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https://github.com/saymrwulf/onnxruntime.git
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Update code regarding some QNN bug fixes (#22222)
### Description Update code regarding some QNN bug fixes: 1. QnnProfile_ExtendedEventData_t.version is not initialized in Qnn 2. Failed to finalize the graph for HardSigmoid with FP16 precision
This commit is contained in:
Hector Li
GitHub
parent
b81e76b9a6
commit
6e3163faa5
4 changed files with 32 additions and 181 deletions
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@ -325,159 +325,6 @@ Status ProcessGridSampleAttributes(QnnModelWrapper& qnn_model_wrapper,
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return Status::OK();
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}
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static Status GetFloatBytes(float f32_val, Qnn_DataType_t qnn_data_type, std::vector<uint8_t>& bytes) {
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switch (qnn_data_type) {
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case QNN_DATATYPE_FLOAT_32: {
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bytes.resize(sizeof(float));
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std::memcpy(bytes.data(), &f32_val, bytes.size());
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break;
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}
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case QNN_DATATYPE_FLOAT_16: {
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bytes.resize(sizeof(MLFloat16));
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const MLFloat16 f16_val(f32_val);
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std::memcpy(bytes.data(), &f16_val, bytes.size());
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break;
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}
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default:
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Qnn Data Type: ", qnn_data_type, " is not supported");
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}
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return Status::OK();
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}
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static Status DecomposeHardSigmoid(QnnModelWrapper& qnn_model_wrapper,
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const NodeUnit& node_unit,
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std::vector<std::string>&& input_names,
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const logging::Logger& logger,
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bool do_op_validation) {
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ORT_UNUSED_PARAMETER(logger);
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const auto& onnx_node_name = utils::GetNodeName(node_unit);
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const auto& input = node_unit.Inputs()[0];
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const auto& output = node_unit.Outputs()[0];
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std::vector<uint32_t> input_shape;
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ORT_RETURN_IF_NOT(qnn_model_wrapper.GetOnnxShape(input.node_arg, input_shape), "Cannot get shape of input 0");
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Qnn_DataType_t qnn_data_type = QNN_DATATYPE_FLOAT_32;
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ORT_RETURN_IF_ERROR(utils::GetQnnDataType(false /*is_quantized*/, input.node_arg.TypeAsProto(), qnn_data_type));
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NodeAttrHelper node_helper(node_unit);
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//
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// Create Mul node.
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//
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const OnnxAttrInfo<float> onnx_alpha_attr{"alpha", 0.2f};
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const OnnxAttrInfo<float> onnx_beta_attr{"beta", 0.5};
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std::string alpha_input_name = MakeString("ort_qnn_ep_", onnx_node_name, "_HardSigmoid_Mul_alpha");
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std::vector<uint8_t> alpha_bytes;
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ORT_RETURN_IF_ERROR(GetFloatBytes(GetOnnxAttr(node_helper, onnx_alpha_attr), qnn_data_type, alpha_bytes));
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QnnTensorWrapper alpha_input(alpha_input_name,
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QNN_TENSOR_TYPE_STATIC,
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qnn_data_type,
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QnnQuantParamsWrapper(),
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{1}, // shape
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std::move(alpha_bytes));
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ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(alpha_input)), "Failed to add alpha input tensor.");
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std::string mul_output_name = MakeString("ort_qnn_ep_", onnx_node_name, "_HardSigmoid_Mul_output");
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std::string mul_node_name = MakeString("ort_qnn_ep_", onnx_node_name, "_HardSigmoid_Mul_node");
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QnnTensorWrapper mul_output(mul_output_name,
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QNN_TENSOR_TYPE_NATIVE,
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qnn_data_type,
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QnnQuantParamsWrapper(),
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std::vector<uint32_t>(input_shape));
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ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(mul_output)), "Failed to add Mul output tensor.");
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ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(mul_node_name,
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QNN_OP_PACKAGE_NAME_QTI_AISW,
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QNN_OP_ELEMENT_WISE_MULTIPLY,
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{input_names[0], alpha_input_name}, // input names
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{mul_output_name}, // output names
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{},
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do_op_validation),
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"Failed to add Mul node.");
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//
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// Create Add node.
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//
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std::string beta_input_name = MakeString("ort_qnn_ep_", onnx_node_name, "_HardSigmoid_Mul_beta");
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std::vector<uint8_t> beta_bytes;
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ORT_RETURN_IF_ERROR(GetFloatBytes(GetOnnxAttr(node_helper, onnx_beta_attr), qnn_data_type, beta_bytes));
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QnnTensorWrapper beta_input(beta_input_name,
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QNN_TENSOR_TYPE_STATIC,
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qnn_data_type,
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QnnQuantParamsWrapper(),
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{1}, // shape
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std::move(beta_bytes));
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ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(beta_input)), "Failed to add beta input tensor.");
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std::string add_output_name = MakeString("ort_qnn_ep_", onnx_node_name, "_HardSigmoid_Add_output");
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std::string add_node_name = MakeString("ort_qnn_ep_", onnx_node_name, "_HardSigmoid_Add_node");
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QnnTensorWrapper add_output(add_output_name,
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QNN_TENSOR_TYPE_NATIVE,
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qnn_data_type,
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QnnQuantParamsWrapper(),
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std::vector<uint32_t>(input_shape));
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ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(add_output)), "Failed to add Add output tensor.");
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ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(add_node_name,
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QNN_OP_PACKAGE_NAME_QTI_AISW,
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QNN_OP_ELEMENT_WISE_ADD,
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{mul_output_name, beta_input_name}, // input names
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{add_output_name}, // output names
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{},
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do_op_validation),
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"Failed to add Add node.");
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//
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// Create ReluMinMax node.
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//
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std::vector<std::string> param_tensor_names;
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// Parameter 'min_value'
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{
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Qnn_Scalar_t min_value = QNN_SCALAR_INIT;
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min_value.dataType = QNN_DATATYPE_FLOAT_32;
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min_value.floatValue = 0.0f;
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QnnParamWrapper qnn_param(node_unit.Index(), node_unit.Name(), QNN_OP_RELU_MIN_MAX_PARAM_MIN_VALUE, min_value);
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param_tensor_names.push_back(qnn_param.GetParamTensorName());
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qnn_model_wrapper.AddParamWrapper(std::move(qnn_param));
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}
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// Parameter 'max_value'
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{
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Qnn_Scalar_t max_value = QNN_SCALAR_INIT;
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max_value.dataType = QNN_DATATYPE_FLOAT_32;
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max_value.floatValue = 1.0f;
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QnnParamWrapper qnn_param(node_unit.Index(), node_unit.Name(), QNN_OP_RELU_MIN_MAX_PARAM_MAX_VALUE, max_value);
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param_tensor_names.push_back(qnn_param.GetParamTensorName());
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qnn_model_wrapper.AddParamWrapper(std::move(qnn_param));
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}
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const std::string& output_name = output.node_arg.Name();
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std::string relu_min_max_node_name = MakeString("ort_qnn_ep_", onnx_node_name, "_HardSigmoid_ReluMinMax_node");
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QnnTensorWrapper output_tensor(output_name,
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qnn_model_wrapper.GetTensorType(output_name),
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qnn_data_type,
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QnnQuantParamsWrapper(),
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std::vector<uint32_t>(input_shape));
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ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(output_tensor)), "Failed to add output tensor.");
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ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(relu_min_max_node_name,
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QNN_OP_PACKAGE_NAME_QTI_AISW,
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QNN_OP_RELU_MIN_MAX,
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{add_output_name}, // input names
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{output_name}, // output names
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std::move(param_tensor_names),
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do_op_validation),
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"Failed to add ReluMinMax node.");
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return Status::OK();
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}
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Status SimpleOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_wrapper,
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const NodeUnit& node_unit,
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std::vector<std::string>&& input_names,
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@ -546,13 +393,8 @@ Status SimpleOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_w
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}
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if (op_type == "HardSigmoid") {
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// direct conversion to ElementWiseNeuron has issue to finalize the graph for FP16 data type
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// still decompose it to Mul, Add, ReluMinMax
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int32_t onnx_data_type = 0;
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ORT_RETURN_IF_ERROR(utils::GetOnnxTensorElemDataType(node_unit.Inputs()[0].node_arg, onnx_data_type));
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if (onnx_data_type == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) {
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return DecomposeHardSigmoid(qnn_model_wrapper, node_unit, std::move(input_names), logger, do_op_validation);
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}
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ORT_RETURN_IF_ERROR(ProcessNodeAttribute(qnn_model_wrapper, node_unit, "alpha",
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QNN_OP_ELEMENT_WISE_NEURON_PARAM_ALPHA,
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@ -1179,17 +1179,16 @@ Status QnnBackendManager::ExtractProfilingEventExtended(
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#endif
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if (!tracelogging_provider_ep_enabled) {
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// QNN issue, the version number not correct, ticket created
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// if (event_data_extended.version == QNN_PROFILE_DATA_VERSION_1) {
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outfile << event_data_extended.v1.timestamp << ","
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<< message << ","
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<< ExtractQnnScalarValue(event_data_extended.v1.value) << ","
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<< unit << ","
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<< "BACKEND"
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<< ","
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<< eventLevel << ","
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<< (event_data_extended.v1.identifier ? event_data_extended.v1.identifier : "NULL") << "\n";
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//}
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if (event_data_extended.version == QNN_PROFILE_DATA_VERSION_1) {
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outfile << event_data_extended.v1.timestamp << ","
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<< message << ","
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<< ExtractQnnScalarValue(event_data_extended.v1.value) << ","
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<< unit << ","
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<< "BACKEND"
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<< ","
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<< eventLevel << ","
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<< (event_data_extended.v1.identifier ? event_data_extended.v1.identifier : "NULL") << "\n";
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}
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} else {
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#ifdef _WIN32
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LogQnnProfileEventAsTraceLogging(
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@ -912,10 +912,28 @@ static GetTestModelFn BuildCastAddTestCase() {
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};
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}
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// A repro of QC case 06838696, accuracy issue for Cast + Op (quantized)
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// the value pair(1, 0.00392156886) at index #1 don't match,
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// which is -0.996078 from 1
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TEST_F(QnnHTPBackendTests, DISABLED_CastAddHTPAccuracyTest) {
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TEST_F(QnnHTPBackendTests, ProfilingTest) {
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onnxruntime::ProviderOptions provider_options;
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#if defined(_WIN32)
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provider_options["backend_path"] = "QnnHtp.dll";
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#else
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provider_options["backend_path"] = "libQnnHtp.so";
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#endif
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provider_options["enable_htp_fp16_precision"] = "1";
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provider_options["profiling_level"] = "detailed";
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provider_options["profiling_file_path"] = "detailed_profile.csv";
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auto input_defs = {TestInputDef<float>({1, 2, 2, 2}, false, -10.0f, 10.0f),
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TestInputDef<float>({1, 2, 2, 2}, false, -10.0f, 10.0f)};
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RunQnnModelTest(BuildOpTestCase<float>("Add", input_defs, {}, {}, kOnnxDomain),
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provider_options,
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13,
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ExpectedEPNodeAssignment::All,
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0.008f);
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}
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TEST_F(QnnHTPBackendTests, CastAddHTPAccuracyTest) {
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ProviderOptions provider_options;
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#if defined(_WIN32)
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provider_options["backend_path"] = "QnnHtp.dll";
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@ -1333,14 +1333,6 @@ TEST_F(QnnHTPBackendTests, UnaryOp_HardSigmoid_F32_as_FP16) {
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}
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// Check that QNN EP can support float16 HardSigmoid on HTP
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// It is using decompose way for FP16 since ElementWiseNeuron failed to finalize the graph with the error below:
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// \HTP\src\hexagon\prepare\tcm_migration.cc:1829:ERROR:no properties registered for q::QNN_HardSigmoid
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// \HTP\HTP\src\hexagon\prepare\graph_prepare.cc:203:ERROR:could not create op: q::QNN_HardSigmoid
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// \HTP\HTP\src\hexagon\prepare\graph_prepare.cc:1238:ERROR:Op 0x101000000010 preparation failed with err:-1
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// Completed stage: Graph Transformations and Optimizations (16361 us)
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// QnnDsp <E> "node" generated: could not create op
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// QnnDsp <E> RouterWindows graph prepare failed 12
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// QnnDsp <E> Failed to finalize graph (id: 1) with err 1002
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TEST_F(QnnHTPBackendTests, UnaryOp_HardSigmoid_FP16) {
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std::vector<float> input_data = GetFloatDataInRange(-5.0f, 5.0f, 16);
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