Programming Model

oneDNN Graph programming model allows users to pass a computation graph and get partitions. Users then compile partitions, bind tensor data, and execute compiled partitions. Partitions are decided by oneDNN Graph implementation, which is the key concept to satisfy the different needs of AI hardware classes using a unified API.

The programming model assumes that the main usage is to support deep learning (DL) frameworks or inference engines. DL frameworks have their own representation for the computation graph. oneDNN Graph API is used to offload or accelerate graph partitions from a framework graph. In the description below, “graph” refers to the graph built by oneDNN Graph implementation, and “framework graph” refers to the graph built by the DL framework.

A deep learning computation graph consists of deep neural network (DNN) operations. A DNN operation is a function that takes input data and returns output data. The input and output data are multi-dimensional arrays called tensors. A DNN operation may consume multiple tensors and produce multiple tensors. A tensor must be produced by a single operation and may be consumed by multiple operations.

oneDNN Graph API uses logical tensor, OP, and graph to represent a computation graph. Logical tensor represents tensor’s metadata, like element data type, shape, and layout. OP represents an operation on a computation graph. OP has kind, attribute, and input and output logical tensors. OPs are added to a graph. Both OP and logical tensor contains a unique ID, so that the graph knows how to connect a producer OP to a consumer OP through a logical tensor. The graph constructed is immutable. The purpose of creating the graph object is to get partitions. After partitions are created, the graph object is not useful anymore. Once users get partitions, users should not add OP to the graph. The order of OPs being added to the graph is considered as the order of OP being executed.

oneDNN Graph defines operation set. Users should convert their DNN operation definition to oneDNN Graph operation for graph construction. For operation outside oneDNN Graph operation set, users may use wild-card OP. The wild-card OP represents any OP. With its input and output logical tensors, it enables the oneDNN Graph implementation to receive a full graph and conduct a complete analysis. User needs to use a special “End” op to indicate output tensors of the graph. For any tensors needs to be alive after a graph being executed, it needs to be connected to a “End” op which consumes the tensor. Users may have multiple “End” ops for one graph. For each OP users add to the graph, users must describe its input and output logical tensors. Users must describe data type for each logical tensor. If tensor’s shape and layout are known, users must describe them along with the logical tensor.

A partition is a connected subgraph in a graph. oneDNN Graph implementation analyzes a graph and returns a number of partitions. The returned partitions completely cover all the OPs of the graph and follow topological order. A partition typically contains multiple Ops. Sometimes a partition may contain just one OP, like a Wildcard OP or unsupported OP. A partition contains a flag to indicate whether the partition is supported and thus can be compiled and executed. User needs to check the flag before using the partition.

Partition’s input and output is also called as port. The ports record the logical tensor information which was passed during graph construction. With the logical tensor ID, users can track the producer and consumer relationship between partitions. The ports also record the data type of corresponding logical tensors.

The returned partitions to users must not form a dependence cycle. For example, a graph contains 3 OPs: A, B, and C. If C consumes A’s output and produces B’s input, oneDNN Graph implementation must not put A and B into one partition. However, if C is not added to the graph, the returned partition may include A and B, since C is not visible to oneDNN Graph implementation. In this case, it is the user’s responsibility to detect the dependence cycle. Once users pass a complete graph, users don’t need to check the dependence cycle among the partitions returned by oneDNN Graph.

A partition needs to be compiled before execution. The compilation lowers down the compute logic to hardware ISA level and generates binary code. The generated code is specialized for the input and output tensor’s metadata. Users must create new logical tensors to pass complete metadata with the compilation API. The logical tensors should fully specify id, data type, shape, and layout, the compilation should succeed. The logical tensors passed during compilation time must match IDs with partition’s ports. The logical tensors must have same data types with the ports with the port of the same ID.

For the output logical tensors, users must either specify a public layout using size and stride for each tensor dimension or request oneDNN Graph implementation to decide a target-specific layout. For the input logical tensors, users must either specify a public layout or using a target-specific layout produced by predecessor partition compilation. For the logical tensor with target-specific layout, it must be produced by a partition and used only by partitions.

A compiled partition represents the generated code specialized for target hardware and tensor metadata passed with compilation API. Users may cache the compiled partition to amortize the compilation cost among many iterations. If tensor metadata is identical, a compiled partition generated in previous iterations may be reused. Alternatively, implementations may reduce the partition compilation cost by caching the compiled partition internally. This optimization falls outside of the scope of this specification.

To execute a compiled partition, users must pass input and output tensors. Input tensors must bind input data buffers to logical tensors. Users may query the compiled partition for output data buffer sizes. If the sizes are known, users may allocate the output data buffers and bind to output tensors. If the sizes are unknown, users must provide an allocator for oneDNN Graph implementation to allocate the output tensor buffer. The execution API takes a compiled partition, input tensors, and return output tensors with the data buffer updated.

An engine represents a target device and context in the system. It needs to be passed as a parameter for partition compilation. A stream abstracts hardware execution resources of a target device. It is required to execute a compiled partition.

_images/programming_concepts.png

The diagram above summarizes the key programming concepts, and how they interact with each other. The arrow indicates the destination object contains or uses the source object. For example, OP contains logical tensor, and compiled partition uses partition.

Logical Tensor

Logical tensor describes the metadata of the input or output tensor, like element data type, number of dimensions, size for each dimension, layout.

Besides helping oneDNN Graph implementation to build the graph, Logical tensor plays a critical role to exchange tensor metadata information between users and oneDNN Graph implementation. Users pass input tensor shape information and get the inferred shape for output tensors from a partition. Users pass logical tensors to compilation API for specifying shape and layout information. Users also use a special logical tensor to allow oneDNN Graph implementation to decide the layout for output tensors. After compilation, users can query the compiled partition for output tensors’ shape, layout, and sizes.

Each logical tensor has an ID. The tensor metadata may include new shape information in the framework graph as it progresses toward execution. As a logical tensor is not mutable, users must create a new logical tensor with the same ID to pass any new additional information to oneDNN Graph implementation. Users should guarantee that the logical tensor ID is unique within the graph which the logcial tensor belongs to.

/// Data Type
enum class data_type {
        undef = dnnl_graph_data_type_undef,
        /// 16-bit/half-precision floating point.
        f16 = dnnl_graph_f16,
        /// non-standard 16-bit (bfloat16 w/ 7 bit mantissa) floating point.
        bf16 = dnnl_graph_bf16,
        /// 32-bit/single-precision floating point.
        f32 = dnnl_graph_f32,
        /// 32-bit signed integer.
        s32 = dnnl_graph_s32,
        /// 8-bit signed integer.
        s8 = dnnl_graph_s8,
        /// 8-bit unsigned integer.
        u8 = dnnl_graph_u8,
};

/// Layout type
enum class layout_type {
        /// undefined layout type
        undef = dnnl_graph_layout_type_undef,
        /// any means that oneDNN graph implementation needs to decide the
        /// layout for the compiled partition.
        any = dnnl_graph_layout_type_any,
        /// strided means that the layout is determined by the strides field.
        strided = dnnl_graph_layout_type_strided,
        /// opaque means that the layout is a target-specific layout decided by
        /// oneDNN graph implementation.
        opaque = dnnl_graph_layout_type_opaque,
};

class logical_tensor {
dnnl_graph_logical_tensor_t data;

/// Constructs a logical tensor object 
logical_tensor(const dnnl_graph_logical_tensor_t &c_data);

/// Constructs a logical tensor object
///
/// @param tid Tensor id
/// @param dtype Data type
/// @param ndims Number of dimension, -1 means it's unknown, 0 means scalar
/// @param ltype Layout type
logical_tensor(size_t tid, data_type dtype, int32_t ndims, layout_type ltype);
 
/// Constructs a logical tensor object
///
/// @param tid Tensor id
/// @param dtype Data type
/// @param adims Tensor dimensions, -1 means a particular axis of dims is
///        unknown, or the axis can be deduced by its size and other axis.
/// @param ltype Layout type
logical_tensor(size_t tid, data_type dtype, const dims_t &adims,
                layout_type ltype);
 
/// Constructs a strided logical tensor object which accepts strides
///
/// @param tid Tensor id
/// @param dtype Data type
/// @param adims Tensor dimensions, -1 means a particular axis of dims is
///        unknown, or the axis can be deduced by its size and other axis.
/// @param strides Tensor strides
logical_tensor(size_t tid, data_type dtype, const dims_t &adims,
                const dims_t &strides);
 
/// Constructs an opaque logical tensor object which accepts layout id
///
/// @param tid Tensor id
/// @param dtype Data type
/// @param adims Tensor dimensions, -1 means a particular axis of dims is
///        unknown, or the axis can be deduced by its size and other axis.
/// @param lid Layout id
logical_tensor(size_t tid, data_type dtype, const dims_t &adims, size_t lid);

/// Returns dimensions of the logical tensor
///
/// @returns A the dimensions vector
dims_t get_dims() const;
 
/// Returns unique id of the logical tensor
///
/// @returns Id number
size_t get_id() const;
 
/// Returns data type of the logical tensor
///
/// @returns The data type
data_type get_data_type() const;
 
/// Returns layout type of the logical tensor
///
/// @returns The layout type
layout_type get_layout_type() const;
 
/// Returns the layout of the tensor
///
/// @returns Layout id
int64_t get_layout_id() const;
 
/// Returns strides of this logical tensor
///
/// @returns A copy of strides vector
dims_t get_strides() const;
 
/// Get memory size required by this logical tensor
///
/// @returns The memory size in bytes
size_t get_mem_size() const;

/// Compares if this and input logical tensor has the same layout and dtype
///
/// @param lt The input logical tensor to be compared
/// @returns @c true if they have the same layout
///        @c false if they have different layout
bool has_same_layout_and_dtype(const logical_tensor &lt) const;
};

OP

OP describes a deep neural network operation. OP contains kind, attribute, and input and output logical tensor.

Conv op contains format attributes for both activation and weight tensor, to indicate the semantics of each dimension of tensors. For example, the 2D conv may specify the dimension order is either NHWC or NCHW. oneDNN Graph uses one letter X to generalize all the spatial dimensions so NXC or NCX are used for the last example. Users should guarantee the OP ID is unique within the graph which the OP is added to.

class op {
/// Constructs an OP object
///
/// @param id The unique id of this op
/// @param akind The op kind specifies which computation is represented by
///     the op, such as Convolution and ReLU.
/// @param debug_string The string added for debug
op(size_t id, kind akind, const std::string &debug_string);
 
/// Contructs an Op object based on input/output tensors and attributes
///
/// @param id The unique id of this op.
/// @param akind The op kind specifies which computation is represented by
///     this op, such as Convolution and ReLU.
/// @param inputs Input logical tensor to be bound to this op.
/// @param outputs Output logical tensor to be bound to this op
/// @param debug_string The string added for debug
op(size_t id, kind akind, const std::vector<logical_tensor> &inputs,
        const std::vector<logical_tensor> &outputs,
        const std::string &debug_string);
 
/// Adds input logical tensor to the op
///
/// @param t Input logical tensor
void add_input(const logical_tensor &t);
 
/// Adds input logical tensors to the op
///
/// @param ts The list of input logical tensors
void add_inputs(const std::vector<logical_tensor> &ts);
 
/// Adds output logical tensor to the op
///
/// @param t Output logical tensor
void add_output(const logical_tensor &t);
 
/// Adds output logical tensors to the op
///
/// @param t The list of output logical tensors
void add_outputs(const std::vector<logical_tensor> &ts);

/// Sets the attribute according to the name and type (int64_t)
///
/// @tparam Type Attribute's type
/// @param name Attribute's name
/// @param a The attribute's value
/// @returns The Op self
template <typename Type,
        requires<std::is_same<Type, int64_t>::value> = true>
op &set_attr(const std::string &name, const Type &a);

/// Sets the attribute according to the name and type (float)
///
/// @tparam Type Attribute's type
/// @param name Attribute's name
/// @param a The attribute's value
/// @returns The Op self
template <typename Type, requires<std::is_same<Type, float>::value> = true>
op &set_attr(const std::string &name, const Type &a);

/// Sets the attribute according to the name and type (bool)
///
/// @tparam Type Attribute's type
/// @param name Attribute's name
/// @param a The attribute's value
/// @returns The Op self
template <typename Type, requires<std::is_same<Type, bool>::value> = true>
op &set_attr(const std::string &name, const Type &a);

/// Sets the attribute according to the name and type (string)
///
/// @tparam Type Attribute's type
/// @param name Attribute's name
/// @param a The attribute's value
/// @returns The Op self
template <typename Type,
        requires<std::is_same<Type, std::string>::value> = true>
op &set_attr(const std::string &name, const Type &a);

/// Sets the attribute according to the name and type
/// (std::vector<int64_t>)
///
/// @tparam Type Attribute's type
/// @param name Attribute's name
/// @param a The attribute's value
/// @returns The Op self
template <typename Type,
        requires<std::is_same<Type, std::vector<int64_t>>::value> = true>
op &set_attr(const std::string &name, const Type &a);

/// Sets the attribute according to the name and type
/// (std::vector<float>)
///
/// @tparam Type Attribute's type
/// @param name Attribute's name
/// @param a The attribute's value
/// @returns The Op self
template <typename Type,
        requires<std::is_same<Type, std::vector<float>>::value> = true>
op &set_attr(const std::string &name, const Type &a);

/// Returns the string format of the Op id and kind
///
/// @returns Op id and kind in string format
std::string to_string() const;
};

Graph

Graph contains a set of OPs. add_op() adds an OP and its logical tensors to a graph. oneDNN Graph implementation accumulates the OPs and logical tensors and constructs and validates the graph as internal state. At the end of graph construction, users may call get_partitions() which returns a set of partitions. After get_partitions(), users shall not add ops to the graph. The graph doesn’t hold any meaning to the user after partitioning. Users should free the graph.

A same logical tensor may appear more than twice in add_op() call, since it is passed with the producer OP and consumer OPs. oneDNN Graph validates logical tensors with the same id should be identical at the graph construction time.

The order of OP being added to the graph is considered as the order of OP being executed. The returned partitions should not contain OP not supported by the oneDNN Graph API implementation. Partitions should not form cyclic dependence within the graph. If user doesn’t pass a complete graph, it is the user’s responsibility to detect any dependence cycle between the partitions and operations not passing to oneDNN Graph implementation.

The logical tensor passed at the graph construction stage might contain incomplete information, for example, dimension and shape information are spatially known. Complete information is not required but helps the oneDNN Graph to form better partition decisions. Adding op to a graph is not thread-safe. Users must create a graph, add op, and get partition in the same thread.

class graph {
public:
/// Constructs a graph session using device information
///
/// @param engine_kind Can be cpu, gpu or any supported engine.
graph(engine::kind engine_kind);

/// Add an op to the graph session to construct DAG for analysis
///
/// @param op An operator/node that represents the entry of frameworks'
///    graph
void add_op(const op &op);

using partition_vec = std::vector<partition>;
/// Get filtered partitions
///
/// @param policy Partition policy, defaults to
///     #dnnl::graph::partition::policy::fusion
/// @return partition_vec A vector storing the partitions
partition_vec get_partitions(partition_policy policy = partition::policy::fusion);
};

Partition

Partition represents a collection of OPS identified by oneDNN Graph implementation as the basic unit for compilation and execution. It contains a list of OP, input ports, output ports, and a flag indicating whether the partition is supported. When a partition is created, it’s assigned with an ID. oneDNN Graph implementation should guarantee the partition ID is globally unique.

Partition can infer the output logical tensor’s shape according to the input logical tensor shape. Users may create input and output logical tensors and pass them as parameters of the shape inference API. After calling the shape inference API, users can get the shape information from the output logical tensor.

Partition can be compiled to generate hardware ISA level binary code specialized for input and output tensors’ metadata. Users must pass as much tensor metadata as possible to get the best performant compiled code. When users pass partition shape information, it is implementation-dependent to decide whether to support the compilation.

Users must create an input logical tensor list and an output logical tensor list to pass the additional tensor metadata as parameters to the compilation API. The input and output logical tensors must match the id of partitions’ ports, which captures the logical tensors information during graph partitioning.

Users must specify strided, any, or opaque as the layout_type for the parameter logical tensors. When users specify any for a logical tensor, the tensor must be an output tensor, and oneDNN Graph implementation decides the best performant layout for the compiled partition. If it is strided, it must use the public data layout described by the logical tensor. For opaque, the parameter logical tensor contains a target-specific layout, which must be determined by the compilation of preceding partitions producing the tensor. If the layout is row-major contiguous, the compilation must succeed. If the layout has a stride, it is implementation dependent whether the compilation succeed. If certain dimension of shape or the rank is unknown, it is implementation dependent whether the compilation succeed. If the compilation succeeds for unknown dimension or rank, the compiled partition should be able to handle any value for that dimension or any rank at the execution time.

class partition {
public:
/// Returns the number of ops in the partition
///
/// @returns Number of ops
size_t get_ops_num() const;
 
/// Returns all op’s id of the partition
///
/// @returns An unordered set of op ids
std::vector<size_t> get_ops();
 
/// Returns the unique id of the partition
///
/// @returns Unique id
size_t get_id() const;

/// Returns the kind of engine used to create the partition
///
/// @returns The engine kind
engine::kind get_engine_kind() const;

/// Compile the partition to generate compiled partition based
/// on the input/output logical tensors. The order of these two lists
/// may have already been changed according to the fwk fused node.
///
/// @param inputs A list of input logical tensors
/// @param outputs A list of output logical tensors
/// @param e The engine used to compile the partition
/// @returns A compiled partition
compiled_partition compile(const std::vector<logical_tensor> &inputs,
        const std::vector<logical_tensor> &outputs, const engine &e) const;
 
/// Infer the shape of outputs
///
/// @param inputs A list of input logical tensors
/// @param outputs A list of output logical tensors
void infer_shape(const std::vector<logical_tensor> &inputs, 
                std::vector<logical_tensor> &outputs);

/// Returns the supporting status of the partition
///
/// @returns @c true if this partition is supported by oneDNN Graph backend
///     @c false if this partition isn't supported by oneDNN Graph backend
bool is_supported() const;

/// Returns a list of input logical tensors from the partition
///
/// @returns A list of input logical tensors
std::vector<logical_tensor> get_in_ports() const;

/// Returns a list of output logical tensors from the partition
///
/// @returns A list of output logical tensor
std::vector<logical_tensor> get_out_ports() const;
};

Tensor

Tensor is an abstraction for multidimensional input and output data needed in the execution of a compiled partition. A tensor contains a logical tensor and a data buffer.

Framework integration code is responsible for managing the tensor’s lifecycle, e.g. free the resource allocated, when it is not used anymore.

class tensor {
logical_tensor_t data;
public:
/// Constructs a tensor object according to the given logical tensor
///
/// @param lt The given logical tensor
/// @param handle Handle of memory buffer to use as an underlying storage,
///    if the ndims in the logical tensor is 0, data handle holds a scalar
tensor(const logical_tensor &lt, void *handle);

/// Returns the underlying memory buffer with the specific type
///
/// @tparam T Type of the request buffer
/// @returns The underlying memory buffer
template <typename T>
typename std::add_pointer<T>::type get_data_handle() const;
 
/// Sets the underlying memory buffer
///
/// @param handle Data handle. For the CPU engine, the data handle
///     is a pointer to the actual data.
void set_data_handle(void *handle);

/// Returns the number of elements in the tensor
///
/// @returns Number of elements
int64_t get_element_num() const;
};

Compiled Partition

A compiled partition represents the generated code specialized for target hardware and meta data described by parameter logical tensors. Compiled partition contains a partition and a handle representing the target specific compiled object.

After the compilation API is invoked, users must query the logical output tensor of the compiled partition to know the output tensor’s layout id and size. The layout id is an opaque identifier for the target-specific layout. Users may pass the layout id for the next partition compilation so that it can be optimized to expect a specific input layout. Users may use the size to allocate the memory buffer of the output tensors for execution.

Framework passes the tensors and compiled partition as parameters to execution API. The parameter logical tensors must be in the same order when they are passed in the compilation API, and their IDs must match with the compiled partition’s internal logical tensors. The layout type of each tensor must be strided or opaque.

The compiled partition may support in-place optimization, which reuses the input tensor data buffer for the output tensor for lower memory footprint and better data locality. For each compiled partition, users can get pairs of input and output ports. For the pair of input and output ports, user can use a same memory buffer when passing input and output tensors along with execution API. The in-place optimization is optional, when users use another memory buffer for the output tensor, oneDNN Graph must update the output tensor.

If users place a tensor with data buffer pointer in outputs, the backend shall use the data buffer provided by users.

Users may convert the parameter tensor with public layout to the target specific layout expected by the compiled partition. A common optimization in deep learning inference is that users may prepack the weight in the target-specific layout required by the compiled partition and cache the reordered weight for late use.

class compiled_partition {
public:
    /// Returns the logical tensor according to tensor id
    ///
    /// @param tid The unique id of required tensor
    /// @returns The logical tensor
    logical_tensor query_logical_tensor(size_t tid) const;

    /// Execute a compiled partition
    ///
    /// @param astream Stream object to run over
    /// @param inputs A list of input tensors in the partition
    /// @param outputs A list of output tensors in the partition
    void execute(stream &astream, const std::vector<tensor> &inputs,
            const std::vector<tensor> &outputs) const;

    /// Returns the in-place ports
    ///
    /// @note
    ///     Each entry of the returned vector is a pair of IDs of input and
    ///     output ports. For in-place ports, users can assign same memory
    ///     buffer when passing tensor along with execution API. The in-place
    ///     optimization is optional, users can always use different memory
    ///     buffers for the execution.
    ///
    /// @returns List of pairs of that indicates input and output use same
    ///     memory buffer.
    std::vector<std::pair<size_t, size_t>> get_inplace_ports() const;

    /// Returns the source partition used to create the compiled partition
    ///
    /// @returns The source partition
    partition get_source_partition() const;
};

/// Executes a compiled partition in a specified stream and returns a SYCL
/// event.
///
/// @param c_partition Compiled partition to execute.
/// @param astream Stream object to run over
/// @param inputs Arguments map.
/// @param outputs Arguments map.
/// @param deps Optional vector with `cl::sycl::event` dependencies.
/// @returns Output event.
inline cl::sycl::event dnnl::graph::sycl_interop::execute(
        compiled_partition &c_partition, stream &astream,
        const std::vector<tensor> &inputs, std::vector<tensor> &outputs,
        const std::vector<cl::sycl::event> &deps = {});

Engine

Engine represents a device and its context. Compiled partitions are associated with engines. A compiled partition should only access the tensor which is associated with the same device and context, no matter the tensor is produced by a compiled partition or created directly by the user.

Engine contains device kind, and a device id or device handle. From the device kind, the engine knows how to generate code for the target device and what kind of device object to be expected. The device id ensures that there is a unique engine being created for each device. The device handle passed from framework allows oneDNN Graph implementation to work on the device specified by the framework.

User programs may access the device directly and interoperate with oneDNN Graph to perform a task on the device. Typically user programs manage the device, which create the device handle and use that to create an oneDNN Graph engine. User programs can generate a tensor on a device and pass it to a compiled partition associated with that engine.

class engine {
public:
/// engine kind
enum class kind {
    /// An unspecified engine
    any = dnnl_graph_any_engine,
    /// CPU engine
    cpu = dnnl_graph_cpu,
    /// GPU engine
    gpu = dnnl_graph_gpu,
};

/// Constructs an engine with specified kind and device_id
///
/// @param akind The kind of engine to construct
/// @param device_id Specify which device to be used
engine(kind akind, int device_id);

/// Returns device handle of the current engine
///
/// @returns Device handle
void *get_device_handle() const;
 
/// Returns device id of the current engine
///
/// @returns Device id
int get_device_id() const;
 
/// Returns concrete kind of the current engine
///
///@returns Kind of engine
kind get_kind() const;
};

/// Constructs an engine from SYCL device and context objects.
///
/// @param adevice SYCL device.
/// @param acontext SYCL context.
///
/// @returns Created engine.
inline engine dnnl::graph::sycl_interop::make_engine(
        const cl::sycl::device &adevice, const cl::sycl::context &acontext);

Stream

Stream is the logical abstraction for execution units. It is created on top of oneDNN Graph engine. For SYCL device, it contains an openCL queue. oneDNN Graph engine may have multiple streams. A compiled partition is submitted to a stream for execution.

class stream {
public:
    /// Constructs a stream for the specified engine.
    /// @param engine Engine to create stream on
    /// @param attr A stream attribute, defaults to nullptr    
    stream(engine &aengine, const stream_attr *attr = nullptr); 

    /// Waits for all compiled partitions executing in the stream to finish.
    /// @returns The stream itself.
    stream &wait();
};

/// Creates an execution stream for a given engine associated with a SYCL
/// queue.
///
/// @param aengine Engine object to use for the stream.
/// @param aqueue SYCL queue to use for the stream.
///
/// @returns An execution stream.
inline stream dnnl::graph::sycl_interop::make_stream(engine aengine,
        const cl::sycl::queue &aqueue);

General API notes

There are certain assumptions on how oneDNN Graph objects behave:

  • Logical tensor behave similarly to trivial types.

  • All other objects behave like shared pointers. Copying is always shallow.

Error Handling

The C++ API throws exceptions for error handling.