[CoreML] Adapt to `MLMultiArray.dataPointer` deprecation (#17726)
### Description This PR addresses https://github.com/microsoft/onnxruntime/issues/17652. The deprecated `MLMultiArray.dataPointer` is replaced with `.getBytesWithHandler`, as suggested by the docs. For now, I am only checking that the output `MLMultiArray` is contiguous, returning unsupported operation when that is not the case. I think this is already better than what we have right now, so we can block unsafe calls to `.dataPointer` (if any..). I would be happy to implement the handling of the non-contiguous case (replacing `memcpy` for such cases) as suggested by @edgchen1, but I am not sure how to reproduce that case to add a corresponding unit-test. Would we have to define a custom `MLCustomLayer` to get a non-contiguous output from a model..? ### Motivation and Context Fix https://github.com/microsoft/onnxruntime/issues/17652. --------- Co-authored-by: nicolo-lucchesi <nicolo.lucchesi@hexagon.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
This commit is contained in:
Nicolò Lucchesi
GitHub
Родитель
41f9379f3c
Коммит
cbb85b4874
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@ -8,6 +8,7 @@
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#include <algorithm>
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#include <cstdint>
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#include <optional>
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#include <unordered_map>
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#include <vector>
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@ -169,6 +170,60 @@ Status CreateInputFeatureProvider(const std::unordered_map<std::string, OnnxTens
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conversion_buffers_out = std::move(conversion_buffers);
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return Status::OK();
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}
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bool IsArrayContiguous(const MLMultiArray* array) {
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int64_t batch_stride = [array.strides[0] longLongValue];
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const auto* shape = array.shape;
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int64_t batch_elems = 1;
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for (unsigned long i = 1; i < shape.count; i++) batch_elems *= [shape[i] longLongValue];
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return batch_stride == batch_elems;
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}
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Status CopyMLMultiArrayBuffer(const void* mlmultiarray_buffer, void* tensor_buffer,
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const MLMultiArray* array_info,
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const OnnxTensorInfo* tensor_info,
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const std::optional<unsigned long> mlmultiarray_buffer_size) {
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if (mlmultiarray_buffer == nullptr) {
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "mlmultiarray_buffer has no data");
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}
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const size_t num_elements = array_info.count;
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const auto onnx_data_type = tensor_info->data_type;
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switch (onnx_data_type) {
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case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
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const auto output_data_byte_size = num_elements * sizeof(float);
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ORT_RETURN_IF_NOT(!mlmultiarray_buffer_size || mlmultiarray_buffer_size == output_data_byte_size,
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"CoreML output buffer size and expected output size differ");
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memcpy(tensor_buffer, mlmultiarray_buffer, output_data_byte_size);
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break;
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}
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case ONNX_NAMESPACE::TensorProto_DataType_INT32: {
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const auto output_data_byte_size = num_elements * sizeof(int32_t);
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ORT_RETURN_IF_NOT(!mlmultiarray_buffer_size || mlmultiarray_buffer_size == output_data_byte_size,
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"CoreML output buffer size and expected output size differ");
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memcpy(tensor_buffer, mlmultiarray_buffer, output_data_byte_size);
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break;
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}
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// For this case, since Coreml Spec only uses int32 for model output while onnx provides
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// int64 for model output data type. We are doing a type casting (int32 -> int64) here
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// when copying the model to ORT
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case ONNX_NAMESPACE::TensorProto_DataType_INT64: {
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ORT_RETURN_IF_NOT(array_info.dataType == MLMultiArrayDataTypeInt32,
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"CoreML output data type is not MLMultiArrayDataTypeInt32");
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ORT_RETURN_IF_NOT(!mlmultiarray_buffer_size || mlmultiarray_buffer_size == num_elements * sizeof(int32_t),
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"CoreML output buffer size and expected output size differ");
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const auto model_output_span = gsl::span{static_cast<const int32_t*>(mlmultiarray_buffer), num_elements};
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const auto output_span = gsl::span{static_cast<int64_t*>(tensor_buffer), num_elements};
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std::transform(model_output_span.begin(), model_output_span.end(), output_span.begin(),
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[](int32_t v) { return static_cast<int64_t>(v); });
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break;
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}
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default:
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL,
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"Output data type is not supported, actual type: ", onnx_data_type);
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}
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return Status::OK();
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}
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} // namespace
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NS_ASSUME_NONNULL_BEGIN
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@ -298,9 +353,9 @@ NS_ASSUME_NONNULL_BEGIN
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "output_features has no value for ", output_name);
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}
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auto* data = [output_value multiArrayValue];
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MLMultiArray* data = [output_value multiArrayValue];
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const auto coreml_static_output_shape = [&]() {
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const auto coreml_static_output_shape = [data]() {
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InlinedVector<int64_t> result;
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result.reserve(data.shape.count);
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for (NSNumber* dim in data.shape) {
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@ -324,41 +379,21 @@ NS_ASSUME_NONNULL_BEGIN
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") do not match");
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}
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const void* model_output_buffer = data.dataPointer;
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if (model_output_buffer == nullptr) {
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "model_output_buffer has no data for ", output_name);
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}
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const auto onnx_data_type = output_tensor_info.data_type;
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switch (onnx_data_type) {
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case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
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const auto output_data_byte_size = num_elements * sizeof(float);
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memcpy(output_buffer, model_output_buffer, output_data_byte_size);
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break;
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}
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case ONNX_NAMESPACE::TensorProto_DataType_INT32: {
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const auto output_data_byte_size = num_elements * sizeof(int32_t);
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memcpy(output_buffer, model_output_buffer, output_data_byte_size);
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break;
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}
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// For this case, since Coreml Spec only uses int32 for model output while onnx provides
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// int64 for model output data type. We are doing a type casting (int32 -> int64) here
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// when copying the model to ORT
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case ONNX_NAMESPACE::TensorProto_DataType_INT64: {
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ORT_RETURN_IF_NOT(data.dataType == MLMultiArrayDataTypeInt32,
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"CoreML output data type is not MLMultiArrayDataTypeInt32");
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const auto model_output_span = gsl::span{static_cast<const int32_t*>(model_output_buffer), num_elements};
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const auto output_span = gsl::span{static_cast<int64_t*>(output_buffer), num_elements};
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std::transform(model_output_span.begin(), model_output_span.end(), output_span.begin(),
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[](int32_t v) { return static_cast<int64_t>(v); });
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break;
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}
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default:
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL,
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"Output data type is not supported, actual type: ", onnx_data_type);
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ORT_RETURN_IF_NOT(IsArrayContiguous(data),
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"Non-contiguous output MLMultiArray is not currently supported");
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__block Status copy_status;
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const auto* tensor_info = &output_tensor_info;
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// `getBytesWithHandler` replaces deprecated `.dataPointer` on new versions
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if (@available(macOS 12.3, iOS 15.4, *)) {
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[data getBytesWithHandler:^(const void* bytes, NSInteger size) {
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copy_status = CopyMLMultiArrayBuffer(bytes, output_buffer, data, tensor_info, size);
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}];
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} else {
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// disable size check as old API does not return buffer length
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copy_status = CopyMLMultiArrayBuffer(data.dataPointer, output_buffer, data, tensor_info, std::nullopt);
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}
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if (!copy_status.IsOK())
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return copy_status;
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}
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}
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}
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