Patent Application

App. No. 17/082,867

Abstract

Claims (67)

1 . A method for processing a computational graph, including:

acquiring a computational graph, wherein nodes in the computational graph represent functions related to the machine learning model, and directed edges in the computational graph represent dependencies between the functions;

determining multiple sequential portions of the computational graph wherein the multiple portions will be executed sequentially and functions corresponding to nodes in each portion can be executed in parallel; and

assigning, to the multiple portions, execution instances for executing functions corresponding to nodes in the corresponding portions, wherein the number of execution instances assigned to each portion is associated with time required to execute functions corresponding to nodes in the portion.

2 . The method according to claim 1 , wherein assigning, to the multiple portions, execution instances includes:

determining, based on functions corresponding to nodes in a first portion of the multiple portions, the type of processing units for providing execution instances assigned to the first portion; and

assigning, to the first portion, the execution instances provided by the processing units of the type.

3 . The method according to claim 1 , wherein assigning, to the multiple portions, execution instances includes:

determining execution instances to be assigned to a first portion of the multiple portions;

dividing the first portion into multiple sub-portions if the execution instances to be assigned to the first portion come from multiple processing units; and

assigning execution instances to each sub-portion, wherein the execution instances assigned to each sub-portion come from different processing units, and the number of execution instances assigned to each sub-portion is associated with time required to execute functions corresponding to nodes in the sub-portion.

4 . The method according to claim 1 , wherein assigning, to the multiple portions, execution instances includes:

assigning a preset number of execution instances to a first portion of the multiple portions; and

wherein the method further includes:

adjusting the execution instances assigned to the first portion during the execution of functions corresponding to nodes in the first portion.

5 . The method according to claim 4 , wherein adjusting the execution instances assigned to the first portion includes:

recycling execution instances among the preset number of execution instances that are not used during the execution of the functions corresponding to the nodes in the first portion.

6 . The method according to claim 4 , wherein adjusting the execution instances assigned to the first portion includes:

determining the number of execution instances that need to be added to the first portion, if it is determined that the preset number is less than the number of execution instances required to execute the functions corresponding to the nodes in the first portion; and

assigning the determined number of execution instances to the first portion.

7 . The method according to claim 1 , wherein determining the multiple portions of the computational graph includes:

determining in-degrees of multiple nodes of the computational graph, wherein an in-degree of a node represents the number of directed edges pointing to the node; and

determining the multiple portions of the computational graph based on the in-degrees.

8 . The method according to claim 7 , wherein determining the multiple portions of the computational graph based on the in-degrees includes iteratively performing the following actions:

selecting a first portion of the computational graph so that each node in the first portion has a preset threshold in-degree; and

removing the first portion and directed edges related to nodes in the first portion from the computational graph, so as to update the computational graph.

9 . The method according to claim 1 , wherein the execution instances are provided by at least one of the following:

a central processing unit; and

a dedicated processing unit.

10 . An electronic device, including:

at least one processing unit; and

at least one memory which is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, wherein the instructions, when being executed by the at least one processing unit, cause the device to perform actions including:

acquiring a computational graph, wherein nodes in the computational graph represent functions related to the machine learning model, and directed edges in the computational graph represent dependencies between the functions;

determining multiple sequential portions of the computational graph wherein the multiple portions will be executed sequentially and functions corresponding to nodes in each portion can be executed in parallel; and

assigning, to the multiple portions, execution instances for executing functions corresponding to nodes in the corresponding portions, wherein the number of execution instances assigned to each portion is associated with time required to execute functions corresponding to nodes in the portion.

11 . The device according to claim 10 , wherein assigning, to the multiple portions, execution instances includes:

determining, based on functions corresponding to nodes in a first portion of the multiple portions, the type of processing units for providing execution instances assigned to the first portion; and

assigning, to the first portion, the execution instances provided by the processing units of the type.

12 . The device according to claim 10 , wherein assigning, to the multiple portions, execution instances includes:

determining execution instances to be assigned to a first portion of the multiple portions;

dividing the first portion into multiple sub-portions if the execution instances to be assigned to the first portion come from multiple processing units; and

assigning execution instances to each sub-portion, wherein the execution instances assigned to each sub-portion come from different processing units, and the number of execution instances assigned to each sub-portion is associated with time required to execute functions corresponding to nodes in the sub-portion.

13 . The device according to claim 10 , wherein assigning, to the multiple portions, execution instances includes:

assigning a preset number of execution instances to a first portion of the multiple portions; and

wherein the operations further include:

adjusting the execution instances assigned to the first portion during the execution of functions corresponding to nodes in the first portion.

14 . The device according to claim 13 , wherein adjusting the execution instances assigned to the first portion includes:

recycling execution instances among the preset number of execution instances that are not used during the execution of the functions corresponding to the nodes in the first portion.

15 . The device according to claim 13 , wherein adjusting the execution instances assigned to the first portion includes:

determining the number of execution instances that need to be added to the first portion, if it is determined that the preset number is less than the number of execution instances required to execute the functions corresponding to the nodes in the first portion; and

assigning the determined number of execution instances to the first portion.

16 . The device according to claim 10 , wherein determining the multiple portions of the computational graph includes:

determining in-degrees of multiple nodes of the computational graph, wherein an in-degree of a node represents the number of directed edges pointing to the node; and

determining the multiple portions of the computational graph based on the in-degrees.

17 . The device according to claim 16 , wherein determining the multiple portions of the computational graph based on the in-degrees includes iteratively performing the following actions:

selecting a first portion of the computational graph so that each node in the first portion has a preset threshold in-degree; and

removing the first portion and directed edges related to nodes in the first portion from the computational graph, so as to update the computational graph.

18 . The device according to claim 10 , wherein the execution instances are provided by at least one of the following:

a central processing unit; and

a dedicated processing unit.

19 . A computer program product tangibly stored in a non-transitory computer-readable medium and including machine-executable instructions, wherein the machine-executable instructions, when being executed, cause a machine to perform steps of a method for processing a computational graph, including:

acquiring a computational graph, wherein nodes in the computational graph represent functions related to the machine learning model, and directed edges in the computational graph represent dependencies between the functions;

determining multiple sequential portions of the computational graph wherein the multiple portions will be executed sequentially and functions corresponding to nodes in each portion can be executed in parallel; and

assigning, to the multiple portions, execution instances for executing functions corresponding to nodes in the corresponding portions, wherein the number of execution instances assigned to each portion is associated with time required to execute functions corresponding to nodes in the portion.

20 . The computer program product according to claim 19 , wherein assigning, to the multiple portions, execution instances includes:

determining, based on functions corresponding to nodes in a first portion of the multiple portions, the type of processing units for providing execution instances assigned to the first portion; and

assigning, to the first portion, the execution instances provided by the processing units of the type.

Assignments (9)