feat(imperative/jit): catch input tensors name when tracing

GitOrigin-RevId: 9c692548663654265f9f9e2753f8637d444cb78d

imperative/python/megengine/jit/tracing.py

@@ -772,7 +772,8 @@ class trace:
                     len(self._output_bindings)
                 )
             )
-        if arg_names is None:
+        without_arg_names = arg_names is None
+        if without_arg_names:
             arg_names = ["arg_%d" % i for i in range(len(self._arg_bindings))]
         if arg_names and not isinstance(arg_names, collections.abc.Sequence):
             arg_names = (arg_names,)
@@ -802,7 +803,7 @@ class trace:
                 dtype=info.dtype,
                 device=dumped_device(info),
                 shape=info.shape or (1,),
-                name=arg_names[i] if arg_names else None,
+                name=info.name if without_arg_names and info.name else arg_names[i],
             )
         for k, h in self._kwarg_bindings.items():
             info = self._tinfo[h]
@@ -889,6 +890,7 @@ class trace:
                     return
                 h, info = self._new_handle()
                 info.external = False
+                info.name = x.c_name
                 info.device = x.device
                 info.dtype = x.dtype
                 info.shape = x.numpy().shape

imperative/python/test/unit/utils/test_dump_naming.py

@@ -203,14 +203,31 @@ def test_with_same_operators(symbolic):
     assert ops[-2].name == "simple.RELU[0]"
 
 
-def test_not_keep_opr_name():
+@pytest.mark.parametrize("symbolic", [False, True])
+def test_not_keep_opr_name(symbolic):
     def f(x):
         return 2 * x
 
-    op = _dump_and_load(f, True, False)[-1]
+    op = _dump_and_load(f, symbolic, False)[-1]
     assert op.name == "MUL(x,const<2>[2])[4]"
 
 
+@pytest.mark.parametrize("tensor_name, var_name", [("data", "data"), (None, "arg_0")])
+def test_catch_input_name(tensor_name, var_name):
+    def f(x):
+        return 2 * x
+
+    func = trace(f, symbolic=True, capture_as_const=True)
+    x = Tensor(np.ones(shape=(2, 3)), name=tensor_name)
+    func(x).numpy()
+    file = io.BytesIO()
+    func.dump(file, optimize_for_inference=False, keep_opr_name=True, keep_var_name=2)
+    file.seek(0)
+    *_, outputs = G.load_graph(file)
+    op = cgtools.get_oprs_seq(outputs)[-1]
+    assert op.inputs[0].name == var_name
+
+
 @pytest.mark.parametrize("symbolic", [False, True])
 def test_quantized_module_auto_naming(symbolic):
     class Simple(M.Module):