arina-grovety commented on code in PR #14765:
URL: https://github.com/apache/tvm/pull/14765#discussion_r1185816248
##########
python/tvm/relay/op/contrib/ethosu.py:
##########
@@ -2000,6 +2000,79 @@ def is_valid(self):
return True
+class ChannelPadParams:
+ """
+ This class will parse a call to a ethosu.pad2d composite function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethos-u.channel-pad"
+ # The ethos-u.channel-pad composite function will be transformed
+ # to the Relay concatenate operation.
+
+ def __init__(self, func_body: Call):
+ from tvm.relay.backend.contrib.ethosu.util import QPadArgs
+
+ # there is no 'layout' attribute in nn.pad
+ layout = "NHWC"
+ self.ifm = TensorParams(
+ tensor=func_body.args[QPadArgs.IFM.value],
+ layout=layout,
+ scale=tvm.relay.Constant(tvm.nd.array(np.array(1.0,
dtype="float32"))),
+ zero_point=func_body.args[QPadArgs.IFM_ZERO_POINT.value],
+ )
+
+ self.ch_padding = self.extract_ch_padding(func_body)
+ self.ofm = TensorParams(
+ tensor=func_body,
+ layout=layout,
+ scale=tvm.relay.Constant(tvm.nd.array(np.array(1.0,
dtype="float32"))),
+ zero_point=func_body.args[QPadArgs.IFM_ZERO_POINT.value],
+ )
+
+ @staticmethod
+ def extract_ch_padding(
+ padding: relay.Call,
+ ) -> Optional[Tuple[int, int]]:
+ """
+ Here we check whether a separate channel-dimension padding operation
can be
+ rewritten as Relay concatenate operation. If the padding specified by
the
+ separate nn.pad operation is not supported by NPU, None will be
returned.
+ This will cause the nn.pad not to be offloaded to NPU.
+ """
+ pad_width = padding.attrs["pad_width"]
+ if len(pad_width) != 4:
+ return None
+ if (
+ list(pad_width[0]) != [0, 0]
+ or list(pad_width[1]) != [0, 0]
Review Comment:
Yes, you are right, spatial and channel pad can of course occur in neural
networks. This is a separate task, we discussed that it will be useful in the
future. We plan to solve it when we have time or a network with such pad
appears.
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