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体验新版 GitCode,发现更多精彩内容 >>
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9bf00cd5
编写于
6月 23, 2021
作者:
B
Baibaifan
提交者:
GitHub
6月 23, 2021
浏览文件
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电子邮件补丁
差异文件
repair npu matmul_grad and comm_init_hccl (#33719)
上级
affddfaa
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
137 addition
and
4 deletion
+137
-4
paddle/fluid/operators/collective/c_comm_init_hccl_op.cc
paddle/fluid/operators/collective/c_comm_init_hccl_op.cc
+29
-0
paddle/fluid/operators/matmul_v2_op_npu.cc
paddle/fluid/operators/matmul_v2_op_npu.cc
+28
-4
python/paddle/fluid/tests/unittests/npu/test_matmulv2_op_npu.py
.../paddle/fluid/tests/unittests/npu/test_matmulv2_op_npu.py
+80
-0
未找到文件。
paddle/fluid/operators/collective/c_comm_init_hccl_op.cc
浏览文件 @
9bf00cd5
...
...
@@ -22,7 +22,11 @@ class Scope;
}
// namespace framework
}
// namespace paddle
#if defined(PADDLE_WITH_ASCEND_CL)
#include "acl/acl.h"
#include "hccl/hccl.h"
#include "hccl/hccl_types.h"
#include "paddle/fluid/platform/collective_helper.h"
#include "paddle/fluid/platform/hccl_helper.h"
#endif
namespace
paddle
{
...
...
@@ -57,6 +61,31 @@ class CCommInitOpAscend : public framework::OperatorBase {
}
platform
::
HCCLCommContext
::
Instance
().
CreateHCCLComm
(
hccl_id
,
rank_ids
,
rank_id
,
device_id
,
rid
);
// Build comm
float
*
buff
;
int32_t
size
=
20
;
std
::
vector
<
float
>
input
(
size
,
0
);
for
(
int32_t
idx
=
0
;
idx
<
size
;
idx
++
)
{
input
[
idx
]
=
1.0
;
}
aclrtMalloc
(
reinterpret_cast
<
void
**>
(
&
buff
),
size
*
sizeof
(
float
),
ACL_MEM_MALLOC_HUGE_FIRST
);
aclrtMemcpy
(
reinterpret_cast
<
void
*>
(
buff
),
size
*
sizeof
(
float
),
input
.
data
(),
size
*
sizeof
(
float
),
ACL_MEMCPY_HOST_TO_DEVICE
);
VLOG
(
3
)
<<
"Build buff data successful."
;
aclrtStream
stream
=
nullptr
;
auto
comm
=
paddle
::
platform
::
HCCLCommContext
::
Instance
().
Get
(
rid
,
place
);
if
(
rank_id
==
0
)
{
stream
=
comm
->
stream
();
}
else
{
auto
dev_ctx
=
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
);
stream
=
static_cast
<
platform
::
NPUDeviceContext
*>
(
dev_ctx
)
->
stream
();
}
platform
::
dynload
::
HcclBroadcast
(
buff
,
size
,
HCCL_DATA_TYPE_FP32
,
0
,
comm
->
comm
(),
stream
);
VLOG
(
3
)
<<
"Build connection successful."
;
#else
PADDLE_THROW
(
platform
::
errors
::
PreconditionNotMet
(
"PaddlePaddle should compile with NPU."
));
...
...
paddle/fluid/operators/matmul_v2_op_npu.cc
浏览文件 @
9bf00cd5
...
...
@@ -138,10 +138,34 @@ class MatMulV2GradNPUKernel : public framework::OpKernel<T> {
}
if
(
dy
)
{
dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
auto
&
runner_dy
=
NpuOpRunner
(
"BatchMatMul"
,
{
*
x
,
*
dout
},
{
*
dy
},
{{
"adj_x1"
,
true
},
{
"adj_x2"
,
false
}});
runner_dy
.
Run
(
stream
);
if
((
x
->
dims
().
size
()
==
3
)
&&
(
dout
->
dims
().
size
()
==
3
)
&&
(
dy
->
dims
().
size
()
==
2
))
{
framework
::
Tensor
dout_
;
TensorCopy
(
*
dout
,
ctx
.
GetPlace
(),
&
dout_
);
ctx
.
template
device_context
<
paddle
::
platform
::
NPUDeviceContext
>()
.
Wait
();
std
::
vector
<
int
>
vec_dim
=
framework
::
vectorize
<
int
>
(
dout_
.
dims
());
std
::
vector
<
int
>
vec_dim_v
{
vec_dim
[
0
]
*
vec_dim
[
1
],
vec_dim
[
2
]};
dout_
.
Resize
(
framework
::
make_ddim
(
vec_dim_v
));
framework
::
Tensor
x_
;
TensorCopy
(
*
x
,
ctx
.
GetPlace
(),
&
x_
);
ctx
.
template
device_context
<
paddle
::
platform
::
NPUDeviceContext
>()
.
Wait
();
std
::
vector
<
int
>
vec_dim_x
=
framework
::
vectorize
<
int
>
(
x_
.
dims
());
std
::
vector
<
int
>
vec_dim_x_v
{
vec_dim_x
[
0
]
*
vec_dim_x
[
1
],
vec_dim_x
[
2
]};
x_
.
Resize
(
framework
::
make_ddim
(
vec_dim_x_v
));
const
auto
&
runner_dy
=
NpuOpRunner
(
"MatMul"
,
{
x_
,
dout_
},
{
*
dy
},
{{
"transpose_x1"
,
true
},
{
"transpose_x2"
,
false
}});
runner_dy
.
Run
(
stream
);
}
else
{
const
auto
&
runner_dy
=
NpuOpRunner
(
"BatchMatMul"
,
{
*
x
,
*
dout
},
{
*
dy
},
{{
"adj_x1"
,
true
},
{
"adj_x2"
,
false
}});
runner_dy
.
Run
(
stream
);
}
}
}
}
...
...
python/paddle/fluid/tests/unittests/npu/test_matmulv2_op_npu.py
浏览文件 @
9bf00cd5
...
...
@@ -206,5 +206,85 @@ class TestMatMulNet(unittest.TestCase):
self
.
assertTrue
(
np
.
allclose
(
npu_loss
,
cpu_loss
))
# The precision is aligned in NPU and GPU separately, which is only used for the usage method.
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_npu
(),
"core is not compiled with NPU"
)
class
TestMatMulNet3_2
(
unittest
.
TestCase
):
def
_test
(
self
,
run_npu
=
True
):
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
SEED
startup_prog
.
random_seed
=
SEED
np
.
random
.
seed
(
SEED
)
self
.
_dtype
=
"float32"
a_np
=
np
.
random
.
random
(
size
=
(
2
,
1
,
3
)).
astype
(
self
.
_dtype
)
b_np
=
np
.
random
.
random
(
size
=
(
2
,
1
,
3
)).
astype
(
self
.
_dtype
)
c_np
=
np
.
random
.
random
(
size
=
(
3
,
2
)).
astype
(
self
.
_dtype
)
d_np
=
np
.
random
.
random
(
size
=
(
3
,
2
)).
astype
(
self
.
_dtype
)
label_np
=
np
.
random
.
randint
(
2
,
size
=
(
2
,
1
)).
astype
(
'int64'
)
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
a
=
paddle
.
static
.
data
(
name
=
"a"
,
shape
=
[
2
,
1
,
3
],
dtype
=
self
.
_dtype
)
b
=
paddle
.
static
.
data
(
name
=
"b"
,
shape
=
[
2
,
1
,
3
],
dtype
=
self
.
_dtype
)
c
=
paddle
.
static
.
data
(
name
=
"c"
,
shape
=
[
3
,
2
],
dtype
=
self
.
_dtype
)
d
=
paddle
.
static
.
data
(
name
=
"d"
,
shape
=
[
3
,
2
],
dtype
=
self
.
_dtype
)
label
=
paddle
.
static
.
data
(
name
=
"label"
,
shape
=
[
2
,
1
],
dtype
=
'int64'
)
sum_1
=
paddle
.
add
(
a
,
b
)
sum_2
=
paddle
.
add
(
c
,
d
)
sum_1
=
paddle
.
cast
(
sum_1
,
'float16'
)
sum_2
=
paddle
.
cast
(
sum_2
,
'float16'
)
if
not
run_npu
:
sum_1
=
paddle
.
cast
(
sum_1
,
'float32'
)
sum_2
=
paddle
.
cast
(
sum_2
,
'float32'
)
result
=
paddle
.
matmul
(
sum_1
,
sum_2
)
if
run_npu
:
result
=
paddle
.
cast
(
result
,
'float32'
)
result
=
paddle
.
reshape
(
result
,
shape
=
[
2
,
2
])
fc_1
=
fluid
.
layers
.
fc
(
input
=
result
,
size
=
8
)
prediction
=
fluid
.
layers
.
fc
(
input
=
fc_1
,
size
=
2
,
act
=
'softmax'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
loss
=
fluid
.
layers
.
reduce_mean
(
cost
)
sgd
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.01
)
sgd
.
minimize
(
loss
)
if
run_npu
:
place
=
paddle
.
NPUPlace
(
0
)
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
print
(
"Start run on {}"
.
format
(
place
))
for
epoch
in
range
(
100
):
pred_res
,
loss_res
=
exe
.
run
(
main_prog
,
feed
=
{
"a"
:
a_np
,
"b"
:
b_np
,
"c"
:
c_np
,
"d"
:
d_np
,
"label"
:
label_np
},
fetch_list
=
[
prediction
,
loss
])
if
epoch
%
10
==
0
:
print
(
"Epoch {} | Prediction[0]: {}, Loss: {}"
.
format
(
epoch
,
pred_res
[
0
],
loss_res
))
return
pred_res
,
loss_res
def
test_npu
(
self
):
cpu_pred
,
cpu_loss
=
self
.
_test
(
False
)
npu_pred
,
npu_loss
=
self
.
_test
(
True
)
self
.
assertTrue
(
np
.
allclose
(
npu_pred
,
cpu_pred
,
atol
=
1e-4
))
self
.
assertTrue
(
np
.
allclose
(
npu_loss
,
cpu_loss
,
atol
=
1e-4
))
if
__name__
==
'__main__'
:
unittest
.
main
()
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