[ET-VK] Allow specifying multiple storage types/memory layouts for an operator + register group norm operator

backends/vulkan/_passes/tag_memory_meta_pass.py

@@ -91,7 +91,7 @@ def __init__(
         self.default_layout: VkMemoryLayout = default_memory_layout
         self.texture_limits = texture_limits
 
-    def propose_node_storage(
+    def propose_node_storage(  # noqa: C901
         self,
         node: torch.fx.Node,
     ) -> Optional[VkStorageType]:
@@ -138,15 +138,23 @@ def propose_node_storage(
         for arg in node.args:
             if isinstance(arg, torch.fx.Node) and utils.is_tensor_node(arg):
                 storage = utils.get_node_storage_type(arg)
+                # Some operators which return multiple output tensors may specify a
+                # different storage type for each output. In this case, the storage type
+                # for the first output is used.
+                if isinstance(storage, (list, tuple)):
+                    storage = storage[0]
                 if storage is not None and storage in valid_storage_types:
                     return storage
 
         # If no storage type has been resolved yet, assume the optimal storage type of
         # the first opinionated user. This search is recursive.
         for user in node.users:
-            optimal_storage = self.propose_node_storage(user)
-            if optimal_storage is not None:
-                return optimal_storage
+            storage = self.propose_node_storage(user)
+            # See above
+            if isinstance(storage, (list, tuple)):
+                storage = storage[0]
+            if storage is not None:
+                return storage
 
         if self.default_storage in valid_storage_types:
             return self.default_storage
@@ -179,15 +187,23 @@ def propose_node_layout(
         for arg in node.args:
             if isinstance(arg, torch.fx.Node) and utils.is_tensor_node(arg):
                 layout = utils.get_node_memory_layout(arg)
+                # Some operators which return multiple output tensors may specify a
+                # different memory layout for each output. In this case, the storage
+                # type for the first output is used.
+                if isinstance(layout, (list, tuple)):
+                    layout = layout[0]
                 if layout is not None and layout in valid_layouts:
                     return layout
 
-        # If no storage type has been resolved yet, assume the optimal storage type of
-        # the first opinionated user. This search is recursive.
+        # If no memory layout has been resolved yet, assume the optimal layout of the
+        # first opinionated user. This search is recursive.
         for user in node.users:
-            optimal_storage = self.propose_node_layout(user, storage)
-            if optimal_storage is not None:
-                return optimal_storage
+            layout = self.propose_node_layout(user, storage)
+            # See above comment
+            if isinstance(layout, (list, tuple)):
+                layout = layout[0]
+            if layout is not None:
+                return layout
 
         # As a last resort, return the default storage type that should be used.
         if self.default_layout in valid_layouts:

backends/vulkan/op_registry.py

@@ -655,6 +655,32 @@ def register_ported_ops_with_prepacking(features: OpFeatures):
     return features
 
 
+@update_features(
+    [
+        exir_ops.edge.aten.native_group_norm.default,
+    ]
+)
+def register_native_group_norm(features: OpFeatures):
+    features.texture_impl = TextureImplFeatures(
+        valid_packed_dims={PackedDim.CHANNELS},
+    )
+    features.handles_own_prepacking = True
+
+    features.optimal_storage = [
+        VkStorageType.TEXTURE_3D,
+        VkStorageType.BUFFER,
+        VkStorageType.BUFFER,
+    ]
+
+    features.optimal_layout = [
+        VkMemoryLayout.TENSOR_CHANNELS_PACKED,
+        VkMemoryLayout.TENSOR_WIDTH_PACKED,
+        VkMemoryLayout.TENSOR_WIDTH_PACKED,
+    ]
+
+    return features
+
+
 # Ported ops that support their own prepacking.
 @update_features(
     [

backends/vulkan/test/test_vulkan_delegate.py

@@ -1898,3 +1898,69 @@ def forward(self, x):
             dynamic_shapes=dynamic_shapes,
             test_inputs=test_inputs,
         )
+
+    def test_vulkan_backend_group_norm(self):
+        class ConvGroupNormModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                # Conv2d: 3 input channels -> 16 output channels
+                self.conv = torch.nn.Conv2d(
+                    in_channels=3,
+                    out_channels=16,
+                    kernel_size=3,
+                    padding=1,
+                    bias=True,
+                )
+                # GroupNorm: 4 groups for 16 channels (16 % 4 == 0)
+                self.group_norm = torch.nn.GroupNorm(
+                    num_groups=4,
+                    num_channels=16,
+                    eps=1e-5,
+                    affine=True,
+                )
+
+            def forward(self, x):
+                x = self.conv(x)
+                x = self.group_norm(x)
+                return x
+
+        # Create sample inputs: [batch, channels, height, width]
+        sample_inputs = (torch.randn(size=(1, 3, 32, 32), dtype=torch.float32),)
+
+        # Test with static shapes first
+        self.lower_module_and_test_output(
+            ConvGroupNormModule(),
+            sample_inputs,
+        )
+
+    def test_vulkan_backend_group_norm_different_groups(self):
+        class GroupNormModule(torch.nn.Module):
+            def __init__(self, num_groups, num_channels):
+                super().__init__()
+                self.group_norm = torch.nn.GroupNorm(
+                    num_groups=num_groups,
+                    num_channels=num_channels,
+                    eps=1e-5,
+                    affine=True,
+                )
+
+            def forward(self, x):
+                return self.group_norm(x)
+
+        # Test different group configurations
+        test_configs = [
+            (2, 8),  # 2 groups, 8 channels
+            (4, 16),  # 4 groups, 16 channels
+            (8, 32),  # 8 groups, 32 channels
+        ]
+
+        for num_groups, num_channels in test_configs:
+            with self.subTest(num_groups=num_groups, num_channels=num_channels):
+                sample_inputs = (
+                    torch.randn(size=(2, num_channels, 16, 16), dtype=torch.float32),
+                )
+
+                self.lower_module_and_test_output(
+                    GroupNormModule(num_groups, num_channels),
+                    sample_inputs,
+                )

backends/vulkan/utils.py

@@ -264,9 +264,19 @@ def set_node_spec_attr(node: torch.fx.Node, attr: str, value):
     if isinstance(spec, TensorSpec):
         setattr(spec, attr, value)
     elif isinstance(spec, (list, tuple)):
-        for s in spec:
-            assert isinstance(s, TensorSpec)
-            setattr(s, attr, value)
+        # Special case if value is a list/tuple of the same length as the
+        # collection of tensors in the node. In this case, treat the value list
+        # as a list of values to set indivudually for each tensor in the node
+        if isinstance(value, (list, tuple)) and len(spec) == len(value):
+            assert len(spec) == len(value)
+            for s, v in zip(spec, value):
+                assert isinstance(s, TensorSpec)
+                setattr(s, attr, v)
+        # Otherwise, set the attribute to value for all tensors in the list
+        else:
+            for s in spec:
+                assert isinstance(s, TensorSpec)
+                setattr(s, attr, value)
     else:
         raise RuntimeError(f"Cannot set attr for spec of type {type(spec)}")