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体验新版 GitCode,发现更多精彩内容 >>
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b0ee1405
编写于
9月 26, 2020
作者:
J
joanna.wozna.intel
提交者:
GitHub
9月 26, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add conv2d bfloat16 support (#27325)
上级
b38e4f28
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
323 addition
and
28 deletion
+323
-28
paddle/fluid/framework/ir/graph_pattern_detector.cc
paddle/fluid/framework/ir/graph_pattern_detector.cc
+1
-2
paddle/fluid/operators/conv_op.cc
paddle/fluid/operators/conv_op.cc
+7
-1
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
+37
-17
paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc
paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc
+2
-1
paddle/fluid/pybind/tensor_py.h
paddle/fluid/pybind/tensor_py.h
+18
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+3
-1
python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py
...luid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py
+208
-0
python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py
...luid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py
+3
-1
python/paddle/fluid/tests/unittests/mkldnn/test_dequantize_mkldnn_op.py
...fluid/tests/unittests/mkldnn/test_dequantize_mkldnn_op.py
+12
-1
python/paddle/fluid/tests/unittests/op_test.py
python/paddle/fluid/tests/unittests/op_test.py
+32
-4
未找到文件。
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @
b0ee1405
...
...
@@ -1894,8 +1894,7 @@ PDNode *patterns::QuantizePlacement::operator()(
PDNode
*
patterns
::
Bfloat16Placement
::
operator
()(
const
std
::
unordered_set
<
std
::
string
>
&
bfloat16_enabled_op_types
)
{
std
::
unordered_set
<
std
::
string
>
supported_op_types
=
std
::
unordered_set
<
std
::
string
>
();
std
::
unordered_set
<
std
::
string
>
supported_op_types
{
"conv2d"
};
if
(
!
bfloat16_enabled_op_types
.
empty
())
{
supported_op_types
=
bfloat16_enabled_op_types
;
}
...
...
paddle/fluid/operators/conv_op.cc
浏览文件 @
b0ee1405
...
...
@@ -166,7 +166,8 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
#endif
if
(
input_data_type
!=
framework
::
proto
::
VarType
::
INT8
&&
input_data_type
!=
framework
::
proto
::
VarType
::
UINT8
)
{
input_data_type
!=
framework
::
proto
::
VarType
::
UINT8
&&
input_data_type
!=
framework
::
proto
::
VarType
::
BF16
)
{
auto
filter_data_type
=
ctx
.
Input
<
Tensor
>
(
"Filter"
)
->
type
();
PADDLE_ENFORCE_EQ
(
input_data_type
,
filter_data_type
,
platform
::
errors
::
InvalidArgument
(
...
...
@@ -455,6 +456,11 @@ void Conv3DOpMaker::Make() {
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"mkldnn_data_type"
,
"(string, default
\"
float32
\"
). Data type of mkldnn kernel"
)
.
SetDefault
(
"float32"
)
.
InEnum
({
"float32"
,
"int8"
,
"bfloat16"
});
AddAttr
<
bool
>
(
"fuse_relu"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"fuse_activation"
,
...
...
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
浏览文件 @
b0ee1405
...
...
@@ -55,12 +55,12 @@ inline MKLDNNMemoryFormat GetWeightsFormat(const MKLDNNMemoryFormat format,
}
}
static
mkldnn
::
memory
::
data_type
GetDstType
(
bool
is_int8
,
static
mkldnn
::
memory
::
data_type
GetDstType
(
bool
is_int8
,
bool
is_bfloat16
,
bool
force_fp32_output
,
std
::
string
fuse_activation
,
bool
fuse_residual_conn
,
const
Tensor
*
residual_param
)
{
auto
dst_dt
=
mkldnn
::
memory
::
data_type
::
f32
;
// uint8_t, int8_t, float
auto
dst_dt
=
mkldnn
::
memory
::
data_type
::
f32
;
if
(
is_int8
)
{
dst_dt
=
(
fuse_activation
==
"relu"
||
fuse_activation
==
"relu6"
)
?
mkldnn
::
memory
::
data_type
::
u8
...
...
@@ -72,6 +72,13 @@ static mkldnn::memory::data_type GetDstType(bool is_int8,
auto
residual_dt
=
framework
::
ToMKLDNNDataType
(
residual_param
->
type
());
if
(
dst_dt
!=
residual_dt
)
dst_dt
=
residual_dt
;
}
}
else
{
if
(
!
force_fp32_output
&&
is_bfloat16
)
{
dst_dt
=
mkldnn
::
memory
::
data_type
::
bf16
;
if
(
fuse_residual_conn
&&
residual_param
)
{
dst_dt
=
framework
::
ToMKLDNNDataType
(
residual_param
->
type
());
}
}
}
return
dst_dt
;
}
...
...
@@ -224,12 +231,15 @@ class ConvMKLDNNHandlerT
src_tz
.
size
(),
chosen_memory_format
);
}
}
const
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
chosen_memory_format
);
const
auto
weights_md
=
platform
::
MKLDNNMemDesc
(
weights_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
MKLDNNMemoryFormat
::
any
);
auto
data_type
=
mkldnn
::
memory
::
data_type
::
f32
;
if
(
ctx
.
Attr
<
std
::
string
>
(
"mkldnn_data_type"
)
==
"bfloat16"
||
std
::
is_same
<
T_out
,
platform
::
bfloat16
>::
value
)
data_type
=
mkldnn
::
memory
::
data_type
::
bf16
;
const
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
data_type
,
chosen_memory_format
);
const
auto
weights_md
=
platform
::
MKLDNNMemDesc
(
weights_tz
,
data_type
,
MKLDNNMemoryFormat
::
any
);
const
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
platform
::
MKLDNNGetDataType
<
T_out
>
(),
chosen_memory_format
);
...
...
@@ -241,8 +251,8 @@ class ConvMKLDNNHandlerT
if
(
bias
)
{
auto
bias_tz
=
framework
::
vectorize
(
bias
->
dims
());
auto
bias_md
=
platform
::
MKLDNNMemDesc
(
bias_tz
,
platform
::
MKLDNNGetDataType
<
T
>
()
,
MKLDNNMemoryFormat
::
x
);
auto
bias_md
=
platform
::
MKLDNNMemDesc
(
bias_tz
,
data_type
,
MKLDNNMemoryFormat
::
x
);
this
->
AcquireForwardPrimitiveDescriptor
(
conv_attr
,
fwd_prop_kind
,
dnnl
::
algorithm
::
convolution_direct
,
...
...
@@ -384,15 +394,21 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
"Operator DNNL Conv must use CPUPlace"
));
bool
is_INT8
=
std
::
is_same
<
T
,
int8_t
>::
value
||
std
::
is_same
<
T
,
uint8_t
>::
value
;
bool
is_BFLOAT16
=
ctx
.
Attr
<
std
::
string
>
(
"mkldnn_data_type"
)
==
"bfloat16"
;
auto
residual_param
=
ctx
.
Input
<
Tensor
>
(
"ResidualData"
);
bool
fuse_residual_conn
=
ctx
.
Attr
<
bool
>
(
"fuse_residual_connection"
);
std
::
string
fuse_activation
=
ctx
.
Attr
<
std
::
string
>
(
"fuse_activation"
);
bool
force_fp32_output
=
ctx
.
Attr
<
bool
>
(
"force_fp32_output"
);
auto
dst_dt
=
GetDstType
(
is_INT8
,
is_BFLOAT16
,
force_fp32_output
,
fuse_activation
,
fuse_residual_conn
,
residual_param
);
if
(
!
is_INT8
)
{
ComputeFP32
<
float
>
(
ctx
);
if
(
dst_dt
==
mkldnn
::
memory
::
data_type
::
f32
)
{
ComputeFP32
<
float
>
(
ctx
);
}
else
if
(
dst_dt
==
mkldnn
::
memory
::
data_type
::
bf16
)
{
ComputeFP32
<
platform
::
bfloat16
>
(
ctx
);
}
}
else
{
std
::
string
fuse_activation
=
ctx
.
Attr
<
std
::
string
>
(
"fuse_activation"
);
bool
fuse_residual_conn
=
ctx
.
Attr
<
bool
>
(
"fuse_residual_connection"
);
bool
force_fp32_output
=
ctx
.
Attr
<
bool
>
(
"force_fp32_output"
);
auto
residual_param
=
ctx
.
Input
<
Tensor
>
(
"ResidualData"
);
auto
dst_dt
=
GetDstType
(
true
,
force_fp32_output
,
fuse_activation
,
fuse_residual_conn
,
residual_param
);
if
(
dst_dt
==
mkldnn
::
memory
::
data_type
::
f32
)
{
ComputeINT8
<
float
>
(
ctx
);
}
else
if
(
dst_dt
==
mkldnn
::
memory
::
data_type
::
u8
)
{
...
...
@@ -1103,6 +1119,10 @@ REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN,
ops
::
kConvMKLDNNFP32
,
ops
::
ConvMKLDNNOpKernel
<
float
,
float
>
);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE
(
conv2d
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
BF16
,
ops
::
kConvMKLDNNFP32
,
ops
::
ConvMKLDNNOpKernel
<
paddle
::
platform
::
bfloat16
,
float
>
);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE
(
conv2d
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
U8
,
ops
::
kConvMKLDNNINT8
,
...
...
paddle/fluid/operators/mkldnn/dequantize_mkldnn_op.cc
浏览文件 @
b0ee1405
...
...
@@ -110,4 +110,5 @@ class DeQuantOpKernel : public framework::OpKernel<T> {
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_KERNEL
(
dequantize
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
ops
::
DeQuantOpKernel
<
uint8_t
>
,
ops
::
DeQuantOpKernel
<
int8_t
>
);
ops
::
DeQuantOpKernel
<
uint8_t
>
,
ops
::
DeQuantOpKernel
<
int8_t
>
,
ops
::
DeQuantOpKernel
<
paddle
::
platform
::
bfloat16
>
);
paddle/fluid/pybind/tensor_py.h
浏览文件 @
b0ee1405
...
...
@@ -41,6 +41,7 @@ namespace detail {
// import numpy as np
// print np.dtype(np.float16).num # 23
constexpr
int
NPY_FLOAT16_
=
23
;
constexpr
int
NPY_UINT16_
=
4
;
// Note: Since float16 is not a builtin type in C++, we register
// paddle::platform::float16 as numpy.float16.
...
...
@@ -60,6 +61,23 @@ struct npy_format_descriptor<paddle::platform::float16> {
static
PYBIND11_DESCR
name
()
{
return
_
(
"float16"
);
}
};
// Note: Since bfloat16 is not a builtin type in C++ and in numpy,
// we register paddle::platform::bfloat16 as numpy.uint16.
template
<
>
struct
npy_format_descriptor
<
paddle
::
platform
::
bfloat16
>
{
static
py
::
dtype
dtype
()
{
handle
ptr
=
npy_api
::
get
().
PyArray_DescrFromType_
(
NPY_UINT16_
);
return
reinterpret_borrow
<
py
::
dtype
>
(
ptr
);
}
static
std
::
string
format
()
{
// Note: "H" represents UINT16.
// Details at:
// https://docs.python.org/3/library/struct.html#format-characters.
return
"H"
;
}
static
PYBIND11_DESCR
name
()
{
return
_
(
"bfloat16"
);
}
};
}
// namespace detail
}
// namespace pybind11
...
...
python/paddle/fluid/framework.py
浏览文件 @
b0ee1405
...
...
@@ -613,7 +613,9 @@ def convert_np_dtype_to_dtype_(np_dtype):
elif
dtype
==
np
.
bool
:
return
core
.
VarDesc
.
VarType
.
BOOL
elif
dtype
==
np
.
uint16
:
return
core
.
VarDesc
.
VarType
.
INT16
# since there is still no support for bfloat16 in NumPy,
# uint16 is used for casting bfloat16
return
core
.
VarDesc
.
VarType
.
BF16
elif
dtype
==
np
.
uint8
:
return
core
.
VarDesc
.
VarType
.
UINT8
elif
dtype
==
np
.
int8
:
...
...
python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_bf16_mkldnn_op.py
0 → 100644
浏览文件 @
b0ee1405
# Copyright (c) 2020 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
import
unittest
import
numpy
as
np
import
struct
import
paddle.fluid.core
as
core
from
paddle.fluid.tests.unittests.op_test
import
OpTest
,
skip_check_grad_ci
,
convert_float_to_uint16
from
paddle.fluid.tests.unittests.test_conv2d_op
import
conv2d_forward_naive
,
TestConv2dOp
def
conv2d_forward_refer
(
input
,
filter
,
group
,
conv_param
):
out
,
in_n
,
out_h
,
out_w
,
out_c
=
conv2d_forward_naive
(
input
,
filter
,
group
,
conv_param
)
return
out
def
conv2d_residual_naive
(
out
,
residual
):
assert
out
.
shape
==
residual
.
shape
out
=
np
.
add
(
out
,
residual
)
return
out
class
TestConv2dBf16Op
(
TestConv2dOp
):
def
setUp
(
self
):
self
.
op_type
=
"conv2d"
self
.
use_cudnn
=
False
self
.
exhaustive_search
=
False
self
.
use_cuda
=
False
self
.
use_mkldnn
=
True
self
.
weight_type
=
np
.
float32
self
.
input_type
=
np
.
float32
self
.
use_mkldnn
=
True
self
.
mkldnn_data_type
=
"bfloat16"
self
.
force_fp32_output
=
False
self
.
init_group
()
self
.
init_dilation
()
self
.
init_test_case
()
self
.
init_fuse_relu
()
self
.
init_fuse_residual
()
self
.
init_data_type
()
self
.
init_force_fp32_output
()
conv2d_param
=
{
'stride'
:
self
.
stride
,
'pad'
:
self
.
pad
,
'dilation'
:
self
.
dilations
}
self
.
input
=
np
.
random
.
random
(
self
.
input_size
).
astype
(
np
.
float32
)
self
.
filter
=
np
.
random
.
random
(
self
.
filter_size
).
astype
(
np
.
float32
)
conv_out
,
_
,
_
,
_
,
_
=
conv2d_forward_naive
(
self
.
input
,
self
.
filter
,
self
.
groups
,
conv2d_param
)
self
.
conv_output_float
=
conv_out
if
self
.
fuse_residual
:
self
.
input_residual
=
np
.
random
.
random
(
self
.
input_residual_size
).
astype
(
np
.
float32
)
self
.
conv_output_float
=
conv2d_residual_naive
(
self
.
conv_output_float
,
self
.
input_residual
)
self
.
conv_output
=
convert_float_to_uint16
(
self
.
conv_output_float
)
self
.
outputs
=
{
'Output'
:
self
.
conv_output
}
elif
self
.
force_fp32_output
:
self
.
outputs
=
{
'Output'
:
self
.
conv_output_float
.
astype
(
np
.
float32
)}
if
self
.
input_type
is
not
np
.
float32
:
self
.
input
=
convert_float_to_uint16
(
self
.
input
)
self
.
inputs
=
{
'Input'
:
self
.
input
.
view
(
self
.
input_type
),
'Filter'
:
OpTest
.
np_dtype_to_fluid_dtype
(
self
.
filter
.
astype
(
self
.
weight_type
))
}
if
self
.
fuse_residual
:
self
.
inputs
[
'ResidualData'
]
=
OpTest
.
np_dtype_to_fluid_dtype
(
convert_float_to_uint16
(
self
.
input_residual
))
self
.
attrs
=
{
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
'groups'
:
self
.
groups
,
'dilations'
:
self
.
dilations
,
'use_cudnn'
:
self
.
use_cudnn
,
'use_mkldnn'
:
self
.
use_mkldnn
,
'mkldnn_data_type'
:
self
.
mkldnn_data_type
,
'force_fp32_output'
:
self
.
force_fp32_output
,
'fuse_residual_connection'
:
self
.
fuse_residual
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
core
.
CPUPlace
())
def
test_check_grad
(
self
):
pass
def
test_check_grad_no_filter
(
self
):
pass
def
test_check_grad_no_input
(
self
):
pass
def
init_test_case
(
self
):
TestConv2dOp
.
init_test_case
(
self
)
self
.
input_size
=
[
1
,
1
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
//
self
.
groups
self
.
input_residual_size
=
[
1
,
2
,
3
,
3
]
self
.
filter_size
=
[
2
,
f_c
,
3
,
3
]
def
init_data_type
(
self
):
self
.
weight_type
=
np
.
float32
self
.
input_type
=
np
.
float32
def
init_force_fp32_output
(
self
):
self
.
force_fp32_output
=
False
def
init_fuse_relu
(
self
):
self
.
fuse_activation
=
"relu"
def
init_fuse_residual
(
self
):
self
.
fuse_residual
=
True
class
TestConv2d
(
TestConv2dBf16Op
):
def
init_test_case
(
self
):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
self
.
input_residual_size
=
[
2
,
6
,
3
,
3
]
assert
np
.
mod
(
self
.
input_size
[
1
],
self
.
groups
)
==
0
f_c
=
self
.
input_size
[
1
]
//
self
.
groups
self
.
filter_size
=
[
6
,
f_c
,
3
,
3
]
def
init_data_type
(
self
):
self
.
input_type
=
np
.
uint16
class
TestWithPad
(
TestConv2d
):
def
init_test_case
(
self
):
TestConv2d
.
init_test_case
(
self
)
self
.
pad
=
[
1
,
1
]
self
.
input_residual_size
=
[
2
,
6
,
5
,
5
]
class
TestWithGroup
(
TestConv2d
):
def
init_group
(
self
):
self
.
groups
=
3
class
TestWithStride
(
TestConv2dBf16Op
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
2
,
2
]
self
.
input_size
=
[
2
,
3
,
6
,
6
]
self
.
input_residual_size
=
[
2
,
6
,
3
,
3
]
assert
np
.
mod
(
self
.
input_size
[
1
],
self
.
groups
)
==
0
f_c
=
self
.
input_size
[
1
]
//
self
.
groups
self
.
filter_size
=
[
6
,
f_c
,
3
,
3
]
def
init_data_type
(
self
):
self
.
input_type
=
np
.
uint16
class
TestWith1x1ForceFP32Output
(
TestConv2dBf16Op
):
def
init_test_case
(
self
):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
input_size
=
[
1
,
3
,
5
,
5
]
assert
np
.
mod
(
self
.
input_size
[
1
],
self
.
groups
)
==
0
f_c
=
self
.
input_size
[
1
]
//
self
.
groups
self
.
filter_size
=
[
6
,
f_c
,
1
,
1
]
def
init_force_fp32_output
(
self
):
self
.
force_fp32_output
=
True
def
init_fuse_residual
(
self
):
self
.
fuse_residual
=
False
class
TestWithInput1x1Filter1x1
(
TestConv2dBf16Op
):
def
init_test_case
(
self
):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
1
,
1
]
self
.
input_residual_size
=
[
2
,
6
,
1
,
1
]
assert
np
.
mod
(
self
.
input_size
[
1
],
self
.
groups
)
==
0
f_c
=
self
.
input_size
[
1
]
//
self
.
groups
self
.
filter_size
=
[
6
,
f_c
,
1
,
1
]
def
init_group
(
self
):
self
.
groups
=
3
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py
浏览文件 @
b0ee1405
...
...
@@ -36,6 +36,7 @@ class TestConv2dInt8Op(TestConv2dOp):
self
.
use_cuda
=
False
self
.
use_mkldnn
=
False
self
.
data_format
=
"NCHW"
self
.
mkldnn_data_type
=
"int8"
self
.
weighttype
=
np
.
float32
self
.
use_mkldnn
=
True
self
.
init_group
()
...
...
@@ -141,7 +142,8 @@ class TestConv2dInt8Op(TestConv2dOp):
'Scale_weights'
:
self
.
scale_weights
,
'Scale_in_eltwise'
:
self
.
scale_in_eltwise
,
'fuse_activation'
:
self
.
fuse_activation
,
'fuse_residual_connection'
:
self
.
fuse_residual
'fuse_residual_connection'
:
self
.
fuse_residual
,
'mkldnn_data_type'
:
self
.
mkldnn_data_type
}
self
.
outputs
=
{
'Output'
:
output
}
...
...
python/paddle/fluid/tests/unittests/mkldnn/test_dequantize_mkldnn_op.py
浏览文件 @
b0ee1405
...
...
@@ -16,7 +16,7 @@ from __future__ import print_function
import
unittest
import
numpy
as
np
from
paddle.fluid.tests.unittests.op_test
import
OpTest
from
paddle.fluid.tests.unittests.op_test
import
OpTest
,
convert_float_to_uint16
class
TestDeQuantizeOp
(
OpTest
):
...
...
@@ -32,6 +32,9 @@ class TestDeQuantizeOp(OpTest):
input
=
(
np
.
random
.
randint
(
0
,
100
,
self
.
input_size
)
-
50
).
astype
(
self
.
data_type
)
output
=
(
input
*
(
1
/
self
.
scale
)).
astype
(
'float'
)
elif
self
.
data_type
==
'uint16'
:
output
=
np
.
random
.
random
(
self
.
input_size
).
astype
(
np
.
float32
)
input
=
convert_float_to_uint16
(
output
)
else
:
input
=
(
np
.
random
.
randint
(
0
,
100
,
self
.
input_size
)).
astype
(
self
.
data_type
)
...
...
@@ -70,5 +73,13 @@ class TestDeQuantizeOp2(TestDeQuantizeOp):
self
.
data_type
=
'uint8'
class
TestDeQuantizeOpBf16
(
TestDeQuantizeOp
):
def
set_scale
(
self
):
self
.
scale
=
1.0
def
set_data_type
(
self
):
self
.
data_type
=
'uint16'
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @
b0ee1405
...
...
@@ -20,6 +20,7 @@ import warnings
import
numpy
as
np
import
random
import
six
import
struct
import
time
import
itertools
import
collections
...
...
@@ -167,6 +168,18 @@ def skip_check_grad_ci(reason=None):
return
wrapper
def
copy_bits_from_float_to_uint16
(
f
):
return
struct
.
unpack
(
'<I'
,
struct
.
pack
(
'<f'
,
f
))[
0
]
>>
16
def
convert_float_to_uint16
(
float_list
):
new_output
=
[]
for
x
in
np
.
nditer
(
float_list
):
new_output
.
append
(
np
.
uint16
(
copy_bits_from_float_to_uint16
(
x
)))
return
np
.
reshape
(
new_output
,
float_list
.
shape
).
view
(
np
.
uint16
)
class
OpTest
(
unittest
.
TestCase
):
@
classmethod
def
setUpClass
(
cls
):
...
...
@@ -242,6 +255,11 @@ class OpTest(unittest.TestCase):
self
.
call_once
=
True
self
.
dtype
=
data_type
def
is_bfloat16_op
(
self
):
return
self
.
dtype
==
np
.
uint16
or
(
hasattr
(
self
,
'mkldnn_data_type'
)
and
getattr
(
self
,
'mkldnn_data_type'
)
is
"bfloat16"
)
def
infer_dtype_from_inputs_outputs
(
self
,
inputs
,
outputs
):
def
is_np_data
(
input
):
return
isinstance
(
input
,
(
np
.
ndarray
,
np
.
generic
))
...
...
@@ -276,8 +294,9 @@ class OpTest(unittest.TestCase):
infer_dtype
(
inputs
,
dtype_set
)
dtype_list
=
[
np
.
dtype
(
np
.
float64
),
np
.
dtype
(
np
.
float32
),
np
.
dtype
(
np
.
float16
),
np
.
dtype
(
np
.
int64
),
np
.
dtype
(
np
.
int32
),
np
.
dtype
(
np
.
int16
),
np
.
dtype
(
np
.
int8
),
np
.
dtype
(
np
.
uint8
),
np
.
dtype
(
np
.
bool
)
np
.
dtype
(
np
.
int64
),
np
.
dtype
(
np
.
int32
),
np
.
dtype
(
np
.
uint16
),
np
.
dtype
(
np
.
int16
),
np
.
dtype
(
np
.
int8
),
np
.
dtype
(
np
.
uint8
),
np
.
dtype
(
np
.
bool
)
]
# check the dtype in dtype_list in order, select the first dtype that in dtype_set
for
dtype
in
dtype_list
:
...
...
@@ -957,6 +976,14 @@ class OpTest(unittest.TestCase):
self
.
op_type
not
in
op_threshold_white_list
.
NEED_FIX_FP64_CHECK_OUTPUT_THRESHOLD_OP_LIST
:
atol
=
0
if
self
.
is_bfloat16_op
():
check_dygraph
=
False
if
hasattr
(
self
,
'force_fp32_output'
)
and
getattr
(
self
,
'force_fp32_output'
):
atol
=
1e-2
else
:
atol
=
2
if
no_check_set
is
not
None
:
if
self
.
op_type
not
in
no_check_set_white_list
.
no_check_set_white_list
:
raise
AssertionError
(
...
...
@@ -1286,8 +1313,9 @@ class OpTest(unittest.TestCase):
no_grad_set
=
set
()
else
:
if
(
self
.
op_type
not
in
no_grad_set_white_list
.
NEED_TO_FIX_OP_LIST
)
and
(
self
.
op_type
not
in
no_grad_set_white_list
.
NOT_CHECK_OP_LIST
):
)
and
(
self
.
op_type
not
in
no_grad_set_white_list
.
NOT_CHECK_OP_LIST
)
and
(
not
self
.
is_bfloat16_op
()):
raise
AssertionError
(
"no_grad_set must be None, op_type is "
+
self
.
op_type
+
" Op."
)
...
...
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