Aleksei-grovety commented on code in PR #14765:
URL: https://github.com/apache/tvm/pull/14765#discussion_r1184956051
##########
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
Review Comment:
This class will parse a call to a ethos-u.channel-pad composite function
##########
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:
Are there networks that have paddings in height, width and channels? If
there are such, then it would be possible to remove width and height
restrictions and add width and height padding processing to the legalization
using depthwise convolution as it is done for pad2d.
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