Alexey-Yazev commented on code in PR #13997:
URL: https://github.com/apache/tvm/pull/13997#discussion_r1111576806
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python/tvm/relay/op/contrib/ethosu.py:
##########
@@ -1375,6 +1375,82 @@ def mean_pattern() ->
tvm.relay.dataflow_pattern.DFPattern:
return pattern
+class SumParams:
+ """
+ This class will parse a call to ethosu.sum composite function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethos-u.sum"
+
+ def __init__(self, func_body: Call):
+ from tvm.relay.backend.contrib.ethosu.util import RequantArgs
+
+ requantize = func_body
+ sum_op = requantize.args[0]
+ attrs = sum_op.attrs
+ cast = sum_op.args[0]
+
+ layout = "NHWC"
+ self.ifm = TensorParams(
+ cast.args[0],
+ layout,
+ requantize.args[RequantArgs.IFM_SCALE.value],
+ requantize.args[RequantArgs.IFM_ZERO_POINT.value],
+ )
+ self.ofm = TensorParams(
+ requantize,
+ layout,
+ requantize.args[RequantArgs.OFM_SCALE.value],
+ requantize.args[RequantArgs.OFM_ZERO_POINT.value],
+ )
+
+ ifm_shape = self.ifm.shape
+ self.height = ifm_shape[0] if len(ifm_shape) in (2, 3) else
ifm_shape[1]
+ self.width = ifm_shape[1] if len(ifm_shape) in (2, 3) else ifm_shape[2]
+ self.keepdims = attrs.keepdims
+
+ self.axis = list(sorted(attrs.axis))
+ if attrs.exclude:
+ self.axis = [i for i in range(len(self.ifm.shape)) if i not in
self.axis]
+
+ def is_valid(self) -> bool:
+ """
+ Checks whether Sum has compatible attributes with HW.
+ """
+
+ ifm_shape_len = len(self.ifm.shape)
+
+ if not check_valid_dtypes([self.ifm], [np.uint8, np.int8, np.int16,
np.int32]):
+ return False
+ if not check_valid_dtypes([self.ofm], [np.int8]):
Review Comment:
It is not possible to support int16 using SHR operation as it supports int8,
uint8, int32, but for example I can use ADD operation instead of SHR. Is there
an operation that is better to use for type conversion or would it be optimal
to use the ADD operation?
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