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Build function bodies according to the imported global opset. (#18833)

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### Description
Build function bodies according to the imported global opset.
Same is for querying ONNX functions.

### Motivation and Context
This addresses issues:
https://github.com/microsoft/onnxruntime/issues/18781
https://github.com/microsoft/onnxruntime/issues/16438
This commit is contained in:
Dmitri Smirnov 2023-12-15 15:56:20 -08:00 committed by GitHub
parent 2952cf82a5
commit 50cbcf9587
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GPG key ID: 4AEE18F83AFDEB23
2 changed files with 77 additions and 12 deletions
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35
onnxruntime/core/graph/graph.cc
View file

@ -582,6 +582,17 @@ bool Node::TryGetFunctionProto(ONNX_NAMESPACE::FunctionProto& onnx_function_prot
onnx_function_proto = *func_template_->onnx_func_proto_;
return true;
} else if (op_) {
auto get_opset_version = [op = op_](Graph* graph) -> std::optional<int> {
if (op->domain() == kOnnxDomain) {
const auto& domain_to_version = graph->DomainToVersionMap();
const auto iter = domain_to_version.find(kOnnxDomain);
if (iter != domain_to_version.cend()) {
return iter->second;
}
}
return {};
};
// Check if this node has a schema defined function proto.
if (op_->HasContextDependentFunction()) {
NodeProto node_proto;
@ -595,8 +606,13 @@ bool Node::TryGetFunctionProto(ONNX_NAMESPACE::FunctionProto& onnx_function_prot
} else
input_types.emplace_back();
}
auto requested_opset_version = get_opset_version(graph_);
if (!requested_opset_version.has_value()) {
requested_opset_version = SinceVersion();
}
ONNX_NAMESPACE::FunctionBodyBuildContextImpl function_body_ctx(node_proto, input_types);
return op_->BuildContextDependentFunction(function_body_ctx, onnx_function_proto);
return op_->BuildContextDependentFunction(function_body_ctx, onnx_function_proto, *requested_opset_version);
} else if (op_->HasFunction()) {
const FunctionProto* function_ptr = nullptr;
// We need to get a function-body suitable for the ONNX opset used by the model.
@ -605,17 +621,12 @@ bool Node::TryGetFunctionProto(ONNX_NAMESPACE::FunctionProto& onnx_function_prot
// as the default-version, which is incorrect in the case of functions belonging to
// non-onnx domains, like MSDOMAIN.
// We use the following as a temporary hack.
function_ptr = op_->GetFunction(SinceVersion(), false);
// TODO: Switch to following, once ONNX issue is fixed.
// auto& map = graph_->DomainToVersionMap();
// const auto iter = map.find(kOnnxDomain);
// if (iter != map.end()) {
// function_ptr = op_->GetFunction(iter->second, true);
// } else {
// function_ptr = op_->GetFunction();
// }
auto requested_opset_version = get_opset_version(graph_);
if (requested_opset_version.has_value()) {
function_ptr = op_->GetFunction(*requested_opset_version, true);
} else {
function_ptr = op_->GetFunction(SinceVersion(), false);
}
if (function_ptr != nullptr) {
onnx_function_proto = *function_ptr;

54
onnxruntime/test/framework/function_test.cc
View file

@ -614,5 +614,59 @@ TEST(FunctionTest, TestInlinedFunctionDoesNotReserrectNonExistingArgs) {
AsSpan(output_names), &fetches, 0));
}
/// <summary>
/// This test covers the issues:
/// https://github.com/microsoft/onnxruntime/issues/16438
/// https://github.com/microsoft/onnxruntime/issues/18781
/// </summary>
TEST(FunctionTest, Test_GH_issue_16438) {
const char* code = R"(
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
producer_name: "pytorch",
producer_version: "2.1.0"
>
torch_jit (float16[5,10,5] input_0) => (double[5,10,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 2, dtype: int = 11> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
aten_special_log_softmax <dim, dtype>(self) => (result_8)
{
tmp = Shape(self)
tmp_0 = Size(tmp)
int64_0 = Constant<value : tensor = int64 int64_0{0}> ()
int64_0_cast = CastLike(int64_0, tmp_0)
self_is_scalar = Equal(tmp_0, int64_0_cast)
self_4 = If(self_is_scalar) <then_branch : graph = thenGraph_8() => (self_2) {
tmp_1 = Constant<value_ints : ints = [0]> ()
self_2 = Unsqueeze(self, tmp_1)
}, else_branch : graph = elseGraph_8() => (self_3) {
self_3 = Identity(self)
}>
result = LogSoftmax<axis : int = @dim>(self_4)
result_5 = Cast<to : int = @dtype>(result)
result_8 = If(self_is_scalar) <then_branch : graph = thenGraph_12() => (result_6) {
result_6 = Squeeze(result_5)
}, else_branch : graph = elseGraph_12() => (result_7) {
result_7 = Identity(result_5)
}>
}
)";
std::string serialized_model;
ParseOnnxSource(code, serialized_model);
SessionOptions session_options;
InferenceSession session_object{session_options, GetEnvironment()};
std::stringstream sstr(serialized_model);
auto status = session_object.Load(sstr);
ASSERT_TRUE(status.IsOK()) << status.ErrorMessage();
status = session_object.Initialize();
ASSERT_TRUE(status.IsOK()) << status.ErrorMessage();
}
} // namespace test
} // namespace onnxruntime