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[WebNN EP] Add a duplicate entry to support new "dataType" (#17841)

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WebNN spec renames "type" as "dataType" at
https://github.com/webmachinelearning/webnn/pull/464, add a duplicate
entry for "dataType" in order to workaround the compatibility issue.
This commit is contained in:
Wanming Lin 2023-10-12 10:13:13 +08:00 committed by GitHub
parent 565bead85f
commit b3cab55d68
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GPG key ID: 4AEE18F83AFDEB23
4 changed files with 45 additions and 44 deletions
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38
onnxruntime/core/providers/webnn/builders/helper.cc
View file

@ -142,5 +142,43 @@ bool IsValidMultidirectionalBroadcast(std::vector<int64_t>& shape_a,
return true;
}
bool SetWebnnDataType(emscripten::val& desc, const int32_t data_type) {
// WebNN changed the name of the MLOperandDescriptor's data type from "type" to "dataType",
// use a duplicate entry temporarily to workaround this API breaking issue.
// TODO: Remove legacy "type" once all browsers implement the new "dataType".
switch (data_type) {
case ONNX_NAMESPACE::TensorProto_DataType_BOOL:
desc.set("type", emscripten::val("uint8"));
desc.set("dataType", emscripten::val("uint8"));
return true;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16:
desc.set("type", emscripten::val("float16"));
desc.set("dataType", emscripten::val("float16"));
return true;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT:
desc.set("type", emscripten::val("float32"));
desc.set("dataType", emscripten::val("float32"));
return true;
case ONNX_NAMESPACE::TensorProto_DataType_INT32:
desc.set("type", emscripten::val("int32"));
desc.set("dataType", emscripten::val("int32"));
return true;
case ONNX_NAMESPACE::TensorProto_DataType_INT64:
desc.set("type", emscripten::val("int64"));
desc.set("dataType", emscripten::val("int64"));
return true;
case ONNX_NAMESPACE::TensorProto_DataType_UINT32:
desc.set("type", emscripten::val("uint32"));
desc.set("dataType", emscripten::val("uint32"));
return true;
case ONNX_NAMESPACE::TensorProto_DataType_UINT64:
desc.set("type", emscripten::val("uint64"));
desc.set("dataType", emscripten::val("uint64"));
return true;
default:
return false;
}
}
} // namespace webnn
} // namespace onnxruntime

3
onnxruntime/core/providers/webnn/builders/helper.h
View file

@ -231,5 +231,8 @@ bool IsSupportedDataType(const int32_t data_type, const WebnnDeviceType device_t
bool IsValidMultidirectionalBroadcast(std::vector<int64_t>& shape_a,
std::vector<int64_t>& shape_b,
const logging::Logger& logger);
bool SetWebnnDataType(emscripten::val& desc, const int32_t data_type);
} // namespace webnn
} // namespace onnxruntime

2
onnxruntime/core/providers/webnn/builders/impl/shape_op_builder.cc
View file

@ -34,7 +34,7 @@ Status ShapeOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
const auto rank = static_cast<int32_t>(input_shape.size());
emscripten::val desc = emscripten::val::object();
desc.set("type", emscripten::val("int64"));
ORT_RETURN_IF_NOT(SetWebnnDataType(desc, ONNX_NAMESPACE::TensorProto_DataType_INT64), "Unsupported data type");
emscripten::val dims = emscripten::val::array();
dims.call<void>("push", rank);
desc.set("dimensions", dims);

46
onnxruntime/core/providers/webnn/builders/model_builder.cc
View file

@ -122,6 +122,7 @@ Status ModelBuilder::RegisterInitializers() {
auto data_type = tensor.data_type();
emscripten::val operand = emscripten::val::object();
if (IsSupportedDataType(data_type, wnn_device_type_)) {
ORT_RETURN_IF_NOT(SetWebnnDataType(desc, data_type), "Unsupported data type");
unpacked_tensors_.push_back({});
std::vector<uint8_t>& unpacked_tensor = unpacked_tensors_.back();
ORT_RETURN_IF_ERROR(onnxruntime::utils::UnpackInitializerData(tensor, unpacked_tensor));
@ -129,37 +130,30 @@ Status ModelBuilder::RegisterInitializers() {
emscripten::val view = emscripten::val::undefined();
switch (data_type) {
case ONNX_NAMESPACE::TensorProto_DataType_BOOL:
desc.set("type", emscripten::val("uint8"));
view = emscripten::val{emscripten::typed_memory_view(num_elements,
reinterpret_cast<uint8_t*>(unpacked_tensor.data()))};
break;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16:
desc.set("type", emscripten::val("float16"));
view = emscripten::val{emscripten::typed_memory_view(num_elements,
reinterpret_cast<uint16_t*>(unpacked_tensor.data()))};
break;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT:
desc.set("type", emscripten::val("float32"));
view = emscripten::val{emscripten::typed_memory_view(num_elements,
reinterpret_cast<float*>(unpacked_tensor.data()))};
break;
case ONNX_NAMESPACE::TensorProto_DataType_INT32:
desc.set("type", emscripten::val("int32"));
view = emscripten::val{emscripten::typed_memory_view(num_elements,
reinterpret_cast<int32_t*>(unpacked_tensor.data()))};
break;
case ONNX_NAMESPACE::TensorProto_DataType_INT64:
desc.set("type", emscripten::val("int64"));
view = emscripten::val{emscripten::typed_memory_view(num_elements,
reinterpret_cast<int64_t*>(unpacked_tensor.data()))};
break;
case ONNX_NAMESPACE::TensorProto_DataType_UINT32:
desc.set("type", emscripten::val("uint32"));
view = emscripten::val{emscripten::typed_memory_view(num_elements,
reinterpret_cast<uint32_t*>(unpacked_tensor.data()))};
break;
case ONNX_NAMESPACE::TensorProto_DataType_UINT64:
desc.set("type", emscripten::val("uint64"));
view = emscripten::val{emscripten::typed_memory_view(num_elements,
reinterpret_cast<uint64_t*>(unpacked_tensor.data()))};
break;
@ -238,35 +232,7 @@ Status ModelBuilder::RegisterModelInputOutput(const NodeArg& node_arg, bool is_i
}
data_type = type_proto->tensor_type().elem_type();
switch (data_type) {
case ONNX_NAMESPACE::TensorProto_DataType_BOOL:
desc.set("type", emscripten::val("uint8"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16:
desc.set("type", emscripten::val("float16"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT:
desc.set("type", emscripten::val("float32"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_INT32:
desc.set("type", emscripten::val("int32"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_INT64:
desc.set("type", emscripten::val("int64"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_UINT32:
desc.set("type", emscripten::val("uint32"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_UINT64:
desc.set("type", emscripten::val("uint64"));
break;
default: {
// TODO: support other type.
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"The ", input_output_type, " of graph doesn't have valid type, name: ", name,
" type: ", type_proto->tensor_type().elem_type());
}
}
ORT_RETURN_IF_NOT(SetWebnnDataType(desc, data_type), "Unsupported data type");
}
if (is_input) {
@ -316,41 +282,35 @@ Status ModelBuilder::AddOperandFromPersistMemoryBuffer(
memcpy(dest, buffer, size);
emscripten::val view = emscripten::val::undefined();
emscripten::val desc = emscripten::val::object();
ORT_RETURN_IF_NOT(SetWebnnDataType(desc, data_type), "Unsupported data type");
switch (data_type) {
case ONNX_NAMESPACE::TensorProto_DataType_BOOL:
view = emscripten::val{emscripten::typed_memory_view(size / sizeof(uint8_t),
reinterpret_cast<const uint8_t*>(dest))};
desc.set("type", emscripten::val("uint8"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16:
view = emscripten::val{emscripten::typed_memory_view(size / sizeof(uint16_t),
reinterpret_cast<const uint16_t*>(dest))};
desc.set("type", emscripten::val("float16"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT:
view = emscripten::val{emscripten::typed_memory_view(size / sizeof(float),
reinterpret_cast<const float*>(dest))};
desc.set("type", emscripten::val("float32"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_INT32:
view = emscripten::val{emscripten::typed_memory_view(size / sizeof(int32_t),
reinterpret_cast<const int32_t*>(dest))};
desc.set("type", emscripten::val("int32"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_INT64:
view = emscripten::val{emscripten::typed_memory_view(size / sizeof(int64_t),
reinterpret_cast<const int64_t*>(dest))};
desc.set("type", emscripten::val("int64"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_UINT32:
view = emscripten::val{emscripten::typed_memory_view(size / sizeof(uint32_t),
reinterpret_cast<const uint32_t*>(dest))};
desc.set("type", emscripten::val("uint32"));
break;
case ONNX_NAMESPACE::TensorProto_DataType_UINT64:
view = emscripten::val{emscripten::typed_memory_view(size / sizeof(uint64_t),
reinterpret_cast<const uint64_t*>(dest))};
desc.set("type", emscripten::val("uint64"));
break;
default:
break;