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!2811 support vm for ParallelConcat

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Merge pull request !2811 from jiangjinsheng/vm_parallel_concat
pull/2811/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent 7304f02410 a3565a75d1
commit bd60db5c11
5 changed files with 150 additions and 1 deletions
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1
mindspore/ops/_op_impl/tbe/__init__.py
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@ -285,3 +285,4 @@ from .mod import _mod_tbe
from .max_pool_grad_grad import _max_pool_grad_grad_tbe
from .max_pool_grad_grad_with_argmax import _max_pool_grad_grad_with_argmax_tbe
from .population_count import _population_count_tbe
from .parallel_concat import _parallel_concat_tbe

80
mindspore/ops/_op_impl/tbe/parallel_concat.py
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@ -0,0 +1,80 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""ParallelConcat op"""
from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
parallel_concat_op_info = TBERegOp("ParallelConcat") \
.fusion_type("OPAQUE") \
.async_flag(False) \
.binfile_name("parallel_concat.so") \
.compute_cost(10) \
.kernel_name("parallel_concat") \
.partial_flag(True) \
.attr("shape", "required", "listInt", "all") \
.attr("N", "required", "int", "all") \
.input(0, "values", False, "dynamic", "all") \
.output(0, "output_data", False, "required", "all") \
.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
.dtype_format(DataType.BOOL_5HD, DataType.BOOL_5HD) \
.dtype_format(DataType.I8_Default, DataType.I8_Default) \
.dtype_format(DataType.I8_5HD, DataType.I8_5HD) \
.dtype_format(DataType.U8_Default, DataType.U8_Default) \
.dtype_format(DataType.U8_5HD, DataType.U8_5HD) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I16_5HD, DataType.I16_5HD) \
.dtype_format(DataType.U16_Default, DataType.U16_Default) \
.dtype_format(DataType.U16_5HD, DataType.U16_5HD) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
.dtype_format(DataType.U32_Default, DataType.U32_Default) \
.dtype_format(DataType.U32_5HD, DataType.U32_5HD) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.dtype_format(DataType.I64_5HD, DataType.I64_5HD) \
.dtype_format(DataType.U64_Default, DataType.U64_Default) \
.dtype_format(DataType.U64_5HD, DataType.U64_5HD) \
.dtype_format(DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F16_5HD, DataType.F16_5HD) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.F32_5HD, DataType.F32_5HD) \
.dtype_format(DataType.BOOL_NHWC, DataType.BOOL_NHWC) \
.dtype_format(DataType.BOOL_NCHW, DataType.BOOL_NCHW) \
.dtype_format(DataType.I8_NHWC, DataType.I8_NHWC) \
.dtype_format(DataType.I8_NCHW, DataType.I8_NCHW) \
.dtype_format(DataType.U8_NHWC, DataType.U8_NHWC) \
.dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \
.dtype_format(DataType.I16_NHWC, DataType.I16_NHWC) \
.dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \
.dtype_format(DataType.U16_NHWC, DataType.U16_NHWC) \
.dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \
.dtype_format(DataType.I32_NHWC, DataType.I32_NHWC) \
.dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \
.dtype_format(DataType.U32_NHWC, DataType.U32_NHWC) \
.dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \
.dtype_format(DataType.I64_NHWC, DataType.I64_NHWC) \
.dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \
.dtype_format(DataType.U64_NHWC, DataType.U64_NHWC) \
.dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \
.dtype_format(DataType.F16_NHWC, DataType.F16_NHWC) \
.dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \
.dtype_format(DataType.F32_NHWC, DataType.F32_NHWC) \
.dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \
.get_op_info()
@op_info_register(parallel_concat_op_info)
def _parallel_concat_tbe():
"""ParallelConcat TBE register"""
return

4
mindspore/ops/operations/__init__.py
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@ -28,6 +28,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
SameTypeShape, ScatterAdd, ScatterSub, ScatterMul, ScatterDiv, ScatterMax, ScatterMin,
ScatterUpdate, ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
Shape, Size, Slice, Split, TransShape,
ParallelConcat,
Squeeze, StridedSlice, Tile, TensorScatterUpdate,
Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin,
UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,
@ -329,7 +330,8 @@ __all__ = [
"InTopK",
"LRN",
"Mod",
"PopulationCount"
"PopulationCount",
"ParallelConcat",
]
__all__.sort()

51
mindspore/ops/operations/array_ops.py
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@ -1463,6 +1463,57 @@ class Concat(PrimitiveWithInfer):
return out
class ParallelConcat(PrimitiveWithInfer):
r"""
Concat tensor in the first dimension.
Concat input tensors along with the first dimension.
Note:
The input tensors are all required to have size 1 in the first dimension.
Inputs:
- **values** (tuple, list) - Tuple or list of input tensors.
Outputs:
Tensor, data type same as `values`.
Examples:
>>> data1 = Tensor(np.array([[0, 1]]).astype(np.int32))
>>> data2 = Tensor(np.array([[2, 1]]).astype(np.int32))
>>> op = P.ParallelConcat()
>>> output = op((data1, data2))
"""
@prim_attr_register
def __init__(self):
"""init ParallelConcat"""
def __infer__(self, values):
x_shp = values['shape']
x_type = values['dtype']
validator.check_integer(f'x_shp length', len(x_shp), 1, Rel.GE, self.name)
first_elem = x_shp[0]
args = {}
for i, elem in enumerate(x_shp[1:]):
j = i + 1
args[f'x_type[{j}]'] = x_type[j]
validator.check_integer(f'x_shp[{j}][0]', elem[0], 1, Rel.EQ, self.name)
validator.check(f"x_shp[0] shape", first_elem, f"x_shp[{j}] shape", elem, Rel.EQ, self.name)
validator.check_tensor_type_same(args, mstype.number_type + (mstype.bool_,), self.name)
ret_shp = x_shp[0].copy()
ret_shp[0] = len(x_shp)
self.add_prim_attr('shape', ret_shp)
self.add_prim_attr('N', len(x_shp))
out = {'shape': ret_shp,
'dtype': x_type[0],
'value': None}
return out
def _get_pack_shape(x_shape, x_type, axis, prim_name):
"""for pack output shape"""
validator.check_value_type("shape", x_shape, [tuple, list], prim_name)

15
tests/ut/python/ops/test_ops.py
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@ -596,6 +596,15 @@ def test_strided_slice_const():
assert (ret.asnumpy() == np.array([], np.float32).reshape([0, 1, 7, 8, 9, 3, 1])).all()
class ParallelConcatNet(nn.Cell):
def __init__(self):
super(ParallelConcatNet, self).__init__()
self.parallel_concat = P.ParallelConcat()
def construct(self, x1, x2):
return self.parallel_concat((x1, x2))
test_case_math_ops = [
('BitwiseAnd', {
'block': P.BitwiseAnd(),
@ -1875,6 +1884,12 @@ test_case_array_ops = [
'desc_inputs': [[1, 3, 24, 24]],
'desc_bprop': [[1, 12, 24, 24]],
}),
('ParallelConcat', {
'block': ParallelConcatNet(),
'desc_inputs': [Tensor([[1, 2]], mstype.float32),
Tensor([[5, 6]], mstype.float32)],
'skip': ['backward'],
}),
]
test_case_other_ops = [

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