# Source code for pytorch_quantization.utils.reduce_amax

```
#
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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# See the License for the specific language governing permissions and
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"""Function to get absolute maximum of a tensor
Follow numpy fashion, which is more generic as pytorch's
"""
import torch
[docs]def reduce_amax(input, axis=None, keepdims=True):
"""Compute the absolute maximum value of a tensor.
Reduces input_tensor along the dimensions given in axis. Unless keepdims is true,
the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true,
the reduced dimensions are retained with length 1.
.. note::
Gradient computeation is disabled as this function is never meant learning reduces amax
Args:
input: Input tensor
axis: The dimensions to reduce. None or int or tuple of ints. If None (the default),
reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
keepdims: A boolean. If true, retains reduced dimensions with length 1. Default True
granularity: DEPRECTED. specifies if the statistic has to be calculated at tensor or channel granularity
Returns:
The reduced tensor.
Raises:
ValueError: Any axis which doesn't make sense or is not supported
ValueError: If unknown granularity is passed in.
"""
with torch.no_grad():
output = input.abs()
if axis is None:
output = torch.max(output)
else:
if isinstance(axis, int):
output, _ = torch.max(output, dim=axis, keepdim=keepdims)
else:
if isinstance(axis, tuple) and len(axis) > input.dim():
raise ValueError("Cannot reduce more axes than tensor's dim.")
for i in axis:
output, _ = torch.max(output, dim=i, keepdim=True)
if not keepdims or output.numel() == 1:
output.squeeze_()
return output
```