Fix bug in ScalarsToAttributePass

The pass should not modify the scalar argument if output is non-float.

Signed-off-by: Oscar Andersson <[email protected]>
Change-Id: I36f6975e8d6f33e5834e44959f6e426808452de1

Author: oscarandersson8218

backends/arm/_passes/cast_int64_pass.py

@@ -5,8 +5,15 @@
 
 # pyre-unsafe
 
+import logging
+
 import torch
+from executorch.backends.arm._passes.arm_pass_utils import is_param_node
 from executorch.exir.pass_base import ExportPass, PassResult
+from torch._export.utils import is_buffer
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
 
 
 class CastInt64ToInt32Pass(ExportPass):
@@ -18,17 +25,31 @@ def _to_int32(self, graph_module: torch.fx.GraphModule):
         for node in graph_module.graph.nodes:
             fake_tensor = node.meta["val"]
             if isinstance(fake_tensor, torch._subclasses.fake_tensor.FakeTensor):
-                if node.meta["val"].dtype == torch.int64:
-                    node.meta["val"] = node.meta["val"].to(torch.int32)
-                    buffer_name = (
-                        self.exported_program.graph_signature.inputs_to_buffers[
-                            node.name
-                        ]
-                    )
-                    new_tensor = self.exported_program.state_dict[buffer_name].to(
-                        torch.int32
-                    )
-                    self.exported_program.state_dict[buffer_name] = new_tensor
+                if node.meta["val"].dtype == torch.int64 and is_param_node(
+                    self.exported_program, node
+                ):
+                    if is_buffer(self.exported_program, node):
+                        node.meta["val"] = node.meta["val"].to(torch.int32)
+                        buffer_name = (
+                            self.exported_program.graph_signature.inputs_to_buffers[
+                                node.name
+                            ]
+                        )
+                        buffer = self.exported_program.state_dict[node.name]
+                        logger.warning(
+                            f"Casting buffer {node.name} from torch.int64 to torch.int32"
+                            f" defined in {node.meta['stack_trace']}"
+                        )
+                        if torch.min(buffer) < torch.iinfo(torch.int32).min:
+                            raise RuntimeError(
+                                f"Buffer {node.name} has value < {torch.iinfo(torch.int32).min}"
+                            )
+                        if torch.max(buffer) > torch.iinfo(torch.int32).max:
+                            raise RuntimeError(
+                                f"Buffer {node.name} has value > {torch.iinfo(torch.int32).max}"
+                            )
+                        buffer_int32 = buffer.to(torch.int32)
+                        self.exported_program.state_dict[buffer_name] = buffer_int32
 
     def call(self, graph_module: torch.fx.GraphModule):
         self._to_int32(graph_module)

backends/arm/_passes/scalars_to_attribute_pass.py

@@ -51,6 +51,11 @@ def call(self, graph_module: GraphModule) -> PassResult:
                 if isinstance(arg, Node):
                     new_args.append(arg)
                     continue
+                if isinstance(arg, int) and not torch.is_floating_point(
+                    get_first_fake_tensor(n)
+                ):
+                    new_args.append(arg)
+                    continue
 
                 prefix = "_tensor_constant_"
                 get_new_attr_name = get_new_attr_name_with_prefix(prefix)

backends/arm/test/ops/test_scalars.py

@@ -75,6 +75,12 @@ def forward(self, x):
             x = 1.0 + x
             return x
 
+    class ShiftInplaceSub(torch.nn.Module):
+        def forward(self, x):
+            x = x >> 4
+            x -= 10
+            return x
+
     # Inplace ops end with '_' (from aten naming)
     ops = [
         ("Add", Add()),
@@ -160,3 +166,6 @@ def test_MI_const(self, test_name: str, op: torch.nn.Module, x):
     @parameterized.expand(tensor_scalar_tests)
     def test_BI(self, test_name: str, op: torch.nn.Module, x, y):
         self._test_add_tosa_BI_pipeline(op, (x, y))
+
+    def test_shift_sub_inplace_tosa_MI(self):
+        self._test_add_tosa_MI_pipeline(self.ShiftInplaceSub(), (torch.IntTensor(5),))