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!5875 Add IFMR op for new backend. 

Merge pull request !5875 from liuxiao93/IFMR-OpenSource-new-backend
This commit is contained in:
mindspore-ci-bot 2020-09-24 15:01:08 +08:00 committed by Gitee
parent 6fd4848a63 0e02df812a
commit 55be3c42a5
6 changed files with 119 additions and 1 deletions
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1
mindspore/ccsrc/backend/kernel_compiler/tbe/tbe_adapter.cc
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@ -137,6 +137,7 @@ static std::map<string, string> tbe_func_adapter_map = {
{"histogram_fixed_width", "histogram_fixed_width_d"},
{"broadcast_to", "broadcast_to_d"},
{"inplace_update", "inplace_update_d"},
{"i_fmr", "ifmr"},
{"matrix_diag", "matrix_diag_d"},
{"matrix_diag_part", "matrix_diag_part_d"},
{"matrix_set_diag", "matrix_set_diag_d"}};

1
mindspore/ops/_op_impl/tbe/__init__.py
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@ -310,3 +310,4 @@ from .population_count import _population_count_tbe
from .parallel_concat import _parallel_concat_tbe
from .adam_apply_one_assign import _adam_apply_one_assign_tbe
from .adam_apply_one_with_decay_assign import _adam_apply_one_with_decay_assign_tbe
from .ifmr import _ifmr_tbe

47
mindspore/ops/_op_impl/tbe/ifmr.py Normal file
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@ -0,0 +1,47 @@
# 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.
# ============================================================================
"""IFMR op"""
from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
ifmr_op_info = TBERegOp("IFMR") \
.fusion_type("OPAQUE") \
.async_flag(False) \
.binfile_name("ifmr.so") \
.compute_cost(10) \
.kernel_name("ifmr") \
.partial_flag(True) \
.attr("min_percentile", "required", "float", "all") \
.attr("max_percentile", "required", "float", "all") \
.attr("search_range", "required", "listFloat", "all") \
.attr("search_step", "required", "float", "all") \
.attr("with_offset", "required", "bool", "all") \
.input(0, "data", False, "required", "all") \
.input(1, "data_min", False, "required", "all") \
.input(2, "data_max", False, "required", "all") \
.input(3, "cumsum", False, "required", "all") \
.output(0, "scale", False, "required", "all") \
.output(1, "offset", False, "required", "all") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.I32_Default,
DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.I32_Default,
DataType.F32_Default, DataType.F32_Default) \
.get_op_info()
@op_info_register(ifmr_op_info)
def _ifmr_tbe():
"""IFMR TBE register"""
return

3
mindspore/ops/operations/__init__.py
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@ -53,7 +53,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
NPUAllocFloatStatus, NPUClearFloatStatus,
NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
Reciprocal, CumSum, HistogramFixedWidth, SquaredDifference, Xdivy, Xlogy,
Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod,
Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod, IFMR,
Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan)
from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, UniformInt, UniformReal,
@ -97,6 +97,7 @@ __all__ = [
'EditDistance',
'CropAndResize',
'TensorAdd',
'IFMR',
'Argmax',
'Argmin',
'ArgMaxWithValue',

61
mindspore/ops/operations/math_ops.py
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@ -3514,3 +3514,64 @@ class Eps(PrimitiveWithInfer):
'dtype': input_x['dtype'],
}
return out
class IFMR(PrimitiveWithInfer):
"""
The TFMR(Input Feature Map Reconstruction).
Args:
min_percentile (float): Min init percentile.
max_percentile (float): Max init percentile.
search_range Union[list(float), tuple(float)]: Range of searching.
search_step (float): Step size of searching.
with_offset (bool): Whether using offset.
Inputs:
- **data** (Tensor) - A Tensor of feature map. With float16 or float32 data type.
- **data_min** (Tensor) - A Tensor of min value of feature map, the shape is :math:`(1)`.
With float16 or float32 data type.
- **data_max** (Tensor) - A Tensor of max value of feature map, the shape is :math:`(1)`.
With float16 or float32 data type.
- **cumsum** (Tensor) - A `1-D` Tensor of cumsum bin of data. With int32 data type.
Outputs:
- **scale** (Tensor) - A tensor of optimal scale, the shape is :math:`(1)`. Data dtype is float32.
- **offset** (Tensor) - A tensor of optimal offset, the shape is :math:`(1)`. Data dtype is float32.
Examples:
>>> data = Tensor(np.random.rand(1, 3, 6, 4).astype(np.float32))
>>> data_min = Tensor([0.1], mstype.float32)
>>> data_max = Tensor([0.5], mstype.float32)
>>> cumsum = Tensor(np.random.rand(4).astype(np.int32))
>>> ifmr = P.IFMR(min_percentile=0.2, max_percentile=0.9, search_range=(1.0, 2.0),
search_step=1.0, with_offset=False)
>>> output = ifmr(data, data_min, data_max, cumsum)
"""
@prim_attr_register
def __init__(self, min_percentile, max_percentile, search_range, search_step, with_offset):
validator.check_value_type("min_percentile", min_percentile, [float], self.name)
validator.check_value_type("max_percentile", max_percentile, [float], self.name)
validator.check_value_type("search_range", search_range, [list, tuple], self.name)
for item in search_range:
validator.check_float_positive("item of search_range", item, self.name)
validator.check('search_range[1]', search_range[1], 'search_range[0]', search_range[0], Rel.GE, self.name)
validator.check_value_type("search_step", search_step, [float], self.name)
validator.check_value_type("offset_flag", with_offset, [bool], self.name)
def infer_shape(self, data_shape, data_min_shape, data_max_shape, cumsum_shape):
validator.check_integer("dims of data_min", len(data_min_shape), 1, Rel.EQ, self.name)
validator.check_integer("data_min[0]", data_min_shape[0], 1, Rel.EQ, self.name)
validator.check_integer("dims of data_max", len(data_max_shape), 1, Rel.EQ, self.name)
validator.check_integer("data_max[0]", data_max_shape[0], 1, Rel.EQ, self.name)
validator.check_integer("dims of cumsum", len(cumsum_shape), 1, Rel.EQ, self.name)
return (1,), (1,)
def infer_dtype(self, data_dtype, data_min_dtype, data_max_dtype, cumsum_dtype):
valid_types = [mstype.float32, mstype.float16]
validator.check_tensor_type_same({"input_value": data_dtype}, valid_types, self.name)
validator.check_tensor_type_same({"input_min": data_min_dtype}, valid_types, self.name)
validator.check_tensor_type_same({"input_max": data_max_dtype}, valid_types, self.name)
validator.check_tensor_type_same({"input_bins": cumsum_dtype}, [mstype.int32], self.name)
return mstype.tensor_type(mstype.float32), mstype.tensor_type(mstype.float32)

7
tests/ut/python/ops/test_ops.py
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@ -1275,6 +1275,13 @@ test_case_math_ops = [
'block': P.Mod(),
'desc_inputs': [[3, 4, 5], [2, 3, 4, 5]],
'desc_bprop': [[2, 3, 4, 5]]}),
('IFMR', {
'block': P.IFMR(min_percentile=0.2, max_percentile=0.9, search_range=(1.0, 2.0),
search_step=1.0, with_offset=False),
'desc_inputs': [[3, 4, 5], Tensor([0.1], mstype.float32), Tensor([0.9], mstype.float32),
Tensor(np.random.rand(4).astype(np.int32))],
'desc_bprop': [],
'skip': ['backward']}),
]
test_case_nn_ops = [