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Check and fix tensor and scalar type promotion (#28299)

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* check and fix tensor and scalar type promotion

* fix else branch error

* fix scalar method error

* fix test_math_op_path unittest

* add future division for unittest

* rm useless bin file
revert-28284-dev/pybind_version
Chen Weihang 4 years ago committed by GitHub
parent fb1e0c93c9
commit 4086f48ea1
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 758 additions and 28 deletions
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46
python/paddle/fluid/dygraph/math_op_patch.py
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@ -149,28 +149,46 @@ def monkey_patch_math_varbase():
reverse=False,
scalar_method=None):
def __impl__(self, other_var):
# tensor and ComplexVariable opetator
# 0. check tensor and ComplexVariable opetator
if isinstance(other_var, ComplexVariable):
# need import paddle in closure
import paddle
math_op = getattr(paddle.incubate.complex.tensor, op_type)
return math_op(self, other_var)
# FIXME(zjl): elementwise_div between integers cannot be converted to scale,
# which may lose accuracy. This is a hot fix for release 1.6.
if scalar_method is not None and not (
op_type == 'elementwise_div' and
self.dtype in _supported_int_dtype_):
if isinstance(other_var, float):
if self.dtype in _supported_int_dtype_:
assert other_var == int(other_var), \
"float value {} cannot convert to integer".format(other_var)
# 1. scalar exists cases
# we need combine the tensor.dtype and scalar.dtype, cast correct object
if isinstance(other_var, float):
# in all cases(+, -, *, /, **, //, %), we need cast tensor.dtype to float
if self.dtype in _supported_int_dtype_:
self = astype(self, 'float32')
# here use `scale` replace `elementwise` to get better performance
# but only +, -, *, / can use this method
if scalar_method is not None:
return scalar_method(self, other_var)
elif isinstance(other_var, int):
return scalar_method(self, float(other_var))
elif isinstance(other_var, int):
# in all cases(+, -, *, /, **, //, %), we can cast it to float
# because the output tensor.dtype depend on the type of input tensor
other_var = float(other_var)
# division is a special case
# NOTE(chenweihang): because we cast tensor to float32 instead float64,
# the division result can only guarantee the numerical accuracy of 6 digits
# after the decimal point. The result of numpy calculation is of float64 type,
# so the calculation result here and the calculation result of numpy are
# different after 6 decimal point. If necessary, we can also use float64 here.
# torch's behavior here is consistent with ours
if op_type == 'elementwise_div' and self.dtype in _supported_int_dtype_:
self = astype(self, 'float32')
# here use `scale` replace `elementwise` to get better performance
# but only +, -, *, / can use this method
if scalar_method is not None:
return scalar_method(self, other_var)
else:
# do nothing
pass
# 2. create varbase for scalar
lhs_dtype = self.dtype
if not isinstance(other_var, core.VarBase):
if reverse:
other_var = create_tensor(
@ -179,6 +197,7 @@ def monkey_patch_math_varbase():
# add fill_op
other_var = create_scalar(value=other_var, dtype=lhs_dtype)
# 3. unify right var type to left var
rhs_dtype = other_var.dtype
if lhs_dtype != rhs_dtype:
other_var = astype(other_var, lhs_dtype)
@ -187,6 +206,7 @@ def monkey_patch_math_varbase():
self = other_var
other_var = tmp
# 4. calculation
axis = -1
math_op = getattr(core.ops, op_type)
return math_op(self, other_var, 'axis', axis)

43
python/paddle/fluid/layers/math_op_patch.py
Unescape View File

@ -215,21 +215,39 @@ def monkey_patch_variable():
reverse=False,
scalar_method=None):
def __impl__(self, other_var):
# FIXME(zjl): elementwise_div between integers cannot be converted to scale,
# which may lose accuracy. This is a hot fix for release 1.6.
if scalar_method is not None and not (
op_type == 'elementwise_div' and
self.dtype in _supported_int_dtype_):
if isinstance(other_var, float):
if self.dtype in _supported_int_dtype_:
assert other_var == int(other_var), \
"float value {} cannot convert to integer".format(other_var)
# 1. scalar exists cases
# we need combine the tensor.dtype and scalar.dtype, cast correct object
if isinstance(other_var, float):
# in all cases(+, -, *, /, **, //, %), we need cast tensor.dtype to float
if self.dtype in _supported_int_dtype_:
self = astype(self, 'float32')
# here use `scale` replace `elementwise` to get better performance
# but only +, -, *, / can use this method
if scalar_method is not None:
return scalar_method(self, other_var)
elif isinstance(other_var, int):
return scalar_method(self, float(other_var))
elif isinstance(other_var, int):
# in all cases(+, -, *, /, **, //, %), we can cast it to float
# because the output tensor.dtype depend on the type of input tensor
other_var = float(other_var)
# division is a special case
# NOTE(chenweihang): because we cast tensor to float32 instead float64,
# the division result can only guarantee the numerical accuracy of 6 digits
# after the decimal point. The result of numpy calculation is of float64 type,
# so the calculation result here and the calculation result of numpy are
# different after 6 decimal point. If necessary, we can also use float64 here.
# torch's behavior here is consistent with ours
if op_type == 'elementwise_div' and self.dtype in _supported_int_dtype_:
self = astype(self, 'float32')
# here use `scale` replace `elementwise` to get better performance
# but only +, -, *, / can use this method
if scalar_method is not None:
return scalar_method(self, other_var)
else:
# do nothing
pass
# 2. create variable for scalar
lhs_dtype = safe_get_dtype(self)
if not isinstance(other_var, Variable):
if reverse:
has_batch_size = False
@ -251,6 +269,7 @@ def monkey_patch_variable():
other_var = create_scalar(
current_block(self), value=other_var, dtype=lhs_dtype)
# 3. unify right var type to left var
rhs_dtype = safe_get_dtype(other_var)
if lhs_dtype != rhs_dtype:
other_var = astype(other_var, lhs_dtype)

10
python/paddle/fluid/tests/unittests/test_math_op_patch.py
Unescape View File

@ -12,15 +12,19 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from __future__ import print_function, division
import unittest
from decorator_helper import prog_scope
import paddle
import paddle.fluid as fluid
import numpy
class TestMathOpPatches(unittest.TestCase):
def setUp(self):
paddle.enable_static()
@prog_scope()
def test_add_scalar(self):
a = fluid.layers.data(name="a", shape=[1])
@ -197,8 +201,8 @@ class TestMathOpPatches(unittest.TestCase):
feed={"a": a_np},
fetch_list=[b])
b_np_actual = (a_np / 7).astype('int64')
self.assertTrue(numpy.array_equal(b_np, b_np_actual))
b_np_actual = (a_np / 7).astype('float32')
self.assertTrue(numpy.allclose(b_np, b_np_actual))
@prog_scope()
def test_equal(self):

318
python/paddle/fluid/tests/unittests/test_tensor_scalar_type_promotion_dynamic.py
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369
python/paddle/fluid/tests/unittests/test_tensor_scalar_type_promotion_static.py
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