[Vulkan] Keep unrepresentable and symbolic scalars out of the delegate

backends/vulkan/op_registry.py

@@ -6,6 +6,7 @@
 
 # pyre-unsafe
 
+import math
 import operator
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
@@ -318,13 +319,26 @@ def register_bool_binary_ops():
 # =============================================================================
 
 
+def is_scalar_value_supported(value: Any, dtype: torch.dtype) -> bool:
+    if type(value) not in (bool, int, float):
+        return False
+    if isinstance(value, float) and math.isnan(value):
+        return False
+    if dtype in utils.INT_T:
+        return -(2**31) <= value <= 2**31 - 1
+    return True
+
+
 @update_features(exir_ops.edge.aten.pow.Tensor_Scalar)
-def register_pow_tensor_scalar():
+def register_binary_scalar_ops():
     return OpFeatures(
         inputs_storage=utils.ANY_STORAGE,
         inputs_dtypes=utils.FP_T,
         supports_resize=True,
         supports_highdim=True,
+        are_node_inputs_supported_fn=lambda node: is_scalar_value_supported(
+            node.args[1], node.meta["val"].dtype
+        ),
     )
 
 
@@ -336,6 +350,9 @@ def register_eq_scalar():
         outputs_dtypes=utils.BOOL_T,
         supports_resize=True,
         supports_highdim=True,
+        are_node_inputs_supported_fn=lambda node: is_scalar_value_supported(
+            node.args[1], node.args[0].meta["val"].dtype
+        ),
     )
 
 
@@ -1882,6 +1899,9 @@ def register_compare_scalar_ops():
         outputs_dtypes=utils.BOOL_T,
         supports_resize=True,
         supports_highdim=True,
+        are_node_inputs_supported_fn=lambda node: is_scalar_value_supported(
+            node.args[1], node.args[0].meta["val"].dtype
+        ),
     )
 
 

backends/vulkan/partitioner/vulkan_partitioner.py

@@ -87,6 +87,7 @@ def __init__(
 
         self.nn_module_blocklist = nn_module_blocklist
         self.nn_module_allowlist = nn_module_allowlist
+        self._node_support: Dict[torch.fx.Node, bool] = {}
 
     def op_node_is_compatible(  # noqa: C901: Function is too complex
         self, node: torch.fx.Node, features: Optional[OpFeatures] = None
@@ -198,10 +199,27 @@ def log_skip(self, node: torch.fx.Node, reason: str) -> None:
     def is_node_supported(
         self, submodules: Mapping[str, torch.nn.Module], node: torch.fx.Node
     ) -> bool:
-        r = self._is_node_supported(node)
-        return r
+        return self._is_node_supported(node)
+
+    def _is_node_supported(self, node: torch.fx.Node) -> bool:
+        if node not in self._node_support:
+            self._node_support[node] = self._check_node_support(node)
+        return self._node_support[node]
+
+    def _check_node_support(self, node: torch.fx.Node) -> bool:  # noqa: C901
+        if any(
+            isinstance(arg.meta.get("val"), (torch.SymFloat, torch.SymBool))
+            for arg in [node, *node.all_input_nodes]
+        ):
+            self.log_skip(node, "symbolic float or bool values are not supported")
+            return False
+
+        if utils.is_symint_node(node) and any(
+            not self._is_node_supported(user) for user in node.users
+        ):
+            self.log_skip(node, "symbolic scalar has an unsupported consumer")
+            return False
 
-    def _is_node_supported(self, node: torch.fx.Node) -> bool:  # noqa: C901
         # Check if tensor node dtype is supported by vulkan
         if utils.is_tensor_node(node) and not utils.io_dtypes_are_supported(node):
             self.log_skip(node, "dtype not supported")

backends/vulkan/test/test_vulkan_dynamic.py

@@ -15,6 +15,8 @@
 
 import torch
 
+import torch.nn.functional as F
+
 from executorch.backends.vulkan.partitioner.vulkan_partitioner import VulkanPartitioner
 
 from executorch.backends.vulkan.serialization.vulkan_graph_schema import (
@@ -203,6 +205,125 @@ def test_constant_bool_mask(self):
                 )
                 self._run(edge, model, inputs, atol=0, rtol=0)
 
+    def test_nan_scalars_fall_back(self):
+        class NanScalar(torch.nn.Module):
+            def __init__(self, kind):
+                super().__init__()
+                self.kind = kind
+
+            def forward(self, x):
+                if self.kind == "where":
+                    return torch.where(x > 0, x, torch.nan)
+                if self.kind == "masked_fill":
+                    return x.masked_fill(x > 0, torch.nan)
+                if self.kind == "full":
+                    return torch.full_like(x, torch.nan)
+                if self.kind == "scalar_tensor":
+                    return torch.scalar_tensor(torch.nan)
+                if self.kind == "pow":
+                    return x**torch.nan
+                return torch.ops.aten.mul.Scalar(x, torch.nan)
+
+        inputs = [(torch.tensor([-1.0, 0.0, 1.0, 2.0]),)]
+        for kind in ("where", "masked_fill", "full", "scalar_tensor", "pow", "mul"):
+            with self.subTest(kind=kind):
+                model = NanScalar(kind)
+                edge = self._lower(model, inputs[0], fully_delegated=False)
+                self._run(edge, model, inputs, atol=0, rtol=0, equal_nan=True)
+
+    def test_dynamic_scalar_values_fall_back(self):
+        class DynamicScalars(torch.nn.Module):
+            def forward(self, x):
+                n = x.shape[0]
+                value = n * 2
+                return (
+                    x**value,
+                    torch.ops.aten.mul.Scalar(x, value),
+                    torch.full((n,), value),
+                    torch.scalar_tensor(value, dtype=torch.int64),
+                    torch.ops.aten.mul.Scalar(x, n * 0.5),
+                    x + torch.full((n,), n * 0.5),
+                    torch.full((n,), 0.5),
+                    F.gelu(x),
+                    torch.clamp(x, max=n * 0.5),
+                    F.leaky_relu(x, negative_slope=n * 0.1),
+                )
+
+        model = DynamicScalars()
+        inputs = [(torch.linspace(-0.9, 4.1, n),) for n in (4, 2, 7, 3, 4)]
+        edge = self._lower(
+            model, inputs[0], ({0: Dim("n", min=2, max=8)},), fully_delegated=False
+        )
+        self.assertTrue(_vulkan_graphs(edge))
+        self._run(edge, model, inputs)
+
+    def test_dynamic_compare_scalars_fall_back(self):
+        class Compare(torch.nn.Module):
+            def __init__(self, op):
+                super().__init__()
+                self.op = op
+
+            def forward(self, x):
+                return self.op(x, x.shape[1])
+
+        inputs = [
+            (torch.arange(2 * s, dtype=torch.float32).reshape(2, s),)
+            for s in (16, 3, 31, 2, 16)
+        ]
+        for op in (torch.eq, torch.ne, torch.lt, torch.le, torch.gt, torch.ge):
+            with self.subTest(op=op):
+                model = Compare(op)
+                edge = self._lower(
+                    model,
+                    inputs[0],
+                    ({1: Dim("s", min=2, max=32)},),
+                    fully_delegated=False,
+                )
+                self.assertEqual(_vulkan_graphs(edge), [])
+                self._run(edge, model, inputs, atol=0, rtol=0)
+
+    def test_compare_scalar_values_fall_back(self):
+        class Compare(torch.nn.Module):
+            def __init__(self, op, value):
+                super().__init__()
+                self.op = op
+                self.value = value
+
+            def forward(self, x):
+                return self.op(x, self.value)
+
+        for op in (torch.eq, torch.ne, torch.lt, torch.le, torch.gt, torch.ge):
+            for x, value in (
+                (torch.tensor([-1.0, 0.0, 1.0, 2.0]), torch.nan),
+                (torch.tensor([-3, 0, 1, 7], dtype=torch.int32), 2**40),
+                (torch.tensor([-(2**40), 0, 2**40, 2**40 + 1]), 2**40),
+            ):
+                with self.subTest(op=op, dtype=x.dtype, value=value):
+                    model = Compare(op, value)
+                    inputs = [(x,)]
+                    edge = self._lower(model, inputs[0], fully_delegated=False)
+                    self.assertEqual(_vulkan_graphs(edge), [])
+                    self._run(edge, model, inputs, atol=0, rtol=0)
+
+    def test_compare_scalar_values(self):
+        class Compare(torch.nn.Module):
+            def __init__(self, op, value):
+                super().__init__()
+                self.op = op
+                self.value = value
+
+            def forward(self, x):
+                return self.op(x, self.value)
+
+        for op in (torch.eq, torch.ne, torch.lt, torch.le, torch.gt, torch.ge):
+            for dtype in (torch.int32, torch.float32):
+                for value in (2.0, 2.5, -1.5):
+                    with self.subTest(op=op, dtype=dtype, value=value):
+                        x = torch.arange(-7, 14, dtype=dtype).reshape(3, 7)
+                        model = Compare(op, value)
+                        edge = self._lower(model, (x,))
+                        self._run(edge, model, [(x,)], atol=0, rtol=0)
+
     def test_4d_reductions(self):
         class Reduce(torch.nn.Module):
             def __init__(self, op, dim):