[WebNN EP] Support axes and fix some validation for Resize

[Honry]

• Supports arbitrary axes for Resize opset 18+
• Check all inputs and attributes more carefully

[Honry]

@fdwr, @guschmue, PTAL, thanks!

[fdwr]

Thanks Wanming.

[fdwr]

ONNX Resize technically has no notion of NCHW or NHWC - it simply has a list of axes, and dimension semantics are irrelevant (especially now that WebNN supports any axes, layout concepts and limitations matter much less now). Additionally ONNX supports 1D/2D/3D/4D... inputs (whereas ironically WebNN's resample2D doesn't actually support 2D input tensors), but it's unclear that this code currently handles 2D input tensors?

I think it would be cleaner (and more robust) if instead of checking a hard-coded axes sizes and explicit values, that we just normalized the sizes and scales up-front, expanding using the ONNX axes and deriving the WebNN axes from those. Then any other less common but legal cases from ONNX models (like 1D cases which are a subset of 2D, or a 3D case with scales of {1,2,2} where the 1.0 scale is actually irrelevant) should just work too.

e.g.

if (input_rank > 4) {
  LOGS(logger, ERROR) << "WebNN resample does not support input rank more than 4D.";
  return false;
}

... if using scales ...

// Expand the scales to get the full scales per axis.
std::vector<float> webnn_scales;
if (has_axes) {
  webnn_scales = GetScales(...);
} else {
  webnn_scales.assign(input_rank, 1.0f);
  ExpandIntoAxes(/*inout*/ webnn_scales, scales, onnx_axes);
}
ResizeRightAligned(/*inout*/ webnn_scales, 4, 1.0f);

// Determine which axes are actually functional, retaining at least 2 axes.
// Remove any axes where scales == 1.
webnn_axes.resize(4);
std::iota(webnn_axes.begin(), webnn_axes.end(), 0);
for (size_t i = webnn_axes.size(); i-- > 0 && webnn_axes.size() > 2; ) {
  if (webnn_scales[i] == 1.0f) {
    webnn_axes.erase(i);
    webnn_scales.erase(i);
  }
}

... else if instead using sizes ...

// Expand the size to get the full output sizes per axis.
std::vector<uint32_t> webnn_sizes;
if (has_axes) {
  webnn_sizes = GetSizes(...);
} else {
  webnn_sizes = input_shape;
  ExpandIntoAxes(/*inout*/ webnn_sizes, sizes, onnx_axes);
}
ResizeRightAligned(/*inout*/ webnn_sizes, 4, 1u);

// Determine which sizes are actually functional, retaining at least 2 axes.
// Remove any axes where output size equals the original input size.
webnn_axes.resize(4);
std::iota(webnn_axes.begin(), webnn_axes.end(), 0);
for (size_t i = webnn_axes.size(); i-- > 0 && webnn_axes.size() > 2; ) {
  if (webnn_sizes[i] == input_sizes[i]) {
    webnn_axes.erase(i);
    webnn_sizes.erase(i);
  }
}

// At this point, axes has 2 elements, and either scales or sizes has 2 elements.

...
// Ensure the input (which may be 1D/2D/3D) is coerced up to WebNN's required 4D.
ResizeRightAligned(/*inout*/ input_shape_4D_with_leading_ones, 4, 1u);

... reshape(input, input_shape_4D_with_leading_ones);

ExpandIntoAxes is a simple templated function equivalent to:

for (size_t i = 0; i < axes.size(); ++i) {
  output[axes[i]] = input[i]
}

ResizeRightAligned is a simple function that inserts leading values:

some_vector.insert(some_vector.begin(), new_size - some_vector.size(), fill_value);

[Honry]

Good proposal, this is actually a requirement for supporting Resize for input shape less than 4D, currently implementation only supports 4D input.

Besides, in onnxruntime, layout transformation will transpose sizes and scales as well if it is NHWC input layout, (I fixed a minor bug to restrict it to only transpose 4D sizes and scales, https://github.com/microsoft/onnxruntime/pull/21954/files, PTAL :) ), this increases the complexity a bit, so can we move this requirement to a follow-up?

[fdwr]

/azp run ONNX Runtime Web CI Pipeline

[azure-pipelines]

Azure Pipelines successfully started running 1 pipeline(s).

[Honry]

@fdwr, thanks for your comments, I've addressed them % the support for input shape < 4D. PTAL, thanks!

[Honry]

Good proposal, this is actually a requirement for supporting Resize for input shape less than 4D, currently implementation only supports 4D input.

Besides, in onnxruntime, layout transformation will transpose sizes and scales as well if it is NHWC input layout, (I fixed a minor bug to restrict it to only transpose 4D sizes and scales, https://github.com/microsoft/onnxruntime/pull/21954/files, PTAL :) ), this increases the complexity a bit, so can we move this requirement to a follow-up?