Computation graph optimization method, data processing method and related product

2:

• The method of claim 1, wherein extracting the operator to construct the view-class operator subgraph comprises:
• associatively caching operator information and an operator sequence number of the operator;
• and
• adding the operator sequence number to the view-class operator subgraph.

3:

• The method of claim 2, wherein extracting the operator to construct the view-class operator subgraph further comprises:
• checking whether the operator information of the operator has been cached;
• generating the operator sequence number for the operator and performing the caching and adding if the operator information of the operator is not cached;
• and
• adding the operator sequence number of the operator, which has been cached, to the view-class operator subgraph if the operator information of the operator has been cached.

6:

• A computing graph optimization method, comprising:
• acquiring a view-class operator subgraph of tensor data in a computing graph, wherein the view-class operator subgraph comprises a view-class source operator associated with the tensor data;
• replacing the source operator with a target operator whose specified function is interchangeable with that of the source operator according to a function of the source operator in the view-class operator subgraph;
• and
• fusing a plurality of contiguous identical target operators into a single target operator to generate a fused view-class operator subgraph.

12:

• A data processing method, comprising:
• acquiring a view-class operator subgraph of to-be-processed tensor data in response to the tensor data being discontiguous in memory, wherein the view-class operator subgraph is constructed or generated according to the method as claimed in claim 1;
• and
• calling a corresponding kernel to perform data moving according to information of the view-class operator subgraph to convert the tensor data into tensor data that is contiguous in memory.

14:

• A data processing method, comprising:
• determining a view-class operator through which a to-be-processed first tensor converts from a memory contiguous state into a memory discontiguous state according to first description information of the first tensor in response to the first tensor being in the memory discontiguous state;
• determining a data moving operator in a computing library that needs to be called according to the view-class operator;
• determining a parameter required for calling the data moving operator to convert the first tensor from the memory discontiguous state into the memory contiguous state according to the first description information;
• and
• calling the data moving operator according to the parameter to convert the first tensor into the memory contiguous state.

16:

• The method of claim 15, wherein determining the view-class operator experienced by the first tensor further comprises:
• determining the view-class operator experienced by the first tensor to be a rearrangement view-class operator when a data size indicated by the first data shape information is consistent with a memory size to which the first tensor is directed, and the first dimension stride information indicates that each dimension stride is arranged in a non-descending order;
• or
• determining the view-class operator experienced by the first tensor to be an expansion view-class operator when there is a dimension stride with a 0 value in the first dimension stride information, and a data size obtained by adjusting the first data shape information according to a position index of the 0 value is consistent with the memory size to which the first tensor data is directed.

18:

• The method of claim 14, wherein determining the data moving operator that needs to be called according to the view-class operator comprises:
• determining the data moving operator that needs to be called to be a data rearrangement operator when the view-class operator is a rearrangement view-class operator;
• or
• determining the data moving operator that needs to be called to be a data expansion operator when the view-class operator is an expansion view-class operator.

19:

• The method of claim 14, wherein determining the parameter required for calling the data moving operator comprises:
• determining second description information of a second tensor serving as an input tensor of the data moving operator;
• and
• determining computation parameter information of the data moving operator.

20:

• The method of claim 19, wherein determining the second description information of the second tensor comprises:
• when the data moving operator is a data rearrangement operator, determining a descending arrangement of first dimension stride information in the first description information as second dimension stride information in the second description information of the second tensor, and
• converting first data shape information in the first description information according to a rule of converting the first dimension stride information into the descending arrangement to obtain second data shape information in the second description information;
• or
• when the data moving operator is a data expansion operator, acquiring a position index of a 0 value from the first dimension stride information in the first description information,
• setting a corresponding position of the first data shape information in the first description information to 1 according to the position index of the 0 value to determine the second data shape information in the second description information, and
• determining the second dimension stride information in the second description information according to the second data shape information and a memory contiguity rule.

22:

• The method of claim 19, wherein determining the computation parameter information of the data moving operator comprises:
• when the data moving operator is a data rearrangement operator, taking the second tensor as an input of the data moving operator,
• taking the first tensor as an output of the data moving operator, and
• deducing the computation parameter information of the data moving operator based on the first description information and the second description information;
• or
• when the data moving operator is a data expansion operator, taking first data shape information as the computation parameter information.

24:

• A computing apparatus configured to optimize a computing graph or perform data processing, comprising:
• a processor configured to perform a program instruction;
• and
• a storage configured to store the program instruction, wherein when the program instruction is loaded and performed by the processor, the processor performs the computing graph optimization method of claim 1.

27:

• A computing apparatus configured to optimize a computing graph or perform data processing, comprising:
• a processor configured to perform a program instruction;
• and
• a storage configured to store the program instruction, wherein when the program instruction is loaded and performed by the processor, the data processing method of claim 12.