Add additional simple operators (MatMulInteger, ConvInteger, Asinh, Acosh, and Atanh (#431)

* Add initial api

* Formatting

* Add more api

* Formatting

* add more operators (asinh, acosh, atanh, MatMulInteger, ConvInteger)

* clang format

* add unit tests for new operators

* clang format

* Add auto api generation

* Formatting

* Fix some compilation errors

* Change handle struct

* Formatting

* Fix reamining compilation errors

* Formatting

* Simplify using ctype

* Formatting

* Initial c++ generation

* Formatting

* Add C++header

* Formatting

* Add test

* Formatting

* Add initial tests

* Formatting

* Try to fix formatting

* Cleanup formatting

* Formatting

* Fix constructors on the same line

* Fix tests

* Formatting

* Fix tidy issues

* Fix tidy issues

* Fix naming issue

* Add onnx API to parse buffer

* Formatting

* Add arguments api

* Formatting

* Fix verify parameters

* Fix cppcheck issues

* Formatting

* Add method to get output shapes and bytes

* Formatting

* Try formatting

* Formatting

* Improve the test coverage

* Formatting

* Add print method

* Formatting

* Fix cppcheck issue

* Fix package dependency

* Add nolint

* Try fix formatting

* Formatting

* formatting

* formatting

* Fix formatting
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>
Co-authored-by: kahmed10 <15948690+kahmed10@users.noreply.github.com>

test/onnx/atanh_test.onnx

+
+atanh_test:=
+
+
x
y"Atanh
+atanh_testZ
+
x
+
+

+
+b
+
y
+
+

+
+B
\ No newline at end of file

test/onnx/convinteger_bias_test.onnx

+convinteger_bias_test:À

+?
+
0
+
1
+
2
3"ConvInteger*
+	dilations@
@
 
*
+strides@
@
 
convinteger_bias_testZ
+
0
+
+

+
+ 
+ Z
+
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+
+

+
+
+Z
+
2
+
+
+
b
+
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+
+

+
+
+B
\ No newline at end of file

test/onnx/gen_onnx.py

@@ -38,6 +38,20 @@ def acos_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def acosh_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Acosh',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
 @onnx_test
 def add_bcast_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
@@ -130,6 +144,20 @@ def asin_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def asinh_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Asinh',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
 @onnx_test
 def atan_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
@@ -144,6 +172,20 @@ def atan_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def atanh_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])
+
+    node = onnx.helper.make_node(
+        'Atanh',
+        inputs=['x'],
+        outputs=['y'],
+    )
+
+    return ([node], [x], [y])
+
+
 @onnx_test
 def averagepool_notset_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
@@ -572,6 +614,22 @@ def conv_relu_maxpool_x2_test():
     return ([node1, node2, node3, node4, node5, node6], [x, y, z, m, n], [out])
 
 
+@onnx_test
+def convinteger_bias_test():
+    x = helper.make_tensor_value_info('0', TensorProto.INT8, [1, 3, 32, 32])
+    y = helper.make_tensor_value_info('1', TensorProto.INT8, [1, 3, 5, 5])
+    z = helper.make_tensor_value_info('2', TensorProto.INT32, [1])
+    out = helper.make_tensor_value_info('3', TensorProto.INT32, [1, 2, 28, 28])
+
+    node = onnx.helper.make_node('ConvInteger',
+                                 inputs=['0', '1', '2'],
+                                 outputs=['3'],
+                                 dilations=[1, 1],
+                                 strides=[1, 1])
+
+    return ([node], [x, y, z], [out])
+
+
 @onnx_test
 def cos_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
@@ -1195,6 +1253,21 @@ def matmul_vv_test():
     return ([node], [m1, m2], [y])
 
 
+@onnx_test
+def matmulinteger_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [3, 6, 16])
+    m2 = helper.make_tensor_value_info('2', TensorProto.INT8, [3, 16, 8])
+    y = helper.make_tensor_value_info('y', TensorProto.INT32, [3, 6, 8])
+
+    node = onnx.helper.make_node(
+        'MatMulInteger',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
 @onnx_test
 def max_test():
     a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])

test/onnx/matmulinteger_test.onnx

+matmulinteger_test:y
+
+
1
+
2
y"
MatMulIntegermatmulinteger_testZ
+
1
+
+
+
+Z
+
2
+
+
+
+b
+
y
+
+
+
+B
\ No newline at end of file

test/onnx/onnx_test.cpp

@@ -38,6 +38,17 @@ TEST_CASE(acos_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(acosh_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::acosh{}, input);
+
+    auto prog = optimize_onnx("acosh_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(add_bcast_test)
 {
     migraphx::program p;
@@ -109,6 +120,17 @@ TEST_CASE(asin_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(asinh_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::asinh{}, input);
+
+    auto prog = optimize_onnx("asinh_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(atan_test)
 {
     migraphx::program p;
@@ -120,6 +142,17 @@ TEST_CASE(atan_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(atanh_test)
+{
+    migraphx::program p;
+    auto input = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {10}});
+    p.add_instruction(migraphx::op::atanh{}, input);
+
+    auto prog = optimize_onnx("atanh_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(averagepool_notset_test)
 {
     migraphx::program p;
@@ -389,6 +422,21 @@ TEST_CASE(conv_relu_maxpool_x2_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(convinteger_bias_test)
+{
+    migraphx::program p;
+    auto l0       = p.add_parameter("0", {migraphx::shape::int8_type, {1, 3, 32, 32}});
+    auto l1       = p.add_parameter("1", {migraphx::shape::int8_type, {1, 3, 5, 5}});
+    auto l2       = p.add_parameter("2", {migraphx::shape::int32_type, {1}});
+    uint64_t axis = 1;
+    auto l3       = p.add_instruction(migraphx::op::quant_convolution{}, l0, l1);
+    auto l4       = p.add_instruction(migraphx::op::broadcast{axis, l3->get_shape().lens()}, l2);
+    p.add_instruction(migraphx::op::add{}, l3, l4);
+
+    auto prog = optimize_onnx("convinteger_bias_test.onnx");
+    EXPECT(p == prog);
+}
+
 TEST_CASE(cos_test)
 {
     migraphx::program p;
@@ -862,9 +910,7 @@ TEST_CASE(matmul_vbm_test)
     auto l1   = p.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {5, 7, 8}});
     auto sl0  = p.add_instruction(migraphx::op::unsqueeze{{0}}, l0);
     auto bsl0 = p.add_instruction(migraphx::op::multibroadcast{{5, 1, 7}}, sl0);
-    std::cout << "ONNX_TEST" << std::endl;
     auto res  = p.add_instruction(migraphx::op::dot{1.0f, 0.0f}, bsl0, l1);
-    std::cout << "After Dot" << std::endl;
     p.add_instruction(migraphx::op::squeeze{{1}}, res);
 
     auto prog = optimize_onnx("matmul_vbm_test.onnx");
@@ -902,6 +948,18 @@ TEST_CASE(matmul_vv_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(matmulinteger_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("1", migraphx::shape{migraphx::shape::int8_type, {3, 6, 16}});
+    auto l1 = p.add_parameter("2", migraphx::shape{migraphx::shape::int8_type, {3, 16, 8}});
+    p.add_instruction(migraphx::op::quant_dot{1, 0}, l0, l1);
+
+    auto prog = optimize_onnx("matmulinteger_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(max_test)
 {
     migraphx::program p;