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Rename Tensor.Size() to Tensor.SizeInBytes() (#1502)

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Rename Tensor.Size() to Tensor.SizeInBytes()
This commit is contained in:
Yufeng Li 2019-07-26 14:15:53 -07:00 committed by GitHub
parent 6f538dc861
commit d6a30485be
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GPG key ID: 4AEE18F83AFDEB23
11 changed files with 17 additions and 17 deletions
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2
include/onnxruntime/core/framework/tensor.h
View file

@ -170,7 +170,7 @@ class Tensor final {
/**
The number of bytes of data.
*/
size_t Size() const {
size_t SizeInBytes() const {
size_t ret;
int64_t l = shape_.Size();
if (l >= static_cast<int64_t>(std::numeric_limits<ptrdiff_t>::max())) {

4
onnxruntime/core/framework/data_transfer.cc
View file

@ -21,8 +21,8 @@ common::Status CPUDataTransfer::CopyTensor(const Tensor& src, Tensor& dst, int /
return Status::OK();
}
// Copying only happens between two same size tensors.
ORT_ENFORCE(src.Size() == dst.Size());
memcpy(dst_data, src_data, src.Size());
ORT_ENFORCE(src.SizeInBytes() == dst.SizeInBytes());
memcpy(dst_data, src_data, src.SizeInBytes());
return Status::OK();
}

2
onnxruntime/core/framework/tensorprotoutils.cc
View file

@ -557,7 +557,7 @@ ONNX_NAMESPACE::TensorProto TensorToTensorProto(const Tensor& tensor, const std:
tensor_proto.set_data_type(tensor_proto_type.tensor_type().elem_type());
tensor_proto.set_raw_data(tensor.DataRaw(), tensor.Size());
tensor_proto.set_raw_data(tensor.DataRaw(), tensor.SizeInBytes());
return tensor_proto;
}

12
onnxruntime/core/providers/cpu/controlflow/loop.cc
View file

@ -302,7 +302,7 @@ void LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector<OrtValue>& last_outpu
Status LoopImpl::ConcatenateLoopOutput(std::vector<OrtValue>& per_iteration_output, int output_index) {
const auto& first_output = per_iteration_output.front().Get<Tensor>();
size_t bytes_per_iteration = first_output.Size();
size_t bytes_per_iteration = first_output.SizeInBytes();
const auto& per_iteration_shape = first_output.Shape();
const auto& per_iteration_dims = per_iteration_shape.GetDims();
@ -317,19 +317,19 @@ Status LoopImpl::ConcatenateLoopOutput(std::vector<OrtValue>& per_iteration_outp
// we can't easily use a C++ template for the tensor element type,
// so use a span for some protection but work in bytes
gsl::span<gsl::byte> output_span = gsl::make_span<gsl::byte>(static_cast<gsl::byte*>(output->MutableDataRaw()),
output->Size());
output->SizeInBytes());
for (int64_t i = 0; i < num_iterations; ++i) {
auto& ort_value = per_iteration_output[i];
auto& iteration_data = ort_value.Get<Tensor>();
// sanity check
if (bytes_per_iteration != iteration_data.Size()) {
if (bytes_per_iteration != iteration_data.SizeInBytes()) {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Inconsistent shape in loop output for output ", output_index,
" Expected:", per_iteration_shape, " Got:", iteration_data.Shape());
}
auto num_bytes = iteration_data.Size();
auto num_bytes = iteration_data.SizeInBytes();
auto src = gsl::make_span<const gsl::byte>(static_cast<const gsl::byte*>(iteration_data.DataRaw()), num_bytes);
auto dst = output_span.subspan(i * bytes_per_iteration, bytes_per_iteration);
gsl::copy(src, dst);
@ -382,8 +382,8 @@ Status LoopImpl::Execute(FeedsFetchesManager* ffm, const FeedsFetchesManager* ca
auto copy_tensor_from_mlvalue_to_output = [this](const OrtValue& input, int output_idx) {
auto& data = input.Get<Tensor>();
Tensor* output = context_.Output(output_idx, data.Shape());
auto src = gsl::make_span<const gsl::byte>(static_cast<const gsl::byte*>(data.DataRaw()), data.Size());
auto dst = gsl::make_span<gsl::byte>(static_cast<gsl::byte*>(output->MutableDataRaw()), output->Size());
auto src = gsl::make_span<const gsl::byte>(static_cast<const gsl::byte*>(data.DataRaw()), data.SizeInBytes());
auto dst = gsl::make_span<gsl::byte>(static_cast<gsl::byte*>(output->MutableDataRaw()), output->SizeInBytes());
gsl::copy(src, dst);
};

2
onnxruntime/core/providers/cpu/controlflow/scan_utils.h
View file

@ -103,7 +103,7 @@ class OutputIterator {
// set the output for the current iteration to zeros. used for short sequence lengths
void ZeroOutCurrent() {
auto* tensor = (**this).GetMutable<Tensor>();
memset(tensor->MutableDataRaw(), 0, tensor->Size());
memset(tensor->MutableDataRaw(), 0, tensor->SizeInBytes());
}
const OrtValue& GetOutput() const {

2
onnxruntime/core/providers/cpu/tensor/scatter.cc
View file

@ -45,7 +45,7 @@ Status CopyScatterData(const Tensor* data_input, const Tensor* indices_input, co
}
const auto input_elements = input_data_shape.Size();
const auto total_input_bytes = data_input->Size();
const auto total_input_bytes = data_input->SizeInBytes();
const auto* src_base = static_cast<const Tdata*>(data_input->DataRaw());
auto* dst_base = static_cast<Tdata*>(data_output->MutableDataRaw());

2
onnxruntime/core/providers/cpu/tensor/size.cc
View file

@ -12,7 +12,7 @@ Status Size::Compute(OpKernelContext* ctx) const {
TensorShape scalar_shape;
Tensor* p_output_tensor = ctx->Output(0, scalar_shape);
auto* p_output_scalar = p_output_tensor->template MutableData<int64_t>();
assert(p_output_tensor->Size() == sizeof(int64_t));
assert(p_output_tensor->SizeInBytes() == sizeof(int64_t));
*p_output_scalar = input_tensor->Shape().Size();

2
onnxruntime/core/providers/cpu/tensor/upsample.cc
View file

@ -342,7 +342,7 @@ Status Upsample<T>::BaseCompute(OpKernelContext* context, const std::vector<floa
Tensor* Y = context->Output(0, Y_dims);
if (no_scale) {
memcpy(Y->MutableDataRaw(), X->DataRaw(), Y->Size());
memcpy(Y->MutableDataRaw(), X->DataRaw(), Y->SizeInBytes());
return Status::OK();
}

2
onnxruntime/core/providers/cuda/gpu_data_transfer.cc
View file

@ -18,7 +18,7 @@ bool GPUDataTransfer::CanCopy(const OrtDevice& src_device, const OrtDevice& dst_
}
common::Status GPUDataTransfer::CopyTensor(const Tensor& src, Tensor& dst, int exec_queue_id) const {
size_t bytes = src.Size();
size_t bytes = src.SizeInBytes();
const void* src_data = src.DataRaw();
void* dst_data = dst.MutableDataRaw();

2
onnxruntime/test/framework/parallel_executor_test.cc
View file

@ -46,7 +46,7 @@ struct TestOp {
// success
Tensor* Y = ctx->Output(0, action_tensor.Shape());
void* target = Y->MutableData<int64_t>();
memcpy(target, action, action_tensor.Size());
memcpy(target, action, action_tensor.SizeInBytes());
break;
}
case 1: {

2
onnxruntime/test/framework/sparse_kernels_test.cc
View file

@ -196,7 +196,7 @@ This operator applies the Abs op element-wise to the input sparse-tensor.
// So, we copy indices/shape from input to output.
// TODO: Extend allocation-planner to enable such sharing.
const auto& input_indices = input->Indices();
memcpy(output->MutableIndices().MutableData<int64_t>(), input_indices.Data<int64_t>(), input_indices.Size());
memcpy(output->MutableIndices().MutableData<int64_t>(), input_indices.Data<int64_t>(), input_indices.SizeInBytes());
return Status::OK();
}
};