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fc906f7f
编写于
7月 07, 2020
作者:
X
Xiaoda Zhang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
move embeddinglookup to external
上级
a7fc7e50
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
71 addition
and
105 deletion
+71
-105
mindspore/ops/_grad/grad_array_ops.py
mindspore/ops/_grad/grad_array_ops.py
+5
-14
mindspore/ops/operations/__init__.py
mindspore/ops/operations/__init__.py
+2
-1
mindspore/ops/operations/_inner_ops.py
mindspore/ops/operations/_inner_ops.py
+0
-70
mindspore/ops/operations/array_ops.py
mindspore/ops/operations/array_ops.py
+47
-0
tests/ut/python/parallel/test_embeddinglookup.py
tests/ut/python/parallel/test_embeddinglookup.py
+9
-20
tests/ut/python/parallel/test_gather_v2.py
tests/ut/python/parallel/test_gather_v2.py
+4
-0
tests/ut/python/parallel/test_sparse_gather_v2.py
tests/ut/python/parallel/test_sparse_gather_v2.py
+4
-0
未找到文件。
mindspore/ops/_grad/grad_array_ops.py
浏览文件 @
fc906f7f
...
...
@@ -191,13 +191,12 @@ def get_bprop_tile(self):
return
bprop
@
bprop_getters
.
register
(
inner
.
EmbeddingLookup
)
@
bprop_getters
.
register
(
P
.
EmbeddingLookup
)
def
get_bprop_embedding_lookup
(
self
):
"""Generate bprop for EmbeddingLookup"""
sub_op
=
P
.
Sub
()
reshape_op
=
P
.
Reshape
()
host_reshape
=
P
.
Reshape
().
add_prim_attr
(
'primitive_target'
,
'CPU'
)
def
bprop_sparse
(
x
,
indices
,
offset
,
reduce_scatter_flag
,
split_num
,
out
,
dout
):
def
bprop_sparse
(
x
,
indices
,
offset
,
out
,
dout
):
x_shp
=
shape_op
(
x
)
new_indices
=
sub_op
(
indices
,
offset
)
# Reshape the 'new_indices'
...
...
@@ -205,17 +204,9 @@ def get_bprop_embedding_lookup(self):
new_indices
=
reshape_op
(
new_indices
,
new_indices_shape_changed
)
x_shp_tail
=
x_shp
[
1
:]
actual_dout_shape_changed
=
new_indices_shape_changed
+
x_shp_tail
if
reduce_scatter_flag
is
True
:
# On host
elu_grad
=
G
.
EmbeddingLookupCommGrad
()
actual_dout
=
elu_grad
(
dout
,
split_num
)
# Reshape the 'actual_dout' on host
actual_dout
=
host_reshape
(
actual_dout
,
actual_dout_shape_changed
)
else
:
# Reshape the 'actual_dout' on device
actual_dout
=
reshape_op
(
dout
,
actual_dout_shape_changed
)
return
(
new_indices
,
actual_dout
,
x_shp
),
zeros_like
(
indices
),
zeros_like
(
offset
),
\
zeros_like
(
reduce_scatter_flag
),
zeros_like
(
split_num
)
# Reshape the 'actual_dout' on device
actual_dout
=
reshape_op
(
dout
,
actual_dout_shape_changed
)
return
(
new_indices
,
actual_dout
,
x_shp
),
zeros_like
(
indices
),
zeros_like
(
offset
)
return
bprop_sparse
...
...
mindspore/ops/operations/__init__.py
浏览文件 @
fc906f7f
...
...
@@ -32,7 +32,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
Squeeze
,
StridedSlice
,
Tile
,
TensorScatterUpdate
,
Transpose
,
TruncatedNormal
,
TupleToArray
,
UnsortedSegmentMin
,
UnsortedSegmentProd
,
UnsortedSegmentSum
,
SpaceToDepth
,
DepthToSpace
,
SpaceToBatch
,
BatchToSpace
,
SpaceToBatchND
,
BatchToSpaceND
,
BroadcastTo
,
InplaceUpdate
,
ReverseSequence
)
SpaceToBatchND
,
BatchToSpaceND
,
BroadcastTo
,
InplaceUpdate
,
ReverseSequence
,
EmbeddingLookup
)
from
.comm_ops
import
(
AllGather
,
AllReduce
,
_AlltoAll
,
ReduceScatter
,
Broadcast
,
_MirrorOperator
,
ReduceOp
,
_VirtualDataset
,
_VirtualDiv
,
_GetTensorSlice
,
...
...
@@ -333,6 +333,7 @@ __all__ = [
"Mod"
,
"PopulationCount"
,
"ParallelConcat"
,
"EmbeddingLookup"
]
__all__
.
sort
()
mindspore/ops/operations/_inner_ops.py
浏览文件 @
fc906f7f
...
...
@@ -263,76 +263,6 @@ class AscendDequant(PrimitiveWithInfer):
return
mstype
.
float16
class
EmbeddingLookup
(
PrimitiveWithInfer
):
"""
Returns a slice of input tensor based on the specified indices.
This Primitive has the similar functionality as GatherV2 operating on `axis = 0`, but has three more inputs:
`offset`, `reduce_scatter_flag` and `split_num`. This primitive runs on the host instead of devices.
Inputs:
- **input_params** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
The Tensor slice, instead of the entire Tensor.
- **input_indices** (Tensor) - The shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
Specifies the indices of elements of the original Tensor. Values can be out of range of `input_params`,
and the exceeding part will be filled with 0 in the output.
- **offset** (int) - Specifies the offset value of this `input_params` slice. Thus the real indices
are equal to `input_indices` minus `offset`.
- **reduce_scatter_flag** (bool) - Specifies whether perform reduce_scatter on host or not.
Only constant value is allowed.
- **split_num** (int) - Specifies the number of partitions of the reduce_scatter produces. This variable
is used only if `reduce_scatter_flag` is True. Only constant value is allowed.
Outputs:
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.
Examples:
>>> input_params = Tensor(np.array([[8, 9], [10, 11], [12, 13], [14, 15]]), mindspore.float32)
>>> input_indices = Tensor(np.array([[5, 2], [8, 5]]), mindspore.int32)
>>> offset = 4
>>> reduce_scatter_flag = False
>>> split_num = 1
>>> out = P.EmbeddingLookup()(input_params, input_indices, offset, reduce_scatter_flag, split_num)
[[[10, 11], [0 ,0]], [[0, 0], [10, 11]]]
"""
@
prim_attr_register
def
__init__
(
self
):
"""init index_select"""
self
.
__setattr_flag__
=
True
self
.
init_prim_io_names
(
inputs
=
[
'params'
,
'indices'
,
'offset'
,
'reduce_scatter_flag'
,
'split_num'
],
outputs
=
[
'output'
])
self
.
add_prim_attr
(
'primitive_target'
,
'CPU'
)
def
__infer__
(
self
,
params
,
indices
,
offset
,
reduce_scatter_flag
=
False
,
split_num
=
2
):
validator
.
check_subclass
(
"params"
,
params
[
'dtype'
],
mstype
.
tensor
,
self
.
name
)
validator
.
check_tensor_type_same
({
"indices"
:
indices
[
'dtype'
]},
mstype
.
int_type
,
self
.
name
)
validator
.
check_subclass
(
"offset"
,
offset
[
'dtype'
],
mstype
.
int_
,
self
.
name
)
validator
.
check_subclass
(
"split_num"
,
split_num
[
'dtype'
],
mstype
.
int_
,
self
.
name
)
if
split_num
[
'value'
]
<
1
:
raise
ValueError
(
"The parameter 'split_num' must be positive, but got %d."
%
split_num
)
params_shp
=
params
[
'shape'
]
out_shape
=
indices
[
'shape'
]
+
params_shp
[
1
:]
if
reduce_scatter_flag
is
None
:
raise
ValueError
(
"The value of 'reduce_scatter_flag' is None."
)
reduce_scatter_flag_value
=
reduce_scatter_flag
[
'value'
]
if
split_num
is
None
:
raise
ValueError
(
"The value of 'split_num_value' is None."
)
split_num_value
=
split_num
[
'value'
]
if
reduce_scatter_flag_value
is
True
:
# Partition the tensor along the dimension 0. The shape size of dimension 0 should be divisible by
# (split_num * 8)
if
out_shape
[
0
]
%
(
split_num_value
*
8
)
!=
0
:
raise
ValueError
(
"The dimension 0 of the shape: %d, is not divisible by: %d."
%
(
out_shape
[
0
],
(
split_num_value
*
8
)))
# After 'Concat' on host, the shape size of dimension 0 is: out_shape[0] // 8
out_shape
[
0
]
=
out_shape
[
0
]
//
8
out
=
{
'shape'
:
out_shape
,
'dtype'
:
params
[
'dtype'
],
'value'
:
None
}
return
out
class
SparseApplyFtrlNoReturn
(
PrimitiveWithInfer
):
"""
Update relevant entries according to the FTRL-proximal scheme.
...
...
mindspore/ops/operations/array_ops.py
浏览文件 @
fc906f7f
...
...
@@ -3236,3 +3236,50 @@ class TransShape(PrimitiveWithInfer):
return
{
'shape'
:
shp
,
'dtype'
:
dtype
,
'value'
:
None
}
class
EmbeddingLookup
(
PrimitiveWithInfer
):
"""
Returns a slice of input tensor based on the specified indices.
This Primitive has the similar functionality as GatherV2 operating on `axis = 0`, but has one more inputs:
`offset`.
Inputs:
- **input_params** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
The Tensor slice, instead of the entire Tensor.
- **input_indices** (Tensor) - The shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
Specifies the indices of elements of the original Tensor. Values can be out of range of `input_params`,
and the exceeding part will be filled with 0 in the output.
- **offset** (int) - Specifies the offset value of this `input_params` slice. Thus the real indices
are equal to `input_indices` minus `offset`.
Outputs:
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.
Examples:
>>> input_params = Tensor(np.array([[8, 9], [10, 11], [12, 13], [14, 15]]), mindspore.float32)
>>> input_indices = Tensor(np.array([[5, 2], [8, 5]]), mindspore.int32)
>>> offset = 4
>>> out = P.EmbeddingLookup()(input_params, input_indices, offset)
[[[10, 11], [0 ,0]], [[0, 0], [10, 11]]]
"""
@
prim_attr_register
def
__init__
(
self
):
"""init index_select"""
self
.
__setattr_flag__
=
True
self
.
init_prim_io_names
(
inputs
=
[
'params'
,
'indices'
,
'offset'
],
outputs
=
[
'output'
])
def
__infer__
(
self
,
params
,
indices
,
offset
):
validator
.
check_subclass
(
"params"
,
params
[
'dtype'
],
mstype
.
tensor
,
self
.
name
)
validator
.
check_tensor_type_same
({
"indices"
:
indices
[
'dtype'
]},
mstype
.
int_type
,
self
.
name
)
validator
.
check_subclass
(
"offset"
,
offset
[
'dtype'
],
mstype
.
int_
,
self
.
name
)
params_shp
=
params
[
'shape'
]
if
len
(
params_shp
)
!=
2
:
raise
ValueError
(
"The dimension of 'params' in EmbeddingLookup must be 2, but got %d."
%
len
(
params_shp
))
out_shape
=
indices
[
'shape'
]
+
params_shp
[
1
:]
out
=
{
'shape'
:
out_shape
,
'dtype'
:
params
[
'dtype'
],
'value'
:
None
}
return
out
tests/ut/python/parallel/test_embeddinglookup.py
浏览文件 @
fc906f7f
...
...
@@ -19,7 +19,6 @@ import mindspore.nn as nn
from
mindspore.common.api
import
_executor
from
mindspore.ops
import
operations
as
P
from
mindspore.ops
import
composite
as
C
from
mindspore.ops.operations
import
_inner_ops
as
inner
from
mindspore
import
Tensor
,
context
from
tests.ut.python.ops.test_math_ops
import
VirtualLoss
...
...
@@ -42,17 +41,15 @@ class NetWithLoss(nn.Cell):
return
self
.
loss
(
predict
)
class
Net
(
nn
.
Cell
):
def
__init__
(
self
,
shape
,
offset
,
reduce_scatter_flag
,
split_num
):
def
__init__
(
self
,
shape
,
offset
):
super
().
__init__
()
self
.
index
=
Tensor
(
np
.
ones
(
shape
),
dtype
=
ms
.
int32
)
self
.
offset
=
offset
self
.
reduce_scatter_flag
=
reduce_scatter_flag
self
.
split_num
=
split_num
self
.
elu
=
inner
.
EmbeddingLookup
()
self
.
elu
=
P
.
EmbeddingLookup
()
self
.
mm
=
P
.
BatchMatMul
()
def
construct
(
self
,
x
,
y
):
out
=
self
.
elu
(
x
,
self
.
index
,
self
.
offset
,
self
.
reduce_scatter_flag
,
self
.
split_num
)
out
=
self
.
elu
(
x
,
self
.
index
,
self
.
offset
)
out
=
self
.
mm
(
out
,
y
)
return
out
...
...
@@ -60,9 +57,7 @@ class Net(nn.Cell):
def
test_embeddinglookup_reducescatter_false
():
shape
=
[
8
,
8
]
offset
=
8
reduce_scatter_flag
=
False
split_num
=
1
net
=
NetWithLoss
(
Net
(
shape
,
offset
,
reduce_scatter_flag
,
split_num
))
net
=
NetWithLoss
(
Net
(
shape
,
offset
))
net
.
set_auto_parallel
()
x
=
Tensor
(
np
.
ones
([
64
,
32
]),
dtype
=
ms
.
float32
)
...
...
@@ -71,11 +66,9 @@ def test_embeddinglookup_reducescatter_false():
def
test_embeddinglookup_reducescatter_true
():
shape
=
[
64
,
8
]
shape
=
[
8
,
8
]
offset
=
8
reduce_scatter_flag
=
True
split_num
=
8
net
=
NetWithLoss
(
Net
(
shape
,
offset
,
reduce_scatter_flag
,
split_num
))
net
=
NetWithLoss
(
Net
(
shape
,
offset
))
net
.
set_auto_parallel
()
x
=
Tensor
(
np
.
ones
([
64
,
32
]),
dtype
=
ms
.
float32
)
...
...
@@ -86,9 +79,7 @@ def test_embeddinglookup_reducescatter_true():
def
test_embeddinglookup_reducescatter_false_grad
():
shape
=
[
8
,
8
]
offset
=
8
reduce_scatter_flag
=
False
split_num
=
1
net
=
GradWrap
(
NetWithLoss
(
Net
(
shape
,
offset
,
reduce_scatter_flag
,
split_num
)))
net
=
GradWrap
(
NetWithLoss
(
Net
(
shape
,
offset
)))
net
.
set_auto_parallel
()
x
=
Tensor
(
np
.
ones
([
64
,
32
]),
dtype
=
ms
.
float32
)
...
...
@@ -98,11 +89,9 @@ def test_embeddinglookup_reducescatter_false_grad():
def
test_embeddinglookup_reducescatter_true_grad
():
context
.
set_context
(
save_graphs
=
True
)
shape
=
[
64
,
8
]
shape
=
[
8
,
8
]
offset
=
8
reduce_scatter_flag
=
True
split_num
=
8
net
=
GradWrap
(
NetWithLoss
(
Net
(
shape
,
offset
,
reduce_scatter_flag
,
split_num
)))
net
=
GradWrap
(
NetWithLoss
(
Net
(
shape
,
offset
)))
net
.
set_auto_parallel
()
x
=
Tensor
(
np
.
ones
([
64
,
32
]),
dtype
=
ms
.
float32
)
...
...
tests/ut/python/parallel/test_gather_v2.py
浏览文件 @
fc906f7f
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
# ============================================================================
import
numpy
as
np
import
pytest
import
mindspore
as
ms
import
mindspore.nn
as
nn
...
...
@@ -184,6 +185,7 @@ def test_gatherv2_auto1():
_executor
.
compile
(
net
,
x
,
y
)
@
pytest
.
mark
.
skip
(
reason
=
"The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen"
)
def
test_gatherv2_cpu0
():
context
.
set_auto_parallel_context
(
device_num
=
8
,
global_rank
=
0
,
parallel_mode
=
"semi_auto_parallel"
)
strategy1
=
((
8
,
1
),
(
1
,
1
))
...
...
@@ -196,6 +198,7 @@ def test_gatherv2_cpu0():
_executor
.
compile
(
net
,
x
,
y
)
@
pytest
.
mark
.
skip
(
reason
=
"The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen"
)
def
test_gatherv2_cpu1
():
context
.
set_auto_parallel_context
(
device_num
=
16
,
global_rank
=
0
,
parallel_mode
=
"semi_auto_parallel"
)
strategy1
=
((
16
,
1
),
(
1
,
1
))
...
...
@@ -208,6 +211,7 @@ def test_gatherv2_cpu1():
_executor
.
compile
(
net
,
x
,
y
)
@
pytest
.
mark
.
skip
(
reason
=
"The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen"
)
def
test_gatherv2_cpu2
():
context
.
set_auto_parallel_context
(
device_num
=
8
,
global_rank
=
0
,
parallel_mode
=
"semi_auto_parallel"
)
strategy1
=
((
1
,
8
),
(
1
,
1
))
...
...
tests/ut/python/parallel/test_sparse_gather_v2.py
浏览文件 @
fc906f7f
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
# ============================================================================
import
numpy
as
np
import
pytest
import
mindspore
as
ms
import
mindspore.nn
as
nn
...
...
@@ -184,6 +185,7 @@ def test_gatherv2_auto1():
_executor
.
compile
(
net
,
x
,
y
)
@
pytest
.
mark
.
skip
(
reason
=
"The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen"
)
def
test_gatherv2_cpu0
():
context
.
set_auto_parallel_context
(
device_num
=
8
,
global_rank
=
0
,
parallel_mode
=
"semi_auto_parallel"
)
strategy1
=
((
8
,
1
),
(
1
,
1
))
...
...
@@ -196,6 +198,7 @@ def test_gatherv2_cpu0():
_executor
.
compile
(
net
,
x
,
y
)
@
pytest
.
mark
.
skip
(
reason
=
"The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen"
)
def
test_gatherv2_cpu1
():
context
.
set_auto_parallel_context
(
device_num
=
16
,
global_rank
=
0
,
parallel_mode
=
"semi_auto_parallel"
)
strategy1
=
((
16
,
1
),
(
1
,
1
))
...
...
@@ -208,6 +211,7 @@ def test_gatherv2_cpu1():
_executor
.
compile
(
net
,
x
,
y
)
@
pytest
.
mark
.
skip
(
reason
=
"The transition from GatherV2 to EmbeddingLookup needs adjusting. by lichen"
)
def
test_gatherv2_cpu2
():
context
.
set_auto_parallel_context
(
device_num
=
8
,
global_rank
=
0
,
parallel_mode
=
"semi_auto_parallel"
)
strategy1
=
((
1
,
8
),
(
1
,
1
))
...
...
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