Skip to content

ORT 1.24.2 release cherry-pick round 2 #27343

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
tianleiwu merged 2 commits into from
Feb 14, 2026
+537 −75
Merged

ORT 1.24.2 release cherry-pick round 2 #27343

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
7693288
Add boundary checks and add tests for SparseTensorProtoToDenseTensorP…
yuslepukhin Feb 13, 2026
5e9dcdc
Remove AzurePowerShell pwsh parameter in tools/ci_build/github/azure-…
edgchen1 Feb 13, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
212 changes: 146 additions & 66 deletions onnxruntime/core/framework/tensorprotoutils.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1741,117 +1741,140 @@ void MakeCpuTensorCopy(const Tensor& src_tensor, Tensor& dst_tensor) {
}

#if !defined(DISABLE_SPARSE_TENSORS)
static Status CopySparseData(size_t n_sparse_elements,
static Status CopySparseData(const std::string& name,
int64_t nnz_elements,
const ONNX_NAMESPACE::TensorProto& indices,
const std::filesystem::path& model_path,
gsl::span<const int64_t>
dims,
std::function<void(size_t from_idx, size_t to_idx)>
copier) {
gsl::span<const int64_t> dense_dims,
int64_t dense_elements,
std::function<void(size_t from_idx, size_t to_idx)> copier) {
Status status = Status::OK();
TensorShape indices_shape(indices.dims().data(), indices.dims().size());
const auto elements = narrow<size_t>(indices_shape.Size());
const int64_t indices_elements = indices_shape.Size();

std::vector<int64_t> indices_values; // used for conversion of smaller size indices
InlinedVector<int64_t> indices_values; // used for conversion of smaller size indices
std::vector<uint8_t> unpack_buffer;
gsl::span<const int64_t> indices_data;
const bool has_raw_data = indices.has_raw_data();
const bool needs_unpack = utils::HasRawData(indices) || utils::HasExternalData(indices);
switch (indices.data_type()) {
case ONNX_NAMESPACE::TensorProto_DataType_INT64:
if (has_raw_data) {
ORT_RETURN_IF_NOT(indices.raw_data().size() == (elements * sizeof(int64_t)),
"Sparse Indices raw data size does not match expected.");
if (needs_unpack) {
ORT_RETURN_IF_NOT(indices.raw_data().size() == (narrow<size_t>(indices_elements) * sizeof(int64_t)),
"Sparse tensor: ", name, " indices raw data size does not match expected: ",
indices_elements * sizeof(int64_t));
ORT_RETURN_IF_ERROR(UnpackInitializerData(indices, model_path, unpack_buffer));
indices_data = ReinterpretAsSpan<const int64_t>(gsl::make_span(unpack_buffer));
} else {
ORT_RETURN_IF_NOT(indices.int64_data_size() == static_cast<int64_t>(elements),
"Sparse indices int64 data size does not match expected");
indices_data = gsl::make_span(indices.int64_data().data(), elements);
ORT_RETURN_IF_NOT(indices.int64_data_size() == indices_elements,
"Sparse tensor: ", name, " indices int64 data size does not match expected: ",
indices_elements);
indices_data = gsl::make_span(indices.int64_data().data(), narrow<size_t>(indices_elements));
}
break;
case ONNX_NAMESPACE::TensorProto_DataType_INT32: {
if (has_raw_data) {
ORT_RETURN_IF_NOT(indices.raw_data().size() == (elements * sizeof(int32_t)),
"Sparse Indices raw data size does not match expected.");
if (needs_unpack) {
ORT_RETURN_IF_NOT(indices.raw_data().size() == (narrow<size_t>(indices_elements) * sizeof(int32_t)),
"Sparse tensor: ", name, " indices raw data size does not match expected: ",
indices_elements * sizeof(int32_t));
ORT_RETURN_IF_ERROR(UnpackInitializerData(indices, model_path, unpack_buffer));
auto int32_span = ReinterpretAsSpan<const int32_t>(gsl::make_span(unpack_buffer));
indices_values.insert(indices_values.cend(), int32_span.begin(), int32_span.end());
unpack_buffer.clear();
unpack_buffer.shrink_to_fit();
} else {
ORT_RETURN_IF_NOT(indices.int32_data_size() == static_cast<int64_t>(elements),
"Sparse indices int32 data size does not match expected");
ORT_RETURN_IF_NOT(indices.int32_data_size() == indices_elements,
"Sparse tensor: ", name, " indices int32 data size does not match expected: ",
indices_elements);
indices_values.insert(indices_values.cend(), indices.int32_data().cbegin(), indices.int32_data().cend());
}
indices_data = gsl::make_span(indices_values);
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_INT16: {
if (has_raw_data) {
ORT_RETURN_IF_NOT(indices.raw_data().size() == (elements * sizeof(int16_t)),
"Sparse Indices raw data size does not match expected.");
if (needs_unpack) {
ORT_RETURN_IF_NOT(indices.raw_data().size() == (narrow<size_t>(indices_elements) * sizeof(int16_t)),
"Sparse tensor: ", name, " indices raw data size does not match expected: ",
indices_elements * sizeof(int16_t));
ORT_RETURN_IF_ERROR(UnpackInitializerData(indices, model_path, unpack_buffer));
auto int16_span = ReinterpretAsSpan<const int16_t>(gsl::make_span(unpack_buffer));
indices_values.insert(indices_values.cend(), int16_span.begin(), int16_span.end());
indices_data = gsl::make_span(indices_values);
unpack_buffer.clear();
unpack_buffer.shrink_to_fit();
} else {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Invalid SparseTensor indices. INT16 indices must be in the raw data of indices tensor");
ORT_RETURN_IF_NOT(indices.int32_data_size() == indices_elements,
"Sparse tensor: ", name, " indices int16 data size does not match expected: ",
indices_elements);
indices_values.insert(indices_values.cend(), indices.int32_data().cbegin(), indices.int32_data().cend());
}
indices_data = gsl::make_span(indices_values);
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_INT8: {
if (has_raw_data) {
ORT_RETURN_IF_NOT(indices.raw_data().size() == elements,
"Sparse Indices raw data size does not match expected.");
if (needs_unpack) {
ORT_RETURN_IF_NOT(indices.raw_data().size() == narrow<size_t>(indices_elements),
"Sparse tensor: ", name, " indices raw data size does not match expected: ",
indices_elements * sizeof(int8_t));
ORT_RETURN_IF_ERROR(UnpackInitializerData(indices, model_path, unpack_buffer));
auto int8_span = ReinterpretAsSpan<const int8_t>(gsl::make_span(unpack_buffer));
indices_values.insert(indices_values.cend(), int8_span.begin(), int8_span.end());
indices_data = gsl::make_span(indices_values);
unpack_buffer.clear();
unpack_buffer.shrink_to_fit();
} else {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Invalid SparseTensor indices. INT8 indices must be in the raw data of indices tensor");
ORT_RETURN_IF_NOT(indices.int32_data_size() == indices_elements,
"Sparse tensor: ", name, " indices int8 data size does not match expected: ",
indices_elements);
indices_values.insert(indices_values.cend(), indices.int32_data().cbegin(), indices.int32_data().cend());
}
indices_data = gsl::make_span(indices_values);
break;
}
default:
return ORT_MAKE_STATUS(
ONNXRUNTIME, INVALID_GRAPH,
"Invalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64");
"Sparse tensor: ", name, " indices. Should be one of the following types: int8, int16, int32 or int64");
}

if (indices_shape.NumDimensions() == 1) {
const auto indices_rank = indices_shape.NumDimensions();
if (indices_rank == 1) {
// flattened indexes
for (size_t i = 0; i < n_sparse_elements; ++i) {
copier(i, narrow<size_t>(indices_data[i]));
for (size_t i = 0, lim = narrow<size_t>(nnz_elements); i < lim; ++i) {
const auto idx = indices_data[i];
ORT_RETURN_IF_NOT(idx >= 0 && idx < dense_elements,
"Sparse tensor: ", name, " index is out of bounds. Got:", idx,
" expected to be in [0, ", dense_elements, ")");

copier(i, narrow<size_t>(idx));
}
} else if (indices_shape.NumDimensions() == 2) {
} else if (indices_rank == 2) {
// entries in format {NNZ, rank}
ORT_ENFORCE(indices_shape[1] > 0 && static_cast<size_t>(indices_shape[1]) == dims.size());
auto rank = static_cast<size_t>(indices_shape[1]);
ORT_ENFORCE(indices_shape[1] > 0 && static_cast<size_t>(indices_shape[1]) == dense_dims.size());
const auto rank = static_cast<size_t>(indices_shape[1]);
auto cur_index = indices_data.begin();
std::vector<size_t> multipliers;
InlinedVector<size_t> multipliers;
multipliers.resize(rank);

// calculate sum of inner dimension elements for each dimension.
// e.g. if shape {2,3,4}, the result should be {3*4, 4, 1}
multipliers[rank - 1] = 1;
for (auto r = rank - 1; r > 0; --r) {
multipliers[r - 1] = SafeInt<size_t>(dims[r]) * multipliers[r];
multipliers[r - 1] = SafeInt<size_t>(dense_dims[r]) * multipliers[r];
}

// calculate the offset for the entry
// e.g. if shape was {2,3,4} and entry was (1, 0, 2) the offset is 14
// as there are 2 rows, each with 12 entries per row
for (size_t i = 0; i < n_sparse_elements; ++i) {
for (size_t i = 0, lim = narrow<size_t>(nnz_elements); i < lim; ++i) {
SafeInt<int64_t> idx = 0;
for (size_t j = 0; j < rank; ++j) {
idx += SafeInt<int64_t>(cur_index[j]) * multipliers[j];
const auto dim_index = cur_index[j];
ORT_RETURN_IF_NOT(dim_index >= 0 && dim_index < dense_dims[j],
"Sparse tensor: ", name, " index is out of bounds. Got:", dim_index,
" expected to be in [0, ", dense_dims[j], ")");
idx += SafeInt<int64_t>(dim_index) * multipliers[j];
}
ORT_RETURN_IF_NOT(idx >= 0 && idx < dense_elements,
"Sparse tensor: ", name, " index is out of bounds. Got:", static_cast<int64_t>(idx),
" expected to be in [0, ", dense_elements, ")");

copier(i, static_cast<size_t>(idx));
cur_index += rank;
Expand All @@ -1860,7 +1883,7 @@ static Status CopySparseData(size_t n_sparse_elements,
ORT_ENFORCE(cur_index == indices_data.end());
} else {
status = ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Invalid SparseTensor indices. Should be rank 0 or 1. Got:", indices_shape);
"Sparse tensor: ", name, " indices shape. Expected to be rank 1 or 2. Got:", indices_shape);
}

return status;
Expand All @@ -1869,53 +1892,110 @@ static Status CopySparseData(size_t n_sparse_elements,
common::Status SparseTensorProtoToDenseTensorProto(const ONNX_NAMESPACE::SparseTensorProto& sparse,
const std::filesystem::path& model_path,
ONNX_NAMESPACE::TensorProto& dense) {
Status status = Status::OK();
Status status;

const auto& sparse_values = sparse.values();
auto type = sparse_values.data_type();
dense.set_data_type(type);
*dense.mutable_name() = sparse_values.name();
const auto& name = sparse_values.name();

SafeInt<size_t> n_sparse_elements = 1;
for (auto dim : sparse_values.dims()) {
n_sparse_elements *= dim;
const auto values_rank = sparse_values.dims_size();
if (values_rank != 1) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Sparse tensor: ", name, " values should be rank 1 for COO format. Got:", values_rank);
}

SafeInt<size_t> n_dense_elements = 1;
auto type = sparse_values.data_type();
dense.set_data_type(type);
*dense.mutable_name() = name;
SafeInt<int64_t> dense_elements = 1;

for (auto dim : sparse.dims()) {
n_dense_elements *= dim;
if (dim < 0) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Sparse tensor: ", name, " dense dims expected to be non-negative. Got:", dim);
}
dense_elements *= dim;
dense.add_dims(dim);
}

const auto dense_dims = gsl::make_span<const int64_t>(dense.dims().data(), dense.dims().size());

SafeInt<int64_t> nnz_elements = 1;
for (auto dim : sparse_values.dims()) {
if (dim < 0) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Sparse tensor: ", name, " tensor dims expected to be non-negative. Got:", dim);
}
nnz_elements *= dim;
}

const auto& indices = sparse.indices();
auto dims = gsl::make_span<const int64_t>(dense.dims().data(), dense.dims().size());
const auto indices_rank = indices.dims_size();
if (indices_rank != 1 && indices_rank != 2) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Sparse tensor: ", name, " indices should be rank 1 or 2 for supported COO format. Got:", indices_rank);
}

if (type != TensorProto_DataType_STRING) {
auto ml_data = DataTypeImpl::TensorTypeFromONNXEnum(type)->GetElementType();
size_t element_size = ml_data->Size();
const auto indices_dims = gsl::make_span(indices.dims().data(), indices.dims().size());

if (indices_dims[0] != nnz_elements) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Sparse tensor: ", name,
" indices outer dimension should match the number of non-zero values. Got:",
indices_dims[0], " expected: ", static_cast<int64_t>(nnz_elements));
}

// need to read in sparse data first as it could be in a type specific field, in raw data, or in external data
std::vector<uint8_t> sparse_data_storage;
ORT_RETURN_IF_ERROR(UnpackInitializerData(sparse_values, model_path, sparse_data_storage));
void* sparse_data = sparse_data_storage.data();
if (indices_rank == 2 && dense_dims.size() != narrow<size_t>(indices_dims[1])) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Sparse tensor: ", name,
" indices is rank 2, its inner dimension should match the rank of the dense tensor. Got:",
indices_dims[1], " expected: ", dense_dims.size());
}

if (indices_rank == 2) {
const auto num_indices = TensorShape(indices_dims).Size();
const int64_t expected_indices_entries = SafeInt<int64_t>(nnz_elements) * indices_dims[1];
if (num_indices != expected_indices_entries) {
return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_GRAPH,
"Sparse tensor: ", name,
" indices is rank 2, it should have NNZ values * indices_dims[1] entries. Got:",
num_indices, " expected: ", expected_indices_entries);
}
}

if (dense_elements == 0) {
// if there are no elements in the dense tensor, we can return early with an empty tensor proto
return status;
}

if (type != ONNX_NAMESPACE::TensorProto_DataType_STRING) {
auto ml_data = DataTypeImpl::TensorTypeFromONNXEnum(type)->GetElementType();
const size_t element_size = ml_data->Size();

// by putting the data into a std::string we can avoid a copy as set_raw_data can do a std::move
// into the TensorProto.
std::string dense_data_storage(n_dense_elements * element_size, 0);
if (n_sparse_elements > 0) {
std::string dense_data_storage(narrow<size_t>(dense_elements) * element_size, 0);
if (nnz_elements > 0) {
// need to read in sparse data first as it could be in a type specific field, in raw data, or in external data
std::vector<uint8_t> values_data;
ORT_RETURN_IF_ERROR(UnpackInitializerData(sparse_values, model_path, values_data));
ORT_RETURN_IF_NOT(values_data.size() == static_cast<size_t>(nnz_elements) * element_size,
"Sparse tensor: ", name, " values data size does not match expected: ",
static_cast<size_t>(nnz_elements) * element_size);
void* sparse_data = values_data.data();
void* dense_data = dense_data_storage.data();

switch (element_size) {
case 1: {
status = CopySparseData(
n_sparse_elements, indices, model_path, dims, [sparse_data, dense_data](size_t from_idx, size_t to_idx) {
name, nnz_elements, indices, model_path, dense_dims, dense_elements,
[sparse_data, dense_data](size_t from_idx, size_t to_idx) {
static_cast<uint8_t*>(dense_data)[to_idx] = static_cast<const uint8_t*>(sparse_data)[from_idx];
});

break;
}
case 2: {
status = CopySparseData(n_sparse_elements, indices, model_path, dims,
status = CopySparseData(name, nnz_elements, indices, model_path, dense_dims, dense_elements,
[sparse_data, dense_data](size_t from_idx, size_t to_idx) {
const auto* src = static_cast<const uint16_t*>(sparse_data) + from_idx;
auto* dst = static_cast<uint16_t*>(dense_data) + to_idx;
Expand All @@ -1925,7 +2005,7 @@ common::Status SparseTensorProtoToDenseTensorProto(const ONNX_NAMESPACE::SparseT
break;
}
case 4: {
status = CopySparseData(n_sparse_elements, indices, model_path, dims,
status = CopySparseData(name, nnz_elements, indices, model_path, dense_dims, dense_elements,
[sparse_data, dense_data](size_t from_idx, size_t to_idx) {
const auto* src = static_cast<const uint32_t*>(sparse_data) + from_idx;
auto* dst = static_cast<uint32_t*>(dense_data) + to_idx;
Expand All @@ -1935,7 +2015,7 @@ common::Status SparseTensorProtoToDenseTensorProto(const ONNX_NAMESPACE::SparseT
break;
}
case 8: {
status = CopySparseData(n_sparse_elements, indices, model_path, dims,
status = CopySparseData(name, nnz_elements, indices, model_path, dense_dims, dense_elements,
[sparse_data, dense_data](size_t from_idx, size_t to_idx) {
const auto* src = static_cast<const uint64_t*>(sparse_data) + from_idx;
auto* dst = static_cast<uint64_t*>(dense_data) + to_idx;
Expand Down
12 changes: 8 additions & 4 deletions onnxruntime/core/framework/tensorprotoutils.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -249,10 +249,14 @@ common::Status ConstantNodeProtoToTensorProto(const ONNX_NAMESPACE::NodeProto& n
void MakeCpuTensorCopy(const Tensor& src_tensor, Tensor& dst_tensor);

#if !defined(DISABLE_SPARSE_TENSORS)
// Convert a SparseTensorProto to a dense TensorProto
// If the SparseTensorProto contains external data then it loads the data and converts to dense tensor proto
// The resulting TensorProto will contain the data as raw data.
// model_path is used for constructing full path for external_data
/// <summary>
// The function supports only COO format with 1D or 2D indices. Values shape is expected to be 1D.
// The function does not support sparse tensors of other formats like CSR/CSC.
/// </summary>
/// <param name="sparse"></param>
/// <param name="model_path">model path is only used if there are references to external data.</param>
/// <param name="dense">The resulting dense tensor proto.</param>
/// <returns>Status</returns>
common::Status SparseTensorProtoToDenseTensorProto(const ONNX_NAMESPACE::SparseTensorProto& sparse,
const std::filesystem::path& model_path,
ONNX_NAMESPACE::TensorProto& dense);
Expand Down
Loading
Loading