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Fusion graph generation method and device and computer readable storage medium

A technology of integrating nodes and operators, applied in the computer field, can solve the problem that cannot meet the needs of machine learning processors, cannot meet the needs of machine learning processor operators or node fusion, and the CPU control flow cannot meet the needs of offline deployment of machine learning processors. and other problems to achieve the effect of meeting the needs of offline deployment

Active Publication Date: 2020-12-11
ANHUI CAMBRICON INFORMATION TECH CO LTD
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the intermediate representation graph (IR graph, Intermediate Representation) with control flow can be obtained through the PyTorch JITScript mechanism, the existing central processing unit (CPU) control flow logic in PyTorch cannot meet the requirements of machine learning processors
On the one hand, CPU control flow cannot meet the offline deployment requirements of machine learning processors; on the other hand, it cannot meet the needs of machine learning processors for control flow operators or node fusion

Method used

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  • Fusion graph generation method and device and computer readable storage medium
  • Fusion graph generation method and device and computer readable storage medium
  • Fusion graph generation method and device and computer readable storage medium

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Embodiment Construction

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of them. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present disclosure.

[0018] It should be understood that the terms "first", "second" and "third" in the claims, specification and drawings of the present disclosure are used to distinguish different objects, rather than to describe a specific sequence. The terms "comprising" and "comprises" used in the specification and claims of this disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude one or more other features, integ...

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Abstract

The invention relates to a fusion graph generation method and device and a computer readable storage medium. The generation device may be included as a computing device in a combined processing device, and the combined processing device may also include an interface device and other processing devices. The computing device interacts with other processing devices to jointly complete the computing operation specified by the user. The combined processing device can further comprise a storage device, and the storage device is connected with the computing device and other processing devices and used for storing data of the computing device and other processing devices. According to the scheme, the IR graph with the control flow can be preprocessed to meet the operator fusion requirement of themachine learning processor, and then the fusion graph of the machine learning processor can be generated based on the preprocessed IR graph.

Description

technical field [0001] This disclosure relates to the field of computers. More specifically, it relates to a method for generating a fusion graph, a generating device and a computer-readable storage medium. Background technique [0002] In the network structure of deep learning, dynamic control flow structures such as loops and conditional branches are generally included. PyTorch is a currently popular deep learning framework, which includes two mechanisms: JIT Trace and JIT Script. However, on machine learning processors, current fusion graphs for machine learning processors generated based on the PyTorch JIT Trace mechanism cannot handle control flow well. Specifically, when the number of loops or conditional judgments are changed, the fusion graph of the machine learning processor generated by PyTorchJIT Trace will run wrong results. Although the intermediate representation graph (IR graph, Intermediate Representation) with control flow can be obtained through the PyTo...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/082G06N3/045
Inventor 不公告发明人
Owner ANHUI CAMBRICON INFORMATION TECH CO LTD
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