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[QNN EP] Bug fix: multiple consumer for cast result in name conflicts #25584

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Merged
HectorSVC merged 3 commits into from
Aug 1, 2025
Merged

[QNN EP] Bug fix: multiple consumer for cast result in name conflicts #25584

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ea0df77
[QNN EP] Bug fix: multiple consumer for indices cast cause conflicts
qti-yuduo Jul 31, 2025
f3f2dc9
Fix CI: compile error becomes runtime error now surface as NotImpleme…
qti-yuduo Jul 31, 2025
5195c0d
Update onnxruntime/core/providers/qnn/builder/opbuilder/gather_op_bui…
qti-yuduo Jul 31, 2025
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2 changes: 1 addition & 1 deletion onnxruntime/core/providers/qnn/builder/opbuilder/base_op_builder.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -348,7 +348,7 @@ Status BaseOpBuilder::ProcessOutputs(QnnModelWrapper& qnn_model_wrapper,
// Insert cast node.
ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(cast_node_info.node_name,
QNN_OP_PACKAGE_NAME_QTI_AISW,
"Cast",
QNN_OP_CAST,
{cast_node_info.input_name},
{cast_node_info.output_name},
{}),
Expand Down
66 changes: 40 additions & 26 deletions onnxruntime/core/providers/qnn/builder/opbuilder/gather_op_builder.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -86,10 +86,10 @@ static bool FixStaticIndices(const std::vector<uint8_t>& onnx_bytes,
}

// Gets the size of input0 on the axis dimension.
static Status GetInpu0AxisDimValue(const QnnModelWrapper& qnn_model_wrapper,
const NodeUnit& node_unit,
int64_t default_axis_value,
/*out*/ int64_t& axis_dim_value) {
static Status GetInput0AxisDimValue(const QnnModelWrapper& qnn_model_wrapper,
const NodeUnit& node_unit,
int64_t default_axis_value,
/*out*/ int64_t& axis_dim_value) {
const auto& input0 = node_unit.Inputs()[0];
std::vector<uint32_t> input0_shape;
ORT_RETURN_IF_NOT(qnn_model_wrapper.GetOnnxShape(input0.node_arg, input0_shape),
Expand All @@ -111,8 +111,9 @@ static Status GetInpu0AxisDimValue(const QnnModelWrapper& qnn_model_wrapper,

// Processes the indices input to Gather operators.
//
// In general, QNN only supports int32/uint32 indices. QNN EP has to add Cast for dynamic int64 indices or
// convert static int64 indices to int32.
// QNN only supports int32 / uint32 as indices tensor data types.
// When indices tensor is an initializer, statically cast values int64 -> int32.
// When dynamic input, add explicit QNN Cast node for int64 -> int32 conversion.
//
// The HTP backend only supports dynamic int64 indices if they are a graph input.
static Status ProcessIndicesInput(QnnModelWrapper& qnn_model_wrapper,
Expand All @@ -121,7 +122,7 @@ static Status ProcessIndicesInput(QnnModelWrapper& qnn_model_wrapper,
const logging::Logger& logger,
std::vector<std::string>& input_names,
bool do_op_validation) {
const auto& input_name = indices_input.node_arg.Name();
const auto& indices_tensor_name = indices_input.node_arg.Name();

TensorInfo indices_info = {};
ORT_RETURN_IF_ERROR(qnn_model_wrapper.GetTensorInfo(indices_input, indices_info));
Expand All @@ -146,28 +147,44 @@ static Status ProcessIndicesInput(QnnModelWrapper& qnn_model_wrapper,
}
}

Qnn_TensorType_t tensor_type = qnn_model_wrapper.GetTensorType(input_name);
std::vector<uint32_t> cast_output_shape(indices_info.shape);
if (qnn_model_wrapper.IsQnnTensorWrapperExist(input_name)) {
LOGS(logger, VERBOSE) << "Tensor already added, skip it: " << input_name;
if (qnn_model_wrapper.IsQnnTensorWrapperExist(indices_tensor_name)) {
LOGS(logger, VERBOSE) << "Tensor already added, skip it: " << indices_tensor_name;
} else {
QnnTensorWrapper input_tensorwrapper(input_name, tensor_type, indices_info.qnn_data_type, QnnQuantParamsWrapper(),
std::move(indices_info.shape), std::move(qnn_indices_bytes));
QnnTensorWrapper input_tensorwrapper(indices_tensor_name,
qnn_model_wrapper.GetTensorType(indices_tensor_name),
indices_info.qnn_data_type, QnnQuantParamsWrapper(),
std::move(indices_info.shape),
std::move(qnn_indices_bytes));
ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(input_tensorwrapper)), "Failed to add tensor.");
}

// Insert QNN Cast op to convert dynamic indices from int64 to int32.
std::string indices_input_name(input_name);
std::string indices_casted_name{indices_tensor_name};
if (indices_info.qnn_data_type == QNN_DATATYPE_INT_64) {
assert(!indices_info.is_initializer);

ORT_RETURN_IF_ERROR(qnn_model_wrapper.AddInt64CastNode(input_name, indices_input_name,
std::move(cast_output_shape),
do_op_validation));
indices_casted_name += "_int32";
if (qnn_model_wrapper.IsQnnTensorWrapperExist(indices_casted_name)) {
LOGS(logger, VERBOSE) << "Tensor already added, skip it: " << indices_casted_name;
} else {
QnnTensorWrapper indices_cast_tensor(indices_casted_name,
QNN_TENSOR_TYPE_NATIVE,
QNN_DATATYPE_INT_32,
QnnQuantParamsWrapper(),
std::move(cast_output_shape));
ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(indices_cast_tensor)),
"Failed to add gather indices cast tensor.");
ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(indices_casted_name,
QNN_OP_PACKAGE_NAME_QTI_AISW,
QNN_OP_CAST,
{indices_tensor_name},
{indices_casted_name},
{},
do_op_validation),
"Failed to add gather indices cast node.");
}
}

input_names.push_back(indices_input_name);

input_names.push_back(indices_casted_name);
return Status::OK();
}

Expand All @@ -181,8 +198,7 @@ Status GatherOpBuilder::ProcessInputs(QnnModelWrapper& qnn_model_wrapper,
ORT_RETURN_IF_ERROR(ProcessInput(qnn_model_wrapper, inputs[0], logger, input_names));

int64_t input0_axis_dim = 0;
ORT_RETURN_IF_ERROR(GetInpu0AxisDimValue(qnn_model_wrapper, node_unit, /*default_axis*/ 0, input0_axis_dim));

ORT_RETURN_IF_ERROR(GetInput0AxisDimValue(qnn_model_wrapper, node_unit, /*default_axis_value=*/0, input0_axis_dim));
return ProcessIndicesInput(qnn_model_wrapper, inputs[1], input0_axis_dim, logger, input_names, do_op_validation);
}

Expand Down Expand Up @@ -312,7 +328,6 @@ Status GatherOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_w
QnnTensorWrapper gather_output_wrapper(gather_output_name, tensor_type, qnn_data_type, quantize_param.Copy(),
std::move(qnn_output_shape));
ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(gather_output_wrapper)), "Failed to add tensor.");

ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(utils::GetNodeName(node_unit),
QNN_OP_PACKAGE_NAME_QTI_AISW,
GetQnnOpType(node_unit.OpType()),
Expand All @@ -328,7 +343,6 @@ Status GatherOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_w
QnnTensorWrapper reshape_output(output_name, reshape_tensor_type, qnn_data_type, std::move(quantize_param),
std::move(target_output_shape));
ORT_RETURN_IF_NOT(qnn_model_wrapper.AddTensorWrapper(std::move(reshape_output)), "Failed to add tensor.");
const static std::string qnn_node_type = "Reshape";
std::string node_output_name = output_name;

if (needs_int64_cast) {
Expand All @@ -337,7 +351,7 @@ Status GatherOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_w
}
ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(output_name,
QNN_OP_PACKAGE_NAME_QTI_AISW,
qnn_node_type,
QNN_OP_RESHAPE,
{gather_output_name},
{node_output_name},
{},
Expand All @@ -350,7 +364,7 @@ Status GatherOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_w
// Insert cast node.
ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(cast_node_info.node_name,
QNN_OP_PACKAGE_NAME_QTI_AISW,
"Cast",
QNN_OP_CAST,
{cast_node_info.input_name},
{cast_node_info.output_name},
{}),
Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/core/providers/qnn/builder/opbuilder/topk.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -257,7 +257,7 @@ Status TopKOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_wra
"Failed to add tensor.");
ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(cast_input_name,
QNN_OP_PACKAGE_NAME_QTI_AISW,
"Cast",
QNN_OP_CAST,
{cast_input_name},
{output_name},
{}),
Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/core/providers/qnn/builder/opbuilder/transpose_op_builder.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ Status TransposeOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_mode
// Insert cast node.
ORT_RETURN_IF_NOT(qnn_model_wrapper.CreateQnnNode(cast_node_info.node_name,
QNN_OP_PACKAGE_NAME_QTI_AISW,
"Cast",
QNN_OP_CAST,
{cast_node_info.input_name},
{cast_node_info.output_name},
{}),
Expand Down
18 changes: 0 additions & 18 deletions onnxruntime/core/providers/qnn/builder/qnn_model_wrapper.h
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Original file line number Diff line number Diff line change
Expand Up @@ -236,24 +236,6 @@ class QnnModelWrapper {
tensor_data_type, quantize_param, do_op_validation, is_for_input, is_for_output);
}

Status AddInt64CastNode(const std::string& input_name, std::string& cast_output_name,
std::vector<uint32_t>&& cast_output_shape, bool do_op_validation) {
cast_output_name = input_name + "_ort_qnn_ep_cast";
QnnTensorWrapper cast_output(cast_output_name, QNN_TENSOR_TYPE_NATIVE, QNN_DATATYPE_INT_32,
QnnQuantParamsWrapper(), std::move(cast_output_shape));
ORT_RETURN_IF_NOT(AddTensorWrapper(std::move(cast_output)), "Failed to add tensor.");
ORT_RETURN_IF_NOT(CreateQnnNode(cast_output_name,
QNN_OP_PACKAGE_NAME_QTI_AISW,
"Cast",
{input_name},
{cast_output_name},
{},
do_op_validation),
"Failed to add node.");

return Status::OK();
}

Status UnpackInitializerData(const ONNX_NAMESPACE::TensorProto& initializer,
std::vector<uint8_t>& unpacked_tensor) const;

Expand Down
5 changes: 4 additions & 1 deletion onnxruntime/test/onnx/main.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -916,7 +916,10 @@ select from 'TF8', 'TF16', 'UINT8', 'FLOAT', 'ITENSOR'. \n)");
ORT_TSTR("sce_none_weights_log_prob_expanded"),
ORT_TSTR("sce_none_weights_expanded"),
ORT_TSTR("convtranspose_3d"),
ORT_TSTR("gather_elements_negative_indices")};
ORT_TSTR("gather_elements_negative_indices"),
ORT_TSTR("rotary_embedding_3d_input_expanded"),
ORT_TSTR("rotary_embedding_expanded"),
ORT_TSTR("rotary_embedding_interleaved_expanded")};

std::unordered_set<std::basic_string<ORTCHAR_T>> all_disabled_tests(std::begin(immutable_broken_tests), std::end(immutable_broken_tests));

Expand Down
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