[Vulkan] Delegate aten.mul.Scalar

backends/vulkan/op_registry.py

@@ -339,7 +339,12 @@ def is_scalar_value_supported(value: Any, dtype: torch.dtype) -> bool:
     return True
 
 
-@update_features(exir_ops.edge.aten.pow.Tensor_Scalar)
+@update_features(
+    [
+        exir_ops.edge.aten.pow.Tensor_Scalar,
+        exir_ops.edge.aten.mul.Scalar,
+    ]
+)
 def register_binary_scalar_ops():
     return OpFeatures(
         inputs_storage=utils.ANY_STORAGE,

backends/vulkan/runtime/graph/ops/glsl/binary_scalar_buffer.yaml

@@ -21,6 +21,8 @@ binary_scalar_buffer:
        - parameter_values: [int32, float]
   shader_variants:
     - NAME: pow_scalar_buffer
+    - NAME: mul_scalar_buffer
+      OPERATOR: X * Y
     - NAME: eq_scalar_buffer
       OPERATOR: X == Y
       IS_COMPARISON_OP: true

backends/vulkan/runtime/graph/ops/glsl/binary_scalar_texture.yaml

@@ -21,6 +21,8 @@ binary_scalar_texture:
        - parameter_values: [int32, float]
   shader_variants:
     - NAME: pow_scalar_texture3d
+    - NAME: mul_scalar_texture3d
+      OPERATOR: X * Y
     - NAME: eq_scalar_texture3d
       OPERATOR: equal(X, Y)
       IS_COMPARISON_OP: true

backends/vulkan/runtime/graph/ops/impl/BinaryScalarOp.cpp

@@ -123,6 +123,10 @@ void pow_tensor_scalar(ComputeGraph& graph, const std::vector<ValueRef>& args) {
   return add_binary_scalar_op_node(graph, args[0], args[1], args[2], "pow");
 }
 
+void mul_tensor_scalar(ComputeGraph& graph, const std::vector<ValueRef>& args) {
+  return add_binary_scalar_op_node(graph, args[0], args[1], args[2], "mul");
+}
+
 void eq_tensor_scalar(ComputeGraph& graph, const std::vector<ValueRef>& args) {
   return add_binary_scalar_op_node(graph, args[0], args[1], args[2], "eq");
 }
@@ -149,6 +153,7 @@ void ge_tensor_scalar(ComputeGraph& graph, const std::vector<ValueRef>& args) {
 
 REGISTER_OPERATORS {
   VK_REGISTER_OP(aten.pow.Tensor_Scalar, pow_tensor_scalar);
+  VK_REGISTER_OP(aten.mul.Scalar, mul_tensor_scalar);
   VK_REGISTER_OP(aten.eq.Scalar, eq_tensor_scalar);
   VK_REGISTER_OP(aten.ne.Scalar, ne_tensor_scalar);
   VK_REGISTER_OP(aten.lt.Scalar, lt_tensor_scalar);

backends/vulkan/test/op_tests/cases.py

@@ -2271,8 +2271,8 @@ def get_index_tensor_inputs():
     return test_suite
 
 
-@register_test_suite("aten.pow.Tensor_Scalar")
-def get_pow_tensor_scalar_inputs():
+@register_test_suite(["aten.pow.Tensor_Scalar", "aten.mul.Scalar"])
+def get_binary_scalar_inputs():
     test_suite = VkTestSuite(
         [
             ((M1,), 2.0),

backends/vulkan/test/test_vulkan_dynamic.py

@@ -362,6 +362,57 @@ def forward(self, x):
                     )
                     self._run(edge, model, inputs, atol=0, rtol=0)
 
+    def test_signed_zero_scalars(self):
+        class SignedZero(torch.nn.Module):
+            def forward(self, x):
+                return (
+                    torch.ops.aten.mul.Scalar(x, -0.0),
+                    torch.full_like(x, -0.0),
+                    torch.scalar_tensor(-0.0, dtype=x.dtype),
+                )
+
+        model = SignedZero()
+        for dtype in (torch.float32, torch.float16):
+            for storage in (VkStorageType.TEXTURE_3D, VkStorageType.BUFFER):
+                with self.subTest(dtype=dtype, storage=storage):
+                    inputs = [(torch.arange(-7, 14, dtype=dtype).reshape(3, 7),)]
+                    edge = self._lower(
+                        model, inputs[0], storage=storage, fully_delegated=False
+                    )
+                    self.assertTrue(_vulkan_graphs(edge))
+                    self.assertTrue(
+                        all(
+                            node.target
+                            in (
+                                operator.getitem,
+                                torch.ops.higher_order.executorch_call_delegate,
+                            )
+                            for node in edge.exported_program().graph.nodes
+                            if node.op == "call_function"
+                        )
+                    )
+                    self._run(
+                        edge, model, inputs, atol=0, rtol=0, check_signed_zero=True
+                    )
+
+    def test_fp16_chained_mul_scalar(self):
+        class ChainedMul(torch.nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.mul.Scalar(
+                    torch.ops.aten.mul.Scalar(x, 1.0006), 1000
+                )
+
+        model = ChainedMul()
+        x = torch.ones(3, 7, dtype=torch.float16)
+        for storage in (VkStorageType.TEXTURE_3D, VkStorageType.BUFFER):
+            with self.subTest(storage=storage):
+                edge = self._lower(model, (x,), storage=storage)
+                operators = [
+                    op.name for graph in _vulkan_graphs(edge) for op in graph.chain
+                ]
+                self.assertEqual(operators.count("aten.mul.Scalar"), 2)
+                self._run(edge, model, [(x,)], atol=0, rtol=0)
+
     def test_integer_scalar_range_fallback(self):
         class LargeScalar(torch.nn.Module):
             def __init__(self, kind, value):