[GitHub] [tvm] mbaret commented on a change in pull request #9442: Arm(R) Ethos(TM)-U NPU BinaryElementwise operators support

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

mbaret commented on a change in pull request #9442:
URL: https://github.com/apache/tvm/pull/9442#discussion_r743845777



##########
File path: python/tvm/relay/backend/contrib/ethosu/legalize.py
##########
@@ -423,11 +640,20 @@ class LegalizeEthosU:
     def transform_module(
         self, mod: tvm.ir.IRModule, ctx: tvm.ir.transform.PassContext
     ) -> tvm.ir.IRModule:
+        """This is the method that replace the operations with hardware/codegen supported

Review comment:
       that replaces

##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -343,5 +343,256 @@ def representative_dataset():
     infra.verify_source(compiled_models, accel_type)
 
 
+@pytest.mark.parametrize("accel_type", ACCEL_TYPES)
+@pytest.mark.parametrize("operator_type", ["ADD", "SUB", "MUL", "MIN", "MAX"])
+@pytest.mark.parametrize(
+    "ifm_shape, ifm2_shape",
+    [
+        ([1, 2, 3, 4], [1, 2, 3, 4]),
+        ([1, 2, 3, 4], [1, 1, 3, 1]),
+        ([1, 1, 3, 1], [1, 2, 3, 4]),
+    ],
+)
+@pytest.mark.parametrize("activation_function", ["NONE", "RELU"])
+def test_ethosu_binary_elementwise(
+    accel_type,
+    operator_type,
+    ifm_shape,
+    ifm2_shape,
+    activation_function,
+):
+    dtype = "int8"
+
+    def create_tflite_graph():
+        tf.config.run_functions_eagerly(True)
+
+        class Model(tf.Module):
+            @tf.function
+            def tf_function(self, lhs, rhs):
+                if operator_type == "ADD":
+                    op = tf.math.add(lhs, rhs)
+                elif operator_type == "SUB":
+                    op = tf.math.subtract(lhs, rhs)
+                elif operator_type == "MUL":
+                    op = tf.math.multiply(lhs, rhs)
+                elif operator_type == "MIN":
+                    op = tf.math.minimum(lhs, rhs)
+                elif operator_type == "MAX":
+                    op = tf.math.maximum(lhs, rhs)
+                if activation_function == "RELU":
+                    op = tf.nn.relu(op)
+                return op
+
+        model = Model()
+        concrete_func = model.tf_function.get_concrete_function(

Review comment:
       Not necessarily something to resolve in this patch, but I wonder if we can abstract some of the boilerplate code here into a utility function.

##########
File path: python/tvm/relay/op/contrib/ethosu.py
##########
@@ -458,6 +458,316 @@ def qnn_avgpool2d_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
     return pattern
 
 
+class BinaryElementwiseParams:
+    """
+    This class will parse a call to a ethosu.binary_elementwise composite function
+    and extract the parameter information.
+    """
+
+    def __init__(self, func_body: Call, operator_type: str, has_quantization_parameters: bool):
+        clip = None
+        if str(func_body.op) == "clip":
+            clip = func_body
+            binary_op = clip.args[0]
+        else:
+            binary_op = func_body
+
+        layout = "NHWC"
+
+        if has_quantization_parameters:
+            self.ifm = TensorParams(
+                binary_op.args[BinaryElementwiseArgs.ifm.value],
+                layout,
+                binary_op.args[BinaryElementwiseArgs.ifm_scale.value],
+                binary_op.args[BinaryElementwiseArgs.ifm_zero_point.value],
+            )
+            self.ifm2 = TensorParams(
+                binary_op.args[BinaryElementwiseArgs.ifm2.value],
+                layout,
+                binary_op.args[BinaryElementwiseArgs.ifm2_scale.value],
+                binary_op.args[BinaryElementwiseArgs.ifm2_zero_point.value],
+            )
+            self.ofm = TensorParams(
+                binary_op,
+                layout,
+                binary_op.args[BinaryElementwiseArgs.ofm_scale.value],
+                binary_op.args[BinaryElementwiseArgs.ofm_zero_point.value],
+            )
+        else:
+            self.ifm = TensorParams(
+                binary_op.args[BinaryElementwiseArgs.ifm.value],
+                layout,
+            )
+            self.ifm2 = TensorParams(
+                binary_op.args[BinaryElementwiseArgs.ifm2.value],
+                layout,
+            )
+            self.ofm = TensorParams(
+                binary_op,
+                layout,
+            )
+        self.activation = clip
+        self.operator_type = operator_type
+
+        def brodcastable(x, y):

Review comment:
       broadcastable (or 'can_broadcast' might be better)




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