[GitHub] [tvm] ekalda commented on a diff in pull request #11010: [microNPU] Match requantize in min/max with activation pattern

[GitBox <gi...@apache.org>]

ekalda commented on code in PR #11010:
URL: https://github.com/apache/tvm/pull/11010#discussion_r853088956


##########
python/tvm/relay/op/contrib/ethosu.py:
##########
@@ -618,19 +618,28 @@ class BinaryElementwiseParams:
     and extract the parameter information.
     """
 
-    def __init__(self, func_body: Call, operator_type: str, has_quantization_parameters: bool):
+    def __init__(self, func_body: Call, operator_type: str, is_quantized_operation: bool):
         from tvm.relay.backend.contrib.ethosu.util import BinaryElementwiseArgs
+        from tvm.relay.backend.contrib.ethosu.util import RequantArgs
 
+        current_call = func_body
         clip = None
-        if str(func_body.op) == "clip":
-            clip = func_body
-            binary_op = clip.args[0]
+        requantize = None
+
+        if is_quantized_operation:
+            if str(current_call.op) == "clip":
+                clip = current_call
+                current_call = clip.args[0]
         else:
-            binary_op = func_body
+            if str(current_call.op) == "qnn.requantize":
+                requantize = current_call
+                clip = current_call.args[0]
+                current_call = clip.args[0]

Review Comment:
   Is there always a clip between requantize and binary op?



##########
tests/python/contrib/test_ethosu/test_legalize.py:
##########
@@ -637,6 +637,18 @@ def verify(ext_func):
         if activation_function == "RELU":
             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

Review Comment:
   A bit unlikely, but to be on the safe side, maybe it's worth setting the seed in the test to make sure we don't end up with these values as a result of the random numbers we generate? 



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