wehub-resource-sync
86 lines
3.0 KiB
Python
86 lines
3.0 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
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"""Unique operator"""
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from tvm import te, tirx
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from tvm.script.ir_builder import IRBuilder
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from tvm.script.ir_builder import tirx as T
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def _calc_adjacent_diff_ir(data, output, binop=tirx.Sub):
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"""Low level IR to calculate adjacent difference in an 1-D array.
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Parameters
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----------
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data : Buffer
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Input 1-D Buffer.
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output: Buffer
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A buffer to store adjacent difference, of the same shape as data. The adjacent difference
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is defined as: output[0] = 0, output[i] = binop(data[i], data[i-1])
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where i > 0 and i < len(data).
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binop: function, optional
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A binary associative op to use for calculating adjacent difference. The function takes two
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TIR expressions and produce a new TIR expression. By default it uses tvm.tirx.Sub to
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compute the adjacent difference.
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"""
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with IRBuilder() as ib:
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data_ptr = T.buffer_proxy(data)
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output_ptr = T.buffer_proxy(output)
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with T.parallel(0, data.shape[0]) as i:
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with T.If(i == 0):
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with T.Then():
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output_ptr[0] = 0
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with T.Else():
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output_ptr[i] = tirx.Cast(output.dtype, binop(data_ptr[i], data_ptr[i - 1]))
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return ib.get()
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def _calc_adjacent_diff(data, out_dtype="int32", binop=tirx.Sub):
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"""Function calculate adjacent difference in an 1-D array.
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Parameters
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----------
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data : tvm.te.Tensor
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Input 1-D tensor.
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output_dtype : str
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The output tensor data type.
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binop: function, optional
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A binary associative op to use for calculating difference. The function takes two
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TIR expressions and produce a new TIR expression. By default it uses tvm.tirx.Sub to
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compute the adjacent difference.
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Returns
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-------
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output : tvm.te.Tensor
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1-D tensor storing the adjacent difference of the input tensor. The adjacent difference
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is defined as: output[0] = 0, output[i] = binop(data[i], data[i-1])
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where i > 0 and i < len(data).
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"""
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return te.extern(
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[data.shape],
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[data],
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lambda ins, outs: _calc_adjacent_diff_ir(ins[0], outs[0], binop=binop),
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dtype=[out_dtype],
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name="_calc_adjacent_diff",
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tag="_calc_adjacent_diff_cpu",
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)
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