wehub-resource-sync
46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. 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,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=invalid-name,consider-using-enumerate,redefined-outer-name
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"""Einsum operator"""
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from . import cpp
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def einsum(subscripts, *operand):
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"""Evaluates the Einstein summation convention on the operands.
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Parameters
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----------
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subscripts : string
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Specifies the subscripts for summation as comma separated list of subscript labels.
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An implicit (classical Einstein summation) calculation is performed unless the
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explicit indicator '->' is included as well as subscript labels of the precise
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output form.
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a_tuple : tuple of tvm.te.Tensor
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These are the Tensors for the operation.
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The only difference of einsum between in tvm and numpy is it needs an extra brackets
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for the tensors. For example, topi.einsum("ij, jk -> ik", (A, B)).
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Returns
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-------
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out : tvm.te.Tensor
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The calculation based on the Einstein summation convention.
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"""
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return cpp.einsum(subscripts, operand)
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