[ET-VK] Update partitioner to account for custom packed arguments

Differential Revision: D65759236

Pull Request resolved: #6753

Author: SS-JIA

backends/vulkan/_passes/insert_prepack_nodes.py

@@ -35,6 +35,10 @@ def prepack_not_required(node: torch.fx.Node) -> bool:
         if not is_param_node(program, node):
             return True
 
+        # Annotate that this node is going to represented as a tensorref in the Vulkan
+        # compute graph. This will be useful for later graph passes.
+        node.meta["vkdg_tensorref"] = True
+
         for user in node.users:
             if user.op == "call_function" and handles_own_prepacking(
                 # pyre-ignore

backends/vulkan/_passes/tag_memory_meta_pass.py

@@ -39,6 +39,30 @@ def set_memory_metadata(
     utils.set_node_spec_attr(node, "vk_memory_layout", layout)
 
 
+def insert_transition_node(
+    graph_module: torch.fx.GraphModule,
+    node: torch.fx.Node,
+    arg: torch.fx.Node,
+    storage: VkStorageType,
+    layout: VkMemoryLayout,
+) -> None:
+    """
+    Insert a clone node to copy the original tensor to a tensor with the desired storage
+    type and memory layout.
+    """
+    with graph_module.graph.inserting_before(node):
+        clone_node = graph_module.graph.create_node(
+            "call_function",
+            exir_ops.edge.aten.clone.default,
+            (arg,),
+        )
+        clone_node.meta["val"] = arg.meta["val"]
+        clone_node.meta["spec"] = deepcopy(arg.meta["spec"])
+        clone_node.meta["spec"].const = False
+        set_memory_metadata(clone_node, storage, layout)
+        arg.replace_all_uses_with(clone_node, lambda x, y=node: x == y)
+
+
 class TagMemoryMetaPass(ExportPass):
     """
     There are a variety of ways that tensors can be represented in Vulkan. The two main
@@ -174,14 +198,33 @@ def propose_node_layout(
         else:
             return next(iter(valid_layouts))
 
+    def should_annotate(self, node) -> bool:
+        if not isinstance(node, torch.fx.Node):
+            return False
+
+        if not isinstance(node.meta["val"], FakeTensor):
+            return False
+
+        # Storage type and memory layout for tensorref will be determined at runtime
+        # so there's no use in setting those attributes ahead of time.
+        if node.meta.get("vkdg_tensorref", False):
+            return False
+
+        return True
+
+    def should_delay_annotation(self, node: torch.fx.Node) -> bool:
+        # For prepack nodes, delay setting the storage type and memory layout as long as
+        # possible. This is to minimize the number of transitions, since it can be
+        # difficult to predict what storage type and memory layout should be used at the
+        # time the prepack node is observed.
+        return node.target == exir_ops.edge.et_vk.prepack.default
+
+    # noqa
     def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
         sorted_nodes: NodeList = topo_sort(list(graph_module.graph.nodes))
 
         for node in sorted_nodes:
-            if not isinstance(node.meta["val"], FakeTensor):
-                continue
-
-            if node.target == exir_ops.edge.et_vk.prepack.default:
+            if not self.should_annotate(node) or self.should_delay_annotation(node):
                 continue
 
             storage = self.propose_node_storage(node)
@@ -191,11 +234,11 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
 
             inserting_transitions_for_node = False
             for i, arg in enumerate(node.args):
-                if not isinstance(arg, torch.fx.Node):
-                    continue
-                if not isinstance(arg.meta["val"], FakeTensor):
+                if not self.should_annotate(arg):
                     continue
 
+                assert isinstance(arg, torch.fx.Node)
+
                 arg_storage = utils.get_node_storage_type(arg)
                 arg_layout = utils.get_node_memory_layout(arg)
 
@@ -215,22 +258,10 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
                         f"[Vulkan Delegate] Inserting transition(s) for {node.format_node()}:"
                     )
 
+                insert_transition_node(graph_module, node, arg, storage, layout)
+
                 logger.info(
                     f"   args {i} ({arg}): ({arg_storage}, {arg_layout}) -> ({storage}, {layout})"
                 )
 
-                # Insert a clone node to copy the original tensor to a tensor with the
-                # desired storage type and memory layout.
-                with graph_module.graph.inserting_before(node):
-                    clone_node = graph_module.graph.create_node(
-                        "call_function",
-                        exir_ops.edge.aten.clone.default,
-                        (arg,),
-                    )
-                    clone_node.meta["val"] = arg.meta["val"]
-                    clone_node.meta["spec"] = deepcopy(arg.meta["spec"])
-                    clone_node.meta["spec"].const = False
-                    set_memory_metadata(clone_node, storage, layout)
-                    arg.replace_all_uses_with(clone_node, lambda x, y=node: x == y)
-
         return PassResult(graph_module, True)

backends/vulkan/op_registry.py

@@ -90,6 +90,9 @@ class OpFeatures:
         # then the insert_prepack_nodes pass will not insert prepack nodes for the args
         # of the op.
         "handles_own_prepacking",
+        # Optional dictionary to specify a custom function to calculate the required
+        # image extents for a particular argument index.
+        "skip_limits_check",
         # Optional check function used during partitioning to determine if a node's
         # inputs are supported by the operator implementation.
         "check_node_fn",
@@ -103,6 +106,7 @@ def __init__(
         optimal_storage: Optional[VkStorageType] = None,
         optimal_layout: Optional[VkMemoryLayout] = None,
         handles_own_prepacking: bool = False,
+        skip_limits_check: Optional[Set[int]] = None,
         check_node_fn: Optional[Callable] = None,
     ):
         self.texture_impl: Optional[TextureImplFeatures] = texture_impl
@@ -111,6 +115,11 @@ def __init__(
         self.optimal_storage: Optional[VkStorageType] = optimal_storage
         self.optimal_layout: Optional[VkMemoryLayout] = optimal_layout
         self.handles_own_prepacking: bool = handles_own_prepacking
+
+        self.skip_limits_check: Set[int] = set()
+        if skip_limits_check is not None:
+            self.skip_limits_check = skip_limits_check
+
         self.check_node_fn: Callable = allow_node
         if check_node_fn is not None:
             self.check_node_fn = check_node_fn
@@ -433,6 +442,7 @@ def register_convolution_op(features: OpFeatures):
     features.optimal_storage = VkStorageType.TEXTURE_3D
     features.optimal_layout = VkMemoryLayout.TENSOR_CHANNELS_PACKED
     features.handles_own_prepacking = True
+    features.skip_limits_check = {1, 2}
     return features
 
 

backends/vulkan/partitioner/vulkan_partitioner.py

@@ -82,8 +82,13 @@ def op_node_is_compatible(
         valid_texture_layouts = utils.possible_node_memory_layouts(
             node, self.texture_limits
         )
-        for arg in node.args:
-            if isinstance(arg, torch.fx.Node) and utils.is_tensor_node(arg):
+
+        for i, arg in enumerate(node.args):
+            if (
+                isinstance(arg, torch.fx.Node)
+                and utils.is_tensor_node(arg)
+                and i not in features.skip_limits_check
+            ):
                 arg_texture_layouts = utils.possible_node_memory_layouts(
                     arg, self.texture_limits
                 )