[BugFix][Runtime] Fix Incorrect node information (#13693)

* [BugFix][Runtime] Fix Incorrect node information

* 1

* 1

python/tvm/contrib/debugger/debug_result.py

@@ -73,21 +73,25 @@ def _update_graph_json(self):
         """update the nodes_list with name, shape and data type,
         for temporarily storing the output.
         """
-        nodes_len = len(self._nodes_list)
-        for i in range(nodes_len):
-            node = self._nodes_list[i]
+        eid = 0
+        for node in self._nodes_list:
             input_list = []
-            for input_node in node["inputs"]:
-                input_list.append(self._nodes_list[input_node[0]]["name"])
-            node["inputs"] = input_list
-            dtype = str("type: " + self._dtype_list[1][i])
-            if "attrs" not in node:
+            if node["op"] == "null":
                 node["attrs"] = {}
                 node["op"] = "param"
-            else:
+                num_outputs = 1
+            elif node["op"] == "tvm_op":
+                for input_node in node["inputs"]:
+                    input_list.append(self._nodes_list[input_node[0]]["name"])
                 node["op"] = node["attrs"]["func_name"]
+                num_outputs = int(node["attrs"]["num_outputs"])
+            else:
+                raise ValueError("")
+            node["inputs"] = input_list
+            dtype = str("type: " + self._dtype_list[1][eid])
             node["attrs"].update({"T": dtype})
-            node["shape"] = self._shapes_list[1][i]
+            node["shape"] = self._shapes_list[1][eid]
+            eid += num_outputs
 
     def _cleanup_tensors(self):
         """Remove the tensor dump file (graph wont be removed)"""

tests/python/unittest/test_runtime_graph_debug.py

@@ -29,7 +29,7 @@
 from tvm._ffi.base import TVMError
 from tvm.contrib import utils
 from tvm.contrib.debugger import debug_executor
-
+from tvm import relay
 
 # Constants for creating simple graphs, fixtures to avoid free globals
 @pytest.fixture
@@ -275,5 +275,29 @@ def test_run_single_node(graph, n, A, myadd):
         mod.run_individual_node(2)
 
 
+@tvm.testing.requires_llvm
+def test_multiple_output():
+    x = relay.var("x", shape=(1, 3, 48, 16), dtype="float32")
+    t = relay.split(x, [12, 16, 32], 2).astuple()
+    x0 = relay.TupleGetItem(t, 0)
+    x1 = relay.TupleGetItem(t, 1)
+    x2 = relay.TupleGetItem(t, 2)
+    x3 = relay.TupleGetItem(t, 3)
+    p0 = relay.const(np.random.uniform(-1, 1, (3, 3, 1, 1)).astype("float32"))
+    y = relay.nn.conv2d(x2, p0, kernel_size=(1, 1), kernel_layout="OIHW", out_dtype="float32") + x3
+
+    func = relay.Function([x], relay.Tuple([x0, x1, y]))
+    mod = tvm.IRModule.from_expr(func)
+    mod = relay.transform.InferType()(mod)
+    target = tvm.target.Target("llvm")
+    device = tvm.cpu()
+    lib = relay.build(mod, target=target)
+    m = debug_executor.GraphModuleDebug(
+        lib["debug_create"]("default", device), [device], lib.get_graph_json(), None
+    )
+    nodes = m.debug_datum.get_graph_nodes()
+    assert nodes[2]["shape"] == [3, 3, 1, 1]
+
+
 if __name__ == "__main__":
     tvm.testing.main()