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[QNN-EP] Einsum equation ReduceSum Multiply on broadcast X #25581

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Merged
HectorSVC merged 1 commit into from
Jul 30, 2025
Merged

[QNN-EP] Einsum equation ReduceSum Multiply on broadcast X #25581

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169 changes: 168 additions & 1 deletion onnxruntime/core/providers/qnn/builder/opbuilder/einsum_op_builder.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -192,6 +192,50 @@
return true;
}

bool IsEquationReduceSumMulBroadcastX(const Equation& equation) {
// E.g., bhwc,wkc->bhwk
const auto& [term_1, term_2, result] = equation;
if (term_1.size() != 4) {
return false;
}
if (term_2.size() != 3) {
return false;
}
if (result.size() != 4) {
return false;
}

// Check contraction over last axis (c)
char c1 = term_1[3];
char c2 = term_2[2];
if (c1 != c2) {
return false;
}

// Check w axis alignment
if (term_1[2] != term_2[0]) {
return false;
}
if (term_1[2] != result[2]) {
return false;
}

// Check k axis alignment
if (term_2[1] != result[3]) {
return false;
}

// Check batch dimensions
if (term_1[0] != result[0]) {
return false;
}
if (term_1[1] != result[1]) {
return false;
}

return true;
}

/**
* @brief Sets the parameter tensor names for a MatMul op.
*
Expand Down Expand Up @@ -305,6 +349,113 @@
return Status::OK();
}

/**
* @brief Creates a ReduceSum, Multiply on broadcasted input X and original input Y.
*
* @param qnn_model_wrapper Pointer to the QnnModelWrapper instance used to manage the QNN model.
* @param node_unit The NodeUnit representing the ONNX node to be converted.
* @param do_op_validation A boolean flag indicating whether to perform operation validation.
* @return Status indicating success or failure of the operation.
*/
Status CreateReduceSumMulBroadcastX(
onnxruntime::qnn::QnnModelWrapper* qnn_model_wrapper,
const onnxruntime::NodeUnit& node_unit,
std::vector<std::string>&& input_names,
bool do_op_validation) {
// Reshape in0 to shape (b, h, w, 1, c) to expand dimension before the contraction axis 'c'.
// Allowing broadcast with in1 for multiplication, aligning the contraction axis for reduce.
onnxruntime::qnn::TensorInfo tensor_info_in0{}, tensor_info_in1{}, tensor_info_out{};
ORT_RETURN_IF_ERROR(qnn_model_wrapper->GetTensorInfo(node_unit.Inputs()[0], tensor_info_in0));
ORT_RETURN_IF_ERROR(qnn_model_wrapper->GetTensorInfo(node_unit.Inputs()[1], tensor_info_in1));
ORT_RETURN_IF_ERROR(qnn_model_wrapper->GetTensorInfo(node_unit.Outputs()[0], tensor_info_out));
const std::vector<uint32_t>& shape_in0 = tensor_info_in0.shape;
const std::vector<uint32_t>& shape_in1 = tensor_info_in1.shape;
ORT_RETURN_IF_NOT(shape_in0.size() == 4, "CreateReduceSumMulBroadcastX expects input 0 to be rank 4");
ORT_RETURN_IF_NOT(shape_in1.size() == 3, "CreateReduceSumMulBroadcastX expects input 1 to be rank 3");
const std::vector<uint32_t> new_shape_in0{shape_in0[0], shape_in0[1], shape_in0[2], 1, shape_in0[3]};
const std::string reshape_out_name = input_names[0] + "_reshaped";
ORT_RETURN_IF_ERROR(qnn_model_wrapper->AddReshapeNode(
/*input_name=*/input_names[0],
/*output_name=*/reshape_out_name,
/*input_shape=*/shape_in0,
/*output_shape=*/new_shape_in0,
/*tensor_data_type=*/tensor_info_in0.qnn_data_type,
/*quantize_param=*/tensor_info_in0.quant_param.Copy(),
/*do_op_validation=*/do_op_validation,
/*is_for_input=*/qnn_model_wrapper->IsGraphInput(input_names[0])));

// Multiply: reshaped in0 * in1
// The output shape of the multiplication is determined by broadcasting the reshaped in0 of
// (b, h, w, 1, c) and in1 (w, k, c) along the matching axes, resulting in (b, h, w, k, c).
const std::string mul_out_name = onnxruntime::qnn::utils::GetNodeName(node_unit) + "_mul";
std::vector<uint32_t> shape_out_mul{new_shape_in0[0], new_shape_in0[1], new_shape_in0[2], shape_in1[1], new_shape_in0[4]};
onnxruntime::qnn::QnnTensorWrapper tensor_wrapper_mul(mul_out_name,
QNN_TENSOR_TYPE_NATIVE,
tensor_info_in0.qnn_data_type,
tensor_info_in0.quant_param.Copy(),
std::move(shape_out_mul));
ORT_RETURN_IF_NOT(qnn_model_wrapper->AddTensorWrapper(std::move(tensor_wrapper_mul)),
"CreateReduceSumMulBroadcastX: failed to AddTensorWrapper");
ORT_RETURN_IF_NOT(qnn_model_wrapper->CreateQnnNode(
/*qnn_node_name=*/mul_out_name,
/*package_name=*/QNN_OP_PACKAGE_NAME_QTI_AISW,
/*qnn_node_type=*/QNN_OP_ELEMENT_WISE_MULTIPLY,
/*input_names=*/{reshape_out_name, input_names[1]},
/*output_names=*/{mul_out_name},
/*param_tensor_names=*/{},
/*do_op_validation=*/do_op_validation),
"CreateReduceSumMulBroadcastX: failed to create Mul node");

std::vector<std::string> param_tensor_names{};

// ReduceSum on last axes={4}, keep_dims=False
// Axis '4' corresponds to the last dimension ('c') of the reshaped tensor (b, h, w, k, c),
// which is the contraction axis for reduce sum op in the einsum equation (bhwc,wkc->bhwk).
std::vector<uint32_t> axes_shape{SafeInt<uint32_t>(1)};
std::vector<uint32_t> axes_value{SafeInt<uint32_t>(4)};
onnxruntime::qnn::QnnParamWrapper param_axes(node_unit.Index(),
node_unit.Name(),
QNN_OP_REDUCE_SUM_PARAM_AXES,
std::move(axes_shape),
std::move(axes_value));
param_tensor_names.push_back(param_axes.GetParamTensorName());
ORT_RETURN_IF_NOT(qnn_model_wrapper->AddParamWrapper(std::move(param_axes)),
"CreateReduceSumMulBroadcastX: failed to add param axes");

Qnn_Scalar_t keep_dims_scalar = QNN_SCALAR_INIT;
keep_dims_scalar.dataType = QNN_DATATYPE_BOOL_8;
keep_dims_scalar.bool8Value = SafeInt<uint8_t>(0);
onnxruntime::qnn::QnnParamWrapper param_keep_dims(node_unit.Index(),
node_unit.Name(),
QNN_OP_REDUCE_SUM_PARAM_KEEP_DIMS,
keep_dims_scalar);
param_tensor_names.push_back(param_keep_dims.GetParamTensorName());
ORT_RETURN_IF_NOT(qnn_model_wrapper->AddParamWrapper(std::move(param_keep_dims)),
"CreateReduceSumMulBroadcastX: failed to add param keep_dims");

const std::string out_name = node_unit.Outputs()[0].node_arg.Name();
Qnn_TensorType_t out_tensor_type = qnn_model_wrapper->IsGraphOutput(out_name) ? QNN_TENSOR_TYPE_APP_READ : QNN_TENSOR_TYPE_NATIVE;
onnxruntime::qnn::QnnTensorWrapper tensor_wrapper_out(out_name,
out_tensor_type,
tensor_info_out.qnn_data_type,
tensor_info_out.quant_param.Copy(),
std::move(tensor_info_out.shape));
ORT_RETURN_IF_NOT(qnn_model_wrapper->AddTensorWrapper(std::move(tensor_wrapper_out)),
"CreateReduceSumMulBroadcastX: failed to AddTensorWrapper");

ORT_RETURN_IF_NOT(qnn_model_wrapper->CreateQnnNode(
/*qnn_node_name=*/out_name,
/*package_name=*/QNN_OP_PACKAGE_NAME_QTI_AISW,
/*qnn_node_type=*/QNN_OP_REDUCE_SUM,
/*input_names=*/{mul_out_name},
/*output_names=*/{out_name},
/*param_tensor_names=*/std::move(param_tensor_names),
/*do_op_validation=*/do_op_validation),
"CreateReduceSumMulBroadcastX: failed to create ReduceSum node");

return Status::OK();
}

} // namespace

namespace onnxruntime {
Expand Down Expand Up @@ -356,9 +507,20 @@
if (!IsEquationMatMul(parsed_equation.value()) &&
!IsEquationMatMulTransposeY(parsed_equation.value()) &&
!IsEquationMatMulBroadcastTransposeY(parsed_equation.value()) &&
!IsEquationMatMulTransposeAll(parsed_equation.value())) {
!IsEquationMatMulTransposeAll(parsed_equation.value()) &&
!IsEquationReduceSumMulBroadcastX(parsed_equation.value())) {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, node_unit.OpType() + " unsupported equation: " + equation);
}
if (IsEquationReduceSumMulBroadcastX(parsed_equation.value())) {
if (IsGpuBackend(qnn_model_wrapper.GetQnnBackendType())) {
// QAIRT 3.36.1: Failed to validate on GPU.
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, node_unit.OpType() + " unsupported equation: " + equation + " on backend GPU");
}
if (node_unit.Inputs()[0].quant_param.has_value()) {
// QAIRT 3.36.1: Failed to finalize QNN graph 1002.
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, node_unit.OpType() + " unsupported equation: " + equation + " for quantized inputs");
}
}
return AddToModelBuilder(qnn_model_wrapper, node_unit, logger, true);
}

Expand Down Expand Up @@ -408,6 +570,11 @@
/*node_unit=*/node_unit,
/*input_names=*/std::move(input_names),
/*do_op_validation=*/do_op_validation));
} else if (IsEquationReduceSumMulBroadcastX(parsed_equation.value())) {
ORT_RETURN_IF_ERROR(CreateReduceSumMulBroadcastX(/*qnn_model_wrapper=*/&qnn_model_wrapper,
/*node_unit=*/node_unit,
/*input_names=*/std::move(input_names),

Check warning on line 576 in onnxruntime/core/providers/qnn/builder/opbuilder/einsum_op_builder.cc

View workflow job for this annotation

GitHub Actions / Optional Lint C++ 

[cpplint] reported by reviewdog 🐶
 Add #include <utility> for move  [build/include_what_you_use] [4]

Raw Output:
onnxruntime/core/providers/qnn/builder/opbuilder/einsum_op_builder.cc:576:  Add #include <utility> for move  [build/include_what_you_use] [4]
/*do_op_validation=*/do_op_validation));
} else {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, node_unit.OpType() + " unsupported equation: " + equation);
}
Expand Down
61 changes: 57 additions & 4 deletions onnxruntime/test/providers/qnn/einsum_op_test.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -189,6 +189,19 @@ TEST_F(QnnCPUBackendTests, EinsumRank4MatMulTransposeAll1) {
/*tolerance=*/1e-4f);
}

TEST_F(QnnCPUBackendTests, EinsumRank4MatMulTransposeAll2) {
const std::vector<int64_t> shape0{1, 7, 1, 7};
const std::vector<int64_t> shape1{1, 9, 1, 7};
const std::vector<float> data0 = GetSequentialFloatData(shape0, /*start=*/-0.1f, /*step=*/0.05f);
const std::vector<float> data1 = GetSequentialFloatData(shape1, /*start=*/-0.1f, /*step=*/0.05f);
RunQnnEinsum<float>(
/*backend=*/kQnnBackendTypeCpu,
/*in0=*/TestInputDef<float>(shape0, /*is_initializer=*/false, std::move(data0)),
/*in1=*/TestInputDef<float>(shape1, /*is_initializer=*/false, std::move(data1)),
/*equation=*/"bkhq,bchk->bchq",
/*tolerance=*/1e-4f);
}

TEST_F(QnnCPUBackendTests, EinsumMatMulBroadcastTransposeY) {
const std::vector<int64_t> shape0{2, 3, 3, 4};
const std::vector<int64_t> shape1{3, 3, 4};
Expand All @@ -202,16 +215,16 @@ TEST_F(QnnCPUBackendTests, EinsumMatMulBroadcastTransposeY) {
/*tolerance=*/1e-4f);
}

TEST_F(QnnCPUBackendTests, EinsumRank4MatMulTransposeAll2) {
const std::vector<int64_t> shape0{1, 7, 1, 7};
const std::vector<int64_t> shape1{1, 9, 1, 7};
TEST_F(QnnCPUBackendTests, EinsumReduceSumMulBroadcastX) {
const std::vector<int64_t> shape0{2, 3, 4, 5};
const std::vector<int64_t> shape1{4, 6, 5};
const std::vector<float> data0 = GetSequentialFloatData(shape0, /*start=*/-0.1f, /*step=*/0.05f);
const std::vector<float> data1 = GetSequentialFloatData(shape1, /*start=*/-0.1f, /*step=*/0.05f);
RunQnnEinsum<float>(
/*backend=*/kQnnBackendTypeCpu,
/*in0=*/TestInputDef<float>(shape0, /*is_initializer=*/false, std::move(data0)),
/*in1=*/TestInputDef<float>(shape1, /*is_initializer=*/false, std::move(data1)),
/*equation=*/"bkhq,bchk->bchq",
/*equation=*/"bhwc,wkc->bhwk",
/*tolerance=*/1e-4f);
}

Expand Down Expand Up @@ -299,6 +312,19 @@ TEST_F(QnnHTPBackendTests, EinsumF16MatMulBroadcastTransposeY) {
/*tolerance=*/1e-2f);
}

TEST_F(QnnHTPBackendTests, EinsumF16ReduceSumMulBroadcastX) {
const std::vector<int64_t> shape0{1, 3, 2, 4};
const std::vector<int64_t> shape1{2, 3, 4};
const std::vector<float> data0 = GetSequentialFloatData(shape0, /*start=*/-0.1f, /*step=*/0.05f);
const std::vector<float> data1 = GetSequentialFloatData(shape1, /*start=*/-0.1f, /*step=*/0.05f);
RunQnnEinsum<float>(
/*backend=*/kQnnBackendTypeHtp,
/*in0=*/TestInputDef<float>(shape0, /*is_initializer=*/false, std::move(data0)),
/*in1=*/TestInputDef<float>(shape1, /*is_initializer=*/false, std::move(data1)),
/*equation=*/"bhwc,wkc->bhwk",
/*tolerance=*/1e-2f);
}

//
// QNN HTP QDQ
//
Expand Down Expand Up @@ -375,6 +401,19 @@ TEST_F(QnnHTPBackendTests, EinsumQdqMatMulBroadcastTransposeY) {
/*tolerance=*/QDQTolerance());
}

// TODO: Re-enable. QAIRT 3.36.1: failed to finalize QNN graph 1002.
TEST_F(QnnHTPBackendTests, DISABLED_EinsumQdqReduceSumMulBroadcastX) {
const std::vector<int64_t> shape0{1, 3, 2, 4};
const std::vector<int64_t> shape1{2, 3, 4};
const std::vector<float> data0 = GetSequentialFloatData(shape0, /*start=*/-0.1f, /*step=*/0.05f);
const std::vector<float> data1 = GetSequentialFloatData(shape1, /*start=*/-0.1f, /*step=*/0.05f);
RunQnnHtpQdqEinsum<uint8_t, uint8_t>(
/*in0=*/TestInputDef<float>(shape0, /*is_initializer=*/false, std::move(data0)),
/*in1=*/TestInputDef<float>(shape1, /*is_initializer=*/false, std::move(data1)),
/*equation=*/"bhwc,wkc->bhwk",
/*tolerance=*/QDQTolerance());
}

#endif // defined(__aarch64__) || defined(_M_ARM64) || defined(__linux__)

#if defined(_M_ARM64)
Expand Down Expand Up @@ -474,6 +513,20 @@ TEST_F(QnnGPUBackendTests, DISABLED_EinsumMatMulBroadcastTransposeY) {
/*tolerance=*/1e-4f);
}

// TODO: Re-enable. Failed on QAIRT 3.36.1.
TEST_F(QnnGPUBackendTests, DISABLED_EinsumReduceSumMulBroadcastX) {
const std::vector<int64_t> shape0{1, 3, 2, 4};
const std::vector<int64_t> shape1{2, 3, 4};
const std::vector<float> data0 = GetSequentialFloatData(shape0, /*start=*/-0.1f, /*step=*/0.05f);
const std::vector<float> data1 = GetSequentialFloatData(shape1, /*start=*/-0.1f, /*step=*/0.05f);
RunQnnEinsum<float>(
/*backend=*/kQnnBackendTypeGpu,
/*in0=*/TestInputDef<float>(shape0, /*is_initializer=*/false, std::move(data0)),
/*in1=*/TestInputDef<float>(shape1, /*is_initializer=*/false, std::move(data1)),
/*equation=*/"bhwc,wkc->bhwk",
/*tolerance=*/1e-4f);
}

#endif // defined(_M_ARM64) GPU tests

} // namespace test
Expand Down
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