anirudhacharya commented on a change in pull request #10605: [MXNET-310]
[ONNX-MXNet] API to import ONNX models into Gluon.
URL: https://github.com/apache/incubator-mxnet/pull/10605#discussion_r192206073
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File path: python/mxnet/contrib/onnx/_import/op_translations.py
##########
@@ -43,32 +43,42 @@ def add(attrs, inputs, cls):
"""Adding two tensors"""
new_attr = {}
if 'broadcast' in attrs and attrs['broadcast'] == 1:
- op_value = translation_utils._fix_bias_shape('broadcast_add', inputs,
cls)
+ broadcast_axis = attrs['axis']
+ op_value = translation_utils._fix_broadcast('broadcast_add', inputs,
+ broadcast_axis, cls)
return op_value, new_attr, inputs
- return 'elemwise_add', new_attr, inputs
+ return 'broadcast_add', new_attr, inputs
def subtract(attrs, inputs, cls):
"""Subtracting two tensors"""
new_attr = {}
if 'broadcast' in attrs and attrs['broadcast'] == 1:
- return 'broadcast_sub', new_attr, inputs
- return 'elemwise_sub', new_attr, inputs
+ broadcast_axis = attrs['axis']
+ op_value = translation_utils._fix_broadcast('broadcast_sub', inputs,
+ broadcast_axis, cls)
+ return op_value, new_attr, inputs
+ return 'broadcast_sub', new_attr, inputs
def multiply(attrs, inputs, cls):
"""Multiply two tensors"""
new_attr = {}
if 'broadcast' in attrs and attrs['broadcast'] == 1:
- op_value = translation_utils._fix_bias_shape('broadcast_mul', inputs,
cls)
+ broadcast_axis = attrs['axis']
+ op_value = translation_utils._fix_broadcast('broadcast_mul', inputs,
+ broadcast_axis, cls)
return op_value, new_attr, inputs
- return 'elemwise_mul', new_attr, inputs
+ return 'broadcast_mul', new_attr, inputs
def divide(attrs, inputs, cls):
"""Divide two tensors"""
new_attr = {}
if 'broadcast' in attrs and attrs['broadcast'] == 1:
- return 'broadcast_div', new_attr, inputs
- return 'elemwise_div', new_attr, inputs
+ broadcast_axis = attrs['axis']
+ op_value = translation_utils._fix_broadcast('broadcast_div', inputs,
+ broadcast_axis, cls)
Review comment:
broadcast_axis comes from the ONNX's axis attribute in operators that
support broadcasting -
https://github.com/onnx/onnx/blob/master/docs/Changelog.md#attributes-103
With OP_SET version 6 broadcasting (1,1) on (4,5) would not be permissible.
If we are broadcasting (5,) on (4,5) the broadcast_axis will be equal to 1. On
the other hand if we broadcast (4,) on (4,5) broadcast axis will be equal to 0.
ONNX with their OP_SET version 7 are updating the broadcast rules to be
aligned with numpy broadcasting rules. When that gets consistently updated in
ONNX repo we will also update the translation code in mxnet.
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