diff --git a/imperative/python/megengine/utils/network_node.py b/imperative/python/megengine/utils/network_node.py
index b44897e9905c3b171efbc4db63e28652b071ac74..0aae12931eae9891a1a538570a77a641ed242d51 100644
--- a/imperative/python/megengine/utils/network_node.py
+++ b/imperative/python/megengine/utils/network_node.py
@@ -392,6 +392,14 @@ class MatrixMul(OpNode):
     type = "MatrixMul"
     opdef = builtin.MatrixMul
 
+    @classmethod
+    def load(cls, opr):
+        obj = super(MatrixMul, cls).load(opr)
+        dim1, dim2 = len(opr.inputs[0].shape), len(opr.inputs[1].shape)
+        obj.params["dimA"] = dim1
+        obj.params["dimB"] = dim2
+        return obj
+
 
 @register_flops(MatrixMul)
 def flops_matmul(opnode: MatrixMul, inputs, outputs):
@@ -431,6 +439,12 @@ class ConvolutionBackwardData(OpNode):
     type = "ConvTranspose"
     opdef = builtin.ConvolutionBackwardData
 
+    @classmethod
+    def load(cls, opr):
+        obj = super(ConvolutionBackwardData, cls).load(opr)
+        obj.params["dtype"] = opr.outputs[0].dtype
+        return obj
+
 
 class DeformableConvForward(OpNode):
     type = "DeformableConv"
diff --git a/imperative/python/test/unit/utils/test_network_node.py b/imperative/python/test/unit/utils/test_network_node.py
index fd2361e465217556b1be6fabbad7295c514ce8e2..57c5fd807e0ff8f8f3f0ea851b9048b250501406 100644
--- a/imperative/python/test/unit/utils/test_network_node.py
+++ b/imperative/python/test/unit/utils/test_network_node.py
@@ -1,6 +1,7 @@
 import io
 import os
 import platform
+from contextlib import contextmanager
 
 import numpy as np
 import pytest
@@ -12,6 +13,7 @@ import megengine.functional as F
 import megengine.module as M
 import megengine.random as rand
 from megengine.core._imperative_rt.core2 import apply
+from megengine.core._trace_option import set_symbolic_shape, use_symbolic_shape
 from megengine.core._wrap import Device
 from megengine.core.ops import builtin
 from megengine.device import (
@@ -26,6 +28,14 @@ from megengine.utils.comp_graph_tools import GraphInference
 from megengine.utils.network import Network as Net
 
 
+@contextmanager
+def override_symbolic_shape(enable: bool):
+    old = use_symbolic_shape()
+    set_symbolic_shape(enable)
+    yield
+    set_symbolic_shape(old)
+
+
 def check_pygraph_dump(trace_func, inp_data, expect_results, max_err=None):
     orig_model = io.BytesIO()
     inp_size = len(inp_data)
@@ -41,6 +51,16 @@ def check_pygraph_dump(trace_func, inp_data, expect_results, max_err=None):
     orig_model.seek(0)
 
     net = Net.load(orig_model)
+
+    # make a graph transform
+    with override_symbolic_shape(False):
+        old_inps = net.input_vars
+        new_inps = [
+            net.make_input_node(shape=inp.shape, dtype=inp.dtype, name=inp.name)
+            for inp in old_inps
+        ]
+        net.replace_vars(dict(zip(old_inps, new_inps)))
+
     file = io.BytesIO()
     net.dump(file, optimize_for_inference=False)
     file.seek(0)
@@ -207,6 +227,17 @@ def test_convtranspose():
     check_pygraph_dump(fwd, [data], [result], 5)
 
 
+def test_convtranspose_int8():
+    @trace(symbolic=True, capture_as_const=True)
+    def fwd(inp, weight):
+        return F.quantized.conv_transpose2d(inp, weight, dtype=dtype.qint8(scale=1.0))
+
+    inp = Tensor(np.random.random((1, 16, 64, 64)), dtype=dtype.qint8(scale=1.0))
+    weight = Tensor(np.random.random((16, 16, 4, 4)), dtype=dtype.qint8(scale=1.0))
+    result = fwd(inp, weight)
+    check_pygraph_dump(fwd, [inp, weight], [result])
+
+
 @pytest.mark.skip(reason="pytest aborted")
 def test_grouplocal():
     n = M.LocalConv2d(3, 32, 32, 32, 3)