forked from huawei/mindspore2022
69 lines
3.1 KiB
Python
69 lines
3.1 KiB
Python
# Copyright 2020-2021 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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"""generate json desc for softmax"""
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from mindspore._extends.graph_kernel.model.model import DataFormat as DF
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from ._utils import Expander, ExpanderInfoValidator as VLD
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from ._utils import infer_shape_from_fractalnz, get_reduced_ori_shape, to_frac_z_axis
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@VLD.add_format(DF.FRAC_NZ)
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@VLD.add_format(DF.DEFAULT)
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@VLD.check_attrs('axis')
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class Softmax(Expander):
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"""Softmax expander"""
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def _expand(self, graph_builder):
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input_x = self.inputs[0]
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processor = self.processor
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axis = self.attrs['axis']
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ori_shape = input_x.shape
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if input_x.data_format == DF.FRAC_NZ:
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ori_shape = infer_shape_from_fractalnz(input_x.shape)
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for i, _ in enumerate(list(axis)):
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if axis[i] < 0:
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axis[i] += len(ori_shape)
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ori_reduced_shape = get_reduced_ori_shape(ori_shape, axis)
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if input_x.data_format == DF.FRAC_NZ:
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axis = to_frac_z_axis(ori_shape, axis)
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ori_dtype = input_x.dtype
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if ori_dtype != "float16" and processor == "aicore":
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input_x_f16 = graph_builder.emit('Cast', [input_x], attrs={'dst_type': 'float16'})
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max_x_f16 = graph_builder.emit('ReduceMax', [input_x_f16], attrs={'reduce_axis': axis, 'keep_dims': True})
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max_x = graph_builder.emit('Cast', [max_x_f16], attrs={'dst_type': ori_dtype})
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else:
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max_x = graph_builder.emit('ReduceMax', [input_x], attrs={'reduce_axis': axis, 'keep_dims': True})
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if ori_dtype == "float16" and processor == "aicore":
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max_x = graph_builder.emit('Cast', [max_x], attrs={'dst_type': "float32"})
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input_x = graph_builder.emit('Cast', [input_x], attrs={'dst_type': "float32"})
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if input_x.data_format == DF.FRAC_NZ:
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max_x = graph_builder.emit('Reshape', [max_x], attrs={'shape': ori_reduced_shape})
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data_sub = graph_builder.emit('Sub', [input_x, max_x])
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data_exp = graph_builder.emit('Exp', [data_sub])
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data_expsum = graph_builder.emit('ReduceSum', [data_exp], attrs={'reduce_axis': axis, 'keep_dims': True})
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if input_x.data_format == DF.FRAC_NZ:
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data_expsum = graph_builder.emit('Reshape', [data_expsum], attrs={'shape': ori_reduced_shape})
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result = graph_builder.emit('RealDiv', [data_exp, data_expsum])
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if ori_dtype == "float16" and processor == "aicore":
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result = graph_builder.emit('Cast', [result], attrs={'dst_type': ori_dtype})
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return result
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