Implement reduce all/any for non-const axes #1657
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This pull request introduces 1 alert when merging a5a4d07 into becdcba - view on LGTM.com new alerts:
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TomWildenhain-Microsoft
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| @@ -3557,29 +3557,29 @@ def func(x): | |||
| self._run_test_case(func, [_OUTPUT], {_INPUT: input_val}) | |||
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| def test_reduce_all(self): | |||
| input_val = np.random.randint(0, 2, (10, 20)).astype(np.bool) | |||
| input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool) | |||
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10 isn't great since there's a only a 1/1024 chance of them all matching. 20 sets of size 2 is good.
Signed-off-by: Tom Wildenhain <tomwi@microsoft.com>
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xadupre
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Aug 11, 2021
| reduce_input = node.input[0] | ||
| if node.type == "All": | ||
| reduce_input = ctx.make_node("Not", [reduce_input]).output[0] | ||
| cast = ctx.make_node("Cast", inputs=[reduce_input], attr={"to": onnx_pb.TensorProto.FLOAT}).output[0] |
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I was wondering why it is necessary to cast into float?
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Hmm, good question. I copied over the existing code and just modified it to do dynamic axes. I think one of the previous ops maybe didn't support non-float values. In any case, float is technically good because it doesn't roll over and is guaranteed to preserve monotonicity of addition. Since we just want to detect if any of the values are non-zero, this is good.
guschmue
approved these changes
Aug 11, 2021
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Signed-off-by: Tom Wildenhain tomwi@microsoft.com