manual merge

test/verify/test_elu.cpp → test/verify/test_sin_half.cpp

@@ -27,14 +27,15 @@
 #include <migraphx/generate.hpp>
 #include <migraphx/make_op.hpp>
 
-struct test_elu : verify_program<test_elu>
+struct test_sin_half : verify_program<test_sin_half>
 {
     migraphx::program create_program() const
     {
         migraphx::program p;
         auto* mm = p.get_main_module();
-        auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {4, 3, 3, 3}});
-        mm->add_instruction(migraphx::make_op("leaky_relu", {{"alpha", 1.0}}), x);
+        migraphx::shape s{migraphx::shape::half_type, {10}};
+        auto x = mm->add_parameter("x", s);
+        mm->add_instruction(migraphx::make_op("sin"), x);
         return p;
     }
 };

test/verify/test_slice_concat_add.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+
+struct test_slice_concat_add : verify_program<test_slice_concat_add>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        migraphx::shape s0{migraphx::shape::float_type, {1, 24, 2, 2}};
+        migraphx::shape s1{migraphx::shape::float_type, {1, 8, 2, 2}};
+        auto x     = mm->add_parameter("x", s0);
+        auto y     = mm->add_parameter("y", s1);
+        auto z     = mm->add_parameter("z", s0);
+        auto slice = mm->add_instruction(
+            migraphx::make_op("slice", {{"axes", {1}}, {"starts", {0}}, {"ends", {8}}}), x);
+        auto concat = mm->add_instruction(migraphx::make_op("concat", {{"axis", 1}}), slice, y, y);
+        mm->add_instruction(migraphx::make_op("add"), concat, z);
+        return p;
+    }
+};

test/verify/test_softmax_large3.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/common.hpp>
+
+struct test_softmax_large3 : verify_program<test_softmax_large3>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm   = p.get_main_module();
+        auto x     = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {2, 4}});
+        auto large = mm->add_literal({migraphx::shape{migraphx::shape::float_type}, {100}});
+        auto add   = migraphx::add_common_op(*mm, migraphx::make_op("mul"), {x, large});
+        mm->add_instruction(migraphx::make_op("softmax", {{"axis", -1}}), add);
+        return p;
+    }
+};

test/verify/test_batchnorm_inference.cpp → test/verify/test_trans_slice.cpp

@@ -27,27 +27,29 @@
 #include <migraphx/generate.hpp>
 #include <migraphx/make_op.hpp>
 
-struct test_batchnorm_inference : verify_program<test_batchnorm_inference>
+struct test_trans_slice : verify_program<test_trans_slice>
 {
-    const size_t width    = 3;
-    const size_t height   = 3;
-    const size_t channels = 3;
-    const size_t batches  = 4;
-
     migraphx::program create_program() const
     {
         migraphx::program p;
         auto* mm = p.get_main_module();
 
-        migraphx::shape s{migraphx::shape::float_type, {batches, channels, height, width}};
-        migraphx::shape vars{migraphx::shape::float_type, {channels}};
-        auto x        = mm->add_parameter("x", s);
-        auto scale    = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 1)));
-        auto bias     = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 2)));
-        auto mean     = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 3)));
-        auto variance = mm->add_literal(migraphx::abs(migraphx::generate_literal(vars, 4)));
-        mm->add_instruction(
-            migraphx::make_op("batch_norm_inference"), x, scale, bias, mean, variance);
+        auto x = mm->add_parameter("x", {migraphx::shape::float_type, {2, 384, 36, 64}});
+        auto transpose =
+            mm->add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 2, 1, 3}}}), x);
+        auto slice1 = mm->add_instruction(
+            migraphx::make_op("slice", {{"axes", {1}}, {"starts", {0}}, {"ends", {12}}}),
+            transpose);
+        auto slice2 = mm->add_instruction(
+            migraphx::make_op("slice", {{"axes", {1}}, {"starts", {12}}, {"ends", {24}}}),
+            transpose);
+        auto transpose2 = mm->add_instruction(
+            migraphx::make_op("transpose", {{"permutation", {0, 1, 3, 2}}}), slice2);
+        auto slice3 = mm->add_instruction(
+            migraphx::make_op("slice", {{"axes", {1}}, {"starts", {24}}, {"ends", {36}}}),
+            transpose);
+        mm->add_return({slice1, transpose2, slice3});
+
         return p;
     }
 };

tools/accuracy/accuracy_checker.py

@@ -116,6 +116,9 @@ def main():
 
     model = migraphx.parse_onnx(model_name, default_dim_value=batch)
 
+    if args.verbose:
+        print(model)
+
     model.compile(migraphx.get_target('gpu'), offload_copy=False)
 
     params = {}

tools/accuracy/requirements.txt

@@ -21,5 +21,5 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 # THE SOFTWARE.
 #####################################################################################
-numpy==1.18.5
+numpy==1.21.6
 onnxruntime==1.10.0

tools/api.py

@@ -21,7 +21,10 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 # THE SOFTWARE.
 #####################################################################################
-import string, sys, re, runpy
+import string
+import sys
+import re
+import runpy
 from functools import wraps
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
@@ -308,18 +311,39 @@ class Parameter:
         return self.substitute('${type} ${name}', prefix=prefix)
 
     def virtual_output_args(self, prefix: Optional[str] = None) -> List[str]:
-        return [
-            '&{prefix}{n}'.format(prefix=prefix or '', n=n)
-            for t, n in self.cparams
-        ]
+        container_type = self.type.remove_generic().basic().str()
+        decl_list: List[str] = []
+        container = (container_type == "std::vector"
+                     or container_type == "vector")
+        for t, n, in self.cparams:
+            if not decl_list and container:
+                decl_list.append('{prefix}{n}.data()'.format(prefix=prefix
+                                                             or '',
+                                                             n=n))
+            else:
+                decl_list.append('&{prefix}{n}'.format(prefix=prefix or '',
+                                                       n=n))
+        return decl_list
 
     def virtual_output_declarations(self,
                                     prefix: Optional[str] = None) -> List[str]:
-        return [
-            'std::remove_pointer_t<{type}> {prefix}{n};'.format(
-                type=Type(t).str(), prefix=prefix or '', n=n)
-            for t, n in self.cparams
-        ]
+        container_type = self.type.remove_generic().basic().str()
+        container = (container_type == "std::vector"
+                     or container_type == "vector")
+        decl_list: List[str] = []
+        for t, n, in self.cparams:
+            if not decl_list and container:
+                inner_t = self.type.inner_type()
+                if inner_t:
+                    decl_list.append(
+                        'std::array<{inner_t}, 1024> {prefix}{n};'.format(
+                            inner_t=inner_t.str(), prefix=prefix or '', n=n))
+            else:
+                decl_list.append(
+                    'std::remove_pointer_t<{type}> {prefix}{n}'.format(
+                        type=Type(t).str(), prefix=prefix or '', n=n))
+                decl_list[-1] += '=1024;' if container else ';'
+        return decl_list
 
     def virtual_output(self, prefix: Optional[str] = None) -> str:
         write = self.virtual_write
@@ -694,9 +718,14 @@ def generate_cpp_header() -> str:
                           [c.generate() for c in cpp_classes])
 
 
-def cwrap(name: str) -> Callable:
+c_type_map: Dict[str, Type] = {}
+
+
+def cwrap(name: str, c_type: Optional[str] = None) -> Callable:
     def with_cwrap(f):
         type_map[name] = f
+        if c_type:
+            c_type_map[name] = Type(c_type)
 
         @wraps(f)
         def decorated(*args, **kwargs):
@@ -917,6 +946,9 @@ def vector_c_wrap(p: Parameter) -> None:
     # Not a generic type
     if not inner:
         return
+    if inner.str() in c_type_map:
+        inner = c_type_map[inner.str()]
+
     t = inner.add_pointer()
     if p.type.is_reference():
         if p.type.is_const():
@@ -927,6 +959,12 @@ def vector_c_wrap(p: Parameter) -> None:
             p.add_size_param()
             p.bad_param('${name} == nullptr or ${size} == nullptr',
                         'Null pointer')
+        elif p.virtual:
+            p.add_param(t)
+            p.add_size_param()
+            p.bad_param('${name} == nullptr or ${size} == nullptr',
+                        'Null pointer')
+            p.virtual_write = '{${name}.begin(), ${name}.begin()+${size}}; // cppcheck-suppress returnDanglingLifetime'
         else:
             p.add_param(t)
             p.bad_param('${name} == nullptr', 'Null pointer')
@@ -946,7 +984,7 @@ def vector_c_wrap(p: Parameter) -> None:
         p.write = ['std::copy(${result}.begin(), ${result}.end(), ${name})']
 
 
-@cwrap('std::string')
+@cwrap('std::string', 'char*')
 def string_c_wrap(p: Parameter) -> None:
     t = Type('char*')
     if p.returns:
@@ -1061,9 +1099,9 @@ struct ${ctype} {
 c_api_virtual_impl = Template('''
 ${return_type} ${name}(${params}) const
 {
-    ${output_decls}
     if (${fname} == nullptr)
         throw std::runtime_error("${name} function is missing.");
+    ${output_decls}
     std::array<char, 256> exception_msg;
     exception_msg.front() = '\\0';
     auto api_error_result = ${fname}(${args});

tools/api/api.cpp

@@ -21,6 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
+#include <migraphx/execution_environment.hpp>
 #include <migraphx/migraphx.h>
 #include <migraphx/rank.hpp>
 #include <migraphx/shape.hpp>
@@ -166,6 +167,13 @@ void set_output_names(tf_options& options, std::vector<const char*> names)
     options.output_node_names = std::vector<std::string>(names.begin(), names.end());
 }
 
+std::vector<argument>
+run_async(program& p, const parameter_map& params, void* s, std::string_view name)
+{
+    execution_environment exec_env{any_ptr(s, name), true};
+    return p.eval(params, exec_env);
+}
+
 template <class Value>
 std::vector<const char*> get_names(const std::unordered_map<std::string, Value>& m)
 {
@@ -265,11 +273,18 @@ struct experimental_custom_op
 template <class CustomOp>
 struct custom_operation
 {
+
     template <class Self, class F>
     static auto reflect(Self&, F)
     {
         return pack();
     }
+
+    value attributes() const
+    {
+        return {{"custom_op", true}, {"target", op.runs_on_offload_target() ? "gpu" : "cpu"}};
+    }
+
     CustomOp op;
     std::string name() const { return op.xobject.name; }
 
@@ -284,6 +299,23 @@ struct custom_operation
     {
         return op.compute(std::move(ctx), std::move(output_shape), std::move(inputs));
     }
+
+    std::ptrdiff_t output_alias(std::vector<shape> inputs) const
+    {
+        auto alias_vec = op.output_alias(std::move(inputs));
+        // TODO: For now, only support one output alias
+        if(alias_vec.empty())
+        {
+            return -1;
+        }
+        if(alias_vec.size() > 1)
+        {
+            MIGRAPHX_THROW("Currently, CustomOps in MIGraphX only supports one output_alias");
+        }
+        return alias_vec.front();
+    }
+
+    bool runs_on_offload_target() const { return op.runs_on_offload_target(); }
 };
 
 template <class CustomOp>

tools/api/migraphx.h

@@ -26,7 +26,6 @@
 
 #include <stdlib.h>
 #include <stdbool.h>
-
 // Add new types here
 // clang-format off
 #define MIGRAPHX_SHAPE_VISIT_TYPES(m) \

tools/convert_onnx_version.py

+#####################################################################################
+# The MIT License (MIT)
+#
+# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#####################################################################################
+import argparse
+import onnx
+from onnx import version_converter
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=
+        'MIGraphX Onnx Model Convertion. Use to convert the opset of the input model to MIGraphX\'s'
+    )
+    req_args = parser.add_argument_group(title='required arguments')
+    req_args.add_argument('--model',
+                          type=str,
+                          required=True,
+                          help='path to onnx file')
+    req_args.add_argument('--output',
+                          type=str,
+                          required=True,
+                          help='path to output onnx file')
+    req_args.add_argument('--opset',
+                          type=int,
+                          required=True,
+                          help='The output opset')
+    req_args.add_argument('--infer_shapes',
+                          action='store_true',
+                          help='Infer shapes for output model')
+    parser.add_argument('--verbose',
+                        action='store_true',
+                        help='show verbose information (for debugging)')
+    args = parser.parse_args()
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    model_path = args.model
+    out_model_path = args.output
+    target_opset = args.opset
+    verbose = args.verbose
+    infer_shapes = args.infer_shapes
+
+    original_model = onnx.load(model_path)
+    if verbose:
+        print(f"The model before conversion:\n{original_model}")
+
+    # A full list of supported adapters can be found here:
+    # https://github.com/onnx/onnx/blob/main/onnx/version_converter.py#L21
+    # Apply the version conversion on the original model
+    converted_model = version_converter.convert_version(
+        original_model, target_opset)
+
+    if infer_shapes:
+        converted_model = onnx.shape_inference.infer_shapes(converted_model)
+
+    if verbose:
+        print(f"The model after conversion:\n{converted_model}")
+
+    # Save the ONNX model
+    onnx.save(converted_model, out_model_path)
+
+
+if __name__ == '__main__':
+    main()

tools/include/context.hpp

@@ -66,12 +66,21 @@ any_ptr get_queue_context(T&)
 {
     return {};
 }
+template <class T>
+void wait_for_context(T&, any_ptr)
+{
+}
+
+template <class T>
+void finish_on_context(T&, any_ptr){}
 
 <%
  interface('context',
            virtual('to_value', returns = 'value', const = True, default = 'to_value_context'),
            virtual('from_value', v = 'const value&', default = 'from_value_context'),
            virtual('get_queue', returns = 'any_ptr', default = 'get_queue_context'),
+           virtual('wait_for', queue = 'any_ptr', returns = 'void', default = 'wait_for_context'),
+           virtual('finish_on', queue = 'any_ptr', returns = 'void', default = 'finish_on_context'),
            virtual('finish', returns = 'void', const = True)) %>
 
     inline void migraphx_to_value(value& v, const context& ctx)

tools/include/operation.hpp

@@ -32,6 +32,8 @@
 #include <utility>
 #include <unordered_map>
 #include <migraphx/reflect.hpp>
+#include <migraphx/dyn_output.hpp>
+#include <migraphx/functional.hpp>
 #include <migraphx/streamutils.hpp>
 #include <migraphx/normalize_attributes.hpp>
 #include <migraphx/argument.hpp>
@@ -199,9 +201,12 @@ auto compute_op(rank<1>,
                 context& ctx,
                 const shape& output_shape,
                 const std::vector<argument>& input)
-    -> decltype(x.compute(auto_any_cast(ctx), output_shape, input))
+    -> decltype(x.compute(auto_any_cast(ctx),
+                          make_compute_output_shape(pack(x, output_shape, input)),
+                          input))
 {
-    return x.compute(auto_any_cast(ctx), output_shape, input);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -220,9 +225,9 @@ compute_op(const T& x, context& ctx, const shape& output_shape, const std::vecto
 
 template <class T>
 auto compute_op(rank<1>, const T& x, const shape& output_shape, const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input))
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input))
 {
-    return x.compute(output_shape, input);
+    return x.compute(make_compute_output_shape(pack(x, output_shape, input)), input);
 }
 
 template <class T>
@@ -244,9 +249,11 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -278,9 +285,17 @@ auto compute_op(rank<4>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(auto_any_cast(ctx), output, inputs, module_args, f))
+                F f) -> decltype(x.compute(auto_any_cast(ctx),
+                                           make_compute_output_shape(pack(x, output, inputs)),
+                                           inputs,
+                                           module_args,
+                                           f))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs, module_args, f);
+    return x.compute(auto_any_cast(ctx),
+                     make_compute_output_shape(pack(x, output, inputs)),
+                     inputs,
+                     module_args,
+                     f);
 }
 
 template <class T, class F>
@@ -290,9 +305,11 @@ auto compute_op(rank<3>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f) -> decltype(x.compute(output, inputs, module_args, f))
+                F f)
+    -> decltype(
+        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
 {
-    return x.compute(output, inputs, module_args, f);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
 
 template <class T, class F>
@@ -302,9 +319,10 @@ auto compute_op(rank<2>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(output, inputs))
+                F)
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs))
 {
-    return x.compute(output, inputs);
+    return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -314,9 +332,12 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>&,
-                F) -> decltype(x.compute(auto_any_cast(ctx), output, inputs))
+                F) -> decltype(x.compute(auto_any_cast(ctx),
+                                         make_compute_output_shape(pack(x, output, inputs)),
+                                         inputs))
 {
-    return x.compute(auto_any_cast(ctx), output, inputs);
+    return x.compute(
+        auto_any_cast(ctx), make_compute_output_shape(pack(x, output, inputs)), inputs);
 }
 
 template <class T, class F>
@@ -348,7 +369,8 @@ auto is_context_free_op(rank<1>,
                         const T& x,
                         const shape& output_shape,
                         const std::vector<argument>& input)
-    -> decltype(x.compute(output_shape, input), std::true_type{});
+    -> decltype(x.compute(make_compute_output_shape(pack(x, output_shape, input)), input),
+                std::true_type{});
 
 template <class T>
 auto is_context_free_op(rank<0>, const T&, const shape&, const std::vector<argument>&)

tools/install_prereqs.sh

@@ -57,7 +57,7 @@ echo "Dependencies are installed at $PREFIX"
 rbuild prepare -d $PREFIX -s develop
 
 # install onnx package for unit tests
-pip3 install onnx==1.8.1 numpy==1.18.5 typing==3.7.4 pytest==6.0.1 packaging==16.8
+pip3 install onnx==1.10.0 numpy==1.21.6 typing==3.7.4 pytest==6.0.1 packaging==16.8
 
 # pin version of protobuf in Python for onnx runtime unit tests
 pip3 install protobuf==3.20.0