diff --git a/docs/Operators.md b/docs/Operators.md
index 47acf23822c..27096fcef0c 100644
--- a/docs/Operators.md
+++ b/docs/Operators.md
@@ -2492,6 +2492,42 @@ This version of the operator has been available since version 10 of the default
 </dl>
 
 
+#### Examples
+
+<details>
+<summary>convinteger</summary>
+
+```python
+
+x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+x_zero_point = np.array([1]).astype(np.uint8)
+w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
+
+# ConvInteger without padding
+convinteger_node = onnx.helper.make_node('ConvInteger',
+    inputs=['x', 'w', 'x_zero_point'],
+    outputs=['y'])
+
+expect(convinteger_node, inputs=[x, w, x_zero_point], outputs=[y],
+       name='test_basic_convinteger')
+
+# ConvInteger with padding
+y_with_padding = np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9]).astype(np.int32).reshape((1, 1, 4, 4))
+
+convinteger_node_with_padding = onnx.helper.make_node('ConvInteger',
+    inputs=['x', 'w', 'x_zero_point'],
+    outputs=['y'],
+    pads=[1, 1, 1, 1],)
+
+expect(convinteger_node_with_padding, inputs=[x, w, x_zero_point], outputs=[y_with_padding],
+       name='test_convinteger_with_padding')
+```
+
+</details>
+
+
 ### <a name="ConvTranspose"></a><a name="convtranspose">**ConvTranspose**</a>
 
   The convolution transpose operator consumes an input tensor and a filter,
@@ -3105,6 +3141,29 @@ This version of the operator has been available since version 10 of the default
 </dl>
 
 
+#### Examples
+
+<details>
+<summary>dequantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('DequantizeLinear',
+    inputs=['x', 'x_scale', 'x_zero_point'],
+    outputs=['y'],)
+
+# scalar zero point and scale
+x = np.array([0, 3, 128, 255]).astype(np.uint8)
+x_scale = np.array([2], dtype=np.float32)
+x_zero_point = np.array([128], dtype=np.uint8)
+y = np.array([-256, -250, 0, 254], dtype=np.float32)
+
+expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+       name='test_dequantizelinear')
+```
+
+</details>
+
+
 ### <a name="Div"></a><a name="div">**Div**</a>
 
   Performs element-wise binary division (with Numpy-style broadcasting support).
@@ -6319,6 +6378,41 @@ This version of the operator has been available since version 10 of the default
 </dl>
 
 
+#### Examples
+
+<details>
+<summary>matmulinteger</summary>
+
+```python
+node = onnx.helper.make_node('MatMulInteger',
+    inputs=['A', 'B', 'a_zero_point', 'b_zero_point'],
+    outputs=['Y'],)
+
+A = np.array([[11, 7, 3],
+    [10, 6, 2],
+    [9, 5, 1],
+    [8, 4, 0], ], dtype=np.uint8)
+
+a_zero_point = np.array([12], dtype=np.uint8)
+
+B = np.array([[1, 4],
+    [2, 5],
+    [3, 6], ], dtype=np.uint8)
+
+b_zero_point = np.array([0], dtype=np.uint8)
+
+output = np.array([[-38, -83],
+    [-44, -98],
+    [-50, -113],
+    [-56, -128], ], dtype=np.int32)
+
+expect(node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output],
+       name='test_matmulinteger')
+```
+
+</details>
+
+
 ### <a name="Max"></a><a name="max">**Max**</a>
 
   Element-wise max of each of the input tensors (with Numpy-style broadcasting support).
@@ -8222,6 +8316,50 @@ This version of the operator has been available since version 10 of the default
 </dl>
 
 
+#### Examples
+
+<details>
+<summary>qlinearconv</summary>
+
+```python
+node = onnx.helper.make_node('QLinearConv',
+    inputs=['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+x = np.array([[255, 174, 162, 25, 203, 168, 58],
+    [15, 59, 237, 95, 129, 0, 64],
+    [56, 242, 153, 221, 168, 12, 166],
+    [232, 178, 186, 195, 237, 162, 237],
+    [188, 39, 124, 77, 80, 102, 43],
+    [127, 230, 21, 83, 41, 40, 134],
+    [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+x_scale = np.array([0.00369204697], dtype=np.float32)
+x_zero_point = np.array([132], dtype=np.uint8)
+
+w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
+
+w_scale = np.array([0.00172794575], dtype=np.float32)
+w_zero_point = np.array([255], dtype=np.uint8)
+
+y_scale = np.array([0.00162681262], dtype=np.float32)
+y_zero_point = np.array([123], dtype=np.uint8)
+
+output = np.array([[0, 81, 93, 230, 52, 87, 197],
+    [240, 196, 18, 160, 126, 255, 191],
+    [199, 13, 102, 34, 87, 243, 89],
+    [23, 77, 69, 60, 18, 93, 18],
+    [67, 216, 131, 178, 175, 153, 212],
+    [128, 25, 234, 172, 214, 215, 121],
+    [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+expect(node, inputs=[x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearconv')
+```
+
+</details>
+
+
 ### <a name="QLinearMatMul"></a><a name="qlinearmatmul">**QLinearMatMul**</a>
 
   Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
@@ -8277,6 +8415,76 @@ This version of the operator has been available since version 10 of the default
 </dl>
 
 
+#### Examples
+
+<details>
+<summary>qlinearmatmul</summary>
+
+```python
+node = onnx.helper.make_node('QLinearMatMul',
+    inputs=['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+#2D
+a = np.array([[208, 236, 0, 238],
+    [3, 214, 255, 29], ], dtype=np.uint8)
+
+a_scale = np.array([0.0066], dtype=np.float32)
+a_zero_point = np.array([113], dtype=np.uint8)
+
+b = np.array([[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]], dtype=np.uint8)
+
+b_scale = np.array([0.00705], dtype=np.float32)
+b_zero_point = np.array([114], dtype=np.uint8)
+
+y_scale = np.array([0.0107], dtype=np.float32)
+y_zero_point = np.array([118], dtype=np.uint8)
+
+output = np.array([[168, 115, 255],
+    [1, 66, 151], ], dtype=np.uint8)
+
+expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearmatmul_2D')
+
+#3D
+a = np.array([[[208, 236, 0, 238],
+    [3, 214, 255, 29]],
+    [[208, 236, 0, 238],
+    [3, 214, 255, 29]]], dtype=np.uint8)
+
+a_scale = np.array([0.0066], dtype=np.float32)
+a_zero_point = np.array([113], dtype=np.uint8)
+
+b = np.array([[[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]],
+    [[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]]], dtype=np.uint8)
+
+b_scale = np.array([0.00705], dtype=np.float32)
+b_zero_point = np.array([114], dtype=np.uint8)
+
+y_scale = np.array([0.0107], dtype=np.float32)
+y_zero_point = np.array([118], dtype=np.uint8)
+
+output = np.array([[[168, 115, 255],
+    [1, 66, 151]],
+    [[168, 115, 255],
+    [1, 66, 151]]], dtype=np.uint8)
+
+expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearmatmul_3D')
+```
+
+</details>
+
+
 ### <a name="QuantizeLinear"></a><a name="quantizelinear">**QuantizeLinear**</a>
 
   The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor.
@@ -8315,6 +8523,28 @@ This version of the operator has been available since version 10 of the default
 </dl>
 
 
+#### Examples
+
+<details>
+<summary>quantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('QuantizeLinear',
+    inputs=['x', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
+y_scale = np.array([2], dtype=np.float32)
+y_zero_point = np.array([128], dtype=np.uint8)
+y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
+
+expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+       name='test_quantizelinear')
+```
+
+</details>
+
+
 ### <a name="RNN"></a><a name="rnn">**RNN**</a>
 
   Computes an one-layer simple RNN. This operator is usually supported
diff --git a/docs/TestCoverage.md b/docs/TestCoverage.md
index 365d40d2c3b..b95aa62c0de 100644
--- a/docs/TestCoverage.md
+++ b/docs/TestCoverage.md
@@ -5,7 +5,7 @@
 * [Overall Test Coverage](#overall-test-coverage)
 # Node Test Coverage
 ## Summary
-Node tests have covered 115/128 (89.84%, 5 generators excluded) common operators.
+Node tests have covered 121/128 (94.53%, 5 generators excluded) common operators.
 
 Node tests have covered 0/0 (N/A) experimental operators.
 
@@ -1379,6 +1379,42 @@ expect(node_with_asymmetric_padding, inputs=[x, W], outputs=[y_with_asymmetric_p
 </details>
 
 
+### ConvInteger
+There are 1 test cases, listed as following:
+<details>
+<summary>convinteger</summary>
+
+```python
+
+x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+x_zero_point = np.array([1]).astype(np.uint8)
+w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
+
+# ConvInteger without padding
+convinteger_node = onnx.helper.make_node('ConvInteger',
+    inputs=['x', 'w', 'x_zero_point'],
+    outputs=['y'])
+
+expect(convinteger_node, inputs=[x, w, x_zero_point], outputs=[y],
+       name='test_basic_convinteger')
+
+# ConvInteger with padding
+y_with_padding = np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9]).astype(np.int32).reshape((1, 1, 4, 4))
+
+convinteger_node_with_padding = onnx.helper.make_node('ConvInteger',
+    inputs=['x', 'w', 'x_zero_point'],
+    outputs=['y'],
+    pads=[1, 1, 1, 1],)
+
+expect(convinteger_node_with_padding, inputs=[x, w, x_zero_point], outputs=[y_with_padding],
+       name='test_convinteger_with_padding')
+```
+
+</details>
+
+
 ### ConvTranspose
 There are 6 test cases, listed as following:
 <details>
@@ -1775,6 +1811,29 @@ expect(node, inputs=[x], outputs=[y],
 </details>
 
 
+### DequantizeLinear
+There are 1 test cases, listed as following:
+<details>
+<summary>dequantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('DequantizeLinear',
+    inputs=['x', 'x_scale', 'x_zero_point'],
+    outputs=['y'],)
+
+# scalar zero point and scale
+x = np.array([0, 3, 128, 255]).astype(np.uint8)
+x_scale = np.array([2], dtype=np.float32)
+x_zero_point = np.array([128], dtype=np.uint8)
+y = np.array([-256, -250, 0, 254], dtype=np.float32)
+
+expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+       name='test_dequantizelinear')
+```
+
+</details>
+
+
 ### Div
 There are 2 test cases, listed as following:
 <details>
@@ -3156,6 +3215,41 @@ expect(node, inputs=[a, b], outputs=[c],
 </details>
 
 
+### MatMulInteger
+There are 1 test cases, listed as following:
+<details>
+<summary>matmulinteger</summary>
+
+```python
+node = onnx.helper.make_node('MatMulInteger',
+    inputs=['A', 'B', 'a_zero_point', 'b_zero_point'],
+    outputs=['Y'],)
+
+A = np.array([[11, 7, 3],
+    [10, 6, 2],
+    [9, 5, 1],
+    [8, 4, 0], ], dtype=np.uint8)
+
+a_zero_point = np.array([12], dtype=np.uint8)
+
+B = np.array([[1, 4],
+    [2, 5],
+    [3, 6], ], dtype=np.uint8)
+
+b_zero_point = np.array([0], dtype=np.uint8)
+
+output = np.array([[-38, -83],
+    [-44, -98],
+    [-50, -113],
+    [-56, -128], ], dtype=np.int32)
+
+expect(node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output],
+       name='test_matmulinteger')
+```
+
+</details>
+
+
 ### Max
 There are 1 test cases, listed as following:
 <details>
@@ -4208,6 +4302,142 @@ expect(node, inputs=[x, y], outputs=[z],
 </details>
 
 
+### QLinearConv
+There are 1 test cases, listed as following:
+<details>
+<summary>qlinearconv</summary>
+
+```python
+node = onnx.helper.make_node('QLinearConv',
+    inputs=['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+x = np.array([[255, 174, 162, 25, 203, 168, 58],
+    [15, 59, 237, 95, 129, 0, 64],
+    [56, 242, 153, 221, 168, 12, 166],
+    [232, 178, 186, 195, 237, 162, 237],
+    [188, 39, 124, 77, 80, 102, 43],
+    [127, 230, 21, 83, 41, 40, 134],
+    [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+x_scale = np.array([0.00369204697], dtype=np.float32)
+x_zero_point = np.array([132], dtype=np.uint8)
+
+w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
+
+w_scale = np.array([0.00172794575], dtype=np.float32)
+w_zero_point = np.array([255], dtype=np.uint8)
+
+y_scale = np.array([0.00162681262], dtype=np.float32)
+y_zero_point = np.array([123], dtype=np.uint8)
+
+output = np.array([[0, 81, 93, 230, 52, 87, 197],
+    [240, 196, 18, 160, 126, 255, 191],
+    [199, 13, 102, 34, 87, 243, 89],
+    [23, 77, 69, 60, 18, 93, 18],
+    [67, 216, 131, 178, 175, 153, 212],
+    [128, 25, 234, 172, 214, 215, 121],
+    [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+expect(node, inputs=[x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearconv')
+```
+
+</details>
+
+
+### QLinearMatMul
+There are 1 test cases, listed as following:
+<details>
+<summary>qlinearmatmul</summary>
+
+```python
+node = onnx.helper.make_node('QLinearMatMul',
+    inputs=['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+#2D
+a = np.array([[208, 236, 0, 238],
+    [3, 214, 255, 29], ], dtype=np.uint8)
+
+a_scale = np.array([0.0066], dtype=np.float32)
+a_zero_point = np.array([113], dtype=np.uint8)
+
+b = np.array([[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]], dtype=np.uint8)
+
+b_scale = np.array([0.00705], dtype=np.float32)
+b_zero_point = np.array([114], dtype=np.uint8)
+
+y_scale = np.array([0.0107], dtype=np.float32)
+y_zero_point = np.array([118], dtype=np.uint8)
+
+output = np.array([[168, 115, 255],
+    [1, 66, 151], ], dtype=np.uint8)
+
+expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearmatmul_2D')
+
+#3D
+a = np.array([[[208, 236, 0, 238],
+    [3, 214, 255, 29]],
+    [[208, 236, 0, 238],
+    [3, 214, 255, 29]]], dtype=np.uint8)
+
+a_scale = np.array([0.0066], dtype=np.float32)
+a_zero_point = np.array([113], dtype=np.uint8)
+
+b = np.array([[[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]],
+    [[152, 51, 244],
+    [60, 26, 255],
+    [0, 127, 246],
+    [127, 254, 247]]], dtype=np.uint8)
+
+b_scale = np.array([0.00705], dtype=np.float32)
+b_zero_point = np.array([114], dtype=np.uint8)
+
+y_scale = np.array([0.0107], dtype=np.float32)
+y_zero_point = np.array([118], dtype=np.uint8)
+
+output = np.array([[[168, 115, 255],
+    [1, 66, 151]],
+    [[168, 115, 255],
+    [1, 66, 151]]], dtype=np.uint8)
+
+expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+       name='test_qlinearmatmul_3D')
+```
+
+</details>
+
+
+### QuantizeLinear
+There are 1 test cases, listed as following:
+<details>
+<summary>quantizelinear</summary>
+
+```python
+node = onnx.helper.make_node('QuantizeLinear',
+    inputs=['x', 'y_scale', 'y_zero_point'],
+    outputs=['y'],)
+
+x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
+y_scale = np.array([2], dtype=np.float32)
+y_zero_point = np.array([128], dtype=np.uint8)
+y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
+
+expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+       name='test_quantizelinear')
+```
+
+</details>
+
+
 ### RNN
 There are 3 test cases, listed as following:
 <details>
@@ -7115,12 +7345,6 @@ expect(node, inputs=[x, y], outputs=[z],
 <br/>
 
 ## &#x1F494;No Cover Common Operators
-### ConvInteger (call for test cases)
-
-
-### DequantizeLinear (call for test cases)
-
-
 ### GlobalLpPool (call for test cases)
 
 
@@ -7136,24 +7360,12 @@ expect(node, inputs=[x, y], outputs=[z],
 ### LpPool (call for test cases)
 
 
-### MatMulInteger (call for test cases)
-
-
 ### MaxRoiPool (call for test cases)
 
 
 ### Multinomial (random generator operator)
 
 
-### QLinearConv (call for test cases)
-
-
-### QLinearMatMul (call for test cases)
-
-
-### QuantizeLinear (call for test cases)
-
-
 ### RandomNormal (random generator operator)
 
 
diff --git a/onnx/backend/test/case/node/convinteger.py b/onnx/backend/test/case/node/convinteger.py
new file mode 100644
index 00000000000..58985abbbe0
--- /dev/null
+++ b/onnx/backend/test/case/node/convinteger.py
@@ -0,0 +1,40 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ConvInteger(Base):
+
+    @staticmethod
+    def export():  # type: () -> None
+
+        x = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.uint8).reshape((1, 1, 3, 3))
+        x_zero_point = np.array([1]).astype(np.uint8)
+        w = np.array([1, 1, 1, 1]).astype(np.uint8).reshape((1, 1, 2, 2))
+
+        y = np.array([12, 16, 24, 28]).astype(np.int32).reshape(1, 1, 2, 2)
+
+        # ConvInteger without padding
+        convinteger_node = onnx.helper.make_node('ConvInteger',
+            inputs=['x', 'w', 'x_zero_point'],
+            outputs=['y'])
+
+        expect(convinteger_node, inputs=[x, w, x_zero_point], outputs=[y],
+               name='test_basic_convinteger')
+
+        # ConvInteger with padding
+        y_with_padding = np.array([1, 3, 5, 3, 5, 12, 16, 9, 11, 24, 28, 15, 7, 15, 17, 9]).astype(np.int32).reshape((1, 1, 4, 4))
+
+        convinteger_node_with_padding = onnx.helper.make_node('ConvInteger',
+            inputs=['x', 'w', 'x_zero_point'],
+            outputs=['y'],
+            pads=[1, 1, 1, 1],)
+
+        expect(convinteger_node_with_padding, inputs=[x, w, x_zero_point], outputs=[y_with_padding],
+               name='test_convinteger_with_padding')
diff --git a/onnx/backend/test/case/node/dequantizelinear.py b/onnx/backend/test/case/node/dequantizelinear.py
new file mode 100644
index 00000000000..4dde6362e0c
--- /dev/null
+++ b/onnx/backend/test/case/node/dequantizelinear.py
@@ -0,0 +1,27 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class DequantizeLinear(Base):
+
+    @staticmethod
+    def export():  # type: () -> None
+        node = onnx.helper.make_node('DequantizeLinear',
+            inputs=['x', 'x_scale', 'x_zero_point'],
+            outputs=['y'],)
+
+        # scalar zero point and scale
+        x = np.array([0, 3, 128, 255]).astype(np.uint8)
+        x_scale = np.array([2], dtype=np.float32)
+        x_zero_point = np.array([128], dtype=np.uint8)
+        y = np.array([-256, -250, 0, 254], dtype=np.float32)
+
+        expect(node, inputs=[x, x_scale, x_zero_point], outputs=[y],
+               name='test_dequantizelinear')
diff --git a/onnx/backend/test/case/node/matmulinteger.py b/onnx/backend/test/case/node/matmulinteger.py
new file mode 100644
index 00000000000..99b8b20e844
--- /dev/null
+++ b/onnx/backend/test/case/node/matmulinteger.py
@@ -0,0 +1,39 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class MatMulInteger(Base):
+
+    @staticmethod
+    def export():  # type: () -> None
+        node = onnx.helper.make_node('MatMulInteger',
+            inputs=['A', 'B', 'a_zero_point', 'b_zero_point'],
+            outputs=['Y'],)
+
+        A = np.array([[11, 7, 3],
+            [10, 6, 2],
+            [9, 5, 1],
+            [8, 4, 0], ], dtype=np.uint8)
+
+        a_zero_point = np.array([12], dtype=np.uint8)
+
+        B = np.array([[1, 4],
+            [2, 5],
+            [3, 6], ], dtype=np.uint8)
+
+        b_zero_point = np.array([0], dtype=np.uint8)
+
+        output = np.array([[-38, -83],
+            [-44, -98],
+            [-50, -113],
+            [-56, -128], ], dtype=np.int32)
+
+        expect(node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output],
+               name='test_matmulinteger')
diff --git a/onnx/backend/test/case/node/qlinearconv.py b/onnx/backend/test/case/node/qlinearconv.py
new file mode 100644
index 00000000000..e860e50cfb7
--- /dev/null
+++ b/onnx/backend/test/case/node/qlinearconv.py
@@ -0,0 +1,48 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class QLinearConv(Base):
+
+    @staticmethod
+    def export():  # type: () -> None
+        node = onnx.helper.make_node('QLinearConv',
+            inputs=['x', 'x_scale', 'x_zero_point', 'w', 'w_scale', 'w_zero_point', 'y_scale', 'y_zero_point'],
+            outputs=['y'],)
+
+        x = np.array([[255, 174, 162, 25, 203, 168, 58],
+            [15, 59, 237, 95, 129, 0, 64],
+            [56, 242, 153, 221, 168, 12, 166],
+            [232, 178, 186, 195, 237, 162, 237],
+            [188, 39, 124, 77, 80, 102, 43],
+            [127, 230, 21, 83, 41, 40, 134],
+            [255, 154, 92, 141, 42, 148, 247], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+        x_scale = np.array([0.00369204697], dtype=np.float32)
+        x_zero_point = np.array([132], dtype=np.uint8)
+
+        w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
+
+        w_scale = np.array([0.00172794575], dtype=np.float32)
+        w_zero_point = np.array([255], dtype=np.uint8)
+
+        y_scale = np.array([0.00162681262], dtype=np.float32)
+        y_zero_point = np.array([123], dtype=np.uint8)
+
+        output = np.array([[0, 81, 93, 230, 52, 87, 197],
+            [240, 196, 18, 160, 126, 255, 191],
+            [199, 13, 102, 34, 87, 243, 89],
+            [23, 77, 69, 60, 18, 93, 18],
+            [67, 216, 131, 178, 175, 153, 212],
+            [128, 25, 234, 172, 214, 215, 121],
+            [0, 101, 163, 114, 213, 107, 8], ], dtype=np.uint8).reshape((1, 1, 7, 7))
+
+        expect(node, inputs=[x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point], outputs=[output],
+               name='test_qlinearconv')
diff --git a/onnx/backend/test/case/node/qlinearmatmul.py b/onnx/backend/test/case/node/qlinearmatmul.py
new file mode 100644
index 00000000000..b58418c7da1
--- /dev/null
+++ b/onnx/backend/test/case/node/qlinearmatmul.py
@@ -0,0 +1,74 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class QLinearMatMul(Base):
+
+    @staticmethod
+    def export():  # type: () -> None
+        node = onnx.helper.make_node('QLinearMatMul',
+            inputs=['a', 'a_scale', 'a_zero_point', 'b', 'b_scale', 'b_zero_point', 'y_scale', 'y_zero_point'],
+            outputs=['y'],)
+
+        #2D
+        a = np.array([[208, 236, 0, 238],
+            [3, 214, 255, 29], ], dtype=np.uint8)
+
+        a_scale = np.array([0.0066], dtype=np.float32)
+        a_zero_point = np.array([113], dtype=np.uint8)
+
+        b = np.array([[152, 51, 244],
+            [60, 26, 255],
+            [0, 127, 246],
+            [127, 254, 247]], dtype=np.uint8)
+
+        b_scale = np.array([0.00705], dtype=np.float32)
+        b_zero_point = np.array([114], dtype=np.uint8)
+
+        y_scale = np.array([0.0107], dtype=np.float32)
+        y_zero_point = np.array([118], dtype=np.uint8)
+
+        output = np.array([[168, 115, 255],
+            [1, 66, 151], ], dtype=np.uint8)
+
+        expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+               name='test_qlinearmatmul_2D')
+
+        #3D
+        a = np.array([[[208, 236, 0, 238],
+            [3, 214, 255, 29]],
+            [[208, 236, 0, 238],
+            [3, 214, 255, 29]]], dtype=np.uint8)
+
+        a_scale = np.array([0.0066], dtype=np.float32)
+        a_zero_point = np.array([113], dtype=np.uint8)
+
+        b = np.array([[[152, 51, 244],
+            [60, 26, 255],
+            [0, 127, 246],
+            [127, 254, 247]],
+            [[152, 51, 244],
+            [60, 26, 255],
+            [0, 127, 246],
+            [127, 254, 247]]], dtype=np.uint8)
+
+        b_scale = np.array([0.00705], dtype=np.float32)
+        b_zero_point = np.array([114], dtype=np.uint8)
+
+        y_scale = np.array([0.0107], dtype=np.float32)
+        y_zero_point = np.array([118], dtype=np.uint8)
+
+        output = np.array([[[168, 115, 255],
+            [1, 66, 151]],
+            [[168, 115, 255],
+            [1, 66, 151]]], dtype=np.uint8)
+
+        expect(node, inputs=[a, a_scale, a_zero_point, b, b_scale, b_zero_point, y_scale, y_zero_point], outputs=[output],
+               name='test_qlinearmatmul_3D')
diff --git a/onnx/backend/test/case/node/quantizelinear.py b/onnx/backend/test/case/node/quantizelinear.py
new file mode 100644
index 00000000000..8fdfdc0e357
--- /dev/null
+++ b/onnx/backend/test/case/node/quantizelinear.py
@@ -0,0 +1,26 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+import onnx
+from ..base import Base
+from . import expect
+
+
+class QuantizeLinear(Base):
+
+    @staticmethod
+    def export():  # type: () -> None
+        node = onnx.helper.make_node('QuantizeLinear',
+            inputs=['x', 'y_scale', 'y_zero_point'],
+            outputs=['y'],)
+
+        x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
+        y_scale = np.array([2], dtype=np.float32)
+        y_zero_point = np.array([128], dtype=np.uint8)
+        y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)
+
+        expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
+               name='test_quantizelinear')
diff --git a/onnx/backend/test/data/node/test_basic_convinteger/model.onnx b/onnx/backend/test/data/node/test_basic_convinteger/model.onnx
new file mode 100644
index 00000000000..aa2dc867604
--- /dev/null
+++ b/onnx/backend/test/data/node/test_basic_convinteger/model.onnx
@@ -0,0 +1,27 @@
+backend-test:�

+$
+
x
+
w
+x_zero_point
y"ConvIntegertest_basic_convintegerZ
+
x
+
+

+

+
+Z
+
w
+
+

+

+
+Z
+x_zero_point
+
+
+
b
+
y
+
+

+

+
+B
diff --git a/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_0.pb b/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_0.pb
new file mode 100644
index 00000000000..33cea026337
--- /dev/null
+++ b/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+

B
xJ		
diff --git a/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_1.pb b/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_1.pb
new file mode 100644
index 00000000000..53980d95daf
--- /dev/null
+++ b/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
+

B
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diff --git a/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_2.pb b/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_2.pb
new file mode 100644
index 00000000000..2ecc7658422
--- /dev/null
+++ b/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/input_2.pb
@@ -0,0 +1 @@
+
Bx_zero_pointJ


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diff --git a/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_basic_convinteger/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_convinteger_with_padding/model.onnx b/onnx/backend/test/data/node/test_convinteger_with_padding/model.onnx
new file mode 100644
index 00000000000..f5d92064b19
--- /dev/null
+++ b/onnx/backend/test/data/node/test_convinteger_with_padding/model.onnx
@@ -0,0 +1,28 @@
+backend-test:�

+7
+
x
+
w
+x_zero_point
y"ConvInteger*
+pads@
@
@
@
�
test_convinteger_with_paddingZ
+
x
+
+

+

+
+Z
+
w
+
+

+

+
+Z
+x_zero_point
+
+
+
b
+
y
+
+

+

+
+B
diff --git a/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/input_0.pb b/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/input_0.pb
new file mode 100644
index 00000000000..33cea026337
--- /dev/null
+++ b/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/input_0.pb
@@ -0,0 +1 @@
+

B
xJ		
diff --git a/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/input_1.pb b/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/input_1.pb
new file mode 100644
index 00000000000..53980d95daf
--- /dev/null
+++ b/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/input_1.pb
@@ -0,0 +1 @@
+

B
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diff --git a/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/input_2.pb b/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/input_2.pb
new file mode 100644
index 00000000000..2ecc7658422
--- /dev/null
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@@ -0,0 +1 @@
+
Bx_zero_pointJ


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diff --git a/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_convinteger_with_padding/test_data_set_0/output_0.pb
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new file mode 100644
index 00000000000..2215049f116
--- /dev/null
+++ b/onnx/backend/test/data/node/test_dequantizelinear/model.onnx
@@ -0,0 +1,21 @@
+backend-test:�

+/
+
x
+x_scale
+x_zero_point
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+
x
+
+
+Z
+x_scale
+
+

+
Z
+x_zero_point
+
+
+
b
+
y
+
+

+B
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new file mode 100644
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--- /dev/null
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new file mode 100644
index 00000000000..227661bbb82
--- /dev/null
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@@ -0,0 +1,26 @@
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+4
+
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+
B
+a_zero_point
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+
A
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+
+Z
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+
+
+Z
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+
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+
+
b
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+
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new file mode 100644
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@@ -0,0 +1,52 @@
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Z
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Z
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w
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+
Z
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+
+

+
Z
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+
+
Z
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+
+
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new file mode 100644
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diff --git a/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_5.pb b/onnx/backend/test/data/node/test_qlinearconv/test_data_set_0/input_5.pb
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