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 the oneDNN Graph implementation, which allows a scalable (no change in user code to benefit from new fusion patterns) and platform aware partitioning.
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 multidimensional 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.
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 (can be incomplete for outputs), 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.
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 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 logical tensor belongs to.
An operation (or OP) describes a deep neural network operation. OP contains kind, attribute, and input and output logical tensor shapes and properties. In particular, activation and weights tensor formats are specified as attributes to the operation.
Each operation has a unique ID and users should guarantee that uniqueness within the graph which the OP is added to.
Graph contains a set of OPs.
dnnl::graph::graph::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. During
the target OP will be validated against its schema. Once the validation fails,
an exception will be thrown out from the API. When
dnnl::graph::graph::add_op() call returns a status. It is the user’s responsibility
to handle the error either by checking the return value of the API or handling
A same logical tensor may appear more than twice in
dnnl::graph::graph::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.
Once the graph is fully described,
dnnl::graph::graph::finalize() should be called. This
prevents any other operation from being added, and allows to call
dnnl::graph::graph::get_partitions() in order to get the set of partitions for that
graph. The graph doesn’t hold any meaning to the user after
partitioning and should freed by the user.
All the OPs added to the graph will be contained in one of the returned
partitions. If an OP is not supported by the oneDNN Graph API implementation,
the corresponding partition will be marked as “not supported”. Users can check
the supporting status of a partition via the
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.
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.
Users can pass the output logical tensors with incomplete shape
DNNL_GRAPH_UNKNOWN_DIM) to partition compilation API. oneDNN Graph
implementation needs calculate the output shapes according to the
given input shapes and schema of the OP. After compilation finished, a
compiled partition will be generated with full shape information for
the input and output logical tensors. Users can query the compiled
partition for the output logical tensors and get the shapes.
Partition can be compiled to generate a
executable object to run the computation for that partition. 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. Typically, the more
information is given before the partition step (e.g. number of
dimensions and tensor dimensions), the most performant the code under
the compiled partition will be.
Users must specify
opaque as the
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.
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.
Tensor is an abstraction for multidimensional input and output data needed in the execution of a compiled partition. A tensor contains a logical tensor, an engine and a data handle.
Users are responsible for managing the tensor’s lifecycle, e.g. free the resource allocated, when it is not used anymore.
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
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.
dnnl::engine) are an abstraction of a computational device. The
graph extension additionally allows to create an engine with specific
host/device allocators to conveniently manage memory inside the Graph
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.
The C++ API throws exceptions for error handling.