[Fx Importer] fix mutation importer with non persistent buffer (#3798)

A non-persistent buffer will not be a part of this module’s
`state_dict`. Hence when setting `experimental_support_mutation=True`
and have non-persistent buffer, the current fx importer will fail to
retrieve a value from `state_dict` and produce `torch.constant.none` to
represent the buffer. This fix get value of non-persistent buffer from
the module's `constants`.

---------

Co-authored-by: Dixin Zhou <dzhou@vdi-ahddp-020.dhcp.mathworks.com>

python/torch_mlir/extras/fx_importer.py

@@ -723,10 +723,17 @@ class FxImporter:
                 # on a symbolic or other non-SSA association. As such, they
                 # are not modeled with mutable IR but will trigger an output
                 # store hook when the final value is produced.
+                if input_spec.persistent:
                     value = prog.state_dict.get(input_spec.target)
                     assert (
-                    not input_spec.persistent or value is not None
-                ), "Expected state_dict value for persistent value"
+                        value is not None
+                    ), "Expected state_dict value for persistent buffer"
+                else:
+                    value = prog.constants.get(input_spec.target)
+                    assert (
+                        value is not None
+                    ), "Expected constants value for non-persistent buffer"
+
                 node = placeholder_nodes[arg.name]
                 mutable_producer_node_name = mutable_buffer_target_producers.get(
                     input_spec.target

test/python/fx_importer/v2.3/mutation_import.py

@@ -107,6 +107,27 @@ def test_frozen_buffer():
     m.operation.verify()
 
 
+@run
+# CHECK-LABEL: test_frozen_buffer_non_persistent
+# CHECK: %[[buffer_literal:.+]] = torch.vtensor.literal
+# CHECK: %[[mul:.+]] = torch.aten.mul.Tensor %arg0, %0
+# CHECK: return %[[mul]]
+def test_frozen_buffer_non_persistent():
+    class Basic(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.register_buffer("buffer", torch.randn(3, 4), persistent=False)
+
+        def forward(self, x):
+            return x * self.buffer
+
+    m = fx.export_and_import(
+        Basic(), torch.randn(3, 4), experimental_support_mutation=True
+    )
+    print(m)
+    m.operation.verify()
+
+
 class ExternalBufferHooks(fx.FxImporterHooks):
     def prepare_module(self, module_op: Operation):
         module_op.context.allow_unregistered_dialects = True