Method and device for intelligent task optimization of large models on the end based on graph attention network

Abstract

Embodiments of the present application provide a method, apparatus, device and computer-readable storage medium for optimizing intelligent tasks of a large model on the end side based on a graph attention network. The method comprises receiving a user request; inputting the user request into a trained intent understanding model to obtain a graph structure of a global intent and subtasks corresponding to the user request; inputting the graph structure of the global intent and the subtask into a graph attention network to obtain a first operation flow; inputting the first operation flow into a large language model to obtain a second operation flow; and executing the task corresponding to the user request through the second operation flow. In this way, the problems that the LLM deployed on the mobile phone end side has no contextual short-term memory and lacks contextual understanding; a single LLM decision is not accurate enough for controlling the mobile phone system; and the cloud framework privacy protection are solved, greatly improving the user experience.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of data processing, and in particular to a method, apparatus, device, and computer-readable storage medium for optimizing large-model intelligent tasks on the edge based on a graph attention network. Background Art

[0002] With the popularity of smartphones and mobile devices, users' demand for intelligent devices is growing. In the existing technology, most of the artificial intelligence systems on mobile phones are based on a single model or rely on cloud computing, which has problems such as slow response speed, high resource consumption, and data privacy security.

[0003] The above defects restrict the widespread application of artificial intelligence on mobile phones and cannot fully meet users' needs for efficiency, reliability and intelligence. Complete reliance on cloud computing will not only lead to slow response speed and high resource consumption, but also involve the risk of privacy leakage. Users' personal data needs to be continuously transmitted to the cloud for processing, which increases the risk of data theft or abuse.

[0004] In addition, the instability and delay of network connection will also affect the user experience. In some cases, uploading users' sensitive data to the cloud may violate data protection regulations, further increasing the risk of privacy security.

[0005] At the same time, the existing single reliance on large language models (LLM) for decision-making is not ideal. Although LLM performs well in processing natural language tasks, its accuracy and real-time performance are still insufficient when used for mobile system operations and decision-making, and it cannot meet users' needs for precise control and efficient decision-making (LLM deployed on the mobile phone side lacks contextual short-term memory capabilities and insufficient understanding of context).

[0006] In summary, existing cloud computing and single model solutions have obvious shortcomings in data privacy protection, response speed and decision-making accuracy. There is an urgent need for a comprehensive solution that can efficiently and reliably handle artificial intelligence tasks while protecting user privacy. Summary of the invention

[0007] According to the embodiments of the present application, a terminal-side large-model intelligent task optimization solution based on a graph attention network is provided to solve the problems that the LLM deployed on the mobile phone side has no contextual short-term memory and lacks contextual understanding: a single LLM decision is not accurate enough for controlling the mobile phone system: and the cloud framework protects privacy, etc., thereby greatly improving the user experience.

[0008] In a first aspect of the present application, a method for optimizing large-model intelligent tasks on a client side based on a graph attention network is provided. The method comprises:

[0009] Receive user requests;

[0010] Inputting the user request into a trained intent understanding model to obtain a graph structure of global intent and subtasks corresponding to the user request;

[0011] Inputting the global intent and the graph structure of the subtask into a graph attention network to obtain a first operation flow;

[0012] The first operation flow is input into a large language model to obtain a second operation flow; and the task corresponding to the user request is executed through the second operation flow.

[0013] Furthermore, the graph structure is composed of multiple nodes, and each node represents a specific operation step.

[0014] Furthermore, the operation steps include inputting content, sending messages and / or searching.

[0015] Furthermore, the graph attention network can calculate the attention score in the following way:

[0016] ;

[0017] in, is a trainable attention vector;

[0018] is a linear transformation matrix;

[0019] and Node and nodes The eigenvector of

[0020] represents the context feature vector;

[0021] Represents a vector concatenation operation.

[0022] Furthermore, the large language model includes:

[0023] The historical operation and feedback data related to the subtask component nodes are obtained through the constructed memory network;

[0024] The memory network stores data through a lightweight local database and performs memory processing through a lightweight neural network model.

[0025] Furthermore, it also includes:

[0026] Sending the second operation stream to the graph attention network;

[0027] The graph attention network updates the connections between the nodes according to the second operation flow.

[0028] Furthermore, it also includes:

[0029] If the current system is Android, the large language model is deployed through llama.cpp; if the current system is iOS, the large language model is deployed through the MLX framework.

[0030] In a second aspect of the present application, a device for optimizing large-scale intelligent tasks on a client side based on a graph attention network is provided. The device includes:

[0031] A receiving module, used for receiving user requests;

[0032] A splitting module, used to input the user request into a trained intent understanding model to obtain a graph structure of global intent and subtasks corresponding to the user request;

[0033] An analysis module, configured to input the global intent and the graph structure of the subtask into a graph attention network to obtain a first operation flow;

[0034] An execution module is used to input the first operation flow into a large language model to obtain a second operation flow; and execute the task corresponding to the user request through the second operation flow.

[0035] In a third aspect of the present application, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0036] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect of the present application is implemented.

[0037] The terminal-side large-model intelligent task optimization method based on the graph attention network provided in the embodiment of the present application receives a user request; inputs the user request into a trained intent understanding model to obtain a graph structure of a global intent and subtasks corresponding to the user request; inputs the graph structure of the global intent and the subtask into a graph attention network to obtain a first operation flow; inputs the first operation flow into a large language model to obtain a second operation flow; and executes the task corresponding to the user request through the second operation flow, thereby greatly improving the user experience.

[0038] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0040] Figure 1 It is a flowchart of a method for optimizing a large model intelligent task on the terminal side based on a graph attention network according to an embodiment of the present application;

[0041] Figure 2 is a flow chart of LLM weight deployment according to an embodiment of the present application;

[0042] Figure 3 A schematic diagram of a data set format based on a graph attention mechanism according to an embodiment of the present application;

[0043] Figure 4 is a schematic diagram of example data based on a graph attention network according to an embodiment of the present application;

[0044] Figure 5 is a core flow chart according to an embodiment of the present application;

[0045] Figure 6 4 is a block diagram of a device-side large model intelligent task optimization device based on a graph attention network according to an embodiment of the present application;

[0046] Figure 7 It is a schematic diagram of the structure of a terminal device or a server suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with 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 the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0048] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0049] Figure 1 A flow chart of a method for optimizing large-scale intelligent tasks on a client side model based on a graph attention network according to an embodiment of the present disclosure is shown. The method includes:

[0050] S110, receiving a user request.

[0051] In some embodiments, the execution entity of the end-side large-model intelligent task optimization method based on the graph attention network can obtain the user request through a wired or wireless connection.

[0052] Furthermore, the execution subject may obtain a user request sent by an electronic device connected to the execution subject for communication, or may obtain a user request pre-stored locally.

[0053] In some embodiments, the user request includes an ambiguous user request, that is, a request that does not include a specific task process. For example, please help me post a Weibo to share today's itinerary and attach relevant pictures.

[0054] S120, input the user request into the trained intent understanding model to obtain a graph structure of the global intent and subtasks corresponding to the user request.

[0055] In some embodiments, the user request is input into the trained intent understanding model, and the user request is parsed by the model to obtain the global intent and subtask corresponding to the user request; the subtask includes a single-step fine-grained operation process. Taking the user request in step S110 as an example, "posting a Weibo" can be used as a global intent, and "writing Weibo content", "selecting pictures" and "posting Weibo" can be used as subtasks. Further, the graph structure related to the global intent and subtasks can be extracted from the constructed database through the intent understanding model; the graph structure can be composed of multiple nodes, each node representing a specific operation step, such as "input content", "select pictures", "send Weibo" and / or "search", etc. Each node can be connected to other nodes through multiple edges. For example, the "input content" node can be connected to the "search" and "send Weibo" nodes at the same time, indicating that there are multiple paths or dependencies between the above operations.

[0056] S130, input the global intent and the graph structure of the subtask into the graph attention network to obtain a first operation flow.

[0057] In some embodiments, the graph structure of the global intent and the subtask is input into the graph attention network (GTA network), and the optimal path is intelligently selected by calculating the weight and priority of each edge, combining the node features and historical data, and planning the specific operation process to obtain the first operation flow. Among them, the big intention (global intention) serves as the context of the operation flow, defining the overall goal and direction of the entire process. For example, when the big intention is "posting a Weibo", all sub-operations (such as "input content", "selecting a picture", and "sending a Weibo") are performed under this context. Contextual information (i.e., the big intention) not only affects GAT's calculation of node importance (i.e., attention weight), but also affects the priority of path selection.

[0058] In some embodiments, Figure 3 As shown in the figure, when designing a task retrieval module based on the graph attention mechanism (GAT), a detailed operating system dataset needs to be constructed first, because the dataset is the most important part for the graph neural network (GNN). The operation flow of the system application is compiled in detail, and each node is defined as the execution process of a small subtask. The dataset format includes node features, edge connections, operation flows and / or context information, which are used to record each operation step and its association in detail. Through this method, the GAT network can finely model the operation steps, and effectively retrieve the subtask execution process related to the current task based on the graph attention mechanism during actual operation. Compared with the graph convolutional network (GCN), the graph attention network (GAT) has more advantages in processing complex and heterogeneous graph structures, and can dynamically assign weights according to the relative importance between nodes to improve the accuracy and efficiency of task retrieval. The above design ensures that the system can accurately identify and call the most relevant subtasks, ensuring the efficiency and accuracy of task execution.

[0059] Specifically, Figure 4 As shown, the subtasks split by the intent understanding model are used as the initial node input and the context, and the similarity between its feature vector and the context feature vector is calculated:

[0060] ;

[0061] is the feature vector of node i;

[0062] According to the calculation results, the node with the highest similarity is selected as the initial node.

[0063] Furthermore, update the characteristics of each node:

[0064] The attention score calculation formula in the graph attention mechanism (GAT) is as follows:

[0065] ;

[0066] After introducing context information, the attention score calculation formula becomes:

[0067] ;

[0068] in, is a trainable attention vector;

[0069] is a linear transformation matrix;

[0070] and Node and nodes The eigenvector of

[0071] represents the context feature vector;

[0072] Represents a vector concatenation operation.

[0073] Taking the calculation of the feature update of node 2 as an example, the attention scores from node 1 to node 2 are calculated based on the edge information of the data input:

[0074] ;

[0075] And node 2 to node 3 to attention score:

[0076] ;

[0077] Normalize its attention weights:

[0078] ;

[0079] Update the characteristics of node 2:

[0080] ;

[0081] And so on, select the node most relevant to the initial node. sequence.

[0082] S140: Input the first operation flow into a large language model to obtain a second operation flow; and execute the task corresponding to the user request through the second operation flow.

[0083] Since the weight file of the core large model (Large Language Model LLM) is too large, directly packaging it into the installation package will result in too high a cost. In order to solve this problem. In this disclosure, a solution based on ID identification is designed, which allows users to download the model weight file online, such as Figure 2As shown in the figure, this method can greatly reduce the difficulty of deployment. If the current system is Android, the large language model can be deployed through llama.cpp; if the current system is iOS, the large language model can be deployed through the MLX framework. This method not only effectively reduces the deployment cost, but also significantly improves the flexibility and scalability of the system.

[0084] In some embodiments, in order to adapt to the LLM on the mobile phone, the following memory network is designed. The network uses a lightweight local database (such as SQLite) for data storage, and reduces memory usage by regularly cleaning and compressing data. Long-term memory data is compressed and stored, and a hierarchical storage strategy is adopted: short-term memory is stored in memory, while long-term memory is stored in local storage and loaded on demand. In addition, a lightweight neural network model (such as TinyBERT) is used for memory processing, and the necessary model components and data are dynamically loaded to avoid memory overflow caused by loading too much data at one time. The above design can effectively reduce the system's memory usage while ensuring efficient memory processing capabilities.

[0085] Furthermore, through the above memory network, historical operations and feedback data related to each node corresponding to the user request are searched; if relevant data is found, it is input as a token to the large language model (LLM) through the memory network, so that the LLM can refer to the information for optimization decision in subsequent steps.

[0086] In some embodiments, the sequence (first operation flow) selected by step S130 is input as a token to the LLM model, and the LLM model receives the operation sequence and further optimizes it to obtain a second operation flow. For example, nodes 2 and 3 in step S130 are optimized to more efficient operations. The task corresponding to the user request is executed based on the second operation flow.

[0087] Furthermore, it also includes:

[0088] Since the edge connections between nodes are fixed, the LLM model can also return the optimized instruction stream to the GAT network, which not only optimizes the connections between existing nodes, but also adds new connections between nodes that were not previously connected, thereby enhancing the connectivity of the entire network. For example, by adding new edge connections, the connectivity between nodes is optimized: add a new edge between nodes 1 and 3 with a weight of 0.5, and add a new edge between nodes 2 and 4 with a weight of 0.4.

[0089] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0090] like Figure 5 As shown, Figure 5The core process of the end-side large-model intelligent task optimization method based on graph attention network is shown.

[0091] For the large model on the mobile phone side, a memory network and a decision-making assistance network (GAT) based on a graph attention network are designed. The memory network provides access to and utilization of historical data, enabling the system to handle repetitive and similar tasks more intelligently, improving the efficiency and continuity of task processing. The decision-making assistance network dynamically adjusts the weights between nodes through GAT, enhances the connection between nodes, including nodes that were not previously connected, thereby optimizing the task execution process and improving the accuracy and efficiency of task processing. This solves the problem that the existing technology lacks memory and utilization of historical operations, and most task decisions rely on a single model.

[0092] A dynamic loading and hierarchical storage mechanism is adopted to store short-term memory in the main memory and long-term memory in the local storage, which is loaded on demand. Dynamic loading and hierarchical storage reduce the system's memory usage, optimize resource usage, and enable the system to run efficiently even when device resources are limited, thus improving the system's practicality and scalability. In addition, local deployment and data storage significantly improve response speed, protect user data privacy, and enhance system security and user trust. This solves the problem in the prior art that the weight files of large models usually need to be loaded at one time, resulting in excessive memory usage, and mainly rely on cloud computing, which poses a risk of data privacy leakage.

[0093] By combining context information with node features, the similarity between the node and the context is calculated, and the best matching node is selected as the initial node. The context matching initial node selection method improves the context understanding ability of task processing, enables the system to select the task starting point more intelligently, and improves the accuracy and efficiency of task processing. It can achieve more intelligent task startup on the terminal device, making task planning more in line with actual needs. It solves the problem that existing technologies usually do not consider context information when processing tasks, resulting in low accuracy of initial node selection.

[0094] In summary, the method disclosed herein not only solves the above problems, but also greatly improves the user experience.

[0095] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0096] The above is an introduction to the method embodiment. The following is a further explanation of the scheme described in this application through an apparatus embodiment.

[0097] Figure 6 A block diagram 600 of a device-side large model intelligent task optimization device based on a graph attention network according to an embodiment of the present application is shown. Figure 6 Shown include:

[0098] Receiving module 610, used to receive a user request;

[0099] A splitting module 620 is used to input the user request into a trained intent understanding model to obtain a graph structure of a global intent and subtasks corresponding to the user request;

[0100] An analysis module 630 is used to input the global intent and the graph structure of the subtask into a graph attention network to obtain a first operation flow;

[0101] The execution module 640 is used to input the first operation flow into the large language model to obtain a second operation flow; and execute the task corresponding to the user request through the second operation flow.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0103] Figure 7 A schematic diagram of the structure of a terminal device or server suitable for implementing an embodiment of the present application is shown.

[0104] like Figure 7 As shown, the terminal device or server includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 702 or the program loaded from the storage part 708 into the random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the terminal device or server are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0105] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage section 708 as needed.

[0106] In particular, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present application are executed.

[0107] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0108] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of the code, and the aforementioned module, program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0109] The units or modules involved in the embodiments described in the present application may be implemented by software or hardware. The units or modules described may also be arranged in a processor. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves.

[0110] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable storage medium stores one or more programs, and when the above programs are used by one or more processors to execute the method described in the present application.

[0111] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for optimizing large-scale intelligent tasks on a device based on a graph attention network, characterized in that: include: Receive user requests; Inputting the user request into a trained intent understanding model to obtain a graph structure of global intent and subtasks corresponding to the user request; Inputting the global intent and the graph structure of the subtask into a graph attention network to obtain a first operation flow, wherein the subtask includes a single-step fine-grained operation flow; Inputting the first operation flow into a large language model to obtain a second operation flow; executing the task corresponding to the user request through the second operation flow; The graph structure is composed of multiple nodes, each node represents a specific operation step; Each node is connected to other nodes through multiple edges, which is used to indicate that there are multiple paths or dependencies between operations; The graph attention network can calculate the attention score as follows: ； in, is a trainable attention vector; is a linear transformation matrix; and Node and nodes The eigenvector of represents the context feature vector; Represents a vector concatenation operation.

2. The method according to claim 1, characterized in that The operation steps include inputting content, sending messages and / or searching.

3. The method according to claim 1, characterized in that The large language model includes: The historical operation and feedback data related to the subtask component nodes are obtained through the constructed memory network; The memory network stores data through a lightweight local database and performs memory processing through a lightweight neural network model.

4. The method according to claim 2, characterized in that: Also includes: Sending the second operation stream to the graph attention network; The graph attention network updates the connections between the nodes according to the second operation flow.

5. The method according to claim 4, characterized in that Also includes: If the current system is Android, the large language model is deployed through llama.cpp; if the current system is iOS, the large language model is deployed through the MLX framework.

6. A device for optimizing large-scale intelligent tasks on a client side based on a graph attention network, characterized in that: include: A receiving module, used for receiving user requests; A splitting module, used to input the user request into a trained intent understanding model to obtain a graph structure of a global intent and subtasks corresponding to the user request, wherein the subtask includes a single-step fine-grained operation process; An analysis module, configured to input the global intent and the graph structure of the subtask into a graph attention network to obtain a first operation flow; An execution module, configured to input the first operation flow into a large language model to obtain a second operation flow; and execute the task corresponding to the user request through the second operation flow; The graph structure is composed of multiple nodes, each node represents a specific operation step; Each node is connected to other nodes through multiple edges, which is used to indicate that there are multiple paths or dependencies between operations; The graph attention network can calculate the attention score as follows: ； in, is a trainable attention vector; is a linear transformation matrix; and Node and nodes The eigenvector of represents the context feature vector; Represents a vector concatenation operation.

7. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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