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[WebNN EP] Support latest WebNN softmax op (#20827)

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Latest WebNN softmax supports N-D input and axis parameter.
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Wanming Lin 2024-06-11 23:27:14 +08:00 committed by GitHub
parent ae4a2e6b3f
commit 043ef5c95f
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2 changed files with 6 additions and 78 deletions
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2
js/web/docs/webnn-operators.md
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@ -80,7 +80,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
| Softsign | ai.onnx(7+) | softsign | ✗ | ✓ | |
| Sin | ai.onnx(7+) | sin | ✗ | ✓ | |
| Slice | ai.onnx(7-9, 10, 11-12, 13+) | slice | ✓ | ✓ | Input 'starts', 'ends', 'axes', and 'steps' if present must be a constant, only supports 'steps' value 1 |
| Softmax | ai.onnx(7-10, 11-12, 13+) | softmax | ✓ | ✓ | Only supports input rank >= 2 |
| Softmax | ai.onnx(7-10, 11-12, 13+) | softmax | ✓ | ✓ | |
| Split | ai.onnx(7-10, 11-12, 13-17, 18+) | split | ✓ | ✓ | Input 'split' if present should be a constant |
| Sqrt | ai.onnx(7-12, 13+) | sqrt | ✓ | ✓ | |
| Squeeze | ai.onnx(7-10, 11-12, 13-20, 21+) | reshape | ✓ | ✓ | Input 'axes' if present should be a constant |

82
onnxruntime/core/providers/webnn/builders/impl/softmax_op_builder.cc
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@ -33,82 +33,16 @@ Status SoftmaxOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
const logging::Logger& logger) const {
const auto& input_defs = node.InputDefs();
emscripten::val input = model_builder.GetOperand(node.InputDefs()[0]->Name());
emscripten::val output = emscripten::val::object();
std::vector<int64_t> input_shape;
ORT_RETURN_IF_NOT(GetShape(*input_defs[0], input_shape, logger), "Cannot get shape");
const auto input_size = input_shape.size();
NodeAttrHelper helper(node);
if (node.SinceVersion() < 13) {
int32_t axis = helper.Get("axis", 1);
axis = static_cast<int32_t>(HandleNegativeAxis(axis, input_size));
// Coerce the input into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}].
if (input_size != 2) {
int32_t first_dim = static_cast<int32_t>(std::reduce(input_shape.begin(), input_shape.begin() + axis,
1, std::multiplies<int64_t>()));
int32_t second_dim = static_cast<int32_t>(std::reduce(input_shape.begin() + axis, input_shape.end(),
1, std::multiplies<int64_t>()));
emscripten::val new_shape = emscripten::val::array(std::vector<int32_t>{first_dim, second_dim});
input = model_builder.GetBuilder().call<emscripten::val>("reshape", input, new_shape);
}
const int32_t default_axis = node.SinceVersion() < 13 ? 1 : -1;
int32_t axis = helper.Get("axis", default_axis);
axis = static_cast<int32_t>(HandleNegativeAxis(axis, input_size));
output = model_builder.GetBuilder().call<emscripten::val>("softmax", input);
// Reshape output to the same shape of input.
if (input_size != 2) {
emscripten::val new_shape = emscripten::val::array();
for (size_t i = 0; i < input_size; i++) {
new_shape.call<void>("push", static_cast<int32_t>(input_shape[i]));
}
output = model_builder.GetBuilder().call<emscripten::val>("reshape", output, new_shape);
}
} else {
int32_t axis = helper.Get("axis", -1);
axis = static_cast<int32_t>(HandleNegativeAxis(axis, input_size));
// Wraparound for transpose the target axis to the last.
// WebNN compute the softmax values of the 2-D input tensor along axis 1.
// https://www.w3.org/TR/webnn/#api-mlgraphbuilder-softmax-method
if (axis != static_cast<int>(input_shape.size() - 1)) {
emscripten::val options = emscripten::val::object();
std::vector<uint32_t> permutation(input_shape.size());
std::iota(permutation.begin(), permutation.end(), 0);
std::rotate(permutation.begin() + axis, permutation.begin() + axis + 1, permutation.end());
options.set("permutation", emscripten::val::array(permutation));
input = model_builder.GetBuilder().call<emscripten::val>("transpose", input, options);
}
// Wraparound for reshape input tensor to 2-D.
if (input_shape.size() != 2) {
uint32_t first_dim = static_cast<uint32_t>(std::reduce(input_shape.begin(), input_shape.begin() + axis,
1, std::multiplies<int64_t>()));
first_dim *= static_cast<uint32_t>(std::reduce(input_shape.begin() + axis + 1, input_shape.end(),
1, std::multiplies<int64_t>()));
uint32_t second_dim = static_cast<uint32_t>(input_shape[axis]);
emscripten::val new_shape = emscripten::val::array(std::vector<uint32_t>{first_dim, second_dim});
input = model_builder.GetBuilder().call<emscripten::val>("reshape", input, new_shape);
}
output = model_builder.GetBuilder().call<emscripten::val>("softmax", input);
// Restore from 2-D to the original shape.
if (input_shape.size() != 2) {
std::vector<uint32_t> new_shape;
std::transform(input_shape.begin(), input_shape.begin() + axis, std::back_inserter(new_shape),
[](int64_t dim) -> uint32_t { return static_cast<uint32_t>(dim); });
std::transform(input_shape.begin() + axis + 1, input_shape.end(), std::back_inserter(new_shape),
[](int64_t dim) -> uint32_t { return static_cast<uint32_t>(dim); });
new_shape.push_back(static_cast<int32_t>(input_shape[axis]));
output = model_builder.GetBuilder().call<emscripten::val>("reshape",
output, emscripten::val::array(new_shape));
}
// Restore the corresponding axis back to the initial position from the last position.
if (axis != static_cast<int>(input_shape.size() - 1)) {
emscripten::val options = emscripten::val::object();
std::vector<uint32_t> permutation(input_shape.size());
std::iota(permutation.begin(), permutation.end(), 0);
std::rotate(permutation.rbegin(), permutation.rbegin() + 1, permutation.rend() - axis);
options.set("permutation", emscripten::val::array(permutation));
output = model_builder.GetBuilder().call<emscripten::val>("transpose", output, options);
}
}
emscripten::val output = model_builder.GetBuilder().call<emscripten::val>("softmax", input, axis);
model_builder.AddOperand(node.OutputDefs()[0]->Name(), std::move(output));
return Status::OK();
}
@ -123,12 +57,6 @@ bool SoftmaxOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& /* initiali
std::vector<int64_t> input_shape;
if (!GetShape(*input_defs[0], input_shape, logger))
return false;
const auto input_size = input_shape.size();
if (input_size < 2) {
LOGS(logger, VERBOSE) << "SoftMax only support input size >= 2d shape, input is "
<< input_size << "d shape";
return false;
}
return true;
}