtype=lhs_dtype)
self.block.append_op(
type=op_type,
inputs={'X': [self],
'Y': [other_var]},
outputs={'Out': out})
return out
comment = OpProtoHolder.instance().get_op_proto(op_type).comment
__impl__.__doc__ = """
{0}
Args:
self(Variable): left hand variable
other_var(Variable|float|int): right hand variable
Returns:
Variable
""".format(comment)
__impl__.__name__ = method_name
return __impl__
# inject methods
for method_name, op_type, reverse in (
("__add__", "elementwise_add", False),
# a+b == b+a. Do not need to reverse explicitly
("__radd__", "elementwise_add", False),
("__sub__", "elementwise_sub", False),
("__rsub__", "elementwise_sub", True),
("__mul__", "elementwise_mul", False),
# a*b == b*a. Do not need to reverse explicitly
("__rmul__", "elementwise_mul", False),
("__div__", "elementwise_div", False),
("__rdiv__", "elementwise_div", True)):
setattr(Variable, method_name,
_elemwise_method_creator_(method_name, op_type, reverse))
Variable.astype = astype
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import decorators
import paddle.v2.fluid as fluid
import numpy
class TestMathOpPatches(unittest.TestCase):
@decorators.prog_scope()
def test_add_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
b = a + 10
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = exe.run(fluid.default_main_program(),
feed={"a": a_np},
fetch_list=[b])
self.assertTrue(numpy.allclose(a_np + 10, b_np))
@decorators.prog_scope()
def test_radd_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
b = 10 + a
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = exe.run(fluid.default_main_program(),
feed={"a": a_np},
fetch_list=[b])
self.assertTrue(numpy.allclose(a_np + 10, b_np))
@decorators.prog_scope()
def test_sub_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
b = a - 10
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = exe.run(fluid.default_main_program(),
feed={"a": a_np},
fetch_list=[b])
self.assertTrue(numpy.allclose(a_np - 10, b_np))
@decorators.prog_scope()
def test_radd_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
b = 10 - a
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = exe.run(fluid.default_main_program(),
feed={"a": a_np},
fetch_list=[b])
self.assertTrue(numpy.allclose(10 - a_np, b_np))
@decorators.prog_scope()
def test_mul_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
b = a * 10
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = exe.run(fluid.default_main_program(),
feed={"a": a_np},
fetch_list=[b])
self.assertTrue(numpy.allclose(a_np * 10, b_np))
@decorators.prog_scope()
def test_rmul_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
b = 10 * a
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = exe.run(fluid.default_main_program(),
feed={"a": a_np},
fetch_list=[b])
self.assertTrue(numpy.allclose(10 * a_np, b_np))
@decorators.prog_scope()
def test_div_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
b = a / 10
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = exe.run(fluid.default_main_program(),
feed={"a": a_np},
fetch_list=[b])
self.assertTrue(numpy.allclose(a_np / 10, b_np))
@decorators.prog_scope()
def test_rdiv_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
b = 10 / a
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32') + 1e-2
b_np = exe.run(fluid.default_main_program(),
feed={"a": a_np},
fetch_list=[b])
self.assertTrue(numpy.allclose(10 / a_np, b_np))
@decorators.prog_scope()
def test_div_two_tensor(self):
a = fluid.layers.data(name="a", shape=[1])
b = fluid.layers.data(name="b", shape=[1])
c = a / b
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = numpy.random.random(size=[10, 1]).astype('float32') + 1e-2
c_np = exe.run(fluid.default_main_program(),
feed={"a": a_np,
'b': b_np},
fetch_list=[c])
self.assertTrue(numpy.allclose(a_np / b_np, c_np))
@decorators.prog_scope()
def test_mul_two_tensor(self):
a = fluid.layers.data(name="a", shape=[1])
b = fluid.layers.data(name="b", shape=[1])
c = a * b
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = numpy.random.random(size=[10, 1]).astype('float32')
c_np = exe.run(fluid.default_main_program(),
feed={"a": a_np,
'b': b_np},
fetch_list=[c])
self.assertTrue(numpy.allclose(a_np * b_np, c_np))
@decorators.prog_scope()
def test_add_two_tensor(self):
a = fluid.layers.data(name="a", shape=[1])
b = fluid.layers.data(name="b", shape=[1])
c = a + b
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = numpy.random.random(size=[10, 1]).astype('float32')
c_np = exe.run(fluid.default_main_program(),
feed={"a": a_np,
'b': b_np},
fetch_list=[c])
self.assertTrue(numpy.allclose(a_np + b_np, c_np))
@decorators.prog_scope()
def test_sub_two_tensor(self):
a = fluid.layers.data(name="a", shape=[1])
b = fluid.layers.data(name="b", shape=[1])
c = a - b
place = fluid.CPUPlace()
exe = fluid.Executor(place)
a_np = numpy.random.random(size=[10, 1]).astype('float32')
b_np = numpy.random.random(size=[10, 1]).astype('float32')
c_np = exe.run(fluid.default_main_program(),
feed={"a": a_np,
'b': b_np},
fetch_list=[c])
self.assertTrue(numpy.allclose(a_np - b_np, c_np))
if __name__ == '__main__':
unittest.main()
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