NXP backend: Fix default params functionality for Amin, Sum

Author: roman-janik-nxp

backends/nxp/backend/ir/converter/node_converters/ops_converters/amin_converter.py

@@ -70,7 +70,12 @@ def convert(self, node: Node):
         t_op.builtin_options = reduce_min_options.ReduceMin(keepdim)
 
         ops = OpsList(middle_op=t_op)
-        dim = get_dim_and_handle_io_formats(self.builder, ops, dim, keepdim)
+        # dim default value is None, it that case no changes to dim or io_formats are needed and all dims are reduced
+        dim = (
+            get_dim_and_handle_io_formats(self.builder, ops, dim, keepdim)
+            if dim is not None
+            else None
+        )
 
         convert_axes_from_attribute(t_op, self.builder, dim)
         self.builder.append_operators(ops.flatten())

backends/nxp/backend/ir/converter/node_converters/ops_converters/sum_dim_int_list_converter.py

@@ -72,7 +72,12 @@ def convert(self, node: Node):
         t_op.builtin_options = sum_options.Sum(keepdim)
 
         ops = OpsList(middle_op=t_op)
-        dim = get_dim_and_handle_io_formats(self.builder, ops, dim, keepdim)
+        # dim default value is None, it that case no changes to dim or io_formats are needed and all dims are reduced
+        dim = (
+            get_dim_and_handle_io_formats(self.builder, ops, dim, keepdim)
+            if dim is not None and dim != []
+            else None
+        )
 
         convert_axes_from_attribute(t_op, self.builder, dim)
         self.builder.append_operators(ops.flatten())

backends/nxp/quantizer/neutron_quantizer.py

@@ -58,6 +58,7 @@
     SqueezePattern,
     SubTensorPattern,
     SumDimIntListPattern,
+    SumPattern,
     TanhInPlacePattern,
     TanhPattern,
     TransposeIntPattern,
@@ -304,6 +305,7 @@ def __init__(self, neutron_target_spec: NeutronTargetSpec, is_qat: bool = False)
                 OpQuantizer(SqueezePattern(is_qat=is_qat), static_qconfig),
                 OpQuantizer(SubTensorPattern(is_qat=is_qat), static_qconfig),
                 OpQuantizer(SumDimIntListPattern(is_qat=is_qat), static_qconfig),
+                OpQuantizer(SumPattern(is_qat=is_qat), static_qconfig),
                 OpQuantizer(TanhPattern(is_qat=is_qat), static_qconfig),
                 OpQuantizer(TanhInPlacePattern(is_qat=is_qat), static_qconfig),
                 OpQuantizer(TransposeIntPattern(is_qat=is_qat), static_qconfig),

backends/nxp/quantizer/patterns.py

@@ -1150,6 +1150,15 @@ def partition_types(self):
         return [torch.ops.aten.squeeze.dims]
 
 
+class SumPattern(SharedSpecPattern):
+    """
+    Quantizer for the `aten.sum.default` operator.
+    """
+
+    def partition_types(self):
+        return [torch.ops.aten.sum.default]
+
+
 class SumDimIntListPattern(SharedSpecPattern):
     """
     Quantizer for the `aten.sum.dim_IntList` operator.

backends/nxp/tests/ir/converter/node_converter/test_amin_converter.py

@@ -57,6 +57,12 @@ def forward(self, x):
         return torch.amin(x, dim=self.dim, keepdim=self.keepdim)
 
 
+class AminDefaultParamsModule(torch.nn.Module):
+    @staticmethod
+    def forward(x):
+        return torch.amin(x)
+
+
 class AminAddModule(AminModule):
     def forward(self, x):
         x = super().forward(x)
@@ -172,6 +178,19 @@ def test__tuple_dims(self, mocker, request, input_shape, dim, keep_dim):
         model = AminModule(dim, keep_dim)
         assert_delegated(model, input_shape, mocker, request)
 
+    @pytest.mark.parametrize(
+        "input_shape",
+        [
+            pytest.param((4, 2), id="2D."),
+            pytest.param((2, 3, 4), id="3D."),
+            pytest.param((1, 3, 3, 7), id="4D."),
+            pytest.param((3, 1, 4, 1, 5), id="5D."),
+        ],
+    )
+    def test__default_params(self, mocker, request, input_shape):
+        model = AminDefaultParamsModule()
+        assert_delegated(model, input_shape, mocker, request)
+
     @pytest.mark.parametrize(
         "input_shape, dim",
         [
@@ -310,7 +329,7 @@ def test__channels_first_input__reducing_all_spatial_dims(
             self, mocker, request, dim
         ):
             # If the spatial dimensions are reduced (removed), the `amin` output will always be equal in channels
-            #  first and channels last, so no `Transpose` ops are added.
+            #  first and channels last, so no `Transpose` ops before `ReduceMin` are added.
             input_shape = (1, 7, 3, 3)
             model = MaxPoolAminModule(dim, False)
 

backends/nxp/tests/ir/converter/node_converter/test_mean_dim_converter.py

@@ -306,7 +306,7 @@ def test__channels_first_input__reducing_all_spatial_dims(
             self, mocker, request, dim
         ):
             # If the spatial dimensions are reduced (removed), the `mean` output will always be equal in channels
-            #  first and channels last, so no `Transpose` ops are added.
+            #  first and channels last, so no `Transpose` ops before `Mean` are added.
             input_shape = (1, 7, 3, 3)
             model = MaxPoolMeanDimModule(dim, False)
 

backends/nxp/tests/ir/converter/node_converter/test_sum_dim_int_list_converter.py

@@ -57,6 +57,12 @@ def forward(self, x):
         return torch.sum(x, dim=self.dim, keepdim=self.keepdim)
 
 
+class SumDefaultParamsModule(torch.nn.Module):
+    @staticmethod
+    def forward(x):
+        return torch.sum(x)
+
+
 class SumAddModule(SumModule):
     def forward(self, x):
         x = super().forward(x)
@@ -172,6 +178,19 @@ def test__tuple_dims(self, mocker, request, input_shape, dim, keep_dim):
         model = SumModule(dim, keep_dim)
         assert_delegated(model, input_shape, mocker, request)
 
+    @pytest.mark.parametrize(
+        "input_shape",
+        [
+            pytest.param((4, 2), id="2D."),
+            pytest.param((2, 3, 4), id="3D."),
+            pytest.param((1, 3, 3, 7), id="4D."),
+            pytest.param((3, 1, 4, 1, 5), id="5D."),
+        ],
+    )
+    def test__default_params(self, mocker, request, input_shape):
+        model = SumDefaultParamsModule()
+        assert_delegated(model, input_shape, mocker, request)
+
     @pytest.mark.parametrize(
         "input_shape, dim",
         [
@@ -314,7 +333,7 @@ def test__channels_first_input__reducing_all_spatial_dims(
             self, mocker, request, dim
         ):
             # If the spatial dimensions are reduced (removed), the `sum` output will always be equal in channels
-            #  first and channels last, so no `Transpose` ops are added.
+            #  first and channels last, so no `Transpose` ops before `Sum` are added.
             input_shape = (1, 7, 3, 3)
             model = MaxPoolSumModule(dim, False)