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handle Concat-11 (#2240)

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Tracy Sharpe 2019-10-23 19:12:40 -07:00 committed by Changming Sun
parent a3286b010a
commit 411b574f86
2 changed files with 32 additions and 15 deletions
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2
onnxruntime/core/optimizer/nchwc_transformer.cc
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@ -684,7 +684,7 @@ void NchwcTransformerImpl::Transform(Node& node) {
if (graph_utils::IsSupportedOptypeVersionAndDomain(node, "Add", {7}) ||
graph_utils::IsSupportedOptypeVersionAndDomain(node, "Sum", {6, 8})) {
TransformAdd(node);
} else if (graph_utils::IsSupportedOptypeVersionAndDomain(node, "Concat", {4})) {
} else if (graph_utils::IsSupportedOptypeVersionAndDomain(node, "Concat", {4, 11})) {
TransformConcat(node);
} else if (graph_utils::IsSupportedOptypeVersionAndDomain(node, "Relu", {6})) {
TransformActivation(node);

45
onnxruntime/test/optimizer/nchwc_optimizer_test.cc
View file

@ -79,27 +79,29 @@ struct NchwcTestHelper {
return &graph_.GetOrCreateNodeArg(name, nullptr);
}
NodeArg* MakeInitializer(const std::vector<int64_t>& shape) {
NodeArg* MakeInitializer(const std::vector<int64_t>& shape, const std::vector<float>& data) {
std::string name = graph_.GenerateNodeArgName("constant");
ONNX_NAMESPACE::TensorProto tensor_proto;
tensor_proto.set_name(name);
tensor_proto.set_data_type(ONNX_NAMESPACE::TensorProto_DataType_FLOAT);
int64_t num_elements = 1;
for (auto& dim : shape) {
tensor_proto.add_dims(dim);
num_elements *= dim;
}
auto random_data = FillRandomData(static_cast<size_t>(num_elements));
tensor_proto.mutable_float_data()->Resize(static_cast<int>(num_elements), 0.0f);
memcpy(tensor_proto.mutable_float_data()->mutable_data(), random_data.data(), random_data.size() * sizeof(float));
tensor_proto.mutable_float_data()->Resize(static_cast<int>(data.size()), 0.0f);
memcpy(tensor_proto.mutable_float_data()->mutable_data(), data.data(), data.size() * sizeof(float));
graph_.AddInitializedTensor(tensor_proto);
return &graph_.GetOrCreateNodeArg(name, nullptr);
}
NodeArg* MakeInitializer(const std::vector<int64_t>& shape) {
int64_t num_elements = std::accumulate(shape.begin(), shape.end(), int64_t(1), std::multiplies<int64_t>{});
return MakeInitializer(shape, FillRandomData(static_cast<size_t>(num_elements)));
}
Node& AddNode(const std::string& op_type,
const std::vector<NodeArg*>& input_args,
const std::vector<NodeArg*>& output_args) {
@ -120,6 +122,21 @@ struct NchwcTestHelper {
return AddNode("Conv", input_args, {output_arg});
}
Node& AddClipNode(NodeArg* input_arg, NodeArg* output_arg, float min, float max) {
int opset_version = graph_.DomainToVersionMap().find(kOnnxDomain)->second;
std::vector<NodeArg*> input_args = {input_arg};
if (opset_version >= 11) {
input_args.push_back(MakeInitializer({1}, {min}));
input_args.push_back(MakeInitializer({1}, {max}));
}
auto& node = AddNode("Clip", input_args, {output_arg});
if (opset_version < 11) {
node.AddAttribute("min", min);
node.AddAttribute("max", max);
}
return node;
}
std::vector<float> FillRandomData(size_t count) {
constexpr int min_fill_value = -23;
constexpr int max_fill_value = 23;
@ -144,7 +161,7 @@ struct NchwcTestHelper {
void NchwcOptimizerTester(const std::function<void(NchwcTestHelper& helper)>& build_test_case,
const std::function<void(NchwcInferenceSession& session)>& check_nchwc_graph,
int opset_version = 10) {
int opset_version = 11) {
// Ignore the test if NCHWc is not supported by the platform.
if (MlasNchwcGetBlockSize() <= 1) {
return;
@ -215,10 +232,10 @@ TEST(NchwcOptimizerTests, ConvNchw) {
auto* conv_output_arg = output_arg;
if (!activation_op_type.empty()) {
conv_output_arg = helper.MakeIntermediate();
auto& act_node = helper.AddNode(activation_op_type, {conv_output_arg}, {output_arg});
if (activation_op_type == "Clip") {
act_node.AddAttribute("min", 0.0f);
act_node.AddAttribute("max", 6.0f);
helper.AddClipNode(conv_output_arg, output_arg, 0.0f, 6.0f);
} else {
helper.AddNode(activation_op_type, {conv_output_arg}, {output_arg});
}
}
@ -255,10 +272,10 @@ TEST(NchwcOptimizerTests, ConvNchwc) {
auto* conv_output_arg = output_arg;
if (!activation_op_type.empty()) {
conv_output_arg = helper.MakeIntermediate();
auto& act_node = helper.AddNode(activation_op_type, {conv_output_arg}, {output_arg});
if (activation_op_type == "Clip") {
act_node.AddAttribute("min", -6.0f);
act_node.AddAttribute("max", 6.0f);
helper.AddClipNode(conv_output_arg, output_arg, -6.0f, 6.0f);
} else {
helper.AddNode(activation_op_type, {conv_output_arg}, {output_arg});
}
}
@ -533,7 +550,7 @@ TEST(NchwcOptimizerTests, ConvAddFusion) {
// Verify that Add or Sum can be fused into a preceding NCHWc Conv node,
// with an optional Relu node following.
std::vector<std::string> op_types = {"Add", "Sum"};
static const int opset_versions[] = {7, 10};
static const int opset_versions[] = {7, 10, 11};
for (auto& op_type : op_types) {
for (auto opset_version : opset_versions) {
test_case(op_type, opset_version, false);