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Return right category of error. Use INVALID_ARGUMENT when the input is incorrect. (#1248)

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* Return the right category of error.

* Return right category of error
This commit is contained in:
Pranav Sharma 2019-06-18 11:19:40 -07:00 committed by GitHub
parent 48df19f53d
commit aae24f7f9e
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GPG key ID: 4AEE18F83AFDEB23
1 changed files with 42 additions and 44 deletions
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86
onnxruntime/core/framework/tensorprotoutils.cc
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@ -55,10 +55,10 @@ static Status UnpackTensorWithRawData(const void* raw_data, size_t raw_data_leng
{
size_t expected_size_in_bytes;
if (!onnxruntime::IAllocator::CalcMemSizeForArray(expected_size, sizeof(T), &expected_size_in_bytes)) {
return Status(onnxruntime::common::ONNXRUNTIME, onnxruntime::common::FAIL, "size overflow");
return Status(onnxruntime::common::ONNXRUNTIME, onnxruntime::common::INVALID_ARGUMENT, "size overflow");
}
if (raw_data_length != expected_size_in_bytes)
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL,
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"UnpackTensor: the pre-allocated size does not match the raw data size, expected ",
expected_size_in_bytes, ", got ", raw_data_length);
if (IsLittleEndianOrder()) {
@ -80,33 +80,32 @@ static Status UnpackTensorWithRawData(const void* raw_data, size_t raw_data_leng
}
} // namespace
namespace onnxruntime {
namespace utils {
// This macro doesn't work for Float16/bool/string tensors
#define DEFINE_UNPACK_TENSOR(T, Type, field_name, field_size) \
template <> \
Status UnpackTensor(const ONNX_NAMESPACE::TensorProto& tensor, const void* raw_data, size_t raw_data_len, \
/*out*/ T* p_data, int64_t expected_size) { \
if (nullptr == p_data) { \
const size_t size = raw_data != nullptr ? raw_data_len : tensor.field_size(); \
if (size == 0) return Status::OK(); \
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT); \
} \
if (nullptr == p_data || Type != tensor.data_type()) { \
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT); \
} \
if (raw_data != nullptr) { \
return UnpackTensorWithRawData(raw_data, raw_data_len, expected_size, p_data); \
} \
if (tensor.field_size() != expected_size) \
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "corrupted protobuf data: tensor shape size(", expected_size, \
") does not match the data size(", tensor.field_size(), ") in proto"); \
auto& data = tensor.field_name(); \
for (auto data_iter = data.cbegin(); data_iter != data.cend(); ++data_iter) \
*p_data++ = *reinterpret_cast<const T*>(data_iter); \
return Status::OK(); \
#define DEFINE_UNPACK_TENSOR(T, Type, field_name, field_size) \
template <> \
Status UnpackTensor(const ONNX_NAMESPACE::TensorProto& tensor, const void* raw_data, size_t raw_data_len, \
/*out*/ T* p_data, int64_t expected_size) { \
if (nullptr == p_data) { \
const size_t size = raw_data != nullptr ? raw_data_len : tensor.field_size(); \
if (size == 0) return Status::OK(); \
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT); \
} \
if (nullptr == p_data || Type != tensor.data_type()) { \
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT); \
} \
if (raw_data != nullptr) { \
return UnpackTensorWithRawData(raw_data, raw_data_len, expected_size, p_data); \
} \
if (tensor.field_size() != expected_size) \
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "corrupted protobuf data: tensor shape size(", expected_size, \
") does not match the data size(", tensor.field_size(), ") in proto"); \
auto& data = tensor.field_name(); \
for (auto data_iter = data.cbegin(); data_iter != data.cend(); ++data_iter) \
*p_data++ = *reinterpret_cast<const T*>(data_iter); \
return Status::OK(); \
}
// TODO: complex64 complex128
@ -134,7 +133,7 @@ Status UnpackTensor(const ONNX_NAMESPACE::TensorProto& tensor, const void* /*raw
}
if (tensor.string_data_size() != expected_size)
return Status(common::ONNXRUNTIME, common::FAIL,
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT,
"UnpackTensor: the pre-allocate size does not match the size in proto");
auto& string_data = tensor.string_data();
@ -161,7 +160,7 @@ Status UnpackTensor(const ONNX_NAMESPACE::TensorProto& tensor, const void* raw_d
}
if (tensor.int32_data_size() != expected_size)
return Status(common::ONNXRUNTIME, common::FAIL,
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT,
"UnpackTensor: the pre-allocate size does not match the size in proto");
for (int iter : tensor.int32_data()) {
*p_data++ = static_cast<bool>(iter);
@ -186,14 +185,14 @@ Status UnpackTensor(const ONNX_NAMESPACE::TensorProto& tensor, const void* raw_d
}
if (tensor.int32_data_size() != expected_size)
return Status(common::ONNXRUNTIME, common::FAIL,
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT,
"UnpackTensor: the pre-allocate size does not match the size in proto");
constexpr int max_value = std::numeric_limits<uint16_t>::max();
for (int i = 0; i < static_cast<int>(expected_size); i++) {
int v = tensor.int32_data()[i];
if (v < 0 || v > max_value) {
return Status(common::ONNXRUNTIME, common::FAIL, "data overflow");
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "data overflow");
}
p_data[i] = MLFloat16(static_cast<uint16_t>(v));
}
@ -220,14 +219,14 @@ Status UnpackTensor(const ONNX_NAMESPACE::TensorProto& tensor, const void* raw_d
}
if (tensor.int32_data_size() != expected_size)
return Status(common::ONNXRUNTIME, common::FAIL,
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT,
"UnpackTensor: the pre-allocate size does not match the size in proto");
constexpr int max_value = std::numeric_limits<uint16_t>::max();
for (int i = 0; i < static_cast<int>(expected_size); i++) {
int v = tensor.int32_data()[i];
if (v < 0 || v > max_value) {
return Status(common::ONNXRUNTIME, common::FAIL, "data overflow");
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "data overflow");
}
p_data[i] = BFloat16(static_cast<uint16_t>(v));
}
@ -235,11 +234,11 @@ Status UnpackTensor(const ONNX_NAMESPACE::TensorProto& tensor, const void* raw_d
return Status::OK();
}
#define CASE_PROTO_TRACE(X, Y) \
case ONNX_NAMESPACE::TensorProto_DataType::TensorProto_DataType_##X: \
if (!IAllocator::CalcMemSizeForArrayWithAlignment<alignment>(size, sizeof(Y), out)) { \
return common::Status(common::ONNXRUNTIME, common::FAIL, "Invalid TensorProto"); \
} \
#define CASE_PROTO_TRACE(X, Y) \
case ONNX_NAMESPACE::TensorProto_DataType::TensorProto_DataType_##X: \
if (!IAllocator::CalcMemSizeForArrayWithAlignment<alignment>(size, sizeof(Y), out)) { \
return common::Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Invalid TensorProto"); \
} \
break;
template <size_t alignment>
@ -248,10 +247,10 @@ common::Status GetSizeInBytesFromTensorProto(const ONNX_NAMESPACE::TensorProto&
size_t size = 1;
for (google::protobuf::int64 dim : dims) {
if (dim < 0 || static_cast<uint64_t>(dim) >= std::numeric_limits<size_t>::max()) {
return common::Status(common::ONNXRUNTIME, common::FAIL, "Invalid TensorProto");
return common::Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Invalid TensorProto");
}
if (!IAllocator::CalcMemSizeForArray(size, static_cast<size_t>(dim), &size)) {
return common::Status(common::ONNXRUNTIME, common::FAIL, "Invalid TensorProto");
return common::Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Invalid TensorProto");
}
}
switch (tensor_proto.data_type()) {
@ -285,7 +284,6 @@ std::vector<int64_t> GetTensorShapeFromTensorShapeProto(const ONNX_NAMESPACE::Te
return tensor_shape_vec;
}
struct UnInitializeParam {
void* preallocated;
size_t preallocated_size;
@ -377,7 +375,7 @@ Status TensorProtoToMLValue(const Env& env, const ORTCHAR_T* tensor_proto_path,
{
if (tensor_proto.data_location() == TensorProto_DataLocation_EXTERNAL) {
if (ele_type == ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING)
return Status(common::ONNXRUNTIME, common::FAIL, "string tensor can not have raw data");
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "string tensor can not have raw data");
std::unique_ptr<ExternalDataInfo> external_data_info;
ORT_RETURN_IF_ERROR(ExternalDataInfo::Create(tensor_proto.external_data(), external_data_info));
@ -393,12 +391,12 @@ Status TensorProtoToMLValue(const Env& env, const ORTCHAR_T* tensor_proto_path,
{
void* file_data;
ORT_RETURN_IF_ERROR(env.ReadFileAsString(full_path.c_str(), external_data_info->GetOffset(),
file_data, raw_data_len, deleter_for_file_data.d));
file_data, raw_data_len, deleter_for_file_data.d));
raw_data = file_data;
}
} else if (tensor_proto.has_raw_data()) {
if (ele_type == ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING)
return Status(common::ONNXRUNTIME, common::FAIL, "string tensor can not have raw data");
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "string tensor can not have raw data");
raw_data = tensor_proto.raw_data().data();
raw_data_len = tensor_proto.raw_data().size();
}
@ -411,7 +409,7 @@ Status TensorProtoToMLValue(const Env& env, const ORTCHAR_T* tensor_proto_path,
int64_t tensor_size = 1;
{
for (auto i : tensor_proto.dims()) {
if (i < 0) return Status(common::ONNXRUNTIME, common::FAIL, "tensor can't contain negative dims");
if (i < 0) return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "tensor can't contain negative dims");
tensor_size *= i;
}
}
@ -426,7 +424,7 @@ Status TensorProtoToMLValue(const Env& env, const ORTCHAR_T* tensor_proto_path,
}
if (preallocated && preallocated_size < size_to_allocate)
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL,
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
"The buffer planner is not consistent with tensor buffer size, expected ",
size_to_allocate, ", got ", preallocated_size);
switch (tensor_proto.data_type()) {