[ET-VK][ez] Fix handling of assert ops

Pull Request resolved: #11258

## Changes

* Apply `RemoveAssertsTransform` as part of `vulkan_preprocess`
* Do not call `RemoveAssertsTransform` before lowering the graph
* Register ops related to asserts to the operator registry as ephemeral ops

## Motivation

assert ops are not implemented in Vulkan, so previously `RemoveAssertsTransform()` is called on the graph before the lowering process.

However, it turns out that the assertion ops are required to properly handle dynamic shapes, because they place constraints on the possible range of symbolic integers. If they are not present, then re-tracing the graph during a recompile (which may occur during a graph transform pass) may fail.

Therefore, instead of calling the transform before lowering, call it inside vulkan_preprocess after a point where subsequent passes will not attempt to trace the graph.
ghstack-source-id: 287878314
@exported-using-ghexport

Differential Revision: [D75686048](https://our.internmc.facebook.com/intern/diff/D75686048/)

Author: SS-JIA

backends/vulkan/_passes/fuse_quantized_ops.py

@@ -17,6 +17,7 @@
 from executorch.exir import ExportedProgram
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass, PassResult
+from executorch.exir.passes import dead_code_elimination_pass
 
 #################
 ## linear_qcnw ##
@@ -224,6 +225,8 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
                 )
 
         graph_module.recompile()
-        graph_module = super().call(graph_module).graph_module
+        dead_code_elimination_pass(graph_module)
 
+        # Re-trace the graph since new nodes were (potentially) inserted
+        graph_module = super().call(graph_module).graph_module
         return PassResult(graph_module, True)

backends/vulkan/_passes/tag_memory_meta_pass.py

@@ -5,7 +5,6 @@
 # LICENSE file in the root directory of this source tree.
 
 import logging
-from copy import deepcopy
 from typing import Any, Optional, Set
 
 import executorch.backends.vulkan.utils as utils

backends/vulkan/op_registry.py

@@ -231,6 +231,13 @@ def update_features_impl(op: OpKey):
         # Symbolic integer ops
         torch.ops.aten.sym_size.int,
         operator.add,
+        operator.lt,
+        operator.gt,
+        operator.ge,
+        operator.le,
+        # Guard and assert ops
+        torch.ops.aten._assert_scalar.default,
+        torch.ops.aten.sym_constrain_range_for_size.default,
     ]
 )
 def register_ephemeral_op(features: OpFeatures):

backends/vulkan/partitioner/vulkan_partitioner.py

@@ -146,10 +146,11 @@ def op_node_is_compatible(  # noqa: C901: Function is too complex
     def node_is_compatible(
         self, node: torch.fx.Node, features: Optional[OpFeatures] = None
     ) -> Tuple[bool, str]:
-        if utils.is_symint_node(node):
-            return node.target in vulkan_supported_ops, "Op is compatible"
-        elif utils.is_tensor_node(node):
+        if utils.is_tensor_node(node):
             return self.op_node_is_compatible(node, features=features)
+        # For non-tensor nodes, just check if the op is registered
+        elif hasattr(node, "target"):
+            return node.target in vulkan_supported_ops, "Op is compatible"
 
         return False, f"Unsupported node type: {node.format_node()}"
 

backends/vulkan/serialization/vulkan_graph_builder.py

@@ -353,7 +353,7 @@ def process_call_function_node(self, node) -> None:
                 # previously encountered, then use the existing Value id.
                 operator_call_args.append(self.get_or_create_value_for(function_arg))
         else:
-            for i, arg_node in enumerate(node.args):
+            for _, arg_node in enumerate(node.args):
                 operator_call_args.append(self.get_or_create_value_for(arg_node))
 
         # Add output node

backends/vulkan/vulkan_preprocess.py

@@ -29,6 +29,7 @@
     SqueezeUnsqueezeInputs,
     TagMemoryMetaPass,
 )
+from executorch.backends.vulkan._passes.remove_asserts import RemoveAssertsTransform
 
 from executorch.backends.vulkan.serialization.vulkan_graph_builder import VkGraphBuilder
 from executorch.backends.vulkan.serialization.vulkan_graph_schema import (
@@ -172,6 +173,7 @@ def preprocess(  # noqa: C901
         program = apply_passes(
             program,
             [
+                RemoveAssertsTransform(),
                 # Since this pass may replace a scalar argument with a tensor argument,
                 # this pass may result in a non ATen compliant graph structure.
                 RemoveLocalScalarDenseOpsTransform(),

examples/models/llama/TARGETS

@@ -148,7 +148,6 @@ runtime.python_library(
         ":source_transformation",
         "//ai_codesign/gen_ai/fast_hadamard_transform:fast_hadamard_transform",
         "//caffe2:torch",
-        "//executorch/backends/vulkan/_passes:vulkan_passes",
         "//executorch/exir/passes:init_mutable_pass",
         "//executorch/examples/models:model_base",
         "//executorch/examples/models:models",

examples/models/llama/export_llama_lib.py

@@ -24,7 +24,6 @@
 import pkg_resources
 import torch
 
-from executorch.backends.vulkan._passes.remove_asserts import remove_asserts
 from executorch.devtools.backend_debug import print_delegation_info
 
 from executorch.devtools.etrecord import generate_etrecord as generate_etrecord_func
@@ -880,9 +879,6 @@ def _to_edge_and_lower_llama(  # noqa: C901
         )
         modelname = f"vulkan_{modelname}"
 
-        # Need to remove asserts from the graph to prevent graph breaks
-        remove_asserts(builder_exported_to_edge.edge_manager.exported_program())
-
     if mps:
         partitioners.append(get_mps_partitioner(use_kv_cache))
         modelname = f"mps_{modelname}"