forked from OSSInnovation/mindspore
!5875 Add IFMR op for new backend.
Merge pull request !5875 from liuxiao93/IFMR-OpenSource-new-backend
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55be3c42a5
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@ -137,6 +137,7 @@ static std::map<string, string> tbe_func_adapter_map = {
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{"histogram_fixed_width", "histogram_fixed_width_d"},
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{"broadcast_to", "broadcast_to_d"},
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{"inplace_update", "inplace_update_d"},
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{"i_fmr", "ifmr"},
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{"matrix_diag", "matrix_diag_d"},
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{"matrix_diag_part", "matrix_diag_part_d"},
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{"matrix_set_diag", "matrix_set_diag_d"}};
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@ -310,3 +310,4 @@ from .population_count import _population_count_tbe
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from .parallel_concat import _parallel_concat_tbe
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from .adam_apply_one_assign import _adam_apply_one_assign_tbe
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from .adam_apply_one_with_decay_assign import _adam_apply_one_with_decay_assign_tbe
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from .ifmr import _ifmr_tbe
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@ -0,0 +1,47 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""IFMR op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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ifmr_op_info = TBERegOp("IFMR") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("ifmr.so") \
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.compute_cost(10) \
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.kernel_name("ifmr") \
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.partial_flag(True) \
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.attr("min_percentile", "required", "float", "all") \
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.attr("max_percentile", "required", "float", "all") \
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.attr("search_range", "required", "listFloat", "all") \
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.attr("search_step", "required", "float", "all") \
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.attr("with_offset", "required", "bool", "all") \
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.input(0, "data", False, "required", "all") \
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.input(1, "data_min", False, "required", "all") \
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.input(2, "data_max", False, "required", "all") \
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.input(3, "cumsum", False, "required", "all") \
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.output(0, "scale", False, "required", "all") \
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.output(1, "offset", False, "required", "all") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.I32_Default,
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DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.I32_Default,
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DataType.F32_Default, DataType.F32_Default) \
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.get_op_info()
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@op_info_register(ifmr_op_info)
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def _ifmr_tbe():
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"""IFMR TBE register"""
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return
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@ -53,7 +53,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
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NPUAllocFloatStatus, NPUClearFloatStatus,
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NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
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Reciprocal, CumSum, HistogramFixedWidth, SquaredDifference, Xdivy, Xlogy,
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Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod,
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Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod, IFMR,
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Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan)
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from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, UniformInt, UniformReal,
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@ -97,6 +97,7 @@ __all__ = [
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'EditDistance',
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'CropAndResize',
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'TensorAdd',
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'IFMR',
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'Argmax',
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'Argmin',
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'ArgMaxWithValue',
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@ -3514,3 +3514,64 @@ class Eps(PrimitiveWithInfer):
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'dtype': input_x['dtype'],
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}
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return out
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class IFMR(PrimitiveWithInfer):
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"""
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The TFMR(Input Feature Map Reconstruction).
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Args:
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min_percentile (float): Min init percentile.
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max_percentile (float): Max init percentile.
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search_range Union[list(float), tuple(float)]: Range of searching.
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search_step (float): Step size of searching.
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with_offset (bool): Whether using offset.
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Inputs:
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- **data** (Tensor) - A Tensor of feature map. With float16 or float32 data type.
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- **data_min** (Tensor) - A Tensor of min value of feature map, the shape is :math:`(1)`.
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With float16 or float32 data type.
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- **data_max** (Tensor) - A Tensor of max value of feature map, the shape is :math:`(1)`.
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With float16 or float32 data type.
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- **cumsum** (Tensor) - A `1-D` Tensor of cumsum bin of data. With int32 data type.
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Outputs:
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- **scale** (Tensor) - A tensor of optimal scale, the shape is :math:`(1)`. Data dtype is float32.
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- **offset** (Tensor) - A tensor of optimal offset, the shape is :math:`(1)`. Data dtype is float32.
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Examples:
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>>> data = Tensor(np.random.rand(1, 3, 6, 4).astype(np.float32))
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>>> data_min = Tensor([0.1], mstype.float32)
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>>> data_max = Tensor([0.5], mstype.float32)
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>>> cumsum = Tensor(np.random.rand(4).astype(np.int32))
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>>> ifmr = P.IFMR(min_percentile=0.2, max_percentile=0.9, search_range=(1.0, 2.0),
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search_step=1.0, with_offset=False)
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>>> output = ifmr(data, data_min, data_max, cumsum)
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"""
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@prim_attr_register
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def __init__(self, min_percentile, max_percentile, search_range, search_step, with_offset):
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validator.check_value_type("min_percentile", min_percentile, [float], self.name)
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validator.check_value_type("max_percentile", max_percentile, [float], self.name)
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validator.check_value_type("search_range", search_range, [list, tuple], self.name)
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for item in search_range:
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validator.check_float_positive("item of search_range", item, self.name)
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validator.check('search_range[1]', search_range[1], 'search_range[0]', search_range[0], Rel.GE, self.name)
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validator.check_value_type("search_step", search_step, [float], self.name)
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validator.check_value_type("offset_flag", with_offset, [bool], self.name)
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def infer_shape(self, data_shape, data_min_shape, data_max_shape, cumsum_shape):
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validator.check_integer("dims of data_min", len(data_min_shape), 1, Rel.EQ, self.name)
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validator.check_integer("data_min[0]", data_min_shape[0], 1, Rel.EQ, self.name)
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validator.check_integer("dims of data_max", len(data_max_shape), 1, Rel.EQ, self.name)
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validator.check_integer("data_max[0]", data_max_shape[0], 1, Rel.EQ, self.name)
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validator.check_integer("dims of cumsum", len(cumsum_shape), 1, Rel.EQ, self.name)
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return (1,), (1,)
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def infer_dtype(self, data_dtype, data_min_dtype, data_max_dtype, cumsum_dtype):
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valid_types = [mstype.float32, mstype.float16]
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validator.check_tensor_type_same({"input_value": data_dtype}, valid_types, self.name)
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validator.check_tensor_type_same({"input_min": data_min_dtype}, valid_types, self.name)
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validator.check_tensor_type_same({"input_max": data_max_dtype}, valid_types, self.name)
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validator.check_tensor_type_same({"input_bins": cumsum_dtype}, [mstype.int32], self.name)
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return mstype.tensor_type(mstype.float32), mstype.tensor_type(mstype.float32)
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@ -1275,6 +1275,13 @@ test_case_math_ops = [
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'block': P.Mod(),
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'desc_inputs': [[3, 4, 5], [2, 3, 4, 5]],
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'desc_bprop': [[2, 3, 4, 5]]}),
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('IFMR', {
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'block': P.IFMR(min_percentile=0.2, max_percentile=0.9, search_range=(1.0, 2.0),
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search_step=1.0, with_offset=False),
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'desc_inputs': [[3, 4, 5], Tensor([0.1], mstype.float32), Tensor([0.9], mstype.float32),
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Tensor(np.random.rand(4).astype(np.int32))],
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'desc_bprop': [],
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'skip': ['backward']}),
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]
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test_case_nn_ops = [
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