Intel® oneAPI Deep Neural Network Developer Guide and Reference
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Visible to Intel only — GUID: GUID-370973BF-4A77-4579-990F-BEED9A060ED6
class dnnl::graph::partition
Overview
A partition object. More…
#include <dnnl_graph.hpp>
class partition: public partition_handle
{
public:
// enums
enum policy;
// construction
partition();
partition(dnnl_graph_partition_t p);
partition(const op& aop, engine::kind ekind);
// methods
size_t get_ops_num() const;
std::vector<size_t> get_ops() const;
size_t get_id() const;
compiled_partition compile(
const std::vector<logical_tensor>& inputs,
const std::vector<logical_tensor>& outputs,
const engine& e
) const;
bool is_supported() const;
std::vector<logical_tensor> get_input_ports() const;
std::vector<logical_tensor> get_output_ports() const;
engine::kind get_engine_kind() const;
};Detailed Documentation
A partition object.
Construction
partition(dnnl_graph_partition_t p)Constructs a partition object.
Parameters:
p |
A raw pointer to the C API handle |
partition(const op& aop, engine::kind ekind)Creates a new partition with a given operator and engine kind.
The API is used to create a partition from an operation directly without creating the graph and calling get_partitions(). The output partition contains only one operation.
Parameters:
aop |
An operation used to create the partition. |
ekind |
Engine kind. |
Methods
size_t get_ops_num() constReturns the number of operations contained in the partition.
Returns:
Number of operations.
std::vector<size_t> get_ops() constReturns all operation IDs contained in the partition.
Returns:
An unordered set of operation IDs.
size_t get_id() constReturns the unique ID of the partition.
Partition ID is generated by the library internally. The ID can be used for debugging purpose or verbose.
Returns:
ID of the partition.
compiled_partition compile(
const std::vector<logical_tensor>& inputs,
const std::vector<logical_tensor>& outputs,
const engine& e
) constCompiles a partition with given input and output logical tensors.
The output logical tensors can contain unknown dimensions. For this case, the compilation will deduce the output shapes according to input shapes. The output logical tensors can also have layout type any. The compilation will choose the optimal layout for output tensors. The optimal layout will be represented as an opaque layout ID saved in the output logical tensor.
Parameters:
inputs |
A list of input logical tensors. |
outputs |
A list of output logical tensors. |
e |
The engine used to compile the partition. |
Returns:
A compiled partition.
bool is_supported() constReturns the supporting status of a partition.
Some operations may not be supported by the library under certain circumstances. During partitioning stage, unsupported partitions will be returned to users with each containing an unsupported operation. Users should check the supporting status of a partition before transforming the computation graph or compiling the partition.
Returns:
true if this partition is supported or false if this partition isn’t supported by the library
std::vector<logical_tensor> get_input_ports() constReturns a list of input logical tensors from the partition.
Returns:
A list of input logical tensors.
std::vector<logical_tensor> get_output_ports() constReturns a list of output logical tensors from the partition.
Returns:
A list of output logical tensor.
engine::kind get_engine_kind() constReturns the engine kind of the partition.
Returns:
The engine kind