RunGraphDistribute

Applicability

Product	Supported or Not
Atlas A3 training products / Atlas A3 inference products	√
Atlas A2 training products / Atlas A2 inference products	√
Atlas 200I/500 A2 inference products	x
Atlas inference products	√
Atlas training products	√

Header File/Library File

Header file: #include <ge/ge_api.h>
Library file: libge_runner.so

Function Usage

Runs the graph corresponding to a specified ID synchronously after the input refdata node is partitioned, and outputs the running result.

Difference between this API and RunGraph: The input refdata node of this API is partitioned, and the output is the output result of each device.

Prototype

      
           Status RunGraphDistribute(uint32_t graph_id, const std::map<int32_t, std::vector<Tensor>> &device_to_inputs, std::map<int32_t, std::vector<Tensor>> &device_to_outputs)

Parameters

Parameter	Input/Output	Description
graph_id	Input	ID of the graph to be run.
device_to_inputs	Input	Input tensors of the computational graph, which are memory allocated on the host. Use const std::map<int32_t, std::vector<Tensor>> as inputs, which correspond to each device ID after partitioning.
device_to_outputs	Output	Output tensors of the computational graph. You do not need to allocate the memory manually. After the running is complete, GE allocates and initializes the memory. Use std::map<int32_t, std::vector<Tensor>> as the output result, which records the computational graph result corresponding to each device ID.

Parameter

Input/Output

Description

graph_id

Input

ID of the graph to be run.

device_to_inputs

Input

Input tensors of the computational graph, which are memory allocated on the host.

Use const std::map<int32_t, std::vector<Tensor>> as inputs, which correspond to each device ID after partitioning.

device_to_outputs

Output

Output tensors of the computational graph. You do not need to allocate the memory manually. After the running is complete, GE allocates and initializes the memory.

Use std::map<int32_t, std::vector<Tensor>> as the output result, which records the computational graph result corresponding to each device ID.

Returns

Parameter	Type	Description
-	Status	SUCCESS: The subgraph running is successful. For details about error codes, see "aclError".

Restrictions

For the full input in graph_id, the input sequence is as follows: Model data input + batch_index + kv.
For the incremental input in graph_id, the input sequence is as follows: Model data input + kv.

Parent topic: Session