[ET-VK] Allow specifying multiple storage types/memory layouts for an operator + register group norm operator (#11974)

## Changes

* Handle cases where an operator needs to specify a separate storage type / memory layout for each individual output.

## Motivation

Required for the group norm operator.

## Future Work

Currently, the `tag_memory_meta_pass` graph pass assumes that all tensors participating in a computation (aside from weights) will have the same storage type and memory layout. As more operators are being added, there are more exceptions to this rule.

The pass may need an update in the near future to make it possible to specify required storage types and memory layouts on a more granular level.

Differential Revision: [D77038781](https://our.internmc.facebook.com/intern/diff/D77038781/)

Author: pytorchbot

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)}")