Fix for monodle failure #629
Conversation
meenakshiramanathan1
requested review from
nvukobratTT,
pilkicTT and
dgolubovicTT
as code owners
meenakshiramanathan1
force-pushed
the
mramanathan/convtranspose2d
branch
from
0a982bf
to
fd53e87
Compare
meenakshiramanathan1
changed the title
meenakshiramanathan1
force-pushed
the
mramanathan/convtranspose2d
branch
3 times, most recently
from
0aeabea
to
f9c284f
Compare
| input_nid = input_[0] | ||
| input_node = graph["nodes"][input_nid] | ||
| if input_node["op"] == "parameter" and input_node["name"].endswith("weight"): | ||
| in_channel = input_node["attrs"]["shape"][0][0][0] |
There was a problem hiding this comment.
Will this handle different shape ranks?
There was a problem hiding this comment.
Yes, inputs are either going to be 3d or 4d for convtranspose2d which is handled here, I have added different shape rank shape inputs in sanity now.
| input_nid = input_[0] | ||
| input_node = graph["nodes"][input_nid] | ||
| if input_node["op"] == "parameter" and input_node["name"].endswith("weight"): | ||
| in_channel = input_node["attrs"]["shape"][0][0][0] |
There was a problem hiding this comment.
How does it work for channel first and channel last cases?
There was a problem hiding this comment.
input_node["attrs"]["shape"][0][0][0] corresponds to the in-channels from the param being sent
In case of below example, it will be 16 and it would be the same for channelfirst and channel last cases
"in_channels, out_channels, kernel_size, stride, padding, groups, bias, dilation, padding_mode, input_shape",
[ 16, 33, (3, 3), 2, 0, 1, True, 1, "zeros", (20, 16, 50, 100)) ]
f9c284f
to
8f9f3b1
Compare
meenakshiramanathan1
force-pushed
the
mramanathan/convtranspose2d
branch
from
8f9f3b1
to
a5ef92b
Compare
| self.padding_bottom, | ||
| ] | ||
|
|
||
| assert self.padding_top == self.padding_bottom, "Padding values for top and bottom must be the same." |
There was a problem hiding this comment.
As this is a TTNN limitation, do we need to assert this here?
Let's track this as their issues, and model it further if we see that this is by design :))
There was a problem hiding this comment.
@nvukobratTT , the TTNN limitation is for a different model i.e mobilenetv2 and not for this model, these changes has been added to modify the padding handled in eval function of convtranspose2d op as a fix for monodle model.
Fix #513