anijain2305 commented on pull request #6430:
URL: https://github.com/apache/incubator-tvm/pull/6430#issuecomment-689703548
Hi @lhutton1 Thanks for the PR! I am trying to understand the need for this
PR.
We can always overwrite the existing convert_op_layout for each operator.
So, I don't think I fully understand that need and usage.
For example, I can use the following code to perform the same operation (see
that I am using a higher level - **plevel = 11**). This will be set by the TVM
user and don't need to be the part of TVM codebase (similar to your usage).
~~~
@reg.register_convert_op_layout("nn.conv2d", level=11)
def custom_layouts(op_name, attrs, args):
if op_name == "nn.conv2d":
data = args[0].checked_type
weight = args[1].checked_type
is_depthwise = is_depthwise_conv2d(data.shape,
attrs['data_layout'],
weight.shape,
attrs['kernel_layout'],
attrs['groups'])
return ['NHWC', 'IHWO'] if is_depthwise else ['NHWC', 'OHWI']
raise ValueError(f'No custom layout defined for {op_name}')
~~~
Does that make sense?
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