wehub-resource-sync
114 lines
3.0 KiB
Python
114 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,consider-using-enumerate,unused-argument,len-as-condition
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"""Elementwise operators"""
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import math as _math
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from tvm import te
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from . import cpp
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def elemwise_sum(xs):
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"""Perform element-wise sum on inputs
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Parameters
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----------
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xs : list of tvm.te.Tensor
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Input arguments.
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Returns
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-------
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y : tvm.te.Tensor
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The result.
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"""
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return cpp.elemwise_sum(xs)
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def full(shape, dtype, fill_value):
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"""Fill tensor with fill_value
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Parameters
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----------
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shape : tuple
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Input tensor shape.
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dtype : str
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Data type
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fill_value : float
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Value to be filled
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Returns
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-------
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y : tvm.te.Tensor
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The result.
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"""
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if isinstance(fill_value, int | float) and (_math.isinf(fill_value) or _math.isnan(fill_value)):
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if not ("float" in dtype or "bfloat16" in dtype):
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raise ValueError("Infinite and NaN require a floating-point dtype.")
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return cpp.full(shape, dtype, fill_value)
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def full_like(x, fill_value):
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"""Construct a tensor with same shape as input tensor,
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then fill tensor with fill_value.
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Parameters
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----------
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x : tvm.te.Tensor
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Input argument.
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fill_value : float
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Value to be filled
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Returns
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-------
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y : tvm.te.Tensor
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The result.
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"""
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return cpp.full_like(x, fill_value)
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def eye(n: int, m: int | None = None, k: int = 0, dtype: str = "float32") -> te.Tensor:
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"""Generate an identity matrix or a matrix with ones on the k-th diagonal.
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Parameters
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----------
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n : int
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Number of rows
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m : int, optional
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Number of columns. If None, defaults to n.
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k : int, optional
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Index of the diagonal. 0 (default) refers to the main diagonal.
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A positive value refers to an upper diagonal, and a negative value
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to a lower diagonal.
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dtype : str, optional
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Data type of the returned array.
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Returns
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-------
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y : tvm.te.Tensor
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The result.
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"""
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m = m if m is not None else n
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return te.compute(
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(n, m),
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lambda i, j: te.if_then_else(i == j - k, te.const(1, dtype), te.const(0, dtype)),
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name="eye",
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)
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