ekalda commented on code in PR #13772:
URL: https://github.com/apache/tvm/pull/13772#discussion_r1071276333
##########
tests/python/contrib/test_ethosu/test_legalize.py:
##########
@@ -951,20 +962,30 @@ def verify(ext_func):
assert op.checked_type.dtype == dtype
assert op.attrs.operator_type == operator_type
assert op.attrs.reversed_operands == reversed_operands
- if activation_function == "RELU":
+ if activation_function != None:
assert str(op.attrs.activation) == "CLIP"
if operator_type in ["MIN", "MAX"]:
- # MIN and MAX with an activation must have a requantize
operation
- # baked into the output. To check the extra requantize node was
- # picked up by the pattern, we can make sure the quantization
- # information is not default.
- assert float(op.attrs.ifm_scale) != 1.0
- assert int(op.attrs.ifm_zero_point) != 0
- assert float(op.attrs.ifm2_scale) != 1.0
- assert int(op.attrs.ifm2_zero_point) != 0
- assert float(op.attrs.ofm_scale) != 1.0
- assert int(op.attrs.ofm_zero_point) != 0
+ if has_separate_requantize:
+ # In case when requantize cannot be fused with MIN/MAX +
CLIP due to hardware constraints
+ # there should be default quantization values since
requantize is separate operation.
+ assert float(op.attrs.ifm_scale) == 1.0
+ assert int(op.attrs.ifm_zero_point) == 0
+ assert float(op.attrs.ifm2_scale) == 1.0
+ assert int(op.attrs.ifm2_zero_point) == 0
+ assert float(op.attrs.ofm_scale) == 1.0
+ assert int(op.attrs.ofm_zero_point) == 0
+ else:
+ # MIN and MAX with an activation must have a requantize
operation
+ # baked into the output. To check the extra requantize
node was
+ # picked up by the pattern, we can make sure the
quantization
+ # information is not default.
+ assert float(op.attrs.ifm_scale) != 1.0
+ assert int(op.attrs.ifm_zero_point) != 0
+ assert float(op.attrs.ifm2_scale) != 1.0
+ assert int(op.attrs.ifm2_zero_point) != 0
+ assert float(op.attrs.ofm_scale) != 1.0
+ assert int(op.attrs.ofm_zero_point) != 0
Review Comment:
Do both of these blocks get run? It looks like we are using the same method
of generating representative dataset (which will determine the qnn params) for
all the tests, so I suspect we will always create IFMs with differing qnn
params and therefore test only one of the patterns here.
##########
tests/python/contrib/test_ethosu/test_legalize.py:
##########
@@ -881,7 +888,7 @@ def verify(ext_func):
([1, 4, 4], [4, 1], False),
],
)
[email protected]("activation_function", ["NONE", "RELU"])
[email protected]("activation_function", [None, tf.nn.relu,
tf.nn.relu6, relu_n1_to_1])
Review Comment:
```suggestion
@pytest.mark.parametrize("activation_function", [None, tf.nn.relu])
```
Since this test looks at the graph entirely at Relay level, where all the
RELUs are just Relay clip operations, I don't think we benefit much from extra
70 tests with just different min and max attributes to clip.
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