[microNPU] Add a legalization test for TFLite PAD (apache#13750)

Added a legalization test for stand-alone pad operation which is legalized to depthwise operation on the NPU.

tests/python/contrib/test_ethosu/test_legalize.py

@@ -674,6 +674,106 @@ def verify(ext_func):
     verify(mod["tvmgen_default_ethos_u_main_0"])
 
 
+@pytest.mark.parametrize("ifm_shape", [(1, 55, 55, 3), (1, 23, 32, 7)])
+@pytest.mark.parametrize("padding", [(0, 1, 0, 0), (1, 1, 1, 1), (1, 1, 5, 5)])
+@pytest.mark.parametrize("const_value", [0, 5, 125, -5])
+def test_tflite_separate_padding_legalize(ifm_shape, padding, const_value):
+    dtype = "int8"
+    kernel_shape = (1, 1)
+    strides = (1, 1)
+    dilation = (1, 1)
+
+    def create_tflite_graph():
+        class Model(tf.Module):
+            @tf.function
+            def tf_function(self, x):
+                return tf.pad(
+                    x,
+                    [[0, 0], [padding[0], padding[2]], [padding[1], padding[3]], [0, 0]],
+                    "CONSTANT",
+                    const_value,
+                )
+
+        model = Model()
+        concrete_func = model.tf_function.get_concrete_function(
+            tf.TensorSpec(ifm_shape, dtype=tf.float32)
+        )
+        # Convert the model
+        def representative_dataset():
+            for _ in range(100):
+                data = np.random.rand(*tuple(ifm_shape))
+                yield [data.astype(np.float32)]
+
+        converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
+        converter.optimizations = [tf.lite.Optimize.DEFAULT]
+        converter.representative_dataset = representative_dataset
+        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+        converter.inference_input_type = tf.int8
+        converter.inference_output_type = tf.int8
+        tflite_model = converter.convert()
+        return tflite_model
+
+    def verify(ext_func):
+        op = ext_func.body
+        ofm_channels = op.attrs.ofm_channels
+
+        # check IFM
+        ifm = op.args[0].checked_type
+        assert list(ifm.shape) == list(ifm_shape)
+        assert str(ifm.dtype) == dtype
+        assert ifm.shape[3] == ofm_channels
+
+        # check OFM
+        ofm = op.checked_type
+        expected_ofm_shape = infra.compute_ofm_shape(
+            ifm_shape, padding, kernel_shape, strides, dilation
+        )
+        assert list(ofm.shape) == list(expected_ofm_shape)
+        assert str(ofm.dtype) == dtype
+        assert ofm.shape[3] == ofm_channels
+
+        # check weights
+        weights_ohwi = op.args[1].data.asnumpy()
+        assert str(weights_ohwi.dtype) == dtype
+        assert weights_ohwi.shape[0] == ofm_channels
+        assert weights_ohwi.shape[1] == kernel_shape[0]
+        assert weights_ohwi.shape[2] == kernel_shape[1]
+        assert weights_ohwi.shape[3] == 1  # only depth multiplier 1 is supported
+
+        # Check that scale_bias matches weight tensor
+        assert list(op.args[2].checked_type.shape)[0] == ofm_channels
+
+        assert list(op.attrs.padding) == list(padding)
+        assert op.attrs.ofm_channels == ofm_channels
+        assert list(op.attrs.strides) == list(strides)
+        assert list(op.attrs.dilation) == list(dilation)
+
+    pad_pattern_table = [
+        (
+            ethosu.PadParams.composite_name,
+            ethosu.pad_pattern(),
+            lambda pat: ethosu.PadParams(pat).is_valid(),
+        )
+    ]
+
+    tflite_graph = create_tflite_graph()
+    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
+
+    mod, params = relay.frontend.from_tflite(
+        tflite_model,
+        shape_dict={"input": ifm_shape},
+        dtype_dict={"input": dtype},
+    )
+
+    mod["main"] = bind_params_by_name(mod["main"], params)
+    mod = partition_ethosu_by_table(mod, pad_pattern_table)
+
+    mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
+        legalize.PadRewriter(), mod["tvmgen_default_ethos_u_main_0"]
+    )
+    verify(mod["tvmgen_default_ethos_u_main_0"])
+
+
 @pytest.mark.parametrize("pooling_type", ["MAX", "AVG"])
 @pytest.mark.parametrize("ifm_shape", [[1, 3, 4, 3], [1, 4, 5, 2]])
 @pytest.mark.parametrize(