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math_op_patch.py
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194 lines (172 loc) · 6.75 KB
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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 __future__ import print_function
from ..framework import Variable, unique_name
from .layer_function_generator import OpProtoHolder
from ..initializer import force_init_on_cpu
def monkey_patch_variable():
def unique_tmp_name():
return unique_name.generate("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_tensor(block, value, dtype, shape):
value = float(value)
tmp_name = unique_tmp_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,
'force_cpu': force_init_on_cpu()
},
stop_gradient=True)
var.stop_gradient = True
return var
def create_scalar(block, value, dtype):
return create_tensor(block, value, dtype, shape=[1])
def create_tensor_with_batchsize(ref_var, value, dtype):
assert isinstance(ref_var, Variable)
value = float(value)
tmp_name = unique_tmp_name()
var = ref_var.block.create_var(name=tmp_name, dtype=dtype)
batch_dim = -1
for i, d in enumerate(ref_var.shape):
if d < 0:
batch_dim = i
break
assert batch_dim != -1
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,
'input_dim_idx': batch_dim,
'output_dim_idx': batch_dim
},
stop_gradient=True)
var.stop_gradient = True
return var
def astype(self, dtype):
"""
Cast a variable to a specified 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 = unique_tmp_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 = unique_tmp_name()
out = self.block.create_var(name=tmp_name, dtype=lhs_dtype)
axis = -1
if other_var.shape[0] == -1:
axis = 0
assert len(self.shape) >= len(other_var.shape), (
"The rank of the first argument of an binary operator cannot "
"be smaller than the rank of its second argument: %s vs %s" %
(len(self.shape), len(other_var.shape)))
self.block.append_op(
type=op_type,
inputs={'X': [self],
'Y': [other_var]},
outputs={'Out': out},
attrs={'axis': axis})
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),
("__truediv__", "elementwise_div", False),
("__rdiv__", "elementwise_div", True),
("__rtruediv__", "elementwise_div", True),
("__pow__", "elementwise_pow", False),
("__rpow__", "elementwise_pow", True),
("__floordiv__", "elementwise_floordiv", False),
("__mod__", "elementwise_mod", False),
# for logical compare
("__eq__", "equal", False),
("__ne__", "not_equal", False),
("__lt__", "less_than", False),
("__le__", "less_equal", False),
("__gt__", "greater_than", False),
("__ge__", "greater_equal", False)):
setattr(Variable, method_name,
_elemwise_method_creator_(method_name, op_type, reverse))
Variable.astype = astype