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[WebNN] Fix some spelling and naming issues #25433

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Jul 19, 2025
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[WebNN] Fix some spelling and naming issues #25433

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2 changes: 1 addition & 1 deletion onnxruntime/core/providers/webnn/builders/helper.h
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Expand Up @@ -84,7 +84,7 @@ inline std::string GetTensorName(const ConstPointerContainer<std::vector<NodeArg
}

template <typename T>
inline std::vector<T> GetNarrowedIntfromInt64(gsl::span<const int64_t> int64_vec) {
inline std::vector<T> GetNarrowedIntFromInt64(gsl::span<const int64_t> int64_vec) {
std::vector<T> vec;
vec.reserve(int64_vec.size());
std::transform(int64_vec.begin(), int64_vec.end(),
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/core/providers/webnn/builders/impl/attention_helper.h
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Expand Up @@ -9,8 +9,8 @@ namespace webnn {
/*
ScaledDotProductAttention Subgraph: The basis for MultiHeadAttention and GroupQueryAttention
inputs: query, key, value, scale, attention mask, and reshape_output_shape (for reshape)
Abbreviatios: B is batch_size, S is query sequence_length, kv_S is key/value sequence length,
N is number of attention heads, H is head size, W is hidden_size
Abbreviations: B is batch_size, S is query sequence_length, kv_S is key/value sequence length,
N is number of attention heads, H is head size, W is hidden_size

query key
| |
Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/core/providers/webnn/builders/impl/base_op_builder.h
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Expand Up @@ -49,7 +49,7 @@ class BaseOpBuilder : public IOpBuilder {
// with opset version 7 or above for opset domain 'ai.onnx'.
// WebNN EP ignores node support for opset less than 7 by
// default as which will be fallback earlier by ONNX Runtime.
// We still set the mininal supported opset to 1 as we couldn't
// We still set the minimal supported opset to 1 as we couldn't
// get the model opset version at this stage.
virtual int GetMinSupportedOpSet(const Node& /* node */) const { return 1; }
virtual int GetMaxSupportedOpSet(const Node& /* node */) const { return 23; }
Expand Down
40 changes: 20 additions & 20 deletions onnxruntime/core/providers/webnn/builders/impl/conv_op_builder.cc
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Expand Up @@ -78,7 +78,7 @@ common::Status SetConvBaseOptions(ModelBuilder& model_builder,
if (output_padding.size() == 1 && is_conv1d) {
output_padding.push_back(0);
}
options.set("outputPadding", emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(output_padding)));
options.set("outputPadding", emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(output_padding)));

// If output shape is explicitly provided, compute the pads.
// Otherwise compute the output shape, as well as the pads if the auto_pad attribute is SAME_UPPER/SAME_LOWER.
Expand All @@ -87,7 +87,7 @@ common::Status SetConvBaseOptions(ModelBuilder& model_builder,
auto_pad_type, pads_out, output_shape, !is_nhwc));

if (output_shape[0] != -1 && output_shape[1] != -1) {
options.set("outputSizes", emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(output_shape)));
options.set("outputSizes", emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(output_shape)));
}
pads = pads_out;
} else {
Expand All @@ -97,13 +97,13 @@ common::Status SetConvBaseOptions(ModelBuilder& model_builder,

const auto group = helper.Get("group", static_cast<uint32_t>(1));
options.set("groups", group);
options.set("strides", emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(strides)));
options.set("dilations", emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(dilations)));
options.set("strides", emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(strides)));
options.set("dilations", emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(dilations)));

// Permute the ONNX's pads, which is [beginning_height, beginning_width, ending_height, ending_width],
// while WebNN's padding is [beginning_height, ending_height, beginning_width, ending_width].
const std::vector<int64_t> padding{pads[0], pads[2], pads[1], pads[3]};
options.set("padding", emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(padding)));
options.set("padding", emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(padding)));

// Add bias if present.
if (input_defs.size() > 2 && op_type != "ConvInteger") {
Expand All @@ -123,7 +123,7 @@ Status AddInitializerInNewLayout(ModelBuilder& model_builder,

const auto& shape = tensor.dims();
std::vector<uint32_t> dims =
GetNarrowedIntfromInt64<uint32_t>(std::vector<int64_t>(std::begin(shape), std::end(shape)));
GetNarrowedIntFromInt64<uint32_t>(std::vector<int64_t>(std::begin(shape), std::end(shape)));

if (is_conv1d) {
// Support conv1d by prepending a 1 size dimension.
Expand Down Expand Up @@ -172,21 +172,21 @@ Status AddInitializerInNewLayout(ModelBuilder& model_builder,
h * w_t +
w;

uint32_t nnapi_idx;
uint32_t wnn_idx;
if (is_conv == 1) { // L_0231
nnapi_idx = out * h_t * w_t * in_t +
h * w_t * in_t +
w * in_t +
in;
wnn_idx = out * h_t * w_t * in_t +
h * w_t * in_t +
w * in_t +
in;
} else { // L_1230 for depthwise conv weight
nnapi_idx = in * h_t * w_t * out_t +
h * w_t * out_t +
w * out_t +
out;
wnn_idx = in * h_t * w_t * out_t +
h * w_t * out_t +
w * out_t +
out;
}

for (size_t i = 0; i < element_size; i++) {
buffer[element_size * nnapi_idx + i] = src[element_size * onnx_idx + i];
buffer[element_size * wnn_idx + i] = src[element_size * onnx_idx + i];
}
}
}
Expand Down Expand Up @@ -234,7 +234,7 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
} else {
input_shape.push_back(1);
}
std::vector<uint32_t> new_shape = GetNarrowedIntfromInt64<uint32_t>(input_shape);
std::vector<uint32_t> new_shape = GetNarrowedIntFromInt64<uint32_t>(input_shape);
common_options.set("label", node.Name() + "_reshape_input");
input = model_builder.GetBuilder().call<emscripten::val>("reshape", input,
emscripten::val::array(new_shape), common_options);
Expand Down Expand Up @@ -283,7 +283,7 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
// Reshape weight to 4D for conv1d.
if (!is_nhwc || !is_constant_weight) {
// The weight_shape has been appended 1's, reshape weight operand.
std::vector<uint32_t> new_shape = GetNarrowedIntfromInt64<uint32_t>(weight_shape);
std::vector<uint32_t> new_shape = GetNarrowedIntFromInt64<uint32_t>(weight_shape);
common_options.set("label", node.Name() + "_reshape_filter");
filter = model_builder.GetBuilder().call<emscripten::val>("reshape",
filter,
Expand Down Expand Up @@ -338,7 +338,7 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
std::vector<int64_t> w_zero_point_shape;
ORT_RETURN_IF_NOT(GetShape(*input_defs[3], w_zero_point_shape, logger), "Cannot get shape of w_zero_point");
w_scale = model_builder.CreateOrGetConstant<float>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT, 1.0f,
GetNarrowedIntfromInt64<uint32_t>(w_zero_point_shape));
GetNarrowedIntFromInt64<uint32_t>(w_zero_point_shape));
} else {
w_zero_point = model_builder.CreateOrGetConstant<uint8_t>(x_type, 0);
w_scale = x_scale;
Expand All @@ -363,7 +363,7 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
const auto& output_defs = node.OutputDefs();
std::vector<int64_t> output_shape;
ORT_RETURN_IF_NOT(GetShape(*output_defs[0], output_shape, logger), "Cannot get output shape");
std::vector<uint32_t> new_shape = GetNarrowedIntfromInt64<uint32_t>(output_shape);
std::vector<uint32_t> new_shape = GetNarrowedIntFromInt64<uint32_t>(output_shape);
common_options.set("label", node.Name() + "_reshape_output");
output = model_builder.GetBuilder().call<emscripten::val>("reshape",
output,
Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/core/providers/webnn/builders/impl/dropout_op_builder.cc
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Expand Up @@ -53,7 +53,7 @@
if (output_defs.size() > 1) {
std::vector<int64_t> mask_shape;
ORT_RETURN_IF_NOT(GetShape(*output_defs[1], mask_shape, logger), "Cannot get mask output's shape");
std::vector<uint32_t> dims = GetNarrowedIntfromInt64<uint32_t>(mask_shape);
std::vector<uint32_t> dims = GetNarrowedIntFromInt64<uint32_t>(mask_shape);

Check warning on line 56 in onnxruntime/core/providers/webnn/builders/impl/dropout_op_builder.cc

View workflow job for this annotation

GitHub Actions / Optional Lint C++ 

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

Raw Output:
onnxruntime/core/providers/webnn/builders/impl/dropout_op_builder.cc:56:  Add #include <vector> for vector<>  [build/include_what_you_use] [4]
emscripten::val one_constant = model_builder.CreateOrGetConstant<uint8_t>(
ONNX_NAMESPACE::TensorProto_DataType_BOOL, 1, dims);

Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/core/providers/webnn/builders/impl/expand_op_builder.cc
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Expand Up @@ -56,7 +56,7 @@ Status ExpandOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
emscripten::val options = emscripten::val::object();
options.set("label", node.Name());

emscripten::val output_shape_arr = emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(output_shape));
emscripten::val output_shape_arr = emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(output_shape));
emscripten::val output = model_builder.GetBuilder().call<emscripten::val>("expand", input, output_shape_arr, options);
model_builder.AddOperand(node.OutputDefs()[0]->Name(), std::move(output));
return Status::OK();
Expand Down
12 changes: 6 additions & 6 deletions onnxruntime/core/providers/webnn/builders/impl/gemm_op_builder.cc
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Expand Up @@ -55,14 +55,14 @@ Status GemmOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
// If the input A is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions.
if (a_shape.size() == 1) {
a_shape.insert(a_shape.begin(), 1);
emscripten::val a_shape_arr = emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(a_shape));
emscripten::val a_shape_arr = emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(a_shape));
common_options.set("label", node.Name() + "_reshape_a");
a = model_builder.GetBuilder().call<emscripten::val>("reshape", a, a_shape_arr, common_options);
}
// If the input B is 1-D, it is promoted to a matrix by appending a 1 to its dimensions.
if (b_shape.size() == 1) {
b_shape.push_back(1);
emscripten::val b_shape_arr = emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(b_shape));
emscripten::val b_shape_arr = emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(b_shape));
common_options.set("label", node.Name() + "_reshape_b");
b = model_builder.GetBuilder().call<emscripten::val>("reshape", b, b_shape_arr, common_options);
}
Expand All @@ -74,7 +74,7 @@ Status GemmOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
// If A or B input is 1-D, we need to reshape the output back to its original shape.
if (a_shape.size() == 1 || b_shape.size() == 1) {
common_options.set("label", node.Name() + "_reshape_output");
emscripten::val output_shape_arr = emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(output_shape));
emscripten::val output_shape_arr = emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(output_shape));
output = model_builder.GetBuilder().call<emscripten::val>("reshape",
output,
output_shape_arr,
Expand All @@ -95,7 +95,7 @@ Status GemmOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
// The scale input should have the same shape as the zero point input.
a_scale = model_builder.CreateOrGetConstant<float>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT,
1.0f,
GetNarrowedIntfromInt64<uint32_t>(a_zero_point_shape));
GetNarrowedIntFromInt64<uint32_t>(a_zero_point_shape));
} else {
// If a_zero_point is not provided, create default scalar for zero_point and scale inputs.
a_zero_point = model_builder.CreateOrGetConstant<uint8_t>(a_type, 0);
Expand All @@ -115,7 +115,7 @@ Status GemmOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
ORT_RETURN_IF_NOT(GetShape(*input_defs[3], b_zero_point_shape, logger), "Cannot get shape of b_zero_point");
b_scale = model_builder.CreateOrGetConstant<float>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT,
1.0f,
GetNarrowedIntfromInt64<uint32_t>(b_zero_point_shape));
GetNarrowedIntFromInt64<uint32_t>(b_zero_point_shape));
} else {
b_zero_point = model_builder.CreateOrGetConstant<uint8_t>(a_type, 0);
b_scale = model_builder.CreateOrGetConstant<float>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT, 1.0f);
Expand Down Expand Up @@ -143,7 +143,7 @@ Status GemmOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
// If A or B input is 1-D, we need to reshape the output back to its original shape.
if (a_shape.size() == 1 || b_shape.size() == 1) {
common_options.set("label", node.Name() + "_reshape_output");
emscripten::val output_shape_arr = emscripten::val::array(GetNarrowedIntfromInt64<uint32_t>(output_shape));
emscripten::val output_shape_arr = emscripten::val::array(GetNarrowedIntFromInt64<uint32_t>(output_shape));
output = model_builder.GetBuilder().call<emscripten::val>("reshape",
output,
output_shape_arr,
Expand Down
6 changes: 3 additions & 3 deletions onnxruntime/core/providers/webnn/builders/impl/gqa_op_builder.cc
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Expand Up @@ -62,9 +62,9 @@ std::vector<int32_t> repeat_sequence(int32_t sequence_length, int32_t kv_num_hea
}

/** GroupQueryAttention SubGraph.
Abbreviatios: B is batch_size, S is sequence_length, W is hidden_size, P is past_sequence_length
N is number of attention heads, kv_N is number of attention heads for kv, H is head size
G is group size, and G=N/kv_N, W=N*H, h=Sqrt(H).
Abbreviations: B is batch_size, S is sequence_length, W is hidden_size, P is past_sequence_length
N is number of attention heads, kv_N is number of attention heads for kv, H is head size
G is group size, and G=N/kv_N, W=N*H, h=Sqrt(H).
GQA inputs: query, key, value, past_key, past_value, seqlens_k, total_sequence_length
Notes: cos_cache, sin_cache inputs are not supported. If the data type of the inputs (qkv and past kv) is float16,
we cast them to float32 to ensure data precision.
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/core/providers/webnn/builders/impl/mha_op_builder.cc
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Expand Up @@ -31,8 +31,8 @@ class MultiHeadAttentionOpBuilder : public BaseOpBuilder {
};

/** MultiHeadAttention SubGraph.
Abbreviatios: B is batch_size, S is sequence_length, W is hidden_size
N is number of attention heads, H is head size
Abbreviations: B is batch_size, S is sequence_length, W is hidden_size
N is number of attention heads, H is head size
Notes: If the datatype of the inputs (qkv and past kv) is float16, we cast them to float32 to ensure data precision.

query key value
Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc
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Expand Up @@ -234,7 +234,7 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
output = model_builder.GetBuilder().call<emscripten::val>("instanceNormalization", input, options);
// Reshape back to the original output shape for 3D input.
if (input_shape.size() != 4) {
std::vector<uint32_t> output_shape = GetNarrowedIntfromInt64<uint32_t>(input_shape);
std::vector<uint32_t> output_shape = GetNarrowedIntFromInt64<uint32_t>(input_shape);
emscripten::val reshape_output_options = emscripten::val::object();
reshape_output_options.set("label", node.Name() + "reshape_output");
output = model_builder.GetBuilder().call<emscripten::val>("reshape",
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/core/providers/webnn/builders/impl/pool_op_builder.cc
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Expand Up @@ -77,7 +77,7 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
}

// Add Padding.
// Usually using autopadding is more efficient than using explicit padding.
// Usually using auto padding is more efficient than using explicit padding.
// Try to see if we can map explicit padding to auto padding.
const auto onnx_strides = helper.Get("strides", std::vector<int64_t>{1, 1});
const auto onnx_pads = helper.Get("pads", std::vector<int64_t>{0, 0, 0, 0});
Expand All @@ -94,7 +94,7 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
auto_pad_type,
pads_out,
!is_nhwc));
pads = GetNarrowedIntfromInt64<uint32_t>(pads_out);
pads = GetNarrowedIntFromInt64<uint32_t>(pads_out);
}
// Permute the ONNX's pads, which is [beginning_height, beginning_width, ending_height, ending_width],
// while WebNN's padding is [beginning_height, ending_height, beginning_width, ending_width].
Expand Down
2 changes: 1 addition & 1 deletion onnxruntime/core/providers/webnn/builders/impl/qdq_op_builder.cc
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Expand Up @@ -99,7 +99,7 @@ Status QDQOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
if (!has_zero_point) {
if (zero_point_shape.empty()) {
// zero_point has the same shape as the scale tensor.
zero_point_shape = GetNarrowedIntfromInt64<uint32_t>(scale_shape);
zero_point_shape = GetNarrowedIntFromInt64<uint32_t>(scale_shape);
}
// Create a zero constant with the same shape as the scale tensor.
// The zero value has been pre-processed in the CreateOrGetConstant function,
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/core/providers/webnn/builders/impl/resize_op_builder.cc
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Expand Up @@ -223,15 +223,15 @@ Status ResizeOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
ORT_RETURN_IF_NOT(GetResizeSizesAndAxes(model_builder.GetGraphViewer(), node, sizes, axes, is_nhwc,
input_shape, logger),
"Error getting Resize sizes");
webnn_sizes = GetNarrowedIntfromInt64<uint32_t>(sizes);
webnn_sizes = GetNarrowedIntFromInt64<uint32_t>(sizes);
options.set("sizes", emscripten::val::array(webnn_sizes));
} else {
ORT_RETURN_IF_NOT(GetResizeScalesAndAxes(model_builder.GetGraphViewer(), node, scales, axes, is_nhwc, logger),
"Error getting Resize scales");
options.set("scales", emscripten::val::array(scales));
}

std::vector<uint32_t> webnn_axes = GetNarrowedIntfromInt64<uint32_t>(axes);
std::vector<uint32_t> webnn_axes = GetNarrowedIntFromInt64<uint32_t>(axes);
options.set("axes", emscripten::val::array(webnn_axes));

emscripten::val input = model_builder.GetOperand(input_defs[0]->Name());
Expand Down
4 changes: 2 additions & 2 deletions onnxruntime/core/providers/webnn/builders/impl/rotaryEmbedding_op_builder.cc
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Expand Up @@ -334,13 +334,13 @@ Status RotaryEmbeddingOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_build
if (input_is_4d) {
// The output is in 4D shape, we need to transpose it back to the original shape.
// Reuse the transpose_options' permutation because the original permutation also
// happens to be its own inverse. (inserve({0, 2, 1, 3} == {0, 2, 1, 3})
// happens to be its own inverse. (inverse({0, 2, 1, 3} == {0, 2, 1, 3})
transpose_options.set("label", node_name + "_transpose_output");
output = wnn_builder.call<emscripten::val>("transpose", output, transpose_options);
} else {
// The output is in 3D shape, we need to reshape it back to the original shape.
// The output shape is same as the input shape.
const std::vector<uint32_t> output_shape = GetNarrowedIntfromInt64<uint32_t>(input_shape);
const std::vector<uint32_t> output_shape = GetNarrowedIntFromInt64<uint32_t>(input_shape);
emscripten::val reshape_output_options = emscripten::val::object();
reshape_output_options.set("label", node_name + "_reshape_output");
output = wnn_builder.call<emscripten::val>(
Expand Down
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