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[WebNN] Refactor op mappings and add input name mapping between ONNX and WebNN #24830

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May 27, 2025
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[WebNN] Refactor op mappings and add input name mapping between ONNX and WebNN #24830

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1923662
[WebNN] Refactor WebNN operation mappings and add input translation s…
NingW101 May 21, 2025
4e6f094
Update onnxruntime/core/providers/webnn/builders/map_info.h
NingW101 May 22, 2025
2365d02
Remove ConvInteger mapping from operation inputs
NingW101 May 22, 2025
6cd3b70
Rename input and operation structures
NingW101 May 22, 2025
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184 changes: 14 additions & 170 deletions onnxruntime/core/providers/webnn/builders/helper.h
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Original file line number Diff line number Diff line change
Expand Up @@ -9,8 +9,8 @@
#include "core/common/inlined_containers.h"
#include <core/graph/basic_types.h>
#include "core/optimizer/initializer.h"
#include "core/providers/common.h"
#include "core/providers/shared/utils/utils.h"
#include "map_info.h"

Check warning on line 13 in onnxruntime/core/providers/webnn/builders/helper.h

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[cpplint] reported by reviewdog 🐶
 Include the directory when naming header files  [build/include_subdir] [4]

Raw Output:
onnxruntime/core/providers/webnn/builders/helper.h:13:  Include the directory when naming header files  [build/include_subdir] [4]

#include <emscripten.h>
#include <emscripten/val.h>
Expand Down Expand Up @@ -201,183 +201,27 @@
const emscripten::val& wnn_limits,
const logging::Logger& logger);

// Some ONNX ops are supported by decomposed WebNN ops.
const std::map<std::string_view, std::vector<std::string_view>> decomposed_op_map = {
{"ConvInteger", {"cast", "conv2d", "dequantizeLinear"}},
{"GroupQueryAttention",
{"add", "cast", "concat", "constant", "cumulativeSum", "div", "expand", "lesser", "matmul", "reshape", "scatterND",
"softmax", "transpose", "where"}},
{"LRN", {"add", "averagePool2d", "div", "mul", "pad", "pow", "transpose"}},
{"MatMulInteger", {"cast", "dequantizeLinear", "matmul"}},
{"MatMulNBits", {"add", "dequantizeLinear", "matmul", "reshape", "transpose"}},
{"MultiHeadAttention", {"add", "cast", "concat", "constant", "div", "matmul", "reshape", "softmax", "transpose"}},
{"RotaryEmbedding", {"add", "concat", "gather", "mul", "reshape", "slice", "split"}},
{"SimplifiedLayerNormalization", {"add", "div", "mul", "pow", "reduceMean", "sqrt"}},
{"SkipSimplifiedLayerNormalization", {"add", "div", "mul", "pow", "reduceMean", "sqrt"}},
};
// ONNX op type to WebNN op type mapping.
const std::map<std::string_view, std::string_view> op_map = {
{"Abs", "abs"},
{"Add", "add"},
{"And", "logicalAnd"},
{"ArgMax", "argMax"},
{"ArgMin", "argMin"},
{"AveragePool", "averagePool2d"},
{"BatchNormalization", "batchNormalization"},
{"Cast", "cast"},
{"Ceil", "ceil"},
{"Clip", "clamp"},
{"Concat", "concat"},
{"Conv", "conv2d"},
{"ConvTranspose", "convTranspose2d"},
{"Cos", "cos"},
{"CumSum", "cumulativeSum"},
{"Div", "div"},
{"DequantizeLinear", "dequantizeLinear"},
{"Dropout", "identity"},
{"DynamicQuantizeLinear", "dynamicQuantizeLinear"},
{"Einsum", "matmul"},
{"Elu", "elu"},
{"Equal", "equal"},
{"Erf", "erf"},
{"Exp", "exp"},
{"Expand", "expand"},
{"Flatten", "reshape"},
{"Floor", "floor"},
{"Gather", "gather"},
{"GatherElements", "gatherElements"},
{"GatherND", "gatherND"},
{"Gelu", "gelu"},
{"Gemm", "gemm"},
{"GlobalAveragePool", "averagePool2d"},
{"GlobalMaxPool", "maxPool2d"},
{"GlobalLpPool", "l2Pool2d"},
{"Greater", "greater"},
{"GreaterOrEqual", "greaterOrEqual"},
{"GRU", "gru"},
{"HardSigmoid", "hardSigmoid"},
{"HardSwish", "hardSwish"},
{"Identity", "identity"},
{"InstanceNormalization", "instanceNormalization"},
{"LayerNormalization", "layerNormalization"},
{"LeakyRelu", "leakyRelu"},
{"Less", "lesser"},
{"LessOrEqual", "lesserOrEqual"},
{"Log", "log"},
{"LpPool", "l2Pool2d"},
{"LSTM", "lstm"},
{"MatMul", "matmul"},
{"Max", "max"},
{"MaxPool", "maxPool2d"},
{"Min", "min"},
{"Mul", "mul"},
{"Neg", "neg"},
{"Not", "logicalNot"},
{"Or", "logicalOr"},
{"Pad", "pad"},
{"Pow", "pow"},
{"PRelu", "prelu"},
{"QuantizeLinear", "quantizeLinear"},
{"Reciprocal", "reciprocal"},
{"ReduceL1", "reduceL1"},
{"ReduceL2", "reduceL2"},
{"ReduceLogSum", "reduceLogSum"},
{"ReduceLogSumExp", "reduceLogSumExp"},
{"ReduceMax", "reduceMax"},
{"ReduceMean", "reduceMean"},
{"ReduceMin", "reduceMin"},
{"ReduceProd", "reduceProduct"},
{"ReduceSum", "reduceSum"},
{"ReduceSumSquare", "reduceSumSquare"},
{"Relu", "relu"},
{"Reshape", "reshape"},
{"Resize", "resample2d"},
{"ScatterElements", "scatterElements"},
{"ScatterND", "scatterND"},
{"Shape", "slice"},
{"Sigmoid", "sigmoid"},
{"Sign", "sign"},
{"Softplus", "softplus"},
{"Softsign", "softsign"},
{"Sin", "sin"},
{"Slice", "slice"},
{"Softmax", "softmax"},
{"Split", "split"},
{"Sqrt", "sqrt"},
{"Squeeze", "reshape"},
{"Sub", "sub"},
{"Tan", "tan"},
{"Tanh", "tanh"},
{"Tile", "tile"},
{"Transpose", "transpose"},
{"Trilu", "triangular"},
{"Unsqueeze", "reshape"},
{"Where", "where"},
{"Xor", "logicalXor"},
};

// WebNN op name to its first input name mapping, only record the name that is different from "input".
// This map is used to determine the first input name of a WebNN op and is utilized by OpSupportLimits.
const std::map<std::string_view, std::string_view> webnn_op_first_input_name_map = {
{"add", "a"},
{"concat", "inputs"},
{"div", "a"},
{"equal", "a"},
{"gemm", "a"},
{"greater", "a"},
{"greaterOrEqual", "a"},
{"lesser", "a"},
{"lesserOrEqual", "a"},
{"logicalAnd", "a"},
{"logicalNot", "a"},
{"logicalOr", "a"},
{"logicalXor", "a"},
{"matmul", "a"},
{"max", "a"},
{"min", "a"},
{"mul", "a"},
{"pow", "a"},
{"sub", "a"},
{"where", "condition"},
};

// Retrieve the first input name of a WebNN op used for validating supported input data types.
// WebNN ops have various first input names such as 'a', 'input', 'inputs', etc.
// Special names other than 'input' are recorded in the webnn_op_first_input_name_map.
// All WebNN op inputs are recorded in op_inputs_map.
inline std::string_view GetWebNNOpFirstInputName(const std::string_view webnn_op_type) {
auto it = webnn_op_first_input_name_map.find(webnn_op_type);
return (it != webnn_op_first_input_name_map.end()) ? it->second : "input";
auto it = op_inputs_map.find(webnn_op_type);
if (it != op_inputs_map.end()) {
for (const auto& input : it->second.inputs) {
if (input.index == 0) {
return input.name;
}
}
}
return "input";
}

inline std::string_view GetWebNNOpType(const std::string_view op_type) {
auto it = op_map.find(op_type);
// Return an empty string if the op_type is not listed in the op_map.
return (it != op_map.end()) ? it->second : "";
auto it = op_inputs_map.find(op_type);
// Return an empty string if the op_type is not listed in the op_inputs_map.
return (it != op_inputs_map.end()) ? it->second.opType : "";
}

const std::map<ONNX_NAMESPACE::TensorProto_DataType, std::string_view> onnx_to_webnn_data_type_map = {
{ONNX_NAMESPACE::TensorProto_DataType_INT4, "int4"},
{ONNX_NAMESPACE::TensorProto_DataType_UINT4, "uint4"},
{ONNX_NAMESPACE::TensorProto_DataType_BOOL, "uint8"},
{ONNX_NAMESPACE::TensorProto_DataType_INT8, "int8"},
{ONNX_NAMESPACE::TensorProto_DataType_UINT8, "uint8"},
{ONNX_NAMESPACE::TensorProto_DataType_FLOAT16, "float16"},
{ONNX_NAMESPACE::TensorProto_DataType_FLOAT, "float32"},
{ONNX_NAMESPACE::TensorProto_DataType_INT32, "int32"},
{ONNX_NAMESPACE::TensorProto_DataType_INT64, "int64"},
{ONNX_NAMESPACE::TensorProto_DataType_UINT32, "uint32"},
{ONNX_NAMESPACE::TensorProto_DataType_UINT64, "uint64"},
};

// This array contains the input/output data types of a WebNN graph that are allowed to be fallback to int32.
constexpr std::array<ONNX_NAMESPACE::TensorProto_DataType, 5> supported_fallback_integer_data_types = {
ONNX_NAMESPACE::TensorProto_DataType_BOOL,
ONNX_NAMESPACE::TensorProto_DataType_INT8,
ONNX_NAMESPACE::TensorProto_DataType_UINT8,
ONNX_NAMESPACE::TensorProto_DataType_UINT32,
ONNX_NAMESPACE::TensorProto_DataType_INT64,
};

bool AreDataTypesSame(const std::string_view op_type,
gsl::span<const int32_t> input_types,
const logging::Logger& logger);
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