Fix GroupNorm dynamic input (#1922)

fix groupnorm dynamic input

coremltools/converters/mil/frontend/torch/ops.py

@@ -1828,10 +1828,17 @@ def group_norm(context, node):
     bias = inputs[3]
     eps = inputs[4]
     n,c = x.shape[0],x.shape[1] # at minimum (N, C) required
-    input_shape = [*x.shape] # n, c, *
     num_groups = builtins.min(num_groups,c)
     new_shape = [n, num_groups, c//num_groups]
-    new_shape += [*x.shape[2:]] # adds remaining dims
+    # optimization for non symbolic shapes. This get rids of 3 mil ops that required on dynamic shapes
+    if not any_symbolic(x.shape[2:]):
+        new_shape += [*x.shape[2:]] # adds remaining dims
+        input_shape = [*x.shape] # n, c, *
+    else:
+        input_shape = mb.shape(x=x)
+        input_shape_sliced = mb.slice_by_size(x=input_shape, begin=[2], size=[-1]) # x_shape[2:]
+        new_shape = mb.concat(values=[new_shape, input_shape_sliced], axis=0)
+
     num_extra_axes = len(x.shape[2:])
     axes_ = [int(i) for i in range(2, 2 + num_extra_axes + 1)]
     weight_shape, bias_shape = [1,c], [1,c]

coremltools/converters/mil/frontend/torch/test/test_torch_ops.py

@@ -1285,6 +1285,36 @@ def test_groupnorm_rank2_input(
             compute_unit=compute_unit,
         )
 
+    @pytest.mark.parametrize(
+        "compute_unit, backend, group_features, eps, affine",
+        itertools.product(
+            compute_units, backends, [(16, 32), (1, 1)], [0.1, 1e-05], [True, False]
+        ),
+    )
+    def test_groupnorm_dynamic(self, compute_unit, backend, group_features, eps, affine):
+        model = nn.GroupNorm(
+            group_features[0], group_features[1], eps=eps, affine=affine
+        )
+        dim_upper_bound = 30 if backend[0] == "mlprogram" else -1
+        converter_input_type = [
+            TensorType(
+                shape=(
+                    6,
+                    group_features[1],
+                    RangeDim(default=10, lower_bound=5, upper_bound=dim_upper_bound),
+                    RangeDim(default=10, lower_bound=5, upper_bound=dim_upper_bound),
+                ),
+                dtype=np.float32,
+            )
+        ]
+        self.run_compare_torch(
+            (6, group_features[1], 10, 10),
+            model,
+            backend=backend,
+            compute_unit=compute_unit,
+            converter_input_type=converter_input_type,
+        )
+
 
 class TestLinear(TorchBaseTest):
     @pytest.mark.parametrize(